From a899dec46f951dffc2018a3976550805ed26f262 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 4 May 2018 14:45:27 -0400
Subject: [PATCH 001/753] fix bugs in attached algorithms

---
 ...ion Strategy Based on Fundamental Factors.html | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html b/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html
index aad7b2f..717c855 100644
--- a/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html	
+++ b/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html	
@@ -13,7 +13,7 @@ <h2>Part 1. Factor Selection</h2>
 	self.AddEquity("SPY") # add benchmark
 	self.numOfCourseSymbols = 200
 	self.numOfPortfolio = 5
-	self._changes = SecurityChanges.None
+	self._changes = None
 	self.flag1 = 1  # variable to control the monthly rebalance of coarse and fine selection function
 	self.flag2 = 0  # variable to control the monthly rebalance of OnData function
 	self.flag3 = 0  # variable to record the number of rebalancing times
@@ -31,12 +31,9 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
         CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
         sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
         top = sortedByVolume[:self.numOfCourseSymbols]
-        list = List[Symbol]()
-        for x in top:
-            list.Add(x.Symbol)
-            return list
+				return [i.Symbol for i in top]
     else:
-        return(List[Symbol]())
+        return []
 </pre>
 2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
 <pre class="prettyprint linenums">def FineSelectionFunction(self, fine):
@@ -50,9 +47,9 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 		self.symbol = [str(x.Symbol.Value) for x in sorted_fine]
 		# factor_value = [x.ValuationRatios.PERatio for x in sorted_fine]
 		self.flag3 = self.flag3 + 1
- 	 	return (List[Symbol]())
+ 	 	return []
 	else:
-		return (List[Symbol]())
+		return []
 </pre>
 <h3>Step 2: Compute the monthly return of portfolios</h3>
 1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
@@ -212,4 +209,4 @@ <h3>Step 2: Calculate equally weighted composite factor scores</h3>
  	<li>At the end of each month, we repeat the above steps to construct the new portfolio and adjust the holding stocks.</li>
 </ul>
 <h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=1a3cfe5a2d072dcaa85471063bc5cd88'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=77c784268221017b324355fe63ae1d6b'></script>

From 69feb4585827b484fc987390015d636450160fd7 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 4 May 2018 18:12:54 -0400
Subject: [PATCH 002/753] fix bugs in universe slection method

---
 ...1 Fundamental Factor Long:Short Strategy.html | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html b/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html
index 53eabaf..c15e2b4 100644
--- a/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html	
+++ b/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html	
@@ -26,7 +26,7 @@ <h3>CoarseSelectionFunction</h3>
 def CoarseSelectionFunction(self, coarse):
     # if the rebalance flag is not 1, return null list to save time.
     if self.reb != 1:
-        return []
+        return return self.long + self.short
 
     # make universe selection once a month
     # drop stocks which have no fundamental data or have too low prices
@@ -42,10 +42,9 @@ <h3>FineSelectionFunction</h3>
 Here is the core function. The process is that we make three sorted list to store the stocks, and then use a dictionary to store the score information. For the dictionary, the keys are Symbols and the values are their scores. Finally we sort the dictionary to get the final rank. we store the top 20 stocks to long in the list self.long and the bottom 20 stocks to short in the list self.short.
 <pre class="prettyprint linenums">
   def FineSelectionFunction(self, fine):
-  # return null list if it's not time to rebalance
+      # return the same symbol list if it's not time to rebalance
       if self.reb != 1:
-          return []
-
+          return self.long+self.short
       self.reb = 0
 
   # drop stocks which don't have the information we need.
@@ -77,11 +76,10 @@ <h3>FineSelectionFunction</h3>
       sorted_symbol = [x[0] for x in self.sorted_stock]
 
       # sotre the top stocks into the long_list and the bottom ones into the short_list
-      self.long = [x for x in sorted_symbol[:self.num_fine]]
-      self.short = [x for x in sorted_symbol[-self.num_fine:]]
+      self.long = [x.Symbol for x in sorted_symbol[:self.num_fine]]
+      self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]]
 
-      topFine = self.long + self.short
-      return [i.Symbol for i in topFine]</pre>
+      return self.long+self.short</pre>
 <h3>Rebalance</h3>
 Our portfolio is rebalanced monthly, so first of all we should write a ScheduledEvent handler in the Initialize function:
 <pre class="prettyprint linenums">
@@ -108,4 +106,4 @@ <h2>Summary</h2>
 For further development, we can try to rank a large number of stocks, and do some portfolio optimization instead of holding stocks equally.
 
 <h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=7a4b97afbe8a2d17de7c5006e394ffa5'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>

From 6d1cd04a9846def87fb944e1ddeb07c4ff262767 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 4 May 2018 18:29:59 -0400
Subject: [PATCH 003/753] convert code snippet to python3

---
 ...rial01 Data Types and Data Structures.html | 88 ++++++++-----------
 ...ns and Objective-Oriented Programming.html |  6 +-
 2 files changed, 42 insertions(+), 52 deletions(-)

diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html
index 4f0da86..11722a1 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html	
@@ -7,48 +7,48 @@ <h2>Basic Variable Types</h2>
 Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
 <pre class="prettyprint linenums">my_string1 = 'Welcome to'
 my_string2 = "QuantConnect"
-print my_string1 + ' ' + my_string2
+print(my_string1 + ' ' + my_string2)
 [out]: Welcome to QuantConnect
 </pre>
 
 An integer is a round number with no values after the decimal point.
 <pre class="prettyprint linenums">my_int = 10
-print my_int
+print(my_int)
 [out]: 10
-print type(my_int)
+print(type(my_int))
 [out]: type 'int'
 </pre>
 
 The built-in function int() can convert a string into an integer.
 <pre class="prettyprint linenums">my_string = "100"
-print type(my_string)
+print(type(my_string))
 [out]: type 'str'
 my_int = int(my_string)
-print type(my_int)
+print(type(my_int))
 [out]: type 'int'
 </pre>
 
 A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
 <pre class="prettyprint linenums">my_float = 1.0
-print type(my_float)
+print(type(my_float))
 [out]: type 'float'
 my_int = 1
-print type(my_int)
+print(type(my_int))
 [out]: type 'int'
 </pre>
 
 As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
 <pre class="prettyprint linenums">my_string = "100"
 my_float = float(my_string)
-print type(my_float)
+print(type(my_float))
 [out]: type 'float'
 </pre>
 
 A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
 <pre class="prettyprint linenums">my_bool = False
-print my_bool
+print(my_bool)
 [out]: False
-print type(my_bool)
+print(type(my_bool))
 [out]: type 'bool'
 </pre>
 
@@ -56,11 +56,11 @@ <h2>Basic Math Operations</h2>
 
 The basic math operators in python are demonstrated below:
 <pre class="prettyprint linenums">
-print "Addition ", 1+1
-print "Subtraction ", 5-2
-print "Multiplication ", 2*3
-print "Division", 10/2
-print "exponent", 2**3
+print("Addition ", 1+1)
+print("Subtraction ", 5-2)
+print("Multiplication ", 2*3)
+print("Division", 10/2)
+print("exponent", 2**3)
 [out]:
 Addition  2
 Subtraction  3
@@ -69,66 +69,56 @@ <h2>Basic Math Operations</h2>
 exponent 8
 </pre>
 
-In Python 2, if you divide an integer by another integer, you will get an integer.
-<pre class="prettyprint linenums">
-print 1/3
-[out]: 0
-print 1.0/3
-[out]: 0.3333333333
-</pre>
-
-To get a float instead of an integer, either the numerator or denominator must be a float as shown above. This isn't a problem in Python 3 since 1/3 will return 0.3333333333
-
 <h2>Data Collections</h2>
 
 <h3>List</h3>
 
 A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
 <pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print my_list
+print(my_list)
 [out]: ['Quant', 'Connect', 1, 2, 3]
 </pre>
 
 The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
 <pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print len(my_list)
+print(len(my_list))
 [out]: 5
-print my_list[0]
+print(my_list[0])
 [out]: Quant
-print my_list[len(my_list) - 1]
+print(my_list[len(my_list) - 1])
 [out]: 3
 </pre>
 
 You can also change the elements in the list by accessing an index and assigning a new value.
 <pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
 my_list[2] = 'go'
-print my_list
+print(my_list)
 [out]: ['Quant', 'Connect', 'go', 2, 3]
 </pre>
 
 A list can also be sliced with a colon:
 <pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
-print my_list[1:3]
+print(my_list[1:3])
 [out]: ['Connect', 1]
 </pre>
 
 The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
-<pre class="prettyprint linenums">print my_list[1:]
+<pre class="prettyprint linenums">print(my_list[1:])
 [out]: ['Connect', 1, 2, 3]
 </pre>
 
 And all elements up to but excluding index 3:
-<pre class="prettyprint linenums">print my_list[:3]
+<pre class="prettyprint linenums">print(my_list[:3])
 [out]: ['Quant', 'Connect', 1]
 </pre>
 
 If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
 <pre class="prettyprint linenums">my_list = ['Hello', 'Quant']
 my_list.append('Hello')
-print my_list
+print(my_list)
 [out]: ['Hello', 'Quant', 'Hello']
 my_list.remove('Hello')
-print my_list
+print(my_list)
 [out]: ['Quant', 'Hello']
 </pre>
 
@@ -142,7 +132,7 @@ <h3>Tuple</h3>
 
 Just like a list, a tuple can be sliced by using index.
 <pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
-print my_tuple[1:]
+print(my_tuple[1:])
 [out]: ('to', 'QuantConnect')
 </pre>
 
@@ -151,7 +141,7 @@ <h3>Set</h3>
 A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
 <pre class="prettyprint linenums">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
 stock_set = set(stock_list)
-print stock_set
+print(stock_set)
 [out]: set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])
 </pre>
 Set is an easy way to remove duplicate elements from a list.
@@ -165,18 +155,18 @@ <h3>Dictionary</h3>
 </pre>
 
 After defining a dictionary, we can access any value by indicating its key in brackets.
-<pre class="prettyprint linenums">print my_dic['GOOG']
+<pre class="prettyprint linenums">print(my_dic['GOOG'])
 [out]: Alphabet
 </pre>
 
 We can also change the value associated with a specified key:
 <pre class="prettyprint linenums">my_dic['GOOG'] = 'Alphabet Company'
-print my_dic['GOOG']
+print(my_dic['GOOG'])
 [out]: Alphabet Company
 </pre>
 
 The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
-<pre class="prettyprint linenums">print my_dic.keys()
+<pre class="prettyprint linenums">print(my_dic.keys())
 [out]: ['GOOG', 'AAPL', 'FB']
 </pre>
 
@@ -184,17 +174,17 @@ <h2>Common String Operations</h2>
 
 A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
 <pre class="prettyprint linenums">my_str = 'Welcome to QuantConnect'
-print my_str[8:]
+print(my_str[8:])
 [out]: to QuantConnect
 </pre>
 
 There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
 <pre class="prettyprint linenums">
-print "Counting the number of e's in this sentence".count('e')
+print("Counting the number of e's in this sentence".count('e'))
 [out]: 6
-print 'The first time e appears in this sentence'.find('e')
+print('The first time e appears in this sentence'.find('e'))
 [out]: 2
-print 'all the a in this sentence now becomes e'.replace('a','e')
+print('all the a in this sentence now becomes e'.replace('a','e'))
 [out]: ell the e in this sentence now becomes e
 </pre>
 
@@ -203,23 +193,23 @@ <h2>Common String Operations</h2>
 splited_list = Time.split(' ')
 date = splited_list[0]
 time = splited_list[1]
-print date, time
+print(date, time)
 [out]: 2016-04-01 09:43:00
 hour = time.split(':')[0]
-print hour
+print(hour)
 [out]: 09
 </pre>
 
 We can replace parts of a string by our variable. This is called string formatting.
 <pre class="prettyprint linenums">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
-print my_time
+print(my_time)
 [out]: Hour: 9, Minute: 43
 </pre>
 
 Another way to format a string is to use the % symbol.
 <pre class="prettyprint linenums">print 'pi is %f' % 3.14
 [out]: pi is 3.140000
-print '%s to %s' % ('Welcome', 'Quantconnect')
+print('%s to %s' % ('Welcome', 'Quantconnect'))
 [out]: Welcome to Quantconnect
 </pre>
 
@@ -227,4 +217,4 @@ <h2>Common String Operations</h2>
 
 <h2>Summary</h2>
 
-We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
\ No newline at end of file
+We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html
index 295e09f..fade877 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html	
@@ -53,8 +53,8 @@ <h2>Built-in Function</h2>
 Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
 
 <strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
-<pre class="prettyprint linenums">tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
-print map(len, tickers)
+<pre class="prettyprint linenums">tickers = [‘AAPL’,’GOOG’,’IBM’,’FB’,’F’,’V’, ‘G’, ‘GE’]
+print(list(map(len,tickers)))
 [out]: [4, 5, 3, 2, 1, 1, 1, 2]
 </pre>
 
@@ -191,4 +191,4 @@ <h2>Summary</h2>
 
 In this chapter we have introduced functions and classes. When we write a QuantConnect algorithm, we would define our algorithm as a class (QCAlgorithm). This means our algorithm inherited the QC API methods from QCAlgorithm class.
 
-In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.
\ No newline at end of file
+In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.

From eac39743351faa5064d658a05f4172211a1c21f0 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 4 May 2018 18:54:04 -0400
Subject: [PATCH 004/753] fix syntax error

---
 .../Tutorial01 Data Types and Data Structures.html            | 4 ++--
 ...torial03 Functions and Objective-Oriented Programming.html | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html
index 11722a1..5e777fb 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html	
@@ -209,8 +209,8 @@ <h2>Common String Operations</h2>
 Another way to format a string is to use the % symbol.
 <pre class="prettyprint linenums">print 'pi is %f' % 3.14
 [out]: pi is 3.140000
-print('%s to %s' % ('Welcome', 'Quantconnect'))
-[out]: Welcome to Quantconnect
+print('%s to %s' % ('Welcome', 'QuantConnect'))
+[out]: Welcome to QuantConnect
 </pre>
 
 %s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html
index fade877..560e7f9 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html	
@@ -53,7 +53,7 @@ <h2>Built-in Function</h2>
 Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
 
 <strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
-<pre class="prettyprint linenums">tickers = [‘AAPL’,’GOOG’,’IBM’,’FB’,’F’,’V’, ‘G’, ‘GE’]
+<pre class="prettyprint linenums">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
 print(list(map(len,tickers)))
 [out]: [4, 5, 3, 2, 1, 1, 1, 2]
 </pre>

From bf16c78d2638a81d01c1719c2364615bb8121245 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 22 May 2018 10:11:24 -0400
Subject: [PATCH 005/753] update the algorithm

---
 .../Tutorial03 Put-Call Parity and Arbitrage Strategies.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html b/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html
index b802cc8..485b070 100644
--- a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html	
+++ b/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html	
@@ -136,7 +136,7 @@ <h3>3. Synthetic Positions</h3>
 </table>
 Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
 <h3>Algorithm</h3>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=69b91d962fb631da4c1d7b6fadb91ec3'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
 <h2>Summary</h2>
 <div class="page" title="Page 264">
 <div class="layoutArea">

From 39f76d3a2b7e54898eb3e312ac2b8e9120dc56f8 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 23 May 2018 15:21:12 -0400
Subject: [PATCH 006/753] update the notebook files and tutorials

---
 ...Types and Data Structures-checkpoint.ipynb | 134 +--
 ...tive-Oriented Programming-checkpoint.ipynb | 150 ++--
 ...04 NumPy and Basic Pandas-checkpoint.ipynb | 478 ++++++++++
 ...-Resampling and DataFrame-checkpoint.ipynb | 839 ++++++++++++++----
 ...Return, Mean and Variance-checkpoint.ipynb |  45 +-
 ... Simple Linear Regression-checkpoint.ipynb | 267 ++++++
 ...ultiple Linear Regression-checkpoint.ipynb | 559 +++---------
 ...Tutorial11 Linear Algebra-checkpoint.ipynb | 127 +--
 ...2 Modern Portfolio Theory-checkpoint.ipynb | 417 +++++++++
 ...pital Asset Pricing Model-checkpoint.ipynb | 325 +++++++
 ...rench Multi-Factor Models-checkpoint.ipynb | 320 +++++++
 ...ial01 Data Types and Data Structures.ipynb | 134 +--
 ...s and Objective-Oriented Programming.ipynb | 150 ++--
 .../Tutorial04 NumPy and Basic Pandas.ipynb   | 213 ++---
 ...al05 Pandas-Resampling and DataFrame.ipynb | 565 ++++++------
 ...06 Rate of Return, Mean and Variance.ipynb |  45 +-
 ...07 Random Variables and Distributions.html |  12 +-
 .../Tutorial09 Simple Linear Regression.html  |   6 +-
 .../Tutorial09 Simple Linear Regression.ipynb |  70 +-
 ...Tutorial10 Multiple Linear Regression.html |  17 +-
 ...utorial10 Multiple Linear Regression.ipynb | 465 +++-------
 .../Tutorial11 Linear Algebra.ipynb           | 127 +--
 ...torial13 Capital Asset Pricing Model.ipynb |  52 +-
 23 files changed, 3572 insertions(+), 1945 deletions(-)
 create mode 100644 Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb
 create mode 100644 Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb
 create mode 100644 Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb
 create mode 100644 Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb
 create mode 100644 Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb

diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb
index 751aa6e..e4566f2 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -16,12 +16,12 @@
    "source": [
     "my_string1 = 'Welcome to'\n",
     "my_string2 = \"QuantConnect\"\n",
-    "print my_string1 + ' ' + my_string2"
+    "print(my_string1 + ' ' + my_string2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -29,59 +29,59 @@
      "output_type": "stream",
      "text": [
       "10\n",
-      "<type 'int'>\n"
+      "<class 'int'>\n"
      ]
     }
    ],
    "source": [
     "my_int = 10\n",
-    "print my_int\n",
-    "print type(my_int)"
+    "print(my_int)\n",
+    "print(type(my_int))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<type 'str'>\n",
-      "<type 'int'>\n"
+      "<class 'str'>\n",
+      "<class 'int'>\n"
      ]
     }
    ],
    "source": [
     "my_string = \"100\"\n",
-    "print type(my_string)\n",
-    "my_int = int(my_string)E\n",
-    "print type(my_int)"
+    "print(type(my_string))\n",
+    "my_int = int(my_string)\n",
+    "print(type(my_int))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<type 'float'>\n"
+      "<class 'float'>\n"
      ]
     }
    ],
    "source": [
     "my_string = \"100\"\n",
     "my_float = float(my_string)\n",
-    "print type(my_float)"
+    "print(type(my_float))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -89,19 +89,19 @@
      "output_type": "stream",
      "text": [
       "False\n",
-      "<type 'bool'>\n"
+      "<class 'bool'>\n"
      ]
     }
    ],
    "source": [
     "my_bool = False\n",
-    "print my_bool\n",
-    "print type(my_bool)"
+    "print(my_bool)\n",
+    "print(type(my_bool))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -111,41 +111,41 @@
       "Addition  2\n",
       "Subtraction  3\n",
       "Multiplication  6\n",
-      "Division  5\n",
+      "Division  5.0\n",
       "exponent 8\n"
      ]
     }
    ],
    "source": [
-    "print \"Addition \", 1+1\n",
-    "print \"Subtraction \", 5-2\n",
-    "print \"Multiplication \", 2*3\n",
-    "print \"Division \", 10/2\n",
-    "print 'exponent', 2**3"
+    "print(\"Addition \", 1+1)\n",
+    "print(\"Subtraction \", 5-2)\n",
+    "print(\"Multiplication \", 2*3)\n",
+    "print(\"Division \", 10/2)\n",
+    "print('exponent', 2**3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\n",
-      "0.333333333333\n"
+      "0.3333333333333333\n",
+      "0.3333333333333333\n"
      ]
     }
    ],
    "source": [
-    "print 1/3\n",
-    "print 1.0/3"
+    "print(1/3)\n",
+    "print(1.0/3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -158,12 +158,12 @@
    ],
    "source": [
     "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -178,14 +178,14 @@
    ],
    "source": [
     "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print len(my_list)\n",
-    "print my_list[0]\n",
-    "print my_list[len(my_list) -1]"
+    "print(len(my_list))\n",
+    "print(my_list[0])\n",
+    "print(my_list[len(my_list) -1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
    "source": [
     "my_list = ['Quant','Connect',1,2,3]\n",
     "my_list[2] = 'go'\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
@@ -219,9 +219,9 @@
    "source": [
     "my_list = ['Quant']\n",
     "my_list.append('Connect')\n",
-    "print my_list\n",
+    "print(my_list)\n",
     "my_list.remove('Quant')\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
@@ -239,7 +239,7 @@
    ],
    "source": [
     "my_list = ['Quant','Connect',1,2,3]\n",
-    "print my_list[1:3]"
+    "print(my_list[1:3])"
    ]
   },
   {
@@ -256,7 +256,7 @@
     }
    ],
    "source": [
-    "print my_list[1:]"
+    "print(my_list[1:])"
    ]
   },
   {
@@ -273,7 +273,7 @@
     }
    ],
    "source": [
-    "print my_list[:3]"
+    "print(my_list[:3])"
    ]
   },
   {
@@ -302,7 +302,7 @@
    ],
    "source": [
     "my_tuple = ('Welcome','to','QuantConnect')\n",
-    "print my_tuple[1:]"
+    "print(my_tuple[1:])"
    ]
   },
   {
@@ -321,7 +321,7 @@
    "source": [
     "stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']\n",
     "stock_set = set(stock_list)\n",
-    "print stock_set"
+    "print(stock_set)"
    ]
   },
   {
@@ -349,7 +349,7 @@
     }
    ],
    "source": [
-    "print my_dic['GOOG']"
+    "print(my_dic['GOOG'])"
    ]
   },
   {
@@ -367,7 +367,7 @@
    ],
    "source": [
     "my_dic['GOOG'] = 'Alphabet Company'\n",
-    "print my_dic['GOOG']"
+    "print(my_dic['GOOG'])"
    ]
   },
   {
@@ -384,7 +384,7 @@
     }
    ],
    "source": [
-    "print my_dic.keys()"
+    "print(my_dic.keys())"
    ]
   },
   {
@@ -402,12 +402,12 @@
    ],
    "source": [
     "my_str = 'Welcome to QuantConnect'\n",
-    "print my_str[8:]"
+    "print(my_str[8:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -421,14 +421,14 @@
     }
    ],
    "source": [
-    "print 'Counting the number of e appears in this sentence'.count('e')\n",
-    "print 'The first time e appears in this sentence'.find('e')\n",
-    "print 'all the a in this sentence now becomes e'.replace('a','e')"
+    "print('Counting the number of e appears in this sentence'.count('e'))\n",
+    "print('The first time e appears in this sentence'.find('e'))\n",
+    "print('all the a in this sentence now becomes e'.replace('a','e'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -445,14 +445,14 @@
     "splited_list = Time.split(' ')\n",
     "date = splited_list[0]\n",
     "time = splited_list[1]\n",
-    "print date, time\n",
+    "print(date, time)\n",
     "hour = time.split(':')[0]\n",
-    "print hour"
+    "print(hour)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -465,12 +465,12 @@
    ],
    "source": [
     "my_time = 'Hour: {}, Minute:{}'.format('09','43')\n",
-    "print my_time"
+    "print(my_time)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -483,28 +483,28 @@
     }
    ],
    "source": [
-    "print 'the pi number is %f'%3.14\n",
-    "print '%s to %s'%('Welcome','Quantconnect')"
+    "print('the pi number is %f'%3.14)\n",
+    "print('%s to %s'%('Welcome','Quantconnect'))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb
index 08b35ef..261e947 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb	
@@ -17,13 +17,13 @@
    "source": [
     "def product(x,y):\n",
     "    return x*y\n",
-    "print product(2,3)\n",
-    "print product(5,10)"
+    "print(product(2,3))\n",
+    "print(product(5,10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,34 +36,34 @@
    ],
    "source": [
     "def say_hi():\n",
-    "    print 'Welcome to QuantConnect'\n",
+    "    print('Welcome to QuantConnect')\n",
     "say_hi()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
-      "[1, 3, 5, 7, 9]\n"
+      "range(0, 10)\n",
+      "range(1, 11)\n",
+      "range(1, 11, 2)\n"
      ]
     }
    ],
    "source": [
-    "print range(10)\n",
-    "print range(1,11)\n",
-    "print range(1,11,2)"
+    "print(range(10))\n",
+    "print(range(1,11))\n",
+    "print(range(1,11,2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -84,14 +84,14 @@
    ],
    "source": [
     "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print 'The length of tickers is {}'.format(len(tickers))\n",
+    "print('The length of tickers is {}'.format(len(tickers)))\n",
     "for i in range(len(tickers)):\n",
-    "    print tickers[i]"
+    "    print(tickers[i])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -104,12 +104,12 @@
    ],
    "source": [
     "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print map(len,tickers)"
+    "print(list(map(len,tickers)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -118,18 +118,18 @@
        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map(lambda x: x**2, range(10))"
+    "list(map(lambda x: x**2, range(10)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -138,18 +138,18 @@
        "[6, 6, 6, 6, 6]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]) "
+    "list(map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -158,7 +158,7 @@
        "[1, 2, 3, 4, 5]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +178,7 @@
        "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
        "[('GOOG', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -225,15 +225,13 @@
    "source": [
     "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
     "price_list.sort(key = lambda x: x[1])\n",
-    "print price_list"
+    "print(price_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class stock:\n",
@@ -250,15 +248,13 @@
     "        self.rate_return = float(self.close)/self.open - 1\n",
     " \n",
     "    def print_return(self):\n",
-    "        print self.rate_return"
+    "        print(self.rate_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "apple = stock('AAPL', 143.69, 144.09, 20109375)\n",
@@ -267,15 +263,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0144653388227\n",
-      "0.000657318141981\n"
+      "0.014465338822744034\n",
+      "0.0006573181419806673\n"
      ]
     }
    ],
@@ -288,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +293,7 @@
        "'Tim Cook'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,15 +305,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['__doc__',\n",
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
        " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
        " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
        " 'ceo',\n",
        " 'close',\n",
        " 'open',\n",
@@ -328,7 +347,7 @@
        " 'volume']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,10 +358,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class child(stock):\n",
@@ -352,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -362,47 +379,38 @@
       "aa\n",
       "100\n",
       "102\n",
-      "0.02\n",
+      "0.020000000000000018\n",
       "None\n"
      ]
     }
    ],
    "source": [
     "aa = child('aa')\n",
-    "print aa.name\n",
+    "print(aa.name)\n",
     "aa.update(100,102)\n",
-    "print aa.open\n",
-    "print aa.close\n",
-    "print aa.print_return()"
+    "print(aa.open)\n",
+    "print(aa.close)\n",
+    "print(aa.print_return())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb
new file mode 100644
index 0000000..32fd4f9
--- /dev/null
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb	
@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[143.73 145.83 143.68 144.02 143.5  142.62] <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
+    "price_array = np.array(price_list)\n",
+    "print(price_array, type(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 3]\n",
+      " [2 4]] <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "Ar = np.array([[1,3],[2,4]])\n",
+    "print(Ar, type(Ar))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Ar.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 3]\n",
+      "[2 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Ar[0])\n",
+    "print(Ar[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first column:  [1 2]\n",
+      "the second column:  [3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('the first column: ', Ar[:,0])\n",
+    "print('the second column: ', Ar[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.log(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.89666666666668\n",
+      "0.9673790478515796\n",
+      "863.38\n",
+      "145.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.mean(price_array))\n",
+    "print(np.std(price_array))\n",
+    "print(np.sum(price_array))\n",
+    "print(np.max(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    143.73\n",
+       "1    145.83\n",
+       "2    143.68\n",
+       "3    144.02\n",
+       "4    143.50\n",
+       "5    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
+    "s = pd.Series(price)\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    143.73\n",
+       "b    145.83\n",
+       "c    143.68\n",
+       "d    144.02\n",
+       "e    143.50\n",
+       "f    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(price,index = ['a','b','c','d','e','f'])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    143.73\n",
+       "5    145.83\n",
+       "4    143.68\n",
+       "3    144.02\n",
+       "2    143.50\n",
+       "1    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.index = [6,5,4,3,2,1]\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "dtype: float64\n",
+      "6    143.73\n",
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[1:])\n",
+    "print(s[:-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.68\n",
+      "6    143.73\n",
+      "5    145.83\n",
+      "4      0.00\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[4])\n",
+    "s[4] = 0\n",
+    "print(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    143.73\n",
+      "1    145.83\n",
+      "2    143.68\n",
+      "3    144.02\n",
+      "4    143.50\n",
+      "5    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "Apple Price List\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = pd.Series(price, name = 'Apple Price List')\n",
+    "print(s)\n",
+    "print(s.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
+      "               '2017-01-05', '2017-01-06'],\n",
+      "              dtype='datetime64[ns]', freq='D')\n",
+      "2017-01-01    143.73\n",
+      "2017-01-02    145.83\n",
+      "2017-01-03    143.68\n",
+      "2017-01-04    144.02\n",
+      "2017-01-05    143.50\n",
+      "2017-01-06    142.62\n",
+      "Freq: D, Name: Apple Price List, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_index = pd.date_range('2017-01-01',periods = len(s),freq = 'D')\n",
+    "print(time_index)\n",
+    "s.index = time_index\n",
+    "print(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6    143.73\n",
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "142.62\n"
+     ]
+    }
+   ],
+   "source": [
+    "s.index = [6,5,4,3,2,1]\n",
+    "print(s)\n",
+    "print(s[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s.iloc[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.68\n"
+     ]
+    }
+   ],
+   "source": [
+    "s.index = time_index\n",
+    "print(s['2017-01-03'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2017-01-02    145.83\n",
+      "2017-01-03    143.68\n",
+      "2017-01-04    144.02\n",
+      "2017-01-05    143.50\n",
+      "Freq: D, Name: Apple Price List, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s['2017-01-02':'2017-01-05'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6    143.73\n",
+      "4    143.68\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "[6    False\n",
+      "5    False\n",
+      "4    False\n",
+      "3     True\n",
+      "2    False\n",
+      "1    False\n",
+      "Name: Apple Price List, dtype: bool]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[s < np.mean(s)] )\n",
+    "print([(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb
index 4fc8445..a67e05c 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import quandl\n",
@@ -15,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,9 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "aapl = aapl_table['Adj. Close']['2017']"
@@ -38,22 +34,320 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-03    114.715378\n",
+      "2017-01-04    114.586983\n",
+      "2017-01-05    115.169696\n",
+      "2017-01-06    116.453639\n",
+      "2017-01-09    117.520300\n",
+      "2017-01-10    117.638817\n",
+      "2017-01-11    118.270913\n",
+      "2017-01-12    117.777088\n",
+      "2017-01-13    117.569682\n",
+      "2017-01-17    118.517825\n",
+      "2017-01-18    118.507948\n",
+      "2017-01-19    118.300542\n",
+      "2017-01-20    118.517825\n",
+      "2017-01-23    118.596837\n",
+      "2017-01-24    118.488195\n",
+      "2017-01-25    120.374604\n",
+      "2017-01-26    120.433863\n",
+      "2017-01-27    120.443739\n",
+      "2017-01-30    120.127692\n",
+      "2017-01-31    119.851150\n",
+      "2017-02-01    127.159749\n",
+      "2017-02-02    126.942467\n",
+      "2017-02-03    127.485673\n",
+      "2017-02-06    128.680728\n",
+      "2017-02-07    129.905412\n",
+      "2017-02-08    130.409113\n",
+      "2017-02-09    131.347379\n",
+      "2017-02-10    131.049809\n",
+      "2017-02-13    132.210332\n",
+      "2017-02-14    133.926319\n",
+      "                 ...    \n",
+      "2017-11-16    171.100000\n",
+      "2017-11-17    170.150000\n",
+      "2017-11-20    169.980000\n",
+      "2017-11-21    173.140000\n",
+      "2017-11-22    174.960000\n",
+      "2017-11-24    174.970000\n",
+      "2017-11-27    174.090000\n",
+      "2017-11-28    173.070000\n",
+      "2017-11-29    169.480000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-01    171.050000\n",
+      "2017-12-04    169.800000\n",
+      "2017-12-05    169.640000\n",
+      "2017-12-06    169.010000\n",
+      "2017-12-07    169.452000\n",
+      "2017-12-08    169.370000\n",
+      "2017-12-11    172.670000\n",
+      "2017-12-12    171.700000\n",
+      "2017-12-13    172.270000\n",
+      "2017-12-14    172.220000\n",
+      "2017-12-15    173.870000\n",
+      "2017-12-18    176.420000\n",
+      "2017-12-19    174.540000\n",
+      "2017-12-20    174.350000\n",
+      "2017-12-21    175.010000\n",
+      "2017-12-22    175.010000\n",
+      "2017-12-26    170.570000\n",
+      "2017-12-27    170.600000\n",
+      "2017-12-28    171.080000\n",
+      "2017-12-29    169.230000\n",
+      "Name: Adj. Close, Length: 249, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-03-01    138.657681\n",
+      "2017-03-02    137.834404\n",
+      "2017-03-03    138.647762\n",
+      "2017-03-06    138.211326\n",
+      "2017-03-07    138.389868\n",
+      "2017-03-08    137.874080\n",
+      "2017-03-09    137.556672\n",
+      "2017-03-10    138.012946\n",
+      "2017-03-13    138.072460\n",
+      "2017-03-14    137.864161\n",
+      "2017-03-15    139.322254\n",
+      "2017-03-16    139.550391\n",
+      "2017-03-17    138.856061\n",
+      "2017-03-20    140.314154\n",
+      "2017-03-21    138.707276\n",
+      "2017-03-22    140.274478\n",
+      "2017-03-23    139.778528\n",
+      "2017-03-24    139.500796\n",
+      "2017-03-27    139.738852\n",
+      "2017-03-28    142.635200\n",
+      "2017-03-29    142.952608\n",
+      "2017-03-30    142.764147\n",
+      "2017-03-31    142.496334\n",
+      "Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl['2017-3'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    120.932434\n",
-       "2017-02-28    136.551200\n",
-       "2017-03-31    143.532630\n",
-       "2017-04-30    144.179981\n",
-       "2017-05-31    156.100000\n",
-       "2017-06-30    155.450000\n",
-       "2017-07-31    153.460000\n",
+       "2017-02-01    127.159749\n",
+       "2017-02-02    126.942467\n",
+       "2017-02-03    127.485673\n",
+       "2017-02-06    128.680728\n",
+       "2017-02-07    129.905412\n",
+       "2017-02-08    130.409113\n",
+       "2017-02-09    131.347379\n",
+       "2017-02-10    131.049809\n",
+       "2017-02-13    132.210332\n",
+       "2017-02-14    133.926319\n",
+       "2017-02-15    134.412350\n",
+       "2017-02-16    134.248686\n",
+       "2017-02-17    134.620649\n",
+       "2017-02-21    135.592711\n",
+       "2017-02-22    135.999390\n",
+       "2017-02-23    135.424088\n",
+       "2017-02-24    135.553035\n",
+       "2017-02-27    135.820848\n",
+       "2017-02-28    135.880362\n",
+       "2017-03-01    138.657681\n",
+       "2017-03-02    137.834404\n",
+       "2017-03-03    138.647762\n",
+       "2017-03-06    138.211326\n",
+       "2017-03-07    138.389868\n",
+       "2017-03-08    137.874080\n",
+       "2017-03-09    137.556672\n",
+       "2017-03-10    138.012946\n",
+       "2017-03-13    138.072460\n",
+       "2017-03-14    137.864161\n",
+       "2017-03-15    139.322254\n",
+       "                 ...    \n",
+       "2017-03-17    138.856061\n",
+       "2017-03-20    140.314154\n",
+       "2017-03-21    138.707276\n",
+       "2017-03-22    140.274478\n",
+       "2017-03-23    139.778528\n",
+       "2017-03-24    139.500796\n",
+       "2017-03-27    139.738852\n",
+       "2017-03-28    142.635200\n",
+       "2017-03-29    142.952608\n",
+       "2017-03-30    142.764147\n",
+       "2017-03-31    142.496334\n",
+       "2017-04-03    142.536010\n",
+       "2017-04-04    143.597342\n",
+       "2017-04-05    142.853418\n",
+       "2017-04-06    142.496334\n",
+       "2017-04-07    142.178926\n",
+       "2017-04-10    142.010303\n",
+       "2017-04-11    140.482777\n",
+       "2017-04-12    140.651400\n",
+       "2017-04-13    139.907475\n",
+       "2017-04-17    140.681157\n",
+       "2017-04-18    140.056260\n",
+       "2017-04-19    139.540472\n",
+       "2017-04-20    141.286216\n",
+       "2017-04-21    141.117593\n",
+       "2017-04-24    142.476496\n",
+       "2017-04-25    143.369205\n",
+       "2017-04-26    142.487208\n",
+       "2017-04-27    142.625281\n",
+       "2017-04-28    142.486415\n",
+       "Name: Adj. Close, Length: 61, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aapl['2017-2':'2017-4']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-03    114.715378\n",
+      "2017-01-04    114.586983\n",
+      "2017-01-05    115.169696\n",
+      "2017-01-06    116.453639\n",
+      "2017-01-09    117.520300\n",
+      "Name: Adj. Close, dtype: float64\n",
+      "Date\n",
+      "2017-12-15    173.87\n",
+      "2017-12-18    176.42\n",
+      "2017-12-19    174.54\n",
+      "2017-12-20    174.35\n",
+      "2017-12-21    175.01\n",
+      "2017-12-22    175.01\n",
+      "2017-12-26    170.57\n",
+      "2017-12-27    170.60\n",
+      "2017-12-28    171.08\n",
+      "2017-12-29    169.23\n",
+      "Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl.head(5))\n",
+    "print(aapl.tail(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    118.093136\n",
+      "2017-02-28    132.456268\n",
+      "2017-03-31    139.478802\n",
+      "2017-04-30    141.728436\n",
+      "2017-05-31    151.386305\n",
+      "2017-06-30    147.233064\n",
+      "2017-07-31    147.706190\n",
+      "2017-08-31    158.856375\n",
+      "2017-09-30    157.606500\n",
+      "2017-10-31    157.811627\n",
+      "2017-11-30    172.214500\n",
+      "2017-12-31    171.893100\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_month = aapl.resample('M').mean()\n",
+    "print(by_month)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-08    115.231424\n",
+      "2017-01-15    117.755360\n",
+      "2017-01-22    118.461035\n",
+      "2017-01-29    119.667448\n",
+      "2017-02-05    124.313346\n",
+      "Freq: W-SUN, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_week = aapl.resample('W').mean()\n",
+    "print(by_week.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-01-31    120.443739\n",
+       "2017-02-28    135.999390\n",
+       "2017-03-31    142.952608\n",
+       "2017-04-30    143.597342\n",
+       "2017-05-31    155.469192\n",
+       "2017-06-30    154.821818\n",
+       "2017-07-31    152.839860\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    164.050000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    175.880000\n",
+       "2017-12-31    176.420000\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,10 +358,92 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "three_day = aapl.resample('3D').mean()\n",
+    "two_week = aapl.resample('2W').mean()\n",
+    "two_month = aapl.resample('2M').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std = aapl.resample('W').std()\n",
+    "max = aapl.resample('W').max()\n",
+    "min = aapl.resample('W').min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.045940\n",
+      "2017-02-28    0.070409\n",
+      "2017-03-31    0.033823\n",
+      "2017-04-30   -0.007736\n",
+      "2017-05-31    0.039829\n",
+      "2017-06-30   -0.073528\n",
+      "2017-07-31    0.033035\n",
+      "2017-08-31    0.047890\n",
+      "2017-09-30   -0.049112\n",
+      "2017-10-31    0.094252\n",
+      "2017-11-30    0.022247\n",
+      "2017-12-31   -0.003357\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
+    "print(monthly_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    119.851150\n",
+      "2017-02-28    135.880362\n",
+      "2017-03-31    142.496334\n",
+      "2017-04-30    142.486415\n",
+      "2017-05-31    152.142689\n",
+      "2017-06-30    143.438008\n",
+      "2017-07-31    148.248489\n",
+      "2017-08-31    164.000000\n",
+      "2017-09-30    154.120000\n",
+      "2017-10-31    169.040000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-31    169.230000\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
+    "print(last_day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "by_week = aapl.resample('W').mean()"
@@ -75,21 +451,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    1.788740\n",
-       "2017-03-31    4.341378\n",
-       "2017-05-31    5.476627\n",
-       "2017-07-31    3.927178\n",
+       "2017-01-31    1.781512\n",
+       "2017-03-31    4.323834\n",
+       "2017-05-31    5.454495\n",
+       "2017-07-31    3.865333\n",
+       "2017-09-30    3.634223\n",
+       "2017-11-30    8.011704\n",
+       "2018-01-31    2.291229\n",
        "Freq: 2M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -101,24 +480,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    120.337440\n",
-       "2017-02-28    136.431689\n",
-       "2017-03-31    143.074505\n",
-       "2017-04-30    143.064546\n",
-       "2017-05-31    152.760000\n",
-       "2017-06-30    144.020000\n",
-       "2017-07-31    149.500000\n",
+       "2017-01-31    119.851150\n",
+       "2017-02-28    135.880362\n",
+       "2017-03-31    142.496334\n",
+       "2017-04-30    142.486415\n",
+       "2017-05-31    152.142689\n",
+       "2017-06-30    143.438008\n",
+       "2017-07-31    148.248489\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    154.120000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    171.850000\n",
+       "2017-12-31    169.230000\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -130,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -143,11 +527,16 @@
        "2017-04-30   -0.007736\n",
        "2017-05-31    0.039829\n",
        "2017-06-30   -0.073528\n",
-       "2017-07-31    0.037546\n",
+       "2017-07-31    0.033035\n",
+       "2017-08-31    0.047890\n",
+       "2017-09-30   -0.049112\n",
+       "2017-10-31    0.094252\n",
+       "2017-11-30    0.022247\n",
+       "2017-12-31   -0.003357\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,28 +548,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0208974076157\n",
-      "0.0476398315185\n",
-      "0.0704090212384\n"
+      "0.02114094011940022\n",
+      "0.04775652864223314\n",
+      "0.09425168306576914\n"
      ]
     }
    ],
    "source": [
-    "print monthly_return.mean()\n",
-    "print monthly_return.std()\n",
-    "print monthly_return.max()"
+    "print(monthly_return.mean())\n",
+    "print(monthly_return.std())\n",
+    "print(monthly_return.max())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -189,12 +578,17 @@
      "text": [
       "Date\n",
       "2017-01-31          NaN\n",
-      "2017-02-28    16.094249\n",
-      "2017-03-31     6.642816\n",
-      "2017-04-30    -0.009959\n",
-      "2017-05-31     9.695454\n",
-      "2017-06-30    -8.740000\n",
-      "2017-07-31     5.480000\n",
+      "2017-02-28    16.029211\n",
+      "2017-03-31     6.615972\n",
+      "2017-04-30    -0.009919\n",
+      "2017-05-31     9.656274\n",
+      "2017-06-30    -8.704681\n",
+      "2017-07-31     4.810482\n",
+      "2017-08-31    15.751511\n",
+      "2017-09-30    -9.880000\n",
+      "2017-10-31    14.920000\n",
+      "2017-11-30     2.810000\n",
+      "2017-12-31    -2.620000\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n",
       "Date\n",
       "2017-01-31         NaN\n",
@@ -203,44 +597,111 @@
       "2017-04-30   -0.000070\n",
       "2017-05-31    0.067770\n",
       "2017-06-30   -0.057214\n",
-      "2017-07-31    0.038050\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print last_day.diff()\n",
-    "print last_day.pct_change()"
+    "print(last_day.diff())\n",
+    "print(last_day.pct_change())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "count    144.000000\n",
-      "mean     140.532385\n",
-      "std       10.956590\n",
-      "min      115.051914\n",
-      "25%      136.314668\n",
-      "50%      143.064944\n",
-      "75%      147.915726\n",
-      "max      156.100000\n",
+      "count    249.000000\n",
+      "mean     149.815713\n",
+      "std       15.065681\n",
+      "min      114.586983\n",
+      "25%      140.651400\n",
+      "50%      151.890000\n",
+      "75%      159.780000\n",
+      "max      176.420000\n",
       "Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print aapl.describe()"
+    "print(aapl.describe())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.000000\n",
+      "2017-02-28    0.133743\n",
+      "2017-03-31    0.048690\n",
+      "2017-04-30   -0.000070\n",
+      "2017-05-31    0.067770\n",
+      "2017-06-30   -0.057214\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_return = last_day.pct_change()\n",
+    "print(daily_return.fillna(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.133743\n",
+      "2017-02-28    0.133743\n",
+      "2017-03-31    0.048690\n",
+      "2017-04-30   -0.000070\n",
+      "2017-05-31    0.067770\n",
+      "2017-06-30   -0.057214\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_return = last_day.pct_change()\n",
+    "print(daily_return.fillna(method = 'bfill'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -252,11 +713,16 @@
        "2017-04-30   -0.000070\n",
        "2017-05-31    0.067770\n",
        "2017-06-30   -0.057214\n",
-       "2017-07-31    0.038050\n",
+       "2017-07-31    0.033537\n",
+       "2017-08-31    0.106251\n",
+       "2017-09-30   -0.060244\n",
+       "2017-10-31    0.096808\n",
+       "2017-11-30    0.016623\n",
+       "2017-12-31   -0.015246\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -268,35 +734,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31     5.880519\n",
-       "2017-02-28     9.093671\n",
-       "2017-03-31     5.417829\n",
-       "2017-04-30     4.073331\n",
-       "2017-05-31    10.167192\n",
-       "2017-06-30    13.180000\n",
-       "2017-07-31    10.730000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "aapl.resample('M').agg(lambda x: max(x) - min(x))"
+    "# aapl.resample('M').agg(lambda x: max(x) - min(x))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -317,12 +764,12 @@
     "        'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}\n",
     "data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')\n",
     "df = pd.DataFrame(dict, index = data_index)\n",
-    "print df"
+    "print(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -342,19 +789,19 @@
     "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
     "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
     "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print data_frame"
+    "print(data_frame)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index([u'AAPL', u'GOOG', u'IBM'], dtype='object')\n",
+      "Index(['AAPL', 'GOOG', 'IBM'], dtype='object')\n",
       "2017-07-03    143.50\n",
       "2017-07-04    144.09\n",
       "2017-07-05    142.73\n",
@@ -371,14 +818,14 @@
     }
    ],
    "source": [
-    "print df.columns\n",
-    "print df.AAPL\n",
-    "print df['GOOG']"
+    "print(df.columns)\n",
+    "print(df.AAPL)\n",
+    "print(df['GOOG'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -386,31 +833,31 @@
      "output_type": "stream",
      "text": [
       "Date\n",
-      "2017-07-24    152.09\n",
-      "2017-07-25    152.74\n",
-      "2017-07-26    153.46\n",
-      "2017-07-27    150.56\n",
-      "2017-07-28    149.50\n",
+      "2018-03-21    171.270\n",
+      "2018-03-22    168.845\n",
+      "2018-03-23    164.940\n",
+      "2018-03-26    172.770\n",
+      "2018-03-27    168.340\n",
       "Name: Close, dtype: float64\n",
       "Date\n",
-      "2017-07-24    21122730.0\n",
-      "2017-07-25    18612649.0\n",
-      "2017-07-26    15172136.0\n",
-      "2017-07-27    32175875.0\n",
-      "2017-07-28    16832947.0\n",
+      "2018-03-21    35247358.0\n",
+      "2018-03-22    41051076.0\n",
+      "2018-03-23    40248954.0\n",
+      "2018-03-26    36272617.0\n",
+      "2018-03-27    38962839.0\n",
       "Name: Adj. Volume, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "df = aapl_table\n",
-    "print df.Close.tail(5)\n",
-    "print df['Adj. Volume'].tail(5)"
+    "print(df.Close.tail(5))\n",
+    "print(df['Adj. Volume'].tail(5))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -434,18 +881,18 @@
       "\n",
       "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
       "Date                                                                      \n",
-      "2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495   \n",
-      "2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856   \n",
-      "2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931   \n",
-      "2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748   \n",
-      "2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913   \n",
-      "2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775   \n",
-      "2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363   \n",
-      "2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460   \n",
-      "2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885   \n",
-      "2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287   \n",
-      "2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807   \n",
-      "2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583   \n",
+      "2016-01-31          1.0   91.581233   94.044912   91.156128   94.044912   \n",
+      "2016-02-29          1.0   94.084911   95.415659   93.880927   93.919781   \n",
+      "2016-03-31          1.0  106.576465  106.751308  105.760531  105.867380   \n",
+      "2016-04-30          1.0   91.297138   92.006223   89.859540   91.054300   \n",
+      "2016-05-31          1.0   97.337844   98.119674   96.575559   97.591939   \n",
+      "2016-06-30          1.0   92.295040   93.594832   92.158220   93.428693   \n",
+      "2016-07-31          1.0  101.823594  102.175417  101.325177  101.843140   \n",
+      "2016-08-31          1.0  103.816156  104.710177  103.796505  104.248477   \n",
+      "2016-09-30          1.0  110.497491  111.391610  109.849008  111.077195   \n",
+      "2016-10-31          1.0  111.666724  112.236603  111.224577  111.558644   \n",
+      "2016-11-30          1.0  110.182071  110.814166  108.908004  109.154917   \n",
+      "2016-12-31          1.0  115.209202  115.752409  114.004271  114.389454   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
@@ -467,12 +914,12 @@
    "source": [
     "aapl_2016 = df['2016']\n",
     "aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])\n",
-    "print aapl_month"
+    "print(aapl_month)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -498,12 +945,12 @@
    ],
    "source": [
     "aapl_bar = aapl_month[['Open', 'High', 'Low', 'Close']]\n",
-    "print aapl_bar"
+    "print(aapl_bar)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -520,12 +967,12 @@
     }
    ],
    "source": [
-    "print aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']]"
+    "print(aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -545,12 +992,12 @@
    ],
    "source": [
     "above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]\n",
-    "print above"
+    "print(above)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -577,7 +1024,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -588,32 +1035,32 @@
    ],
    "source": [
     "aapl_bar['rate_return'] = aapl_bar.Close.pct_change()\n",
-    "print aapl_bar"
+    "print(aapl_bar)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "             Open   High    Low  Close rate_return\n",
-      "Date                                              \n",
-      "2016-01-31  False  False  False  False        True\n",
-      "2016-02-29  False  False  False  False       False\n",
-      "2016-03-31  False  False  False  False       False\n",
-      "2016-04-30  False  False  False  False       False\n",
-      "2016-05-31  False  False  False  False       False\n",
-      "2016-06-30  False  False  False  False       False\n",
-      "2016-07-31  False  False  False  False       False\n",
-      "2016-08-31  False  False  False  False       False\n",
-      "2016-09-30  False  False  False  False       False\n",
-      "2016-10-31  False  False  False  False       False\n",
-      "2016-11-30  False  False  False  False       False\n",
-      "2016-12-31  False  False  False  False       False\n",
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n",
+      "2016-02-29  False  False  False  False        False\n",
+      "2016-03-31  False  False  False  False        False\n",
+      "2016-04-30  False  False  False  False        False\n",
+      "2016-05-31  False  False  False  False        False\n",
+      "2016-06-30  False  False  False  False        False\n",
+      "2016-07-31  False  False  False  False        False\n",
+      "2016-08-31  False  False  False  False        False\n",
+      "2016-09-30  False  False  False  False        False\n",
+      "2016-10-31  False  False  False  False        False\n",
+      "2016-11-30  False  False  False  False        False\n",
+      "2016-12-31  False  False  False  False        False\n",
       "\n",
       "------------------ separate line -----------------\n",
       "\n",
@@ -627,33 +1074,33 @@
    ],
    "source": [
     "missing = aapl_bar.isnull()\n",
-    "print missing\n",
-    "print '\\n------------------ separate line -----------------\\n'\n",
-    "print missing.describe()"
+    "print(missing)\n",
+    "print('\\n------------------ separate line -----------------\\n')\n",
+    "print(missing.describe())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "             Open   High    Low  Close rate_return\n",
-      "Date                                              \n",
-      "2016-01-31  False  False  False  False        True\n"
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n"
      ]
     }
    ],
    "source": [
-    "print missing[missing.rate_return == True]"
+    "print(missing[missing.rate_return == True])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -695,15 +1142,15 @@
    ],
    "source": [
     "drop = aapl_bar.dropna()\n",
-    "print drop\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(drop)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "fill = aapl_bar.fillna(0)\n",
-    "print fill"
+    "print(fill)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -723,12 +1170,12 @@
     "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
     "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
     "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print data_frame"
+    "print(data_frame)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -772,15 +1219,15 @@
    "source": [
     "log_price = np.log(aapl_bar.Close)\n",
     "log_price.name = 'log_price'\n",
-    "print log_price\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(log_price)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "concat = pd.concat([aapl_bar, log_price], axis = 1)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -813,15 +1260,15 @@
    ],
    "source": [
     "df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])\n",
-    "print df_volume\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(df_volume)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])\n",
-    "print df_2017"
+    "print(df_2017)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -870,12 +1317,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar,df_volume],axis = 1)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -898,12 +1345,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -936,12 +1383,12 @@
    ],
    "source": [
     "append = aapl_bar.append(df_2017)\n",
-    "print append"
+    "print(append)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -974,12 +1421,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1013,12 +1460,12 @@
    "source": [
     "df_2017.columns = ['Change', 'High','Low','Close']\n",
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1051,27 +1498,41 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb
index 3980d14..8c4c7b6 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb	
@@ -9,19 +9,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.02\n"
+      "0.020000000000000018\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",
     "rate_return = 102.0/100 - 1\n",
-    "print rate_return"
+    "print(rate_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -66,88 +66,79 @@
     "#take log return\n",
     "aapl['log_price'] = np.log(aapl.Close)\n",
     "aapl['log_return'] = aapl.log_price.diff()\n",
-    "print aapl"
+    "print(aapl)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0273081001636\n"
+      "0.0273081001636184\n"
      ]
     }
    ],
    "source": [
     "month_return = aapl.log_return.sum()\n",
-    "print month_return"
+    "print(month_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.94597446551\n"
+      "4.94597446550658\n"
      ]
     }
    ],
    "source": [
-    "print np.mean(aapl.log_price)"
+    "print(np.mean(aapl.log_price))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.000142032804482\n"
+      "0.00014203280448152512\n"
      ]
     }
    ],
    "source": [
-    "print np.var(aapl.log_price)"
+    "print(np.var(aapl.log_price))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb
new file mode 100644
index 0000000..c870f22
--- /dev/null
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb	
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import quandl\n",
+    "spy_table = quandl.get('LSE/SPY5')\n",
+    "amzn_table = quandl.get('WIKI/AMZN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spy = spy_table.loc['2016',['Last Close']]\n",
+    "amzn = amzn_table.loc['2016',['Close']]\n",
+    "spy_log = np.log(spy['Last Close']).diff().dropna()\n",
+    "amzn_log = np.log(amzn.Close).diff().dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spy</th>\n",
+       "      <th>amzn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-12-22</th>\n",
+       "      <td>-0.004462</td>\n",
+       "      <td>-0.005543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-23</th>\n",
+       "      <td>0.001372</td>\n",
+       "      <td>-0.007531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-28</th>\n",
+       "      <td>0.000928</td>\n",
+       "      <td>0.000946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-29</th>\n",
+       "      <td>-0.005671</td>\n",
+       "      <td>-0.009081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-30</th>\n",
+       "      <td>0.002086</td>\n",
+       "      <td>-0.020172</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 spy      amzn\n",
+       "Date                          \n",
+       "2016-12-22 -0.004462 -0.005543\n",
+       "2016-12-23  0.001372 -0.007531\n",
+       "2016-12-28  0.000928  0.000946\n",
+       "2016-12-29 -0.005671 -0.009081\n",
+       "2016-12-30  0.002086 -0.020172"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([spy_log,amzn_log],axis = 1).dropna()\n",
+    "df.columns = ['spy','amzn']\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'amzn_return')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b031b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize = (15,10))\n",
+    "plt.scatter(df.spy,df.amzn)\n",
+    "plt.xlabel('spx_return')\n",
+    "plt.ylabel('amzn_return')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   amzn   R-squared:                       0.044\n",
+      "Model:                            OLS   Adj. R-squared:                  0.040\n",
+      "Method:                 Least Squares   F-statistic:                     10.63\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
+      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
+      "No. Observations:                 235   AIC:                            -1214.\n",
+      "Df Residuals:                     233   BIC:                            -1207.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept   1.234e-05      0.001      0.010      0.992      -0.002       0.002\n",
+      "spy            0.4921      0.151      3.261      0.001       0.195       0.789\n",
+      "==============================================================================\n",
+      "Omnibus:                       51.597   Durbin-Watson:                   2.255\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              570.651\n",
+      "Skew:                           0.405   Prob(JB):                    1.22e-124\n",
+      "Kurtosis:                      10.591   Cond. No.                         127.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.formula.api as sm\n",
+    "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pamameters:  Intercept    0.000012\n",
+      "spy          0.492112\n",
+      "dtype: float64\n",
+      "residual:  Date\n",
+      "2016-12-22   -0.003360\n",
+      "2016-12-23   -0.008219\n",
+      "2016-12-28    0.000477\n",
+      "2016-12-29   -0.006303\n",
+      "2016-12-30   -0.021211\n",
+      "dtype: float64\n",
+      "fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('pamameters: ',model.params)\n",
+    "print('residual: ', model.resid.tail())\n",
+    "print('fitted values: ',model.predict()[-6:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1d630588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (15,10))\n",
+    "plt.scatter(df.spy,df.amzn)\n",
+    "plt.xlabel('spx_return')\n",
+    "plt.ylabel('amzn_return')\n",
+    "plt.plot(df.spy,model.predict(),color = 'red')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb
index e02840b..3ae3a01 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -17,14 +15,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 98,
+   "metadata": {},
    "outputs": [],
    "source": [
     "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "spy_table = quandl.get('BCIW/_SPXT')\n",
+    "goog_table = quandl.get('WIKI/GOOG')\n",
     "amzn_table = quandl.get('WIKI/AMZN')\n",
     "ebay_table = quandl.get('WIKI/EBAY')\n",
     "wal_table = quandl.get('WIKI/WMT')\n",
@@ -33,31 +29,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>spy</th>\n",
+       "      <th>goog</th>\n",
        "      <th>amzn</th>\n",
        "      <th>ebay</th>\n",
        "      <th>wal</th>\n",
@@ -75,7 +71,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2016-12-23</th>\n",
-       "      <td>0.001351</td>\n",
+       "      <td>-0.001708</td>\n",
        "      <td>-0.007531</td>\n",
        "      <td>0.008427</td>\n",
        "      <td>-0.000719</td>\n",
@@ -83,7 +79,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-27</th>\n",
-       "      <td>0.002254</td>\n",
+       "      <td>0.002074</td>\n",
        "      <td>0.014113</td>\n",
        "      <td>0.014993</td>\n",
        "      <td>0.002298</td>\n",
@@ -91,7 +87,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-28</th>\n",
-       "      <td>-0.008218</td>\n",
+       "      <td>-0.008246</td>\n",
        "      <td>0.000946</td>\n",
        "      <td>-0.007635</td>\n",
        "      <td>-0.005611</td>\n",
@@ -99,7 +95,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-29</th>\n",
-       "      <td>-0.000247</td>\n",
+       "      <td>-0.002883</td>\n",
        "      <td>-0.009081</td>\n",
        "      <td>-0.001000</td>\n",
        "      <td>-0.000722</td>\n",
@@ -107,7 +103,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-30</th>\n",
-       "      <td>-0.004601</td>\n",
+       "      <td>-0.014113</td>\n",
        "      <td>-0.020172</td>\n",
        "      <td>-0.009720</td>\n",
        "      <td>-0.002023</td>\n",
@@ -118,39 +114,39 @@
        "</div>"
       ],
       "text/plain": [
-       "                 spy      amzn      ebay       wal      aapl\n",
+       "                goog      amzn      ebay       wal      aapl\n",
        "Date                                                        \n",
-       "2016-12-23  0.001351 -0.007531  0.008427 -0.000719  0.001976\n",
-       "2016-12-27  0.002254  0.014113  0.014993  0.002298  0.006331\n",
-       "2016-12-28 -0.008218  0.000946 -0.007635 -0.005611 -0.004273\n",
-       "2016-12-29 -0.000247 -0.009081 -0.001000 -0.000722 -0.000257\n",
-       "2016-12-30 -0.004601 -0.020172 -0.009720 -0.002023 -0.007826"
+       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
+       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
+       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
+       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
+       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "spy = spy_table.loc['2016',['Close']]\n",
+    "goog = goog_table.loc['2016',['Close']]\n",
     "amzn = amzn_table.loc['2016',['Close']]\n",
     "ebay = ebay_table.loc['2016',['Close']]\n",
     "wal = wal_table.loc['2016',['Close']]\n",
     "aapl = aapl_table.loc['2016',['Close']]\n",
-    "spy_log = np.log(spy.Close).diff().dropna()\n",
+    "goog_log = np.log(goog.Close).diff().dropna()\n",
     "amzn_log = np.log(amzn.Close).diff().dropna()\n",
     "ebay_log = np.log(ebay.Close).diff().dropna()\n",
     "wal_log = np.log(wal.Close).diff().dropna()\n",
     "aapl_log = np.log(aapl.Close).diff().dropna()\n",
-    "df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
-    "df.columns = ['spy','amzn','ebay','wal','aapl']\n",
+    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
+    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
     "df.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -159,28 +155,29 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.254\n",
-      "Model:                            OLS   Adj. R-squared:                  0.242\n",
-      "Method:                 Least Squares   F-statistic:                     20.94\n",
-      "Date:                Tue, 22 Aug 2017   Prob (F-statistic):           7.21e-15\n",
-      "Time:                        10:14:46   Log-Likelihood:                 684.88\n",
-      "No. Observations:                 251   AIC:                            -1360.\n",
-      "Df Residuals:                     246   BIC:                            -1342.\n",
-      "Df Model:                           4                                         \n",
+      "Dep. Variable:                  Close   R-squared:                       0.346\n",
+      "Model:                            OLS   Adj. R-squared:                  0.319\n",
+      "Method:                 Least Squares   F-statistic:                     12.51\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
+      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
+      "No. Observations:                 124   AIC:                            -827.3\n",
+      "Df Residuals:                     118   BIC:                            -810.4\n",
+      "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept      0.0001      0.001      0.134      0.894      -0.002       0.002\n",
-      "spy            1.0468      0.170      6.155      0.000       0.712       1.382\n",
-      "ebay          -0.0795      0.058     -1.364      0.174      -0.194       0.035\n",
-      "wal           -0.0865      0.089     -0.976      0.330      -0.261       0.088\n",
-      "aapl           0.1529      0.084      1.831      0.068      -0.012       0.317\n",
+      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
+      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
+      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
+      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
+      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
+      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
       "==============================================================================\n",
-      "Omnibus:                       70.404   Durbin-Watson:                   1.979\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1938.463\n",
-      "Skew:                          -0.310   Prob(JB):                         0.00\n",
-      "Kurtosis:                      16.600   Cond. No.                         179.\n",
+      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
+      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
+      "Kurtosis:                       7.142   Cond. No.                         410.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
@@ -189,13 +186,13 @@
     }
    ],
    "source": [
-    "model = sm.ols(formula = 'amzn ~ spy+ebay+wal+aapl',data = df).fit()\n",
-    "print model.summary()"
+    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
+    "print(model2.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -204,25 +201,25 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.234\n",
-      "Model:                            OLS   Adj. R-squared:                  0.231\n",
-      "Method:                 Least Squares   F-statistic:                     76.13\n",
-      "Date:                Tue, 22 Aug 2017   Prob (F-statistic):           3.88e-16\n",
-      "Time:                        10:14:49   Log-Likelihood:                 681.59\n",
-      "No. Observations:                 251   AIC:                            -1359.\n",
-      "Df Residuals:                     249   BIC:                            -1352.\n",
+      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
+      "Model:                            OLS   Adj. R-squared:                  0.348\n",
+      "Method:                 Least Squares   F-statistic:                     134.7\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
+      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
+      "No. Observations:                 251   AIC:                            -1401.\n",
+      "Df Residuals:                     249   BIC:                            -1394.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept   9.876e-05      0.001      0.097      0.923      -0.002       0.002\n",
-      "spy            1.0796      0.124      8.725      0.000       0.836       1.323\n",
+      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
+      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
       "==============================================================================\n",
-      "Omnibus:                       68.122   Durbin-Watson:                   2.011\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2089.718\n",
-      "Skew:                          -0.110   Prob(JB):                         0.00\n",
-      "Kurtosis:                      17.134   Cond. No.                         122.\n",
+      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
+      "Skew:                          -0.374   Prob(JB):                         0.00\n",
+      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
@@ -231,43 +228,28 @@
     }
    ],
    "source": [
-    "simple = sm.ols(formula = 'amzn ~ spy',data = df).fit()\n",
-    "print simple.summary()"
+    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
+    "print(simple.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 123,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NotFoundError",
-     "evalue": "(Status 404) (Quandl Error QECx02) You have submitted an incorrect Quandl code. Please check your Quandl codes and try again.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-6-3f8aaae839bf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfama_table\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquandl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'KFRENCH/FACTORS5_D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/get.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(dataset, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'column_index'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'column_index'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'column_index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'code'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle_column_not_found\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m     \u001b[0;31m# Array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/model/dataset.pyc\u001b[0m in \u001b[0;36mdata\u001b[0;34m(self, **options)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mupdated_options\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUtil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_options\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mData\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mupdated_options\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mNotFoundError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhandle_not_found_error\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/operations/list.pyc\u001b[0m in \u001b[0;36mall\u001b[0;34m(cls, **options)\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUtil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstructed_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mresponse_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mUtil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_dates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/connection.pyc\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(cls, http_verb, url, **options)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mabs_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s/%s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mApiConfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi_base\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_verb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabs_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/connection.pyc\u001b[0m in \u001b[0;36mexecute_request\u001b[0;34m(cls, http_verb, url, **options)\u001b[0m\n\u001b[1;32m     42\u001b[0m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m200\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_api_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/quandl/connection.pyc\u001b[0m in \u001b[0;36mhandle_api_error\u001b[0;34m(cls, resp)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_klass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_letter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mQuandlError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNotFoundError\u001b[0m: (Status 404) (Quandl Error QECx02) You have submitted an incorrect Quandl code. Please check your Quandl codes and try again."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fama_table = quandl.get('KFRENCH/FACTORS5_D')"
+    "from datetime import datetime\n",
+    "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
+    "fama_table = pd.read_csv(url)\n",
+    "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
+    "fama_table.index = index\n",
+    "fama_table = fama_table.iloc[:,1:]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 124,
+   "metadata": {},
    "outputs": [],
    "source": [
     "fama = fama_table['2016']\n",
@@ -278,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -290,8 +272,8 @@
       "Dep. Variable:                  Close   R-squared:                       0.387\n",
       "Model:                            OLS   Adj. R-squared:                  0.375\n",
       "Method:                 Least Squares   F-statistic:                     30.97\n",
-      "Date:                Mon, 31 Jul 2017   Prob (F-statistic):           2.21e-24\n",
-      "Time:                        11:25:10   Log-Likelihood:                 709.59\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
+      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
       "No. Observations:                 251   AIC:                            -1407.\n",
       "Df Residuals:                     245   BIC:                            -1386.\n",
       "Df Model:                           5                                         \n",
@@ -319,19 +301,19 @@
    ],
    "source": [
     "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
-    "print fama_model.summary()"
+    "print(fama_model.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "data": {
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0jPWsyveaUzpu9DJsvRHQJOATVWUgNkCNs4aAK8AdHXcwkZ7gx30/rvSxLOtg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tSSekKyzoGQk8ZYLHnt7VccjZvyyVIXXib+NnnMWxIM35Bn7DqalHQeQYYvMS9o1FXh8gtDlcRY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uaX1SOjVhP8s+P5/PzJ/ETav/yX/8dgOdqQytHe5azj0N+HLLMuSRG+yPCftyMnzl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\nOom5c+d2n+fhhx9m06ZNrFu3Dms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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f1b3b10>"
+       "<matplotlib.figure.Figure at 0x1a160d0128>"
       ]
      },
      "metadata": {},
@@ -343,21 +325,20 @@
     "plt.figure(figsize = (15,7.5))\n",
     "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
     "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])\n",
     "plt.legend()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f1b30d0>"
+       "<matplotlib.figure.Figure at 0x103c271d0>"
       ]
      },
      "metadata": {},
@@ -372,15 +353,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "residual mean:  -2.31112163493e-16\n",
-      "residual variance:  0.000205113416293\n"
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -391,14 +372,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PTJ7dr9tcsc6gQpLV5eN7gpyIw408aRUMVTnsPgwioCs9/ct0v96Z4gSMiul4AHAIbnUN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\nAAAAoDghFAAAAADFCaEAAAAAKE4IBQA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Z7fBZ0YMdgJ1AOMVQzKoFwKhcXO9saiJN1tHDBy7bvoke7ADsHIb1AQBTYXj4zmbY5mQdObiU2265MUv7F1OSLO1fzG233Cj4A2BH0HMKAJgKw8N3NsM2J08PdgB2KuEUADA1Lq5nwyRqQxm2CQCMyrA+AIBdpF0banVtPTVP1YY6ubK6rec1bBMAGFWj4VQp5VWllLOllIdKKcc63H9VKeX21v2/XUq5vrX8r5RS7i2l3N/6/+ZNj/lE6znva/177vTeEQDAbJtUbSg1kQCAUTU2rK+UsjfJTyb5K0keTvKpUsqdtdbf37Ta25I8Vmv9zlLKm5O8P8mbknw5yWtqrY+UUv58klNJNv/l85Za65mpvBEAgDkyydpQhm0CAKNosufUdyd5qNb62Vrr15P8UpLXbVnndUl+tvXzR5L8pVJKqbWu1FofaS1/IMnTSylXTaXVAABzrFsNKLWhAICmNBlOLSX50qbbD+fy3k+XrVNrPZ/kK0mevWWd1ydZqbU+sWnZz7SG9P14KaV0evFSyttLKWdKKWfOnTu3nfcBADA31IYCAGZNk+FUp9CoDrNOKeXFuTjU729tuv8ttdYbk/zF1r8f7PTitdYP1lqXa63L11577VANBwCYV2pDAQCzprGaU7nYU+r5m24/L8kjXdZ5uJSyL8kzkzyaJKWU5yX5v5L8jVrrZ9oPqLWutv7/01LKL+Ti8MGfm9SbAABm08mV1Zw4dTaPrK3nuv2LOXr4gACmRW0oAGCWNNlz6lNJXlBKuaGU8rQkb05y55Z17kzy1tbPb0hyutZaSyn7k9yV5NZa6z3tlUsp+0opz2n9vJDk+5P83oTfBwAwY06urObWO+7P6tp6apLVtfXcesf9Obmy2nTTAADYorFwqlVD6h25ONPeHyT5cK31gVLKe0spr22t9tNJnl1KeSjJjyY51lr+jiTfmeTHW7Wl7iulPDfJVUlOlVJ+N8l9SVaT/NT03hUAMAtOnDqb9Y0Lly1b37iQE6fONtQiAAC6aXJYX2qtH0vysS3LfmLTz3+W5I0dHve+JO/r8rQvHWcbAYD588ja+lDLAQBoTpPD+gAAJuK6/YtDLQcAoDnCKQBgxzl6+EAWF/ZetmxxYW+OHj7QUIsAAOim0WF9AACT0J6Jzmx9sDOZjZNJcnzB9AmnAIAd6cjBJRcTsAO1Z+NsT3rQno0zic882+b4gmYY1gcAAMwNs3EySY4vaIZwCgAAmBtm42SSHF/QDOEUAAAwN8zGySQ5vqAZwikAgF3o5MpqDh0/nRuO3ZVDx0/n5Mpq002CgZiNk0lyfEEzFEQHANhlFPxlnpmNk0lyfEEzSq216TY0bnl5uZ45c6bpZgAATMWh46ez2qF+ytL+xdxz7OYGWgQA7ESllHtrrcv91jOsDwBgl1HwFwCYJYb1AQDsMtftX+zYc0rBX7o5ubJqmBMAE6PnFADALqPgL8No1yhbXVtPzVM1yhTRB2Bc9JwCACZOr4vZMs6Cv/btznfi1NlLxfPb1jcu5MSps/Y1AGMhnAKAGTfvF/9mhpusUY+PIweXtr397dv+5v3zm4y/RtlO2CYAjJdhfQAww3bCcJpevS7YnqaPD/u2t6b3z7h0q0U2So2ynbJNABgv4RQAzLCdcPFvZrjJafr4sG97a3r/jEu/GmUnV1Zz6Pjp3HDsrhw6frpn0LRTtgmTN8xxBcw/w/oAYIbthIt/M8NNTtPHx6zv26aHjzW9f8alV42yYYd27pRtwmQZMgy7j55TADDDxjmcpinjnhnOt+lPafr4mOVZ/2Zh+FjT+2ecjhxcyj3Hbs7njr869xy7+bLAapieUDtpmzA5etjB7iOcAoAZNssX/4M6cnApt91yY5b2L6YkWdq/mNtuuXHkmeGaDhxmyajHx7gCvnHu23GbhYvbnfD57WfYnlC7YZuwfXrYwe5jWB8AzLBew2nmyThmhktMab/VKMfHuIfLjGvfjltTF7dbhxK+/qVLufvBc3P9+e1l2KGdO+WcxmTN+pBhYPyEUwAw42b14r8Jvk2/0rDHx24J+Jq4uO0U/P3Kvasz05tsEo4ePnDZe07694RyTmOrraHuK154bX7l3tWhjitgvhnWBwDMDfVqtm+3BHxNDB+bhaGE0zbLQzuZD52Ga//Kvat5/UuXHFewi+g5BQDMjVF6aXC5cfcoanpGvG6aGD62W4K/rfSEYju6hbp3P3gu9xy7uaFWAdMmnAKAXWxWg4Vu1KvZvnEGfOOuXzXu43HaoYk6OTC83RrqApcTTgHALjXuYGFa9NLYnnEGfOOsX/UPT96fD33yi6mt27MWdA1Czz4YnlAXSIRTALBr7ZbC2FxpXAHfuHo8nFxZvSyYahv1eGwqeNWzD4Yn1AUS4RQA7FqGUrBd4+rxcOLU2SuCqbZRjscmg9fd1LNv3oYFbzbPbd9phLpAIpwCgF3LUAq2a1w9HnoFUKMcj5MKXucp0Jh0Wzv1Tnvn7fflPR99IO96zYtndrsk8zukeSfbTaEu0JlwCgDGYJ4uWtsMpdjZpnFMbu7xsLq2nr2lXOqhtPn+froFpSUZ6XicRPA6qUBjEvtpGuFLp95pSfLY4xszE/R027aGNAPMnj1NNwAA5l37QnB1bT01T10InlxZbbppPR05uJTbbrkxS/sXU5Is7V/Mbbfc6OJsB5jmMXnk4FKOHj6QxYW9uVAvDs4b9vXaj9+sJHnLy751pOOx0/NtN3jtFWiMalL7aRJt3apXL7Rxv9bJldUcOn46Nxy7K4eOnx5o+/TatoY0A8wePacAYJvm+Vt4Qyl2pmkck5t7pewp5VIwNcrrjbvmzCRq2Ewi0JjUfppG+NKtd9q4X2vUXmC9tq0hzQCzRzgFANvkW3hmzaSPya2BwdZgapTXG3dQOu7nm0SgMan91K2te0rJyZXVsWyXTsOCt7ZhHEYN8Hpt2w+86SZDmgFmjHAKALbJt/C7w9b6Na944bW5+8Fz2+qZM6m6UJM+JrvVG5rU682CSdRom9R+6hYcXah1bPWg2o9/950PZG1944r7v/bE+bEEYaMGeL22rdnhAGaPcAoAtklh8Z2v09Cin//kFy/d3x5qdOYLjw4cWE2yaPV2j8l2aNYucn6h1ixtej+D9OwZtZj5rOoWaCTJoeOnRwo5JnXuaL/+j33409sabjnI6xw5uJSTK6t5z0cfyGOPPxVSra2PpzD6qAFev227nZ518zgBBsCsK7VLN+zdZHl5uZ45c6bpZgAwx1ys7GyHjp/uWV+nrSTZ/JfV4sLerkXmuz3n0v7F3HPs5m209qJRj8mtodlm7ffTDq76+fzxV4/U9mkYx2e207bqtc8n1Y5ubjh2Vzr9pV+SfG7M+2ZSx/N2tvG4tu3m59l/9UK++mfns/HkU1t22H0OsJuUUu6ttS73XU84JZwCAHrrdpE/iG4X59MMDobRL4hr96DqVW8oSfYvLuQZV+1rJLDtF0p0CjzaswO+78iNA7/OpAPG7Zpm+yZ5PDcZ/vcKazeblX0OMGsGDacM6wMA6KPfzGS9dBsCN6u1yvoN2Xtkbf2yIW6ra+tX9Bhb2FPyta+fv1SLaJxDFvsZZLhkp5pZNcmHPvnFLH/bswZu46xPhjDNIceTPJ6bnFV00Ppqs7LPAebVnqYbAMDudHJlNYeOn84Nx+7KoeOnc3JltekmQVdHDx/I4sLekR7b7eK803POQq2yfmFC+/4jB5dyz7Gb8/njr84H3nRTlvYvpuRiD5JvePq+bFy4stbRj3340xP/rPea3a2tW5BQW4/vZfO5a08pHde5bv/iTJzjjhxcym233HjZvpnU8LNZPZ63a9DQqelQmcmYhc8x7BZ6TgEwdZMsBA2T0KkY9mNfeyKPbzzZ83G9Ls5ndcawXkP2ur2fzYWxT5w6e1lh7M3GOVtcN4P0ZurVE65XD7mt566txcaTi9voFS+8dqBz3DSGq02r19GsHs/bNUivyZ0QwnElf6vAdKk5FTWnAKZt1uu0wCD61aHaW0r+6V97SeMXMaMEIP1m6+v2mEFq8yST/ax3O79sroH1zMWFS0MOt9pbSj5z218d6rn3lpIna720fbsVjN/8vsdRTH0SZn1yh2m3r9N+Wthb8oyn7ctX1jdmchsxHv5WgfFQcwqAmTXrdVqYf9O4gO3Vo2IWQoZk9G/+R+ltM2htnmSyn/Wjhw/k6Ec+fdmwwj0ll9XA6hZMJZ17Q7V1a/eTtV5W9PtHbr+v7+N7DT9s6riZ9Z4iTbRvp/YIoz9/q8B0CacAmLpZLQS92816j4lBTesCttvwt2uuXsi7XvPimdh23QKQ93z0gbG3b5gLtq2f9bEfe1vypSdr8uSFwUYLLPU4Dw167uq13uZeaZ2M+8J3mG07i4HZZk21r8mC7DTH3yowXQqiAzB1O7Vw7jxrBzqra+upeSrQmbfirydXVvNjH/5034LY49Cp2PQ/f9NNWfmJV87MhWy3oOOxxzfGvm+7XbBtLRm+9bM+7mPvxKmz2XhytLIV/c5Dg567uq3XrkXVq4bROC98h9223Y6X1bX1mTgX6MnCNPlbBaZLzyl2lJ3yrT/sdIZJzJ5Z7zExiPaFeLdhWZO4gB1nj4pJ/A7rNfRw3Pu2U0+yxYW9ef1Ll3L3g+e6vq9xH3vD7OfNdagG2eaDnru6rddv6OO4L3yH3ba9jpdZGN6nJwvT5G8VmC7hFDvGrNdJAC5nmMRs2Qk9Evpd+M/yBWyn32FHf/nTec9HH8ja46MXXT56+EDeOUD9o35tG+TibNQLuXEfe90CjJLLR/uVXKw99Yyr9uUDb7pp4G076Lmr03rdalEl6VtwfhTDbtteMzXOQljdLQDVk4VJ8bcKTI9wih1jJ3zrD9CUndAjoVeYsfkCdhZ72Xb6HbbxZM1jj18s3D3qFy5HDi7l3Xc+0LEA+CD7dtgvfka5kBv3sdevB9fq2vplQdU0v8zq9l6vuXphIrN/9du2nT4Lt91y47YDzU7G8bnTkwVg51Jzih1jJ3zrD9CUnVBbo1uYsbeUSzPnzWptrUF+V41aN+vdr33xyPu2V0H1Q8dP54Zjd+XQ8dPb2n6veOG1Qy3vp1MtsNtuuTHvO3Jj7jl2c5b2L26tlz6RmmSdHD18IAt7t1bhSr76Z+cncgz2+lx3+ywk3YvCjxoYjvNzd+TgUu45dnM+d/zVuefYzYIpgB2iZ8+pUsp/2+v+WuvvjLc5MLqd8K0/sDPMYs+cfnZCj4RuPWbawVSy/V62k9q3vWr9bDbKFy7b2be9Cqpvt1dX290Pnuu4/Oc/+cXc/eC5kXvYdHtMk19mdevJtvFknUhP7177/tDx010/C+MePqd3++4zj78HgWb1G9b3T3vcV5OMv/8xjEgdAmAWzHP9u3mvrTFICNNvNrJe7/8fnrw/H/rkFycyHKxXrZ/NRv3CZdR9O2hoNmrQcHJltefzT+Lz09SXWe2L9U5DLJPJhWPd9n2vkG7cYbXe7bvLPP8eBJrTM5yqtb5iWg2B7doJ3/oD82+eegg0/c32JF6/Xwgz6mxkJ1dWLwum2sa1b7f+Dnvm4kK+9vXz2bjw1CtO4wuXrfvkFS+8Nr9y72rf0CwZPmhoX8D2s75xIe+8/b5LPXomEQROettuvVjv5Lr9i1P9TPYL6cYZVuvdvrvM0+9BYHYMXBC9lPLnk7woydPby2qtPzeJRsGo5v1bf2D+zUsPgaa/2W7q9UedjezEqbNXBFNt2923WwOJ9sxx0w4PO+2TX7l39VIh8XY7vvbE+ZELrG/Wb3bFrcZ1jEzyy6xu+6zfe11c2JtXvPDaqX4mphnS6d3eW9NfFIzbvPweBGbLQOFUKeVdSV6ei+HUx5J8X5L/lEQ4BQCbzEsPgaa/2W7q9dvP3Ws2sk4Xir0uqrazb/uFdNMKCk+cOtvxuF3fuJC7Hzx32UxynXoBjRI0DDJcsFN7xtVTbdzbtte+7HX8LLWOsWE+E/M2853e7d11O27OfOHRy0Lhedpe8/J7EJgtg/acekOSlyRZqbX+T6WUb07ybyfXLACYT/PSQ2C732xv9+K46aLU3cKYZy4udLxQfObiQsfeQiXZ1r5tOiQ8ubKao7/86Ww82a1f2OXbgcDHAAAgAElEQVT7pL3f1zcuZG8puVDrpXBl0PaeXFnNu+98YOQ2N937YpjeUe192e1ifWn/4qXg70d6BKZbX//oRz59acjn6tp6jn7k00mG72E1zR7nerd31u24mVR9u2mYl9+DwGwZNJxar7U+WUo5X0r5piR/nOTbJ9gugCQ7r6s7O9+89BDYzjfb4xiS1/Q3690unkpJxwvFpy/syeLC3svuK0ne8rJv3da+bXr4y7vvfKBnMJU8tU+27vcLtV664Oy1DTafxzvV0hpWk70vRukd9cjaej7wppv6XqwP+pl4z0cfuGL7bVyoec9HH5i580zi93g/3Y6bSdW3m4Z5+T0IzJY9A653ppSyP8lPJbk3ye8k+c/bffFSyqtKKWdLKQ+VUo51uP+qUsrtrft/u5Ry/ab7bm0tP1tKOTzocwLzo30RsLq2npqnLgJOrqw23TTo6cjBpdxz7OZ87virc8+xm2fyD/Kjhw9kcWHvZcsG/Wa7Vw+Rabz+OBw5uJTbbrkxS/sXU3KxB8ttt9yYtcc7z6S29vjGFet/4E035X1HbtxWO7oFLdMKYLrNHNe2eZ+Mst+3nsfX1je2FUwt7CmN9r7o1zuqk+v2L3Y93jafGwb9TDzW5RjttrxJfo/3N8xnveleg8OYh9+DwGwZqOdUrfXvtH7816WUX0vyTbXW393OC5dS9ib5ySR/JcnDST5VSrmz1vr7m1Z7W5LHaq3fWUp5c5L3J3lTKeVFSd6c5MVJrkvy66WUP9d6TL/nBOZE08NdYCfbzjfb4+jtMwvfrHcaZtRtuF87YBh3+17xwmuvmAVwVoa/bB2uN8p+H7boeT/f8PR9Ey8Kv/WYTJ46TnsVxe/XO6rf8TPIZ2LeQh2/x/vr1Iuz5MqeU4maTcDOVmrt/+1VKeV7Oy2vtf4/I79wKf99knfXWg+3bt/aes7bNq1zqrXOb5VS9iX5/5Jcm+TY5nXb67Ue1vM5O/nGb/zG+tKXvnTUtwJMyCc/+ydd73vZtz97ii0BNlv54lqeOH9l4HDVvr05+K37G2jR+Hz5q0/ks+e+lic3/X20p5R8+7XPyHO+4aqJv1aSfPM3PT03POcZY32tbs584bGcv/DkFcv37d2T5W+75rJlo+z3XufxrUop2VtKzj95ZXs2G/b8/+WvPpEvPbqeJ85fyFX79ub5z1q8Yl9++atP5PNffvyK1y6lJEn6/b3c3gaDvNawNj9nL/v27Mny9df0XGfapvl7fBLbflq2tn3/1Qs596dPTOU8BDBpv/mbv3lvrXW533qD1pw6uunnpyf57lwc3ndz59UHspTkS5tuP5zke7qtU2s9X0r5SpJnt5Z/cstj21+/9HvOJEkp5e1J3p4kV13lJA+z6Kp9e7teCAHNef6zFjsGOM9/1vx/q9++8JvGRe6XHl2/IphK0nVo4SCGvUC//tlX5zPnvnZZ+FJKyfXPvvqKdZ//rMWO6/ba793O41vt27sn1z/76ktt7RaEJU8FaoO8v60B4BPnL+Sz576W5Kl9/eWvPnHF+2ob5Evczcf+c77hqrEeK90CzK1KKbn+OVfus6ZN6/f4IPt5lnU6br7x6fvmNmwDGMWgw/pes/l2KeX5Sf7JNl+7dHqpAdfptrxTDa2Ov81rrR9M8sEkWV5erp/4xCe6NhRoRrfpyrfW6QCmT5Hj7bvh2F0d/0gpST5x/NVDP1/7nHnNpnPm15Icedm3ZvnbntV1fw26LzvN7Lewp+R9b3xJ133f6Ty+sKfkG56+L2uPb3R9vZMrq/mR2+/rOoyu7cLC3ryzx++EQ8dP57mdZmXcv5hPtGbJu+k9/zHf3Kf2Viclmfix3639W/3zN900k5+/UX+PD3t+GWQ/A9CMdi/kfgbtObXVw0n+/IiP3fwcz990+3lJHumyzsOtYX3PTPJon8f2e05gTsxCTRrYqbYbLpkWfvvGPWNhp/o+NcnPf/KLuf0/f+lSqLS6tp533n5f3n3nA3n3a1888L48cersFTP7bTxZe9YPGvU8fuTgUt55+31929SvftEgdbL6FYXvZGn/Yu6ZQugxSB23pVY9tEka9Xwxyv4fZTbQpme9BGD7BgqnSin/e57qgbQnyU1JPr3N1/5UkheUUm5IspqLBc7/xy3r3JnkrUl+K8kbkpyutdZSyp1JfqGU8s9ysSD6C3Jx9sAywHMCc8QFMIzfKBd/DGeQi/lOhZC3Uwy914X41lApuRjKDLPfRw0ARj2PL3UJ74Z5/XEHgMl4CtYPGvZ0a/8429LPds8Xw+7/UYqoT2I/AzBdnYbBdXImF2tM3ZuLQdHfr7X+9e28cK31fJJ3JDmV5A+SfLjW+kAp5b2llNe2VvvpJM8upTyU5EfzVCH0B5J8OMnvJ/m1JH+31nqh23Nup50AsNP0uvjbDU6urObQ8dO54dhdOXT89NhnQGtfzK+2ZndbXVvP0Y98Oje95z9e9ppHDi7ltltuzNL+xZRcDGO2M2x5lAvxYfZ7t+ffU8pEZpE7evhAFhf61ybq9b47PcfWQOeaqxf6vsaekrHso6Tz8XHrHfd33Iad2t8eHDGOtgxi2ueLUULQQfYzALNt0JpTPzuJF6+1fizJx7Ys+4lNP/9Zkjd2eew/TvKPB3lOABjFTq2rNA9DYCa17afRa6zTxfzGhXpp+NjW1xzX6x49fGCgOk1bDbrfO/X0SpILtV56P8n4hmJvHRL2zMWFfO3r57Nx4al32C+AGGRY2bte8+Ic/cinL3verfaWkhN/7fLaWv2O0W73D9MzqF/720HrJM9R0z5fjNILShkAgPlXes1CUkq5P10KiidJrfW7JtGoaVteXq5nzpxpuhkAzJCdXJD/0PHTHS/+plVHp59u2/71L13K3Q+e29bF5zTee7dC55N6zc0hyOLCnjy+8eRQjx+mHSdXVvNjH/50LnT4+7Hkyj8ax/2Z6RT4JNsPJTY/b9L5j9/9iwu5712vvLR+r/NDr/u7BYglyeeGKIQ/rXPUtM8XO/ncC7AblVLurbUu91uv37C+70/ymlwcOvdrSd7S+vexJB/ZbiMBYFbt5KFvsz4Eptu2/9AnvzjQUKheuvX2WF1bH9sQv0GH142j58nWIWKPbzyZhT0l11y9cGkYWq9ha8Ps93aA0ymYSjoHOuP+zBw5uJR7jt2czx1/9aVgZNAhcoM+bzdr6xuXnrff+aHX/d2Oj2GHZU7rHDXt88W4h7sCMB96DuurtX4hSUoph2qthzbddayUck+S906ycQDQlHkY+jaqYYbANDG0sds23hp+9CuS3EmvAtPjGuLXbfhbp7Z0M+h27ziE8Mmaq5+2Lys/0b2XT5JcvbAn/+uAF/3dnmMQ2/nM9NsOoxTP7qfXMdJ+3n7nh173f+BNN42lEP60zlFNDJkzGQrA7jNQzakkzyil/A+11v+UJKWUv5DkGZNrFgA0a6fP/jTIxV9Ts/r1m6Fss2EvxPsFR9sNNpIrL+b3X72Qr/7Z+ctmzOsVRnTa7kd/+dN5z0cfyNrjG5eFA4MEFEcOLuXMFx7Nhz75xcsCvnqptPblrz1onaRBDfOZ2fz6W2tMdTr+JhHQHD18IO+8/b6ez9vv/NDt/j3l4ja/7ZYbtx32TPMcJSwCYNIGna3vbUl+spTy+VLK55P8H0n+54m1CgAaNutD36ahqaGNvWYo22rYC/HNQ4a6GUfPk83DxFZ+4pU58caXDDxMqVtvqMce37hi6NqgQ8TufvBc155nbb1mkRt1m5TW83zHrR/L9X1mR9z6+mvrG1cUKd/a5nENkdvsyMGlrkMh28/b7/zQbabBzcXjNw9PHCX4mdVz1KRnwwRgZxoonKq13ltrfUmS70ryklrrTbXW35ls0wCgOeqeNDe0sdO2f8vLvnVsF+Lt4KhbQDWpnieDhhGDbN92SDNoQDHIvhylTlI/7WipXaeqV02oQXtnbW7zpAKad73mxT2fd+sxes3VC7lq3578yO335dDx00ku9o7aW66MVccV8M7iOapXwLmd5xR2Aex8PYf1lVL+eq3150spP7pleZKk1vrPJtg2AGjUbh/K0uTQxk7bfvnbnjXWujedhvjNQs+TQYc1PrK2PnA9oEH25bB1kjrNzte2pyRPdrmz29DJQUPPzW2eVD2kQZ63fYx2G/562y035skuxePHFfBO8hw1Sr25cdUAa7/26tr6ZcfZtIYWAzB9/WpOtetKfeOkGwIAzJZZC2/GfSHeRKHnQQxbUH2Q7TLIvuwVYHXbVkk6Pm+/tncKZwYJ5Todf5MKaAZ93n49ziYd8PYLkUYJmUatNzeO3pZbX3scEyEAMPv6zdb3b1r/v2c6zQEAZsWshjej6HaBPou947Zu962FwZPhQ8JB9mW/AKvXtnr3nQ9kbX0jSfL0hT25at+eS7c76RTOdAvl2r2wljYFYoeOn56ZY3IaM/N10y9EGjVkGrUH1DjCuEGGd26351kTs5AC0NtAs/WVUv5JkvclWU/ya0lekuSdtdafn2DbAGBkLj7GYxbDm2E1NevgIAYNzcZxPPfbl51CsVKSH7n9vkv1rbo9/onzT176+bHHu4dSSfdwpv3cm4Ou5GIwtfkxs7YvR+lxNq629guRRg2ZRu0BNY7eloMET9vpeTbL5wOA3WzQ2fpeWWv9r0m+P8nDSf5ckqMTaxUAbMMkivIyv5qadbCfYY7TYQqqb0f7dT7wppvyxPknO84QuFWvni7tcuDtwuD9inYfObiUZ1x15Xen7f01i/uyyVnzug2DbAc8o4ZMo86COI4i7f1eY7vbdhaPIQAG7DmVpD2f7l9N8ou11kdLh9lHAGAWjKsoLztDU7MO9jPLx2m3tv3oh+/Lu+98IF9Z37jUC6jXdqy5GFDcc+zmgV97lP01zn05bC+1Xr2jJtlL5+TKatei9O2AZ9RhdtvpAbXd3padXrv9PpfG0PNsVs8HALvdoOHUR0spD+bisL6/U0q5NsmfTa5ZADA6Fx9s1uSsg73M8nHarQ1P1lwactcOWvZfvdBzKN+w76fX/vraE+c71rIa174cNUzaPHPfiVNnLw2F/NoT5ycWQJ44dbZjMFWSSyHSqCFTk/XmJv3as3o+ANjtBgqnaq3HSinvT/Jfa60XSimPJ3ndZJsGAKNx8TE7ZqH216zNOtg2K8dpp300yMx5ycWg5ap9e3rO0Dfs++nWc+b6Zy/mP3/+sY6Pefzr53NyZXUsgc+oYVKnYKubcQSQ3Z6j5qmAZztBT5P15ib52rN6PgDY7QaqOVVKuTrJ303yr1qLrkuyPKlGAcB2NFkDhqfMSu2vcdTBmYRZOE677aNXvPDaK9rWzVfWN3LbLTfmmqsXrrhvlPdz5OBSXv/SpWwuIFGT/L+fefSyGQs3e+zxjbEcW/1qOPUyyCxzbeMIILs9x9KW5dOqVzYvZvV8MC0nV1Zz6Pjp3HDsrhw6flotRmBmDDqs72eS3JvkL7RuP5zkl5P835NoFABsR5NDUnjKLNVUmsVZB2fhOO22j+5+8Fxuu+XG/NiHP50LtXMg1NaelW7zsLbtvp+7Hzx3xZC13q3Y/rE1SA2nXgbtDTWuAFIPoNHN4vlgGsxUCMyyQcOp76i1vqmU8gNJUmtdLyqiAzDD5v3iYxaGw23XLNdUmhVNH6e99lG7XVsDkM22hiHjej+jHiNbHzfM52iQGk69dBsKec3VC7n6afvG/lmehXBzmnbCObFps/SFAcBWg4ZTXy+lLKb1ZVIp5TuSPDGxVgHALjbv3263LyK79XRR+2s2nFxZzZ5SOvaMau+jrQHI/qsXUmsum61vEsdkt6CnW8+mre1Ohv8cDVLDqZduPZne9ZoXT+xz23S4OS3zfk6cFb4wAGZZ33Cq1UPqXyf5tSTPL6V8KMmhJD802aYBwO40z99ub72I3Mqwo9nQ3k+dgqlJ9YYaRreg5/UvXcrdD57L6tr6FUHV1nYP+znqFohtreHUzbh6Ms17D6FJtH+S58R5397DmJVJGAA66RtO1VprKeXvJXllkpfl4pdWf6/W+uVJNw4AdqN5/na7V1HopR1+4TdPuu2nvaXMRHHoQYKefqFCr89Rp8eOo4bTdoO8cfYQmkTo0u85J9XDaVLnxN3WI0udMmCWDTqs75NJvr3WetckGwMAzPe3290uFkuSe47dPN3G0FW3/fRkrTNzUd4v6Ol3f7fP0TMXFzoGErfdcmNuu+XGmSxQP2wPoUmELoM856R6OE3qnDjPvVRHsdvqlAHzZdBw6hVJ/lYp5QtJvpbWkP9a63dNrGUAsEvN87fb8xysjWJehwTN4n4aZFsOs727fY5KSddA4p5jN89sgfphTCJ0GeQ5J9XDaVLnxFnppTrN88huqVMGzJ89A673fUm+I8nNSV6T5Ptb/wMAY3bk4FJuu+XGLO1fTMnF4XCzMNRqEEcPH8jiwt7Lls1LsDasdk+S1bX11DzVk+TkymrX9Q8dP50bjt2VQ8dPd11vGmZtPw2yLYfd3t0+R2uPb3RcfxaGzXYLB4cNDScRugzynONq/1aTOidOqr3DGPa4BtipBuo5VWv9wqQbAgA8ZV6/3d5Nw0aG6Z0ya7VtZm0/DbItR+kN1OlzdOLU2ZnrNdY2rh5Ck+gZN8hzTrLX59Zj9sSps5ctH8Us9FLdbUMLAboZdFgfAMBA5jVYG9YwvVNm8QJ0lvbTINtyXL2BZiGQ6GZcoeEk3uMgzznJ0HMSAe/W9j5zcSGlJD9y+305cersVALbWRlaCNA04RQAwAiG6Z3iArS3QbbluHoDzVqvsa3GERpO4j0O+pyTCj0nFfC229tU78ZZrP8G0AThFADACIbpneICtLN2IejVtfWLs+1sum/rthxnb6BZ6jU2KZN4j01ut0kHvE31bpzlnnwA0yScAiDJ/M46Ni9s351nmN4pLkCvtLWnSk0uBVRLHbblrPd4YrImHfA21bvRcQ1wkXAKgJkr1rzT2L4716A9SebhAnTaAWqnnirtYOqeYzd3fMwg21sQPBnDbNdJ7INJB7xN9m7cDT35APoRTgHQczhD+34XeqMb93ARF9/zaZYvQJsIUCfRU2U3BcHTPA8Ms10ntQ8mHfDq3QjQLOEUAF0vBtsXFbvhQm+SxnkRvpsuvneTpgPHJurtTKKnyizOijgJ0z4PDLNdJ7kPJhnwzkPvRoCdbE/TDQCged0uBveW0rNHFYPptn1HuQjv18uN+dMOGlbX1lPzVNBwcmV1am1oot7O0cMHsriw97Jl2+2psltmRZz2eWCY7TrP++DIwaXcc+zmfO74q3PPsZsFUwBTJJwCoOtF4oVaO64/DxcZs2ScF+HzfOFHZ7MQOI4zQB3UkYNLue2WG7N/ceHSsqcvbO9P0ybeRxOmfR4YZrvO2z44ubKaQ8dP54Zjd+XQ8dNTDYUBeIpwCoBLF4lL+xdTcrEgcft2J7N6kTGrum3fUb6Vn7cLP/qbhcBxEr2YBvXE+Scv/fzY4xvb6jXW5PuYpmmfB4bZrvO0D5rstSgUA7icmlMAJOley0OB2PEYV60URXt3niZnCWtrqt7OuOsT7Za6QdM+DwyzXedpHzRVo0ztQIArldplyMZusry8XM+cOdN0MwBmUtOFmrmSfbKzbL1QTS4GDaP2rpsnNxy7K53+Ei1JPnf81dNuzlxxHti+po6/Q8dPdwykl/Yv5p5jN0/sdQGaUEq5t9a63G89PacA6GmSsyMxGvtkZ5mnnibjNgu9xuaV88D2NXX8zcJQXoBZI5wCAGjYbg0aDFOlSU0df0JZgCsJpwAAaMRu7jVG85o6/oSy88lQWpgsNaei5hQAADA9go75sptrA8J2qTkFAAAwg3brUN551dTMjrCb7Gm6AQAAADCrFLGHyRNOAQAAQBfditUrYg/jI5wCYNc5ubKaQ8dP54Zjd+XQ8dM5ubLadJMAgBl19PCBLC7svWyZIvYwXmpOAbCrbC1qurq2nlvvuD9J1I0AAK5gZlGYPOEUALuKoqYAwLAUsYfJMqwPgF1FUVMAAJgtwikAdhVFTQEAYLYIpwDYVRQ1BQCA2aLmFAC7iqKmAAAwW4RTAOw6ipoCAMDsMKwPAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABoTCPhVCnlWaWUj5dS/rD1/zVd1ntra50/LKW8tbXs6lLKXaWUB0spD5RSjm9a/4dKKedKKfe1/v3Nab0nAAAAAIbX1Gx9x5L8Rq31eCnlWOv239+8QinlWUnelWQ5SU1ybynlziRPJPnfaq13l1KeluQ3SinfV2v9D62H3l5rfcfU3gkAADTs5MpqTpw6m0fW1nPd/sW84oXX5u4Hz126ffTwAbOUAjCzmhrW97okP9v6+WeTHOmwzuEkH6+1PlprfSzJx5O8qtb6eK317iSptX49ye8ked4U2gwAADPn5Mpqbr3j/qyuracmWV1bz89/8ouX3b71jvtzcmW16aYCQEdNhVPfXGv9oyRp/f/cDussJfnSptsPt5ZdUkrZn+Q1SX5j0+LXl1J+t5TykVLK87s1oJTy9lLKmVLKmXPnzo36PgAAoFEnTp3N+saFnuusb1zIiVNnp9QiABjOxMKpUsqvl1J+r8O/1w36FB2W1U3Pvy/JLyb5F7XWz7YWfzTJ9bXW70ry63mqd9aVT1TrB2uty7XW5WuvvXbAJgEAwGx5ZG19rOsBwLRNrOZUrfUvd7uvlPJfSinfUmv9o1LKtyT54w6rPZzk5ZtuPy/JJzbd/mCSP6y1/vNNr/knm+7/qSTvH6HpAAAwN67bv5jVAYKn6/YvTqE1ADC8pob13Znkra2f35rkVzuscyrJK0sp17Rm83tla1lKKe9L8swk79z8gFbQ1fbaJH8w5nYDAMBMOXr4QBYX9vZcZ3Fhb44ePjClFgHAcJqare94kg+XUt6W5ItJ3pgkpZTlJD9ca/2btdZHSyn/KMmnWo95b2vZ85L8gyQPJvmdUkqS/Mta679N8r+UUl6b5HySR5P80DTfFAAAF22dPc5scZPT3q5m6wNgXpVaa/+1drjl5eV65syZppsBALAjtGeP21yke3Fhb2675UYBCQDsIqWUe2uty/3Wa6rnFAAAO1Sn2ePas8VNO5zSgwsAZp9wCgCAseo2K9y0Z4vb2oNrdW09t95xf5IIqABghjRVEB0AgB2q26xw054trlcPLgBgdginALY4ubKaQ8dP54Zjd+XQ8dM5ubLadJMA5kqn2eOamC1uVnpwAQC9GdYHsIkhIADb12n2uCZqPV23fzGrHYKoaffgAgB6E04BbDJLRXwB5tmRg0uNnzePHj7QcdbAaffgAgB6E04BbGIICMDOMSs9uACA3oRTAJsYAgKws8xCDy4AoDcF0QE2mZUivgAAALuFnlMAmxgCAgDjdXJl1e9VAHoSTgFsYQgIAIyHWXABGIRhfQAAwET0mgUXANqEUwAAwESYBReAQQinAACAieg2261ZcAHYTDgFAABMhFlwARiEgugAAMBEmAUXgEEIpwAAgIkxCy4A/RjWBwAAAEBjhFMAAAAANMawPoABnVxZVTMDAABgzIRTAAM4ubKaW++4P+sbF5Ikq2vrufWO+5NEQAUAALANhvUBDODEqbOXgqm29Y0LOXHqbEMtAgAA2BmEUwADeGRtfajlAAAADEY4BTCA6/YvDrUcAACAwQinAAZw9PCBLC7svWzZ4sLeHD18oKEWAQAA7AwKogMMoF303Gx9AAAA4yWcAhjQkYNLwigAAIAxM6wPAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABojHAKAAAAgMYIpwAAAABoTCPhVCnlWaWUj5dS/rD1/zVd1ntra50/LKW8ddPyT5RSzpZS7mv9e25r+VWllNtLKQ+VUn67lHL9dN4RAAAAAKNoqufUsSS/UWt9QZLfaN2+TCnlWUneleR7knx3kndtCbHeUmu9qfXvj1vL3pbksVrrdyb5QJL3T/JNAAAAALA9+xp63dcleXnr559N8okkf3/LOoeTfLzW+miSlFI+nuRVSX6xz/O+u/XzR5L8y1JKqbXWnq05ezZ5+ct7rgIAAADA+DXVc+qba61/lCSt/5/bYZ2lJF/adPvh1rK2n2kN6fvxUkrZ+pha6/kkX0ny7E4NKKW8vZRyppRyZmNjY3vvBgAAAICRTKznVCnl15P8Nx3u+geDPkWHZe0eUG+pta6WUr4xya8k+cEkP9fnMZcvrPWDST6YJMvLyzWf+MSAzQIAAACgr9IpprnSxMKpWutf7nZfKeW/lFK+pdb6R6WUb0nyxx1WezhPDf1Lkufl4vC/1FpXW///aSnlF3KxJtXPtR7z/CQPl1L2JXlmkke3/24AAAAAmISmhvXdmaQ9+95bk/xqh3VOJXllKeWaViH0VyY5VUrZV0p5TpKUUhaSfH+S3+vwvG9IcrpvvSkAAAAAGtNUQfTjST5cSnlbki8meWOSlFKWk/xwrfVv1lofLaX8oySfaj3mva1lz8jFkGohyd4kv57kp1rr/HSSf19KeSgXe0y9eXpvCQAAAIBhFR2LLtacOnPmTNPNAADYtU6urObEqbN5ZG091+1fzNHDB3Lk4FL/BwIAM6uUcm+tdbnfek31nAIAgCQXg6lb77g/6xsXkiSra+u59Y77k0RABQC7wP/f3r3HWlaWdwD+vWFQqRoBCwiDFRIpDdpW61RjbBtvMNpEoZVaTaNjqjFN25gWS8QY47UWRLuXHi4AABAfSURBVIOxtk2oNiGt9UYsoqZOENQYm1gGMSpVHGq9DFABAaMVL+jbP/Ya2TOekZlzzt7fXJ4nOdl7fetb67wrnJdzzm/W+s6oNacAACBJcuHW638aTO10149+nAu3Xj+oIgBgmYRTAAAMddOdd+3TOABwcBFOAQAw1AlHHrFP4wDAwUU4BQDAUOduPjVHHH7YLmNHHH5Yzt186qCKONRddu2NecL5V+Xk8z6cJ5x/VS679sbRJQEc1CyIDgDAUDsXPffX+tgfWKAfYPmEUwAADHfWozf6xZ/9ws9boN/XKMBieKwPAABgYoF+gOUTTgEAAEws0A+wfMIpAACAiQX6AZbPmlMAAAATC/QDLJ9wCgAAYI4F+gGWy2N9AAAAAAwjnAIAAABgGOEUAAAAAMMIpwAAAAAYRjgFAAAAwDDCKQAAAACGEU4BAAAAMIxwCgAAAIBhhFMAAAAADCOcAgAAAGAY4RQAAAAAwwinAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAAAAMI5wCAAAAYBjhFAAAAADDCKcAAAAAGEY4BQAAAMAwwikAAAAAhhFOAQAAADCMcAoAAACAYYRTAAAAAAwjnAIAAABgGOEUAAAAAMMIpwAAAAAYRjgFAAAAwDDCKQAAAACGEU4BAAAAMIxwCgAAAIBhhFMAAAAADCOcAgAAAGAY4RQAAAAAwwinAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAAAAMI5wCAAAAYBjhFAAAAADDDAmnquroqrqiqrZPr0ftYd6Wac72qtoyjT2wqj4793FbVb1l2veCqrp1bt+LlnldAAAAAOybUXdOnZfkyu4+JcmV0/YuquroJK9K8rgkj03yqqo6qru/092P2vmR5GtJ3j936Hvm9r998ZcCAAAAwGqNCqfOTHLJ9P6SJGetMGdzkiu6+/buviPJFUmeNj+hqk5JcmySTy6wVgAAAAAWZFQ4dVx335wk0+uxK8zZmOQbc9s7prF5z83sTqmeG3tWVX2uqi6tqofuqYCqenFVbauqbbfeeuvqrgIAAACANVlYOFVVH62qL6zwcebenmKFsd5t+zlJ3jW3/cEkJ3X3ryX5aO65O+tnT9R9cXdv6u5NxxxzzF6WBAAAAMB62rCoE3f3U/e0r6q+WVXHd/fNVXV8kltWmLYjyRPntk9M8vG5c/x6kg3dfc3c5/zW3Px/THLB6qoHAAAAYBlGPdZ3eZIt0/stST6wwpytSc6oqqOmv+Z3xjS203Oz611TmYKunZ6Z5IvrVjEAAAAA625hd07di/OTvLeqXpjk60n+IEmqalOSP+nuF3X37VX1uiRXT8e8trtvnzvHs5P87m7nfUlVPTPJ3UluT/KCBV4DAAAAAGtUu64lfmjatGlTb9u2bXQZAAAAAAeNqrqmuzfd27xRj/UBAAAAgHAKAAAAgHGEUwAAAAAMI5wCAAAAYBjhFAAAAADDCKcAAAAAGEY4BQAAAMAwwikAAAAAhhFOAQAAADCMcAoAAACAYYRTAAAAAAwjnAIAAABgGOEUAAAAAMMIpwAAAAAYRjgFAAAAwDDCKQAAAACGEU4BAAAAMIxwCgAAAIBhhFMAAAAADCOcAgAAAGAY4RQAAAAAwwinAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAAAAMI5wCAAAAYBjhFAAAAADDCKcAAAAAGEY4BQAAAMAwwikAAAAAhhFOAQAAADCMcAoAAACAYYRTAAAAAAwjnAIAAABgGOEUAAAAAMMIpwAAAAAYRjgFAAAAwDAbRhcAAAAw0mXX3pgLt16fm+68KycceUTO3Xxqznr0xtFlARwyhFMAAMAh67Jrb8zL3//53PWjHydJbrzzrrz8/Z9PEgEVwJJ4rA8AADhkXbj1+p8GUzvd9aMf58Kt1w+qCODQI5wCAAAOWTfdedc+jQOw/oRTAADAIeuEI4/Yp3EA1p9wCgAAOGSdu/nUHHH4YbuMHXH4YTl386mDKgI49FgQHQAAOGTtXPTcX+sDGEc4BQAAHNLOevRGYRTAQB7rAwAAAGAY4RQAAAAAwwinAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAAAAMMyScqqqjq+qKqto+vR61h3kfqao7q+pDu42fXFWfno5/T1XdZxq/77R9w7T/pMVfDQAAAACrNerOqfOSXNndpyS5ctpeyYVJnrfC+AVJLpqOvyPJC6fxFya5o7sfnuSiaR4AAAAA+6lR4dSZSS6Z3l+S5KyVJnX3lUm+Mz9WVZXkyUkuXeH4+fNemuQp03wAAAAA9kOjwqnjuvvmJJlej92HYx+c5M7uvnva3pFk4/R+Y5JvTOe9O8m3p/k/o6peXFXbqmrbrbfeuopLAAAAAGCtNizqxFX10SQPWWHXK9Z66hXGei/27TrYfXGSi5Nk06ZNK84BAAAAYLEWFk5191P3tK+qvllVx3f3zVV1fJJb9uHUtyU5sqo2THdHnZjkpmnfjiQPTbKjqjYkeVCS21d3BQAAAAAs2qjH+i5PsmV6vyXJB/b2wO7uJB9LcvYKx8+f9+wkV03zAQAAANgPjQqnzk9yelVtT3L6tJ2q2lRVb985qao+meR9mS1svqOqNk+7XpbknKq6IbM1pd4xjb8jyYOn8XOy578CCAAAAMB+oNxYNFtzatu2baPLAAAAADhoVNU13b3p3uaNunMKAAAAAIRTAAAAAIwjnAIAAABgGOEUAAAAAMMIpwAAAAAYRjgFAAAAwDDCKQAAAACGEU4BAAAAMIxwCgAAAIBhhFMAAAAADCOcAgAAAGAY4RQAAAAAwwinAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGqu0fXMFxV3Zrkawv+NL+Y5LYFfw44UOgHuId+gF3pCbiHfoBd6YkDz8O6+5h7myScWpKq2tbdm0bXAfsD/QD30A+wKz0B99APsCs9cfDyWB8AAAAAwwinAAAAABhGOLU8F48uAPYj+gHuoR9gV3oC7qEfYFd64iBlzSkAAAAAhnHnFAAAAADDCKfWSVUdXVVXVNX26fWoPczbMs3ZXlVb5sbvU1UXV9WXq+pLVfWs5VUP62+tPTG3//Kq+sLiK4bFWUs/VNUvVNWHp+8N11XV+cutHtZHVT2tqq6vqhuq6rwV9t+3qt4z7f90VZ00t+/l0/j1VbV5mXXDoqy2J6rq9Kq6pqo+P70+edm1w3pby/eIaf8vVdV3q+qvllUz60s4tX7OS3Jld5+S5MppexdVdXSSVyV5XJLHJnnV3C8or0hyS3f/cpLTknxiKVXD4qy1J1JVv5/ku8spFxZqrf3wpu7+lSSPTvKEqnr6csqG9VFVhyX5uyRPz+znnOdW1Wm7TXthkju6++FJLkpywXTsaUmek+QRSZ6W5O+n88EBay09keS2JM/o7l9NsiXJPy+naliMNfbDThcl+fdF18riCKfWz5lJLpneX5LkrBXmbE5yRXff3t13JLkisx+ykuSPk/xNknT3T7r7tgXXC4u2pp6oqgckOSfJ65dQKyzaqvuhu7/X3R9Lku7+YZLPJDlxCTXDenpskhu6+yvT1/G7M+uLefN9cmmSp1RVTePv7u4fdPf/JLlhOh8cyFbdE919bXffNI1fl+R+VXXfpVQNi7GW7xGpqrOSfCWzfuAAJZxaP8d1981JMr0eu8KcjUm+Mbe9I8nGqjpy2n5dVX2mqt5XVccttlxYuFX3xPT+dUnenOR7iywSlmSt/ZAkmb5fPCOzu6/gQHKvX9/zc7r77iTfTvLgvTwWDjRr6Yl5z0pybXf/YEF1wjKsuh+q6v5JXpbkNUuokwXaMLqAA0lVfTTJQ1bY9Yq9PcUKY53Zf4cTk3yqu8+pqnOSvCnJ81ZVKCzJonqiqh6V5OHd/Ze7P08O+6sFfo/Yef4NSd6V5K3d/ZV9rxCG+rlf3/cyZ2+OhQPNWnpitrPqEZk92nTGOtYFI6ylH16T5KLu/u50IxUHKOHUPujup+5pX1V9s6qO7+6bq+r4JLesMG1HkifObZ+Y5ONJvpXZ3SH/No2/L7NnamG/tsCeeHySx1TVVzP7/9SxVfXx7n5iYD+1wH7Y6eIk27v7LetQLizbjiQPnds+MclNe5izYwpjH5Tk9r08Fg40a+mJVNWJmf3u8Pzu/u/FlwsLtZZ+eFySs6vqjUmOTPKTqvp+d79t8WWznjzWt34uz2xBwkyvH1hhztYkZ1TVUdMit2ck2drdneSDueeXkqck+a/FlgsLt5ae+IfuPqG7T0ryW0m+LJjiALfqfkiSqnp9Zj+E/cUSaoVFuDrJKVV1clXdJ7MFzi/fbc58n5yd5KrpZ6TLkzxn+ktNJyc5Jcl/LqluWJRV98T0iPeHk7y8uz+1tIphcVbdD93929190vR7w1uSvEEwdWASTq2f85OcXlXbk5w+baeqNlXV25Oku2/PbB2dq6eP105jyew52VdX1ecye5zvpUuuH9bbWnsCDiar7ofpX8dfkdlfr/lMVX22ql404iJgtab1Qf48s8D1i0ne293XVdVrq+qZ07R3ZLZ+yA2Z/UGM86Zjr0vy3sz+4e4jSf6su3+87GuA9bSWnpiOe3iSV07fEz5bVSutZQgHhDX2AweJmv2DFAAAAAAsnzunAAAAABhGOAUAAADAMMIpAAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAB7Cq+u7oGgAA1kI4BQAAAMAwwikAgCWoqt+sqs9V1f2q6v5VdV1VPXK3ORdU1Z/Obb+6ql5aVQ+oqiur6jNV9fmqOnOF8z+xqj40t/22qnrB9P4xVfWJqrqmqrZW1fELvFQAgH0inAIAWILuvjrJ5Ulen+SNSf6lu7+w27R3J/nDue1nJ3lfku8n+b3u/o0kT0ry5qqqvfm8VXV4kr9NcnZ3PybJPyX567VcCwDAetowugAAgEPIa5NcnVnY9JLdd3b3tVV1bFWdkOSYJHd099engOkNVfU7SX6SZGOS45L87158zlOTPDLJFVOedViSm9fjYgAA1oNwCgBgeY5O8oAkhye5X5L/W2HOpUnOTvKQzO6kSpI/yiysekx3/6iqvjodP+/u7HpX/M79leS67n78elwAAMB681gfAMDyXJzklUnemeSCPcx5d5LnZBZQXTqNPSjJLVMw9aQkD1vhuK8lOa2q7ltVD0rylGn8+iTHVNXjk9ljflX1iHW5GgCAdeDOKQCAJaiq5ye5u7v/taoOS/IfVfXk7r5qfl53X1dVD0xyY3fvfPzunUk+WFXbknw2yZd2P393f6Oq3pvkc0m2J7l2Gv9hVZ2d5K1TaLUhyVuSXLeYKwUA2DfV3aNrAAAAAOAQ5bE+AAAAAIYRTgEAAAAwjHAKAAAAgGGEUwAAAAAMI5wCAAAAYBjhFAAAAADDCKcAAAAAGEY4BQAAAMAw/w+uQXnHb2sZKQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f974450>"
+       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
       ]
      },
      "metadata": {},
@@ -407,7 +388,7 @@
    ],
    "source": [
     "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df.spy,simple.resid)\n",
+    "plt.scatter(df.goog,simple.resid)\n",
     "plt.axhline(0.05,color = 'r')\n",
     "plt.axhline(-0.05,color = 'r')\n",
     "plt.axhline(0,color = 'black')\n",
@@ -418,397 +399,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 140,
+   "metadata": {},
    "outputs": [],
    "source": [
+    "from pandas.core import datetools\n",
     "from statsmodels.stats import diagnostic as dia"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p-value of Heteroskedasticity:  0.144075842844\n"
+      "p-value of Heteroskedasticity:  0.14407584284381386\n"
      ]
     }
    ],
    "source": [
     "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
-    "print 'p-value of Heteroskedasticity: ', het[-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2.8288103783041767, nan, 2.8496563023857964, 0.092640488871836887)"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.spy))"
+    "print('p-value of Heteroskedasticity: ', het[-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(251,)"
+       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "simple.resid.shape"
+    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(251,)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.spy.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# fama-french factor explore##\n",
-    "fama_table = quandl.get('KFRENCH/FACTORS5_M')\n",
-    "fama = fama_table['2008':'2016']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'fama' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-6a47d2860ee7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfama\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfama\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mfama\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'fama' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "fama = fama.apply(lambda x: x/100)\n",
-    "fama.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2016-08-31    0.013548\n",
-       "2016-09-30    0.084895\n",
-       "2016-10-31   -0.058389\n",
-       "2016-11-30   -0.050972\n",
-       "2016-12-31   -0.000933\n",
-       "Freq: M, Name: Close, dtype: float64"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "amzn = amzn_table.loc['2008':'2016',['Close']]\n",
-    "amzn = amzn.resample('M').agg(lambda x: x[-1])\n",
-    "amzn_log = np.log(amzn.Close).diff().dropna()\n",
-    "amzn_log.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MKT</th>\n",
-       "      <th>SMB</th>\n",
-       "      <th>HML</th>\n",
-       "      <th>RMW</th>\n",
-       "      <th>CMA</th>\n",
-       "      <th>RF</th>\n",
-       "      <th>Close</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-08-31</th>\n",
-       "      <td>0.0050</td>\n",
-       "      <td>0.0152</td>\n",
-       "      <td>0.0318</td>\n",
-       "      <td>-0.0124</td>\n",
-       "      <td>-0.0056</td>\n",
-       "      <td>0.0002</td>\n",
-       "      <td>0.013548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-09-30</th>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>0.0172</td>\n",
-       "      <td>-0.0134</td>\n",
-       "      <td>-0.0185</td>\n",
-       "      <td>-0.0005</td>\n",
-       "      <td>0.0002</td>\n",
-       "      <td>0.084895</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-10-31</th>\n",
-       "      <td>-0.0202</td>\n",
-       "      <td>-0.0397</td>\n",
-       "      <td>0.0415</td>\n",
-       "      <td>0.0136</td>\n",
-       "      <td>0.0022</td>\n",
-       "      <td>0.0002</td>\n",
-       "      <td>-0.058389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-11-30</th>\n",
-       "      <td>0.0486</td>\n",
-       "      <td>0.0703</td>\n",
-       "      <td>0.0844</td>\n",
-       "      <td>-0.0068</td>\n",
-       "      <td>0.0383</td>\n",
-       "      <td>0.0001</td>\n",
-       "      <td>-0.050972</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-31</th>\n",
-       "      <td>0.0182</td>\n",
-       "      <td>0.0032</td>\n",
-       "      <td>0.0352</td>\n",
-       "      <td>0.0095</td>\n",
-       "      <td>-0.0021</td>\n",
-       "      <td>0.0003</td>\n",
-       "      <td>-0.000933</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               MKT     SMB     HML     RMW     CMA      RF     Close\n",
-       "Date                                                                \n",
-       "2016-08-31  0.0050  0.0152  0.0318 -0.0124 -0.0056  0.0002  0.013548\n",
-       "2016-09-30  0.0025  0.0172 -0.0134 -0.0185 -0.0005  0.0002  0.084895\n",
-       "2016-10-31 -0.0202 -0.0397  0.0415  0.0136  0.0022  0.0002 -0.058389\n",
-       "2016-11-30  0.0486  0.0703  0.0844 -0.0068  0.0383  0.0001 -0.050972\n",
-       "2016-12-31  0.0182  0.0032  0.0352  0.0095 -0.0021  0.0003 -0.000933"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_df = pd.concat([fama,amzn_log],axis = 1).dropna()\n",
-    "new_df = new_df.rename(columns = {'Mkt-RF':'MKT'})\n",
-    "new_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  Close   R-squared:                       0.022\n",
-      "Model:                            OLS   Adj. R-squared:                  0.012\n",
-      "Method:                 Least Squares   F-statistic:                     2.333\n",
-      "Date:                Wed, 19 Jul 2017   Prob (F-statistic):              0.130\n",
-      "Time:                        15:40:51   Log-Likelihood:                 102.72\n",
-      "No. Observations:                 107   AIC:                            -201.4\n",
-      "Df Residuals:                     105   BIC:                            -196.1\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0198      0.009      2.184      0.031       0.002       0.038\n",
-      "SMB            0.5555      0.364      1.527      0.130      -0.166       1.277\n",
-      "==============================================================================\n",
-      "Omnibus:                        2.017   Durbin-Watson:                   1.703\n",
-      "Prob(Omnibus):                  0.365   Jarque-Bera (JB):                1.503\n",
-      "Skew:                          -0.149   Prob(JB):                        0.472\n",
-      "Kurtosis:                       3.499   Cond. No.                         40.2\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+      "0.2668279319999998\n"
      ]
     }
    ],
    "source": [
-    "m = sm.ols(formula = 'Close ~ MKT + SMB + HML',data = new_df).fit()\n",
-    "print m.summary()"
+    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb
index 36f6ff6..06e6da5 100644
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -13,10 +11,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([1,2,3])\n",
@@ -26,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,19 +32,19 @@
       "[[1 2 3]\n",
       " [2 2 1]\n",
       " [3 2 1]]\n",
-      "<type 'numpy.ndarray'>\n"
+      "<class 'numpy.ndarray'>\n"
      ]
     }
    ],
    "source": [
     "matrix = np.column_stack((a,b,c))\n",
-    "print matrix\n",
-    "print type(matrix)"
+    "print(matrix)\n",
+    "print(type(matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -63,12 +59,12 @@
    ],
    "source": [
     "matrix2 = np.array([a,b,c])\n",
-    "print matrix2"
+    "print(matrix2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +89,7 @@
    ],
    "source": [
     "x = np.dot(A,B)\n",
-    "print x"
+    "print(x)"
    ]
   },
   {
@@ -108,7 +104,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-893b341bebb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-7-ac89428c1137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)"
      ]
     }
@@ -119,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -132,83 +128,49 @@
       "\n",
       "-------------seperation line------------\n",
       "\n",
-      "[[ 0.   -1.    1.  ]\n",
-      " [-0.25  2.   -1.25]\n",
-      " [ 0.5  -1.    0.5 ]]\n"
+      "[[ 3.70074342e-17 -1.00000000e+00  1.00000000e+00]\n",
+      " [-2.50000000e-01  2.00000000e+00 -1.25000000e+00]\n",
+      " [ 5.00000000e-01 -1.00000000e+00  5.00000000e-01]]\n"
      ]
     }
    ],
    "source": [
-    "print matrix\n",
-    "print '\\n-------------seperation line------------\\n'\n",
-    "print np.linalg.inv(matrix)"
+    "print(matrix)\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.linalg.inv(matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]\n",
-      " [  0.00000000e+00   1.00000000e+00   1.11022302e-16]\n",
-      " [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]\n",
+      "[[ 1.00000000e+00 -6.66133815e-16  3.33066907e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00 -2.22044605e-16  1.00000000e+00]]\n",
       "\n",
       "-------------seperation line------------\n",
       "\n",
-      "[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]\n",
-      " [  6.66133815e-16   1.00000000e+00   0.00000000e+00]\n",
-      " [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]\n"
+      "[[ 1.00000000e+00  0.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00 -4.44089210e-16]\n",
+      " [-1.11022302e-16  0.00000000e+00  1.00000000e+00]]\n"
      ]
     }
    ],
    "source": [
     "inverse = np.linalg.inv(matrix)\n",
-    "print np.dot(matrix,inverse)\n",
-    "print '\\n-------------seperation line------------\\n'\n",
-    "print np.dot(inverse,matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [1 2 3]\n",
-      " [3 3 3]]\n"
-     ]
-    },
-    {
-     "ename": "LinAlgError",
-     "evalue": "Singular matrix",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-a9681b1c2fd3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0maa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0maa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0minv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m    524\u001b[0m     \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m     \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    527\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mLinAlgError\u001b[0m: Singular matrix"
-     ]
-    }
-   ],
-   "source": [
-    "aa = np.array([[1,2,3],[1,2,3],[3,3,3]])\n",
-    "print aa\n",
-    "inv = np.linalg.inv(aa)"
+    "print(np.dot(matrix,inverse))\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.dot(inverse,matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -225,12 +187,12 @@
     "A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])\n",
     "B = np.array([[8],[-11],[-3]])\n",
     "inv_A = np.linalg.inv(A)\n",
-    "print np.dot(inv_A,B)"
+    "print(np.dot(inv_A,B))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -244,36 +206,27 @@
     }
    ],
    "source": [
-    "print np.linalg.solve(A,B)"
+    "print(np.linalg.solve(A,B))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb
new file mode 100644
index 0000000..6999639
--- /dev/null
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb	
@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import quandl\n",
+    "from cvxopt import solvers\n",
+    "from cvxopt import matrix\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import minimize\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class stock(object):\n",
+    "    def __init__(self,ticker):\n",
+    "        self.ticker = ticker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tickers = ['KO','JNJ','PFE','NKE','PG','WMT','MMM','IBM']\n",
+    "stocks = []\n",
+    "leng = len(tickers)\n",
+    "for i in tickers:\n",
+    "    vars()[i] = stock(i)\n",
+    "    stocks.append(vars()[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf = quandl.get('USTREASURY/LONGTERMRATES')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf = rf['LT Composite > 10 Yrs']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    table = quandl.get('WIKI/%s'%i.ticker)\n",
+    "    i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
+    "    i.mean = np.mean(i.rate)*252\n",
+    "    i.std = np.std(i.rate)*np.sqrt(252)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'float' object has no attribute 'shape'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-42-265a85598a64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmean_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstd_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcov_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmean_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstd_list\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstock_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m   3083\u001b[0m             \u001b[0mw\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0maweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3085\u001b[0;31m     \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3086\u001b[0m     \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_sum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1162\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1163\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1164\u001b[0m             \u001b[0mscl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'shape'"
+     ]
+    }
+   ],
+   "source": [
+    "stock_list = [x.ticker for x in stocks]\n",
+    "rate_list = [x.rate for x in stocks]\n",
+    "mean_list = [x.mean for x in stocks]\n",
+    "std_list = [x.std for x in stocks]\n",
+    "cov_matrix = np.cov(rate_list)\n",
+    "df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def min_var_generator(rate):\n",
+    "    def target(x, sigma, mean,r):\n",
+    "        sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
+    "        return sr_inv\n",
+    "\n",
+    "    x = np.ones(leng)/leng\n",
+    "    mean = mean_list\n",
+    "    sigma = cov_matrix\n",
+    "    r = rf\n",
+    "    c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
+    "        {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
+    "    bounds = [(-1,1) for i in range(leng)]\n",
+    "    res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
+    "#     return res['x']\n",
+    "    return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0.20992676,  0.27623188, -0.00295919, -0.10248824,  0.31125431,\n",
+       "         0.23690646, -0.10606759,  0.1771956 ]), 0.1455789711354554)"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_var_generator(0.06)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
+    "simu_var = []\n",
+    "for i in simu_rate:\n",
+    "    try:\n",
+    "        res = min_var_generator(i)\n",
+    "        simu_var.append(res[1])\n",
+    "    except:\n",
+    "        print i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rate      0.179200\n",
+       "std       0.240626\n",
+       "sharpe    0.630854\n",
+       "Name: 1518, dtype: float64"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
+    "port_df.head()\n",
+    "port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
+    "opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
+    "opt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Simulation#\n",
+    "monte_rate, monte_std = [],[]\n",
+    "for i in range(100000):\n",
+    "    w = np.random.dirichlet(np.ones(leng),size=1)\n",
+    "    monte_rate.append(np.dot(w,mean_list))\n",
+    "    monte_std.append(np.sqrt(np.dot(np.dot(w,cov_matrix),w.reshape(8,1)))*np.sqrt(252))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simu_df = pd.DataFrame({'x':monte_std,'y':monte_rate})\n",
+    "simu_df['sharpe'] = (simu_df['y'] - rf)/simu_df['x']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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Sy+XSwoULFRUVqod7NVC/nvVVp35bKaKnHM6oQMcEUM5RQAEAAABAJeTxeDRv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y7oq2cekW6qrqSUiJN13RDAaDwfCDYwQog8Fg+BGpq6vj1VdfZeLEiWzatIkOHTrw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\nERERERGRZKUClIiIiIiIiIiIJKv/A907XhlB7lZHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118f8a990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
+    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
+    "plt.scatter(0,rf,color = 'r',s = 30)\n",
+    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
+    "plt.annotate('risk-free',(0,rf),size = 15)\n",
+    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
+    "for i in stocks:\n",
+    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
+    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
+    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
+    "plt.xlim(0)\n",
+    "plt.xlabel('Standard Deviation')\n",
+    "plt.ylabel('Expected Return')\n",
+    "plt.axhline(rf,ls = '--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "###three stocks version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def efficient_frontier(tickers):\n",
+    "    stocks = []\n",
+    "    leng = len(tickers)\n",
+    "    for i in tickers:\n",
+    "        vars()[i] = stock(i)\n",
+    "        stocks.append(vars()[i])\n",
+    "    \n",
+    "    rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
+    "    rf = (rf.ix[-1][0]/100)\n",
+    "    for i in stocks:\n",
+    "        table = quandl.get('WIKI/%s'%i.ticker)\n",
+    "        i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
+    "        i.mean = np.mean(i.rate)*252\n",
+    "        i.std = np.std(i.rate)*np.sqrt(252)\n",
+    "        \n",
+    "    stock_list = [x.ticker for x in stocks]\n",
+    "    rate_list = [x.rate for x in stocks]\n",
+    "    mean_list = [x.mean for x in stocks]\n",
+    "    std_list = [x.std for x in stocks]\n",
+    "    cov_matrix = np.cov(rate_list)\n",
+    "    df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
+    "    print df\n",
+    "    \n",
+    "    def min_var_generator(rate):\n",
+    "        def target(x, sigma, mean,r):\n",
+    "            sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
+    "            return sr_inv\n",
+    "\n",
+    "        x = np.ones(leng)/leng\n",
+    "        mean = mean_list\n",
+    "        sigma = cov_matrix\n",
+    "        r = rf\n",
+    "        c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
+    "            {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
+    "        bounds = [(-1,1) for i in range(leng)]\n",
+    "        res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
+    "        return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))\n",
+    "    \n",
+    "    simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
+    "    simu_var = []\n",
+    "    for i in simu_rate:\n",
+    "        try:\n",
+    "            res = min_var_generator(i)\n",
+    "            simu_var.append(res[1])\n",
+    "        except:\n",
+    "            print i\n",
+    "    \n",
+    "    port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
+    "    port_df.head()\n",
+    "    port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
+    "    opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
+    "    return port_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         mean       std\n",
+      "PG   0.052920  0.177927\n",
+      "IBM  0.053190  0.222997\n",
+      "KO   0.072506  0.188207\n"
+     ]
+    }
+   ],
+   "source": [
+    "new = efficient_frontier(['PG','IBM','KO'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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Wac+fr/u7XaCISnfIRHSWMcx4AnB6KKAAAAAAoBw6cOCAJkyYoNTUVP3222+6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RAAAg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LQyZ3Tnafw2a4ehIwkhhBAvTn9/CZ5KluC9MSaGDmBIjRs3JiIiAgsLi5c67vPPP2fz5s0o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\nUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115569390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
+    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
+    "plt.scatter(new['std'],new['rate'],c = new['sharpe'],s = 0.5)\n",
+    "plt.scatter(0,rf,color = 'r',s = 30)\n",
+    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
+    "plt.annotate('risk-free',(0,rf),size = 15)\n",
+    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
+    "for i in stocks:\n",
+    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
+    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
+    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
+    "plt.xlim(0)\n",
+    "plt.xlabel('Standard Deviation')\n",
+    "plt.ylabel('Expected Return')\n",
+    "plt.axhline(rf,ls = '--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb
new file mode 100644
index 0000000..9990b04
--- /dev/null
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb	
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from googlefinance import getQuotes\n",
+    "import json\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats.mstats import normaltest\n",
+    "import time\n",
+    "from cvxopt import matrix\n",
+    "import seaborn as sns\n",
+    "import statsmodels.tsa.stattools as ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class stock(object):\n",
+    "    def __init__(self,ticker):\n",
+    "        self.ticker = ticker\n",
+    "tickers = [\"MMM\", \"AXP\", \"AAPL\", \"BA\", \"CAT\", \"CVX\", \"CSCO\",\"KO\",\n",
+    "                \"DIS\",\"DD\",\"XOM\",\"GE\",\"GS\",\"HD\",\"IBM\",\"INTC\",\"JPM\",\"MCD\",\n",
+    "                \"MRK\",\"MSFT\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"WMT\"]         \n",
+    "stocks = []\n",
+    "for i in tickers:\n",
+    "    vars()[i] = stock(i)\n",
+    "    stocks.append(vars()[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    table = quandl.get('WIKI/%s'%i.ticker,start_date = '2012-03-21',end_date = '2015-01-01')\n",
+    "    i.rate = np.log(table['Adj. Close']).diff().dropna()\n",
+    "    i.mean = np.mean(i.rate)*252\n",
+    "    i.std = np.std(i.rate)*np.sqrt(252)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spy = quandl.get('LSE/SPY5')\n",
+    "spy = np.log(spy['Last Close']).diff().dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class rolling(object):\n",
+    "    def __init__(self,ticker,series,spy_series):\n",
+    "        self.ticker = ticker\n",
+    "        self.prices = series\n",
+    "        self.spy = spy_series\n",
+    "        self.df = pd.concat([self.prices,self.spy],axis = 1).dropna()\n",
+    "        self.df.columns = ['SPY','%s'%self.ticker]\n",
+    "        self.prices = self.df['%s'%self.ticker]\n",
+    "        self.spy = self.df['SPY']\n",
+    "        \n",
+    "    def roll(self, length):\n",
+    "        df_leng = self.df.shape[0]\n",
+    "        beta, beta_p, inter, inter_p,resid = [],[],[],[],[]\n",
+    "        loop = df_leng - length\n",
+    "        for i in range(loop):\n",
+    "            x = sm.add_constant(self.spy[i:i+length])\n",
+    "            model = sm.OLS(self.prices[i:i+length],x).fit()\n",
+    "            beta.append(model.params[1])\n",
+    "            beta_p.append(model.pvalues[1])\n",
+    "            inter.append(model.params[0])\n",
+    "            inter_p.append(model.pvalues[0])\n",
+    "        beta_df = pd.DataFrame({'beta':beta,'beta_p':beta_p,'inter':inter,'inter_p':inter_p},index = self.df.index[length:])\n",
+    "        self.beta_df = beta_df\n",
+    "        self.mean_beta = np.mean(beta)\n",
+    "        self.std_beta = np.std(beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    i.r = rolling(i.ticker, i.rate,spy)\n",
+    "    i.r.roll(21*6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      mean_beta   sd_beta  sd_beta_p\n",
+      "MMM    0.391424  0.106368   0.000174\n",
+      "AXP    0.260624  0.055665   0.000784\n",
+      "AAPL   0.095790  0.041038   0.095875\n",
+      "BA     0.190906  0.070859   0.023826\n",
+      "CAT    0.191012  0.063119   0.019710\n",
+      "CVX    0.280610  0.089215   0.011553\n",
+      "CSCO   0.132116  0.067679   0.115558\n",
+      "KO     0.181293  0.166132   0.309038\n",
+      "DIS    0.254268  0.063585   0.000340\n",
+      "DD     0.247352  0.057332   0.002610\n",
+      "XOM    0.292914  0.150067   0.026876\n",
+      "GE     0.256557  0.075137   0.010253\n",
+      "GS     0.237748  0.029856   0.000318\n",
+      "HD     0.219472  0.061830   0.012097\n",
+      "IBM    0.212634  0.112679   0.055888\n",
+      "INTC   0.152051  0.073508   0.057509\n",
+      "JPM    0.222741  0.057927   0.008795\n",
+      "MCD    0.243713  0.113179   0.189099\n",
+      "MRK    0.170320  0.062014   0.049013\n",
+      "MSFT   0.211149  0.104175   0.008382\n",
+      "NKE    0.115547  0.066427   0.252076\n",
+      "PFE    0.247528  0.092036   0.012432\n",
+      "PG     0.197096  0.159905   0.280305\n",
+      "UTX    0.318099  0.081374   0.000436\n",
+      "UNH    0.135254  0.032307   0.036826\n",
+      "VZ     0.170965  0.087304   0.082649\n",
+      "WMT    0.193440  0.062965   0.062210\n"
+     ]
+    }
+   ],
+   "source": [
+    "tickers = [x.ticker for x in stocks]\n",
+    "mean_betas = [x.r.mean_beta for x in stocks]\n",
+    "sd_betas = [x.r.std_beta for x in stocks]\n",
+    "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n",
+    "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n",
+    "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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na5Nz9cvfzbaKjqcGN+Wld9YDcCpKrWu/HSxJNbL8K/NY+KncW7gb4CO2XtX5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Lc/ZtfJfI9dMYZGLHhMLRGpVeg0R94yYKB0N/CGzkgNLJmn2XDRPiJVUB4a09kcsEvJ2taevt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10Pej0eA8PwGTXC4L6+spK473+kOCkZM1sbOr70AlaurgAUp6Ry5aefuV5WhiAIBM9+B4W5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\nr6xEn5dLfWUFzhHmb4+cz/+DIJPhMWIE/rOv7XI2hAZtJQqP1nu18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yUilc8fEUy4uxOP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f9ifI14EzsZlM/v6wXuVDjV28W2MXWy6zcHkDu3i7CbsYqLGL914PoX9vDwDmLT7Ljn3x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\n1RqB+SuDWf+VeFzS9mNxxN5VMfP5zkTGZ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bLt0A1Wbw3XzvvNnvTq7AaA3NQIB1s5XZ/6\nE4C1C4bRua0TVyLTmDbnf/yYi3owzP43gQNCIpEIwI+CIMyq+P+HgKUgCPMkEsk8IF8QhMUSiUQO\n7APOV9xTAxHVfmqLIAgL9c1f0GiI2bSZbh/NRG5vR9D873AO6IilR9URiUlnz2Nkbs6AH74mNTiE\nm9t30+mtqVh5eNBr3mdIZTJKVDlcmPMNTp07IpXJ9C6H21eiyU7J4I1Vc0i5cYcjK7bx0pJZOula\n9PCn6+j+rHrja63rLs09efnHjzCWmxB68Byn1u9h3Cev1PvuURu30OPjGcjt7bgwbyHOAR2x8nCr\n9u4XMLYwZ+Cir0gJDuHGtt0EvP06xlaWdH3/LeR2tuQlJROy6Fce/7mq+BWXryKT63+cXmxINJkp\nGby39guSriew77ftvLH0A510bXq2p9fY/ix97Rudex0GdmH0W8/onfc9woNjSEtSsmjzbG5FJ/DH\nkh3MW/2eTroRkx+jXZeWlJeVs/C9FYQFx9CpV1sAigqLObrjHH61fLDXxr266FlRF4FfLsSli3Zd\nJJ4R62LQYrEurm/dTZd3XkdqYkzr8WPIS04hLymlMr2RmZz+33xe+f9zcxfg2q3hDrLz5yJJvJvO\nPwe/JjI8nu++/ouNmz+rNe03C1+lXXsfrWuubvbM/+ZlNv3R+I5C0Gi4+edmOs+aiam9HZe/+g7H\nzh2xqKafqefOY2RhTq+FX5N2MYTb23fjP30qAGbOTnSf/0Wj879HcOB1ku4q+XvvJ0RH3OXHb3ex\n6s8ZtaY9cyICczMTrWu+LVz55scpLP56Z6PyFzQaUrb8je+M9zGys+PWwm+x7tgJuVtVORjb2+M5\n5RWUx49oPSs1MaHZy69i6uxCmUpF3HffYNXOH5m5ud5yxF6OJjM5gxm/f0HSjQT2/7adabXoZ+ue\n7ek5pj+/vK6rnyWFxQTvOYtna+8G5SloNNzYtJmAD8U2EFLRBqrb6JRz5zG2MKfP91+juBhC3Lbd\ndHhrauX9m1u249BB9+i+Lp98gImVZYPkuIdUAl+N78iLq4JQ5BSx570BHI9SEJemfVzkgWspfLk7\nQuf51afjMDOWMbl3w96/dhkkzHuxCy8tPoMiq4jdc4dw4loKcSm52jJcSmT+n9rOhS4tHOja0pFR\nc44CsHX2IHq2duLijYx68xU0Gu78/Tdt3n8fEzs7ohYswLZTJ8zdq+oi47zYX3b+9lsyL13i7q5d\ntJw2jawrV9CUldFx3jzUJSWEz5uHY/fumDo6krB1K7b+/rR680005eVoSht+VHNYUAyKJCVLts4m\nLiqB9Yt38NUaXXs9cvJj+HcV7fWCGSu4FhRD596ive71eGdenvV0g/OsyT37sHmfaB+WfLOL1X/V\nYR+OR2Bmrm0fuvdqyRszRmBkJGPFTwf4c+1Jpr8/SudZQaMhbMNW+n46AzN7W07P/R7Xrh2xrtZP\nJJwW+4knfpxPUtBlorfspvu7r5ObnEpS8BUe//4LirNzOL/wF55YPA+JVIrXgF40f2IgV1Zt0MrP\nuUMb2k18EqlMRtSW3cTuO8KQ93TlukfMpRiUyRnM3vA5CTEJ7Ph5O+/9pmsf2vXyp9+T/Vjw0rda\n14/9dZTOAzvTd2w/FAkK1sxeRbu/vqwzv9q4ESLK8OH6z0m8nsA/v27n7V90ZWjby5/eY/ux+FVt\nGZTJGZzaepw3f5yJuZU5+aqmP6J50/YzrNxwhN9/eqvJf7suoi7GkJGcwbxNs7kTk8CWpTv4ePn7\nOumGTBhEqwBRT375cDlRF2Pw79m20fneCY1GlZLOS8vnorh5h5OrtjLphw910p1auZXBb03GtZUP\ne75eQUJoND5d/Tm+fDP9XxqHZ/uWRB0PIvSfE/R+rv6P3uRr0eQpMnjq5y9Rxt4heO0WRn37kU66\noN+30mfaczi29OHEwhUkX4vGM0DsKwqU2aSEx2DhaFeZ3tLZgWFfvoeppTlJV6MIWrO51t+tSfTF\nGNKTM5hbUf5bl+7gw1rKv31vfwaM68dXLy7Qur575V56DO1Gz2E9uBEay741+5ky+4V6861OyPnr\nJCdmsH73p1yPvMuv3+3klw0zddJ9/t2LWFjKEQSBrz/eyLnjYTw2LIBrl+O4cDaKFZtnYWJihCpL\nP92QSiXMe7cPL39yGIWygJ2/jeVk0F3i7qoq0yxYebHy3y8+2Y52LRwq///79nDMTI2YNEp7stPA\n/02awglTAoyXSCSOdSWQSCQmwE7giiAI8youFwmC0Lnan97OB4Cc23cwd3HG3NkJqZERbj27k35V\n2yOXfjUcj37iLLpL9y5kRl9HEARkpiaVzgZ1WRk8wJqc2OAI2j/eA4lEgkcbX0oKisjPytFJ59HG\nF0t7G53r3h1bYSwXBzXurX3Iy1TppKmJ6vYdLFycqr17N9JDw7TSpIeG4dFPnA1yrfbuNt7NkNuJ\n3ldLD3c0ZWViGQDlxcXcOXwCv7H6nyEfExxJ58HdkUgkNGvrQ1F+EXm1lEOztj5Y1VIOTUFoYCR9\nh3dDIpHQwt+HwvwiVErtgb2p3IR2XVoCYGRshE8rT7LSq8p85++HGPXc4xibGDcoT9WtO5g7V9WF\ne69upNWoi7TQMDyr1YWyoi6MTE2xb90CqXHdeeWnplGam4996xYNkgfgzKkwRo3thUQioUOn5uTn\nFZGRoVsXdeHu4UjL1p5IHmCxWu7tO5g5O2NWUS4uPbujvKatnxlXw3HtI+qnU7cuZMeI5dKUBJ6O\nYtjorkgkEvw7epOfV4wyI1cnXWFhCds2nWXK1CFa132au+Dl49zo/AvvxGPi5ISJk1gONt26kxt2\nTSuNiYMjZp6eINEub1MXV0ydxSgQY1tbjKysKM9v3KD6enX9bONDcUEd+tmmbv08uekg/Z4djFED\ndUOnDfTojrKGjc4IDcetr9gGnGu0gYzQa5g5OmJR7SPtQejkZUdCZgGJWYWUqQX2XU3mCf+Gn79+\nIVZJfkn5g8nQ3J6E9HwSMwooU2vYf+kuQwLc638QEAQwNZZibCTFxFiKsUyKMre4Qc/mx8cjd3ZG\nXtEO7bt3JztM205lX7uGY2+xLuy7diU3JkasC4kETWkpglqNpqwMqUyGzMyM8sJC8m7exKlfPwCk\nRkYY6eEcuxIYSf8Ke92yvQ+FeUVk12Kv/btWs9etPcnKqL+PbCiBp6IYPqZh9mFrLfahR5/WGBmJ\nYwn/jl5kpNduZ7Nv3cHSxQkLZ0ekRkZ49uqK4op2+StCw/HqL/YT7j0CyIi6gSAIKK6E4dmrKzJj\nYyycHbF0cSL71h0AHNu0xNjSQic/5w7tKsc4dn6+FGXdv8wiL0TQ7QnRPvi0E/vv3Ezdd/Fp54O1\nQy32QQLFhWJbLC4owqa2NPUQHRRBlyGiDF5tfSgqqF0Gr7a1y3DpUBC9x/TD3Epsg5a2VnrLUB/n\nL10nS5Vff8ImJPxCJD0r6sa3om5yapSLidyEVgFVetKspSeqB9ST25ciaDtIHNu6tRbHtgU1+oyC\nrBxKi4pxa+2LRCKh7aAe3LokOm9VKel4+IvjFq/ObYgLCtPJozYSQ8JpPkDM16mVL6UFRRRma+db\nmJ1DWVExTq3EfJsP6EFiSFXfErJxJ12fH6fVpzq3bo6ppdg2nFr6UtCAsTZAxIVIetRT/gC+7Xxq\nbfeKBEVl3bQKaEHEhcgG5VudoDNRDBkp2sq2HbwpyCsmU6lrpyws5QCo1RrKy8sr33//jgtMfGkQ\nJibi3LOtvX660bG1EwkpuSQq8igr13Dg9G0G96l7km70oObsP3WrSv6rqeQXlumVp4H/XZrCAVEO\nrAZ0XYEiRsBWIFYQhE+bID8tirOzkdtXeTfldrYUZ2drpSnJVlWmkcpkGJmZUZZfAIDqVjyBs+dz\n4YuvaffSc42KfgDIy8zByrEqnMrKwZa8WoxTQwg/FkzzrvWHLRZXey8Aub0dxdmqOtPUfPd7KC5f\nxdq7GbKKD+DYnfvwGT4EmYn2LE9DyM1UYVOtHGwcbchV6lcOUYFh/DZ9IZu/WUdORnb9D9QgKyMX\ne+cqGeydbMm6jwwFeUVcPR+Ff7dWANy5kURWuorOfRoeOlqcrcLMoQF14VBVF8bmunVRF6kXL+PW\ns6tWaGF9pKepcHGtWsbj7GJLRlrt5TlvzgYmP/01a1YeaNKP/xKVtn6a2tlSUkM/S1UqTKu1UVm1\nNlqUoSRk3reELlyC6mZso+VQpufi7FrVJpxcbFDW8pGwdtkRJk4ZgKm8YR/XDaVcpcLYrqoujO3s\nKFPpPzAsvBOPoC7HxNGpUXLkKVVYVwv7tNZTP1PiEsnJyKZVD91ohLqoaaNN7XXbQEmNNnDPTpUX\nF3Pn4BF8n6xl1lYi4drin7k0bwHJp881WB5XGzmpqqLK/ytyinG10Q0JHt7RjUOzHmP5lG642cob\n/PsNwcXOjNSswioZsopwsatFhq6eHPhqKL+91Rs3e/H+1VuZBF/PIHjpGIJ/GsO5SAW3UhvmkCpV\nqTCxr2qHJra2lNWij/fSSO45GfLzse/SBamJCaEffcS1Tz/FbehQjCwsKMnMxMjKitt//EHE119z\ne+NG1CUlDS6LrIxcHKrba2dbsu/jKC3IKyL0fBTtu7aqvBZyJpxPpyxi6ed/kFmHjbsfGem5OLvU\nbx9+X3aESVMGIL+PfTjwTwg9+7au9V5RtgqzGn12UY2PqupppDIZRuZmlOYXUJSdU+NZW4qyG25D\nEs5eqFyyURe5yhxsnarysHWyJUcP+zB8ynCuHL/C/Elfsmb2ap56R/+olJoy2Dja1uqAqAtlUjrK\n5AxWvP8zy2b+xI2QGL1l+C+So8zBtpqe2DrZorpP3RTmFxERFEXriomWxpKfqcKy2tjG0sFWZ3It\nPysHSwdb7TQVH/YOzdy4fUl0CsSev0qesmH6WZitwqJavuYOthTWcKAVZqmwsK/K18LelsIKnbgb\nEo65ve19l1fEnrqAZ+eGjfNUyhzsapS/Prrh4edB2DmxHMLORVBcWEJBTsPGf/dQZuTgVG0c4+hi\nQ2Ydzs7Z76xm4hPzMDOX039wRwCS7yqJvBbPjJd+5sNpy7kRdVev/F0dzUnNqJJZoSzExVHX8Qng\n7myJp6sVQddS9crj/woSyb/391+lqZahLAOel0gktbm7PwZKBUGoGVNpJpFIrlX7m9hEsuiFrZ8v\n/RZ8Sa8vP+X2/sOoSx+tdy7yVAiKuLv0HP/4v5JfXlIKN7buxv9lcW19bkIihekZuHbr/K/kX5M2\nPdsz648veWfFp7To0pqdS/56qPmpy9WsmL+JJ57pj7O7AxqNhr9/28Pkt598qPnqS0rwZTx6dXso\nv/3N96+ybfeX/L7xI65eieXA3uCHko++mNrY0GfxArrP+5yWk54hetU6youK6n+wkcReTyY5KZMB\nj3d4aHk8CGU5KhLXr8XzxZeRSP/9FYQajYYja/5h2NRx/1qe8f/sx2voYIzkug6ArrM/pMf8z+n8\nwTsknTxN9o3GO6hqciJKQf9vjjNiyWnO3cxg8aTG7Q30QDJcS2HgRwcYNfco56PTWPR6DwC8nS3x\nc7Oi7wf76fPBfnq1daZbyzoDEJuMgjt3kEilBPzwA50XLCD12DGKMzIQ1GoK7t7FZeBAOsyZg9TE\nhJTDhx+KDOpyNb/N28SwZ/rj7CGG9nbp58/SHXNYuPEjOnRvxcpvNj+UvGOvJ5OSmMmAwXXbh41r\nTiCTSRk6qstDkaGx3NhzCKlUhmffHg81n9BTofQY1oMvt8xn6oJp/L3wTzQazUPNsyYatQZlcgbT\nFr3D5M+msGvpVoryC+t/8H8ItVrN+m828thTA3B0f/i24X4Meec5wg8FsnnWD5QWFyMzatwknz6U\nl5QS8c8ROk+oe7lRauRN4k4G0eX5f2es99SbY4kNu8X30xYTFx6HraMNEtnD68cX/DaNzYfnUlZa\nzrUQcY8jdbmavJxCfv5jBq/PGM23n21q8mjTe4we1JzD5+LRaB7O7xv4/58m2YRSEIRciUSyEZgB\n1PxCCAT6SCSSVoIg3Kx2vUgQhPt+5UokkmnANIBBH39A+3G668bkdnYUZ1V5VIuzVcjt7LTSmNrZ\nUpwlzsJp1GrKi4p0whUt3d0wksvJT07Bxrdha3uvHDhL2JEgANxaepGnrPLO5mWqsNIz/PDOtRsE\nbTvKc9/NwOg+4fj3kFe81z2Ks7Irl1XUTGNWy7sXZWUT+ssqOk17GQsXcUY1O+42OXfucnrW52jU\nGkpz87j43Y/0/Ex3DeY9Lu47x+XDYjl4tPIip1o55ChzsHZseDmYW1fVS9dhvTmydm+Dnju+K5DT\n+8QPZ982zbSWU2RlqLCvQ4Z1i7bj4unI8AkDASguLCEpXsF3M5aJ8mflsfTTtby38LX7bkQpt7Ol\nKLMBdZFZVRdlhbrtsDZy7yYhqDUNapfbNp9i945AANq19yFNkVV5Lz1NhZOLnc4zzhXXLCzkDB/V\ng6jIO4x+Ur+NP+vC1FZbP0uyVZjW0E8TW1tKqumnuqKNSiSSymUpVj7emDk7UqhIx7qB+rlry3n2\n7xLXI7bxb0a6oqpNZKTl4Ois3SaiwhO4EZ3EhBELUKs1ZGflM+O1Ffyydnqj3r06Rra2lGVX1UVZ\ndjbGtg3fgEpdVMSdZb/i+uRTmDdv2OZ+97i47xyhFXbKvaUXudVCcnP10M/SohLSE1L545PfAMjP\nzmXzV2uYPHfqfTeirGmjS7J024BpjTZwz07l3L5D+uVQ4rbtorywCKRim2g2ZFClnTextsapS2dy\nb8dj17r+2T5FTjFutlXRBq42chQ52t2WqlqY6NaLCXw6Wr+N9OojLbsIN/uqZQqu9maVm01WylBQ\ntY/C1jPxfPKsOIs1tIsH125lUVixDORMhIIuLRy4HKusN18TW1tKs6raYalKhXEt+lialYWpnR1C\nhT4aWVqi3LcPG39/pEZGSK2tsfLzoyAhAauWLTGxs8OyeXNAXLaReujQfeU4ujOQUxWOzuZtm5FZ\n3V6nq7Bzqr1Nrv1hO66ejoyYOLDympVNlQ0dNKYXm5fvr7ccQLQP+6rbh7T724fI8ASuRyfx7IgF\nqMtF+/Duayv4tcI+HNwTwoWz0Sxd/UadkWpmdrYU1eizzexsak1j5lChC4VFmFhaYGZnU+NZFWZ2\n9duQhLNBKK5G0vezmbXKFbjnHMEHRfvQrJUXqmpRh6oMFTZ69N8XD11k2ndvAODTzpey0nIKcgqw\nsrt/qHfQ3nNcOiTK4FlDhhylqvblHnVg42hLszbeyIxk2Ls64OjphDJZSbPWDdvP6b/EmX8COX9A\nLBfv1l6oqumJKkOFbR118/eSbTh5OPH4MwNrvV8fYQfPEnnsAgAuLbzIrza2yc9U6SwjtrS3qYx4\nqExTERFh7+nKU/PeBiA7OZ07l6PqzPf6kTPcPCHm6+jnTUG1fAszVZjba7d3c3tbCqpFRRRkqTC3\nsyUvLYP89Ez2fvxd5bP7P/2eUQs+wszWmqyEZC6s/pshn05Hfp89hM7+E8iFivL3au1Fdo3y10c3\nbBxtmPrVqwCUFJUQdjYcc8v6N+Pcu+08h/4R7VSrds3IqDaOUabl4OBctwwmpsb0HuhP0JlIuvZq\nhaOLLX0f74BEIqFNey+kEik5qgJs7Rq2j5JCWYibU5W9dXU0J01ZexTHqMeaM+/XCw363f+L/IcD\nE/41mvIUjKVAKLC+xvWzwAbgkEQi6ScIQoPjcQRBWI24vIMZQadqdaNZ+3pTmJZOYYYSuZ0tqRdD\n6PTma1ppnDt3JDkwCNsWzUkLCcW+bWskEon4jL0dUpmMImUmBakKzBwdasumVrqOGkDXUQMAiAuJ\nInT/WdoO6ELKjTuYmstr3euhLhS3Ejm8bAsT5k/HooFrFm18vSnQevfLdHrzVe13D+hIcmAwdi2a\nowgJxaHi3csKCrny4zJaTxiHXauqDxrvwQPxHix2WoUZmVz5adl9nQ8gnlTRc0x/AG5ciuLivnN0\nGNiFpOsJyC3keu31kJeVU5n+enAETs1cGvTckPH9GDJeXIN87UI0x3cF0mtwALeiEzC3lGPraK3z\nzI41BykqKOK1T6pOlTC3NGP5/qoNQhe8u4xJb4+t9xQMm+badZESfJmA6dp14dKlI0mBwdi1FOvC\nsV3rBi2pSAkKwb13w6IfJkwexITJgwA4dyaCbZtPMWxEdyLD47G0NMOpxsC+vFxNXl4RdnaWlJWp\nCTwTQY9eTbdBkJWvN0Vp6RRlKDG1syXtYgj+b2jrp2PnjiguBGHTojkZl0OxbSOWS2lunuiIkEop\nSs+gMC0dM6eGz+aMn9SX8ZP6AhB0NoZdW88zeHhnoiPuYmEpx9FJu02Mm9CHcRP6AJCanMWnM9Y1\nifMBwNzbh5L0dEqVGRjZ2pFzOYRmr77eoGc15eUkrFqOXc/elSdj6EN1/bxZoZ/tB3Yh6YZ++im3\nMOOTLVUba63/5FeGvvZkvadgWPl6U5herQ1cqqUNBHQk9bzYBtIvh2JXYae6za7a7Oz2P/uQmZrS\nbMgg1CUlCBoBIzM56pISsiJjal+mUQvhiSp8HC3wtDcnLaeIMQEezPwzVCuNk5UpGXniMoIh/q7c\nSm/ajezC47PwcbbE09GCtOwiRvfw4v1V2pFHTjZyMipO4RgS4E5cxTKLlKxCJg5ojuyABIkEerZ2\nYv3Rmzp51Ialjw/F6ekUK5WY2NqSFRKC3+va7dC2UyeUQUFY+fmRdeUK1m3aIJFIMLW3J/fGDZx6\n90ZdUkJefDyuQ4ZgYmODqZ0dRQoFZq6u5MbEYOZ+//0shj7dj6FPi/b66oVoju4MpPeQAOKiEjCz\nlGNXi73etvoghflFvP6p9ilA2crcyvRXAiNx927YXi3V7cOFszHs2lJlHyxrsQ9PTejDU9Xswyfv\nrqt0Plw8f52//zjNr2unIzere+mibXNv8hXpFKQrMbO3JSn4Ct3e0t5s2rVLR+6eC8a+ZXNSLl2t\n7Cdcu3Tk8vL1+I0YTHF2DvmKdOz8fO77jmlhUcTtP0a/L97HyLR2ufo92Z9+T4r2ITo4isA95wgY\n1IWEmATkFmZ6ffzbOdsSe/UmPYb1JC1BQXlZGZa29X/c9B7bn95jRRmuX4ziwt5zdHqsC4nXE5Cb\n6ydDuz4dCDsdSrdhPSnIyUeZlIG9W8PHdf8lBo7rx8Bxop5EBkdx5p9Auj4ewJ2YBMwszGrda2Df\n2oMUFxTz/IeNDyruNHIAnUaKY9v4y5GEHTxLq35dUdwUx7YWNfoMC3sbTMzkpN6Ix7WVDzGnLtFp\nVMU4UpWHua0VgkbDpR2H6TCsX535thk2kDbDxOeSQiO5fuQsvn26ooy9g7G5GeY1nHXmdjYYm8nJ\nuBmPY0sfbp+9RJvhA7Hz8mDimqpt5Xa8M5fRCz5Gbm1JvjKL00vW0P/tKdi43398OWBcPwZUK/+z\n1cpfXkf518W90y+kUilH/z5OrxE9G/Tc2Al9GTtBtFMXA6PZu+08jw3rzPXIu5hbynGoYSuLCkso\nLCzBwdEadbmaS+djaN/ZF4A+A/0JuxxH524tSErIoKy8HBvb+ifB7hFxIwMfD2s8XS1JUxYy6rHm\nfPDdaZ10zZvZYG1pwtXo9Ab/toH/ezSZA0IQhCyJRLINeA1YV+PeTolE4gwclkgkAwVBaLLdo6Qy\nGW1fmMiVxb8gaDR49O+DpYc7sbv2YuPrjXNAJzwG9CVi9XrOfjwHYwtzO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qOt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2cAX3+9HkdHWwBefdWfF18corMMR9bu4PZ5TVmMmDoWp5aVy+L4xn2EHw2lMDef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\nwWAwGAwGt/N/S3w2JE4ATzkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11663fbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cov_matrix = np.corrcoef(beta_list)\n",
+    "cov_df = pd.DataFrame(cov_matrix)\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v448vrefjt73Eaf/5MN97YAlrtjYYJkmSJEmSBpxh0gAaWpjgv99+DL95/8nc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8rx/ApMeh2v3DyKi/qHdehaAyJVCHbdmDgGRbu3ukqInqQ+THJLZZLMTBJCCOEeEiYJIYRoV6Up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\n7f8DDi7hGT7PuDIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1164b3110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n",
+    "plt.plot(KO.r.beta_df['beta'])\n",
+    "plt.plot(PG.r.beta_df['beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   beta   R-squared:                       0.870\n",
+      "Model:                            OLS   Adj. R-squared:                  0.870\n",
+      "Method:                 Least Squares   F-statistic:                     3363.\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          6.92e-225\n",
+      "Time:                        12:43:08   Log-Likelihood:                 704.82\n",
+      "No. Observations:                 505   AIC:                            -1406.\n",
+      "Df Residuals:                     503   BIC:                            -1397.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const         -0.0097      0.004     -2.285      0.023      -0.018      -0.001\n",
+      "beta           0.9690      0.017     57.988      0.000       0.936       1.002\n",
+      "==============================================================================\n",
+      "Omnibus:                       11.655   Durbin-Watson:                   0.068\n",
+      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               11.842\n",
+      "Skew:                           0.369   Prob(JB):                      0.00268\n",
+      "Kurtosis:                       3.133   Cond. No.                         6.50\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "-3.19434626171\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(PG.r.beta_df['beta'])\n",
+    "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n",
+    "print(coint.summary())\n",
+    "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n",
+    "print(adf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116b40890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(df['sd_beta'],df['sd_beta_p'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GWZdSzCwCAABgLtq2CxUWQQYzi8ZtaG17TAEAADjM2rYPFRax0CYfx3Fl0XzWAgAAwOL4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\nRERERCRPxSIREREREREREcn7/0SUm9TtupAjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e3db810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "GE.r.beta_df['beta_p'].plot()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb
new file mode 100644
index 0000000..951f57e
--- /dev/null
+++ b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb	
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.formula.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats.mstats import normaltest\n",
+    "import time\n",
+    "from cvxopt import matrix\n",
+    "import seaborn as sns\n",
+    "import statsmodels.tsa.stattools as ts\n",
+    "from scipy import stats\n",
+    "from scipy.optimize import minimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fama_5 = quandl.get('KFRENCH/FACTORS5_D')\n",
+    "fama_5 = fama_5/100\n",
+    "fama_5 = fama_5.rename(columns = {'Mkt-RF':'mkt'})\n",
+    "spy = quandl.get('LSE/SPY5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spy = np.log(spy['Last Close']).diff().dropna()\n",
+    "spy.name = 'spy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mkt</th>\n",
+       "      <th>SMB</th>\n",
+       "      <th>HML</th>\n",
+       "      <th>RMW</th>\n",
+       "      <th>CMA</th>\n",
+       "      <th>RF</th>\n",
+       "      <th>spy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-06-23</th>\n",
+       "      <td>0.0024</td>\n",
+       "      <td>0.0077</td>\n",
+       "      <td>-0.0050</td>\n",
+       "      <td>-0.0035</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-26</th>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>0.0069</td>\n",
+       "      <td>0.0025</td>\n",
+       "      <td>0.0017</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>0.000738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-27</th>\n",
+       "      <td>-0.0084</td>\n",
+       "      <td>-0.0025</td>\n",
+       "      <td>0.0130</td>\n",
+       "      <td>0.0031</td>\n",
+       "      <td>0.0032</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-28</th>\n",
+       "      <td>0.0102</td>\n",
+       "      <td>0.0082</td>\n",
+       "      <td>0.0021</td>\n",
+       "      <td>-0.0018</td>\n",
+       "      <td>-0.0007</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-29</th>\n",
+       "      <td>-0.0083</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>0.0135</td>\n",
+       "      <td>-0.0025</td>\n",
+       "      <td>0.0036</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.005262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               mkt     SMB     HML     RMW     CMA       RF       spy\n",
+       "Date                                                                 \n",
+       "2017-06-23  0.0024  0.0077 -0.0050 -0.0035  0.0005  0.00003 -0.000615\n",
+       "2017-06-26  0.0004  0.0005  0.0069  0.0025  0.0017  0.00003  0.000738\n",
+       "2017-06-27 -0.0084 -0.0025  0.0130  0.0031  0.0032  0.00003 -0.000779\n",
+       "2017-06-28  0.0102  0.0082  0.0021 -0.0018 -0.0007  0.00003 -0.000205\n",
+       "2017-06-29 -0.0083  0.0007  0.0135 -0.0025  0.0036  0.00003 -0.005262"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([fama_5,spy],axis = 1).dropna()\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                    spy   R-squared:                       0.340\n",
+      "Model:                            OLS   Adj. R-squared:                  0.339\n",
+      "Method:                 Least Squares   F-statistic:                     210.6\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          3.73e-110\n",
+      "Time:                        16:03:03   Log-Likelihood:                 4457.8\n",
+      "No. Observations:                1229   AIC:                            -8908.\n",
+      "Df Residuals:                    1225   BIC:                            -8887.\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept   9.267e-05      0.000      0.503      0.615      -0.000       0.000\n",
+      "mkt            0.5700      0.023     24.636      0.000       0.525       0.615\n",
+      "SMB           -0.0753      0.039     -1.942      0.052      -0.151       0.001\n",
+      "HML            0.0658      0.039      1.670      0.095      -0.011       0.143\n",
+      "==============================================================================\n",
+      "Omnibus:                      142.776   Durbin-Watson:                   2.657\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              609.967\n",
+      "Skew:                          -0.472   Prob(JB):                    3.53e-133\n",
+      "Kurtosis:                       6.319   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = sm.ols(formula = 'spy ~ mkt+SMB+HML',data = df).fit()\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  small   R-squared:                       0.995\n",
+      "Model:                            OLS   Adj. R-squared:                  0.995\n",
+      "Method:                 Least Squares   F-statistic:                 9.912e+04\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
+      "Time:                        17:13:18   Log-Likelihood:                 8506.2\n",
+      "No. Observations:                1517   AIC:                        -1.700e+04\n",
+      "Df Residuals:                    1513   BIC:                        -1.698e+04\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept     -0.0001   2.29e-05     -6.254      0.000      -0.000   -9.82e-05\n",
+      "mkt            1.0407      0.003    403.477      0.000       1.036       1.046\n",
+      "SMB            0.8707      0.005    181.700      0.000       0.861       0.880\n",
+      "HML            0.1050      0.005     21.744      0.000       0.095       0.114\n",
+      "==============================================================================\n",
+      "Omnibus:                       84.277   Durbin-Watson:                   1.984\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              260.380\n",
+      "Skew:                          -0.213   Prob(JB):                     2.88e-57\n",
+      "Kurtosis:                       4.985   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "small_table = quandl.get('NASDAQOMX/NQUSS')\n",
+    "small = np.log(small_table['Index Value']).diff().dropna()\n",
+    "small.name = 'small'\n",
+    "small_df = pd.concat([fama_5,small],axis = 1).dropna()\n",
+    "small_model = sm.ols(formula = 'small~mkt+SMB+HML',data = small_df).fit()\n",
+    "print(small_model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  large   R-squared:                       0.998\n",
+      "Model:                            OLS   Adj. R-squared:                  0.998\n",
+      "Method:                 Least Squares   F-statistic:                 2.425e+05\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
+      "Time:                        17:12:41   Log-Likelihood:                 9634.6\n",
+      "No. Observations:                1517   AIC:                        -1.926e+04\n",
+      "Df Residuals:                    1513   BIC:                        -1.924e+04\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept     -0.0001   1.09e-05    -11.144      0.000      -0.000   -9.98e-05\n",
+      "mkt            0.9891      0.001    806.811      0.000       0.987       0.992\n",
+      "SMB           -0.1468      0.002    -64.463      0.000      -0.151      -0.142\n",
+      "HML           -0.0076      0.002     -3.305      0.001      -0.012      -0.003\n",
+      "==============================================================================\n",
+      "Omnibus:                       97.595   Durbin-Watson:                   1.959\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              365.063\n",
+      "Skew:                          -0.190   Prob(JB):                     5.34e-80\n",
+      "Kurtosis:                       5.373   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "large_table = quandl.get('NASDAQOMX/NQUSL')\n",
+    "large = np.log(large_table['Index Value']).diff().dropna()\n",
+    "large.name = 'large'\n",
+    "large_df = pd.concat([fama_5,large],axis = 1).dropna()\n",
+    "large_model = sm.ols(formula = 'large~mkt+SMB+HML',data = large_df).fit()\n",
+    "print(large_model.summary())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb
index 751aa6e..e4566f2 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -16,12 +16,12 @@
    "source": [
     "my_string1 = 'Welcome to'\n",
     "my_string2 = \"QuantConnect\"\n",
-    "print my_string1 + ' ' + my_string2"
+    "print(my_string1 + ' ' + my_string2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -29,59 +29,59 @@
      "output_type": "stream",
      "text": [
       "10\n",
-      "<type 'int'>\n"
+      "<class 'int'>\n"
      ]
     }
    ],
    "source": [
     "my_int = 10\n",
-    "print my_int\n",
-    "print type(my_int)"
+    "print(my_int)\n",
+    "print(type(my_int))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<type 'str'>\n",
-      "<type 'int'>\n"
+      "<class 'str'>\n",
+      "<class 'int'>\n"
      ]
     }
    ],
    "source": [
     "my_string = \"100\"\n",
-    "print type(my_string)\n",
-    "my_int = int(my_string)E\n",
-    "print type(my_int)"
+    "print(type(my_string))\n",
+    "my_int = int(my_string)\n",
+    "print(type(my_int))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<type 'float'>\n"
+      "<class 'float'>\n"
      ]
     }
    ],
    "source": [
     "my_string = \"100\"\n",
     "my_float = float(my_string)\n",
-    "print type(my_float)"
+    "print(type(my_float))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -89,19 +89,19 @@
      "output_type": "stream",
      "text": [
       "False\n",
-      "<type 'bool'>\n"
+      "<class 'bool'>\n"
      ]
     }
    ],
    "source": [
     "my_bool = False\n",
-    "print my_bool\n",
-    "print type(my_bool)"
+    "print(my_bool)\n",
+    "print(type(my_bool))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -111,41 +111,41 @@
       "Addition  2\n",
       "Subtraction  3\n",
       "Multiplication  6\n",
-      "Division  5\n",
+      "Division  5.0\n",
       "exponent 8\n"
      ]
     }
    ],
    "source": [
-    "print \"Addition \", 1+1\n",
-    "print \"Subtraction \", 5-2\n",
-    "print \"Multiplication \", 2*3\n",
-    "print \"Division \", 10/2\n",
-    "print 'exponent', 2**3"
+    "print(\"Addition \", 1+1)\n",
+    "print(\"Subtraction \", 5-2)\n",
+    "print(\"Multiplication \", 2*3)\n",
+    "print(\"Division \", 10/2)\n",
+    "print('exponent', 2**3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\n",
-      "0.333333333333\n"
+      "0.3333333333333333\n",
+      "0.3333333333333333\n"
      ]
     }
    ],
    "source": [
-    "print 1/3\n",
-    "print 1.0/3"
+    "print(1/3)\n",
+    "print(1.0/3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -158,12 +158,12 @@
    ],
    "source": [
     "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -178,14 +178,14 @@
    ],
    "source": [
     "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print len(my_list)\n",
-    "print my_list[0]\n",
-    "print my_list[len(my_list) -1]"
+    "print(len(my_list))\n",
+    "print(my_list[0])\n",
+    "print(my_list[len(my_list) -1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
    "source": [
     "my_list = ['Quant','Connect',1,2,3]\n",
     "my_list[2] = 'go'\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
@@ -219,9 +219,9 @@
    "source": [
     "my_list = ['Quant']\n",
     "my_list.append('Connect')\n",
-    "print my_list\n",
+    "print(my_list)\n",
     "my_list.remove('Quant')\n",
-    "print my_list"
+    "print(my_list)"
    ]
   },
   {
@@ -239,7 +239,7 @@
    ],
    "source": [
     "my_list = ['Quant','Connect',1,2,3]\n",
-    "print my_list[1:3]"
+    "print(my_list[1:3])"
    ]
   },
   {
@@ -256,7 +256,7 @@
     }
    ],
    "source": [
-    "print my_list[1:]"
+    "print(my_list[1:])"
    ]
   },
   {
@@ -273,7 +273,7 @@
     }
    ],
    "source": [
-    "print my_list[:3]"
+    "print(my_list[:3])"
    ]
   },
   {
@@ -302,7 +302,7 @@
    ],
    "source": [
     "my_tuple = ('Welcome','to','QuantConnect')\n",
-    "print my_tuple[1:]"
+    "print(my_tuple[1:])"
    ]
   },
   {
@@ -321,7 +321,7 @@
    "source": [
     "stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']\n",
     "stock_set = set(stock_list)\n",
-    "print stock_set"
+    "print(stock_set)"
    ]
   },
   {
@@ -349,7 +349,7 @@
     }
    ],
    "source": [
-    "print my_dic['GOOG']"
+    "print(my_dic['GOOG'])"
    ]
   },
   {
@@ -367,7 +367,7 @@
    ],
    "source": [
     "my_dic['GOOG'] = 'Alphabet Company'\n",
-    "print my_dic['GOOG']"
+    "print(my_dic['GOOG'])"
    ]
   },
   {
@@ -384,7 +384,7 @@
     }
    ],
    "source": [
-    "print my_dic.keys()"
+    "print(my_dic.keys())"
    ]
   },
   {
@@ -402,12 +402,12 @@
    ],
    "source": [
     "my_str = 'Welcome to QuantConnect'\n",
-    "print my_str[8:]"
+    "print(my_str[8:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -421,14 +421,14 @@
     }
    ],
    "source": [
-    "print 'Counting the number of e appears in this sentence'.count('e')\n",
-    "print 'The first time e appears in this sentence'.find('e')\n",
-    "print 'all the a in this sentence now becomes e'.replace('a','e')"
+    "print('Counting the number of e appears in this sentence'.count('e'))\n",
+    "print('The first time e appears in this sentence'.find('e'))\n",
+    "print('all the a in this sentence now becomes e'.replace('a','e'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -445,14 +445,14 @@
     "splited_list = Time.split(' ')\n",
     "date = splited_list[0]\n",
     "time = splited_list[1]\n",
-    "print date, time\n",
+    "print(date, time)\n",
     "hour = time.split(':')[0]\n",
-    "print hour"
+    "print(hour)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -465,12 +465,12 @@
    ],
    "source": [
     "my_time = 'Hour: {}, Minute:{}'.format('09','43')\n",
-    "print my_time"
+    "print(my_time)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -483,28 +483,28 @@
     }
    ],
    "source": [
-    "print 'the pi number is %f'%3.14\n",
-    "print '%s to %s'%('Welcome','Quantconnect')"
+    "print('the pi number is %f'%3.14)\n",
+    "print('%s to %s'%('Welcome','Quantconnect'))"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb
index 08b35ef..261e947 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb	
@@ -17,13 +17,13 @@
    "source": [
     "def product(x,y):\n",
     "    return x*y\n",
-    "print product(2,3)\n",
-    "print product(5,10)"
+    "print(product(2,3))\n",
+    "print(product(5,10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,34 +36,34 @@
    ],
    "source": [
     "def say_hi():\n",
-    "    print 'Welcome to QuantConnect'\n",
+    "    print('Welcome to QuantConnect')\n",
     "say_hi()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
-      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
-      "[1, 3, 5, 7, 9]\n"
+      "range(0, 10)\n",
+      "range(1, 11)\n",
+      "range(1, 11, 2)\n"
      ]
     }
    ],
    "source": [
-    "print range(10)\n",
-    "print range(1,11)\n",
-    "print range(1,11,2)"
+    "print(range(10))\n",
+    "print(range(1,11))\n",
+    "print(range(1,11,2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -84,14 +84,14 @@
    ],
    "source": [
     "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print 'The length of tickers is {}'.format(len(tickers))\n",
+    "print('The length of tickers is {}'.format(len(tickers)))\n",
     "for i in range(len(tickers)):\n",
-    "    print tickers[i]"
+    "    print(tickers[i])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -104,12 +104,12 @@
    ],
    "source": [
     "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print map(len,tickers)"
+    "print(list(map(len,tickers)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -118,18 +118,18 @@
        "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map(lambda x: x**2, range(10))"
+    "list(map(lambda x: x**2, range(10)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -138,18 +138,18 @@
        "[6, 6, 6, 6, 6]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]) "
+    "list(map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -158,7 +158,7 @@
        "[1, 2, 3, 4, 5]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +178,7 @@
        "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -199,7 +199,7 @@
        "[('GOOG', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -225,15 +225,13 @@
    "source": [
     "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
     "price_list.sort(key = lambda x: x[1])\n",
-    "print price_list"
+    "print(price_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class stock:\n",
@@ -250,15 +248,13 @@
     "        self.rate_return = float(self.close)/self.open - 1\n",
     " \n",
     "    def print_return(self):\n",
-    "        print self.rate_return"
+    "        print(self.rate_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [],
    "source": [
     "apple = stock('AAPL', 143.69, 144.09, 20109375)\n",
@@ -267,15 +263,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0144653388227\n",
-      "0.000657318141981\n"
+      "0.014465338822744034\n",
+      "0.0006573181419806673\n"
      ]
     }
    ],
@@ -288,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +293,7 @@
        "'Tim Cook'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -309,15 +305,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['__doc__',\n",
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
        " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
        " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
        " 'ceo',\n",
        " 'close',\n",
        " 'open',\n",
@@ -328,7 +347,7 @@
        " 'volume']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,10 +358,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [],
    "source": [
     "class child(stock):\n",
@@ -352,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -362,47 +379,38 @@
       "aa\n",
       "100\n",
       "102\n",
-      "0.02\n",
+      "0.020000000000000018\n",
       "None\n"
      ]
     }
    ],
    "source": [
     "aa = child('aa')\n",
-    "print aa.name\n",
+    "print(aa.name)\n",
     "aa.update(100,102)\n",
-    "print aa.open\n",
-    "print aa.close\n",
-    "print aa.print_return()"
+    "print(aa.open)\n",
+    "print(aa.close)\n",
+    "print(aa.print_return())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb
index 2119ec7..32fd4f9 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -20,14 +18,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 143.73  145.83  143.68  144.02  143.5   142.62] <type 'numpy.ndarray'>\n"
+      "[143.73 145.83 143.68 144.02 143.5  142.62] <class 'numpy.ndarray'>\n"
      ]
     }
    ],
    "source": [
     "price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
     "price_array = np.array(price_list)\n",
-    "print price_array, type(price_array)"
+    "print(price_array, type(price_array))"
    ]
   },
   {
@@ -40,13 +38,13 @@
      "output_type": "stream",
      "text": [
       "[[1 3]\n",
-      " [2 4]] <type 'numpy.ndarray'>\n"
+      " [2 4]] <class 'numpy.ndarray'>\n"
      ]
     }
    ],
    "source": [
     "Ar = np.array([[1,3],[2,4]])\n",
-    "print Ar, type(Ar)"
+    "print(Ar, type(Ar))"
    ]
   },
   {
@@ -63,7 +61,7 @@
     }
    ],
    "source": [
-    "print Ar.shape"
+    "print(Ar.shape)"
    ]
   },
   {
@@ -81,8 +79,8 @@
     }
    ],
    "source": [
-    "print Ar[0]\n",
-    "print Ar[1]"
+    "print(Ar[0])\n",
+    "print(Ar[1])"
    ]
   },
   {
@@ -100,8 +98,8 @@
     }
    ],
    "source": [
-    "print 'the first column: ', Ar[:,0]\n",
-    "print 'the second column: ', Ar[:,1]"
+    "print('the first column: ', Ar[:,0])\n",
+    "print('the second column: ', Ar[:,1])"
    ]
   },
   {
@@ -118,38 +116,36 @@
     }
    ],
    "source": [
-    "print np.log(price_array) "
+    "print(np.log(price_array))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "143.896666667\n",
-      "0.967379047852\n",
+      "143.89666666666668\n",
+      "0.9673790478515796\n",
       "863.38\n",
       "145.83\n"
      ]
     }
    ],
    "source": [
-    "print np.mean(price_array)\n",
-    "print np.std(price_array)\n",
-    "print np.sum(price_array)\n",
-    "print np.max(price_array)"
+    "print(np.mean(price_array))\n",
+    "print(np.std(price_array))\n",
+    "print(np.sum(price_array))\n",
+    "print(np.max(price_array))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd"
@@ -157,80 +153,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0    143.73\n",
-      "1    145.83\n",
-      "2    143.68\n",
-      "3    144.02\n",
-      "4    143.50\n",
-      "5    142.62\n",
-      "dtype: float64\n"
-     ]
+     "data": {
+      "text/plain": [
+       "0    143.73\n",
+       "1    145.83\n",
+       "2    143.68\n",
+       "3    144.02\n",
+       "4    143.50\n",
+       "5    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
     "s = pd.Series(price)\n",
-    "print s"
+    "s"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "a    143.73\n",
-      "b    145.83\n",
-      "c    143.68\n",
-      "d    144.02\n",
-      "e    143.50\n",
-      "f    142.62\n",
-      "dtype: float64\n"
-     ]
+     "data": {
+      "text/plain": [
+       "a    143.73\n",
+       "b    145.83\n",
+       "c    143.68\n",
+       "d    144.02\n",
+       "e    143.50\n",
+       "f    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "s = pd.Series(price,index = ['a','b','c','d','e','f'])\n",
-    "print s"
+    "s"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6    143.73\n",
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "dtype: float64\n"
-     ]
+     "data": {
+      "text/plain": [
+       "6    143.73\n",
+       "5    145.83\n",
+       "4    143.68\n",
+       "3    144.02\n",
+       "2    143.50\n",
+       "1    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "s.index = [6,5,4,3,2,1]\n",
-    "print s"
+    "s"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -252,13 +257,13 @@
     }
    ],
    "source": [
-    "print s[1:]\n",
-    "print s[:-2]"
+    "print(s[1:])\n",
+    "print(s[:-2])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -277,14 +282,14 @@
     }
    ],
    "source": [
-    "print s[4]\n",
+    "print(s[4])\n",
     "s[4] = 0\n",
-    "print s"
+    "print(s)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -304,13 +309,13 @@
    ],
    "source": [
     "s = pd.Series(price, name = 'Apple Price List')\n",
-    "print s\n",
-    "print s.name"
+    "print(s)\n",
+    "print(s.name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -332,14 +337,14 @@
    ],
    "source": [
     "time_index = pd.date_range('2017-01-01',periods = len(s),freq = 'D')\n",
-    "print time_index\n",
+    "print(time_index)\n",
     "s.index = time_index\n",
-    "print s"
+    "print(s)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -359,13 +364,13 @@
    ],
    "source": [
     "s.index = [6,5,4,3,2,1]\n",
-    "print s\n",
-    "print s[1]"
+    "print(s)\n",
+    "print(s[1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -377,7 +382,7 @@
     }
    ],
    "source": [
-    "print s.iloc[1]"
+    "print(s.iloc[1])"
    ]
   },
   {
@@ -395,7 +400,7 @@
    ],
    "source": [
     "s.index = time_index\n",
-    "print s['2017-01-03']"
+    "print(s['2017-01-03'])"
    ]
   },
   {
@@ -416,60 +421,56 @@
     }
    ],
    "source": [
-    "print s['2017-01-02':'2017-01-05']"
+    "print(s['2017-01-02':'2017-01-05'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2017-01-01    143.73\n",
-      "2017-01-03    143.68\n",
-      "2017-01-05    143.50\n",
-      "2017-01-06    142.62\n",
+      "6    143.73\n",
+      "4    143.68\n",
+      "2    143.50\n",
+      "1    142.62\n",
       "Name: Apple Price List, dtype: float64\n",
-      "2017-01-04    144.02\n",
-      "Freq: D, Name: Apple Price List, dtype: float64\n"
+      "[6    False\n",
+      "5    False\n",
+      "4    False\n",
+      "3     True\n",
+      "2    False\n",
+      "1    False\n",
+      "Name: Apple Price List, dtype: bool]\n"
      ]
     }
    ],
    "source": [
-    "print s[s < np.mean(s)] \n",
-    "print s[(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))]"
+    "print(s[s < np.mean(s)] )\n",
+    "print([(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb
index 43eede9..a67e05c 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import quandl\n",
@@ -15,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,10 +23,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "aapl = aapl_table['Adj. Close']['2017']"
@@ -36,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -75,47 +71,47 @@
       "2017-02-13    132.210332\n",
       "2017-02-14    133.926319\n",
       "                 ...    \n",
-      "2017-07-10    144.473805\n",
-      "2017-07-11    144.941906\n",
-      "2017-07-12    145.151057\n",
-      "2017-07-13    147.172854\n",
-      "2017-07-14    148.437722\n",
-      "2017-07-17    148.955620\n",
-      "2017-07-18    149.473519\n",
-      "2017-07-19    150.409720\n",
-      "2017-07-20    149.732468\n",
-      "2017-07-21    149.662751\n",
-      "2017-07-24    151.475396\n",
-      "2017-07-25    152.122770\n",
-      "2017-07-26    152.839860\n",
-      "2017-07-27    149.951579\n",
-      "2017-07-28    148.895863\n",
-      "2017-07-31    148.248489\n",
-      "2017-08-01    149.443640\n",
-      "2017-08-02    156.504989\n",
-      "2017-08-03    154.941334\n",
-      "2017-08-04    155.758020\n",
-      "2017-08-08    159.433108\n",
-      "2017-08-09    160.409148\n",
-      "2017-08-10    155.270000\n",
-      "2017-08-11    157.480000\n",
-      "2017-08-14    159.850000\n",
-      "2017-08-15    161.600000\n",
-      "2017-08-16    160.950000\n",
-      "2017-08-17    157.870000\n",
-      "2017-08-18    157.500000\n",
-      "2017-08-21    157.210000\n",
-      "Name: Adj. Close, Length: 159, dtype: float64\n"
+      "2017-11-16    171.100000\n",
+      "2017-11-17    170.150000\n",
+      "2017-11-20    169.980000\n",
+      "2017-11-21    173.140000\n",
+      "2017-11-22    174.960000\n",
+      "2017-11-24    174.970000\n",
+      "2017-11-27    174.090000\n",
+      "2017-11-28    173.070000\n",
+      "2017-11-29    169.480000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-01    171.050000\n",
+      "2017-12-04    169.800000\n",
+      "2017-12-05    169.640000\n",
+      "2017-12-06    169.010000\n",
+      "2017-12-07    169.452000\n",
+      "2017-12-08    169.370000\n",
+      "2017-12-11    172.670000\n",
+      "2017-12-12    171.700000\n",
+      "2017-12-13    172.270000\n",
+      "2017-12-14    172.220000\n",
+      "2017-12-15    173.870000\n",
+      "2017-12-18    176.420000\n",
+      "2017-12-19    174.540000\n",
+      "2017-12-20    174.350000\n",
+      "2017-12-21    175.010000\n",
+      "2017-12-22    175.010000\n",
+      "2017-12-26    170.570000\n",
+      "2017-12-27    170.600000\n",
+      "2017-12-28    171.080000\n",
+      "2017-12-29    169.230000\n",
+      "Name: Adj. Close, Length: 249, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print aapl"
+    "print(aapl)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -151,12 +147,12 @@
     }
    ],
    "source": [
-    "print aapl['2017-3']"
+    "print(aapl['2017-3'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -227,7 +223,7 @@
        "Name: Adj. Close, Length: 61, dtype: float64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -253,28 +249,28 @@
       "2017-01-09    117.520300\n",
       "Name: Adj. Close, dtype: float64\n",
       "Date\n",
-      "2017-08-08    159.433108\n",
-      "2017-08-09    160.409148\n",
-      "2017-08-10    155.270000\n",
-      "2017-08-11    157.480000\n",
-      "2017-08-14    159.850000\n",
-      "2017-08-15    161.600000\n",
-      "2017-08-16    160.950000\n",
-      "2017-08-17    157.870000\n",
-      "2017-08-18    157.500000\n",
-      "2017-08-21    157.210000\n",
+      "2017-12-15    173.87\n",
+      "2017-12-18    176.42\n",
+      "2017-12-19    174.54\n",
+      "2017-12-20    174.35\n",
+      "2017-12-21    175.01\n",
+      "2017-12-22    175.01\n",
+      "2017-12-26    170.57\n",
+      "2017-12-27    170.60\n",
+      "2017-12-28    171.08\n",
+      "2017-12-29    169.23\n",
       "Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print aapl.head(5)\n",
-    "print aapl.tail(10)"
+    "print(aapl.head(5))\n",
+    "print(aapl.tail(10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -289,19 +285,23 @@
       "2017-05-31    151.386305\n",
       "2017-06-30    147.233064\n",
       "2017-07-31    147.706190\n",
-      "2017-08-31    157.444303\n",
+      "2017-08-31    158.856375\n",
+      "2017-09-30    157.606500\n",
+      "2017-10-31    157.811627\n",
+      "2017-11-30    172.214500\n",
+      "2017-12-31    171.893100\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "by_month = aapl.resample('M').mean()\n",
-    "print by_month"
+    "print(by_month)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -320,29 +320,34 @@
    ],
    "source": [
     "by_week = aapl.resample('W').mean()\n",
-    "print by_week.head()"
+    "print(by_week.head())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    120.932434\n",
-       "2017-02-28    136.551200\n",
-       "2017-03-31    143.532630\n",
-       "2017-04-30    144.179981\n",
-       "2017-05-31    156.100000\n",
-       "2017-06-30    155.450000\n",
-       "2017-07-31    153.460000\n",
+       "2017-01-31    120.443739\n",
+       "2017-02-28    135.999390\n",
+       "2017-03-31    142.952608\n",
+       "2017-04-30    143.597342\n",
+       "2017-05-31    155.469192\n",
+       "2017-06-30    154.821818\n",
+       "2017-07-31    152.839860\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    164.050000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    175.880000\n",
+       "2017-12-31    176.420000\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,10 +358,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
     "three_day = aapl.resample('3D').mean()\n",
@@ -366,10 +369,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [],
    "source": [
     "std = aapl.resample('W').std()\n",
@@ -379,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -394,19 +395,23 @@
       "2017-05-31    0.039829\n",
       "2017-06-30   -0.073528\n",
       "2017-07-31    0.033035\n",
-      "2017-08-31    0.004505\n",
+      "2017-08-31    0.047890\n",
+      "2017-09-30   -0.049112\n",
+      "2017-10-31    0.094252\n",
+      "2017-11-30    0.022247\n",
+      "2017-12-31   -0.003357\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
-    "print monthly_return"
+    "print(monthly_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -421,22 +426,24 @@
       "2017-05-31    152.142689\n",
       "2017-06-30    143.438008\n",
       "2017-07-31    148.248489\n",
-      "2017-08-31    157.210000\n",
+      "2017-08-31    164.000000\n",
+      "2017-09-30    154.120000\n",
+      "2017-10-31    169.040000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-31    169.230000\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
-    "print last_day"
+    "print(last_day)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "by_week = aapl.resample('W').mean()"
@@ -444,21 +451,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    1.788740\n",
-       "2017-03-31    4.341378\n",
-       "2017-05-31    5.476627\n",
-       "2017-07-31    3.927178\n",
+       "2017-01-31    1.781512\n",
+       "2017-03-31    4.323834\n",
+       "2017-05-31    5.454495\n",
+       "2017-07-31    3.865333\n",
+       "2017-09-30    3.634223\n",
+       "2017-11-30    8.011704\n",
+       "2018-01-31    2.291229\n",
        "Freq: 2M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,24 +480,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Date\n",
-       "2017-01-31    120.337440\n",
-       "2017-02-28    136.431689\n",
-       "2017-03-31    143.074505\n",
-       "2017-04-30    143.064546\n",
-       "2017-05-31    152.760000\n",
-       "2017-06-30    144.020000\n",
-       "2017-07-31    149.500000\n",
+       "2017-01-31    119.851150\n",
+       "2017-02-28    135.880362\n",
+       "2017-03-31    142.496334\n",
+       "2017-04-30    142.486415\n",
+       "2017-05-31    152.142689\n",
+       "2017-06-30    143.438008\n",
+       "2017-07-31    148.248489\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    154.120000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    171.850000\n",
+       "2017-12-31    169.230000\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -512,11 +527,16 @@
        "2017-04-30   -0.007736\n",
        "2017-05-31    0.039829\n",
        "2017-06-30   -0.073528\n",
-       "2017-07-31    0.037546\n",
+       "2017-07-31    0.033035\n",
+       "2017-08-31    0.047890\n",
+       "2017-09-30   -0.049112\n",
+       "2017-10-31    0.094252\n",
+       "2017-11-30    0.022247\n",
+       "2017-12-31   -0.003357\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -528,28 +548,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0208974076157\n",
-      "0.0476398315185\n",
-      "0.0704090212384\n"
+      "0.02114094011940022\n",
+      "0.04775652864223314\n",
+      "0.09425168306576914\n"
      ]
     }
    ],
    "source": [
-    "print monthly_return.mean()\n",
-    "print monthly_return.std()\n",
-    "print monthly_return.max()"
+    "print(monthly_return.mean())\n",
+    "print(monthly_return.std())\n",
+    "print(monthly_return.max())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -564,7 +584,11 @@
       "2017-05-31     9.656274\n",
       "2017-06-30    -8.704681\n",
       "2017-07-31     4.810482\n",
-      "2017-08-31     8.961511\n",
+      "2017-08-31    15.751511\n",
+      "2017-09-30    -9.880000\n",
+      "2017-10-31    14.920000\n",
+      "2017-11-30     2.810000\n",
+      "2017-12-31    -2.620000\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n",
       "Date\n",
       "2017-01-31         NaN\n",
@@ -574,44 +598,48 @@
       "2017-05-31    0.067770\n",
       "2017-06-30   -0.057214\n",
       "2017-07-31    0.033537\n",
-      "2017-08-31    0.060449\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print last_day.diff()\n",
-    "print last_day.pct_change()"
+    "print(last_day.diff())\n",
+    "print(last_day.pct_change())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "count    159.000000\n",
-      "mean     141.555690\n",
-      "std       11.555853\n",
+      "count    249.000000\n",
+      "mean     149.815713\n",
+      "std       15.065681\n",
       "min      114.586983\n",
-      "25%      137.695538\n",
-      "50%      142.853418\n",
-      "75%      149.842024\n",
-      "max      161.600000\n",
+      "25%      140.651400\n",
+      "50%      151.890000\n",
+      "75%      159.780000\n",
+      "max      176.420000\n",
       "Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "print aapl.describe()"
+    "print(aapl.describe())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -626,19 +654,23 @@
       "2017-05-31    0.067770\n",
       "2017-06-30   -0.057214\n",
       "2017-07-31    0.033537\n",
-      "2017-08-31    0.060449\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "daily_return = last_day.pct_change()\n",
-    "print daily_return.fillna(0)"
+    "print(daily_return.fillna(0))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -653,28 +685,23 @@
       "2017-05-31    0.067770\n",
       "2017-06-30   -0.057214\n",
       "2017-07-31    0.033537\n",
-      "2017-08-31    0.060449\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
       "Freq: M, Name: Adj. Close, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "daily_return = last_day.pct_change()\n",
-    "print daily_return.fillna(method = 'bfill')"
+    "print(daily_return.fillna(method = 'bfill'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -686,11 +713,16 @@
        "2017-04-30   -0.000070\n",
        "2017-05-31    0.067770\n",
        "2017-06-30   -0.057214\n",
-       "2017-07-31    0.038050\n",
+       "2017-07-31    0.033537\n",
+       "2017-08-31    0.106251\n",
+       "2017-09-30   -0.060244\n",
+       "2017-10-31    0.096808\n",
+       "2017-11-30    0.016623\n",
+       "2017-12-31   -0.015246\n",
        "Freq: M, Name: Adj. Close, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -702,35 +734,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31     5.880519\n",
-       "2017-02-28     9.093671\n",
-       "2017-03-31     5.417829\n",
-       "2017-04-30     4.073331\n",
-       "2017-05-31    10.167192\n",
-       "2017-06-30    13.180000\n",
-       "2017-07-31    10.730000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "aapl.resample('M').agg(lambda x: max(x) - min(x))"
+    "# aapl.resample('M').agg(lambda x: max(x) - min(x))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -751,12 +764,12 @@
     "        'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}\n",
     "data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')\n",
     "df = pd.DataFrame(dict, index = data_index)\n",
-    "print df"
+    "print(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -776,19 +789,19 @@
     "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
     "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
     "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print data_frame"
+    "print(data_frame)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index([u'AAPL', u'GOOG', u'IBM'], dtype='object')\n",
+      "Index(['AAPL', 'GOOG', 'IBM'], dtype='object')\n",
       "2017-07-03    143.50\n",
       "2017-07-04    144.09\n",
       "2017-07-05    142.73\n",
@@ -805,14 +818,14 @@
     }
    ],
    "source": [
-    "print df.columns\n",
-    "print df.AAPL\n",
-    "print df['GOOG']"
+    "print(df.columns)\n",
+    "print(df.AAPL)\n",
+    "print(df['GOOG'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -820,31 +833,31 @@
      "output_type": "stream",
      "text": [
       "Date\n",
-      "2017-07-24    152.09\n",
-      "2017-07-25    152.74\n",
-      "2017-07-26    153.46\n",
-      "2017-07-27    150.56\n",
-      "2017-07-28    149.50\n",
+      "2018-03-21    171.270\n",
+      "2018-03-22    168.845\n",
+      "2018-03-23    164.940\n",
+      "2018-03-26    172.770\n",
+      "2018-03-27    168.340\n",
       "Name: Close, dtype: float64\n",
       "Date\n",
-      "2017-07-24    21122730.0\n",
-      "2017-07-25    18612649.0\n",
-      "2017-07-26    15172136.0\n",
-      "2017-07-27    32175875.0\n",
-      "2017-07-28    16832947.0\n",
+      "2018-03-21    35247358.0\n",
+      "2018-03-22    41051076.0\n",
+      "2018-03-23    40248954.0\n",
+      "2018-03-26    36272617.0\n",
+      "2018-03-27    38962839.0\n",
       "Name: Adj. Volume, dtype: float64\n"
      ]
     }
    ],
    "source": [
     "df = aapl_table\n",
-    "print df.Close.tail(5)\n",
-    "print df['Adj. Volume'].tail(5)"
+    "print(df.Close.tail(5))\n",
+    "print(df['Adj. Volume'].tail(5))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -868,18 +881,18 @@
       "\n",
       "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
       "Date                                                                      \n",
-      "2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495   \n",
-      "2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856   \n",
-      "2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931   \n",
-      "2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748   \n",
-      "2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913   \n",
-      "2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775   \n",
-      "2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363   \n",
-      "2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460   \n",
-      "2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885   \n",
-      "2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287   \n",
-      "2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807   \n",
-      "2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583   \n",
+      "2016-01-31          1.0   91.581233   94.044912   91.156128   94.044912   \n",
+      "2016-02-29          1.0   94.084911   95.415659   93.880927   93.919781   \n",
+      "2016-03-31          1.0  106.576465  106.751308  105.760531  105.867380   \n",
+      "2016-04-30          1.0   91.297138   92.006223   89.859540   91.054300   \n",
+      "2016-05-31          1.0   97.337844   98.119674   96.575559   97.591939   \n",
+      "2016-06-30          1.0   92.295040   93.594832   92.158220   93.428693   \n",
+      "2016-07-31          1.0  101.823594  102.175417  101.325177  101.843140   \n",
+      "2016-08-31          1.0  103.816156  104.710177  103.796505  104.248477   \n",
+      "2016-09-30          1.0  110.497491  111.391610  109.849008  111.077195   \n",
+      "2016-10-31          1.0  111.666724  112.236603  111.224577  111.558644   \n",
+      "2016-11-30          1.0  110.182071  110.814166  108.908004  109.154917   \n",
+      "2016-12-31          1.0  115.209202  115.752409  114.004271  114.389454   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
@@ -901,12 +914,12 @@
    "source": [
     "aapl_2016 = df['2016']\n",
     "aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])\n",
-    "print aapl_month"
+    "print(aapl_month)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -932,12 +945,12 @@
    ],
    "source": [
     "aapl_bar = aapl_month[['Open', 'High', 'Low', 'Close']]\n",
-    "print aapl_bar"
+    "print(aapl_bar)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -954,12 +967,12 @@
     }
    ],
    "source": [
-    "print aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']]"
+    "print(aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -979,12 +992,12 @@
    ],
    "source": [
     "above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]\n",
-    "print above"
+    "print(above)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1011,7 +1024,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1022,32 +1035,32 @@
    ],
    "source": [
     "aapl_bar['rate_return'] = aapl_bar.Close.pct_change()\n",
-    "print aapl_bar"
+    "print(aapl_bar)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "             Open   High    Low  Close rate_return\n",
-      "Date                                              \n",
-      "2016-01-31  False  False  False  False        True\n",
-      "2016-02-29  False  False  False  False       False\n",
-      "2016-03-31  False  False  False  False       False\n",
-      "2016-04-30  False  False  False  False       False\n",
-      "2016-05-31  False  False  False  False       False\n",
-      "2016-06-30  False  False  False  False       False\n",
-      "2016-07-31  False  False  False  False       False\n",
-      "2016-08-31  False  False  False  False       False\n",
-      "2016-09-30  False  False  False  False       False\n",
-      "2016-10-31  False  False  False  False       False\n",
-      "2016-11-30  False  False  False  False       False\n",
-      "2016-12-31  False  False  False  False       False\n",
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n",
+      "2016-02-29  False  False  False  False        False\n",
+      "2016-03-31  False  False  False  False        False\n",
+      "2016-04-30  False  False  False  False        False\n",
+      "2016-05-31  False  False  False  False        False\n",
+      "2016-06-30  False  False  False  False        False\n",
+      "2016-07-31  False  False  False  False        False\n",
+      "2016-08-31  False  False  False  False        False\n",
+      "2016-09-30  False  False  False  False        False\n",
+      "2016-10-31  False  False  False  False        False\n",
+      "2016-11-30  False  False  False  False        False\n",
+      "2016-12-31  False  False  False  False        False\n",
       "\n",
       "------------------ separate line -----------------\n",
       "\n",
@@ -1061,33 +1074,33 @@
    ],
    "source": [
     "missing = aapl_bar.isnull()\n",
-    "print missing\n",
-    "print '\\n------------------ separate line -----------------\\n'\n",
-    "print missing.describe()"
+    "print(missing)\n",
+    "print('\\n------------------ separate line -----------------\\n')\n",
+    "print(missing.describe())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "             Open   High    Low  Close rate_return\n",
-      "Date                                              \n",
-      "2016-01-31  False  False  False  False        True\n"
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n"
      ]
     }
    ],
    "source": [
-    "print missing[missing.rate_return == True]"
+    "print(missing[missing.rate_return == True])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1129,15 +1142,15 @@
    ],
    "source": [
     "drop = aapl_bar.dropna()\n",
-    "print drop\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(drop)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "fill = aapl_bar.fillna(0)\n",
-    "print fill"
+    "print(fill)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1157,12 +1170,12 @@
     "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
     "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
     "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print data_frame"
+    "print(data_frame)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1206,15 +1219,15 @@
    "source": [
     "log_price = np.log(aapl_bar.Close)\n",
     "log_price.name = 'log_price'\n",
-    "print log_price\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(log_price)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "concat = pd.concat([aapl_bar, log_price], axis = 1)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1247,15 +1260,15 @@
    ],
    "source": [
     "df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])\n",
-    "print df_volume\n",
-    "print '\\n---------------------- separate line--------------------\\n'\n",
+    "print(df_volume)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
     "df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])\n",
-    "print df_2017"
+    "print(df_2017)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1304,12 +1317,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar,df_volume],axis = 1)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1332,12 +1345,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1370,12 +1383,12 @@
    ],
    "source": [
     "append = aapl_bar.append(df_2017)\n",
-    "print append"
+    "print(append)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1408,12 +1421,12 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1447,12 +1460,12 @@
    "source": [
     "df_2017.columns = ['Change', 'High','Low','Close']\n",
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1485,27 +1498,41 @@
    ],
    "source": [
     "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print concat"
+    "print(concat)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb
index 3980d14..8c4c7b6 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb	
@@ -9,19 +9,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.02\n"
+      "0.020000000000000018\n"
      ]
     }
    ],
    "source": [
     "import numpy as np\n",
     "rate_return = 102.0/100 - 1\n",
-    "print rate_return"
+    "print(rate_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -66,88 +66,79 @@
     "#take log return\n",
     "aapl['log_price'] = np.log(aapl.Close)\n",
     "aapl['log_return'] = aapl.log_price.diff()\n",
-    "print aapl"
+    "print(aapl)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0273081001636\n"
+      "0.0273081001636184\n"
      ]
     }
    ],
    "source": [
     "month_return = aapl.log_return.sum()\n",
-    "print month_return"
+    "print(month_return)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.94597446551\n"
+      "4.94597446550658\n"
      ]
     }
    ],
    "source": [
-    "print np.mean(aapl.log_price)"
+    "print(np.mean(aapl.log_price))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.000142032804482\n"
+      "0.00014203280448152512\n"
      ]
     }
    ],
    "source": [
-    "print np.var(aapl.log_price)"
+    "print(np.var(aapl.log_price))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
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diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html b/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html
index 8b10dee..fe2526d 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html	
@@ -30,7 +30,7 @@ <h3>Uniform Distribution</h3>
 plt.grid()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot1.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot1.png" alt="plot1" />
 Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
 <pre class="prettyprint linenums">print len([x for x in series if x &lt;= 3])/float(len(series))
 [out]: 0.4956
@@ -111,7 +111,7 @@ <h3>Binomial Distribution</h3>
 plt.grid()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot2.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot2.png" alt="plot2" />
 Another good property of binomial distribution is that its mean and variance are simple enough:
 \[\bar{u} = np\]
 \[\sigma^2 = np(1-P)\]
@@ -129,7 +129,7 @@ <h2>Normal Distribution</h2>
 norm.plot.density()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot3.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot3.png" alt="plot3" />
 Financial data is highly disordered and is considered to has lots of noise. Most of the time we believe those noise follows normal distribution. It's also widely believed that the return on an asset over a short period of time follows normal distribution. Let's check it with the daily logarithm rates of return on SPY:
 <pre class="prettyprint linenums">import quandl
 quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
@@ -144,13 +144,13 @@ <h2>Normal Distribution</h2>
 spy.log_return.plot()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot4.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot4.png" alt="plot4" />
 This is a classic daily return chart. Let's now plot the density chart of the returns:
 <pre class="prettyprint linenums">plt.figure(figsize = (20,10))
 spy.log_return.plot.density()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot5.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot5.png" alt="plot5" />
 If we observe the x-axis and y-axis carefully, we can see the return of asset is not a standard normal distribution. The peak of the standard normal distribution plot is around 0.4, while it's over 0.6 for this chart. This is because the standard deviation \(\sigma\) of the return is obviously not 1.
 We can demonstrate the normal distribution with different mean and variance by simulation:
 <pre class="prettyprint linenums">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
@@ -162,6 +162,6 @@ <h2>Normal Distribution</h2>
 df.plot.density()
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial07-plot6.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial07-plot6.png" alt="plot6" />
 <h2>Summary</h2>
 In this chapter we introduced random variable, the difference between discrete random distribution and continuous random distribution, and most importantly, normal distribution. In the next chapter we will introduce how to use these distributions to test our idea or generating trading signals.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html
index d53dd0e..66d8bba 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html	
@@ -58,7 +58,7 @@ <h2>Introduction</h2>
 plt.scatter(df.spy,df.amzn)
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial9-plot1.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial9-plot1.png" alt="plot1" />
 The plot is scattered, but we can see they are approximately correlated: generally the higher SPX's daily return is, the higher Amazon stock's return is. This is called <strong>positively correlated</strong>. We will cover it in the following tutorials.
 <h2>Slope and Intercept</h2>
 It's natural that we want to model the relation between these two rates of return. Intuitively we use a straight line to model it, this is called <strong>Linear Regression</strong>. In order to find the best straight line, it's natural to think that the vertical distances between the points of the data set and the fitted line should be minimized. Those vertical distances are called <strong>residual</strong>. Our objective is to make the sum of squared residuals as small as possible. This method is called <strong>ordinary least square</strong>, or <strong>OLS</strong> method.
@@ -76,7 +76,7 @@ <h2>Python Implementation</h2>
 model = sm.ols(formula = 'amzn~spy',data = df).fit()
 print model.summary()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial9-regression%20table.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial9-plot2.png" alt="plot2" />
 We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
 <pre class="prettyprint linenums">print 'pamameters: ',model.params
 [out]: pamameters:  Intercept    0.000012
@@ -99,7 +99,7 @@ <h2>Python Implementation</h2>
 plt.plot(df.spy,model.predict(),color = 'red')
 plt.show()
 </pre>
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial9-plot2.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial9-head.png" alt="head" />
 The red line is the fitted linear regression straight line.
 As we can see there are lot of statistical results in the summary table. Now let's talk about some important statistical parameters.
 <h2>Parameter Significance</h2>
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb
index 345940d..c870f22 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
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+   "execution_count": 1,
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@@ -15,7 +15,7 @@
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    "cell_type": "code",
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+   "execution_count": 2,
    "metadata": {},
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@@ -27,13 +27,26 @@
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+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
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       "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe tbody tr th {\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
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@@ -87,7 +100,7 @@
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-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
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@@ -105,9 +118,19 @@
    "outputs": [
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F6XDs1FruvO901jfOJ0nWzq7nzvtOX/q9zykA2yVcAmPX72TWiSvsrKPHz1z6LLatb5zP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\nO902AJJsAr4L/IDem0aTpbnHAEkOB15H7+yYJse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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c69ef90>"
+       "Text(0,0.5,'amzn_return')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b031b70>"
       ]
      },
      "metadata": {},
@@ -119,13 +142,12 @@
     "plt.figure(figsize = (15,10))\n",
     "plt.scatter(df.spy,df.amzn)\n",
     "plt.xlabel('spx_return')\n",
-    "plt.ylabel('amzn_return')\n",
-    "plt.show()"
+    "plt.ylabel('amzn_return')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -137,8 +159,8 @@
       "Dep. Variable:                   amzn   R-squared:                       0.044\n",
       "Model:                            OLS   Adj. R-squared:                  0.040\n",
       "Method:                 Least Squares   F-statistic:                     10.63\n",
-      "Date:                Mon, 31 Jul 2017   Prob (F-statistic):            0.00128\n",
-      "Time:                        11:00:50   Log-Likelihood:                 608.99\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
+      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
       "No. Observations:                 235   AIC:                            -1214.\n",
       "Df Residuals:                     233   BIC:                            -1207.\n",
       "Df Model:                           1                                         \n",
@@ -163,12 +185,12 @@
    "source": [
     "import statsmodels.formula.api as sm\n",
     "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
-    "print model.summary()"
+    "print(model.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -190,21 +212,21 @@
     }
    ],
    "source": [
-    "print 'pamameters: ',model.params\n",
-    "print 'residual: ', model.resid.tail()\n",
-    "print 'fitted values: ',model.predict()[-6:]"
+    "print('pamameters: ',model.params)\n",
+    "print('residual: ', model.resid.tail())\n",
+    "print('fitted values: ',model.predict()[-6:])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Qqg4NK/78X+ZdNkSSFmczFb/7hY2gr61fOMiPT8yOn6wVdr2YVDfcxVniGTQI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\n6E24BAAAoDfhEgAAgN6ESwAAAHr7f3qUFpWQd341AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f466c90>"
+       "<matplotlib.figure.Figure at 0x1a1d630588>"
       ]
      },
      "metadata": {},
@@ -223,21 +245,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html
index 4546617..31c5e25 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html	
@@ -232,9 +232,7 @@ <h2>Python Implementation</h2>
 fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA', data = fama_df).fit()
 print fama_model.summary()
 </pre>
-
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial10-fama.png?raw=true" alt="" width="600" height="450" />
-
+<img src="images/Tutorial10-fama.png" alt="fama" />
 The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
 
 <pre class="prettyprint linenums">
@@ -250,9 +248,7 @@ <h2>Python Implementation</h2>
 plt.legend()
 plt.show()
 </pre>
-
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial10-compare.png?raw=true" alt="" width="600" height="450" />
-
+<img src="images/Tutorial10-compare.png" alt="compare" />
 Although it's hard to see from the chart above, the predicted return from multiple regression is closer to the actual return. Usually we don't plot the predictions to determine which model is better; we read the summary table.
 
 <h2>Model Significance Test</h2>
@@ -286,8 +282,7 @@ <h3>Normality</h3>
 fama_model.resid.plot.density()
 plt.show()
 </pre>
-
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial10-residual.png?raw=true" alt="" width="600" height="450" />
+<img src="images/Tutorial10-residual.png" alt="residual" />
 
 As seen from the plot, the residual is normally distributed. By the way, the residual mean is always zero, up to machine precision:
 
@@ -309,9 +304,7 @@ <h3>Homoskedasticity</h3>
 plt.ylabel('residual')
 plt.show()
 </pre>
-
-<img class="aligncenter size-full wp-image-2206" src="https://github.com/ShallweXiaowei/Tutorials/blob/master/Tutorial%20Series/Introduction%20to%20Financial%20Python/images/Tutorial10-variance.png?raw=true" alt="" width="600" height="450" />
-
+<img src="images/Tutorial10-variance.png" alt="variance" />
 As seen from the chart, the residuals' variance doesn't increase with X. The three outliers do not change our conclusion. Although we can plot the residuals for simple regression, we can't do this for multiple regression, so we use statsmodels to test for heteroskedasticity:
 
 <pre class="prettyprint linenums">from statsmodels.stats import diagnostic as dia
@@ -324,4 +317,4 @@ <h3>Homoskedasticity</h3>
 
 <h2>Summary</h2>
 
-In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
\ No newline at end of file
+In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb
index 8b76b24..3ae3a01 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -17,14 +15,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 98,
+   "metadata": {},
    "outputs": [],
    "source": [
     "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "spy_table = quandl.get('BCIW/_SPXT')\n",
+    "goog_table = quandl.get('WIKI/GOOG')\n",
     "amzn_table = quandl.get('WIKI/AMZN')\n",
     "ebay_table = quandl.get('WIKI/EBAY')\n",
     "wal_table = quandl.get('WIKI/WMT')\n",
@@ -33,31 +29,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>spy</th>\n",
+       "      <th>goog</th>\n",
        "      <th>amzn</th>\n",
        "      <th>ebay</th>\n",
        "      <th>wal</th>\n",
@@ -75,7 +71,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2016-12-23</th>\n",
-       "      <td>0.001351</td>\n",
+       "      <td>-0.001708</td>\n",
        "      <td>-0.007531</td>\n",
        "      <td>0.008427</td>\n",
        "      <td>-0.000719</td>\n",
@@ -83,7 +79,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-27</th>\n",
-       "      <td>0.002254</td>\n",
+       "      <td>0.002074</td>\n",
        "      <td>0.014113</td>\n",
        "      <td>0.014993</td>\n",
        "      <td>0.002298</td>\n",
@@ -91,7 +87,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-28</th>\n",
-       "      <td>-0.008218</td>\n",
+       "      <td>-0.008246</td>\n",
        "      <td>0.000946</td>\n",
        "      <td>-0.007635</td>\n",
        "      <td>-0.005611</td>\n",
@@ -99,7 +95,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-29</th>\n",
-       "      <td>-0.000247</td>\n",
+       "      <td>-0.002883</td>\n",
        "      <td>-0.009081</td>\n",
        "      <td>-0.001000</td>\n",
        "      <td>-0.000722</td>\n",
@@ -107,7 +103,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2016-12-30</th>\n",
-       "      <td>-0.004601</td>\n",
+       "      <td>-0.014113</td>\n",
        "      <td>-0.020172</td>\n",
        "      <td>-0.009720</td>\n",
        "      <td>-0.002023</td>\n",
@@ -118,39 +114,39 @@
        "</div>"
       ],
       "text/plain": [
-       "                 spy      amzn      ebay       wal      aapl\n",
+       "                goog      amzn      ebay       wal      aapl\n",
        "Date                                                        \n",
-       "2016-12-23  0.001351 -0.007531  0.008427 -0.000719  0.001976\n",
-       "2016-12-27  0.002254  0.014113  0.014993  0.002298  0.006331\n",
-       "2016-12-28 -0.008218  0.000946 -0.007635 -0.005611 -0.004273\n",
-       "2016-12-29 -0.000247 -0.009081 -0.001000 -0.000722 -0.000257\n",
-       "2016-12-30 -0.004601 -0.020172 -0.009720 -0.002023 -0.007826"
+       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
+       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
+       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
+       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
+       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "spy = spy_table.loc['2016',['Close']]\n",
+    "goog = goog_table.loc['2016',['Close']]\n",
     "amzn = amzn_table.loc['2016',['Close']]\n",
     "ebay = ebay_table.loc['2016',['Close']]\n",
     "wal = wal_table.loc['2016',['Close']]\n",
     "aapl = aapl_table.loc['2016',['Close']]\n",
-    "spy_log = np.log(spy.Close).diff().dropna()\n",
+    "goog_log = np.log(goog.Close).diff().dropna()\n",
     "amzn_log = np.log(amzn.Close).diff().dropna()\n",
     "ebay_log = np.log(ebay.Close).diff().dropna()\n",
     "wal_log = np.log(wal.Close).diff().dropna()\n",
     "aapl_log = np.log(aapl.Close).diff().dropna()\n",
-    "df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
-    "df.columns = ['spy','amzn','ebay','wal','aapl']\n",
+    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
+    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
     "df.tail()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
@@ -159,28 +155,29 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.254\n",
-      "Model:                            OLS   Adj. R-squared:                  0.242\n",
-      "Method:                 Least Squares   F-statistic:                     20.94\n",
-      "Date:                Tue, 22 Aug 2017   Prob (F-statistic):           7.21e-15\n",
-      "Time:                        11:09:04   Log-Likelihood:                 684.88\n",
-      "No. Observations:                 251   AIC:                            -1360.\n",
-      "Df Residuals:                     246   BIC:                            -1342.\n",
-      "Df Model:                           4                                         \n",
+      "Dep. Variable:                  Close   R-squared:                       0.346\n",
+      "Model:                            OLS   Adj. R-squared:                  0.319\n",
+      "Method:                 Least Squares   F-statistic:                     12.51\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
+      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
+      "No. Observations:                 124   AIC:                            -827.3\n",
+      "Df Residuals:                     118   BIC:                            -810.4\n",
+      "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept      0.0001      0.001      0.134      0.894      -0.002       0.002\n",
-      "spy            1.0468      0.170      6.155      0.000       0.712       1.382\n",
-      "ebay          -0.0795      0.058     -1.364      0.174      -0.194       0.035\n",
-      "wal           -0.0865      0.089     -0.976      0.330      -0.261       0.088\n",
-      "aapl           0.1529      0.084      1.831      0.068      -0.012       0.317\n",
+      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
+      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
+      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
+      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
+      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
+      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
       "==============================================================================\n",
-      "Omnibus:                       70.404   Durbin-Watson:                   1.979\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1938.463\n",
-      "Skew:                          -0.310   Prob(JB):                         0.00\n",
-      "Kurtosis:                      16.600   Cond. No.                         179.\n",
+      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
+      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
+      "Kurtosis:                       7.142   Cond. No.                         410.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
@@ -189,13 +186,13 @@
     }
    ],
    "source": [
-    "model = sm.ols(formula = 'amzn ~ spy+ebay+wal+aapl',data = df).fit()\n",
-    "print model.summary()"
+    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
+    "print(model2.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -204,25 +201,25 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.234\n",
-      "Model:                            OLS   Adj. R-squared:                  0.231\n",
-      "Method:                 Least Squares   F-statistic:                     76.13\n",
-      "Date:                Tue, 22 Aug 2017   Prob (F-statistic):           3.88e-16\n",
-      "Time:                        11:09:07   Log-Likelihood:                 681.59\n",
-      "No. Observations:                 251   AIC:                            -1359.\n",
-      "Df Residuals:                     249   BIC:                            -1352.\n",
+      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
+      "Model:                            OLS   Adj. R-squared:                  0.348\n",
+      "Method:                 Least Squares   F-statistic:                     134.7\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
+      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
+      "No. Observations:                 251   AIC:                            -1401.\n",
+      "Df Residuals:                     249   BIC:                            -1394.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept   9.876e-05      0.001      0.097      0.923      -0.002       0.002\n",
-      "spy            1.0796      0.124      8.725      0.000       0.836       1.323\n",
+      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
+      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
       "==============================================================================\n",
-      "Omnibus:                       68.122   Durbin-Watson:                   2.011\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2089.718\n",
-      "Skew:                          -0.110   Prob(JB):                         0.00\n",
-      "Kurtosis:                      17.134   Cond. No.                         122.\n",
+      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
+      "Skew:                          -0.374   Prob(JB):                         0.00\n",
+      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
@@ -231,21 +228,19 @@
     }
    ],
    "source": [
-    "simple = sm.ols(formula = 'amzn ~ spy',data = df).fit()\n",
-    "print simple.summary()"
+    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
+    "print(simple.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import urllib2\n",
     "from datetime import datetime\n",
     "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
-    "response = urllib2.urlopen(url)\n",
-    "fama_table = pd.read_csv(response)\n",
+    "fama_table = pd.read_csv(url)\n",
     "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
     "fama_table.index = index\n",
     "fama_table = fama_table.iloc[:,1:]"
@@ -253,107 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Mkt-RF</th>\n",
-       "      <th>SMB</th>\n",
-       "      <th>HML</th>\n",
-       "      <th>RMW</th>\n",
-       "      <th>CMA</th>\n",
-       "      <th>RF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1963-07-01</th>\n",
-       "      <td>-0.67</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>-0.31</td>\n",
-       "      <td>0.01</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>0.012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-07-02</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>-0.27</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>-0.08</td>\n",
-       "      <td>-0.19</td>\n",
-       "      <td>0.012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-07-03</th>\n",
-       "      <td>0.63</td>\n",
-       "      <td>-0.17</td>\n",
-       "      <td>-0.09</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>-0.33</td>\n",
-       "      <td>0.012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-07-05</th>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.08</td>\n",
-       "      <td>-0.28</td>\n",
-       "      <td>0.07</td>\n",
-       "      <td>-0.33</td>\n",
-       "      <td>0.012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-07-08</th>\n",
-       "      <td>-0.63</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>-0.16</td>\n",
-       "      <td>-0.31</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>0.012</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Mkt-RF   SMB   HML   RMW   CMA     RF\n",
-       "1963-07-01   -0.67  0.00 -0.31  0.01  0.15  0.012\n",
-       "1963-07-02    0.79 -0.27  0.26 -0.08 -0.19  0.012\n",
-       "1963-07-03    0.63 -0.17 -0.09  0.19 -0.33  0.012\n",
-       "1963-07-05    0.40  0.08 -0.28  0.07 -0.33  0.012\n",
-       "1963-07-08   -0.63  0.04 -0.16 -0.31  0.13  0.012"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -377,8 +272,8 @@
       "Dep. Variable:                  Close   R-squared:                       0.387\n",
       "Model:                            OLS   Adj. R-squared:                  0.375\n",
       "Method:                 Least Squares   F-statistic:                     30.97\n",
-      "Date:                Tue, 22 Aug 2017   Prob (F-statistic):           2.21e-24\n",
-      "Time:                        11:24:12   Log-Likelihood:                 709.59\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
+      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
       "No. Observations:                 251   AIC:                            -1407.\n",
       "Df Residuals:                     245   BIC:                            -1386.\n",
       "Df Model:                           5                                         \n",
@@ -406,19 +301,19 @@
    ],
    "source": [
     "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
-    "print fama_model.summary()"
+    "print(fama_model.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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0jPWsyveaUzpu9DJsvRHQJOATVWUgNkCNs4aAK8AdHXcwkZ7gx30/rvSxLOtg\n6CDAkge2mLwOr/VLOiNGFphgO5lsjqhuBHZ17jqcNg85UvmSzmXM8PU+AZoNrngdeGqrPuB7rv9F\nRkbbONZXWlBc66q1XoavEPAVz/CB0c8vJZ1ixRQWsVd4RUN/zAj4djXuIpPLENHLeyMcT6Sp87nm\nDPhMPqeP39z9m9x/x8Okh1/PoeFneduDb+MjP/4IFyIXlvzfIMRUhYDPWWJJ5ypk+B479xi/9r1f\n4x9e+IcV/17ziuX79+ovg4bLJeATVWUgOlDIOrxm42to8DTwcO/DFT6VdXWHuvE6vOxq2rUsf57X\n4bV+SeeUpeuj8TSa3bjQr3PX4bZ7UFqanDu4vGsZep+E9muNYM/XVNUlneFUmOHURXKJzfzsTGn/\nHUF30HprGdCMD/KZ3oV6+MB4fUiGT6wYh83Bu3a8q+KL2Aeixh0x84dCuWWd43GdGk+WRCYxb8Bn\n2lzXyE7vXbRH/pp7dt7D91/5Pm/+9pv5y2f+0lJrKkR1K5R0FhnaYkxus694hu/Rs4/yoac/RFZl\nK/uDMZrvn/W3QMtOuCQBn6geoXioEPA5bA5+8fJf5MkLT1oqu2Al3aFurmm5Bqdtaf17Jp/DZ/0M\nX2EH38bCDj4wSjrddg+aTSfrrFm+ks7EOPQ9Dx2vNT72N80a2qKUYjQ5yqHBQ/zolR9Zugz5pZGX\nAMgmNvNsb2k/F4OuIOF02DJTcxOZBF7zKIWSzoUDvnp3vfTwiZX11ivfSo2zpqJZvr5oHwFXgM2B\nzUD5AV84oeN2Gz8EigV8YKxneOliht/Y8Xs8+rZHeUfnO3jg9APc/q3b+UTXJxhOVO/dMWENpU7p\ntGk26j31K7pw9eHeh/nwjz/MvpZ93LDxhkIwWhFmhq+m2Qj4Rs+AnqzceYQoQygWYoN/Q+HjOzvu\nRM/p/ODcDyp4KmsaTgxzJnxm2co5AbxOr2Umi8+rkOHbYKxkcBgZvlp3LR6HF2xpMo5lXMtw7ieg\nctBxC9lclqMeHw8lQ3zu0Of4k6f/hF95+Fe44b4buPk/b+buR+/mD5/8Q0tnpY8OHQUgm9xE97mx\nkuYt1LpryeQylrkZkMgk8JjBZ4kBX51HMnxihVlhEftAbID2mvbCwJVyLn4z2RyRZAany/ghUGrA\npxT8+PQwzb5m/vT6P+Xhtz7M7R23c9/L93Hbt27jH57/B0s1AYvqEtWjaGj4nf6iz230NK5Yhu/B\nMw/ypz82ye4zAAAgAElEQVT5U65rvY7Pv/7zNPuaKxvwmSsZ/C3QssO4UBk+VbnzCFGiZCbJaHJ0\n2iCJnY072RrcykO9Mq1zJrN/b7kGtkCV9PBN9IOnDlx+hmNGSafPUYPT5sRj96BpadKOGmPISjaz\n9O/X+wQ4/bBpP5994V94Z+YMH/Ek+eLhL3Jk6AhBV5DbOm7jj6/7Yz77us9S667lxcEXl/59V8ix\n4WM4s23Y8RFNZXipv3gmNOgKAtZZvp7MJPHk8gGfr9GYSl2kpLPeU894ctwyWcrVJgHfKnnnjncC\n8O8nKrOIvT/azwb/BhrcRrBWToZvImm8Ydqdxl20UgK+PZvqqPM5eerk5HqG9pp2PnbDx3jgLQ9w\ny+Zb+Ndj/8qt99/KPx3+J2L6ws22QswUTUfxO/3YtOJvY43exhXp4ft2z7f5s5/8GQfaDvDZ138W\nn9NHjbOGWJHm8RVlLl33N0Frvq9H+vhEFbgUNyZ0Ts3waZrGHR138Pyl5wvDx4ShO9SN3+lnR+OO\nZfszq6akM7+DbzSaQrMbS9fByFBi00nZ8zcCF5jcWLLeJ2HrDeBw8fiZo/gyDh7oG+Tgu7r53tu/\nx5fe9CX+7Po/455d93Dz5pvZ27zXsvsjlVIcGT6CSm3mxiubAEoq6zQDPivcpM/kMug5HW8uA04f\n2B3g8hcd2lLvriedS1v/9b1CJOBbJYVF7Ke+uerBjVKK/mg/G2s2LirDF07ka9Htxt2TBm/xgM9u\n07jxyiae7hmadTdla+1W/vamv+Wbb/4m+9v287lDn+PW+2/lq8e+SjIjpWeiNBPpiaITOk0rkeG7\n/9T9fPRnH+XnNv4c/+91/6+wtNjv9BPVo+RUhdaSxIaMwQIONzR0gN0lAZ+oCuYOvpmj4m/vuB2A\nR3ofWfUzWVlXqItrWq7BYe4jWwZVM7QlP6jD7OFryC/W9jm8aFqOqN14P15yH9/4BRg5XejfG0sN\n49G9XJFO4ppnfcXe5r30hnstERzNNBAbYDQ5SjzSzu72Wq5o9pcU8Jl77KyQ4TOvE73ZjDGNFcAV\nKF7Smb8psJLtHVYmAd8qunvn3UT0CA+cfmBVv+9EeoJ4Js5G/0Y8Dg9eh7esF/x4PA1AVjPulNW7\n60v6ups7mxmKpDgxMPcdts76Tj7zus9w3+33sbNxJ3/3/N9x27du476X77N0w7Owhmg6WnRCp8nM\n8C1XKcd/nfwv/uKZv+CG9hv4zOs+g8fhKXwu4AqgUJW7ixgdNMo5AexOaLpKBreIqmAOF5sZ8G0K\nbOKalmt4qPehdVuONdNgfJBzE+eWtZwTqqWkc6AQ8A3H0jid8cLNbL/TCPQmcBnPXWofX++Txq8d\ntwAQy45iz+Szh/OsZtjbvBeAI0NHlva9V8CRYeNMenwTrUE313c0ltTHZ6WSzmTWCPg82QyYN33d\nxXs2zeq08dT6XM0gAd8qMhexf+3411Z1EXtf1Ghw3lhjvEE2eBrKalw1M3xpNYHX4cXn9JX0dTd1\nNgPwdM/Qgs+7uulqvvjGL/KVX/gKmwOb+fhzH+euh+4ilU2VfEax/kT1aNGBLaZGTyOpbGpZsuv3\nvXwff/XsX3HTppv4zGs/g9vunvZ5s6cwshylRIsRG4KalsmPW3bA4InKnEWIMoTiITQ0Wn2tsz53\nxxV3cDZ8lhOj8loGo5wTYP+G5RvYAvnF61YO+DJpYzBVoaQzjeaYDPhqXMb1yZiWz3oudWJy7xNQ\n02q8jwI6Y5Axgp/5VjPsbtqNTbNZsqzz6NBRnDYXuWQbrUEP13c0ltTHF3TnAz4LrGYwX5/eTHpK\nhq94SWddPgu8Xid1SsC3yu7ZdQ990T6euPDEqn1P867phhqjL6Lc0bRmwJfMTZTUv2dqDXrY3haY\n1se3kOvaruOrt36VP7zmDzkTPiP9GmJBkXSk5IDPLENeah/fN058g48/93Fu2XwLn77l07jsrlnP\nMctMK9aXGh0Ef/Pkxy07YOIiJK1XXiTEVKFYiCZv05z/rt502Ztw2pw8dEaGt4AR8AVcAbbXb1/W\nP9fr8Bo9UlatsiksXZ8s6cQWK5Tr+fM3pMPKDPiWcOMtl4Pep4zsnqYRTUdRtiQZ3fheM1czmHxO\nH531nRwaOrT4771Cjg0fo917JeAoBHwAzxQp66x1Waek0wz4PHpqSsBXU9JaBpAM35Jomnarpmkn\nNU07rWnah+f4vFvTtP/Mf/45TdO25h9v1DTtCU3TopqmfXY5zmJ1r9tsLGL/2vGvrdr3NJeut/uN\nO2L1nvICvvG48cYfy4yXFfCBUdZ58JVRYqnSJmVpmsa2+m2ANd5YhHVF9TJKOj355etL6OO796V7\n+b9d/5fXb3k9f3/z3895UQqTi+ArNqkzNjg9w1cY3CKZEWFtU5euz1TrruWmTTfx6NlHyeSWYfJi\nlesOdXNt67XYbfZl/XN9DiNgsmwfn7mDLx/wDcWi5LRUIcMXcJsBX/7ydik9fJeOGUFdvn/v7Ljx\nvZO6MexkvpJOMMo6jw4dXdVqrmL0nM7xkeM0uq4EoK3WQ3PAzZUtNUX7+PxOP3bNbom+xEIPn56Y\nEfCVluGTHr5F0jTNDnwO+EVgJ/CrmqbtnPG09wJjSqkrgU8Dn8g/ngT+HPjgUs9RLew2O+/e8W5e\nGHyhsAtlpfVH+/E6vIWm23J3kpkZvog+tqiAT88qnjlT+oV2oVbcAqUDVWHoJFjoh8pqiabLKOn0\n5gO+RWb4vnLsK3zy4Cd542Vv5JM3fxKnff4lx4WAr8jdxhWRSRmZPP+Mkk6QwS3C8qYuXZ/LnR13\nMpIc4bmB51bxVNYTioU4HznP/tblLecECsOnLFvWWdjBly/pTBrZGvP6JuAyAz7NeN5SApRC/97N\nAPQMG987ksm/v85T0glGwBfPxDk9fnrx33+ZnRk/QzKbxK86sGnQ6DduWl7f0UD32dEF+/g0TSPg\nCljiRvxkhm9KwFdCD1/AGcChOSTDtwQHgNNKqV6lVBr4D+AtM57zFuDf8r//JvB6TdM0pVRMKfUT\njMBv3XjrNmMR+2pl+fqj/Wz0b0TTjDfABk8DY8mxkpvfx+M6fpedsVT5Ad+1W+vxuew8daq0sk6Y\nrBWvWA9UNRk5A597NZx4sNInWVVKKSLpyKpk+P7l6L/w98//PbduvZW/velvcdrmD/agwhm+qSsZ\nTLWbjQlmMrhFWJhSatbS9Zl+ftPPE3AF1v1OPrN/78CG5R3YApMBXzVk+DLZHJG0cfFulusFzQyf\nGbsspaSz9wlo3l7IJk7N8KVtngUzfPua9wFYqo+vMEQmuZnmgBuH3QgBru9oJJbOcqxIH1+tu9YS\nN+ILGb5UvKwePk3TjOXr0sO3aO3AhSkfX8w/NudzlFIZIAw0lvNNNE17v6ZpBzVNOzg0VHrwYEV+\np593dL6D77/y/UJ/3UoaiA0UBraAkeFL59Ilv6GHEzq1PiejydGyAz63w87PdTSWF/BZaBqU5b3y\nU0DB2CuVPsmqSmaTZFSm5LUM9Z56NLSySzn+6fA/8Y8v/CO3XX4b/+fn/09J488r2sNnBnxTSzo1\nTQa3CMsLp8IkMokFM3wuu4tf2PoLPH7+ceK6RQOSVdAV6qLWXUtnfeey/9nmUDbrZvj6jfI9T5Cx\nuI5mN14HZg9fndd4/41kM6DZF1/SqSfhlWcK5ZwAFyMh4zfZIFF73YIZvk2BTTR4GiwV8B0bPka9\nu55wNEhrcHKy9KsvNy7Hi5V1Bl1BS1yXJbL5oS3pWFk9fGC8TsrN8MX0GG954C1879z3yj6rlVTN\n0Bal1JeUUtcppa5rbm4u/gUW987t+UXsL6/8Iva+aN+0gM8M2kq9+A0n0gR9GTK5TKFOvhw3X9XM\n+dE454ZLuwCWgK8MF/KlTbHqvglSLrNc0nytFOOwOahz15Wc4VNK8flDn+dzhz7HnR138vEbP17y\nrquKTumMmhm+lumPt+40SjplpL2wqFDcuJheKMMHRllnIpPgR+d/tBrHsqTuUDfXtV6HTVv+S7hC\nhs+qAfWUHXwjsRSa3biuMK9Nat3G+288kwBPcPEZvgvPQSZRWMcAEIpeQmW9XFZfx4QWnHdoCxjZ\npL3Nezk0aJ3BLUeHj3J109UMTaSnBXyl9vEFXUFL9PAl8vsPPdn0lLUMAcgkIbtwf2+5MyzAqJLr\nDfdClf/4XI53iz5g85SPN+Ufm/M5mqY5gFpgebcgV5kNNRtWZRF7TI8xkZ6Y9kPUDPhKfdGPx3V8\n3sS0ry3Hzfn1DKVm+Vx2Fx67xxKlA5Z3ocv4NTpY2XOssohu/BAvtaQTJnfxFaOU4rOHPssXDn+B\nt1zxFv7qhr8qazCCGfBVJsOXfx3UzLgp1rITEqMQvbT6ZxKiBIVp0kUCvn0t+2ivaefh3odX41iW\n0x/tpy/ax/625e/fg2ro4eufnNCZX8kAkxk+n9M8fxLcwcWvZeh9EmwO2HpD4aGR1BBka9nc4GOU\nwIIlnWC8Vs9HzltiSEg0HeXM+Bl2N+0mNJGkbUrAB6X18QXd1sjwFfbw5ZTxdwxGSSeUNKmznLVk\nQGFi/NTESTVajoCvG9imadrlmqa5gF8BZjYUPQi8J//7dwCPK9meyj077yGqR/l2z7dX7HuYL9T2\nmskqW7PWvdSAL5zQ8XiMN3+zF6oclzX6uazRx9NllnVa4Y3F0uKjMHzK+H1sfQV8Zoav1JJOMF67\nxTJ8Sin+8YV/5EtHvsTbt72dj93wsbKn4Nk0G36nvzI9fGbgPzPDJ4NbhMUNxIyAr9U/ewffVDbN\nxu0dt/PswLMMxddXZQMY5ZzAigV85pROawd8xvXMSCxdyPCZQ1u8+YAvmUnkA75FZvh6n4BN+ydL\nBoGIPoKLeup9LoZyAYgt/PPESgvYj48cR6G4qn4X4YROa3D6/thS+viscl1W6OFTanpJJxQP+Dz1\njCfLK+mcucu6Wi054Mv35H0AeAw4AfyXUuolTdM+pmnam/NP+zLQqGnaaeB/AoXVDZqmnQP+Hvg1\nTdMuzjHhc83a3bybV7W8iq+f+PqKje41Az5zBx9Mlj6UetdpPKHjchl30cx9ZuW6ubOZn50ZIZUp\n7b8z6A7K0JZiLhqN+3gbJkv51gkz4Ct1SicYr92FMnxKKT79/Kf58rEvc1fnXXz05z666JIpv9Nf\nmSmdsSFw1XB0UOdNn36KwUh+HlZL/m1VBrcIiwrFQ7hsrpKqSG7vuJ2cyvHds99dhZNZS3eom3p3\nPVfWXbkif74ZMFmypDObMaoUzJLOaArNHqfGGSiU3HvtZsCXNEo6F9PDFx+F/kPT+vcAErlRauwN\nNPhdhDI1C5Z0Auxq3IVDc1iirPPIsBF0NudXMrTOyPCV0sdnBnw5NX8WcDUUpnQqZUznhCkZviKr\nGdx1hNPhsq65B2IDuO3uRSU8rGRZCsCVUt9VSnUqpa5QSv1N/rGPKqUezP8+qZS6Syl1pVLqgFKq\nd8rXblVKNSilapRSm5RS6+qK5J6dK7uIvbCDb0qGr1DSWUJaWylFOKFjc8amfW25bu5sJqFnOXiu\ntKyiVe4kWdqF54ySk21vXHc9fBO68dooq6RzgQyfUopPHvwkX3npK/zKVb/Cn1//50vqjwk4A5XL\n8PmbebZ3hFOXojx4KD/Rzt9kZP1kcIuwqFDUWMlQyr+7jtoOdjXu4pHeR1bhZNahlDL699pWpn8P\nLF7SGRsElZ22dF2zx6bNFnDanaBspHJJI/uzmJ6zs08Dalr/XiaXIatNUOdqpt7nIqTXgB6H9PyB\nscfhYXvDdksMbjk2fIwtgS0kkkagNzPgK6WPr9ZdS07lKtOuMEUyk8RtcxoBTGEtQ/7XVPEMX07l\nyrq+7Iv2scG/oTDpvlpVzdCWteq1m19Le0079x6/d0X+/P5o/6y7pl6HF7fdXVJJZ1LPkc7k0GzG\nPyKzHLRc13c04rRrJZd1WmXfi6Vd6IK2PVC3xbjTuI528S0mw9fobSSeic+6kFFK8YnuT/C141/j\nXTvexUde/ZElv7H7XZXK8BkB3yujxg/khw73T37OHNwihAUNxOZfuj6XO6+4kxOjJzg9Zp09Zyvt\nYvQiA7GBFSvnBIuXdBZWMkyWdLpcCerzC7VNmnKTyi6hpLP3SWOVTfu1hYeG4kOgKZp9LTT4nUYP\nHxTN8u1t2cux4WPoOb38cyyjo0NH2d1s9O+BsXR9pmJ9fFYZqJfIJPCa65FmlXQu/PddaGkqo4+v\nP9o/LWlSrSTgqzC7zc7dO+/mxcEXV6TOuz/az4aaDdPuBmqaVvLy9fFEGoCsFiHgCiy4cHohfreD\n/VsbSh7cEnQFZWjLQrI69D0Pm19tZG5UzihDWScWFfDNsYsvp3L8zXN/wzdOfIO7d97Nh/Z/aFnu\n4gWcgcrcBY0OQU0L50eNi7XDF8OcNafjtuyEoZchV9lyHCHmUmzp+ky3br0Vu2ZfV8NbCvv32pZ/\n/57JbXejoVlzD19h6bpZ0pnG7ozPuhFtw0U6lzKCgcWUdPY+AZf/PNgnJzOfGc23x/hbqfe7GFVm\nwFdkcEvzPpLZJKfGTpV/jmUSioUYTAyyu2k3g/mArzUwV8C3cB+fuSO50pM6E5kEHi3/dzNraEuR\nks78zYFy+vj6o/1V378HEvBZwi9d+Usrtoh9IDbARv/sF2q9u7TRtOGEcVdKJ7Lk+uWbOpt5ORQh\nFE4Wfa708BVx6ZhRTrL5wOSS7XU0uCWiR7BptsLd6FI0evMBX76PL6dy/PWzf81/nvxPfn3Xr/PH\n1/3x8pRsxEfx292VW7zub+b8SIxrL6tH06Zk+Vp2GK+Z8XOrfy4hFpDJZRiMDxad0DlVo7eR12x8\nDY+cfaTiPUWrpSvURaOnkY7ajhX7Hpqm4XP6qiLDNxpLgz1WmNBpsuMmk0tNrmUoZ0bg6FkYOzer\nf+/06EUALqvdSIPPxYjKBxolDm6pZB/fseFjAMaEznASj9NG0Dt7zVCxPr5alzEYp9IZvmQ2iUfL\nD1ObupYBipd0lpnhi+txxlJjtHhLf2+yKgn4LMBcxP6DV36w7IvYp+3gO/Jf8I/74NJxGjwNJQV8\n43Ej4EupiUX375nM9QxP9xTP8gVdQSJ6ZMWG2VQ9cx3D5ldPLtleR6sZoukofqe/rABtaoYvp3J8\n7JmP8d+n/pvf3P2b/NG1f7R89flf+yVq+g+vfklnNgPxEXK+Zi6OJXj15Q3s39rAdw71oZSCll3G\n82Rwi7CYofgQOZUrK8MHcEfHHYRiIZ6/9PwKncw6lFJ0D3Szv23/ivcSeR1eiwZ8feDwgNe4aB+J\npchpcwR8mgtd5TN8Od3Yz1aq3ieNXztumfbwK2FjT+QVDRuNDF+JJZ1t/jZafC0V7eM7MnwEh83B\nVQ1XcSmSojXomfM1VKyPz8zwVbr6KplJ4jWr1golnSWuZfCUN6XenB78qe8O8rVnXyn/sBYiAZ9F\nrMQi9mQmyWhylA2+Vvjun8C33gdjZ+H0D43lkyXc4TAzfLHM+JIDvu1tAVoC7pLKOs1SvYpkSarB\nhecguAlq2ydH8K+jwS1RPUrAWXo5J0xm+IYTw3z0px/l/p77ef+e9/P7r/r95buAGj4NA4epSU6s\n/ms3PgIowvZ6MjnFlgYfb967kTNDMY4PTEDzVcbzZHCLsJhSl67P9Notr8Xn8K2Lss7zkfMMJgZX\ntH/P5HV4rTml09zBl3+/Ho5HyZEulOmZHJqHrEpOlvuV08fX+6SRQWzaNu3hvkgIlbNzZWMrDX4X\no4UM38IBn7mAvZKrGY4NH2N7/XbcdjeXwslZA1umWqiPz+zhC6crX9LpVRqgTQZ6Ja5lMG8OjKdK\nK+k0VzLk9HoC7tlZ0WoiAZ9FbKjZwJsue9OyLmI370xsfPE+6PoiXP8/ILARQkdL7uEL5zN8EX18\n2iSsxdA0jZs6m/lJzzDZ3MIlFoXmYOnjm9uFLqOcEyaXbK+jDN9EeqKsHXwwOWH2sy9+lu+c+Q6/\nu/d3+b1X/d7y3i1/+SEAapIR4pn46mao8yW9l3LGv50tjT5u270Bh03jwcP9xvjq+q0yuEVYTqlL\n12fyOry84bI38P1z3yeVTa3E0SxjpffvTeVzWLikM1/OmcnmmMj3ks3s4XPa3GRJgccoQSyrj+9C\nF2y9sRBUmoYSg6hMLS1BD3U+JxP4yGr2ohk+MPr4+qJ9Fdkbmc1leWn4Ja5uuhqAS5FiAd/8fXzm\nrsNKX5clM0m8YAT05t+TM9/eUaSk0+Pw4HV4S87wmavNlF4/ZxlsNZGAz0Lu3nn3si5i7+99HICN\nw2fh7V+GWz8OG/ZC6AgNngYSmURhgeV8jKEtOSbSS8/wgVHWGU7oHL648N2VQsCnS8A3S7gPwhcm\nAz5PHdhd6yvDl47OXsmQScOLXzcG2szBZXcRcAUYS43xgX0f4Hf2/c7yH+yEkWnwp42LpVhmFQe3\n5AP+C2nj/5ctDT4a/C5+flsTDx3qJ5dTxuAWCfiExZg3J8st6QSjrDOqR3nywpPLfCpr6R7optnb\nzNbg1hX/Xl6H17pDWwLGTYGxuF5Yuj4zw+e0eciRntLXVeJ1RCYNkQFomN0jOZYawparxeO043bY\nCbidxO11RYe2gDGpE6hIWWdvuJd4Js6e5j0opQiFk7TNWLo+1UJ9fB67B6fNWfEevngmjic3ZQcf\ngM1mZPmKDG0B4wZBqRm+/mg/Ds2JytRQ613c0EKrkIDPQnY37+aalmuWvohdKej6Z/qf+AsANt71\nddj9DuNzbbth+BT1DiMNXuwuRzihY3cmUKhlCfhuvLIJmwZPnVw4OLFKrbglXTT79/IBn6aBv3l9\nBXx6tHBToODQ1+E7/wN6n5r36+7qvIsPH/gwv7X3t5b/UBP90HcQmrcTyE/CjJXww2cxvnj4i3zw\nqQ+SzqYnH8z//fcmfDjtGhtqjX1ab9nXTn84yfPnx4zBLSOnIbO2syGiuoRiIYKuID5n6UOYTAfa\nDtDibVnTZZ1KKbpCXavSvwcW7eHL5WBiYHJCZyyF5jCC0pkZPpfNQ05LTynpLPE6ItIPKKjdNOtT\nsewIHm3yGqje7yJiry06tAVgR8MOnDZnRQa3HB0+ChgDW8IJnVQmt2CGrzngZts8fXyaphF0BSs+\npTOZSeLJ5SYDepOrpuhaBqDkCjcwSjprXS2AjaBHAj6xjMxF7I9feHxxf4CegAd+B777QQaar8Sh\n2WnecsPk5zfsAZWjPmmkvUdTC7/ox+M6Qb9xcdjgXXrAV+93sWdTXdHBLVbZ92JJF7rA4TV28Jn8\nzeuqpDOSjkwv6czf5ACM7Oc8/ujaP+JdO961Mod6Ob8E+tW/hT8f8K1UH9/P+n/GY+ce48M//vDk\nzaH83/+piIdN9T7sNuPC8I07W/E4bXznUJ+R4ctlYLhnRc4lxGKEYqGyyzlNdpud2zpu4ycXf1Jy\nmVa1OTtxlpHkyIquY5jKklM648PGABZzQmc0jWY3Ar6ZGT633YPSUpMBQaklnWFz7cP0nWtKKVJq\njIBzesA3RrCkkk6X3cWuxl0VyfAdGTpCwBVgS3ALlyaMa7mFAj4wyjrn6+OrdddW/LosmU3izWXn\nCPj8JWX46jx1Ja9lGIgNELAbcxKCkuETy+mWzbewqWYT9760iEXs4xfgy2+Ew/8Bt3yEvi0HaPW3\n4bBNqTtu2w1AQ8S4OCwlw+f3Gm/8S13LYLq5s5nDF8YZi6XnfY45tKXSbyyWdOE5aL8G7E70rM7n\nD32eYV/9ulrLENVnlHS+8tPJUkVzV9NqO/EQNHVCxy3U5MeAr1TAN54ap85dxw9e+QEfe/ZjxhTO\n2BDY3ZwcN8o5TX63gzfsaOW7R0PoTduNB2Vwi7CQcpeuz3RHxx1kVIbHzj22jKeyju4BY//eavTv\ngUWHthRWMpgZvvRkSeeMKZ0euwdN09HNwV6lDm0JG6sXqN08/VunJ1CaToO7ufBYg89prGYoMrTF\ntLd5L8dHjk+vylgFx4aPsbtpNzbNtuDS9anMPr6jfbMzeUFXsOLXZYlMAk9WnxzUYnLXFO3hg/xa\nshLXMvRF+/DZjNVXkuETy8pus/Pune/m0NCh8qc6PfonMHoO3vlfcMuHGIgPzL5rWncZuGupHzsP\nlBbweTxGwDezbGKxbupsJqfgJ6fnf6NcaGhLLqf46HeOcfhC6Ysz1ww9AQOHC+Wc3znzHb5w+Av8\nwIWxdHsdUEoRTUenL13v+pIxqtvfPHlhsJrio3DuJ7DjTghspMbM8K3Qaobx1DhvuOwN/Pbe3+Zb\nPd/iUwc/hYoOFpauTw34AN68dyOjsTQ/G6sDm1P6+ISllLt0faarGq5iW/22NVvW2RXqotXXyubA\n5uJPXoxUZFpmxJIlnTMDvmgKzR5DQ5tV3u9xeMGWJmk3B3mUmuHLV4fUTs/whaKXAGjzTb5G6/0u\nhrL+kjJ8APta9pHOpTkxuno32+J6nJ7xnsmBLQssXZ/q1R1GJvPZ3tkVYEF3sKKtNkopY2hLVp+n\npLOEDJ+7rqQevkQmwWhyFBdNOO0aHmd1h0zVffo16peu/CUCzkB5i9hHe+Hko3D9b0PnmwCj2bSw\ng8+kadC2m/oho6SrWB3zeFzH5TLu9C1HSSfA3k211HqdPL3Aegavw4vD5phz+frhi+Pc+8wrfPvF\nCmVyKqn/RaMkb/OryeayfOXYVwAYsNuMDE85C2arVCKTIKuykyWdE/3GsJRX3W1MoTTv0q6mU98D\nlYXtd4DTQ01+Qe1KZPiUUkykJqhz1/G7e3+Xd25/J/cev5cvRU+S8TYxkcxwWeP0gO/mq5oJehx8\n5+iwMW5cAj5hEXE9TjgVXnRJp+mOjjs4PHSY8xPnl+lk1qCU4uClgxxoO7Ay/XupKHzpFrj/NwsP\nWXJK58T0csvRWBrNESfoCk6vYsKYxKhpOSLKZTxQaoZvog98jeD0Tnu4J790vT3YWniswediQPdD\nMn1UL/4AACAASURBVDzvoLCpzAXshwdXr6zzxOgJcirHniaj/eNS2Aj4WhYY2gLQVDN/H1+tq7Il\nnZlchqzK4s1M6dE0ldjD1+BpIKbHimZbzenBtmwDtV7nqvTPriQJ+Cxo6iJ2cyRsUV3/DDY7XPde\nAPSszmB8cHbAB7BhD4FLJ3DYHCUObYli02zU5i9il8pht3HjtiaeOjVklKLNwWwOnuuN5YcnjLtt\nZ4bW4Y6+C88Zv246wA/P/5DzkfPYNBt9Wtbob0iszR6WqcybAIWSzoNfAZWD/e817v5WIsN34iFj\nL+LGVxlnqzHuBK9EwBfVo2RUhjp3HZqm8aEDH+LNV7yZz6oR7nUZb+mbZ2T43A47v3j1Bh57KUS2\nabsEfMIyQjFjB99SMnwAt11+Gxoaj/Q+shzHsowz42cYTY6uXDnn9//UGOR09seQ7wf2Oo0MX07N\n7uGqmIl+sDmMKg6Mkk6PKzHnuiifw3j/G0+njXH9yRKHjIQvzjmw5eyYceG/tW7yeqre7yKUNZev\nFx8A0uxrZqN/I4eGVm9wy9EhY2DL1JUMdT4nHqe96Nde39HIwXOj6DP6+Cqd4TOnx3r01OwMX4kl\nnWbPZ7Hr38IOvnRd1ZdzggR8lvXOHflF7CdKWMSenIAXvga73gpB4y5pKB5CodjonyPga9uNpsep\ndwaK1jGPx406+Tp3HXZb8TeJUt28rZnBSIqXQ/PfjZk34Dtu9KqdHlyPAV8XNF6J8jXw5aNfZmtw\nK/vb9tOfy6/XKLGfoJqZQVTQFTSmTT7/Fei81cjuBTcZd2lXM9OZisKZx2HHHYWdQDUB49/dSkzp\nNEtRzJ1INs3GX77mL3ltKsen7UM4gi/OyvABvHnfRmLpLD1sgfHz5S0jFmKFmAHfUjN8bf42DrQd\n4KHeh+a9kViNVnT/3slH4fmvQvN2IzMy9DJgVNgARdc2raqJfmOPsM24bB2JpnE4E7P69wD8+Qzd\neDJmBAUl9/D1GT9DZjg/YQR82xonSz2N5etmwFdiH1/L3tUb3KIUR4eP0F7TTqPXmL8QCqdoKzKw\nxVTYxzejjy/oChLRI6u7Y3YK8zXp0RPT1zJAyUNbzPakYmWdZsJFT9cTqPKBLSABn2W1+dt409Y3\ncX/P/cX7gA79u/Fm/erJvWLmC3XODF9+cEu9zbVgSWc2p4ikMmRt0WVZyTDVTZ3GXbqFyjqDrtl3\nks6PxDl5KUJb0MNAOEkkWbyUYk7puJEVrdCb1qIoZWT4Nr+aZ/qf4cToCX796l9nc2Az/ea+wnUw\nuKWQ4XPVwPEHjVLWA/lypOBG0ONQ4gSuZXH6h5BJGuWced7gZmxKEdGXP6gyR2JPvdBxYOOToQF2\nqHo8G/+bs/GuWV93fUcjzQE3PxrND18afHnZzyZEucwdfEsN+ABu77idC5ELHBkus//dwrpD3Wz0\nb2RTYHYgsiTRIXjw96B1N9z1VeOxC8b7hpkhs1RZ50RfoX8PjJJOmyO+YMAXScWMsr+Se/jmzvCF\nYpfIZfy0100GGPU+F6PkSwrLGNwyGB8s3ORYUQ9+gKOvPMHupt2FhwYjSVpKDPjMPr6us9OvEc0b\njXO126yGZNYI+Lz5tQyRpM777j1olJ+6AlBC37z5mimW8OiP9eOwOUgkfAQ91b10HSTgs7T37HyP\nsYj99AKL2HM56PoibNoPm64tPFwI+ObK8DVdBXYX9dncgintSFJHKcgwsWwTOk1ttR62twV4aoGA\nL+AOzHpT+UG+nPM3f/5yAM4MLTKDcuJB+O4HjemO1WK011jyuvkA/3LsX2jxtXBnx52017QzqkeJ\na9q6WM1gZvhqnDXQ/c/QcAV0vM74pNlsH17F/s6XHzb6Prb8XOEhrbYdf04RW4HA07wrOe1CJzGG\nO5flruRrsKU38Wc//RBdA9ODPrtN4449G7j/Qr40e/ClZT/bLN/8DaPkVoh5DMQGsGk2mn3NxZ9c\nxBsveyNuu5uHz6yN4S05laP7UvfyZ/eUMoK95AS8/Z+NDJ+vsRDwmRk+Sy1fn+ifFvCNxFLkbLFZ\nKxkAalxGwBpO5TN8paxlSE5AKjxnwDecGEJlgjTVTPa+LSbDt69lH8Cq7OMbvnSMATJcXbOl8Fix\npetTNdW4qfU66RufHvRXemWWeRPCqxS4A5y6FOEHxy9xz5e7ODmWMzJ8uYVLkc0ERrGSzv5oPxv9\nG4kks1W/kgEk4LO0XU27uKblGr5x4htkcpm5n9TzfSMQePVvT3t4IDaAhjZ3X4TDBc3baUgnFszw\njceN7FkyNzFnnfxS3dTZTPe5UWKpuf/b5irp/OHxS3S21vC67cZelJ5Li7zLZPYwVVOWI9+/d7im\nnu5QN+/Z+R6cdmchqB9w2NfF8nXzJkBgvM/4/+TA+wplPoX9SavVx5dJw6nH4KpfBPuUO4C1m6hR\nOaIr8Pcxs6QTKPy9D6TquDzz+2wJbuH3Hv89LkSm7yR8y752zmYb0O3e1VnN8PJ3jf9/hJhHKBai\n2ds8a/DGYtS4anjt5tfyvXPfQy9hkIbV9Yz1EE6FObBhmffvvfBvcOpReMNfQMsOoxR90wG4mM/w\nOS2W4VNqVsA3HEuRITLn9HAz4IumE+AJllbSaQ6FmTGhE2BCH8ap6nHaJy+ZG/xORlU+w1dCDx9A\nZ30nHrtnVco6j+rGmfZEjaAmk80xHE0V3cE3VVONi+FoatpjZsBXqeXrZkmnVylwBRiLGf/OW2vd\n3H9sHFAofeFEQKk9fObgw4mETq0EfGKlFRaxn59nEftzXzDq2ne+ZdrDfdE+mn3NOO3zvEg37KE+\nPrbgCz6cMP4hxbPjy17SCcY+Pj2r5pwEBbMDvnBcp+vcKG/Y0cqWBh8uu43Tix3cYl7sDlVZwOeu\n5csXf0Stu5Z3dL4DmCzb7XO61kWGr1DS+dK3jYb8vb86+clCwLdKkzrPPm2UC22/c/rjwY34czmi\nyblf20sxV0mnWcrbE/XS0djK51//eRSKT3V/atrX7t1Uy5bGGl6xbVn5wS3pGGQSMHZuZb+PqGpL\nWbo+lzs67mA8Nc5P+6uoemMe3aH8/r3WZczwjZyB730ELr95+o3izQeM4S3x0ckMn1V28SXGjPeS\nfMCXyeYIJ+Lk0OfM8AUKAZ/Zw1dCNmqeHXwA8ewIPtv0a6B6n4sx8iWeJZZ0Om1Orm66elUyfEdz\ncexKsf3cQQCGo2lyqvjS9amaatwMR6ZPsjRvNFYqw1cY2pIzMnzj+evUf33Pfjo2GlNU/+7hF8jm\n5u/jDbqCaGhFe/j6on1s8G9gIpGRoS1i5ZmL2Odc0TB4AnqfNPqXZgR2A7GBucs5TW17qE/FiOrR\neUfTGv+QMiSzsRUJ+K7bWo/XaZ+3rDPoChJJRwoN+E+eGiSbU7xhZysOu43Lm/ycvrSeAr4uzmza\nwxMXn+Cd299ZuAtrBnwDvtp10cNnlnQGjj8Me34ZvFN+4Ne0gmZbvQzfyw8Zo6A7bpn+eLCdQC5H\ndAXugo6nxmfvnsoH+i/HPGxu8LGhZgPv3/N+Hr/wOD/r/1nhaZqm8ea9G3k+uYFcaIVLOs2LoPFX\n1sW6ELE4S126PtNr2l9Dvbt+Tezk6wp1salmExtqlikgzmbgW+83qhF+6QuTlRFQ2O3Kxe5CwGeZ\nDN+MHXxjcb2wdH2uDF/QPSXD564tLcNnBnzB/8/eeQfIVddr/3Om993ZvpstyaYXSAglBAxFuoYu\nKiAggle8Nu5Vea+v2K/YrqLiFRBFBREUROkCoYUSkhBSSK+bbLbv7MxO7+f943dmdmanz84m4XWf\nfwIzZ86c2Tlzzu/7fZ7v86QzfOFYmKjkpUpXl/Z4lVFLXFIT0FQVLekEMce3c2Tn5BrihP3s0EjM\njMQwHnwTAs6xDL5SC74cDN/RKviSpi2ymOFz+cX6tcFm4KOnzwfg6Xf28LmH3iUYye7RoFFpsOlt\neRVuwWgQR9BBo6mFcCyOzTg1wzeFSUZqEHuGDGDtPaAxwIk3ZrwuawZfKpqOp0ax283F8o0GIkga\ncVGtVAZfKvQaNctn1uY0brHpbMTkWLKj8+L2Aeosepa0igX+rAZLeQxf0C0CViXV+6fgC7hgcAf3\nG9UYNUaumXdN8qk6Yx1alZYeg/lfInzdG/aiRsIYCQg5ZyrUGrA2H5kZvngMdj4Ds88D7bibqK1F\nzPBNQvC6K+jCqrOmu+Yqks6heBUdSiTDdQuuo9XSyo/W/YhIfEzedsniFnbF21AFHJN7viQWQRH/\nv4TUeAqlQ5blijN8WpWWC2dcyCuHXjlqxhKVQCweE/l7lZRzvv5T6HkHVt6ZKV1sOQEkNXSvPfZM\nW5IFnzhmhy+EpBHrgmymLTaDGQB/JFD8DN/oYfH5renNh0G/aKbVGhvSHteoVVQZtXjVVSW5Yy+u\nX0xUjrLdMYkKC/8w/Ro1reYmkdu7+wX6lYKvWJdOEJLOofEFn/4YknTqrTj9YdQqCZtBg8ogZipv\nXdHC89v7ufa3a3H6shMadr09L8OXMJOq1orvfYrhm8IRweWzLs8MYvePwOa/wPEfBVN6MRaLxxjw\nDWQUfKOBCJ/8/ToOO/3QuBB7THQ/cjkVjfrDSGqxYJ0Mhg+ErLPL4adrOFNznbiwuENuwtE4r+0a\n4tz5DahUwvp+VoOF7hF/zi5OTgztEv92nC5MUI5mkeTqLi47r+cdejUqnvV3ceXsK9NkLCpJRYul\nhV6t9l9iYe0Ju7HEZaSO06FxYeYGtpaxeYzJRPc68feef3Hmcxo9FpUW7ySYHoyGRjMXOd5B4pKG\nUczJSAa9Ws9tJ9/G/tH9PLLzkeSmsxut+Kvniv+ZTOMWX4qcdUrWOYUsGAmOEI6HK8rwgZB1huNh\nVh1cVdH9Hknscu7CE/ZUzrDl8AZ47Udw3Edh0ZWZz+vM0LQIutcde6YtnnSGb8QbTjJ82SSd1UrB\n54v4xQxf2FPQyIPRw6KgHBc/1etNxIY0ZLykxqTDLdnEOqJILG4QAeyTmsfnG2ZAraHBPks0QHc+\nlcLwFWfaAoLh8wSjaWuso83wJU1b4jLoLbj8kbFQdJ343i9bUMX/XrOU93pGufKet+geyTyP7QY7\nrjymagnjQ6tGfO9TM3xTOCIwaU18ZK4IYk8EQfLuH4WmfZxZC8BQYIioHM3omm7rHeXVXUO8smsI\nDDbsyk02F63t8qcwfJNU8CXjGfZkFiqpF5a1Bxx4QlHOnd+YfH5Wg4W4DPtLdepMzC4tvFz8e7RY\nPv8I3LsC7j0TlJyfnOhexx+rbCCpuGHhDRlPt5hb6FVJ/xqSTsceLLFoJruXgG3akSn4dj4Nah3M\nOi/r0xaNCW88e3dxInCFXJkFn2+QoM6OjIr2lND1s9rO4vSW07l70904AmOLknmLlwHgPDCJ5gGp\nMifnwcl7nym8b1Gp0PXxOK7uODpsHTy1/6mK7vdIoqLze2EfPP5psfj/0E9yb9e2DHrexajSAccY\nwyephGQfEbouqXMzfNVGMVvnjwZELAOIoi/ve/RkNWzZOyIW/u1VmYopu1mJZiih4Ksx1NBubWfz\n4ORde/3uHjxqFY2WaTDvw7D3JRxOF2qVRK2lhILPKrYdSWHJdGodRo3xqIWvJ2IZDLIMehsuf4Rq\nk1KM6RTX1LCXDx3XzJ9uWobDG+byX7/F9t70463WV+eNZUistQ2SkPJOuXRO4YjhmnnXoEIlgthj\nEZEhN+OMrAxHojMxzZJ+8Rr2ih/tbiXsvKZ2DpBf0mnQi4vqZBV802tNtNeYsso6rcqP1x12s2r7\nAAatitNnjenoZzeKi3rJss6hnaAxwpwLxv7/aODVH0JwVNwsHrwsr9PXyKG3eNxqZWXnyqyLoxZL\nCz2EBVv5//m8lGd4F1ZJnZZ7lwbbNLFAmMy/gyyLaI/Os0UHOQssOhteufI5j66QK92hE8A7hFtV\njUGrot46dkOXJInbTrmNQDTAXRvvSj5+7kkLcchWeve8W/HjSyJV5jTF8L1/MLz3iF1DKhW6Ph6S\nJPHhzg+zvn89fd7szTRvKJq2kD3WsL5/PR22DhrNjYU3LoQXbhdu3pffkz7zPB6tp0DEh0lp0AQi\nx0rB1yOKPcWrwOEN5Z3hq1Jm+IJRRdIJhef4RruzRjJ0OcXCv9OepeAz6RiOW0uSdIKIZ9g0tCnp\nT1BpDLq7AGi0tYr7ZMRPVd8bNFj1qBWFVDFIxFCMn+Oz6qyMho+OpHMsliGelHTaTaJBkWD4Et/1\nKTNq+NtnlxOXZX7x0u60/dgN9rymhb3eXjSSBnVc3GuncvimcMSQFsS+9VFxAUwJWk9FojMxftDb\nofxodytRBvZGEcjpzHFDdAUiGA3ixzVZBZ8kSZw5p5639jkIRdMXx6n2v6t2DPKBWfUYdWNyixl1\nZlQS7C01mmFwO7G6udy13o+stx6dgm9oF6z/LZz4Sbj6ERg5AH+6MvtNKR7jIfdOQhLcuChzXhNE\ncT8SDxOIBYsKHk3Dgdfhng/A8J7SP8eRhmMfnqADi7kxw6goiappYm6sGKlsueh/D1yHYH6OohOw\nGKoJSqTNz1UCWSWdvkGGqaa9xiSkLSnorOrkmvnX8Piex9nmEBLO1hozvbpOVEOTGM3gHxYMqKVx\nquB7v2DjQ/CrE2HvS0fk7SoZuj4eKzvFb/PZA89mff6zf9rADfevy/rc0UY0HmXDwIbKyDl3Pw/v\n3A+nfR5mrMi/bZt4P2OfCK4/ZiSd4yIZROi6D5WkSjaGU6FT65BllWCDEg25fHN88bjyHpkM32HP\nAHJcS0d1ZhZxjVnLQNQimraFJKMpWFy/mJHgCIe9k+MmPaC4VDdWz4TpHwBDNbMdrxYdup5AnUUU\nUuMLvip91VFj+ALRABKgU+lAoxcMX4J90yuuqeEx1desBivzmqxJwiMBu96OM+TMWXT3entpMjfh\nDYnvdYrhm8IRxfULrscX8fH4hl+BffoYQzUOuW6iwykFnyzLVLWcjEqWGXHsztgHCIZPq/ejVWlF\nyPUk4Yw59fjDMTZ0pS/QEzN8uwaH6HEFOH9BeqdTr1HTUWsuneEb3MFBdTs/XbUHj3XW0cnie+F2\n0Y06++viJvzRP0LfZnj4aoiku3f5ejfwsFnPOVXz6KzqzLq7pFOnRl16NMOB1aKAeeAyMVN4LGP9\nb/Gq1FhqZubeJrEwmEynzh1PCYnR3A/l3MRiFAsEf4WjMrIyfL5h+qJW2mvMWV9zy+JbsBvs/GDt\nD5I3OE3zAtqiB9nZV/lweEAsgky14lrlmpJ0HvMY2Q/P3Sb+u3fjEXnLPl8fBrUh83yuANqsbSyp\nX8LT+5/OWNRtPOTk9T3D7Ox3E40Vv1A/Utg5shNvxMspTRM0bPENwxOfh4aF8MFvFN6+ugMsjWh6\nNqBVaY8tSWda6HoYgz5Ila4q3bxKgSRJSLKWUDSYwvDlKVB8QxALZ2X4Bv0DyJEqmqqMGc/ZzTp6\nImaQY5BnHmw8Ftcrc3yTFM8wqDDnDdXTRWN0zoUs9q+hxZr5t8qHJMM3LpohW0bykUIwGsSAGkkn\n1qQuf5jqJMOXKPjS14R2kw6nf1zBZ7ATjUfx5cjs6/X1Ms0yDbcS+zBl2jKFI4qFdQtZWj2HP8ed\nRE/+dMZwcQK93l5qDDXJwesEHEqHw+mPMOwNo2peTHU8jtN9KOt+Rv0RNFoRyTCeNagkls+sRauW\neG3cHF+C4dvY04skwdnzMoemZ9Zb2FNKNIN/BLwDbI+KTl6fruPIM3x7V8GeF+CMr4JZkajOvUjI\nbbregMduFLJdBY++93s8ahU3HZ9jZo2xgq9XoynduMXZBYZqcUN88LJj1+kz7IOND+HVm7GZ6nNv\nZ1Nu2pM5x7fzaWg/bez7ywKzIsXyOPdX7G0jsQj+qD+d4ZNlZO8gh0LmtPm9VFh1Vm5deiubhzYn\n7epb552ERQry2tpJknX6HGCqEwXfZDF88Ti8cSdsf3Jy9v+vgoRdv0oN5vrJz2hU0O/rp8ncNGn3\nl5WdK9nr2ssu5660x3/96j4AIjGZbucxUtSkIDm/NxGGT5bhyS+KQuTK+0BTxOyWJEHrydC9DpPW\ndIwVfGPs24gvjE4XzNsokGQ9oVhQxDJAfklnMoMvs+AbCQ0hx2zUmnUZz9WYdAzFlCKjhDm+WdWz\nMGvNkxbAPqD4MjSYlCb5/JXY8LJMVdpaJzEekOHUqbMdNUlnMBrEKKmShbzTH8GenOHLZPgAqk1a\nXP50pU3iHpprji/hdJ/Io56KZZjCEcf1QYkerYaX6zMvTAn0enuzZvClUtp7BjxgbcIuSzgVF6rx\ncAWEE9ZkyTkTsOg1nNRRw2u70gsNs9aMhMSuwQFOaKtOm01KYFaDhS6Hr/gurZK/96ZHFI87Y61C\nelaiBr9sxKLw/NfBPgOWfSb9ueM/Kgbqdz0LT3wO4nHCsTAPDK1lWTjGohnZzUGA5Pfdq9GUzvC5\nDkLTcXDNX0WcwZ+uELOFxxq2/BVCo3jUmvyMc5Lhm6SCz7FPLIjzyDkBrFbBsPsqyG4lbKTTCr6Q\nGykWoi9mSzp0ZsOlsy5lYe1C7txwJ76ID2vb8QDs375+cmZJ/MNgrhWsgbsHoqXPS8myzFObezPk\n3oCIxXjqi7Dq2/D8//3/fnZ1UrH6J3B4vbDrn3bSWE7pJKPSkQzjccH0C9CoNDy9byyTb1e/hxe3\nDyQNw/aXE+0zyVjXv44ZVTOoM+ZuKBXExgdh1zNwzjezuxnnQtsp4DyAUaU/NoLXg27RjExl+Lxh\nVBo/dkPm/F4CEjrC8RSGL989zZ274PNER9BTk3QHT4UwbVH2X0LBp1apWVS3iC1DW4p+TSkYCI9i\nlUlm9QbazyIg61jqf7Ok/Ri0aix6zTEn6TTKgN5GMBIjEImNmbaoNSKqbFxxbzfpcPnDxFPC2BPn\nTrY5vlAsxFBgiGZLM+5glCatD/0vFgln/Pcxpgq+9xM8/Zy161Xa1CYe2PNYzs36fH1ZM/iGvSHm\nNoqL064BD0gSNRozzhxZJKOBCHGVZ9ILPhCyzp39nqR1MIi4AYvWyqDPxbkLsg+uz26wEInJHMxi\nu5sVyszSqyPiRrrBr7CGR4rle/cP4r3O/172juspnxbSmy1/gedu45n9zzAkR7jZPEt0X3Og3lSP\nRtLQo1GXx/DZp0PHcvjoA6KYefhqOFYG9hM49DZxazO+WBCLLk/BZ20SeUqTlcW3Q3H+y2Uao8Bs\nFYsHr6dyMtlEwVdlSOlsK4zssFyVk+ED8Xv62rKvMRQY4r4t90H9PABqfft499AkyDp9w2MMnxwX\npgglYvPhUb7w8Eae2jxuzjgWhX98Vixq25eLfR9+pzLH/a+GQ2th9Y9h8dXCrr9hPjj2lFWgl4pK\nh66PR7WhmhXTVvDsgWeJxUXT4O5X92LSqfnuJaIIKtnleZIRiUd4d+Ddick5R/bDc/8F01fAqZ8r\n7bWt4n2NsnxsMHwe5befwvA5fMK0JZtDZwJqdETklBm+Mhi+uBwnFB/BrMmc3wPB8DlkZf+lGrfU\nL2GXc9ekFNUDMR+N0hgjORBQsTp+PLNGXitp1hCg1qLLmH87qpLOWFAp+CxJ9i0p6QQxKpOF4YvL\n4AlFk48lzH6yZfEljJ4Sks4mfUREg8SjGdu+nzBV8L2f8M79qONRPjHvWjYPbc6q/47L8Zyh6w5f\niPnNVqpNWnYrMki7oYaReCjrzd3ljxAlS8E3CZ30MxPxDOPcOtWYkNQBzpufveCb1SAW/kXLOgd3\nENVa6KOGeU1WXnXWJh+fdARc8PL3xU04X7Gw4stw2hdg/X28sv4upkWiLGs/J++uRRZfc+mSzrAf\nvANEqzrE/885Hy6/Fw6+BX+9IU1aetQRdOE31xGX41i1mYP6SaiU8NzJmuHb+TQ0L4HqtrybWarb\nAfB6CkRulICsDJ8SxTFMFe15GD4QsyOXzLyEB7Y/wKGwi7itjfnqbp7aPAl/K79DSF7t08X/lyHr\n3Dcoftdbe1K687EI/O0m0RQ555vC9Eitg21/n/gx/6sh6BZ2/VVtcNGPxWMNC8TCxjG5Jk6RWITh\nwPCkMnwgZJ1DgSHW9q/lkMPPk5t7+cSpHUyvM1Nj1rHvGGP4tju244/6y5dzxqLw+GdApRFjAqoS\nl3ktS0ClxaTIx486EkoN69h5MuILE5O8eRk+NXoi8VBxM3yjPaA1i9GGFDiDTmQphl2XnWm1m3WM\nJAo+f2kF3+L6xcTlOFuHt5b0umIwGA/TqB4b6el3B3k+dhKm4EDJ87l1Fj3DnkxJZyAaIHIU1gf+\nqB9DikMnMObSCULWOW6GL1EQulLm+BL5jdkYvoTTfYu5BXcwQr1e+Zz6yfOyOBKYKvjeL4gEhdPW\nnAu4bPHNWHXjgtgVJIJss91Ehz1h6ix65jRYhaQTsFuacaokGE6fcQhGYoSicUKyO73g2/gQ3LVU\nuEpWEPObrdRb9bw2ruALh/WYDOFkYTceM5XHi75pD+5g0NiJJEl87OQ2DkWriemsY2Hsk4nVPxHO\nkRfckZetQ5LgvO8RPeETrA8NsDwQQGo/teDuWyzT6NUZSpN0KnLDr73iodeldHOP+wh8+Kew53nB\nopTYEZw0BJx4jeLmmpfhAyWaYRIc0Ny9QvpWQM4JYFF+N17fQMXefjQkCp+0gk/5vkew0WrPNBYY\nj1uX3opWpeUn63+CqnEBJxj6eXpLb2XNK6IhscAy1YFdaSaUIW09MCw6tcmCLxqCv14P2/8hfkcr\nvixs5meeIx47Vs7V9wueu02wo1fcN8aENC4Q/05yE2zAP4CMPKkMH8CZbWdi1Vp5et/T3Lt6HxqV\nips+MAOAzjrzMcfwJeb3Tmo8qbwdvHEnHF4nruFZJIoFoTVC8/EYw4Fjg+FLNO4USWc0FsfpDxOW\nPXkZPo1kICqHRAEgqQowfEokw7j78qBfXFsbTJn+AQA1qZLOEhm+4+uFpH4yAtgHpDiN2rG4macc\nggAAIABJREFUoAF3kJfiS5ElNewsLZuyzqLLKukEjsocXzAaxBAXBV9iLi8p6QRR4IfGm7aI550p\nc3z5GL5e31i0mTsQpV6rFIqF1h3HOKYKvvcLtv5NMDfLbsGkNXHVnKtYdWjVWBC7glwZfP5wlEAk\nRq1Fz+xGC7sUp84aeyejajXRvvTh4dFABKQwUTlEjVEp+GIReOUOIRd5+OMVnfOSJIkzZtfzxt5h\nYorO2heK4vVrsZmjOYf6LXoNLVWGZAGbF7IMgzvYE29lVr2Fk6fXABJuS+fkSzod+2DtvXDCJ6D5\n+MLbSxJbl30Kr0rF8pgKmhdn3cwbinLNfW+zo8/NNMs0RdJZQsGnsC67w7U8sCZlQX7yTYI9ee9R\nsSg8FuajAi48euFCWbjga5kchm/nM+Lf+ZcU3DRxjL4SZjsKITvDJ5okKksDek1hF7Z6Uz2fWfwZ\nXj38Km9U1dAS6cbl9bNmf+WOMznPYq4VnXm1riyGL1Hwbe9zEwv54ZFrxIzrh/4HlqdI1RZdIZiA\nw8emzf4xia1/g80PC/Oo9mVjj9fOFuzQJBu3JNykJ7vg06v1nD/9fF48uIpHN+znIye10qjY08+s\nt7B/+Nhi+Nb3r2dW9SxqjdllhHnR8y689kMhzT3+qvIPovUUTEE3gWNhhi9xHVcYPqdfrE3iRAsU\nfHpihEQRp7fmj2UYPZw1dP2wR/gbTLNmP0drTDpC6IiojSXN8IEomjqrOitu3BIJeXCoJBpTGvUD\n7iCjWIi1nw47ns7z6kzUWfQ4fJmSTuCoyDqD0SDGWFQp+MRxpRV8OnNOhi/VqdOsNaNVaRkJZuYf\nJzL46k31jAYi1OoSDF8eZdH7AFMF3/sBsgxr74b6+dB5FgBXz7saFSoe2vFQ2qaJgm98Bl/CVrfO\nomNukxVPMMqAO4TdLmz+Xb0b0rZ3+SNIGkX2mQg23fq4YE1O/xI49sJjnxLykQrhzLn1uPwRthwW\ni9rX9wwRixnRaoN5Xzer0VpcNIN3EAIjrPU1sqStmlkNFlQS9GiOgFPnC98QM3vFWGMrWNO/DgmJ\nZTeuBm32/JxNh1y8tc/B6t1DtFhacKggWArDpyzCu+UGHl53iEA4xRzjA/+ZlJbyyh3F73OyEHDi\n1QnJok2bPew8iapWIdOpdKG64ymxIK6fW3BTs1YUp54cLmDlIDnDl+pO5xsijoS1pviF8yfmf4IO\nWwc/8u4kJkdYpB/iiU0VLJATix9TrZDYVreXVfDtH/ahkkAO+wg9cKXIh7vkV2LWNRVzLgS1fkrW\nWSxGD8PT/yEMWs64Lf05jQ5qZ006wzdZoevZsLJzJcFYANm8lVvOGIt06aw3M+wNM+ovQ5omy/DW\nXXB4Q+Ftc8DhDXHD/ev43tPbeX3PEN5QkI2DG8uTc4b9wmnV0ijYvYmg7RSMsSj+EqIGJg3uXqEU\nUO6BI74wkkY0gvIVfFqVnjgKM6W35Wf43D1Z2dD9I6Kh3lGV/Ry1GjSoVRJ+TXXJBR8IWefmoc0V\nNc0aduxGlqQxh05gwB3CqFWjXniJkGqXoGiqs+hx+sNpCpBEZNbRMG4JRAMYYlHQWZKMXSFJZ4Lh\nS5V0SpKEXW/PyvD1eHtoNDeiUWlwByPUaKYYvikcKRx8S+SkLftMUnKQCGJ/fM/jeFNO7gQVPd6l\nc9gnLnx1Fj2zG8aMW+xKF9E5mK4jHw1EkNRiv7XGWnFze/MXwujhnG+LG8reVfBi8QVMIayYVYck\nkZR1vrB9AK1kJirn7zLOqrewb9CX5sCUFUrHemOohRPa7Ri0aqbXmdkebREsia+CDEcq9r8m3NJW\n/CdYs88iZsOavjUsrF1IVR5ZzrZewbIeHPEnF059gVIKvoP4MaK21DEaiPD3jSmMsSIt5YTrhKnD\nmv8tfr+VhixDwIlHuekXxfBFA5UNX/ePiNiM+RcXtblBbUCDhK+CXdDR0Ch6tT49csU7iAsrbXXF\ndx91ah23nXwbXSEHf7ZZubLNzfNb+wlGsrhhloOEvMmkzL5Ud4CzNElnPC7TNezjnE4jf9T9CGPP\n23DFb2DpdZkbG2ww+zzYdgzLOmX52GDK4zH4+y3i3yvvE85249EwHwa2TephJAq+yWb4ADqti5Aj\n1Uybtj1tzrWzXhkJKIfle+NOkaf6l0+UrXZ5cfsAr+0e4oE1XVz3u3Wc8tPfE4gGCLhn0F2sEVly\nZ98Qi/nLfg3G3LNtRaHtFGHaEi5COTPZGJ/B5w0hqcXfJt8Mn05lII6yUNfbcs/wRUPgHRBzrONw\ncLQPWZborMle8KlUEnaTFrequiyn7yUNSxgNjdLl7ir5tbkw4NwLQKN1jLHsdwdpqjIgzfuweGBH\n8bLOOqseWRaFdgJVOtFwPDoMXwBjLAJ6W3ZJZxbTlkRB6PSNi2YwVOec4Uv4YLgDEarVicbBVME3\nhcnG2rvFBfz4j6U9nAxi3/N48rFeby82nS1jQZzI4Ku16JjTmDA68VCjF7S/c2Rv2mLE5Q8nGb4a\nQw3sewkGtwnGR6WCEz8Jp/47vP1reOf3FfmYdrOO41urWb17iGgszis7B+mori14UZnVYCEQidHj\nKjBvoLB4e+KtLGkTncH5TTbW+RrTnq8o4jFhGV/VXpJbmjfsZcvQFk5tyT+7t71P/G0OOfxJGW9v\nDtfVbJCdB+imgQsXNbOwxcbv3zyQ3m2UJLj4F7DgUvE5Nv6p6H1XFBE/xCN4NOLCXdQMH1Q2mmH3\n8yJgt4j5PRAdRLNKhyceyZgpKBfZQtdjnkEG4/kdOrPhjNYzWNFyOnfbqzjO1o0nFOXVXRUKiU9K\nOpWCr4wsvn53kFgkyB3eb3KCtJfHO78roktyYeHl4O2HQ2vKOuRJRTwGP1sA7z5wtI9EsFJdr8NF\nP4KazuzbNCwUM5cVOm+zoc/Xh11vx6DJrl6oJB5c0014dAlOeSvDgbGFeWe9YOFLnuPb8yK89F3o\n+IA451Z9u6zjWrPfQb1Vz6Zvns/vbjiJRZ1DIEs8+KqaFT9+hXN++mqS/csaTZJ6POt/K+4xnWeV\ndSxpqGrFpDlGcvjGZfA5fCIuCvIzfDq1AVlKFHzW3EV5ckYwU9LZ6+1HjlppqTLnfB+7SYdLspVs\n2gJjAeyVlHUOKLnKDbb25GOD7iANVr0onKedJIzHikS9RdxzU7P4EgxfYqb8SCIYDWCU5aSkU6dR\nYdSmjDJkmeGzGbVIUjrDB+Rk+BLRZrIs4w5GsakVldkUwzeFSYXzoJgbWnoD6NIXdAvrFnJi44k8\ntOMhoopdbC6HzsTQbZ1FT61FT51Fx+4BT7JDNhIPppkquAKR5EW1xlAj2D1rMxyXMhdw3vdg1nnw\n7FfgwOqKfNwz59SzqdvFyzsHcfojzG9sJBwPE4zmlnXOVgrYgrLOwe34NNX4dfZk0Tu3ycpbbmVR\nOjQJEqaNf4KBrXDed3LKMrPhnYF3iMkxljcvz7vd9l5R8HU5fMnvvYdI0ZEK8ZEuumL1dNSa+NTp\nM9gz6OWNveNuXCq1MHXoPBue/EJJ3cGKQWHqvGpxYc/r0gkpBV8FZYo7nhL7bVla9EssGiM+SVWx\n43CFXBmLnPBoP8Oyjfba3IuSXLjtlP8iJKl4LPgOdRYdT1bKrXM8w2efLgKgA8U3Iw4M+zhRtZv6\n0fe42/YFHg0WMLGYcyFojMemrNM3LGy9u944usfRuwle/m/RwFlybe7tGuaLfyfRzGqyIxkS8IWi\n/P6tA5xUdy5x4jx34Lnkc+01JjQqqbQsPsc+eOwmaFoE1z4qGp/v3F/ydyvLMm/tc7C8sxazXsM5\n8xux1Rxibs0cVn3pQ9z+4fm0VBt5cM1BrvvdOk747ovc/Mf1PPj2wXT2z+cQua0NC8TcdYVgtDbj\nlyvE+E8E7p40hm/EFy6K4TOkFnyGPJLOPKHrQ4Eh5KiNRlvu0Hq7WYdDtpalEJpRNQOrzprVcb1c\nDChjPU32WcnHEgwfIBqWvRvHPncB1FnEZ0+NZjiaM3yBaACDLIPegtMfxm7Spns86MwwjplWqySq\njFpcgXSGz26wZzB84ViYocAQ0yzT8IVjxOIyNinB8E3N8E1hMrH+PkDKnFlRcP2C6+n19fLSoZcA\ncRPNNhPhUAq+GrPo1sxusLJrYMzW2KlSQd9YCKg7EEGl6OTtzkOioFt2S3p2nFoDH/mdmPf4y3Xi\nRjhBnDmnjrgMdzy7A51axXEtYkGQ78IyS5Hl7C0UzTC4g/1SG8e3VqNRi1N/bpOVXrmGmNZS+cVN\n0A0vfw/aThXsQwlY07sGo8bIkoYlObcJhGPsG/Ki06jodQWo0taikVT0FpvFJ8tIzi4OyQ2015hY\nubiZOoue37/ZlbmtRg8f+xNMO1HMbu5/taTPM2EohYJH+d4KMnyJAfwib2oFEfYJlnveyvwOq+Ng\n0VrwqqSKOYaOhkYzCj7ZOygiGUpk+ACmV03nOm0jT8guls/38dKOQTzBClht+4eFM15CWlaGU+f+\nYR8LpC4APO3nsK3HnV+2rbeIWJHtTwhG7ViCV3FqnYymUrEI++FvN4O5Hlb+PP95nCj4JtG4ZbJD\n1xN4eN0hXP4IXzn7DObXzOfp/WPshlator3WVDzDF/KIjFK1Bj7+Z9GEPfv/Csnyk18sKbt035CX\nIU+I02aKsYpwLMymwU2c3HQysxos3LyikwdvWsamb53H7244iSuXtrJrwMM3/rF1jP17ahvDj9yC\nHHAKuXMJTcVCMFa1E5UgUqlraDmIBCAwkiHpVBUxw2dQG5CkGOFYWGF9cqwh8hR8o+FhiFVRZdRm\nPJdAjUnHUMwirnklSrZVkorj64+vKMM36B9EH49jq54OiMbCgDtEk2JUxDxlJCFhQFYAyYIvJZrB\nqhOFz5Ge4ZNlmUAshCEuJ106q4269I10FnG/Hvdd2E26NJdOEOePc9yMfb+vHxmZFksLbqVAtEgB\nMSOuzn0evB8wVfAdywj7hARowSU57ZXPbD2Tdms7D2x/AFmW6fX2Zjh0gujOWA0aDAr1PbfJyt4B\nT1KL7dRoxJygApc/gkrjxagxYnz7XtBZ4aQbMw/AUCVysCQV/PljJXXws2FxazU2g4Yuh59TZ9bS\nYBILxnwXFrtZR51Fx97BPAWfLCMP7mBTsJklbWNdwXlNVkDCae6svEnBGz8ThdeFBWIYsmBN3xqW\nNi5Fp9bl3GbXgIe4LFjRuAz97jDNOrvI4vMWUfB5B1HFgnTL9bTXmtBr1Fy7rJ2Xdw4m3RHToLeI\njnbtbHj4miMbdJ1g+ACNpMGgLrCwsTSK8PVKMXx7V0E0WLScMwGzvgqvqrIM33hJpzY4zLBcRUcZ\nBR/Av7WcRV00RjcPEYpGeWFbBWIkfMNgrBnLACsji+/AkI/j1N3IliY6O6bjCUXpdhaYa1p4uXCp\nPfhmWYc9aUgYKQ3tPnrF6AtfF2Zbl98Dppr829qnC7Y0S8HX4+3BVQEzj35f/6QzfKFojPte389p\nM2tZ2m7n4pkXs92xnf2u/cltOuuKdOqMx0W+nWMvXPUHYUQEglG45Jcwsg9e/WHRx7Zmn2CElisF\n35ahLYRioQzDFpNOsH/fu2wRq796Ni99+cwk++db+wB13S/wP5GruPn5YCb7NwGYaoS5TeBo/pay\nyC0dvjBGQwiVpEoWHtlgUOac3UF/ftOWRDPOlqmM8sUcGFU1OV3CQaw/+qMWcX8ow9V0Sf0S9rn2\n4anQvORA0EFjLI5kEPcJlz9COBqnIVHw1c0SXgxFKnVqFUlnajSDRqXBorUccYYvFBPHYJTHYhnS\n5vdArFPiUTGbmYJqkzZT0mmw4w65kwo5IOl832IRGXwAJoLv+/k9mCr4jm1sfljozpd9NucmapWa\nTyz4BFuGtrD68Gr8UX/2DD5vKNmpASGD9IVjDLgjVOmrGDHVQP8YwzcaiKDT+anR2US+1UmfFMVd\nNtTMEOyPswse/eSEnDs1ahUrZosQ9vPmNySlA55I/ovhzHoLewbz5ewcRgp72Rlv5YT2sa5gm92E\nSafmkLq9sgyfs0uYnCy+WrBiJaDf18+B0QNFyzkvWiQWTQcdPlrMjfRoNMVFMyiL70NyA212USxc\ne2o7WrXEH9/qyv4aox2uexws9fCnK2Fgcq3bk1AWmF5krDpr3hswoISvN1duhm/H06KAaT+tpJdZ\njTWi4Bst/jhkWebe1/ZxOEtxk8HwhX1oYwG8anvmja9IWJqWcKvTxT7vThqat1VG1ukfHpvfA8GA\nQEnGLQeGvRyn7UZqWsSiFnHt2dpTYIEx+3zQmo49WWeC4YuFKp5hWhR2PSdkh6d9HjrPLLy9Sg0N\n8zIKvlcOvcJl/7iMH64vvrDJBk/YgzfinXSG7/F3exhwh/j3s4S87aIZF6GSVGks38x6M13D/mQc\nUE6s/rEw37rgDphxRvpznWeJyJ237hKy2SLw1j4H06qNSWZ+ff96JCRObMx9v5AkiZn1Cvt3eQM/\nMD2Is/4UPCd8JpP9K2b2Lw+MSsHnP7y2rNdXBOMy+EBIOvX6ANX6alRS7iWsSSP+rq6gT0g6c8Uy\njB4WrLc2PcPUH/ETxY9Nkz10PYEas5bDYaXZVoZxy+L6xcjIvDf0XuGNi8BgxEMDqmSTud8txmGS\nDB8IpcrBt4QRWQFY9Br0GlVGFp9NZzviM3yJmVIh6bQpks4sDB9kGLdUG7VpsQwgGD4ZOa1wTYau\nW1pwB8Ra1hgPvO/n92Cq4Dt2EY/D2/dAywnQdkreTS+deSlWnZX/eed/AHLO8NVZxn4YcxpFZ2z3\ngAe73o7TZEtn+AIRNDo/teGgYEryFJ0ATD8dVt4J+1+B579W7KfMiouOa8KoVXPugsai7X9nN1rY\nO+jNbW+ssHe74q2c0Da2YFapJOY0WtkeaRZFUhEXwKLw4rdEllUZMxVv970NwKnN+Q1btvWOYjVo\nOH2WuCEdGvEzzdKqMHxFFHyKvM5jbMWsF059DVYDFy9u4dF3upPdrQxYm+D6J0BjgAcvL8tuv2Qo\nDJ+HaGE5ZwK2lsoUfNGwMGyZ+6HsjoZ5YNZZ8ak1JR1H72iQHzy3k7te2pv2uCzLmQWfIt2VLfWF\ni+BcaJjPxV4fxxubkWue4Y39h5MS8LLhHxGRDAkYq8FQXdK50j3koiPWDY2LmNNkQaOS2NpbYIGh\nM4tZvu1PVjQyZsLwprCmR1rW6RkQM15Nx5UUC0PDgjTVwyM7H+HWV28lGAvS7eme0CElHTotk8fw\nRWNx7nltH4tbqzh9ljgX64x1LG9ezjP7nyEuCzfXmfUWwrF41gZLEjufgVd/AIuvEW7Z2XD+f4sm\nx5OfF5m1eRCPy7y938GpnbXJ3+36gfXMq5mXweBn34FwWpUkNfZrf8d3L1+cZP++sXJB2uzfku+8\nyE1/yDL7VwBGhT0L9FU+GLxoZGP4vGE0Wn9eOSeAUSngRoN+IemMhTJYH7FBT1bDlkToeq2hPu/7\n2E06huMK01iGcctxdcchIVVM1jkQC9AojTX3B5SCL20Ocf5KYUC267nxL8+AJEnUWfRpM3wgooGO\nNMOX8HIwxWXQWXAFsjB8yYIvnQCwm3QZLp01SlZh6hxfj7cHtaSm0dSYlHTqZf/7fn4Ppgq+Yxf7\nXhYWy8s+W1AOmAhiT1j7Ziv4HN4wteaxH/ychkTB56XGUINTqxeLUmXw2OUPo1a7qfEMCqOWLKGk\nGVh6nXDxXPcbWHdfkR80Ex8+rpl3bj+X5irjmFa8kFNnvQV3MJrmJJUGZZHltc0ekzYomNdkZY1H\nuahXwqnz4FuCFT391qwykUJY07uGWkMtc+xz8m63vc/NgmYbDVY9Rq2agw4/zVUzGNaoCXn6Cr+R\nsvjW1HSkPfyp02fgC8f46/o8izr7dLj+H+Im+sCloATUThoSM3yxEBZtkQVf1bSSmLWc6FoNodGi\n4xhSYdFahNFMCQVfn+I2+8x7fWkxCZ6Ih5gcS18QKtJdra34uI8M2Kej0hj5L+NMgnEXmpqXePa9\nIs6ffPANpxd8yvsUW/CFo3H0o/vQEGWbrRatWjRmtvYU0VFeeLlYeHW9XvJhTxq8g6JBAjA4yZmf\nqZBleOLfRbf7it+mz2AXQsN88A4Q9w5x54Y7+f7a77Ni2grO7zifAd/EZL/J0HVTjoLPeVDMCXsG\nyo6yeOa9Pg46/Pz72bPSmiErZ66k19fLxsGNQBFOnYM7Rb5dy1LR1Mx1Pzba4UP/Ixqnb92V99h2\n9ntw+iPJ+b1QLMTmwc3F5++9cSd0vy3eT5GWJti/mz4wI2327yMntrJ7sHT2L8GQ+Yd2Zi+UjgQS\n103bGBPs8IVQaQoXfBadUvCFfJC4ZmaTdY4ezjoykyj4Gs0Ned+nxqzDKSvFQBnGLRadhdn22Wwa\nmnhhHZfjDMoRGjVjBl5jBV/Kuqd5iYihKNKts86qz8rwHemCL5Xhk/UWXP5wMlQ9CZ3y2cc5dVab\ndBmSzmqDOIdSC75eXy+NJpHBN6oUfLqYf4rhm8IkYu3dYg6pSLOPq+ddjUYS7MP4DD4QuvfaFIav\nyqSl0aZnd79w6nQm7mGKrNMdiKCVhqmJRkQRVyzO/Y7osD/3f2DfK8W/LgWSJCUZp2LdoGYpBWxO\n45bBHQxRw8yOzAv73CYrGwNNye0mhHgc/vk10TEs5e+WeLkc5+2+tzm15dS8jE0sLrOzz8OCFhuS\nJNFeY+Kgw8+0KlG89RXTgXd2MUgtzbXpN85F06o4ebqdP67pyi9zapgP1z4mio4HL68cO5oNASdI\narzRYN65jTTYpokO8USzz3Y8LS72nWeV/FKLzoJXoqQZvt5RcYP2hqK8sH1sYT2q2IqnLnRiCnNk\nri29sZCEIt87ztnLZbMuQ1fzJo9u3lj+/iBT0gnCuKVI05Zup5+5chcb9Ho+vvM+7t58N4um2djW\n6y4cUjz7PPF9bXs8/3ZHEt5+cT5Wtx9Zhm/db8T86fn/LSSapaBhPmHga6v/D/dvvZ+PzvkoPz/7\n50yvms5wYDht7qVUFAxd/8e/i0bST+fAT2bCH1bCs18VEUCH1hbMvZNlmbtf3cfsBgvnzU9vhnyw\n7YMYNUae2idmmJJZfNmcOgMueOQaIRP+2J8Km6IsuATmXyJm+Yb35txszf70+b3Ng5sJx8Oc0pRf\nzQMIh8VXfyDWBnliSrLN/uVk/9Z0ZSyGEwxZgGiaodsRhbtXjJLoxgqYEV8YWfLldegEMCszfJ5Q\nCjuT7bzJUfAdcoumRKstv+zYbtbhQKxTymH4QMg63xt6L8k6lwtn0ElEggbdWFNwwC0KtYZUhk+S\nYN6HBbFQRPRKvUWXwfDZ9EdB0hkbK/h8kplITE6Gqiehzy7ptJu0+MIxwtGxv7FdL86h1GiGPm/f\nWAafonLSRn1TM3xTmCQM7RY36ZNuAk1u045UNJmbuGjGRdj19gxJSDQWx+kPp83wgZB17h4UBd9I\nTIk9UGSdPr8HvxTCbmuHxgXFH7tKDVf+FurnwqM3wPCe4l+bBcUyfIWiGSJ929gRm5Ym50xgXpON\nXmqJaswTn+Pb8gj0bYJzv50Ro1EM9jj3MBIcKSjnPDDsIxCJsaBZ3Gjaa00cGhmLZuj1FWZo4iMH\n6IrXZ7Xzv/H0GXSPBHhpR4FOfutJ8PGHhJHBnz+acZGtGIIuMNrxRDzFM3y2aRMPX4/HhJxr1rll\nOeBZtBbCyIRLYPj6R8VNrc6i428bxhzyEjel1ILPMywKyaq6CRR8kJTvfWnpl9Cp9OyJPVQ41zIX\n4jFF0jm+4JsOrkNFBaPvH/IxX3WIx23i/P7tlt9SXzvMiC9M32juiBZAzOLMvUiYEhSQ1hWLF7b1\nF/4t5IN3UEih6+cfOYZvcAe88A0x13jyzSW/3F3dwWebGnh2aD1fWvolbj/1djQqDY2mRmJyDEeg\ndDYjgX5fPxpJQ50xx3zU8G6YeQ5c+CMxbxQNwqY/w9O3wv3nww/b4WcL4aGr4MVvwuZHRFESEefG\nyzsH2dnv4bNnzUSlSm+cmbQmzmk/hxe6XiAUC1Fj1lFt0rJvPMMX8sDjnxbn7MceLE7lAoJ10xrg\nqS/mPNfX7Btmeq2JlmpRlKzrX4dKUrG0sUDkS9gv2EZzA3z4Z0WbgRVk/57Yxr89uCHtNQmGLyBJ\ncHhdUe9TcXj60uSWYi0TIYK3MMOnF8fvDikzfJDJ8AVHhfQvS8F3wCmurZ3V+a+tNSYdI3Ki4Cvv\nN7GkYQmeiCfNTKgcDPjFNaop5XfV7w5SY9ah16jTN15wqfhdFeHWKSSdR5/hS0g6jfE4rqhYG2dK\nOpXifpyks1pxqE9tbCTOoVSnzh5vT0roumhqqSO+KYZvCpOEdfeCWpfdFTMPbj/1dv784T9nMEMj\n/jCyTNoMH4iCb++gl2qdndGwm7itNcnwnRb6JzFJoqbzg6Ufv94qnDtVWuHcOQHmR6PSYNaaC87w\nNVj1WPUa9mRj+OIxVI7d7JbTDVsSSDh1jphmTKz7HvLCqu+IYNNFHylrF4n5vYKGLUrg+kLFzKKj\nxsShEX+S3e0JFr7xxEcOCIfOLO6O5y9oZFq1kfvfLMJgYubZcOXvoGcDPHLt5Mh/Ak4wVuONeEub\n4YOJzfEdXi9mO8uQcwKYtaKY9oY9RYdY97qCmHVqPnZyG6/vGWJQkeQkCr7Uho5bKfgam9rKOr4k\nGhaAd4C6uMR1825GY9nFXWueLG9fAScgZ2H4pkMsLBZxBXBg2MsMVRcvmo2c13EedoOdVxx3AdHi\nZZ0BJxx4rZxPkIbnt/Vzy5828JPnJ9AM8g6ApUGw4o49kz9fGA2JCAa9FS7935Jdgvt9/dzw5ld5\n16DnDvMCbj7u5uR9JeGs2e8vX8bd5+ujwdSAWqXOfDLsE7+56afDqbcIB8ybV8HXDsOt78HVf4Fz\nvgUdpwkGaM2v4e+fgXtXwB3NyHedhPEfn+Iblie5RPeOaDqOc0a9uPNiPBEPqw+L/NgNy4D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tSSekKyzoGQk8ZYLHnt7VccjZvyyVIXXib+NnnMWxIM35Bn7DqalHQeQYYvMS9o1FXh8gtDlcRY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uaX1SOjVhP8s+P5/PzJ/ETav/yX/8dgOdqQytHe5azj0N+HLLMuSRG+yPCftyMnzl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\nOom5c+d2n+fhhx9m06ZNrFu3Dms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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c4c0ad0>"
+       "<matplotlib.figure.Figure at 0x1a160d0128>"
       ]
      },
      "metadata": {},
@@ -430,21 +325,20 @@
     "plt.figure(figsize = (15,7.5))\n",
     "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
     "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])\n",
     "plt.legend()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10c61a610>"
+       "<matplotlib.figure.Figure at 0x103c271d0>"
       ]
      },
      "metadata": {},
@@ -459,15 +353,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "residual mean:  -2.31112163493e-16\n",
-      "residual variance:  0.000205113416293\n"
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -478,14 +372,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PTJ7dr9tcsc6gQpLV5eN7gpyIw408aRUMVTnsPgwioCs9/ct0v96Z4gSMiul4AHAIbnUN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\nAAAAoDghFAAAAADFCaEAAAAAKE4IBQA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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d43bb10>"
+       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
       ]
      },
      "metadata": {},
@@ -494,7 +388,7 @@
    ],
    "source": [
     "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df.spy,simple.resid)\n",
+    "plt.scatter(df.goog,simple.resid)\n",
     "plt.axhline(0.05,color = 'r')\n",
     "plt.axhline(-0.05,color = 'r')\n",
     "plt.axhline(0,color = 'black')\n",
@@ -505,210 +399,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 140,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
-      "  from pandas.core import datetools\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "from pandas.core import datetools\n",
     "from statsmodels.stats import diagnostic as dia"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "p-value of Heteroskedasticity:  0.144075842844\n"
+      "p-value of Heteroskedasticity:  0.14407584284381386\n"
      ]
     }
    ],
    "source": [
     "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
-    "print 'p-value of Heteroskedasticity: ', het[-1]"
+    "print('p-value of Heteroskedasticity: ', het[-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(2.8288103783041767, nan, 2.8496563023857964, 0.092640488871836887)"
+       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.spy))"
+    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2668279319999998\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb
index 36f6ff6..06e6da5 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb	
@@ -2,10 +2,8 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -13,10 +11,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "a = np.array([1,2,3])\n",
@@ -26,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,19 +32,19 @@
       "[[1 2 3]\n",
       " [2 2 1]\n",
       " [3 2 1]]\n",
-      "<type 'numpy.ndarray'>\n"
+      "<class 'numpy.ndarray'>\n"
      ]
     }
    ],
    "source": [
     "matrix = np.column_stack((a,b,c))\n",
-    "print matrix\n",
-    "print type(matrix)"
+    "print(matrix)\n",
+    "print(type(matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -63,12 +59,12 @@
    ],
    "source": [
     "matrix2 = np.array([a,b,c])\n",
-    "print matrix2"
+    "print(matrix2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +89,7 @@
    ],
    "source": [
     "x = np.dot(A,B)\n",
-    "print x"
+    "print(x)"
    ]
   },
   {
@@ -108,7 +104,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-893b341bebb8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-7-ac89428c1137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)"
      ]
     }
@@ -119,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -132,83 +128,49 @@
       "\n",
       "-------------seperation line------------\n",
       "\n",
-      "[[ 0.   -1.    1.  ]\n",
-      " [-0.25  2.   -1.25]\n",
-      " [ 0.5  -1.    0.5 ]]\n"
+      "[[ 3.70074342e-17 -1.00000000e+00  1.00000000e+00]\n",
+      " [-2.50000000e-01  2.00000000e+00 -1.25000000e+00]\n",
+      " [ 5.00000000e-01 -1.00000000e+00  5.00000000e-01]]\n"
      ]
     }
    ],
    "source": [
-    "print matrix\n",
-    "print '\\n-------------seperation line------------\\n'\n",
-    "print np.linalg.inv(matrix)"
+    "print(matrix)\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.linalg.inv(matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]\n",
-      " [  0.00000000e+00   1.00000000e+00   1.11022302e-16]\n",
-      " [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]\n",
+      "[[ 1.00000000e+00 -6.66133815e-16  3.33066907e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00 -2.22044605e-16  1.00000000e+00]]\n",
       "\n",
       "-------------seperation line------------\n",
       "\n",
-      "[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]\n",
-      " [  6.66133815e-16   1.00000000e+00   0.00000000e+00]\n",
-      " [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]\n"
+      "[[ 1.00000000e+00  0.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00 -4.44089210e-16]\n",
+      " [-1.11022302e-16  0.00000000e+00  1.00000000e+00]]\n"
      ]
     }
    ],
    "source": [
     "inverse = np.linalg.inv(matrix)\n",
-    "print np.dot(matrix,inverse)\n",
-    "print '\\n-------------seperation line------------\\n'\n",
-    "print np.dot(inverse,matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [1 2 3]\n",
-      " [3 3 3]]\n"
-     ]
-    },
-    {
-     "ename": "LinAlgError",
-     "evalue": "Singular matrix",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-a9681b1c2fd3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0maa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0maa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0minv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36minv\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m    524\u001b[0m     \u001b[0msignature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'D->D'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'd->d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    525\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m     \u001b[0mainv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    527\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[0;34m(err, flag)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mLinAlgError\u001b[0m: Singular matrix"
-     ]
-    }
-   ],
-   "source": [
-    "aa = np.array([[1,2,3],[1,2,3],[3,3,3]])\n",
-    "print aa\n",
-    "inv = np.linalg.inv(aa)"
+    "print(np.dot(matrix,inverse))\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.dot(inverse,matrix))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -225,12 +187,12 @@
     "A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])\n",
     "B = np.array([[8],[-11],[-3]])\n",
     "inv_A = np.linalg.inv(A)\n",
-    "print np.dot(inv_A,B)"
+    "print(np.dot(inv_A,B))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -244,36 +206,27 @@
     }
    ],
    "source": [
-    "print np.linalg.solve(A,B)"
+    "print(np.linalg.solve(A,B))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb
index 12cde27..9990b04 100644
--- a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb	
+++ b/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb	
@@ -69,15 +69,6 @@
     "spy = np.log(spy['Last Close']).diff().dropna()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 43,
@@ -171,7 +162,7 @@
     "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n",
     "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n",
     "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n",
-    "print df"
+    "print(df)"
    ]
   },
   {
@@ -194,8 +185,7 @@
     "cov_matrix = np.corrcoef(beta_list)\n",
     "cov_df = pd.DataFrame(cov_matrix)\n",
     "plt.figure(figsize = (20,10))\n",
-    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")\n",
-    "plt.show()"
+    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")"
    ]
   },
   {
@@ -218,8 +208,7 @@
     "plt.figure(figsize = (20,10))\n",
     "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n",
     "plt.plot(KO.r.beta_df['beta'])\n",
-    "plt.plot(PG.r.beta_df['beta'])\n",
-    "plt.show()"
+    "plt.plot(PG.r.beta_df['beta'])"
    ]
   },
   {
@@ -263,20 +252,11 @@
    "source": [
     "x = sm.add_constant(PG.r.beta_df['beta'])\n",
     "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n",
-    "print coint.summary()\n",
+    "print(coint.summary())\n",
     "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n",
-    "print adf"
+    "print(adf)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -295,19 +275,9 @@
    ],
    "source": [
     "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df['sd_beta'],df['sd_beta_p'])\n",
-    "plt.show()"
+    "plt.scatter(df['sd_beta'],df['sd_beta_p'])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -333,21 +303,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

From e441f61e36575728869758edcc086e509f75bd77 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 09:22:22 -0400
Subject: [PATCH 007/753] Sync master

---
 .idea/deployment.xml | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 .idea/deployment.xml

diff --git a/.idea/deployment.xml b/.idea/deployment.xml
new file mode 100644
index 0000000..2f031c8
--- /dev/null
+++ b/.idea/deployment.xml
@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PublishConfigData">
+    <serverData>
+      <paths name="cloud_beta">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+      <paths name="www.beta">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+      <paths name="www.master">
+        <serverdata>
+          <mappings>
+            <mapping local="$PROJECT_DIR$" web="/" />
+          </mappings>
+        </serverdata>
+      </paths>
+    </serverData>
+  </component>
+</project>
\ No newline at end of file

From 1ae3b69e6cddf95217cdd1557a71241a1eeb0d04 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Thu, 24 May 2018 09:24:25 -0400
Subject: [PATCH 008/753] Delete deployment.xml

---
 .idea/deployment.xml | 28 ----------------------------
 1 file changed, 28 deletions(-)
 delete mode 100644 .idea/deployment.xml

diff --git a/.idea/deployment.xml b/.idea/deployment.xml
deleted file mode 100644
index 2f031c8..0000000
--- a/.idea/deployment.xml
+++ /dev/null
@@ -1,28 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PublishConfigData">
-    <serverData>
-      <paths name="cloud_beta">
-        <serverdata>
-          <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
-          </mappings>
-        </serverdata>
-      </paths>
-      <paths name="www.beta">
-        <serverdata>
-          <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
-          </mappings>
-        </serverdata>
-      </paths>
-      <paths name="www.master">
-        <serverdata>
-          <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
-          </mappings>
-        </serverdata>
-      </paths>
-    </serverData>
-  </component>
-</project>
\ No newline at end of file

From 698474fecef21abe9ef01ba0f6817912daba56fa Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 09:25:14 -0400
Subject: [PATCH 009/753] Update git ignore

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index cc92ca8..f2cf507 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
 */.ipynb_checkpoints/*
+.idea/
+Strategy Tutorials/Tutorial11 Fundamental Factor Long

From d70ff4ab6f222a3acc77703d4dd1f6d75121684f Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 09:26:31 -0400
Subject: [PATCH 010/753] Undo deletion

---
 .gitignore | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index f2cf507..e3b4353 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,2 @@
 */.ipynb_checkpoints/*
 .idea/
-Strategy Tutorials/Tutorial11 Fundamental Factor Long

From ecaae6082c089f4a3168b2482985edb436fc6790 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 10:07:17 -0400
Subject: [PATCH 011/753] Start reformatting ofthe tutorials

---
 01 API Tutorials/00 Overview/00 About.html    |   20 +
 .../00 Overview/01 What Will I learn %3F.html |    8 +
 .../00 Overview/02 Tutorials.html             |   46 +
 ...al00 Introduction to QuantConnect API.html |   81 -
 ...al01 Consolidating Data to Build Bars.html |   90 -
 ...Tutorial02 Desktop Charting with LEAN.html |   22 -
 ...03 How do I use the API File Provider.html |   16 -
 .../Tutorial04 Scheduled Events.html          |   70 -
 ...torial05 Tracking and Managing Orders.html |  138 --
 ...orial06 Using Options in QuantConnect.html |  246 ---
 .../images/data library screen shot.png       |  Bin 209426 -> 0 bytes
 ... Ranking Strategy on Dow 30 Companies.html |  126 --
 ...eversion and Momentum in Forex Market.html |  195 ---
 ...irs Trading - Copula vs Cointegration.html |  452 -----
 ...al04 The Dynamic Breakout II Strategy.html |   64 -
 ...orial05 Dual Thrust Trading Algorithm.html |   49 -
 ... Can Crude Oil Predict Equity Returns.html |   94 -
 ...orrelation and Cointegration Approach.html |  328 ----
 ...w Frequency Compoment of Forex Market.html |  182 --
 ...Strategy Based on Fundamental Factors.html |  212 ---
 ...hort-Term Reversal Strategy in Stocks.html |   81 -
 ...undamental Factor Long:Short Strategy.html |  109 --
 .../images/dual thrust price range.png        |  Bin 46470 -> 0 bytes
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 .../images/low-frequency-component-less.jpg   |  Bin 6037 -> 0 bytes
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 .../images/out-of-sample-trend-estimation.jpg |  Bin 6585 -> 0 bytes
 .../images/trading-signal-100.jpg             |  Bin 46559 -> 0 bytes
 .../images/trading-signal-1600.jpg            |  Bin 43407 -> 0 bytes
 .../Tutorial00 Applied Options.html           |  101 --
 .../Tutorial01 Covered Call.html              |   86 -
 .../Tutorial02 Bull Call Spread.html          |   75 -
 .../Tutorial03 Long Straddle.html             |   64 -
 .../Tutorial04 Long Strangle.html             |   77 -
 .../Tutorial05 Butterfly Spread.html          |  105 --
 .../Tutorial06 Iron Condor.html               |  110 --
 .../Tutorial07 Iron Butterfly.html            |  112 --
 .../Tutorial08 Protective Collar.html         |   85 -
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 ...Types and Data Structures-checkpoint.ipynb |  512 ------
 ...ical Operations and Loops-checkpoint.ipynb |  372 ----
 ...tive-Oriented Programming-checkpoint.ipynb |  418 -----
 ...04 NumPy and Basic Pandas-checkpoint.ipynb |  478 -----
 ...-Resampling and DataFrame-checkpoint.ipynb | 1540 -----------------
 ...Return, Mean and Variance-checkpoint.ipynb |  146 --
 ... Simple Linear Regression-checkpoint.ipynb |  267 ---
 ...ultiple Linear Regression-checkpoint.ipynb |  487 ------
 ...Tutorial11 Linear Algebra-checkpoint.ipynb |  234 ---
 ...2 Modern Portfolio Theory-checkpoint.ipynb |  417 -----
 ...pital Asset Pricing Model-checkpoint.ipynb |  325 ----
 ...rench Multi-Factor Models-checkpoint.ipynb |  320 ----
 ...al00 Introduction to Financial Python.html |  119 --
 ...rial01 Data Types and Data Structures.html |  220 ---
 ...ial01 Data Types and Data Structures.ipynb |  512 ------
 ...torial02 Logical Operations and Loops.html |  194 ---
 ...orial02 Logical Operations and Loops.ipynb |  372 ----
 ...ns and Objective-Oriented Programming.html |  194 ---
 ...s and Objective-Oriented Programming.ipynb |  418 -----
 .../Tutorial04 NumPy and Basic Pandas.html    |  229 ---
 .../Tutorial04 NumPy and Basic Pandas.ipynb   |  478 -----
 ...ial05 Pandas-Resampling and DataFrame.html |  678 --------
 ...al05 Pandas-Resampling and DataFrame.ipynb | 1540 -----------------
 ...l06 Rate of Return, Mean and Variance.html |  122 --
 ...06 Rate of Return, Mean and Variance.ipynb |  146 --
 ...07 Random Variable and Distributions.ipynb |  364 ----
 ...07 Random Variables and Distributions.html |  167 --
 ...dence Interval and Hypothesis Testing.html |  134 --
 ...ence Interval and Hypothesis Testing.ipynb |  219 ---
 .../Tutorial09 Simple Linear Regression.html  |  126 --
 .../Tutorial09 Simple Linear Regression.ipynb |  267 ---
 ...Tutorial10 Multiple Linear Regression.html |  320 ----
 ...utorial10 Multiple Linear Regression.ipynb |  487 ------
 .../Tutorial11 Linear Algebra.html            |  225 ---
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 .../Tutorial12 Modern Portfolio Theory.html   |  155 --
 .../Tutorial12 Modern Portfolio Theory.ipynb  |  431 -----
 ...utorial13 Capital Asset Pricing Model.html |   92 -
 ...torial13 Capital Asset Pricing Model.ipynb |  325 ----
 ...ial14 Fama-French Multi-Factor Models.html |   96 -
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 .../images/placeholder.txt                    |    0
 ...e and Construction of Neural Networks.html |    0
 ...nstruction of Support Vector Machines.html |    0
 ...tion of Market Signals by Naive Bayes.html |    0
 ...n Gain to Construct ID3 Decision Tree.html |    0
 ...d Class Prediction with Random Forest.html |    0
 ... and Making Predictions with AdaBoost.html |    0
 ...plementation of Q-Learning Algorithms.html |    0
 ...rend Analysis with Apriori Algorithms.html |    0
 ...assification with K-Nearest Neighbors.html |    0
 ...esses to Construct Genetic Algorithms.html |    0
 .../images/placeholder.txt                    |    0
 .../Tutorial00 Introduction to Options.html   |  115 --
 ...utorial01 General Features of Options.html |  128 --
 ...-Call Parity and Arbitrage Strategies.html |  158 --
 ...Call Parity and Arbitrage Strategies.ipynb |   99 --
 ...stic Processes and Monte Carlo Method.html |  180 --
 ...tic Processes and Monte Carlo Method.ipynb |  214 ---
 ...n Pricing Black Scholes Merton Model.ipynb |  106 --
 ...ns Pricing Black Scholes Merton Model.html |  140 --
 .../Tutorial06 The Greek Letters.html         |  229 ---
 .../Tutorial06 The Greek Letters.ipynb        |  318 ----
 ...cal Volatility and Implied Volatility.html |  192 --
 ...al Volatility and Implied Volatility.ipynb |  438 -----
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 Tutorial Series/README.md                     |  124 --
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 create mode 100644 01 API Tutorials/00 Overview/01 What Will I learn %3F.html
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 delete mode 100644 API Tutorials/Tutorial03 How do I use the API File Provider.html
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 delete mode 100644 API Tutorials/images/data library screen shot.png
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 delete mode 100644 Strategy Tutorials/Tutorial03 Pairs Trading - Copula vs Cointegration.html
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diff --git a/01 API Tutorials/00 Overview/00 About.html b/01 API Tutorials/00 Overview/00 About.html
new file mode 100644
index 0000000..8166e1e
--- /dev/null
+++ b/01 API Tutorials/00 Overview/00 About.html	
@@ -0,0 +1,20 @@
+<p>In this tutorial series we introduce the core concepts of the QuantConnect/LEAN API with the aim of answering the
+    commonly asked questions about using QuantConnect. </p>
+<p>The core of the documentation can be found on the <a href="/docs">documentation page</a>, but these videos should
+    give you an even deeper understanding. The QuantConnect underlying technology (LEAN) is written in C#, and most of
+    these videos are in C# -- however the API is virtually the same in Python and F#. If you're a Python user just click
+    the "Python" code snippet button on the documentation page to view the python examples.</p>
+<div class="row tutorials-page-about">
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+        <h3>6 Tutorials</h3>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+        <h3>6 Backtests</h3>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-code" aria-hidden="true"></i></p>
+        <h3>25 Code Snippets</h3>
+    </div>
+</div>
\ No newline at end of file
diff --git a/01 API Tutorials/00 Overview/01 What Will I learn %3F.html b/01 API Tutorials/00 Overview/01 What Will I learn %3F.html
new file mode 100644
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+++ b/01 API Tutorials/00 Overview/01 What Will I learn %3F.html	
@@ -0,0 +1,8 @@
+<div class="row tutorials-learn">
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Consolidating Data</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Desktop Charting</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using API File Provider</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Scheduling Events</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Order Management</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using Options</div>
+</div>
\ No newline at end of file
diff --git a/01 API Tutorials/00 Overview/02 Tutorials.html b/01 API Tutorials/00 Overview/02 Tutorials.html
new file mode 100644
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+++ b/01 API Tutorials/00 Overview/02 Tutorials.html	
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+<table class="table qc-table tutorials-table">
+    <tbody>
+    <tr onclick="window.document.location='/tutorials/consolidating-data-build-bars/';" class="clickable">
+        <td>1</td>
+        <td>
+            <h2>Consolidating Data to Build Bars</h2>
+            Create any bar size by combining data together with Consolidators.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/desktop-charting-with-lean/';" class="clickable">
+        <td>2</td>
+        <td>
+            <h2>Desktop Charting with LEAN</h2>
+            Setting up desktop charting with the open source algorithmic trading engine LEAN.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/use-api-file-provider/';" class="clickable">
+        <td>3</td>
+        <td>
+            <h2>Downloading Data with the API File Provider</h2>
+            Guide to downloading and interacting with your QuantConnect data repository.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/scheduled-events-lean/';" class="clickable">
+        <td>4</td>
+        <td>
+            <h2>Using Scheduled Events</h2>
+            Scheduling events to trigger code at specific times of day.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/tracking-and-managing-orders/';" class="clickable">
+        <td>5</td>
+        <td>
+            <h2>Tracking and Managing Orders</h2>
+            Simple guide to the basics of tracking and managing orders in QuantConnect.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect/';" class="clickable">
+        <td>6</td>
+        <td>
+            <h2>Using Options in QuantConnect</h2>
+            The key API methods for requesting option data financial and focusing in on specific contracts.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    </tbody>
+</table>
\ No newline at end of file
diff --git a/API Tutorials/Tutorial00 Introduction to QuantConnect API.html b/API Tutorials/Tutorial00 Introduction to QuantConnect API.html
deleted file mode 100644
index 9d1e092..0000000
--- a/API Tutorials/Tutorial00 Introduction to QuantConnect API.html	
+++ /dev/null
@@ -1,81 +0,0 @@
-<h2>About</h2>
-<p>In this tutorial series we introduce the core concepts of the QuantConnect/LEAN API with the aim of answering the commonly asked questions about using QuantConnect. </p>
-
-<p>The core of the documentation can be found on the <a href="/docs">documentation page</a>, but these videos should give you an even deeper understanding. The QuantConnect underlying technology (LEAN) is written in C#, and most of these videos are in C# -- however the API is virtually the same in Python and F#. If you're a Python user just click the "Python" code snippet button on the documentation page to view the python examples.</p>
-<div class="row tutorials-page-about">
-<div class="col-xs-12 col-md-4">
-<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-<h3>6 Tutorials</h3>
-</div><div class="col-xs-12 col-md-4">
-<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-<h3>6 Backtests</h3>
-</div><div class="col-xs-12 col-md-4">
-<p><i class="fa fa-code" aria-hidden="true"></i></p>
-<h3>25 Code Snippets</h3>
-</div>
-</div>
-
-<h2>What Will I learn ?</h2>
-<div class="row tutorials-learn">
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Consolidating Data</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Desktop Charting</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using API File Provider</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Scheduling Events</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Order Management</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using Options</div>
-</div>
-<h2>Tutorials</h2>
-<table class="table qc-table tutorials-table">
-<tbody>
-<tr onclick="window.document.location='/tutorials/consolidating-data-build-bars/';"  class="clickable" >
-<td>1</td>
-<td>
-<h2>Consolidating Data to Build Bars</h2>
-Create any bar size by combining data together with Consolidators.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-    
-
-<tr onclick="window.document.location='/tutorials/desktop-charting-with-lean/';"  class="clickable" >
-<td>2</td>
-<td>
-<h2>Desktop Charting with LEAN</h2>
-Setting up desktop charting with the open source algorithmic trading engine LEAN.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-
-<tr onclick="window.document.location='/tutorials/use-api-file-provider/';"  class="clickable" >
-<td>3</td>
-<td>
-<h2>Downloading Data with the API File Provider</h2>
-Guide to downloading and interacting with your QuantConnect data repository.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-
-<tr onclick="window.document.location='/tutorials/scheduled-events-lean/';"  class="clickable" >
-<td>4</td>
-<td>
-<h2>Using Scheduled Events</h2>
-Scheduling events to trigger code at specific times of day.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-
-<tr onclick="window.document.location='/tutorials/tracking-and-managing-orders/';"  class="clickable" >
-<td>5</td>
-<td>
-<h2>Tracking and Managing Orders</h2>
-Simple guide to the basics of tracking and managing orders in QuantConnect.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-
-<tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect/';"  class="clickable" >
-<td>6</td>
-<td>
-<h2>Using Options in QuantConnect</h2>
-The key API methods for requesting option data financial and focusing in on specific contracts.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-
-
-</tbody>
-</table>
\ No newline at end of file
diff --git a/API Tutorials/Tutorial01 Consolidating Data to Build Bars.html b/API Tutorials/Tutorial01 Consolidating Data to Build Bars.html
deleted file mode 100644
index 5d1de5b..0000000
--- a/API Tutorials/Tutorial01 Consolidating Data to Build Bars.html	
+++ /dev/null
@@ -1,90 +0,0 @@
-<code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
-
-Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized once. There are three key steps to create and register a consolidator:
-<ol>
- 	<li>Create the consolidator object.</li>
- 	<li>Bind an event handler to handle the new bars.</li>
- 	<li>Register it with the subscription manager to start receiving data.</li>
-</ol>
-In <b>C#</b> this looks like:
-<pre class="prettyprint linenums">public class ConsolidatorDemoAlgorithm : QCAlgorithm
-{
-	public override void Initialize()
-	{
-		// backtest parameters
-		SetStartDate(2016, 1, 1);
-		SetEndDate(DateTime.Now);
-
-		// cash allocation
-		SetCash(25000);
-
-		//assets or universe selection
-		AddEquity("SPY", Resolution.Minute);
-
-		//create a consolidator object; for tradebars; for a timespan of 30 minutes
-		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
-
-		//bind event handler to data consolidated event.
-		thirtyMinutes.DataConsolidated += OnHalfHour;
-
-		//register the consolidator for data.
-		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
-	}
-
-	//event handler for data!
-	public void OnHalfHour(object sender, TradeBar bar) {
-		Debug(Time.ToString("u") + " " + bar);
-	}
-
-	public override void OnData(Slice data)
-	{ }
-}</pre>
-In <b>Python</b>:
-<pre class="prettyprint linenums">from datetime import datetime, timedelta
-class DataConsolidationAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
-
-        self.SetStartDate(2016,1,1)  #Set Start Date
-        self.SetEndDate(datetime.now())   #Set End Date
-        # Find more symbols here: http://quantconnect.com/data
-        self.AddEquity("SPY", Resolution.Minute)
-
-        # define our 30 minute trade bar consolidator. we can
-        # access the 30 minute bar from the DataConsolidated events
-        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
-
-        # attach our event handler. The event handler is a function that will
-        # be called each time we produce a new consolidated piece of data.
-        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
-
-        # this call adds our 30-minute consolidator to
-        # the manager to receive updates from the engine
-        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
-
-    def ThirtyMinuteBarHandler(self, sender, bar):
-        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
-        self.Debug(str(self.Time) + " " + str(bar))
-    def OnData(self, data):
-        pass
-</pre>
-The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
-
-In <b>C#</b> this looks like:
-<pre class="prettyprint linenums">// From tick data sources
-var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
-
-//from renko bars
-var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
-</pre>
-
-There are two key points to remember:
-<ol>
- 	<li>Request a smaller resolution than what you want to produce.</li>
- 	<li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.</li>
-</ol>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.</p>
-https://www.youtube.com/watch?v=bbZy__qd1aA
-
-&nbsp;
diff --git a/API Tutorials/Tutorial02 Desktop Charting with LEAN.html b/API Tutorials/Tutorial02 Desktop Charting with LEAN.html
deleted file mode 100644
index ff69fd4..0000000
--- a/API Tutorials/Tutorial02 Desktop Charting with LEAN.html	
+++ /dev/null
@@ -1,22 +0,0 @@
-With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
-
-Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
-
-Two configuration changes are required for desktop charting to work:
-<ol>
- 	<li>Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file.
-<pre class="prettyprint">  "environment": "backtesting-desktop",</pre>
-</li>
- 	<li>Insert your API token and user id into configuration file. This is required for chart streaming.
-<pre class="prettyprint">  // To get your api access token go to quantconnect.com/account
-  "job-user-id": "....",
-  "api-access-token": "...........",</pre>
-</li>
-</ol>
-With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
-
-If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
-
-In the tutorial video below we demonstrate this feature on LEAN:
-
-https://www.youtube.com/watch?v=m6llfznP4d4
\ No newline at end of file
diff --git a/API Tutorials/Tutorial03 How do I use the API File Provider.html b/API Tutorials/Tutorial03 How do I use the API File Provider.html
deleted file mode 100644
index fbaf5a9..0000000
--- a/API Tutorials/Tutorial03 How do I use the API File Provider.html	
+++ /dev/null
@@ -1,16 +0,0 @@
-Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually; but using the API File Provider we provide a way for you to automatically install this into your LEAN project.
-
-Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.
-
-<img src="images/data library screen shot.png" alt="Data library collection for FXCM EURUSD data" />
-
-Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.
-
-This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:
-<ol>
- 	<li>Insert your <code>job-user-id</code> and <code>api-access-token</code> into the relevant config fields. You can find these on your <a href="/account">Account</a> page.</li>
- 	<li>Update the <code>data-provider</code> configuration to refer to the APIDataProvider class. This is called <code>QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider</code>.</li>
-</ol>
-Check out the video below where we guide you step by step through how to use the API Data Provider.
-
-https://www.youtube.com/watch?v=uyHp_jyeSpA
\ No newline at end of file
diff --git a/API Tutorials/Tutorial04 Scheduled Events.html b/API Tutorials/Tutorial04 Scheduled Events.html
deleted file mode 100644
index 9237c12..0000000
--- a/API Tutorials/Tutorial04 Scheduled Events.html	
+++ /dev/null
@@ -1,70 +0,0 @@
-Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
-
-The scheduling is set with two rules: the <code>DateRules</code> and <code>TimeRules</code> classes. The schedule manager takes one of each and executes an action code block.
-In C# this looks like:
-<pre class="prettyprint linenums">Schedule.On(DateRules.On(2013, 10, 7), TimeRules.At(13, 0), () =&gt;
-{
-         Debug("Specific Time: Fired at : " + Time);
-});</pre>
-Or in Python:
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.On(2013, 10, 7), self.TimeRules.At(13, 0), Action(self.SpecificTime))
-def SpecificTime(self):
-    self.Log("SpecificTime: Fired at : {0}".format(self.Time))</pre>
-The date rules can be specified as below:
-<table class="table table-striped qc-table">
-<thead>
-<tr>
-<th colspan="2">Scheduled Events Date Rules</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="50%"><code>DateRules.On(2013, 10, 7)</code></td>
-<td>On a specific date</td>
-</tr>
-<tr>
-<td><code>DateRules.EveryDay("SPY")</code></td>
-<td>Every trading day for SPY</td>
-</tr>
-<tr>
-<td><code>DateRules.Every(DayOfWeek.Monday, ...)</code></td>
-<td>On specific days of week</td>
-</tr>
-<tr>
-<td><code>DateRules.EveryDay()</code></td>
-<td>Every day</td>
-</tr>
-<tr>
-<td><code>DateRules.MonthStart("SPY")</code></td>
-<td>SPY trading start of each month</td>
-</tr>
-</tbody>
-</table>
-The time rules trigger specify when on the day the event should be triggered. They can be specified as below:
-<table class="table table-striped qc-table">
-<thead>
-<tr>
-<th colspan="2">Scheduled Events Time Rules</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="50%"><code>TimeRules.At(13, 0)</code></td>
-<td>At a specific time, 1:00pm</td>
-</tr>
-<tr>
-<td><code>TimeRules.AfterMarketOpen("SPY", 10)</code></td>
-<td>Run 10 minutes after market open for SPY</td>
-</tr>
-<tr>
-<td><code>TimeRules.BeforeMarketClose("SPY", 10)</code></td>
-<td>Run 10 minutes before market close for SPY</td>
-</tr>
-<tr>
-<td><code>TimeRules.Every(TimeSpan.FromMinutes(10))</code></td>
-<td>Every 10 minutes during day specified</td>
-</tr>
-</tbody>
-</table>
-For a demonstration see the tutorial video below:
-https://youtu.be/SUu4MKlm93I
diff --git a/API Tutorials/Tutorial05 Tracking and Managing Orders.html b/API Tutorials/Tutorial05 Tracking and Managing Orders.html
deleted file mode 100644
index bd19104..0000000
--- a/API Tutorials/Tutorial05 Tracking and Managing Orders.html	
+++ /dev/null
@@ -1,138 +0,0 @@
-<h2>Overview</h2>
-Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
-
-When you place a trade you receive an <code>OrderTicket</code> for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
-
-You can place several types of orders including:
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Supported Order Types</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="25%">Market Order</td>
-<td><code>MarketOrder("SPY", 100);</code></td>
-</tr>
-<tr>
-<td>Limit Order</td>
-<td><code>var ticket = LimitOrder("SPY", 100, 100.10m);</code></td>
-</tr>
-<tr>
-<td>Stop Market Order</td>
-<td><code>var ticket = StopMarketOrder("SPY", 100, 100.10m);</code></td>
-</tr>
-<tr>
-<td>Stop Limit Order</td>
-<td><code>var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code></td>
-</tr>
-<tr>
-<td>Market On Open Order</td>
-<td><code>var ticket = MarketOnOpen("SPY", 100);</code></td>
-</tr>
-<tr>
-<td>Market On Close Order</td>
-<td><code>var ticket = MarketOnClose("SPY", 100);</code></td>
-</tr>
-</tbody>
-</table>
-<h2>Updating Orders</h2>
-Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
-<pre class="prettyprint linenums">var currentStopPrice = _ticket.Get(OrderField.StopPrice);</pre>
-Or update the order fields (LimitPrice, StopPrice, Tag or Quantity):
-<pre class="prettyprint linenums">_ticket.Update(new UpdateOrderFields
-{
-    LimitPrice = newLongLimit,
-    Tag = "Update #" + (longOrder.UpdateRequests.Count + 1)
-});</pre>
-You can also cancel your order if required:
-<pre class="prettyprint linenums">_ticket.Cancel();</pre>
-In <b>Python</b>, you can update order like this:
-<pre class="prettyprint linenums"># Retrive the 'StopPrice' for an order from the ticket
-currentStopPrice = _ticket.Get(OrderField.StopPrice)
-# update the order fields (LimitPrice, StopPrice, Tag or Quantity)
-updateOrderFields = UpdateOrderFields()
-updateOrderFields.LimitPrice = newLongLimit
-updateOrderFields.Tag = "Update #{0}".format(len(_ticket.UpdateRequests) + 1)
-_ticket.Update(updateOrderFields)
-# cancel your order if required
-_ticket.Cancel()
-</pre>
-In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
-https://www.youtube.com/watch?v=HykXfstdNW0
-
-<h2>Full Python Example</h2>
-This is a complete port from C# to Python of this Quick Start Lesson.
-<pre class="prettyprint linenums">
-```Lesson 6 - Tracking and Managing Orders: 
-Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
-```
-import numpy as np
-from decimal import Decimal
-
-class BasicTemplateAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        self._limitTicket = None
-        self._stopMarketTicket = None
-        self._stopLimitTicket = None
-        self.SetCash(25000)
-        self.SetStartDate(2009,1,1)
-        self.SetEndDate(2009,6,1)
-        self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
-        
-    def OnData(self, slice):
-        if self._limitTicket is None:
-        
-            self.MarketOrder(self.spy, 100)
-            
-            self._limitTicket = self.LimitOrder(
-                self.spy, 
-                100, 
-                slice["SPY"].Close * Decimal(0.9), 
-                "limit order")
-                
-            self._stopMarketTicket = self.StopMarketOrder(
-                self.spy,
-                -100,
-                slice["SPY"].Close * Decimal(0.95),
-                "stop market")
-                
-            self._stopLimitTicket = self.StopLimitOrder(
-                self.spy,
-                -100,
-                slice["SPY"].Close * Decimal(0.9),
-                slice["SPY"].Close * Decimal(0.8),
-                "stop market")
-
-
-    def OnEndOfDay(self):
-        ```End of each day, plot our asset & order prices```
-        if self._limitTicket is None:
-            return
-        
-        self.Plot("Order Tickets", "SPY", self.Portfolio['SPY'].Price)
-        
-        if self._stopMarketTicket.Status != OrderStatus.Filled:
-            self.Plot("Order Tickets", "Stop Price", 
-                      self._stopMarketTicket.Get(OrderField.StopPrice))
-            
-        if self._stopLimitTicket.Status != OrderStatus.Filled:
-            self.Plot("Order Tickets", "Limit Price", 
-                      self._stopLimitTicket.Get(OrderField.LimitPrice))
-
-    def OnOrderEvent(self, OrderEvent):
-        ```Event when the order is filled. Debug log the order fill. :OrderEvent:``` 
-        
-        if OrderEvent.FillQuantity == 0:
-            return
-        
-        fetched = self.Transactions.GetOrderById(OrderEvent.OrderId)
-        
-        self.Debug("{} was filled. Symbol: {}. Quantity: {}. Direction: {}"
-                   .format(str(fetched.Type), 
-                           str(OrderEvent.Symbol), 
-                           str(OrderEvent.FillQuantity), 
-                           str(OrderEvent.Direction)))
-</pre>
diff --git a/API Tutorials/Tutorial06 Using Options in QuantConnect.html b/API Tutorials/Tutorial06 Using Options in QuantConnect.html
deleted file mode 100644
index 93086b9..0000000
--- a/API Tutorials/Tutorial06 Using Options in QuantConnect.html	
+++ /dev/null
@@ -1,246 +0,0 @@
-QuantConnect provides US options trade and quotes price data for approximately 4000 symbols, each of which has roughly 10 strikes on average. Data is available starting January 1st, 2010. In this tutorial, we will discuss how to use QuantConnect to start your options trading algorithm.
-<h2> Step 1: Add Options</h2>
-Before trading options, you need to add options for a given underlying equity and set the resolution in step <code>Initialize</code> with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Method</th>
-<th>Parameters</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
-resolution,
-fillDataForward)</td>
-<td><strong>underlying(string):</strong> The underlying equity symbol
-<strong>resolution:</strong> Tick, Second, Minute, Hour, or Daily. Default is minute
-<strong>fillDataForward(bool):</strong> If true, returns the last available data even if none in that time slice. The default value is true.</td>
-</tr>
-</tbody>
-</table>
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2017, 01, 01)  #Set Start Date
-    self.SetEndDate(2017, 06, 30)  #Set End Date
-    self.SetCash(50000)  #Set Strategy Cash
-    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
-    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
-    self.symbol = option.Symbol
-</pre>
-The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
-<h2>Step 2: Filter Contracts - Coarse Selection</h2>
-After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Method</th>
-<th>Parameters</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a>(
-min strike,
-max strike,
-minexpiry,
-maxExpiry)</td>
-<td><strong>min Strike, max Strike:</strong> The min and max strike rank relative to market price
-<b><b>min Expiry, max Expiry: </b></b>The range of time to expiration to include, for example, TimeSpan.FromDays(10) would exclude contracts expiring in less than 10 days</td>
-</tr>
-</tbody>
-</table>
-Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
-
-For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
-
-For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.
-<pre class="prettyprint linenums"># filter the contracts with strikes between (market price - 10, market price + 10)
-option.SetFilter(-10,10)
-# filter the contracts which expires more than 30 days but no longer than 60 days
-option.SetFilter(TimeSpan.FromDays(30),TimeSpan.FromDays(60))
-# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
-option.SetFilter(-10, +10, timedelta(0), timedelta(180))</pre>
-<div class="page" title="Page 242">
-<div class="layoutArea">
-<div class="column">
-<h2>Step 3: Choose Contracts - Fine Selection</h2>
-For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
-
-<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
-
-<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
-
-<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Properties of Option Contract x</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><code>x.Symbol.Value</code></td>
-<td> Get the string of option contract's symbol</td>
-</tr>
-<tr>
-<td><code>x.AskPrice, x.BidPrice</code></td>
-<td> Get the ask price,  Get the bid price</td>
-</tr>
-<tr>
-<td><code>x.Expiry</code></td>
-<td> Get the expiration date</td>
-</tr>
-<tr>
-<td><code>x.Strike</code></td>
-<td> Get the strike price</td>
-</tr>
-<tr>
-<td><code>x.ImpliedVolatility</code></td>
-<td> Get the implied volatility</td>
-</tr>
-<tr>
-<td><code>x.Greeks</code></td>
-<td>Get the Greeks letter</td>
-</tr>
-<tr>
-<td><code>x.Right</code></td>
-<td>Get the right being purchased
-x.Right ＝ OptionRight.Call  call option[right to buy]
-x.Right ＝ OptionRight.Put  put option[right to sell]</td>
-</tr>
-<tr>
-<td><code>x.UnderlyingLastPrice</code></td>
-<td>Get the last price the underlying security traded at</td>
-</tr>
-<tr>
-<td><code>x.UnderlyingSymbol</code></td>
-<td>Gets the underlying security's symbol</td>
-</tr>
-</tbody>
-</table>
-We can print out the details of the contract after filtering with Python data frame to show these properties. Assume today is 01/03/2017.  The stock price at 01/03/2017 09:31:00 is $776.01 per share. Here we use  <code>SetFilter(-1, +1, timedelta(0), timedelta(60)) </code>to filter the contracts.
-<pre class="prettyprint linenums">def OnData(self,slice):
-    for i in slice.OptionChains:
-        if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
-</pre>
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Symbol</th>
-<th>type(call 0, put 1)</th>
-<th>Strike</th>
-<th>Expiry</th>
-<th>Ask Price</th>
-<th>Bid Price</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>GOOG 170217C00780000</td>
-<td>0</td>
-<td>780.0</td>
-<td>2017-02-17</td>
-<td>26.4</td>
-<td>27.9</td>
-</tr>
-<tr>
-<td>GOOG 170120P00782500</td>
-<td>1</td>
-<td>782.5</td>
-<td>2017-01-20</td>
-<td>14.7</td>
-<td>16.3</td>
-</tr>
-<tr>
-<td>GOOG 170120C00782500</td>
-<td>0</td>
-<td>782.5</td>
-<td>2017-01-20</td>
-<td>9.4</td>
-<td>10.2</td>
-</tr>
-<tr>
-<td>GOOG 170120P00780000</td>
-<td>1</td>
-<td>780.0</td>
-<td>2017-01-20</td>
-<td>13.4</td>
-<td>15.0</td>
-</tr>
-<tr>
-<td>GOOG 170120C00780000</td>
-<td>0</td>
-<td>780.0</td>
-<td>2017-01-20</td>
-<td>10.6</td>
-<td>11.5</td>
-</tr>
-<tr>
-<td>GOOG 170217P00780000</td>
-<td>1</td>
-<td>780.0</td>
-<td>2017-02-17</td>
-<td>28.9</td>
-<td>30.8</td>
-</tr>
-<tr>
-<td>GOOG 170120P00777500</td>
-<td>1</td>
-<td>777.5</td>
-<td>2017-01-20</td>
-<td>12.2</td>
-<td>13.7</td>
-</tr>
-<tr>
-<td>GOOG 170120C00777500</td>
-<td>0</td>
-<td>777.5</td>
-<td>2017-01-20</td>
-<td>11.8</td>
-<td>12.9</td>
-</tr>
-</tbody>
-</table>
-&nbsp;
-
-Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
-<pre class="prettyprint linenums">for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
-</pre>
-Finally, we trade the options by using the contract's symbol.
-<pre class="prettyprint linenums">if len(contracts) == 0: continue
-# trade the contracts with the farthest expiration
-symbol = contracts[0].Symbol
-self.MarketOrder(symbol, 1)
-self.MarketOnCloseOrder(symbol, -1)
-
-</pre>
-<h2>Algorithm</h2>
-This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c4467dd2b39f215cd685caaac9069ef3'></script>
-<h2>Summary</h2>
-After mastering the basic knowledge of options market, this tutorial we take a close at how to use Quantconnect to customize your own options trading. For example, <span lang="EN-GB">how you can access an option chain, </span><span lang="EN-GB">how to view the details of the contract as a Python data frame, and the most important how to trade the specific option contract.</span>
-
-Next chapter we will examine some important topics of options like the payoff, Put-Call parity, and the synthetic positions. By learning all those concepts, we will start some brief hedging strategies involving options.
-
-</div>
-</div>
-</div>
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diff --git a/Strategy Tutorials/Tutorial01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html b/Strategy Tutorials/Tutorial01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html
deleted file mode 100644
index 26e26e9..0000000
--- a/Strategy Tutorials/Tutorial01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html	
+++ /dev/null
@@ -1,126 +0,0 @@
-<h2>Abstract</h2>
-This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM) [ref]<a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>[/ref], a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
-<ol>
-	<li>How to use historical data</li>
-	<li>Set an event handler</li>
-	<li>Conduct linear regression</li>
-	<li>Build your own functions in the QuantConnect Algorithm Lab</li>
-</ol>
- The implementation of the strategy demonstrates that stocks beat the market last month are likely to beat the market again in the subsequent month. This algorithm performs well when the market is smooth. However when the market volatility increases the model fails to capture alpha and it performs poorly. What we learn from this is that market fluctuations decrease the significance level of the linear regression coefficients, especially when we are using daily returns to fit the model.
-<h2>CAPM Theory</h2>
-The capital asset pricing model (CAPM) describes the relationship between systematic risk and expected return for assets, typically stocks. The formula for calculating the expected return of an asset given its risk is as follows:
-
-\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \]
-
-where:
-
-\[r_f = Risk Free Rate\]
-\[\beta = Beta of the security\]
-
-\[r_m = Expected market return\]
-
-\[\epsilon = Tracking error\]
-
-This formula can be better understood if we refactor the formula as seen below:
-
-\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \]
-
-The left side of the equation gives us the difference between the asset return and risk free rate, the <strong>"excess return"</strong>. If we regress the <strong>market excess return</strong> against the <strong>asset excess return </strong>the slope represents the <strong>"beta"</strong> of the asset. Therefore, beta can also be calculated by the equation:
-
-\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\]
-
-So beta can be described as:
-
-\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\]
-
-The formula above indicates that beta can be explained as<strong> "correlated relative volatility"</strong>. To make this simpler, beta can be calculated by doing a simple linear regression which can be viewed as a factor to explain the return, and the tracking error can represent alpha.
-
-To make this theory more convenient for our algorithm, we change the above formula into the following form:
-
-\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\]
-
-<strong>r*(1-β) </strong>on the right hand side of the equation is a very small item, making it negligible in the context of the Dow 30 companies. If we regress the stocks return with the return of the benchmark, the slope and intercept will be beta and alpha.
-
-<h2>Method</h2>
-Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:
-<ol>
- 	<li>Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.</li>
- 	<li>Conduct simple linear regression on the return of each stock against a benchmark (S&amp;P 500 index, SPY).</li>
- 	<li>Rank the stocks by their intercepts.</li>
- 	<li>Liquidate all our positions and purchase the first 2 stocks in our sorted list.</li>
-</ol>
-Dow Jones components change very infrequently, with the last change being on March 19th, 2015 [ref]<strong> <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a></strong>[/ref]. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
-<h3>Step 1: Setup Event Handler</h3>
-In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))</pre>
-<h3>Step 2: Linear Regression Function</h3>
-In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope.
-<pre class="prettyprint linenums">def regression(self,x,y):
-    x = np.array(x)
-    x = np.diff(x)/x[:-1]
-    y = np.array(y)
-    y = np.diff(y)/y[:-1]
-    A = np.vstack([x, np.ones(len(x))]).T
-    result = np.linalg.lstsq(A, y)[0]
-    beta = result[0]
-    alpha = result[1]
-    return(alpha,beta)</pre>
-<h3>Step 3: History Function</h3>
-Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
-<pre class="prettyprint linenums">def get_regression_data(self,symbol,history):
-    symbol_price = []
-    benchmark_price = []
-    for i in history:
-        bar = i[symbol]
-        benchmark = i[self.benchmark]
-        symbol_price.append(bar.Close)
-        benchmark_price.append(benchmark.Close)
-
-    result = self.regression(symbol_price,benchmark_price)
-    return result</pre>
-<h3>Step 4: Rebalance Function:</h3>
-This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: <a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>.
-<pre class="prettyprint linenums">def rebalance(self):
-    # get historical stock symbols and prices, then put them in tuples
-    history = self.History(self.regression_dates, Resolution.Daily)
-    filter = []
-    for i in self.symbols:
-        filter.append((i,self.get_regression_data(i, history)[0]))
-    # sort the filter by alpha
-    filter.sort(key = lambda x : x[1],reverse = True)
-    sorted_symbols = []
-    for i in range(2):
-    	sorted_symbols.append(filter[i][0])
-    # get the symbols of our holding stocks
-    holding_list = []
-    for i in self.Portfolio:
-    	if i.Value.Invested:
-    		holding_list.append(i.Value.Symbol)
-    # if we have holdings and we are not going to hold them anymore, sell them
-    if holding_list:
-    	for i in holding_list:
-    		if i not in sorted_symbols:
-    			self.Liquidate(i)
-    # Long the 2 stock in our list.
-    for i in sorted_symbols:
-    	self.SetHoldings(i,1)</pre>
-<h2>Conclusion</h2>
-We have demonstrated that during a smooth market, the stocks that beat the market last month are likely to beat the market again in the subsequent month. When there is market fluctuation, the significance level of linear regression will reduce and the model performance will decrease.
-
-We can understand this by looking at the covariance of the asset(x) and the benchmark (y). As the covariance reduces to zero, the beta will decrease.
-
-\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \b{x})^2}\]
-
-As an experiment, we tested the algorithm on market data from 2015. This was a much more volatile period for the market with a fluctuation that returned a mean close to zero and dropped neaerly 10% from Aug 18th to Aug 25th of that year. The algorithm performed quite poorly in this year with a return rate of -11.58%.
-
-The risks associated with this strategy include a high drawdown, lack of hedging and not stop-loss. Since we are using leverage, the risk is increased and it has a margin call in January as a result.
-
-We can improve the performance by applying the following techniques:
-
-<ol>
-	<li>Conduct optimizations: we can implement mean-variance analysis to determine the asset allocation each month and select more stocks to trade. This will lower our risk and manage the portfolio more scientifically.</li>
-	<li>Take beta into consideration: If we want to be more aggressive, we can select targets by a combination of alpha and beta. This means we choose stocks with a high alpha that are more volatile than the market. If we are conservative investors however, we can make the strategy market-neutral, which means the portfolio would not be affected by the market performance. For example, if we long two stocks with beta 1 and -1 respectively at the same position size, our portfolio becomes market-neutral.</li>
-</ol>
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=f49f71017808fe1b55d1f274e5baa012&bm='></script>
diff --git a/Strategy Tutorials/Tutorial02 Combining Mean Reversion and Momentum in Forex Market.html b/Strategy Tutorials/Tutorial02 Combining Mean Reversion and Momentum in Forex Market.html
deleted file mode 100644
index 9c2d533..0000000
--- a/Strategy Tutorials/Tutorial02 Combining Mean Reversion and Momentum in Forex Market.html	
+++ /dev/null
@@ -1,195 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research [ref]<strong>Alina F. Serban,</strong> Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>[/ref] which was based on research in the equity market by Ronald J. Balvers and Yangru Wu [ref]<strong>Ronald J. Balvers, Yangru Wu,</strong> Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
-
-In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity, UIP [ref]<strong>Investopedia</strong>, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>[/ref]). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang [ref]<strong>Chiang, T., Jiang, C., 1995. </strong>Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref].
-
-We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
-<h2>Introduction</h2>
-The strategy is centered on <strong>uncovered interest parity (UIP)</strong> theory. UIP states that the change in the exchange rate should incorporate any interest rate differentials between the two currencies. By looking for patterns in the deviation from UIP we can potential generate abnormal returns.
-<h3>Interest Parity Conditions</h3>
-UIP states that an investor who borrows money in their home country and lends it in another country with a higher interest rate should expect a zero return due to the changes in exchange rate. In other words:
-
-\[1+r_t = (1+r^{i}_t)(\frac{F^{i}_t}{S^{i}_t})\]
-Where \(r_t\) is the domestic interest rate, \(r^{i}\) is the foreign interest rate, \(S^{i}\) is the spot exchange rate and \(F^{i}\) is the forward rate. We can also replace F forward rate with expected spot rate:
-
-\[1+r_t = (1+r^{i}_t)(\frac{E(S^{i}_{t+1})}{S^{i}_t})\]
-
-Taking logs of the above two equations, we obtain:
-
-\[r_t - r^{i}_t = \ln F^{i}_t - \ln S^{i}_t\]
-
-\[r_t - r^{i}_t =\ln S^{i}_{t+1} - \ln S^{i}_t\]
-
-The deviation from UIP is denoted by y and defined as follows:
-
-\[y^{i}_{t+1} =\ln S^{i}_{t+1} -\ln F^{i}_t\]
-<h3>Model and Parameter Estimation</h3>
-Fama and French and Summers [ref]<strong>Fama, E., 1984. </strong>Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.[/ref] constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
-
-\[x^{i}_t = (1 - \delta ^{i})\mu ^{i} + \delta ^{i}x^{i}_{t-1} + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
-
-Using the equation above Serban adapts it to find the abnormal return in the forex market. The \(\delta \) represents the speed of mean reversion and can differ by country, while the \(\rho \) represents the momentum strength and can vary by country and by lag. The parameter \(\mu \) also varies by country. Accounting for these changes:
-
-\[y^{i}_t = -(1 - \delta ^{i})(x^{i}_{t-1} - \mu ^{i}) + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
-<h3>Trading Strategy</h3>
-The trading strategy from the paper allows \(\mu \) to change by country, while let \(\rho \) and \(\delta \) stay fixed. By applying Ordinary Least Squared(OLS) regression, the model estimates the return y for each currency. We construct the portfolio by taking a long position on the currency with the highest expected return and taking a short position on the currency with the lowest expected return. We hold these positions for one month, and repeat the process each month. There are two exceptions to this strategy: if all expected returns are positive, we take a long position only, and vice versa.
-
-To limit the number of parameters we need to estimate and find a solution easily we only allow \(\mu \) to change by country. According to the paper if we let ρ stay fixed and J =3, we can obtain the highest return for this strategy. If so the equation can be simplified as:
-
-\[y^{i}_t = -(1-\delta )(x^{i}_{t-1} - \mu ^{i}) + \rho (x^{i}_{t-1} - x^{i}_{t-4}) + \epsilon ^{i}_t\]
-
-When applying the above equation, we found that the scale of the mean reversion for each currency are very different, and this difference in scale is large enough to affect the accuracy of our rank. We made an adjustment to standardize the mean-reversion. While calculating \(\mu \) (the mean of the log prices) we also calculated standard deviation σ. In this tutorial we replace \(x-\mu \) with \(\frac{x - \mu}{\sigma } \). This captures the mean reversion factor better than the author's technique.
-<h3>Data Description</h3>
-The paper used monthly exchange rate data for the Canadian Dollar/USD, German Mark/Euro, UK Pound/USD and Japanese Yen/USD, from 1978 to 2008. Due to data availability, we used Euro/USD instead of German Mark/Euro, and the earliest data available starts from 2004. Each time we launch the strategy we use all of the available historical data prior to the start date to build the OLS model and uses that model for the entire backtest. The paper used 1/3 of their data as the training dataset and the rest of the test set. We directly test our model on backtesting, because QuantConnect makes this easier.
-<h2>Method</h2>
-In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution.
-<h3>Step 1: Request Historical Data</h3>
-The first function takes two arguments: symbol and number of daily data points requested. This function requests historical <a href="https://www.quantconnect.com/docs#Consolidating-Data-TradeBars-vs-QuoteBars">QuoteBars</a> and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">DataFrame</a>. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
-<pre class="prettyprint linenums">def get_history(self,symbol, num):
-    data = {}
-    dates = []
-    history = self.History([symbol], num, Resolution.Daily).loc[symbol]['close'] #request the historical data for a single symbol
-    for time in history.index:
-        t = time.to_pydatetime().date()
-        dates.append(t)
-    dates = pd.to_datetime(dates)
-    df = pd.DataFrame(history)
-    df.reset_index(drop=True)
-    df.index = dates
-    df.columns = ['price']
-    return df
-
-def calculate_return(self,df):
-    #calculate the mean for further use
-    mean = np.mean(df.price)
-    # cauculate the standard deviation
-    sd = np.std(df.price)
-    # pandas method to take the last datapoint of each month.
-    df = df.resample('BM',how = lambda x: x[-1])
-    # the following three lines are for further experiment purpose
-    # df['j1'] = df.price.shift(1) - df.price.shift(2)
-    # df['j2'] = df.price.shift(2) - df.price.shift(3)
-    # df['j3'] = df.price.shift(3) - df.price.shift(4)
-    # take the return as depend variable
-    df['log_return'] = df.price - df.price.shift(1)
-    # calculate the reversal factor
-    df['reversal'] = (df.price.shift(1) - mean)/sd
-    # calculate the momentum factor
-    df['mom'] = df.price.shift(1) - df.price.shift(4)
-    df = df.dropna() #remove nan value
-    return (df,mean,sd)</pre>
-<h3>Step 2: Build Predictive Model</h3>
-The concat function requests history and joins the results into a single DataFrame. As \(\mu \) varies by country so we assign the mean and standard deviation to the symbol for each currency for future use. The OLS function takes the resulting DataFrame to conduct an OLS regression. We write it into a function because it's easier to change the formula here if we need.
-<pre class="prettyprint linenums">def concat(self):
-    # we requested as many daily tradebars as we can
-    his = self.get_history(self.quoted[0].Value,20*365)
-    # get the clean DataFrame for linear regression
-    his = self.calculate_return(his)
-    # add property to the symbol object for further use.
-    self.quoted[0].mean = his[1]
-    self.quoted[0].sd = his[2]
-    df = his[0]
-    # repeat the above procedure for each symbols, and concat the dataframes
-    for i in range(1,len(self.quoted)):
-        his = self.get_history(self.quoted[i].Value,20*365)
-        his = self.calculate_return(his)
-        self.quoted[i].mean = his[1]
-        self.quoted[i].sd = his[2]
-        df = pd.concat([df,his[0]])
-    df = df.sort_index()
-    # remove outliers that outside the 99.9% confidence interval
-    df = df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() &lt; 3).all(axis=1)]
-    return df
-
-def OLS(self,df):
-    res = sm.ols(formula = 'return ~ reversal + mom',data = df).fit()
-    return res</pre>
-<h3>Step 3: Apply Predictive Model</h3>
-The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
-<pre class="prettyprint linenums">def predict(self,symbol):
-    # get current month in string
-    month = str(self.Time).split(' ')[0][5:7]
-    # request the data in the last three months
-    res = self.get_history(symbol.Value,33*3)
-    # pandas method to take the last datapoint of each month
-    res = res.resample('BM',how = lambda x: x[-1])
-    # remove the data points in the current month
-    res = res[res.index.month != int(month)]
-    # calculate the variables
-    res = self.calculate_input(res,symbol.mean,symbol.sd)
-    res = res.ix[0]
-    # take the coefficient. The first one will not be used for sum-product because it's the intercept
-    params = self.formula.params[1:]
-    # calculate the expected return
-    re = sum([a*b for a,b in zip(res[1:],params)]) + self.formula.params[0]
-    return re
-
-def calculate_input(self, df, mean, sd):
-    df['reversal'] = (df.price - mean)/sd
-    df['mom'] = df.price - df.price.shift(3)
-    df = df.dropna()
-    return df
-</pre>
-There are a few points of note:
-<ol>
- 	<li>We need historical TradeBars for the last three months. To do this we requested 99 bars and use a pandas DataFrame to extract a data point for the end of each month.</li>
- 	<li>We use event schedule to execute the strategy at the first trading day, however, sometimes the first day of the month could be on the 2nd if the 1st falls on a weekend. To fix this we remove the data from the current month, leaving only the last 3 months of data.</li>
- 	<li>We start from the second element of res (res[1:]) because res and params are different lengths. This was hard to detect because Python would not throw error when running [a*b for a,b in zip(res,params)] even if the length of the two lists are different.</li>
- 	<li>This function also used pandas DataFrame methods extensively. For more information please refer to <a href="http://pandas.pydata.org">pandas</a>.</li>
-</ol>
-<h3>Step 4: Initializing the Model</h3>
-In the Initialize function we prepare the data and conduct a linear regression. The class property 'self.formula' is the result of the OLS regression. We will use this object each time we rebalance the portfolio.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2013,6,1)
-    self.SetEndDate(2016,6,1)
-    self.SetCash(10000)
-    self.syls = ['EURUSD','GBPUSD','USDCAD','USDJPY']
-    self.quoted = []
-    for i in range(len(self.syls)):
-        self.quoted.append(self.AddForex(self.syls[i],Resolution.Daily,Market.Oanda).Symbol)
-    df = self.concat()
-    self.Log(str(df))
-    self.formula = self.OLS(df)
-    self.Log(str(self.formula.summary()))
-    self.Log(str(df))
-    self.Log(str(df.describe()))
-    for i in self.quoted:
-        self.Log(str(i.mean) + '   ' + str(i.sd))
-    self.Schedule.On(self.DateRules.MonthStart(), self.TimeRules.At(9,31), Action(self.action))</pre>
-<h3>Step 5: Performing Monthly Rebalancing</h3>
-Every month we rebalance the portfolio using the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Schedule Event</a> helper method. The predicted returns are added to the rank array and then sorted by return. The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
-<pre class="prettyprint linenums">def action(self):
-    rank = []
-    long_short = []
-    for i in self.quoted:
-        rank.append((i,self.predict(i)))
-# rank the symbols by their expected return
-    rank.sort(key = lambda x: x[1],reverse = True)
-# the first element in long_short is the one with the highest expected return, which we are going to long, and the second one is going to be shorted.
-    long_short.append(rank[0])
-    long_short.append(rank[-1])
-    self.Liquidate()
-
-# the product &lt; 0 means the expected return of the first one is positive and that of the second one is negative--we are going to long and short.
-    if long_short[0][1]*long_short[1][1] &lt; 0:
-        self.SetHoldings(long_short[0][0],1)
-        self.SetHoldings(long_short[1][0],-1)
-        # this means we long only because all of the expected return is positive
-    elif long_short[0][1] &gt; 0 and long_short[1][1] &gt; 0:
-        self.SetHoldings(long_short[0][0],1)
-# short only
-    else:
-        self.SetHoldings(long_short[1][0],-1)</pre>
-<h2>Results</h2>
-The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
-<img class=" wp-image-565 alignleft" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/06/WX20170602-123108@2x-300x162.png" alt="" width="522" height="282" />
-
-From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are -4.074 and 1.417 for the reversal factor and momentum factor respectively. The p-value of the reversal factor is very small which means this factor has a very high significance level.
-<h3>Backtest Sensitivity Results</h3>
-We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
-<img class="alignnone wp-image-583" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/06/WX20170602-153018@2x-1-300x63.png" alt="" width="471" height="99" />
-The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
-<h2>Conclusion</h2>
-The paper demonstrates there are inefficiencies in the UIP which can be exploited with a hybrid momentum and mean reversion strategy. Although the sample size of the paper is much larger than ours the parameter and significance level of the two models are very close. The strategies we discussed above keep \(\rho \) fixed and only allow \(\mu \) to change by country. If we let \(\rho \) change by lag the significance level of the model might increase, but this could potentially make modeling more difficult by introducing multicollinearity. To test this we wrote this implementation in the algorithm and commented out the lines. If you are interested in exploring this extension to the model you can change these lines to test your strategy.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c1eea4df64cf986eba481f5e5e376e8a'></script>
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-<h2>Abstract</h2>
-We investigate two pairs trading methods and compare the results. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
-
-In this tutorial, we will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a> [ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref] </em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012)[ref]Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012.[/ref]. The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
-<h2>Framework of Copula</h2>
-<h3>1. Definition</h3>
-Given a random vector \(X_1,X_2,...,X_p\), its marginal cumulative distribution functions (CDFs) are \(F_i(x) = P[X_i \leq x]\). By applying the probability integral transform to each component, the marginal distributions of \((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\) are uniform (from <a href="https://en.wikipedia.org/wiki/Copula_(probability_theory)">Wikipedia</a>).
-
-Then the copula of \(X_1,X_2,...,X_p\) is defined as the joint cumulative distribution function of \(U_1,U_2,...,U_p\), for which the marginal distribution of each variable U is uniform as  \(U(0,1)\).
-
-\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
-
-Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.[ref]Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>[/ref]
-<h3>2. Bivariate Copulas</h3>
-Since this research focuses on bivariate copulas (for pairs trading we have 2 random variables) some probabilistic properties are specified.
-
-Let X and Y be two random variables with cumulative probability function \(F_1(X)\) and \(F_2(Y)\). \(U=F_1(X), V=F_2(Y)\) which are uniformly distributed.  Then the copula function is \(C(u,v)=P(U\leq u,V\leq v)\). Taking the partial derivative of the copula function over U and V would give the conditional distribution function as follows:
-
-\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\]
-
-\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\]
-<h3>3. Archimedean Copulas</h3>
-There are many copula functions that enable us to describe dependence structures between variables, other than the Gaussian assumption. Here we will focus three of these; the <em>Clayton</em>, <em>Gumbel</em> and <em>Frank</em> copula formulas from the Archimedean class.
-
-Archimedean copulas[ref]Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>[/ref] are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\) [ref]LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.[/ref].
-
-\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
-
-The probability density function is:
-
-\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\]
-
-Where \(\phi_{(2)}^{-1}\) is the inverse of the second derivative of the generator function.
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: left;">Copula</th>
-<th style="text-align: center;">Copula function C(u,v;θ)</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: left;">Clayton Copula</td>
-<td style="text-align: left;">\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Gumbel Copula</td>
-<td style="text-align: left;">\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Frank Copula</td>
-<td>\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]</td>
-</tr>
-</tbody>
-</table>
-&nbsp;
-
-Genest and MacKay (1986) [ref]Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283[/ref] proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
-
-\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
-
-So we can easily estimate the parameter in Archimedean copulas if we know Kendall’s tau rank measure and the generator function. Please refer to step 3 to see the formulas.
-<h2>Part I: Copula Method</h2>
-ETFs have many different stock sectors and asset classes which provide us a wide range of pairs trading candidates. Our data set consists of daily data of the ETFs traded on the NASDAQ or the NYSE.
-
-We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
-<h3>Step 1: Selecting the Paired Stocks</h3>
-The general method of pair selection is based on both fundamental and statistical analysis.[ref]<strong>Jean Folger</strong>. Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>[/ref]
-<h4><strong>1) Assemble a list of potentially related pairs</strong></h4>
-Any random pairs could be correlated. It is possible that those variables are not causally related to each other, but because of a spurious relationship due to either coincidence or the presence of a certain third, unseen factor. Thus, it is important for us to start with a list of securities that have something in common. For this demonstration, we choose some of the most liquid ETFs traded on the Nasdaq or the NYSE.  The relationship for those potentially related pairs could be due to an index, sector or asset class overlap. e.g. QQQ and XLK are two ETFs which track the market leading indices.
-<h4><strong>2) Filter the trading pair with statistical correlation</strong></h4>
-<div class="page" title="Page 12">
-<div class="section">
-<div class="layoutArea">
-<div class="column">
-
-To determine which stock pairs to include in the analysis, correlations between the pre-selected ETF pairs are analyzed. Below are three types of correlation measures we usually use in statistics:
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="3">Correlation Measurement Techniques</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="33%">Pearson correlation</td>
-<td>\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]</td>
-</tr>
-<tr>
-<td>Kendall rank correlation</td>
-<td>\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]</td>
-</tr>
-<tr>
-<td>Spearman rank correlation</td>
-<td>\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]</td>
-</tr>
-<tr>
-<td colspan="2"> \(n\) = number of value in each data set
-\(n_c\) = number of concordant
-\(n_d\) = number of discordant
-\(d_i\) = the difference between the ranks of corresponding values \(x_i\) and \(y_i\)</td>
-</tr>
-</tbody>
-</table>
-</div>
-We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading.
-
-We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
-
-\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n where n is the number of price data
-<pre class="prettyprint linenums">def _pair_selection(self):
-    tick_syl =  [["QQQ","AGG","XME","TNA","FAS","XLF","EWC","QLD"],["XLK","JNK","EWG","TLT","FAZ","XLU","EWA","QID"]]
-    logreturn={}
-    for i in range(2):
-        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol for x in tick_syl[i]]
-        history = self.History(self.lookbackdays,Resolution.Daily)
-        # generate the log return series of different ETFs
-        for j in range(len(tick_syl[i])):
-            close_price = []
-            for slice in history:
-                bar = slice[syl[j]]
-                close_price.append(bar.Close)
-            logreturn[tick_syl[i][j]] = np.diff(np.log([float(z) for z in close_price]))
-    # estimate coefficients of different correlation measures
-    tau_coef,pr_coef,sr_coef= [],[],[]
-    for i in range(len(tick_syl[i])):
-        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
-        tau_coef.append(kendalltau(tik_x, tik_y)[0])
-        pr_coef.append(pearsonr(tik_x, tik_y)[0])
-        sr_coef.append(spearmanr(tik_x, tik_y)[0])
-    index_max = tau_coef.index(max(tau_coef))
-    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
-</pre>
-<h3>Step 2: Estimating Marginal Distributions of log-return</h3>
-In order to construct the copula, we need to transform the log-return series \(R_x\) and \(R_y\) to two uniformly distributed values u and v. This can be done by estimating the marginal distribution functions of \(R_x\) and \(R_y\) and plugging the return values into a distribution function. As we make no assumptions about the distribution of the two log-return series, here we use the empirical distribution function to approach the marginal distribution \(F_1(R_x)\) and \(F_2(R_y)\). The Python ECDF function from the statsmodel library gives us the Empirical CDF as a step function.
-<h3>Step 3: Estimating Copula Parameters</h3>
-As discussed above, we estimate the copula parameter theta by the relationship between the copula and the dependence measure Kendall’s tau, for each of the Archimedean copulas.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Copula</th>
-<th style="text-align: center;">Kendall's tau</th>
-<th style="text-align: center;">parameter θ</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Clayton Copula</td>
-<td>\[\frac{\theta}{\theta +2}\]</td>
-<td>\[\theta=2\tau(1-\tau)^{-1}\]</td>
-</tr>
-<tr>
-<td>Gumbel Copula</td>
-<td>\[1-\theta^{-1}\]</td>
-<td>\[\theta=(1-\tau)^{-1}\]</td>
-</tr>
-<tr>
-<td>Frank Copula</td>
-<td>\[1+4[D_1(\theta)-1]/\theta\]</td>
-<td>\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]</td>
-</tr>
-<tr>
-<td colspan="3">\[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]</td>
-</tr>
-</tbody>
-</table>
-</div>
-<pre class="prettyprint linenums">def _parameter(self, family, tau):
-    if  family == 'clayton':
-        return 2*tau/(1-tau)
-    elif family == 'frank':
-        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
-    	# frank_fun is the squared difference
-    	# Minimize the frank_fun would give the parameter theta for the frank copula
-      integrand = lambda t: t/(np.exp(t)-1)
-    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
-      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
-    elif family == 'gumbel':
-        return 1/(1-tau)</pre>
-</div>
-</div>
-<h3>Step 4: Selecting the Best Fitting Copula</h3>
-Once we get the parameter estimation for the copula functions, we use the AIC criteria to select the copula that provides the best fit in algorithm initialization.
-
-\[AIC=-2L(\theta)+2k\]
-
-where \(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\) is the log-likelihood function and k is the number of parameters, here k=1.
-
-The density functions of each copula function are as follows:
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;">Copula</th>
-<th style="text-align: center;">Density function c(u,v;θ)</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: left;">Clayton Copula</td>
-<td style="text-align: left;">\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]</td>
-</tr>
-<tr>
-<td>Gumbel Copula</td>
-<td>\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Frank Copula</td>
-<td style="text-align: left;">\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]</td>
-</tr>
-<tr>
-<td colspan="2">\[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]</td>
-</tr>
-</tbody>
-</table>
-<div class="page" title="Page 23">
-<div class="layoutArea">
-<div class="column">
-<pre class="prettyprint linenums">def _lpdf_copula(self, family, theta, u, v):
-    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
-    if  family == 'clayton':
-        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
-    elif family == 'frank':
-        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
-        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
-        pdf = num/denom
-    elif family == 'gumbel':
-        A = (-np.log(u))**theta + (-np.log(v))**theta
-        c = np.exp(-A**(1/theta))
-        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
-    return np.log(pdf)
-</pre>
-The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" &amp; "XLK".
-<pre class="prettyprint linenums">self.family = ['clayton', 'frank', 'gumbel']
-tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
-AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
-for i in self.family:
-    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
-    # Replace nan with zero and inf with finite numbers in lpdf list
-    lpdf = np.nan_to_num(lpdf)
-    loglikelihood = sum(lpdf)
-    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
-    # choose the copula with the minimum AIC
-    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
-</pre>
-&nbsp;
-<h3>Step 5: Generating the Trading Signals</h3>
-The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I(2013)[ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref], the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
-
-Given current returns \(R_x, R_y\) of stock X and stock Y, we define the "mis-pricing indexes" are:
-
-\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\]
-
-\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
-
-For further <span class="s1">mathematical proof</span>, please refer to Xie W, Wu Y(2013)[ref]Xie W, Wu Y. Copula-based pairs trading strategy[C]//Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.[/ref]
-
-The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
-
-Gumbel Copula
-
-\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\]
-
-\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]
-
-</div>
-</div>
-</div>
-Clayton Copula
-
-\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
-
-\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
-
-Frank Copula
-
-\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \]
-
-\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]
-
-After selection of trading pairs and the best-fitted copulas, we take the following steps for trading. Please note we implement the Steps 1, 2, 3 and 4 on the first day of each month using the daily data for the last 12 months, which means our empirical distribution functions and copula parameters theta estimation are updated once a month. In summary each month:
-<ol>
- 	<li>During the 12 months' rolling formation period, daily close prices are used to calculate the daily log returns for the pair of ETFs and then compute Kendall's rank correlation.</li>
- 	<li>Estimate the marginal distribution functions of log returns of X and Y, which are ecdf_x and ecdf_y separately.</li>
- 	<li>Plug Kendall's tau into copula parameter estimation functions to get the value of theta.</li>
- 	<li>Run linear regression over the two price series. The coefficient is used to determine how many shares of stock X and Y to buy and sell. For example, if the coefficient is 2, for every X share that is bought or sold, 2 units of Y are sold or bought.</li>
-</ol>
-<pre class="prettyprint linenums">def _set_signal(self):
-	history = self.History(self.lookbackdays,Resolution.Daily)
-	# generate the log return series of paired stocks
-    # logreturn_trade is a dictionary to store the history log return series of paired stocks each trading day
-    price = {}
-    logreturn_trade = {}
-    for j in range(len(self.syl)):
-        close = []
-        for slice in history:
-            bar = slice[self.syl[j]]
-            close.append(bar.Close)
-        price[self.ticker[j]] = close
-        logreturn_trade[self.ticker[j]] = np.diff(np.log([float(z) for z in close]))
-
-    x, y = logreturn_trade[self.ticker[0]], logreturn_trade[self.ticker[1]]
-
-    # estimate Kendall'rank correlation each trading day
-    tau = kendalltau(x, y)[0]
-
-    # etstimate the copula parameter: theta
-    self.theta = self._parameter(self.copula, tau)
-
-    # simulate the empirical distribution function for returns of two paired stocks
-    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
-    # run linear regression over the two history return series
-    ols = linear_model.LinearRegression()
-    self.coef = ols.fit(np.array(price[self.ticker[0]]).reshape(-1,1), np.array(price[self.ticker[1]])).coef_
-
-</pre>
-Finally during the trading period, each day we convert today's returns to u and v by using empirical distribution functions ecdf_x and ecdf_y. After that, two mispricing indexes are calculated every trading day by using the estimated copula C.  The algorithm constructs short positions in X and long positions in Y on the days that \(MI_{Y|X}&lt;0.05\) and \(MI_{X|Y}&gt;0.95\). It constructs short position in Y and long positions in X on the days that \(MI_{Y|X}&gt;0.95\) and \(MI_{X|Y}&lt;0.05\).
-<pre class="prettyprint linenums">def OnData(self,data):
-    for i in self.syl:
-        self.price_list[i].append(self.Portfolio[i].Price)
-    # compute today's log return of 2 stocks
-    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
-    else:
-        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
-        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
-    # Convert the two returns to uniform values u and v using the empirical distribution functions
-    u_value = self.ecdf_x(return_x)
-    v_value = self.ecdf_y(return_y)
-    # Compute the mispricing indices for u and v by using estimated copula
-    self._misprice_index(self.copula, self.theta, u_value, v_value)
-    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
-        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-        else:
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
-        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
-        else:
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)</pre>
-<h2 class="page" title="Page 5">Part II: Cointegration Method</h2>
-For the cointegration pairs trading method, we choose the same ETF pair "GLD" &amp; "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
-<h3 class="page" title="Page 5">Step 1: Generate the Spread Series</h3>
-At the start of each month, we generate the log price series of two ETFs with the daily close. Then the spread series is estimated using regression analysis based on log price series data.
-
-For equities X and Y, we run linear regression over the log price series and get the coefficient β.
-
-\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
-<h3>Step 2: Compute the Threshold</h3>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
-
-</div>
-<pre class="prettyprint linenums">def _set_signal(self):
-    history = self.History(self.numdays,Nullable[Resolution](Resolution.Daily))
-    logprice = {}
-    for j in range(len(self.syl)):
-        close = []
-        for slice in history:
-            bar = slice[self.syl[j]]
-            close.append(bar.Close)
-        logprice[self.ticker[j]] = np.log([float(z) for z in close])    # generate the log return series of stock price
-    # run linear regression over the two history log price series
-    reg = linear_model.LinearRegression()
-    x,y = logprice[self.ticker[0]],logprice[self.ticker[1]]
-    reg.fit([[n] for n in x],y)
-    self.coef = reg.coef_    # reg.intercept_
-    self.spread = y - self.coef * x  #compute the spread series based on regression result
-    self.mean, self.std = np.mean(self.spread),np.std(self.spread)
-
-    logprice_x = np.log(float(self.Portfolio[self.syl[0]].Price))
-    logprice_y = np.log(float(self.Portfolio[self.syl[1]].Price))
-
-    syl_x, syl_y = self.syl[0],self.syl[1]
-    quantity = float(self.CalculateOrderQuantity(syl_x,0.4))
-
-    current_spread = float(logprice_y) - self.coef * float(logprice_x)
-
-    if current_spread > self.mean + self.threshold * self.std:
-        self.Sell(syl_y, np.exp(self.coef) * quantity)
-        self.Buy(syl_x,  quantity)
-    elif current_spread < self.mean - self.threshold * self.std:
-        self.Buy(syl_y, np.exp(self.coef) * quantity )
-        self.Sell(syl_x,  quantity)
-    if self.Portfolio[self.syl[0]].Quantity != 0 and self.Portfolio[self.syl[1]].Quantity != 0:
-        if  current_spread < 0.5 * self.std and current_spread > -0.5 * self.std:
-            self.Liquidate()
-</pre>
-</div>
-</div>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-<h3>Step 3: Set up the Trading Signals</h3>
-On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread&gt;mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its <span class="s1">equilibrium (defined as less than half a standard deviation from zero spread).</span>
-
-The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
-
-</div>
-</div>
-</div>
-<h2>Conclusion</h2>
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;">method</th>
-<th style="text-align: center;">Transactions</th>
-<th style="text-align: center;">Profit</th>
-<th style="text-align: center;">Sharpe Ratio</th>
-<th style="text-align: center;">Drawdown</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: center;">Copula</td>
-<td style="text-align: center;">347</td>
-<td style="text-align: center;">325.4%</td>
-<td style="text-align: center;">1.005</td>
-<td style="text-align: center;">20.2%</td>
-</tr>
-<tr>
-<td style="text-align: center;">Cointegration</td>
-<td style="text-align: center;">33</td>
-<td style="text-align: center;">71.2%</td>
-<td style="text-align: center;">0.781</td>
-<td style="text-align: center;">15.6%</td>
-</tr>
-</tbody>
-</table>
-Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
-
-Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method.
-
-It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
-<h2>Backtest</h2>
-Backtest for copula method
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56f45d1234400db5d5dbbdb8dab1da44'></script>
-Backtest for cointegration method
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d5ac7544325b16b3536fa13dc2d34214'></script>
diff --git a/Strategy Tutorials/Tutorial04 The Dynamic Breakout II Strategy.html b/Strategy Tutorials/Tutorial04 The Dynamic Breakout II Strategy.html
deleted file mode 100644
index 6f79c26..0000000
--- a/Strategy Tutorials/Tutorial04 The Dynamic Breakout II Strategy.html	
+++ /dev/null
@@ -1,64 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
-
-First we decide the look-back period based on the change rate of volatility, then we make trading decisions based on the highest high and lowest low from the look back period as well as a Bollinger Bands indicator. It is an auto adaptive trading system that can adjust its buy and sell rules depending on the performance of these rules in the past. In addition to Forex markets it is widely used in future and equity markets. You can refer to <a href="https://www.youtube.com/watch?v=MxGVqSvLqDQ">this video</a> to learn more about dynamic break out II.
-
-The original Dynamic Break Out system was developed by George Pruitt for Futures Magazine in 1996. The logic behind the dynamic breakout system is that the volatility component changes the lookback period, then the enter and exit points are decided by the highest high and lowest low price over the lookback period.  The newer version of the Dynamic Break Out is just like the original, except we introduce the Bollinger Band and adjust the number of look back days using the market volatility, so different market conditions perform better with different parameters. In addition, the stop loss signal is fixed in version one, but in version two the liquidate point is based on the moving average indicator and the length of moving average is dynamically changed with the look-back period.
-
-We backtested the strategy on EURUSD and GBPUSD over 6 years period.  The result suggests a drawdown of 20% and the strategy caught the market turning points. It is especially profitable in a trending market.
-<h2>Method</h2>
-<h3>Step 1: Determine the look back periods</h3>
-
-The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.[ref]<strong>Robert C. Miner(October 20, 2008)</strong>. High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>[/ref]   <b> </b>
-
-To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
-<pre class="prettyprint linenums">close = self.History(self.syl, 31, Resolution.Daily)['close']
-   todayvol = np.std(close[1:self.numdays+1])
-   yesterdayvol = np.std(close[0:self.numdays])
-   deltavol = (todayvol - yesterdayvol) / todayvol
-   self.numdays = round(self.numdays * (1 + deltavol)) # the number of days must be integer
-</pre>
-
-Though the look back days are dynamic, it needs to be restricted within an acceptable range of 20 to 60.
-
-<h3>Step 2: Choose the algorithm buy/sell point</h3>
-
-For a buy setup, the close price of the previous day must be above the upper Bollinger Band. In addition the ask price must be above the highest high of the most recent N days. Where N is the look back days from Step-1.
-
-For a sell setup, the close price of previous day must be below the lower Bollinger Band and the ask price must be below the lowest low of the most recent N days. The length of the Bollinger Band calculation is the same number of look back days that is generated by Step-1.
-
-Bollinger Band is a popular technical indicator. k is a constant. Here we choose k=2.
-\[ Upper Band = moving\ average + k\times standard\  deviation \]
-
-\[ Lower Band = moving\ average - k\times standard\  deviation \]
-
-&nbsp;
-
-QuantConnect provides more than 100 technical indicators for you to use in your algorithm. These are provided as class objects in Python. A full list of the indicators and their properties can be found in the reference table of <a href="https://www.quantconnect.com/docs#Indicators">the documentation page</a>.
-<pre class="prettyprint linenums">self.bolband = self.BB(self.syl,self.numdays,decimal.Decimal(2),MovingAverageType.Exponential,Resolution.Daily)
-     self.upband = self.bolband.UpperBand
-     self.lowband = self.bolband.LowerBand
-</pre>
-<h3>Step 3: Choose the algorithm liquidation point</h3>
-The exit signal for an existing holding is determined by calculating a simple moving average of closing prices for the past look back days. That is to say, we liquidate a long position if the current price is lower than the moving average of the close price over the look back period, and vice versa for selling a short position.
-<pre class="prettyprint linenums">self.buypoint = max(self.high)
-self.sellpoint = min(self.low)
-historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
-self.longLiqPoint = np.mean(historyclose)
-self.shortLiqPoint = np.mean(historyclose)
-self.yesterdayclose = historyclose.iloc[-1]
-</pre>
-<h2>Conclusion</h2>
-For six years backtesting of EURUSD, the overall statistics show an annual rate of return of 2.3% and with a Sharpe Ratio of 0.31. EURUSD has a significant uptrend from 2010 to 2012. This momentum strategy outperforms the market and seems to be profitable from 2010 to 2014.  The maximum drawdown occurs in May 2015 to December 2015 and is roughly 14%. From our results we find the strategy works best in an trending forex market.
-
-In contrast, GBPUSD is pretty volatile during the tested period from 2010 to 2016. Our testing demonstrated a negative annual rate of return with a drawdown of approximately 19%.
-
-When the volatility decreases, the price tends to continue following the current trend. Volatility causes the the look back days to decrease when computing the bollinger bands, making it easier to enter a trade.  If the market volatility increases we increase the look back days in order to filter the fake signals, making it harder to enter a trade.
-
-Here we use the standard deviation of price as a measure of market volatility. To improve the model we could choose other measures of volatility like standard deviation of logarithm return series or other stochastic volatility measures.
-
-<h2>Algorithm</h2>
-Backtest for EURUSD
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=87d3047e8c358ea9b4779e0b5d238a74'></script>
-Backtest for GBPUSD
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4bacf930111e3502993b7e7c9b315be0'></script>
diff --git a/Strategy Tutorials/Tutorial05 Dual Thrust Trading Algorithm.html b/Strategy Tutorials/Tutorial05 Dual Thrust Trading Algorithm.html
deleted file mode 100644
index e586713..0000000
--- a/Strategy Tutorials/Tutorial05 Dual Thrust Trading Algorithm.html	
+++ /dev/null
@@ -1,49 +0,0 @@
-<h2>Abstract</h2>
-The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
-
-In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
-<h2>Method</h2>
-<h3>Step 1 : Initialization of algorithm</h3>
-Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2016, 4, 12)
-    self.SetEndDate(2017, 3, 19)
-    self.SetCash(100000)
-    equity = self.AddSecurity(SecurityType.Equity, "AAON", Resolution.Hour)
-</pre>
-Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
-</pre>
-Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
-<h3>Step 2:  Implementation of algorithm</h3>
-<img src="images/dual thrust price range.png" alt="dual thrust price range”/>
-
-In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
-<pre class="prettyprint linenums">history = self.History([self.syl.Value], 4, Resolution.Daily)
-  self.high = history.loc[self.syl.Value]['high']
-  self.low = history.loc[self.syl.Value]['low']
-  self.close = history.loc[self.syl.Value]['close']
-</pre>
-<h3>Step 3: Trading Implementation</h3>
-The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
-<img src="images/dual thrust trading.png" alt="dual thrust trading”/>
-
-This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
-<pre class="prettyprint linenums"> holdings = self.Portfolio[self.syl].Quantity
-        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
-            if holdings &gt;= 0:
-                self.SetHoldings(self.syl, 1)
-            else:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, 1)
-        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, -1)
-            else:
-                self.SetHoldings(self.syl, -1)</pre>
-<h2>Conclusion</h2>
-We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
-<h2>Algorithm</h2>
-Backtest result for ETF: SPY from 2004 to 2017
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c3816a8e2704a903c6c929f743fc6a62'></script>
diff --git a/Strategy Tutorials/Tutorial06 Can Crude Oil Predict Equity Returns.html b/Strategy Tutorials/Tutorial06 Can Crude Oil Predict Equity Returns.html
deleted file mode 100644
index 49e0446..0000000
--- a/Strategy Tutorials/Tutorial06 Can Crude Oil Predict Equity Returns.html	
+++ /dev/null
@@ -1,94 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". [ref]<strong>Gerben, Driesprong (2007)</strong>. Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>[/ref] by?by Gerben, Ben and Benjamin (2007).
-
-If the predicted return is larger than the risk-free rate, the portfolio is fully invested in stock; if the predicted return is lower than the risk-free rate, the portfolio is invested in short-term U.S T-bills. The backtesting period starts in 1980 and is divided into an in-sample period where regression analysis is made and an out?of sample period where the regression result is embedded "statically" into the strategy.
-
-In our implementation of the strategy we adapt the method of the original paper to make it more applicable to the current market. We have set our backtesting period to be from 2010 to 2017 and we refresh our regression analysis each month to form a rolling dynamic projection. This is because?empirical evidence shows?us the correlation between oil and stocks is not as strong as in the 1980's. We use?the price of S&amp;P GSCI? Crude Oil Total Return Index ETNs to represent spot oil price, and import T-bill data from Quandl by defining a custom class.?We use the "Schedule" API to trigger an event every month automatically?and the "History" function to retrieve data for regression analysis.
-
-Our analysis shows this strategy under performs the market in recent years. In the 9 year analysis period the algorithm was mostly long the S&amp;P500 index and only 9 trades were performed as the markets were strongly bullish. The trades could potentially simply be due to the weakening of the relationship between stocks and oil.
-<h2>Background</h2>
-We assume the predicted return of the stock is proportional to the return of oil. This can be represented by the regression equation:
-
-\[r^{stock}_t=a_0+a_1r^{oil}_{t-1}+e_t\]
-
-with
-
-\[e_t=r^{stock}_t-E_{t-1}[r^{stock}_t]\].
-
-The independent variable is the return of the oil and the dependent variable is the return of the stock. We use the monthly returns over a regression period of 2 years, giving us 22 observations to regress.?Every month regression analysis is conducted, and we use the estimated coefficient from the regression to compute the expected stock return with the given return of oil.
-<h2>Method</h2>
-The algorithm implementation consists of mainly three parts: Defining the custom imported data, initialization of the strategy parameters, and monthly re-balancing of the portfolio.
-<h3>Step 1: Defining Custom Imported Data</h3>
-We import T-Bill data from Quandl - a marketplace for financial, economic and alternative data. This requires defining a small class that tells QuantConnect how to interpret?the Quandl data.
-<pre class="prettyprint linenums">class TBill(PythonData):
-    def GetSource(self, config, date, isLiveMode):
-        return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/USTREASURY/BILLRATES.csv?api_key=XXXXXXXXX&amp;order=asc", SubscriptionTransportMedium.RemoteFile)
-    def Reader(self, config, line, date, isLiveMode):
-        tbill = TBill()
-        tbill.Symbol = config.Symbol
-        # Example Line Format:
-        # Date      4 Wk Bank Discount Rate   
-        # 2017-06-01 		0.8    
-        if not (line.strip() and line[0].isdigit()): return None
-        try:
-            data = line.split(',')
-            value = float(data[1])*0.01
-            value = decimal.Decimal(value)
-            if value == 0: return None
-            tbill.Time = datetime.strptime(data[0], "%Y-%m-%d")
-            tbill.Value = value
-            tbill["Close"] = float(value)
-            return tbill;
-        except ValueError:
-            return None
-</pre>
-We first provide the source of the data as a URL to Quandl's API in the GetSource method. We need to make sure the data is?organized in ascending order which is done with the?"order=asc" parameter.?You need to substitute the API key in the URL for your personal Quandl API token.
-
-The Reader method parses a line of the data file. When using custom data you need to minimally set?the Time property?and Value property. In this example we find the value property from the headings in the spreadsheet is the second column and we reference it with data[1]. We set the Close property to the same value.
-
-In our "Initialize" function we use the following commands to add the custom data into our portfolio.
-<pre class="prettyprint linenums">self.AddData(TBill, "tbill")
-self.tbill = self.Securities["tbill"].Symbol
-</pre>
-<h3>Step 2: Initialization of the Strategy Parameters</h3>
-In our?"Initialize" function we set the cash amount, start-end date as well as other parameters that are specific to this strategy.?We set two parameters for the regression analysis period:
-<pre class="prettyprint linenums">self.regPeriod = 24
-self.daysInMonth = 21
-</pre>
-The variable "regPeriod" indicates how many months we are going to take into consideration in our regression analysis. We assume 21 days per month and request a historical period of approximately 2 years. We jump back in steps of 21 days and assume it is roughly 1 month of return.
-
-We need to set up "Schedule" function in "Initialize" so as to trigger the monthly re-balancing function every month.
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy),Action(self.MonthlyReg))
-</pre>
-<h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
-Every month we reconstruct the regression analysis to determine whether to be 100% long stocks or T-Bill contracts. We perform this re-balancing in the MonthlyReg function at the start of each month.?We use the History function to retrieve historical data for oil and stocks?and then divide the T-Bill rate by 12 to make it comparable to the monthly expected return of stocks.
-<pre class="prettyprint linenums">oilHist = self.History(self.oil, self.regPeriod*self.daysInMonth, Resolution.Daily)
-spyHist = self.History(self.spy, self.regPeriod*self.daysInMonth, Resolution.Daily)
-oilSeries = [float(x.Close) for x in oilHist][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
-spySeries = [float(x.Close) for x in spyHist][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
-rf = float(self.Securities[self.tbill].Price)/12.0
-</pre>
-Then we make an OLS regression by using "numpy" to make the prediction on next month's stock return.
-<pre class="prettyprint linenums">x = np.array(oilSeries)
-x = (np.diff(x)/x[:-1])
-y = np.array(spySeries)
-y = (np.diff(y)/y[:-1])
-A = np.vstack([x[:-1],np.ones(len(x[:-1]))]).T
-beta, alpha = np.linalg.lstsq(A,y[1:])[0]
-yPred = alpha + x[-1]*beta
-</pre>
-Finally, we compare the expected return of stocks with risk-free rate. If the former is larger than the latter, we invest fully in stocks; otherwise we liquidate our holdings. Because we cannot purchase T-Bill contracts the performance is likely slightly underestimated.
-<pre class="prettyprint linenums">if yPred &gt; rf:
-	self.SetHoldings(self.spy, 1)
-else:
-	self.Liquidate(self.spy)</pre>
-<h2>Conclusion</h2>
-We backtested this strategy over the period beginning in 2010 and ending in 2017. It has a sharpe ratio of 0.72 beating the benchmark's 0.6 over a similar period.
-
-Although the annual return closely matches that of the paper,?it is largely a coincidence of the strong bull market in recent years.?If we look at the monthly regression results, we could find that in most cases, the p-value is not small enough to reject the null hypothesis that there is no correlation between oil and stocks. So the investment decisions based on the insignificant statistical results are almost meaningless. The performance of this strategy cannot effectively beat the benchmark, mostly?due to?the weakened correlation between oil and stocks.
-
-Further research and backtesting could be conducted on assets other than oil that have a stronger relationship with stocks.
-<h2>Algorithm</h2>
-Strategy code, as well as backtesting result, is attached below. We also put other choices of implementation in the comments.
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=60a13d51f2c96b543355c8dafaa69b27&bm=Benchmark'></script>
\ No newline at end of file
diff --git a/Strategy Tutorials/Tutorial07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html b/Strategy Tutorials/Tutorial07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html
deleted file mode 100644
index 35884ce..0000000
--- a/Strategy Tutorials/Tutorial07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html	
+++ /dev/null
@@ -1,328 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work.[ref]<strong>George J. Miao </strong>High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>[/ref]We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
-
-This strategy is especially profitable when the market is performing poorly. The profit is resulted from mispricing, and mispricings are likely to happen when the market goes down or volatility increases.
-
-To explore this strategy further, we design this strategy to be flexible. We can change the data resolution into 5 minutes, 10 minutes or even 30 minutes by simply changing a parameter. It's also essential to choose optimized entering, closing and stop loss threshold. Everyone can has his/her own version of this strategy.
-<h2>Introduction</h2>
-High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012[ref]<strong>Cartea &amp; Penalva, 2012 </strong>Where is the value in high frequency trading?<strong><a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a></strong>[/ref]).
-<div class="page" title="Page 14">
-<div class="layoutArea">
-<div class="column">
-
-Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006[ref]<strong>Gatev, Goetzmann, &amp; Rouwenhorst, 2006 </strong>Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>[/ref]) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
-
-</div>
-<h3>Data Description</h3>
-In order to have more pairs with high correlation, we select stocks in a specific industry. Economically, we prefer traditional sectors because the companies in these sector are more likely to be close substitutes. If we selected N stocks, the number of pairs can be calculated by \(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\). In the demonstrated strategy we used 80 stocks, so we have 3160 pairs in total. We used minute data and aggregate them into lower resolution, thus 1 minute is the highest resolution for this strategy.
-<h3>Correlation Approach</h3>
-Correlations measure the relationship between two stocks that have price trends. They tend to move together, and thus are correlated. Correlation filter is the first step to screen the candidate pairs.
-
-Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
-
-\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}}\]
-
-Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
-
-However, the pairs trading based on a correlation approach alone would have a disadvantage of instabilities over time. Correlation coefficients do not necessarily imply  mean-reversion between the prices of the two stock pairs. In order to overcome the above issue, a cointegration approach was further used as the second-step of the selection process for the pairs.
-<h3>Cointegration Approach</h3>
-The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger[ref]<strong>Engle and Granger</strong> Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236">Online Copy</a></strong>[/ref]Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
-
-The definition of cointegration is the following: assume that \(x_t\) and \(y_t\) are two time series that were non-stationary. If there exists a parameter \(\gamma\) such that the following equation:
-
-\[z_t = y_t - \gamma x_t\]
-
-was a stationary process, then \(x_t\) and \(y_t\) would be cointegrated. This process is a powerful tool for investigating common asset trends in multivariate time series.
-
-In our case, Let \(p_t^A\) and \(p_t^B\) be the prices of two stocks A and B respectively. If it is assumed that {\({p_t^A, p_t^B}\)} is individually non-stationary, there exists the parameter \(\gamma\) such that the following equation was a stationary process
-
-\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]
-
-where \(\mu\) is a mean of the cointegration model. \(\epsilon_t\) is a stationary, mean-revering process and was referred to as a <em>cointegration residual</em>. The parameter \(\gamma\) is known as a <em>cointegration coefficient. </em>The equation above represents a model of cointegrated pair for stocks A and B.
-
-It's essential to understand how the conitegration residual together with the cointegration coefficient determines our trading direction. If \(\epsilon\) is positive, in a given confidence interval, this is a signal that stock A is relatively overpriced and stock B is relatively underpriced, and we are going to long B and short A; If If \(\epsilon\) is negative, we are going to long A and short B.
-<h3>Cointegration Verification(optional reading part)</h3>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-In the Engle-Granger method(Engle &amp; Granger, 1987), we first set up a cointegration regression between stock A and stock B as stated in the equation above, and then estimate the regression parameters \(\mu\) and \(\gamma\) using an ordinary least squares(OLS). Subsequently, we tested the regression residual \(epsilon_t\) to determine whether or not it was stationary.
-
-The most popular stationary test in the area of cointegration, the Augmented Dickey Fuller (ADF) test, was used on the regression residual \(\epsilon\) to determine whether it had a unit root.
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-Testing for the presence of the unit root in the regression residual using the ADF test was given by
-
-\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \DeltaZ_{t-i} + \mu_t\]
-
-where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
-
-The number of lag order p in the equation is usually unknown and therefore had to be estimated. To determine the number of lag p, the information criteria for lag order selection was used. Here we choose Bayesian Information Criterion(BIC)
-
-\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]
-
-Where T is the sample size.
-
-The unit root test for the regression residual \(\epsilon\) using the ADF test was then carried out under the null hypothesis \(H_0 : \gamma = 0\) versus the alternative hypothesis \(H_1 : \gamma &lt; 0\). A statistical value of the ADF test was obtained by
-
-\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]
-
-The test result in the equation above is compared with the critical value of the ADF test. If the test result is less than the critical value, then the null hypothesis is rejected. This means the regression residual \(\epsilon\) is stationary. Thus, the two stock prices {\({p_t^A, p_t^B}\)} are cointegrated.
-<h3>Pairs Trading Strategy</h3>
-The pairs trading strategy uses trading signals based on the regression residual \(\epsilon\) and were modeled as a mean-reverting process.
-
-In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
-
-The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
-
-In the training period, each of the training data contained a 3-month period, which is a dynamic rolling window size. Immediately after the training period, we begin our one-month trading period, and the dynamic rolling window automatically shift ahead to record the new prices of the stocks in each pair. After the first trading period, we use the updated stock prices to select our pairs for trading again, and begin another trading period.
-<h3>Parameter Adjustment</h3>
-The performance of the strategy is sensitive to the parameters. There are  mainly four parameter to adjust: Opening Threshold, Closing Threshold, Stop-loss Threshold and data resolution.
-
-Opening threshold represents by how many times the residual \(\epsilon\) exceed the standard deviation, which is calculated by \(\frac{\epsilon - \bar{\epsilon}}{\sigma}\). By default we set it to 2.32 and -2.32, which is the critical value for 99% confidence interval if we assume the residual follows normal distribution.
-
-Closing threshold is calculated in the same way as opening threshold, we set it to 0.5 by default to close early to prevent further divergence.
-
-Stop-loss Threshold is set to 4.5. This depends on the level of mispricing we can bear. The higher degree our tolerance to risk is, the higher we can set this parameter. However, if we set this number too low, we may have too many pairs closed before reversion to stop loss.
-
-</div>
-&nbsp;
-<h2>Method</h2>
-In this trading strategy we would define a class named 'pairs'. We manage pairs instead of stocks directly to make it's more convenient for us to calculate correlation and cointegration, update stock prices in the pair and trade on the selected pairs.
-<h3>Step 1: Pairs Class Definition</h3>
-The pairs is made up of two stocks, stock A and stock B. This class has several properties. The basic properties include symbols of stock A and stock B, the pandas DataFrame that contains time and prices of the two stocks, the current error, the error of the last datapoint, and the lists to record stock prices for update purpose. Instead of updating the DataFrame every 5 minutes, we record the prices in lists to update the DataFrame monthly. This would speed up the algorithm at least 10 times because manipulating DataFrame is very time consuming.
-The cor_update method is used every month to update the correlation between the two stocks in this pair. The cointegration_test method is also used monthly to do OLS regression, conduct ADF test, and calculate the mean and standard deviation of the residual. The method also assign these calculated values as properties to the pair object.
-<pre class="prettyprint linenums">class pairs(object):
-    def __init__(self, a, b):
-        self.a = a
-        self.b = b
-        self.name = str(a) + ':' + str(b)
-        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
-    # The number of bars in the rolling window would be determined by the resolution, so we get this
-      information from the shape of the DataFrame here.
-        self.num_bar = self.df.shape[0]
-        self.cor = self.df.corr().ix[0][1]
-    # Set the initial signals to be 0
-        self.error = 0
-        self.last_error = 0
-        self.a_price = []
-        self.a_date = []
-        self.b_price = []
-        self.b_date = []
-
-    def cor_update(self):
-        self.cor = self.df.corr().ix[0][1]
-
-    def cointegration_test(self):
-        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
-    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
-      the tuple is the test value.
-        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
-        self.mean_error = np.mean(self.model.resid)
-        self.sd = np.std(self.model.resid)
-
-    def price_record(self,data_a,data_b):
-        self.a_price.append(float(data_a.Close))
-        self.a_date.append(data_a.EndTime)
-        self.b_price.append(float(data_b.Close))
-        self.b_date.append(data_b.EndTime)
-
-    def df_update(self):
-        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
-                 [self.a_date]).dropna()
-        self.df = pd.concat([self.df,new_df])
-        self.df = self.df.tail(self.num_bar)
-    # after updating the DataFrame, we empty the lists for the incoming data
-        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
-            list = []
-</pre>
-<h3>Step 2: Generate and Clean Pairs</h3>
-The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
-The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
-<pre class="prettyprint linenums">def generate_pairs(self):
-    for i in range(len(self.symbols)):
-        for j in range(i+1,len(self.symbols)):
-            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
-
-    self.pair_list = [x for x in self.pair_list if x.cor &gt; self.pair_threshold]
-
-    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
-
-    if len(self.pair_list) &gt; self.pair_num:
-        	self.pair_list = self.pair_list[:self.pair_num]
-
-def pair_clean(self,list):
-    l = []
-    l.append(list[0])
-    for i in list:
-        symbols = [x.a for x in l] + [x.b for x in l]
-        if i.a not in symbols and i.b not in symbols:
-            l.append(i)
-        else:
-            pass
-    return l
-</pre>
-<h3>Step 3: Warming up Period</h3>
-This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
-<pre class="prettyprint linenums">if len(self.symbols[0].prices) &lt; self.num_bar:
-    for symbol in self.symbols:
-        if data.ContainsKey(i) is True:
-    	    symbol.prices.append(float(data[symbol].Close))
-            symbol.dates.append(data[symbol].EndTime)
-        else:
-            self.Log('%s is missing'%str(symbol))
-            self.symbols.remove(symbol)
-    self.data_count = 0
-    return</pre>
-<h3>Step 4: Pairs Selection</h3>
-This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
-<pre class="prettyprint linenums">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
-    if self.generate_count == 0:
-        for symbol in self.symbols:
-        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
-
-        self.generate_pairs()
-        self.generate_count +=1
-        self.Log('pair list length:'+str(len(self.pair_list)))
-
-        for pair in self.pair_list:
-            pair.cor_update()
-    # Update the DataFrame and correlation selection
-    if len(self.pair_list[0].a_price) != 0:
-        for pair in self.pair_list:
-    	    pair.df_update()
-            pair.cor_update()
-
-    self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
-    # Cointegration test
-    for pair in self.selected_pair:
-        pair.cointegration_test()
-
-    self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
-    self.selected_pair.sort(key = lambda x: x.adf)
-    # If no pair passed the two-stage test, return.
-    if len(self.selected_pair) == 0:
-        self.Log('no selected pair')
-        self.count += 1
-        return
-    # clean the pair to avoid overlapping stocks.
-    self.selected_pair = self.pair_clean(self.selected_pair)
-    # assign a property to the selected pair, this is a signal that would be used for trading.
-    for pair in self.selected_pair:
-        pair.touch = 0
-        self.Log(str(pair.adf) + pair.name)
-    # limit the number of selected pairs.
-    if len(self.selected_pair) &gt; self.selected_num:
-        self.selected_pair = self.selected_pair[:self.selected_num]
-
-    self.count +=1
-    self.data_count = 0
-    return
-</pre>
-<h3>Step 5: Trade Period</h3>
-It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
-<h4>Updating Pairs</h4>
-This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
-<pre class="prettyprint linenums">num_select = len(self.selected_pair)
-for pair in self.pair_list:
-    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
-        i.price_record(data[i.a],data[i.b])
-    else:
-        self.Log('%s has no data'%str(pair.name))
-        self.pair_list.remove(pair)
-
-for pair in self.selected_pair:
-    pair.last_error = pair.error
-
-for pair in self.trading_pairs:
-    pair.last_error = pair.error</pre>
-<h4>Opening Pairs Trading</h4>
-This is most complex part. For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
-When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
-<pre class="prettyprint linenums">for i in self.selected_pair:
-    price_a = float(data[i.a].Close)
-    price_b = float(data[i.b].Close)
-    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
-    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
-    self.trading_pairs:
-        if i.touch == 0:
-            if i.error &lt; i.mean_error - self.open_size*i.sd and i.last_error &gt; i.mean_error -
-            self.open_size*i.sd:
-                i.touch += -1
-            elif i.error &gt; i.mean_error + self.open_size*i.sd and i.last_error &lt; i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error &gt; i.mean_error - self.open_size*i.sd and i.last_error &lt; i.mean_error -
-            self.open_size*i.sd:
-                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
-                i.record_model = i.model
-                i.record_mean_error = i.mean_error
-                i.record_sd = i.sd
-                self.trading_pairs.append(i)
-                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
-                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
-                i.touch = 0
-         elif i.touch == 1:
-             if i.error &lt; i.mean_error + self.open_size*i.sd and i.last_error &gt; i.mean_error +
-             self.open_size*i.sd:
-             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
-             i.record_model = i.model
-             i.record_mean_error = i.mean_error
-             i.record_sd = i.sd
-             self.trading_pairs.append(i)
-             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
-             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
-             i.touch = 0
-         else:
-             pass
-    else:
-        pass
-</pre>
-<h4>Closing Pairs Trading</h4>
-This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
-<pre class="prettyprint linenums">for i in self.trading_pairs:
-    price_a = float(data[i.a].Close)
-    price_b = float(data[i.b].Close)
-    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
-    if ((i.error &lt; i.record_mean_error + self.close_size*i.record_sd and i.last_error &gt;i.record_mean_error + self.close_size*i.record_sd) or (i.error &gt; i.record_mean_error -
-    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
-    self.stop_loss*i.record_sd:
-        self.Log('close %s to stop loss'%str(i.name))
-        self.Liquidate(i.a)
-        self.Liquidate(i.b)
-        self.trading_pairs.remove(i)
-    else:
-        pass</pre>
-<h2>Result</h2>
-We used 10-minute resolution data to backtest the strategy from Jan 2013 to Dec 2016. To demonstrate the in sample training results, we randomly selected a training period that from 2016-09-07 to 2013-11-30.
-<h3>Training Result</h3>
-The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
-
-<img class="alignnone wp-image-1119" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/06/WechatIMG6478-300x193.jpeg" alt="" width="455" height="293" />
-
-The upper part of the following chart plots the stock prices of pair ING vs TCB. The lower part plots by how many times standard deviation the residual deviate from its mean. There are 5 trading opportunities if we set the opening threshold to be 2.32.
-
-<img class="alignnone size-full wp-image-1127" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/06/resid.png" alt="" width="1185" height="565" />
-
-The following chart is the density plot of the residual error. From the shape we can see the error is approximately normal distributed.
-
-<img class="alignnone size-full wp-image-1130" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/06/error.png" alt="" width="720" height="504" />
-
-<strong>Backtesting Result</strong>
-
-The strategy is considered to be market neutral strategy because it a long/short strategy betting on price convergence. Out backtested beta is -0.112, which is within our expectation.
-
-Theoretically, the higher resolution we use, the higher win rate is because on one hand the higher resolution would increase the number of datapoint in our training period, which would make it's harder to past the two-stage test; on the other hand the higher resolution data would let us capture minor profit more accurately. However, there is a trade off between performance and backtesting time. The higher resolution will lead backtesting time to increase drastically.
-
-The number of stocks in the initialize step would also affect our performance. Theoretically, the more stock we have, we better pairs we are likely to pick. But too many stocks would also be time consuming.
-
-what's worth mentioning is that the optimized parameters are different for each sector. It depends on the features of the price patterns in the specific industry. Plotting the pairs prices and the residual to observe is good option to adjust the thresholds.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4a8fa9de2d7f60be9fb0e0bdd307ccd1'></script>
-
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
diff --git a/Strategy Tutorials/Tutorial08 The Momentum Strategy Based on the Low Frequency Compoment of Forex Market.html b/Strategy Tutorials/Tutorial08 The Momentum Strategy Based on the Low Frequency Compoment of Forex Market.html
deleted file mode 100644
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-<h2>Abstract</h2>
-Trend estimation is a family of methods to detect and predict tendencies and trends in price series just using the history information. Moving average is a commonly used trend following trading tool. Lots of momentum trading strategies in the Forex market are based on the moving average rule, in which signals are triggered if the close is above or below the moving average. But MA has the time lag, therefore can't be used to predict the turning points of market price changes.
-
-In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009) [ref]Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>[/ref].
-
-This strategy exploits short-term momentum in the non-linear trend component of the exchange rate which is generated by Hodrick-Prescott Filter (rather than the exchange rate itself) and uses the MA(1, 2) rule to measure this momentum. The strategy was tested on seven kinds of exchange rates and the results shows less robustness and the performance is sensitive to the change of model parameters.
-
-<h2>Introduction</h2>
-Hodrick-Prescott Filter decomposes a time series\(y_t\) into two components: the cyclical part(which is short-term) and the trend part(which is long term).
-
-\[y_t=\x _t +c_t\]
-
-The filter is the solution to the following optimization problem for \(x_t\)
-
-\[\min _{x_t}\left[\sum_{t=1}^n(y_t-x_t)^2+\lambda\sum_{t=2}^{n-1}[(x_{t+1}-x_t)-(x_{t}-x_{t-1})^2] \right]\]
-
-[ref]Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>[/ref]The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
-
-\[\min_{\bf x}{\parallel {\bf{y}}-{\bf{x}}\parallel}_2^2+\lambda {\parallel D\bf x\parallel}_2^2\]
-
-where \({\bf y}=(y_1,y_2,...,y_n),{\bf x}=(x_1,x_2,...,x_n)\in {\rm I\!R}^n\),\(\parallel\cdot\parallel_2\) is the Euclidean norm. D is the (n-2)*n matrix:
-
-\[
-
-\left[
-\begin{matrix}
-1 &amp; -2 &amp; 1 &amp; \\
-&amp; 1 &amp; -2 &amp; 1 \\
-&amp; &amp; &amp; \ddots &amp;\\
-&amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
-&amp; &amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
-\end{matrix}
-\right]
-
-\]
-
-The solution of this optimization problem is given by solving the following linear system:
-
-\[y=(I+2\lambda D^TD)^{-1}x\]
-<pre class="prettyprint linenums">def hpfilter(self,X, lamb=1600):
-    X = np.asarray(X, float)
-    if X.ndim &gt; 1:
-    X = X.squeeze()
-    nobs = len(X)
-    I = sparse.eye(nobs,nobs)
-    offsets = np.array([0,1,2])
-    data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1)
-    K = sparse.dia_matrix((data, offsets), shape=(nobs-2,nobs))
-    use_umfpack = True
-    self.trend = spsolve(I+lamb*K.T.dot(K), X,use_umfpack=use_umfpack)
-    cycle = X - self.trend
-</pre>
-    
-<h2>Method</h2>
-
-This low-frequency momentum trading strategies are applied to daily data on seven kinds of exchange rates. We use five years history data before January 2011 for initial estimation of the trend model. Daily exchange rates for the period January 2011 to May 2017 is used for out of sample trading.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2011,1,1)
-    self.SetEndDate(2017,5,30)
-    self.SetCash(100000)
-    self.numdays = 360*5  # set the length of training period
-    self.syl = self.AddSecurity(SecurityType.Forex, "EURUSD", Resolution.Daily).Symbol
-    self.n,self.m = 2, 1
-    self.trend = None
-    self.SetBenchmark(self.syl)
-    self.MA_rules = None
-    history = self.History(self.numdays,Resolution.Daily)
-    self.close = [slice[self.syl].Close for slice in history]</pre>
-
-<h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
-The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(\1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
-
-<img src="images/low-frequency-component-more.jpg" alt="low-frequency-component-more” />
-
-If we just plot the curve for the first 100 days, we find that the smaller the λ, the more apparent the trend. The curve does not change too much as the λ  smaller than 100. Thus here we choose  λ=100 to extract the trend of daily price data. This trend is our low-frequency component.
-<p style="text-align: center;"><img src="images/low-frequency-component-less.jpg" alt="low-frequency-component-less”/>
-</p>
-
-<p style="text-align: center;">Out-of-sample EUR/USD Trend Estimation</p>
-
-<h3><img src="images/out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation”/> Step 2: Setting up the Moving Average Rule</h3>
-
-Moving average (MA) rules are very commonly used to generate buy and sell signals from data on the spot exchange rate. The MA rule compares a short-run moving average of the current and lagged exchange rate with a long-run moving average.
-
-\[MA(m,n)=\frac{1}{m}\sum_{i=0}^{m-1}S_{t-i}-\frac{1}{n}\sum_{i=0}^{n-1}S_{t-i}\]
-
-
-For HP filter, the non-linear trend is estimated recursively as the paper did. The initial estimation was undertaken using 3 years history data before 2011. The estimation period is then rolled forward each day through the trading period from January 2011 to May 2017.
- 
-<h3>Step 3: Generating the Trading Signals</h3>
-We generate buy and sell signals by applying an MA(1, 2) rule to the estimated low-frequency component. For MA(m,n), m must be 1 which denotes the current value of low-frequency component. n should be small since large n would generate large time lag, the judgment of turning points is not accurate.
-
-A buy signal is generated when the current day’s low-frequency trend is higher than the last day’s low-frequency trend and a sell signal is generated when it is lower.
-<pre class="prettyprint linenums">def OnData(self,data):
-    self.close.append(self.Portfolio[self.syl].Price)
-    self.hpfilter(self.close[-self.numdays:len(self.close)+1], 100)
-    self.MA_rules_today = (np.mean(self.trend[-self.m : len(self.trend)]) - np.mean(self.trend[-self.n : len(self.trend)]))
-    self.MA_rules_yesterday = (np.mean(self.trend[-self.m-1: len(self.trend)-1]) - np.mean(self.trend[-self.n-1 : len(self.trend)-1]))
-    holdings = self.Portfolio[self.syl].Quantity
-
-    if self.MA_rules_today &gt; 0 and self.MA_rules_yesterday &lt; 0:
-       self.SetHoldings(self.syl, 1)
-    elif self.MA_rules_today &lt; 0 and self.MA_rules_yesterday &gt; 0:
-       self.SetHoldings(self.syl, -1)
-</pre>
-<p style="text-align: center;">Trading Signals when λ=1600</p>
-
-<img src="images/trading-signal-1600.jpg" alt="trading-signal.jpg”/>
-
-<p style="text-align: center;">Trading Signals when λ=100</p>
-<img src="images/trading-signal-100.jpg" alt="trading-signal-100.jpg"/>
-
-The above charts are the in-sample trading signals after applying MA rules on the low-frequency component. The trend curve is more smooth with larger λ. Thus when we applied MA rules, a less smooth trend will trigger more trading opportunities.
-<h2>Conclusion</h2>
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;" colspan="8">Strategy Performance for different currencies in Forex</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Currency</td>
-<td>USDCAD</td>
-<td>EURUSD</td>
-<td> USDCHF</td>
-<td> EURGBP</td>
-<td>CADUSD</td>
-<td>USDNOK</td>
-<td>USDZAR</td>
-</tr>
-<tr>
-<td>Sharp Ratio</td>
-<td>0.361</td>
-<td>0.375</td>
-<td> 0.131</td>
-<td> 0.337</td>
-<td> 0.052</td>
-<td> 0.054</td>
-<td> 0.195</td>
-</tr>
-<tr>
-<td> Total Trades</td>
-<td> 14</td>
-<td> 11</td>
-<td> 12</td>
-<td> 11</td>
-<td> 6</td>
-<td> 16</td>
-<td> 9</td>
-</tr>
-<tr>
-<td> Annual Return</td>
-<td> 3.128%</td>
-<td> 3.272%</td>
-<td> 1.162%</td>
-<td> 2.689%</td>
-<td> 0.043%</td>
-<td> 0.093%</td>
-<td> 2.201%</td>
-</tr>
-<tr>
-<td> Max Drawdown</td>
-<td> 10.3%</td>
-<td> 8.3%</td>
-<td> 24.8%</td>
-<td> 17.6%</td>
-<td> 22.5%</td>
-<td> 30.3%</td>
-<td> 23%</td>
-</tr>
-</tbody>
-</table>
-    
-The table reports the strategy performance statistics during six and a half years backtesting period. From the table we can see, most of them have the higher maximum drawdown. The number of total trades is small because we applied MA rules on the smoothed trend component.  As the author indicated in the paper, we still find that the performance of this strategy is very sensitive to the choice of lag parameters in MA rules and in a non-monotonic way.
-
-The strategy does not generate more stable profits in Forex market generally. That might because that the HP filter technique was designed to be viewed as a trend curve through the entire set of data. When we applied it in trading strategy, the entry of new data into the filter model can cause the trend line to change the trend through past data, makes it harder to identify the trend accurately.
-
-We also tried the kernel regression method in the paper. However, because the distribution parameter in the Python kernel regression package can not be adjusted. The appropriate trend component is not being extracted well from kernel regression. The smoothing effect of the regression is not good as expected. Thus here we choose not to move on to the trading part of kernel regression method.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5188517de3244425b56af3e717c90cb9'></script>
-
diff --git a/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html b/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html
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--- a/Strategy Tutorials/Tutorial09 Stock Selection Strategy Based on Fundamental Factors.html	
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-<h2>Abstract</h2>
-In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em>Ayhan Yüksel[ref]<em>Factor </em>Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>[/ref]
-<h2>Part 1. Factor Selection</h2>
-QuantConnect provides Morningstar fundamentals data for US Equities. Valuation Ratios is daily data. For others like operation ratios and financial statements data are available for multiple periods depending on the property. Please refer to <a href="https://www.quantconnect.com/data#fundamentals/usa/morningstar">Data Library</a> for detailed factors available.
-
-The algorithm is designed to test the significance of one factor each time.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2005,01,01)  #Set Start Date
-	self.SetEndDate(2012,03,01)    #Set End Date
-	self.SetCash(50000)            #Set Strategy Cash
-	self.UniverseSettings.Resolution = Resolution.Daily
-	self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
-	self.AddEquity("SPY") # add benchmark
-	self.numOfCourseSymbols = 200
-	self.numOfPortfolio = 5
-	self._changes = None
-	self.flag1 = 1  # variable to control the monthly rebalance of coarse and fine selection function
-	self.flag2 = 0  # variable to control the monthly rebalance of OnData function
-	self.flag3 = 0  # variable to record the number of rebalancing times
-        # store the monthly returns of different portfolios in a dataframe
-	self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1))
-        # schedule an event to fire at the first trading day of SPY
-	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))</pre>
-<h3>Step 1: Ranking the stocks by factor values</h3>
-1. First, we sort the stocks by daily dollar volume and take the top stocks with the highest dollar volumes as our candidates. There is a convenient way using our universe selection API. Universes are refreshed every day by default, but can be refreshed as often as required. This is controlled by the variable <code>UniverseSettings.Resolution</code>. You can refer to the <a href="https://www.quantconnect.com/docs#Universes">documentation</a> for more details.  Here we use <a href="https://www.quantconnect.com/docs#Scheduled-Events">Scheduled events API</a> to trigger code to run at the first trading day each month and use three flag variables to control the rebalancing of <code>CoarseSelection</code>, <code>FineSelection</code> and <code>Ondata</code> functions.
-
-Coarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property <code>x.HasFundamentalData</code> to exclude them from our candidate stocks pool.
-<pre class="prettyprint linenums"># sort the data by daily dollar volume and take the top entries
-def CoarseSelectionFunction(self, coarse):
-    if self.flag1:
-        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
-        sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
-        top = sortedByVolume[:self.numOfCourseSymbols]
-				return [i.Symbol for i in top]
-    else:
-        return []
-</pre>
-2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
-<pre class="prettyprint linenums">def FineSelectionFunction(self, fine):
-	if self.flag1:
-		self.flag1 = 0
-		self.flag2 = 1
-		# filter the fine by deleting equities wit zero factor value
-		filtered_fine = [x for x in fine if x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths != 0 ]
-		# sort the fine by reverse order of factor value
-		sorted_fine = sorted(filtered_fine, key=lambda x: x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths, reverse=True)
-		self.symbol = [str(x.Symbol.Value) for x in sorted_fine]
-		# factor_value = [x.ValuationRatios.PERatio for x in sorted_fine]
-		self.flag3 = self.flag3 + 1
- 	 	return []
-	else:
-		return []
-</pre>
-<h3>Step 2: Compute the monthly return of portfolios</h3>
-1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
-<pre class="prettyprint linenums">sorted_symbol = self.symbol
-self.AddEquity("SPY") # add benchmark
-for x in sorted_symbol:
-	self.AddEquity(x)
-history = self.History(20,Resolution.Daily)
-monthly_return =[]
-new_symbol_list =[]
-for j in range(len(sorted_symbol)):
-	try:
-		daily_price = []
-		for slice in history:
-			bar = slice[sorted_symbol[j]]
-			daily_price.append(float(bar.Close))
-		new_symbol_list.append(sorted_symbol[j])
- 		monthly_return.append(daily_price[-1] / daily_price[0] - 1)
-	except:
-		self.Log("No history data for " + str(sorted_symbol[j]))
-		del daily_price
-# the length of monthly_return list should be divisible by the number of portfolios
-monthly_return = monthly_return[:int(math.floor(len(monthly_return) / self.numOfPortfolio) * self.numOfPortfolio)]
-</pre>
-<div class="page" title="Page 15">
-<div class="layoutArea">
-<div class="column">
-
-2. We divide the stocks into 5 portfolios and compute the average monthly returns of each portfolio. Then we add the monthly return of benchmark "SPY" at the last line of the data frame <code>df_return</code>.
-<pre class="prettyprint linenums">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
-# calculate the average return of different portfolios
-port_avg_return = np.mean(reshape_return,axis=1).tolist()
-# add return of "SPY" as the benchmark  to the end of the return list
-benchmark_syl = self.AddEquity("SPY").Symbol
-history_benchmark = self.History(20,Resolution.Daily)
-benchmark_daily_price = [float(slice[benchmark_syl].Close) for slice in history_benchmark]
-benchmark_monthly_return = (benchmark_daily_price[-1]/benchmark_daily_price[0]) - 1
-port_avg_return.append(benchmark_monthly_return)
-self.df_return[str(self.flag3)] = port_avg_return
-</pre>
-<h3>Step 3: Generate the metrics to test the factor significance</h3>
-After getting the monthly returns of portfolios and the benchmark, we compute the average annual return and excess return over benchmark of each portfolio across the whole backtesting period. Then We generate three metrics to judge the significance of each factor.
-<ol>
- 	<li>The first metrics is the correlation between the portfolio' returns and their rank. The absolute value of the correlation coefficient should larger than 0.8.</li>
- 	<li>If the return of the rank first portfolio larger than the portfolio at the bottom of the return rankings, we define it the win portfolio and the loss portfolio and vice versa. The win probability is the probability that the win portfolio return outperform the benchmark return. The loss probability is the probability that the loss portfolio return underperform the benchmark.  If the factor is significant, both loss and win probability should greater than 0.4.</li>
- 	<li>The excess return of win portfolio should be greater than 0.25, while the excess return of loss portfolio should be lower than 0.05.</li>
-</ol>
-<pre class="prettyprint linenums">def calculate_criteria(self,df_port_return):
-	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
-	annual_return = (total_return+1)**(1./6)-1
-	excess_return = annual_return - np.array(annual_return)[-1]
-	correlation = annual_return[0:5].corr(pd.Series([5,4,3,2,1],index = annual_return[0:5].index))
-	# higher factor with higher return
-	if np.array(total_return)[0] &gt; np.array(total_return)[-2]:
-		loss_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
-		win_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
-		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
-		win_port_excess_return = np.array(excess_return)[0]
-		loss_port_excess_return = np.array(excess_return)[-2]
-	# higher factor with lower return
-	else:
-		loss_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
-		win_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
-		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
-		win_port_excess_return = np.array(excess_return)[-2]
-		loss_port_excess_return = np.array(excess_return)[0]
-	test_result = {}
-	test_result["correelation"]=correlation
-	test_result["win probality"]=win_prob
-	test_result["loss probality"]=loss_prob
-	test_result["win portfolio excess return"]=win_port_excess_return
-	test_result["loss portfolio excess return"]=loss_port_excess_return
-
-	return test_result</pre>
-</div>
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;" colspan="8">Factor Significance Testing Result</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> Factor</td>
-<td> FCFYield</td>
-<td> BuyBackYield</td>
-<td> PriceChange1M</td>
-<td>TrailingDividendYield</td>
-<td> EVToEBITDA</td>
-<td> RevenueGrowth</td>
-<td>BookValuePerShare</td>
-</tr>
-<tr>
-<td> The correlation</td>
-<td> -0.936</td>
-<td> -0.987</td>
-<td> 0.918</td>
-<td> -0.981</td>
-<td> 0.939</td>
-<td>0.89</td>
-<td>-0.92</td>
-</tr>
-<tr>
-<td>Win Probability</td>
-<td>0.630</td>
-<td>0.639</td>
-<td> 1</td>
-<td> 0.667</td>
-<td> 0.722</td>
-<td> 0.69</td>
-<td>0.69</td>
-</tr>
-<tr>
-<td>Loss probability</td>
-<td> 0.426</td>
-<td>0.472</td>
-<td> 1</td>
-<td> 0.518</td>
-<td> 0.472</td>
-<td> 0.42</td>
-<td>0.40</td>
-</tr>
-<tr>
-<td> Excess Return(Win)</td>
-<td> 0.324</td>
-<td> 0.212</td>
-<td>0.303</td>
-<td> 0.225</td>
-<td> 0.414</td>
-<td> 0.23</td>
-<td> 0.27</td>
-</tr>
-<tr>
-<td> Excess Return(Loss)</td>
-<td> 0.060</td>
-<td> 0.037</td>
-<td> -1.67</td>
-<td> 0.043</td>
-<td> 0.042</td>
-<td> 0.07</td>
-<td> 0.06</td>
-</tr>
-</tbody>
-</table>
-</div>
-</div>
-We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.
-<h2>Part 2. Stock Selection</h2>
-<h3>Step 1: Rank the stocks by factor values</h3>
-First, we remove the  stocks without fundamental data or have zero factor value. For each pre-selected factor, we rank the stocks by those factor values. The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive.
-<h3>Step 2: Calculate equally weighted composite factor scores</h3>
-The second step is using different selected factor variables to calculate an equally weighted composite factor score for each stock.
-<ul>
- 	<li>First, according to the factor order, we place our universe stocks into 5 distinct quintile portfolios, named P1, P2, P3, P4 and P5. The ranking of portfolios sets out the preference of the factor model, i.e. the first portfolio (P1) corresponds to the “most preferred” stocks, while the fifth (P5) corresponds to the “least preferred” stocks.Suppose there are n stocks in total. Then the stocks fall into the first rank portfolio will have score p, the stocks fall into the second rank portfolio will get score p-1 and so on. Then we can get a score for every stock. We did the same calculation for each factor.</li>
- 	<li>Second, we calculate a “Composite Factor Score” by combining the six-factor scores and using an equal weighting scheme. Then we get composite factor score for each stock.</li>
- 	<li> Third, we then rank the stocks in our universe according to their Composite Factor Scores and choose the highest ranked 20 stocks to construct our portfolios at the beginning of each month.</li>
- 	<li>At the end of each month, we repeat the above steps to construct the new portfolio and adjust the holding stocks.</li>
-</ul>
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=77c784268221017b324355fe63ae1d6b'></script>
diff --git a/Strategy Tutorials/Tutorial10 Short-Term Reversal Strategy in Stocks.html b/Strategy Tutorials/Tutorial10 Short-Term Reversal Strategy in Stocks.html
deleted file mode 100644
index 60ab3e3..0000000
--- a/Strategy Tutorials/Tutorial10 Short-Term Reversal Strategy in Stocks.html	
+++ /dev/null
@@ -1,81 +0,0 @@
-<h2>Abstract</h2>
-This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". [ref]<strong>Groot, Wilma (2011)</strong>. Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>[/ref] The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
-
-One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
-
-In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
-
-In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
-<h2>Method</h2>
-The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
-<h3>Step 1:?Initialization</h3>
-In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use <em>self._numOfWeeks</em> to count?the number of weeks that have passed since the start date, and <em>self.</em>_LastDay to indicate whether it is a new week. <em>self._ifWarmUp</em> is true when the <em>self._numOfWeeks</em> is 3, which means as long as next week's data come, we can make our investment decisions.??<em>self._stocks</em> is a list containing all the symbols of the 100 stocks that are taken into consideration. <em>self._values</em> is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
-<pre class="prettyprint linenums">def Initialize(self):
-        self.SetStartDate(2002, 1, 3)
-        self.SetEndDate(2016, 12, 1)
-        self.SetCash(1000000)
-
-        self.UniverseSettings.Resolution = Resolution.Daily
-        self.AddUniverse(self.CoarseSelectionFunction)
-        self._numberOfSymbols = 100
-        self._numberOfTradings = 0.1 * self._numberOfSymbols
-
-        self._numOfWeeks = 0
-        self._LastDay = -1
-        self._ifWarmUp = False
-
-        self._stocks = []
-        self._values = {}</pre>
-Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
-<pre class="prettyprint linenums">def CoarseSelectionFunction(self, coarse):
-        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-        top100 = sortedByDollarVolume[:self._numberOfSymbols]
-        list = List[Symbol]()
-        for x in top100:
-                list.Add(x.Symbol)
-        return list
-</pre>
-<h3>Step 2:?Warm Up</h3>
-Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
-<pre class="prettyprint linenums">self._stocks = []
-self.uni_symbol = None
-symbols = self.UniverseManager.Keys
-for i in symbols:
-        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
-                self.uni_symbol = i
-        for i in self.UniverseManager[self.uni_symbol].Members:
-                self._stocks.append(i.Value.Symbol)
-                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
-
-</pre>
-We get all the symbols of qualified stocks from UniverseManager and keep them in <em>self._stocks</em> which is a list. Then we create for each key in the dictionary <em>self._values</em> a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
-<pre class="prettyprint linenums">for stock in self._stocks:
-        self._values[stock].append(self.Securities[stock].Price)</pre>
-<h3>Step 3:?Weekly Rebalancing</h3>
-After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
-<pre class="prettyprint linenums">returns = {}
-for stock in self._stocks:
-        newPrice = self.Securities[stock].Price
-        oldPrice = self._values[stock].pop(0)
-        self._values[stock].append(newPrice)
-        returns[stock] = newPrice/oldPrice
-</pre>
-Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
-<pre class="prettyprint linenums">newArr = [(v,k) for k,v in returns.items()]
-newArr.sort()
-for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
-        self.SetHoldings(stock, 0)
-for ret, stock in newArr[0:self._numberOfTradings]:
-        self.SetHoldings(stock, 0.5/self._numberOfTradings)
-for ret, stock in newArr[-self._numberOfTradings:]:
-        self.SetHoldings(stock, -0.5/self._numberOfTradings)
-</pre>
-Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
-<h2>Conclusion</h2>
-In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead.
-
-If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&amp;P 500 benchmark.
-
-Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=7534f7fbeb9019ab5fbfa0d95a6d0839'></script>
\ No newline at end of file
diff --git a/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html b/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html
deleted file mode 100644
index c15e2b4..0000000
--- a/Strategy Tutorials/Tutorial11 Fundamental Factor Long:Short Strategy.html	
+++ /dev/null
@@ -1,109 +0,0 @@
-<h2>Introduction</h2>
-In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm[ref]A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>[/ref]. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
-
-<h3>Factors</h3>
-The paper strategy used three factors together to rank stocks: value, quality and momentum.
-
-Value: The most commonly used measure for value is P/B ratio(price-to-book value). Intuitively, the stocks with high P/B ratio are likely to be overpriced, and those stocks are labeled as growth stock. On the other hand, the stocks with low P/B value are considered to be value stocks. Following this logic, we use book value per share as a measure for value in our algorithm: the stocks with high book value per share rank high.
-
-Quality: Quality is a comprehensive factor. The paper used total profits over asset, gross margins, and free cash flow over assets. For simplicity, we used only operation margin as our quality factor. Here we assume that the companies with high operation margin are profitable, and their stocks are the quality ones.
-
-Momentum: The paper strategy is quarterly rebalanced, so it used recent one-year return, three-month and returns around earning events as measures for momentum. While our algorithm is monthly rebalanced, we simply use recent monthly return as our momentum factor.
-
-<h3>Ranking</h3>
-Ranking is the core process for stock selection. We first rank all the stocks according to each factor, then assign weights to each factor to get the final rank.
-
-Specifically, in our algorithm we have 250 stocks in total. We rank them according to their book values per share, operation margins and one-month returns by descending order. For each stock, its index in each sorted list is its score on each factor. e.g. If stock A ranks 1st by value, 10th by quality and 30th by momentum, its scores on the value, quality and momentum are 1, 10 and 30 respectively.
-
-The last step is to calculate the final score of each stocks. We use the same weight as the paper does: 40% on value, 40% on quality and 20% on momentum. In this way, stock A's final score is 1*0.4 + 10*0.4 + 30*0.2 = 10.4. Finally, we can rank all the stocks by their scores by <strong>ascending</strong> order. It worth attention that the stock with the <strong>lowest</strong> score is the best and it ranks 1st, and the one with the highest score is the worst.
-
-<h2>Implementation</h2>
-In this implementation, the FineSelectionFunction would be the core part because we have to rank the stocks in this process. We also need a ScheduledEvent handler to rebalance the portfolio every month. We would introduce the process step by step.
-
-<h3>CoarseSelectionFunction</h3>
-This function is a filter for the whole asset universe(around 8000 stocks). We select the top 250 with highest dollar volume to ensure liquidity. We also filter out the stocks without fundamental information or with a too low price(less than $5).
-<pre class="prettyprint linenums">
-def CoarseSelectionFunction(self, coarse):
-    # if the rebalance flag is not 1, return null list to save time.
-    if self.reb != 1:
-        return return self.long + self.short
-
-    # make universe selection once a month
-    # drop stocks which have no fundamental data or have too low prices
-    selected = [x for x in coarse if (x.HasFundamentalData)
-                and (float(x.Price) > 5)]
-
-    sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
-    top = sortedByDollarVolume[:self.num_coarse]
-    return [i.Symbol for i in top]
-</pre>
-
-<h3>FineSelectionFunction</h3>
-Here is the core function. The process is that we make three sorted list to store the stocks, and then use a dictionary to store the score information. For the dictionary, the keys are Symbols and the values are their scores. Finally we sort the dictionary to get the final rank. we store the top 20 stocks to long in the list self.long and the bottom 20 stocks to short in the list self.short.
-<pre class="prettyprint linenums">
-  def FineSelectionFunction(self, fine):
-      # return the same symbol list if it's not time to rebalance
-      if self.reb != 1:
-          return self.long+self.short
-      self.reb = 0
-
-  # drop stocks which don't have the information we need.
-  # you can try replacing those factor with your own factors here
-
-      filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
-                                      and x.ValuationRatios.PriceChange1M
-                                      and x.ValuationRatios.BookValuePerShare]
-
-      self.Log('remained to select %d'%(len(filtered_fine)))
-
-      # rank stocks by three factor.
-      sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-      sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
-      sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-
-      stock_dict = {}
-
-      # assign a score to each stock, you can also change the rule of scoring here.
-      for i,ele in enumerate(sortedByfactor1):
-          rank1 = i
-          rank2 = sortedByfactor2.index(ele)
-          rank3 = sortedByfactor3.index(ele)
-          score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
-          stock_dict[ele] = score
-
-      # sort the stocks by their scores
-      self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
-      sorted_symbol = [x[0] for x in self.sorted_stock]
-
-      # sotre the top stocks into the long_list and the bottom ones into the short_list
-      self.long = [x.Symbol for x in sorted_symbol[:self.num_fine]]
-      self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]]
-
-      return self.long+self.short</pre>
-<h3>Rebalance</h3>
-Our portfolio is rebalanced monthly, so first of all we should write a ScheduledEvent handler in the Initialize function:
-<pre class="prettyprint linenums">
-def Initialize(self):
-# Use SPY as a benchmark for market open
-    self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))
-Our rebalanced method is straightforward: We first liquidate the stocks that are no longer in the long/short list, and then assign equal weight to the stocks we are going to long or short.
-def rebalance(self):
-# if this month the stock are not going to be long/short, liquidate it.
-    long_short_list = self.long + self.short
-    for i in self.Portfolio.Values:
-        if (i.Invested) and (i.Symbol not in long_short_list):
-            self.Liquidate(i.Symbol)
-
-    # Assign each stock equally. Always hold 10% cash to avoid margin call
-    for i in self.long:
-        self.SetHoldings(i,0.9/self.num_fine)
-
-    for i in self.short:
-        self.SetHoldings(i,-0.9/self.num_fine)
-</pre>
-<h2>Summary</h2>
-The paper strategy was backtest at a 60-year timespan. It's persistent, systematic and intuitive. Although our version is very different from the paper one, the logic and intuition behind are the same. It has a better performance than the paper strategy because we added short positions.
-For further development, we can try to rank a large number of stocks, and do some portfolio optimization instead of holding stocks equally.
-
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>
diff --git a/Strategy Tutorials/images/dual thrust price range.png b/Strategy Tutorials/images/dual thrust price range.png
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diff --git a/Tutorial Series/Applied Options/Tutorial00 Applied Options.html b/Tutorial Series/Applied Options/Tutorial00 Applied Options.html
deleted file mode 100644
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--- a/Tutorial Series/Applied Options/Tutorial00 Applied Options.html	
+++ /dev/null
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-<h2>About</h2>
-<p class="p1"><span class="s1">The goal of this series is to introduce the common options strategies to those who already have basic knowledge of options markets and most importantly, we will teach users how to start your simple options trading algorithm on QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an interesting relationship between profit and stock price. We will primarily talk about the strategies like the Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will demonstrate the QuantConnect algorithm implemented in Python. </span></p>
-
-&nbsp;
-
-&nbsp;
-
-<div class="row tutorials-page-about">
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-<h3>8 Tutorials</h3>
-</div>
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-<h3>10 Backtests</h3>
-</div>
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-code" aria-hidden="true"></i></p>
-<h3>45 Code Snippets</h3>
-</div>
-</div>
-
-&nbsp;
-
-&nbsp;
-
-<h2>What Will I Learn?</h2>
-<div class="row tutorials-learn">
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
-
-&nbsp;
-
-&nbsp;
-
-</div>
-<table class="table qc-table tutorials-table">
-<tbody>
-<tr onclick="window.document.location='/tutorials/applied-options-covered-call/';"  class="clickable" >
-<td>1</td>
-<td>
-<h2>Covered Call</h2>
-A brief introduction to Covered Call Strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';"  class="clickable" >
-<td>2</td>
-<td>
-<h2>Bull Call Spread</a></h2>
-A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-long-straddle';"  class="clickable" >
-<td>3</td>
-<td>
-<h2>Long Straddle</h2>
-A brief introduction to Long Straddle strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-long-strangle';"  class="clickable" >
-<td>4</td>
-<td>
-<h2>Long Strangle</h2>
-A brief introduction to Long Strangle strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';"  class="clickable" >
-<td>5</td>
-<td>
-<h2>Butterfly Spread</h2>
-A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-iron-condor';"  class="clickable" >
-<td>6</td>
-<td>
-<h2>Iron Condor</h2>
-A brief introduction to Iron Condor strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';"  class="clickable" >
-<td>7</td>
-<td>
-<h2>Iron Butterfly</h2>
-A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/applied-options-protective-collar';"  class="clickable" >
-<td>8</td>
-<td>
-<h2>Protective Collar</h2>
-A brief introduction to Protective Collar strategy and the algorithm implemented in python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-</tbody>
-</table>
diff --git a/Tutorial Series/Applied Options/Tutorial01 Covered Call.html b/Tutorial Series/Applied Options/Tutorial01 Covered Call.html
deleted file mode 100644
index 9b4f714..0000000
--- a/Tutorial Series/Applied Options/Tutorial01 Covered Call.html	
+++ /dev/null
@@ -1,86 +0,0 @@
-<h2>Definition</h2>
-A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
-
-The payoff  is as follows:
-<pre class="prettyprint linenums">import numpy as np
-import matplotlib.pyplot as plt
-%pylab inline
-price = np.arange(110,230,1) # the stock price at expiration date
-strike = 160 # the strike price
-premium = 7.5 # the option premium
-# the payoff of short call position
-payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
-# the payoff of long stock postion
-payoff_long_stock = [i-strike for i in price]
-# the payoff of covered call
-payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
-plt.figure(figsize=(20,11))
-plt.plot(price, payoff_short_call, label = 'short call')
-plt.plot(price, payoff_long_stock, label = 'long stock')
-plt.plot(price, payoff_covered_call, label = 'covered call')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/covered_call.png" alt="covered call strategy payoff"/>
-<h2>Implementation</h2>
-<h3>Step 1: Initialize the Algorithm</h3>
-At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
-<pre class="prettyprint linenums">def Initialize(self):
-   self.SetStartDate(2016, 1, 1)
-   self.SetEndDate(2016, 3, 1)
-   self.SetCash(100000)
-   equity = self.AddEquity("IBM", Resolution.Minute)
-   option = self.AddOption("IBM", Resolution.Minute)
-   self.symbol = option.Symbol
-   # set our strike/expiry filter for this option chain
-   option.SetFilter(-3, +3, timedelta(0), timedelta(30))
-   # use the underlying equity as the benchmark
-   self.SetBenchmark(equity.Symbol)
-   self.call = "IBM" # Initialize the call contract
-</pre>
-Second, in the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
-<h3>Step 2: Choose the Call Options Contract</h3>
-First, purchase the underlying stock for 100 shares or <span class="s1">an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.</span>
-
-Second, filter out the call options from candidate contracts.<code>call = [x for x in chain if x.Right == 0]</code> For call options, the right is 0, for put options, the right is 1.
-
-Third, select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
-<pre class="prettyprint linenums">def TradeOptions(self,slice):
-		if slice.OptionChains.Count == 0: return
-    for i in slice.OptionChains:
-        if i.Key != self.symbol: continue
-        chain = i.Value
-        call = [x for x in chain if x.Right == 0] # filter the call options contracts
-        # sorted the contracts according to their expiration dates and choose the ATM options
-        contracts = sorted(sorted(call, key = lambda x: abs(chain.Underlying.Price - x.Strike)),
-                                        key = lambda x: x.Expiry, reverse=True)
-        if len(contracts) == 0: return
-        contract = contracts[0]
-        self.call = contract.Symbol
-        self.Sell(self.call, 1) # short the call options
-        if self.Portfolio["IBM"].Quantity == 0:
-            self.Buy("IBM",100)     # buy 100 the underlying stock
-            self.Log("The stock price at time 0 S(0): {}".format(self.Securities["IBM"].Price))
-</pre>
-Fourth, at the expiration date we print out the price and position information
-<pre class="prettyprint linenums">def OnData(self,slice):
-    if not self.Portfolio[self.call].Invested and self.Time.hour != 0 and self.Time.minute == 1:
-        self.TradeOptions(slice) # sell the call option
-
-    # if the option contract expires, print out the price and position information
-    if slice.Delistings.Count > 0:
-        if [x.Key == self.call for x in slice.Delistings]:
-            self.Log("stock IBM quantity: {0}".format(self.Portfolio["IBM"].Quantity))
-            self.Log("{0} quantity: {1}".format(self.call.Value, self.Portfolio[self.call].Quantity))
-            self.Log("The stock price at Expiry S(T): {}".format(self.Securities["IBM"].Price))
-</pre>
-<h2>Summary</h2>
-From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=bf562b82727fb0070c774d9e828caa68'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=cf74ca719d2579ec5afb2603bedd4b7a'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial02 Bull Call Spread.html b/Tutorial Series/Applied Options/Tutorial02 Bull Call Spread.html
deleted file mode 100644
index 33189d1..0000000
--- a/Tutorial Series/Applied Options/Tutorial02 Bull Call Spread.html	
+++ /dev/null
@@ -1,75 +0,0 @@
-<h2>Definition</h2>
-Bull Call Spread is an option strategy involved with two call option contracts with the same expiration but different strikes. The strategy buys the call options with a lower strike and sells the same amount of call options with a higher strike price.
-
-This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss from the dropping price of the stock but still create a ceiling to the profit while the price is increasing.
-
-Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
-<pre class="prettyprint linenums">price = np.arange(800,1100,1)
-k_low = 900 # lower strike price for call
-k_high = 1000 # higher strike price for call
-premium_low = 20 # premium of call option with lower strike
-premium_high = 2 # premium of call option with higher strike
-# long call with lower strike
-payoff_long_call = [max(-premium, i-k_low-premium_low ) for i in price]
-# short call with higher strike
-payoff_short_call = [min(premium, -(i-k_high-premium_high)) for i in price]
-payoff = np.sum([payoff_long_call, payoff_short_call], axis=0)
-plt.figure(figsize=(20,11))
-plt.plot(price, payoff_long_call, label = 'long call')
-plt.plot(price, payoff_short_call, label = 'short call')
-plt.plot(price, payoff, label = 'Bull Call Spread')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Bull Call Spread Payoff at Expiration',fontsize = 20)
-plt.grid(True)
-</pre>
-<img class="alignnone size-full wp-image-2585" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/bull_call_spread.png" alt="" width="1187" height="886" />
-
-From the payoff plot we can see, the maximum profit of the strategy is the difference between call option strike price sold and call option strike price purchased minus the difference between buying lower strike call premium and selling higher strike call premium.
-<h2>Implementation</h2>
-Step 1: First, you need to initialize the algorithm including set the start date, end date and the cash required. Then use <code>option.SetFilter(-6, 6, timedelta(30), timedelta(60))</code> to filter the candidate contracts which expire in 30 days to 60 days from now on. The strike price range involves both ITM and OTM options. Then we get the option chains of GOOG.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2016, 5, 1)
-	self.SetEndDate(2016, 10, 1)
-	self.SetCash(200000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-6, 6, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Choose the contracts with the same expiration date. For demonstration purpose here we sorted the contracts by their expiration dates and choose the options with the furthest expiration date in the option chain.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-	for i in optionchain:
-		if i.Key != self.symbol: continue
-		chain = i.Value
-		# sorted the optionchain by expiration date and choose the furthest date
-		expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-</pre>
-Step 3: Filter the call options from the contracts which expire on the furthest expiration date in the option chain.
-<pre class="prettyprint linenums">call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 4: Sort the call options with the same expiration date according to their strike price. Then buy the call option with the lowest strike price and sell the call with the highest strike price.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-	if len(call_contracts) == 0: continue
-	# call option contract with lower strike
-	self.call_low = call_contracts[0]
-	# call option contract with higher strike
-	self.call_high = call_contracts[-1]
-	self.Buy(self.call_low.Symbol, 1)
-	self.Sell(self.call_high.Symbol ,1)
-</pre>
-Note here you need to add the following rules in <code>OnData(self,slice)</code> method because you only need to trade options once and wait until the contracts expire. If you already had securities invested in the portfolio, then you do not need to trade new options.
-<pre class="prettyprint linenums">if not self.Portfolio.Invested:
-	self.TradeOptions(optionchain)
-</pre>
-<h2>Summary</h2>
-This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
-<h2>Algorithm</h2>
-Backtest (SetFilter)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=341ee20242a302f09496e71ecc5edcfd'></script>
-Backtest (OptionChainProvider)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=bdad11049a41b9a5563ffb2f86d5c760'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial03 Long Straddle.html b/Tutorial Series/Applied Options/Tutorial03 Long Straddle.html
deleted file mode 100644
index bea2081..0000000
--- a/Tutorial Series/Applied Options/Tutorial03 Long Straddle.html	
+++ /dev/null
@@ -1,64 +0,0 @@
-<h2>Definition</h2>
-Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(750,1000,1)
-strike = 900 # strike price for both call and put
-premium_call = 20 # premium of call option
-premium_put = 10 # premium of put option
-# payoff for the long call
-payoff_long_call = [max(-premium_call, i-strike-premium_call) for i in price]
-# payoff for the long put
-payoff_long_put = [max(-premium_put, strike-i-premium_put) for i in price]
-payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_call, label = 'Long Call')
-plt.plot(price, payoff_long_put, label = 'long put')
-plt.plot(price, payoff, label = 'Long Straddle')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Straddle Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/long_straddle.png" alt="long straddle strategy payoff"/>
-Given this plot, if the stock price moves sharply at the expiration either up or down, the Long Straddle will collect positive profit. the potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial, because the stock price can fall to zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be reached if the stock price is exactly equal to the strike price at expiration. Both options will expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 01)
-	self.SetEndDate(2017, 6, 30)
-	self.SetCash(100000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-5, 5, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)</pre>
-Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the optionchain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-	# sorted the contracts according to their strike prices
-	call_contracts = sorted(call,key = lambda x: x.Strike)
-	if len(call_contracts) == 0: continue
-	self.call = call_contracts[0]
-</pre>
-Step 3: According to the call option contract, choose the put option with the same strike price and the same expiration date. Then buy the call option and the put option at the same time and wait until expiration.
-<pre class="prettyprint linenums"> for i in chain:
-	if i.Expiry == expiry and i.Right == 1 and i.Strike ==call_contracts[0].Strike:
-	    self.put = i
-self.Buy(self.call.Symbol ,1)
-self.Buy(self.put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-There are three possible outcomes at expiration for the Long Straddle strategy. If the stock price is at the strike price at expiration, then both the call and the put become worthless and no stock position is created. If the stock price is above the strike price at expiration, the put option expires worthless, the long call is exercised, the stock is purchased at the strike price and a long stock position for is created. If the stock price is below the strike price at expiration, the call expires worthless, the long put is exercised, the stock is sold at the strike price and a short stock position is created. In this algorithm, the undelying asset is GOOG stock. We purchase both the $820 put and the $820 call at time 0. At the expiration, the share price of GOOG rises to 930 then the call option is exercised and the put options become worthless. After expiration, we hold long position for 100 shares of GOOG stock.
-<h2>Algorithm</h2>
-Backtest (Using SetFilter)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ef8d4c43b0e9f785519174510955f13a'></script>
-Backtest (Using OptionChainProvider)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=74ef2b0ad22bc82753ec3d6ea4703451'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial04 Long Strangle.html b/Tutorial Series/Applied Options/Tutorial04 Long Strangle.html
deleted file mode 100644
index cf44671..0000000
--- a/Tutorial Series/Applied Options/Tutorial04 Long Strangle.html	
+++ /dev/null
@@ -1,77 +0,0 @@
-<h2>Definition</h2>
-Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,1000,1)
-# Suppose the undelying price at time 0 is 830
-k_call = 870 # The strike price of OTM call
-k_put = 795 # The strike price of OTM put
-premium_call = 8 # premium of call option
-premium_put = 10 # premium of put option
-# payoff for the long call
-payoff_long_call = [max(-premium_call, i-k_call-premium_call) for i in price]
-# payoff for the long put
-payoff_long_put = [max(-premium_put, k_put-i-premium_put) for i in price]
-payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_call, label = 'Long Call')
-plt.plot(price, payoff_long_put, label = 'long put')
-plt.plot(price, payoff, label = 'Long Strangle')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Strangle Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/long_strangle.png" alt="long strangle strategy payoff"/>
-<div class="play-box">
-
-From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both a put and a call option. The maximum loss occurs when the stock price falls between the strike price of two options. All options are worthless on expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm including setting the start and end date, setting the cash and filtering the options contracts. Note here in SetFilter, the strike price should range from negative to positive because we need to choose out-of-the-money put and call options from candidate contracts. The strike price of OTM call should be greater than ATM options and the strike price of OTM put should be lower than ATM options.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(100000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-15, 15, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Sort the option chain by expiration date and choose an expiration date you want to trade. For demonstration purpose, here we choose options with the furthest expiration date in candidate contracts. Then filter out the call options which expire on that date.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the option chain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 3: Sort the call options by their expiration date and choose the deep OTM contract which has the largest strike price.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-if len(call_contracts) == 0: continue
-# choose the deep OTM call option
-self.call = call_contracts[-1]
-</pre>
-Step 4: Select the put options which have the same expiration date with the call option and sort the put options by strike price. Then choose the deep out-of-the-money put which has the minimum strike price among all the available put options.
-<pre class="prettyprint linenums">put_contracts = sorted([i for i in chain if i.Expiry == expiry and i.Right == 1], key = lambda x: x.Strike)
-# choose the deep OTM put option
-self.put = put_contracts[0]
-</pre>
-Step 5: Buy the call and the put options at the same time and wait until expiration.
-<pre class="prettyprint linenums">self.Buy(self.call.Symbol ,1)
-self.Buy(self.put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16. Therefore we conclude the call option is exercised then we get 100 long stocks position. The put option expires worthless.
-
-You can enter into Long Strangle if you have no clear idea of market direction but forecast there will be a great movement in the underlying asset. As the options you buy are all out of the money, that reduces the cost of this strategy. But because the call and the put options are all out of the money, the stock will need to move even more significantly than long straddle to gain positive profit from this strategy.
-
-</div>
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=33274c008409580598b3b12f64aed847'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=7f244df15dc01b09786598e8bb406415'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial05 Butterfly Spread.html b/Tutorial Series/Applied Options/Tutorial05 Butterfly Spread.html
deleted file mode 100644
index 6ddde24..0000000
--- a/Tutorial Series/Applied Options/Tutorial05 Butterfly Spread.html	
+++ /dev/null
@@ -1,105 +0,0 @@
-<h2> Definition</h2>
-Butterfly Spread strategy involves four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Name</th>
-<th> Strategy</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> Long butterfly spread with calls</td>
-<td> Buy 1 ITM call, sell 2 ATM call, buy 1 OTM call</td>
-</tr>
-<tr>
-<td> Long butterfly spread with puts</td>
-<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
-</tr>
-<tr>
-<td> Short butterfly spread with calls</td>
-<td> Sell 1 ITM call, buy 2 ATM call, sell 1 OTM call</td>
-</tr>
-<tr>
-<td> Short butterfly spread with puts</td>
-<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
-</tr>
-</tbody>
-</table>
-Butterfly Spread strategy consists of three legs with a total of four options. In this tutorial, we take Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
-
-The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(800,1100,1)
-# Suppose the undelying price at time 0 is 935
-k_itm = 915 # the strike price of ITM call
-k_otm = 955 # the strike price of OTM call
-k_atm = 935 # the strike price of ATM call
-premium_itm = 45 # the premium of ITM call
-premium_otm = 15 # the premium of OTM call
-premium_atm = 25 # the premium of ATM call
-# payoff for the long ITM call position
-payoff_itm_long = [max(-premium_itm, i-k_itm-premium_itm) for i in price]
-# payoff for the long OTM call position
-payoff_otm_long = [max(-premium_otm, i-k_otm-premium_otm) for i in price]
-# payoff for the 2 short ATM call position
-payoff_atm_short = [min(2*premium_atm, -2*(i-k_atm-premium_atm)) for i in price]
-# payoff for Butterfly Spread Strategy
-payoff = np.sum([payoff_itm_long,payoff_otm_long,payoff_atm_short], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_itm_long, label = 'Long ITM Call')
-plt.plot(price, payoff_otm_long, label = 'Long OTM Call')
-plt.plot(price, payoff_atm_short, label = 'Short 2 ATM Call')
-plt.plot(price, payoff, label = 'Long Call Butterfly Spread')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Call Butterfly Spread Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/butterfly_spread.png" alt="butterfly strategy strategy payoff"/>
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(150000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-9, 9, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the optionchain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
-<pre class="prettyprint linenums"># sorted the contracts according to their strike prices
-call_contracts = sorted(call,key = lambda x: x.Strike)
-if len(call_contracts) == 0: continue
-# choose OTM call
-self.otm_call = call_contracts[-1]
-# choose ITM call
-self.itm_call = call_contracts[0]
-# choose ATM call
-self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-</pre>
-Step 4: Purchase 1 ITM call option and 1 OTM call option, then sell 2 ATM call option.
-<pre class="prettyprint linenums">self.Sell(self.atm_call.Symbol ,2)
-self.Buy(self.itm_call.Symbol ,1)
-self.Buy(self.otm_call.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option strike at $855, 1 ITM call option strike at $810 and sell 2 ATM options strike at 835. At the expiry 05/19/2017, the share price is $930, then the long positions of the ITM option and the OTM option are all exercised, we buy 100 GOOG  shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised. We have to sell 200  GOOG shares to option holder at $835. Then we no longer hold positions after the expiration of this Long Call Butterfly Spread. Since the stock price had a sharp increase during the life of options, we make no profits from this strategy.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9ea3de0a9067624820464164334e15bd'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=196889bfa6dfba635dcdb218a8db4437'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial06 Iron Condor.html b/Tutorial Series/Applied Options/Tutorial06 Iron Condor.html
deleted file mode 100644
index 6b44e08..0000000
--- a/Tutorial Series/Applied Options/Tutorial06 Iron Condor.html	
+++ /dev/null
@@ -1,110 +0,0 @@
-<h2>Definition</h2>
-Iron Condor is an option strategy which involves four option contracts.All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Position</th>
-<th>Strike</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>long 1 OTM put</td>
-<td> A</td>
-</tr>
-<tr>
-<td>short 1 OTM put</td>
-<td> B</td>
-</tr>
-<tr>
-<td>short 1 OTM call</td>
-<td> C</td>
-</tr>
-<tr>
-<td>long 1 OTM call</td>
-<td> D</td>
-</tr>
-</tbody>
-</table>
-The Iron Condor is the combination of a bear put spread and a bull call spread in which the strike price of the long put is lower than the strike price of the long call. If the stock price is between the two short strike prices when the options expire, the strategy will be profitable.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,950,1)
-k_call_higher = 850 # the strike price of OTM call(Higher k)
-k_call_lower = 840 # the strike price of OTM call(Lower k)
-k_put_higher = 760 # the strike price of OTM put(Higher k)
-k_put_lower = 750 # the strike price of OTM put(Lower k)
-premium_call_higher = 1 # the premium of OTM call(Higher k)
-premium_call_lower = 10 # the premium of OTM call(Lower k)
-premium_put_higher = 11 # the premium of oTM put(Higher k)
-premium_put_lower = 1 # the premium of OTM put(Lower k)
-# payoff for the long put position
-payoff_long_put = [max(premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
-# payoff for the short put position
-payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_call_lower, -(i-k_call_lower+premium_call_lower)) for i in price]
-# payoff for the long call position
-payoff_long_call = [max(premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
-# payoff for Long Iron Condor Strategy
-payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
-plt.plot(price, payoff, label = 'Long Iron Condor',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Iron Condor Strategy Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/iron_condor.png" alt="iron condor strategy payoff"/>
-Here the strike price is A(750), B(760), C(850) and D(860). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 2, 1)
-	self.SetEndDate(2017, 3, 31)
-	self.SetCash(150000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-20, 20, timedelta(0), timedelta(40))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Break the candidate options into two parts: call and put options.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# filter the call and put options on the contracts
-	call = [i for i in chain if i.Right == 0]
-	put = [i for i in chain if i.Right == 1]
-</pre>
-Step 3: Sort the call and put options by their strike price respectively.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-put_contracts = sorted(put,key = lambda x: x.Strike)
-</pre>
-Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
-<pre class="prettyprint linenums">if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
-# Buy 1 OTM Put (Lower Strike)
-self.otm_put_lower = put_contracts[0]
-self.Buy(self.otm_put_lower.Symbol ,1)
-# Sell 1 OTM Put
-self.otm_put = put_contracts[15]
-self.Sell(self.otm_put.Symbol ,1)
-# Sell 1 OTM Call
-self.otm_call = call_contracts[-15]
-self.Sell(self.otm_call.Symbol ,1)
-# Buy 1 OTM Call (Higher Strike)
-self.otm_call_higher = call_contracts[-1]
-self.Buy(self.otm_call_higher.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
-<h2>Algorithm</h2>
-Backtesing using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=bb86142c5d6bd999cdca77b6d8119628'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9fe9114a22cee9e8623558b2ac8b4334'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial07 Iron Butterfly.html b/Tutorial Series/Applied Options/Tutorial07 Iron Butterfly.html
deleted file mode 100644
index e2afa5a..0000000
--- a/Tutorial Series/Applied Options/Tutorial07 Iron Butterfly.html	
+++ /dev/null
@@ -1,112 +0,0 @@
-<h2>Definition</h2>
-Iron Butterfly is an option strategy which involves four option contracts. All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C.
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Position</th>
-<th>Strike</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Buy 1 OTM put</td>
-<td> A</td>
-</tr>
-<tr>
-<td>Sell 1 ATM put</td>
-<td> B</td>
-</tr>
-<tr>
-<td>Sell 1 ATM call</td>
-<td> B</td>
-</tr>
-<tr>
-<td>Buy 1 OTM call</td>
-<td> C</td>
-</tr>
-</tbody>
-</table>
-Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,950,1)
-k_atm = 830 # the strike price of ATM call &amp; put
-k_otm_put = 800 # the strike price of OTM put
-k_otm_call = 860 # the strike price of OTM call
-premium_otm_put = 2 # the premium of OTM put
-premium_atm_put = 7 # the premium of ATM put
-premium_atm_call = 8 # the premium of ATM call
-premium_otm_call = 1 # the premium of OTM call
-# payoff for the long put position
-payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
-# payoff for the short put position
-payoff_short_put = [min(premium_atm_put, -(k_atm-i-premium_atm_put)) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_atm_call, -(i-k_atm-premium_atm_call)) for i in price]
-# payoff for the long call position
-payoff_long_call = [max(-premium_otm_call, i-k_otm_call-premium_otm_call) for i in price]
-# payoff for Iron Butterfly Strategy
-payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
-plt.plot(price, payoff, label = 'Iron Butterfly',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Iron Butterfly Strategy Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/iron_butterfly.png" alt="iron butterfly strategy payoff"/>
-From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of ATM options. In this condition, all options expire worthless and you keep all premiums received. We can see the Iron Butterfly has more narrow structures than the Iron Condor. However, the profit can be higher than with the Iron Condor as you receive more premium by selling ATM options than OTM options.
-
-The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two conditions, two puts or two calls are exercised and the other two options expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 2, 1)
-	self.SetEndDate(2017, 3, 31)
-	self.SetCash(300000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-10, 10, timedelta(0), timedelta(30))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Break the candidate contracts into the call and put options.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-    for i in optionchain:
-        if i.Key != self.symbol: continue
-	chain = i.Value
-	# filter the call and put options from the contracts
-	call = [i for i in chain if i.Right == 0]
-	put = [i for i in chain if i.Right == 1]
-</pre>
-Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-put_contracts = sorted(put,key = lambda x: x.Strike)
-if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
-</pre>
-Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
-<pre class="prettyprint linenums"># Sell 1 ATM Put
-self.atm_put = sorted(put_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-self.Sell(self.atm_put.Symbol ,1)
-# Sell 1 ATM Call
-self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-self.Sell(self.atm_call.Symbol ,1)
-# Buy 1 OTM Call
-self.otm_call = call_contracts[-1]
-self.Buy(self.otm_call.Symbol ,1)
-# Buy 1 OTM Put
-self.otm_put = put_contracts[0]
-self.Buy(self.otm_put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of ATM call, after expiration we hold 100 short positions of GOOG stock.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=66cf0da6069a3cdf9a3db53dc77f68fd'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=fd29a25361c8565f3fcd65c1c9e7dccd'></script>
diff --git a/Tutorial Series/Applied Options/Tutorial08 Protective Collar.html b/Tutorial Series/Applied Options/Tutorial08 Protective Collar.html
deleted file mode 100644
index dbd06eb..0000000
--- a/Tutorial Series/Applied Options/Tutorial08 Protective Collar.html	
+++ /dev/null
@@ -1,85 +0,0 @@
-<h2>Definition</h2>
-Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums"># Protective Collar
-price = np.arange(700,950,1)
-# assume at time 0, the price of the undelying stock is 830
-k_otm_put = 800 # the strike price of OTM put
-k_otm_call = 860 # the strike price of OTM call
-premium_otm_put = 6 # the premium of OTM put
-premium_otm_call = 2 # the premium of OTM call
-# payoff for the long put position
-payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_otm_call, -(i-k_otm_call-premium_otm_call)) for i in price]
-# payoff for the underlying stock
-payoff_stock = price - 830
-# payoff for the Protective Collar Strategy
-payoff = np.sum([payoff_long_put,payoff_short_call,payoff_stock], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_stock, label = 'Underlying Stock',linestyle='--')
-plt.plot(price, payoff, label = 'Protective Collar',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Protective Collar Strategy - Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="images/protective_collar.png" alt="protective collar strategy payoff"/>
-
-According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset add the net credit from the premium. It occurs when the stock price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with limit risk and limit profit.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(1000000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-10, +10, timedelta(0), timedelta(30))
-	# use the underlying equity as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Choose the expiration date for your options traded and break the options into the call and put contracts. The choice of expiration date depends on the holding period of stocks in your portfolio.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-    for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# choose the furthest expiration date within 30 days from now on
-	expiry = sorted(chain, key = lambda x: x.Expiry)[-1]
-	# filter the call options contracts
-	call = [x for x in chain if x.Right == 0 and x.Expiry == expiry]
-	# filter the put options contracts
-	put = [x for x in chain if x.Right == 1 and x.Expiry == expiry]
-</pre>
-Step 3: Choose the deep in-the-money call and put options in the list and then sell the call options and buy the put options.
-<pre class="prettyprint linenums">self.otm_call = sorted(call, key = lambda x: x.Strike)[-1]
-self.otm_put = sorted(put, key = lambda x: x.Strike)[0]
-if (self.otm_call is None) or (self.otm_put is None): continue
-self.Sell(self.otm_call.Symbol, 1) # sell the OTM call
-self.Buy(self.otm_put.Symbol, 1) # buy the OTM put
-</pre>
-Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
-<pre>def OnData(self,slice):
-    optionchain = slice.OptionChains
-    for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    chains = i.Value
-    contract_list = [x for x in chains]
-    if (slice.OptionChains.Count == 0) or (len(contract_list) == 0): return
-    # if you don't hold options and stocks, buy the stocks and trade the options
-    if not self.Portfolio.Invested:
-	self.Buy("GOOG",100)	 # buy 100 shares of the underlying stock
-	self.TradeOptions(optionchain)   # sell OTM call and buy OTM put
-</pre>
-<h2>Summary</h2>
-In this algorithm, at the beginning  01/04/2016, we purchased 100 GOOG shares. At the same time, we purchased a $715 put at $6 and sells a $772.5 call at $2.45. The share price of GOOG is $739.32, which is between the strike prices of two out-the-money options. At the expiry 01/15/2016, the share price of GOOG drops to $714.32. The call option expire worthless but the put option is exercised. Then we sell 100 GOOG shares at $715. Then we hold neither option positions and stock positions.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=1c5489477989a11a13f5ba5832d8fb04'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f55a1e05b90092e5b146152a704eddc'></script>
diff --git a/Tutorial Series/Applied Options/images/bull_call_spread.png b/Tutorial Series/Applied Options/images/bull_call_spread.png
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diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb
deleted file mode 100644
index e4566f2..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial01 Data Types and Data Structures-checkpoint.ipynb	
+++ /dev/null
@@ -1,512 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Welcome to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string1 = 'Welcome to'\n",
-    "my_string2 = \"QuantConnect\"\n",
-    "print(my_string1 + ' ' + my_string2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "<class 'int'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_int = 10\n",
-    "print(my_int)\n",
-    "print(type(my_int))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'str'>\n",
-      "<class 'int'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string = \"100\"\n",
-    "print(type(my_string))\n",
-    "my_int = int(my_string)\n",
-    "print(type(my_int))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'float'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string = \"100\"\n",
-    "my_float = float(my_string)\n",
-    "print(type(my_float))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "<class 'bool'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_bool = False\n",
-    "print(my_bool)\n",
-    "print(type(my_bool))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Addition  2\n",
-      "Subtraction  3\n",
-      "Multiplication  6\n",
-      "Division  5.0\n",
-      "exponent 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Addition \", 1+1)\n",
-    "print(\"Subtraction \", 5-2)\n",
-    "print(\"Multiplication \", 2*3)\n",
-    "print(\"Division \", 10/2)\n",
-    "print('exponent', 2**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3333333333333333\n",
-      "0.3333333333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(1/3)\n",
-    "print(1.0/3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 1, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "Quant\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print(len(my_list))\n",
-    "print(my_list[0])\n",
-    "print(my_list[len(my_list) -1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 'go', 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant','Connect',1,2,3]\n",
-    "my_list[2] = 'go'\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect']\n",
-      "['Connect']\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant']\n",
-    "my_list.append('Connect')\n",
-    "print(my_list)\n",
-    "my_list.remove('Quant')\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Connect', 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant','Connect',1,2,3]\n",
-    "print(my_list[1:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Connect', 1, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_list[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_list[:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "my_tuple = ('Welcome','to','QuantConnect')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('to', 'QuantConnect')\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_tuple = ('Welcome','to','QuantConnect')\n",
-    "print(my_tuple[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']\n",
-    "stock_set = set(stock_list)\n",
-    "print(stock_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "my_dic = {'AAPL':'AAPLE', 'FB':'FaceBook', 'GOOG':'Alphabet'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alphabet\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_dic['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alphabet Company\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_dic['GOOG'] = 'Alphabet Company'\n",
-    "print(my_dic['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['GOOG', 'AAPL', 'FB']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_dic.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_str = 'Welcome to QuantConnect'\n",
-    "print(my_str[8:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n",
-      "2\n",
-      "ell the e in this sentence now becomes e\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Counting the number of e appears in this sentence'.count('e'))\n",
-    "print('The first time e appears in this sentence'.find('e'))\n",
-    "print('all the a in this sentence now becomes e'.replace('a','e'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2016-04-01 09:43:00\n",
-      "09\n"
-     ]
-    }
-   ],
-   "source": [
-    "Time = '2016-04-01 09:43:00'\n",
-    "splited_list = Time.split(' ')\n",
-    "date = splited_list[0]\n",
-    "time = splited_list[1]\n",
-    "print(date, time)\n",
-    "hour = time.split(':')[0]\n",
-    "print(hour)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hour: 09, Minute:43\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_time = 'Hour: {}, Minute:{}'.format('09','43')\n",
-    "print(my_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the pi number is 3.140000\n",
-      "Welcome to Quantconnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('the pi number is %f'%3.14)\n",
-    "print('%s to %s'%('Welcome','Quantconnect'))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial02 Logical Operations and Loops-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial02 Logical Operations and Loops-checkpoint.ipynb
deleted file mode 100644
index 4270da9..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial02 Logical Operations and Loops-checkpoint.ipynb	
+++ /dev/null
@@ -1,372 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 1 == 0\n",
-    "print 1 == 1\n",
-    "print 1 != 0\n",
-    "print 5 >= 5\n",
-    "print 5 >= 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n",
-      "False\n",
-      "True\n",
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 2 > 1 and 3 > 2\n",
-    "print 2 > 1 and 3 < 2 \n",
-    "print 2 > 1 or 3 < 2\n",
-    "print 2 < 1 and 3 < 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print (3 > 2 or 1 < 3) and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))\n",
-    "print 3 > 2 or 1 < 3 and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-5-91506eaa57cd>, line 5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-91506eaa57cd>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    elif statement2:\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
-     ]
-    }
-   ],
-   "source": [
-    "if statement1:\n",
-    "    # if the statement1 is true, execute the code here.\n",
-    "    # code.....\n",
-    "    # code.....\n",
-    "elif statement2:\n",
-    "    # if the statement 1 is false, skip the codes above to this part.\n",
-    "    # code......\n",
-    "    # code......\n",
-    "else:\n",
-    "    # if none of the above statements is True, skip to this part\n",
-    "    # code......"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "i==0 is True\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = 0\n",
-    "if i == 0:\n",
-    "    print 'i==0 is True'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "q is false\n"
-     ]
-    }
-   ],
-   "source": [
-    "p = 1 > 0\n",
-    "q = 2 > 3\n",
-    "if p and q:\n",
-    "    print 'p and q is true'\n",
-    "elif p and not q:\n",
-    "    print 'q is false'\n",
-    "elif q and not p:\n",
-    "    print 'p is false'\n",
-    "else:\n",
-    "    print 'None of p and q is true'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = 0\n",
-    "while i < 5:\n",
-    "    print i\n",
-    "    i += 1 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in [1,2,3,4,5]:\n",
-    "    print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['AAPL', 'GOOG', 'IBM', 'FB', 'F', 'V', 'G', 'GE']\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "selected = ['AAPL','IBM']\n",
-    "new_list = []\n",
-    "for i in stocks:\n",
-    "    if i not in selected:\n",
-    "        new_list.append(i)\n",
-    "print stocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "FB\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "for i in stocks:\n",
-    "    print i\n",
-    "    if i == 'FB':\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "F\n",
-      "V\n",
-      "G\n",
-      "GE\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "for i in stocks:\n",
-    "    if i == 'FB':\n",
-    "        continue\n",
-    "    print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 4, 9, 16, 25]\n"
-     ]
-    }
-   ],
-   "source": [
-    "squares = []\n",
-    "for i in [1,2,3,4,5]:\n",
-    "    squares.append(i**2)\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 4, 9, 16, 25]\n"
-     ]
-    }
-   ],
-   "source": [
-    "list = [1,2,3,4,5]\n",
-    "squares = [x**2 for x in list]\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['AAPL', 'IBM']\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "selected = ['AAPL','IBM']\n",
-    "new_list = [x for x in stocks if x in selected]\n",
-    "print new_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]\n",
-      "['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]\n",
-    "print [str(x)+' vs '+str(y) for x in ['AAPL','GOOG','IBM','FB'] for y in ['F','V','G','GE'] if x!=y]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb
deleted file mode 100644
index 261e947..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial03 Functions and Objective-Oriented Programming-checkpoint.ipynb	
+++ /dev/null
@@ -1,418 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "50\n"
-     ]
-    }
-   ],
-   "source": [
-    "def product(x,y):\n",
-    "    return x*y\n",
-    "print(product(2,3))\n",
-    "print(product(5,10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Welcome to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "def say_hi():\n",
-    "    print('Welcome to QuantConnect')\n",
-    "say_hi()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "range(0, 10)\n",
-      "range(1, 11)\n",
-      "range(1, 11, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(range(10))\n",
-    "print(range(1,11))\n",
-    "print(range(1,11,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The length of tickers is 8\n",
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "FB\n",
-      "F\n",
-      "V\n",
-      "G\n",
-      "GE\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print('The length of tickers is {}'.format(len(tickers)))\n",
-    "for i in range(len(tickers)):\n",
-    "    print(tickers[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4, 4, 3, 2, 1, 1, 1, 2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print(list(map(len,tickers)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x: x**2, range(10)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[6, 6, 6, 6, 6]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted([5,2,3,4,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "sorted(price_list, key = lambda x: x[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('GOOG', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "sorted(price_list, key = lambda x: x[1],reverse = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "price_list.sort(key = lambda x: x[1])\n",
-    "print(price_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class stock:\n",
-    "    def __init__(self, ticker, open, close, volume):\n",
-    "        self.ticker = ticker\n",
-    "        self.open = open\n",
-    "        self.close = close\n",
-    "        self.volume = volume\n",
-    "        self.rate_return = float(close)/open - 1\n",
-    " \n",
-    "    def update(self, open, close):\n",
-    "        self.open = open\n",
-    "        self.close = close\n",
-    "        self.rate_return = float(self.close)/self.open - 1\n",
-    " \n",
-    "    def print_return(self):\n",
-    "        print(self.rate_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "apple = stock('AAPL', 143.69, 144.09, 20109375)\n",
-    "google = stock('GOOG', 898.7, 911.7, 1561616)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.014465338822744034\n",
-      "0.0006573181419806673\n"
-     ]
-    }
-   ],
-   "source": [
-    "apple.ticker\n",
-    "google.print_return()\n",
-    "google.update(912.8,913.4)\n",
-    "google.print_return()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Tim Cook'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "apple.ceo = 'Tim Cook'\n",
-    "apple.ceo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'ceo',\n",
-       " 'close',\n",
-       " 'open',\n",
-       " 'print_return',\n",
-       " 'rate_return',\n",
-       " 'ticker',\n",
-       " 'update',\n",
-       " 'volume']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(apple)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class child(stock):\n",
-    "    def __init__(self,name):\n",
-    "        self.name = name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aa\n",
-      "100\n",
-      "102\n",
-      "0.020000000000000018\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "aa = child('aa')\n",
-    "print(aa.name)\n",
-    "aa.update(100,102)\n",
-    "print(aa.open)\n",
-    "print(aa.close)\n",
-    "print(aa.print_return())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb
deleted file mode 100644
index 32fd4f9..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial04 NumPy and Basic Pandas-checkpoint.ipynb	
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[143.73 145.83 143.68 144.02 143.5  142.62] <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
-    "price_array = np.array(price_list)\n",
-    "print(price_array, type(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 3]\n",
-      " [2 4]] <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "Ar = np.array([[1,3],[2,4]])\n",
-    "print(Ar, type(Ar))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Ar.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 3]\n",
-      "[2 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Ar[0])\n",
-    "print(Ar[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the first column:  [1 2]\n",
-      "the second column:  [3 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('the first column: ', Ar[:,0])\n",
-    "print('the second column: ', Ar[:,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.log(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.89666666666668\n",
-      "0.9673790478515796\n",
-      "863.38\n",
-      "145.83\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.mean(price_array))\n",
-    "print(np.std(price_array))\n",
-    "print(np.sum(price_array))\n",
-    "print(np.max(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    143.73\n",
-       "1    145.83\n",
-       "2    143.68\n",
-       "3    144.02\n",
-       "4    143.50\n",
-       "5    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
-    "s = pd.Series(price)\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    143.73\n",
-       "b    145.83\n",
-       "c    143.68\n",
-       "d    144.02\n",
-       "e    143.50\n",
-       "f    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = pd.Series(price,index = ['a','b','c','d','e','f'])\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6    143.73\n",
-       "5    145.83\n",
-       "4    143.68\n",
-       "3    144.02\n",
-       "2    143.50\n",
-       "1    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.index = [6,5,4,3,2,1]\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "dtype: float64\n",
-      "6    143.73\n",
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[1:])\n",
-    "print(s[:-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.68\n",
-      "6    143.73\n",
-      "5    145.83\n",
-      "4      0.00\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[4])\n",
-    "s[4] = 0\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0    143.73\n",
-      "1    145.83\n",
-      "2    143.68\n",
-      "3    144.02\n",
-      "4    143.50\n",
-      "5    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "Apple Price List\n"
-     ]
-    }
-   ],
-   "source": [
-    "s = pd.Series(price, name = 'Apple Price List')\n",
-    "print(s)\n",
-    "print(s.name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
-      "               '2017-01-05', '2017-01-06'],\n",
-      "              dtype='datetime64[ns]', freq='D')\n",
-      "2017-01-01    143.73\n",
-      "2017-01-02    145.83\n",
-      "2017-01-03    143.68\n",
-      "2017-01-04    144.02\n",
-      "2017-01-05    143.50\n",
-      "2017-01-06    142.62\n",
-      "Freq: D, Name: Apple Price List, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "time_index = pd.date_range('2017-01-01',periods = len(s),freq = 'D')\n",
-    "print(time_index)\n",
-    "s.index = time_index\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6    143.73\n",
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "142.62\n"
-     ]
-    }
-   ],
-   "source": [
-    "s.index = [6,5,4,3,2,1]\n",
-    "print(s)\n",
-    "print(s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "145.83\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s.iloc[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.68\n"
-     ]
-    }
-   ],
-   "source": [
-    "s.index = time_index\n",
-    "print(s['2017-01-03'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2017-01-02    145.83\n",
-      "2017-01-03    143.68\n",
-      "2017-01-04    144.02\n",
-      "2017-01-05    143.50\n",
-      "Freq: D, Name: Apple Price List, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s['2017-01-02':'2017-01-05'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6    143.73\n",
-      "4    143.68\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "[6    False\n",
-      "5    False\n",
-      "4    False\n",
-      "3     True\n",
-      "2    False\n",
-      "1    False\n",
-      "Name: Apple Price List, dtype: bool]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[s < np.mean(s)] )\n",
-    "print([(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb
deleted file mode 100644
index a67e05c..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial05 Pandas-Resampling and DataFrame-checkpoint.ipynb	
+++ /dev/null
@@ -1,1540 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "aapl_table = quandl.get('WIKI/AAPL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aapl = aapl_table['Adj. Close']['2017']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-03    114.715378\n",
-      "2017-01-04    114.586983\n",
-      "2017-01-05    115.169696\n",
-      "2017-01-06    116.453639\n",
-      "2017-01-09    117.520300\n",
-      "2017-01-10    117.638817\n",
-      "2017-01-11    118.270913\n",
-      "2017-01-12    117.777088\n",
-      "2017-01-13    117.569682\n",
-      "2017-01-17    118.517825\n",
-      "2017-01-18    118.507948\n",
-      "2017-01-19    118.300542\n",
-      "2017-01-20    118.517825\n",
-      "2017-01-23    118.596837\n",
-      "2017-01-24    118.488195\n",
-      "2017-01-25    120.374604\n",
-      "2017-01-26    120.433863\n",
-      "2017-01-27    120.443739\n",
-      "2017-01-30    120.127692\n",
-      "2017-01-31    119.851150\n",
-      "2017-02-01    127.159749\n",
-      "2017-02-02    126.942467\n",
-      "2017-02-03    127.485673\n",
-      "2017-02-06    128.680728\n",
-      "2017-02-07    129.905412\n",
-      "2017-02-08    130.409113\n",
-      "2017-02-09    131.347379\n",
-      "2017-02-10    131.049809\n",
-      "2017-02-13    132.210332\n",
-      "2017-02-14    133.926319\n",
-      "                 ...    \n",
-      "2017-11-16    171.100000\n",
-      "2017-11-17    170.150000\n",
-      "2017-11-20    169.980000\n",
-      "2017-11-21    173.140000\n",
-      "2017-11-22    174.960000\n",
-      "2017-11-24    174.970000\n",
-      "2017-11-27    174.090000\n",
-      "2017-11-28    173.070000\n",
-      "2017-11-29    169.480000\n",
-      "2017-11-30    171.850000\n",
-      "2017-12-01    171.050000\n",
-      "2017-12-04    169.800000\n",
-      "2017-12-05    169.640000\n",
-      "2017-12-06    169.010000\n",
-      "2017-12-07    169.452000\n",
-      "2017-12-08    169.370000\n",
-      "2017-12-11    172.670000\n",
-      "2017-12-12    171.700000\n",
-      "2017-12-13    172.270000\n",
-      "2017-12-14    172.220000\n",
-      "2017-12-15    173.870000\n",
-      "2017-12-18    176.420000\n",
-      "2017-12-19    174.540000\n",
-      "2017-12-20    174.350000\n",
-      "2017-12-21    175.010000\n",
-      "2017-12-22    175.010000\n",
-      "2017-12-26    170.570000\n",
-      "2017-12-27    170.600000\n",
-      "2017-12-28    171.080000\n",
-      "2017-12-29    169.230000\n",
-      "Name: Adj. Close, Length: 249, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-03-01    138.657681\n",
-      "2017-03-02    137.834404\n",
-      "2017-03-03    138.647762\n",
-      "2017-03-06    138.211326\n",
-      "2017-03-07    138.389868\n",
-      "2017-03-08    137.874080\n",
-      "2017-03-09    137.556672\n",
-      "2017-03-10    138.012946\n",
-      "2017-03-13    138.072460\n",
-      "2017-03-14    137.864161\n",
-      "2017-03-15    139.322254\n",
-      "2017-03-16    139.550391\n",
-      "2017-03-17    138.856061\n",
-      "2017-03-20    140.314154\n",
-      "2017-03-21    138.707276\n",
-      "2017-03-22    140.274478\n",
-      "2017-03-23    139.778528\n",
-      "2017-03-24    139.500796\n",
-      "2017-03-27    139.738852\n",
-      "2017-03-28    142.635200\n",
-      "2017-03-29    142.952608\n",
-      "2017-03-30    142.764147\n",
-      "2017-03-31    142.496334\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl['2017-3'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-02-01    127.159749\n",
-       "2017-02-02    126.942467\n",
-       "2017-02-03    127.485673\n",
-       "2017-02-06    128.680728\n",
-       "2017-02-07    129.905412\n",
-       "2017-02-08    130.409113\n",
-       "2017-02-09    131.347379\n",
-       "2017-02-10    131.049809\n",
-       "2017-02-13    132.210332\n",
-       "2017-02-14    133.926319\n",
-       "2017-02-15    134.412350\n",
-       "2017-02-16    134.248686\n",
-       "2017-02-17    134.620649\n",
-       "2017-02-21    135.592711\n",
-       "2017-02-22    135.999390\n",
-       "2017-02-23    135.424088\n",
-       "2017-02-24    135.553035\n",
-       "2017-02-27    135.820848\n",
-       "2017-02-28    135.880362\n",
-       "2017-03-01    138.657681\n",
-       "2017-03-02    137.834404\n",
-       "2017-03-03    138.647762\n",
-       "2017-03-06    138.211326\n",
-       "2017-03-07    138.389868\n",
-       "2017-03-08    137.874080\n",
-       "2017-03-09    137.556672\n",
-       "2017-03-10    138.012946\n",
-       "2017-03-13    138.072460\n",
-       "2017-03-14    137.864161\n",
-       "2017-03-15    139.322254\n",
-       "                 ...    \n",
-       "2017-03-17    138.856061\n",
-       "2017-03-20    140.314154\n",
-       "2017-03-21    138.707276\n",
-       "2017-03-22    140.274478\n",
-       "2017-03-23    139.778528\n",
-       "2017-03-24    139.500796\n",
-       "2017-03-27    139.738852\n",
-       "2017-03-28    142.635200\n",
-       "2017-03-29    142.952608\n",
-       "2017-03-30    142.764147\n",
-       "2017-03-31    142.496334\n",
-       "2017-04-03    142.536010\n",
-       "2017-04-04    143.597342\n",
-       "2017-04-05    142.853418\n",
-       "2017-04-06    142.496334\n",
-       "2017-04-07    142.178926\n",
-       "2017-04-10    142.010303\n",
-       "2017-04-11    140.482777\n",
-       "2017-04-12    140.651400\n",
-       "2017-04-13    139.907475\n",
-       "2017-04-17    140.681157\n",
-       "2017-04-18    140.056260\n",
-       "2017-04-19    139.540472\n",
-       "2017-04-20    141.286216\n",
-       "2017-04-21    141.117593\n",
-       "2017-04-24    142.476496\n",
-       "2017-04-25    143.369205\n",
-       "2017-04-26    142.487208\n",
-       "2017-04-27    142.625281\n",
-       "2017-04-28    142.486415\n",
-       "Name: Adj. Close, Length: 61, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aapl['2017-2':'2017-4']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-03    114.715378\n",
-      "2017-01-04    114.586983\n",
-      "2017-01-05    115.169696\n",
-      "2017-01-06    116.453639\n",
-      "2017-01-09    117.520300\n",
-      "Name: Adj. Close, dtype: float64\n",
-      "Date\n",
-      "2017-12-15    173.87\n",
-      "2017-12-18    176.42\n",
-      "2017-12-19    174.54\n",
-      "2017-12-20    174.35\n",
-      "2017-12-21    175.01\n",
-      "2017-12-22    175.01\n",
-      "2017-12-26    170.57\n",
-      "2017-12-27    170.60\n",
-      "2017-12-28    171.08\n",
-      "2017-12-29    169.23\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl.head(5))\n",
-    "print(aapl.tail(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    118.093136\n",
-      "2017-02-28    132.456268\n",
-      "2017-03-31    139.478802\n",
-      "2017-04-30    141.728436\n",
-      "2017-05-31    151.386305\n",
-      "2017-06-30    147.233064\n",
-      "2017-07-31    147.706190\n",
-      "2017-08-31    158.856375\n",
-      "2017-09-30    157.606500\n",
-      "2017-10-31    157.811627\n",
-      "2017-11-30    172.214500\n",
-      "2017-12-31    171.893100\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "by_month = aapl.resample('M').mean()\n",
-    "print(by_month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-08    115.231424\n",
-      "2017-01-15    117.755360\n",
-      "2017-01-22    118.461035\n",
-      "2017-01-29    119.667448\n",
-      "2017-02-05    124.313346\n",
-      "Freq: W-SUN, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "by_week = aapl.resample('W').mean()\n",
-    "print(by_week.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    120.443739\n",
-       "2017-02-28    135.999390\n",
-       "2017-03-31    142.952608\n",
-       "2017-04-30    143.597342\n",
-       "2017-05-31    155.469192\n",
-       "2017-06-30    154.821818\n",
-       "2017-07-31    152.839860\n",
-       "2017-08-31    164.000000\n",
-       "2017-09-30    164.050000\n",
-       "2017-10-31    169.040000\n",
-       "2017-11-30    175.880000\n",
-       "2017-12-31    176.420000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aapl.resample('M').max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "three_day = aapl.resample('3D').mean()\n",
-    "two_week = aapl.resample('2W').mean()\n",
-    "two_month = aapl.resample('2M').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "std = aapl.resample('W').std()\n",
-    "max = aapl.resample('W').max()\n",
-    "min = aapl.resample('W').min()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.045940\n",
-      "2017-02-28    0.070409\n",
-      "2017-03-31    0.033823\n",
-      "2017-04-30   -0.007736\n",
-      "2017-05-31    0.039829\n",
-      "2017-06-30   -0.073528\n",
-      "2017-07-31    0.033035\n",
-      "2017-08-31    0.047890\n",
-      "2017-09-30   -0.049112\n",
-      "2017-10-31    0.094252\n",
-      "2017-11-30    0.022247\n",
-      "2017-12-31   -0.003357\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
-    "print(monthly_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    119.851150\n",
-      "2017-02-28    135.880362\n",
-      "2017-03-31    142.496334\n",
-      "2017-04-30    142.486415\n",
-      "2017-05-31    152.142689\n",
-      "2017-06-30    143.438008\n",
-      "2017-07-31    148.248489\n",
-      "2017-08-31    164.000000\n",
-      "2017-09-30    154.120000\n",
-      "2017-10-31    169.040000\n",
-      "2017-11-30    171.850000\n",
-      "2017-12-31    169.230000\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
-    "print(last_day)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "by_week = aapl.resample('W').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    1.781512\n",
-       "2017-03-31    4.323834\n",
-       "2017-05-31    5.454495\n",
-       "2017-07-31    3.865333\n",
-       "2017-09-30    3.634223\n",
-       "2017-11-30    8.011704\n",
-       "2018-01-31    2.291229\n",
-       "Freq: 2M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "twomon = aapl.resample('2M').std()\n",
-    "twomon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    119.851150\n",
-       "2017-02-28    135.880362\n",
-       "2017-03-31    142.496334\n",
-       "2017-04-30    142.486415\n",
-       "2017-05-31    152.142689\n",
-       "2017-06-30    143.438008\n",
-       "2017-07-31    148.248489\n",
-       "2017-08-31    164.000000\n",
-       "2017-09-30    154.120000\n",
-       "2017-10-31    169.040000\n",
-       "2017-11-30    171.850000\n",
-       "2017-12-31    169.230000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
-    "last_day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    0.045940\n",
-       "2017-02-28    0.070409\n",
-       "2017-03-31    0.033823\n",
-       "2017-04-30   -0.007736\n",
-       "2017-05-31    0.039829\n",
-       "2017-06-30   -0.073528\n",
-       "2017-07-31    0.033035\n",
-       "2017-08-31    0.047890\n",
-       "2017-09-30   -0.049112\n",
-       "2017-10-31    0.094252\n",
-       "2017-11-30    0.022247\n",
-       "2017-12-31   -0.003357\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
-    "monthly_return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.02114094011940022\n",
-      "0.04775652864223314\n",
-      "0.09425168306576914\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(monthly_return.mean())\n",
-    "print(monthly_return.std())\n",
-    "print(monthly_return.max())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31          NaN\n",
-      "2017-02-28    16.029211\n",
-      "2017-03-31     6.615972\n",
-      "2017-04-30    -0.009919\n",
-      "2017-05-31     9.656274\n",
-      "2017-06-30    -8.704681\n",
-      "2017-07-31     4.810482\n",
-      "2017-08-31    15.751511\n",
-      "2017-09-30    -9.880000\n",
-      "2017-10-31    14.920000\n",
-      "2017-11-30     2.810000\n",
-      "2017-12-31    -2.620000\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n",
-      "Date\n",
-      "2017-01-31         NaN\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(last_day.diff())\n",
-    "print(last_day.pct_change())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    249.000000\n",
-      "mean     149.815713\n",
-      "std       15.065681\n",
-      "min      114.586983\n",
-      "25%      140.651400\n",
-      "50%      151.890000\n",
-      "75%      159.780000\n",
-      "max      176.420000\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.000000\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "print(daily_return.fillna(0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.133743\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "print(daily_return.fillna(method = 'bfill'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-02-28    0.133743\n",
-       "2017-03-31    0.048690\n",
-       "2017-04-30   -0.000070\n",
-       "2017-05-31    0.067770\n",
-       "2017-06-30   -0.057214\n",
-       "2017-07-31    0.033537\n",
-       "2017-08-31    0.106251\n",
-       "2017-09-30   -0.060244\n",
-       "2017-10-31    0.096808\n",
-       "2017-11-30    0.016623\n",
-       "2017-12-31   -0.015246\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "daily_return.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# aapl.resample('M').agg(lambda x: max(x) - min(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              AAPL    GOOG     IBM\n",
-      "2017-07-03  143.50  898.70  155.58\n",
-      "2017-07-04  144.09  911.71  153.67\n",
-      "2017-07-05  142.73  906.69  152.36\n",
-      "2017-07-06  144.18  918.59  152.94\n",
-      "2017-07-07  143.77  926.99  153.49\n"
-     ]
-    }
-   ],
-   "source": [
-    "dict = {'AAPL': [143.5, 144.09, 142.73, 144.18, 143.77],'GOOG':[898.7, 911.71, 906.69, 918.59, 926.99],\n",
-    "        'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}\n",
-    "data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')\n",
-    "df = pd.DataFrame(dict, index = data_index)\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     AAPL    GOOG\n",
-      "0  143.50  898.70\n",
-      "1  144.09  911.71\n",
-      "2  142.73  906.69\n",
-      "3  144.18  918.59\n",
-      "4  143.77  926.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
-    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
-    "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print(data_frame)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['AAPL', 'GOOG', 'IBM'], dtype='object')\n",
-      "2017-07-03    143.50\n",
-      "2017-07-04    144.09\n",
-      "2017-07-05    142.73\n",
-      "2017-07-06    144.18\n",
-      "2017-07-07    143.77\n",
-      "Freq: D, Name: AAPL, dtype: float64\n",
-      "2017-07-03    898.70\n",
-      "2017-07-04    911.71\n",
-      "2017-07-05    906.69\n",
-      "2017-07-06    918.59\n",
-      "2017-07-07    926.99\n",
-      "Freq: D, Name: GOOG, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.columns)\n",
-    "print(df.AAPL)\n",
-    "print(df['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2018-03-21    171.270\n",
-      "2018-03-22    168.845\n",
-      "2018-03-23    164.940\n",
-      "2018-03-26    172.770\n",
-      "2018-03-27    168.340\n",
-      "Name: Close, dtype: float64\n",
-      "Date\n",
-      "2018-03-21    35247358.0\n",
-      "2018-03-22    41051076.0\n",
-      "2018-03-23    40248954.0\n",
-      "2018-03-26    36272617.0\n",
-      "2018-03-27    38962839.0\n",
-      "Name: Adj. Volume, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = aapl_table\n",
-    "print(df.Close.tail(5))\n",
-    "print(df['Adj. Volume'].tail(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close      Volume  Ex-Dividend  \\\n",
-      "Date                                                                    \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0   \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0   \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0   \n",
-      "2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0   \n",
-      "2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0   \n",
-      "2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0   \n",
-      "2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0   \n",
-      "2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0   \n",
-      "2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0   \n",
-      "2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0   \n",
-      "2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0   \n",
-      "2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0   \n",
-      "\n",
-      "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
-      "Date                                                                      \n",
-      "2016-01-31          1.0   91.581233   94.044912   91.156128   94.044912   \n",
-      "2016-02-29          1.0   94.084911   95.415659   93.880927   93.919781   \n",
-      "2016-03-31          1.0  106.576465  106.751308  105.760531  105.867380   \n",
-      "2016-04-30          1.0   91.297138   92.006223   89.859540   91.054300   \n",
-      "2016-05-31          1.0   97.337844   98.119674   96.575559   97.591939   \n",
-      "2016-06-30          1.0   92.295040   93.594832   92.158220   93.428693   \n",
-      "2016-07-31          1.0  101.823594  102.175417  101.325177  101.843140   \n",
-      "2016-08-31          1.0  103.816156  104.710177  103.796505  104.248477   \n",
-      "2016-09-30          1.0  110.497491  111.391610  109.849008  111.077195   \n",
-      "2016-10-31          1.0  111.666724  112.236603  111.224577  111.558644   \n",
-      "2016-11-30          1.0  110.182071  110.814166  108.908004  109.154917   \n",
-      "2016-12-31          1.0  115.209202  115.752409  114.004271  114.389454   \n",
-      "\n",
-      "            Adj. Volume  \n",
-      "Date                     \n",
-      "2016-01-31   64416504.0  \n",
-      "2016-02-29   35216277.0  \n",
-      "2016-03-31   25888449.0  \n",
-      "2016-04-30   68531478.0  \n",
-      "2016-05-31   42307212.0  \n",
-      "2016-06-30   35836356.0  \n",
-      "2016-07-31   27733688.0  \n",
-      "2016-08-31   29662406.0  \n",
-      "2016-09-30   36379106.0  \n",
-      "2016-10-31   26419398.0  \n",
-      "2016-11-30   36162258.0  \n",
-      "2016-12-31   30586265.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_2016 = df['2016']\n",
-    "aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])\n",
-    "print(aapl_month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close\n",
-      "Date                                        \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82\n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_bar = aapl_month[['Open', 'High', 'Low', 'Close']]\n",
-    "print(aapl_bar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open    High     Low   Close\n",
-      "Date                                      \n",
-      "2016-03-31  109.72  109.90  108.88  108.99\n",
-      "2016-04-30   93.99   94.72   92.51   93.74\n",
-      "2016-05-31   99.60  100.40   98.82   99.86\n",
-      "2016-06-30   94.44   95.77   94.30   95.60\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close\n",
-      "Date                                        \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82\n"
-     ]
-    }
-   ],
-   "source": [
-    "above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]\n",
-    "print(above)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_bar['rate_return'] = aapl_bar.Close.pct_change()\n",
-    "print(aapl_bar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Open   High    Low  Close  rate_return\n",
-      "Date                                               \n",
-      "2016-01-31  False  False  False  False         True\n",
-      "2016-02-29  False  False  False  False        False\n",
-      "2016-03-31  False  False  False  False        False\n",
-      "2016-04-30  False  False  False  False        False\n",
-      "2016-05-31  False  False  False  False        False\n",
-      "2016-06-30  False  False  False  False        False\n",
-      "2016-07-31  False  False  False  False        False\n",
-      "2016-08-31  False  False  False  False        False\n",
-      "2016-09-30  False  False  False  False        False\n",
-      "2016-10-31  False  False  False  False        False\n",
-      "2016-11-30  False  False  False  False        False\n",
-      "2016-12-31  False  False  False  False        False\n",
-      "\n",
-      "------------------ separate line -----------------\n",
-      "\n",
-      "         Open   High    Low  Close rate_return\n",
-      "count      12     12     12     12          12\n",
-      "unique      1      1      1      1           2\n",
-      "top     False  False  False  False       False\n",
-      "freq       12     12     12     12          11\n"
-     ]
-    }
-   ],
-   "source": [
-    "missing = aapl_bar.isnull()\n",
-    "print(missing)\n",
-    "print('\\n------------------ separate line -----------------\\n')\n",
-    "print(missing.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Open   High    Low  Close  rate_return\n",
-      "Date                                               \n",
-      "2016-01-31  False  False  False  False         True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(missing[missing.rate_return == True])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34     0.000000\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
-     ]
-    }
-   ],
-   "source": [
-    "drop = aapl_bar.dropna()\n",
-    "print(drop)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "fill = aapl_bar.fillna(0)\n",
-    "print(fill)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     AAPL    GOOG\n",
-      "0  143.50  898.70\n",
-      "1  144.09  911.71\n",
-      "2  142.73  906.69\n",
-      "3  144.18  918.59\n",
-      "4  143.77  926.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
-    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
-    "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print(data_frame)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2016-01-31    4.578210\n",
-      "2016-02-29    4.571510\n",
-      "2016-03-31    4.691256\n",
-      "2016-04-30    4.540525\n",
-      "2016-05-31    4.603769\n",
-      "2016-06-30    4.560173\n",
-      "2016-07-31    4.646408\n",
-      "2016-08-31    4.664382\n",
-      "2016-09-30    4.727830\n",
-      "2016-10-31    4.732155\n",
-      "2016-11-30    4.705197\n",
-      "2016-12-31    4.752037\n",
-      "Freq: M, Name: log_price, dtype: float64\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open      High     Low   Close  rate_return  log_price\n",
-      "Date                                                                \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037\n"
-     ]
-    }
-   ],
-   "source": [
-    "log_price = np.log(aapl_bar.Close)\n",
-    "log_price.name = 'log_price'\n",
-    "print(log_price)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "concat = pd.concat([aapl_bar, log_price], axis = 1)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                Volume  Split Ratio\n",
-      "Date                               \n",
-      "2016-10-31  26419398.0          1.0\n",
-      "2016-11-30  36162258.0          1.0\n",
-      "2016-12-31  30586265.0          1.0\n",
-      "2017-01-31  49200993.0          1.0\n",
-      "2017-02-28  23482860.0          1.0\n",
-      "2017-03-31  19661651.0          1.0\n",
-      "2017-04-30  20247187.0          1.0\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open     High     Low   Close\n",
-      "Date                                       \n",
-      "2016-10-31  113.65  114.230  113.20  113.54\n",
-      "2016-11-30  111.56  112.200  110.27  110.52\n",
-      "2016-12-31  116.65  117.200  115.43  115.82\n",
-      "2017-01-31  121.15  121.390  120.62  121.35\n",
-      "2017-02-28  137.08  137.435  136.70  136.99\n",
-      "2017-03-31  143.72  144.270  143.01  143.66\n",
-      "2017-04-30  144.09  144.300  143.27  143.65\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])\n",
-    "print(df_volume)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])\n",
-    "print(df_2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return      Volume  \\\n",
-      "Date                                                                    \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN   \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN   \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN   \n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN   \n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN   \n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN   \n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN   \n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN   \n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN   \n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0   \n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0   \n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0   \n",
-      "2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0   \n",
-      "2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0   \n",
-      "2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0   \n",
-      "2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0   \n",
-      "\n",
-      "            Split Ratio  \n",
-      "Date                     \n",
-      "2016-01-31          NaN  \n",
-      "2016-02-29          NaN  \n",
-      "2016-03-31          NaN  \n",
-      "2016-04-30          NaN  \n",
-      "2016-05-31          NaN  \n",
-      "2016-06-30          NaN  \n",
-      "2016-07-31          NaN  \n",
-      "2016-08-31          NaN  \n",
-      "2016-09-30          NaN  \n",
-      "2016-10-31          1.0  \n",
-      "2016-11-30          1.0  \n",
-      "2016-12-31          1.0  \n",
-      "2017-01-31          1.0  \n",
-      "2017-02-28          1.0  \n",
-      "2017-03-31          1.0  \n",
-      "2017-04-30          1.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar,df_volume],axis = 1)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open    High     Low   Close  rate_return      Volume  \\\n",
-      "Date                                                                  \n",
-      "2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0   \n",
-      "2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0   \n",
-      "2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0   \n",
-      "\n",
-      "            Split Ratio  \n",
-      "Date                     \n",
-      "2016-10-31          1.0  \n",
-      "2016-11-30          1.0  \n",
-      "2016-12-31          1.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Close      High     Low    Open  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
-      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
-      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
-      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
-      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "append = aapl_bar.append(df_2017)\n",
-    "print(append)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Close      High     Low    Open  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
-      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
-      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
-      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
-      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            Change   Close      High     Low    Open  rate_return\n",
-      "Date                                                             \n",
-      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
-      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
-      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
-      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
-      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
-      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
-      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_2017.columns = ['Change', 'High','Low','Close']\n",
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            Change   Close      High     Low    Open  rate_return\n",
-      "Date                                                             \n",
-      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
-      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
-      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
-      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
-      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
-      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
-      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb
deleted file mode 100644
index 8c4c7b6..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial06 Rate of Return, Mean and Variance-checkpoint.ipynb	
+++ /dev/null
@@ -1,146 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.020000000000000018\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "rate_return = 102.0/100 - 1\n",
-    "print(rate_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "               Open   Close  log_price  log_return\n",
-      "Date                                              \n",
-      "2017-03-01  137.890  139.79   4.940141         NaN\n",
-      "2017-03-02  140.000  138.96   4.934186   -0.005955\n",
-      "2017-03-03  138.780  139.78   4.940070    0.005884\n",
-      "2017-03-06  139.365  139.34   4.936917   -0.003153\n",
-      "2017-03-07  139.060  139.52   4.938208    0.001291\n",
-      "2017-03-08  138.950  139.00   4.934474   -0.003734\n",
-      "2017-03-09  138.740  138.68   4.932169   -0.002305\n",
-      "2017-03-10  139.250  139.14   4.935481    0.003311\n",
-      "2017-03-13  138.850  139.20   4.935912    0.000431\n",
-      "2017-03-14  139.300  138.99   4.934402   -0.001510\n",
-      "2017-03-15  139.410  140.46   4.944923    0.010521\n",
-      "2017-03-16  140.720  140.69   4.946559    0.001636\n",
-      "2017-03-17  141.000  139.99   4.941571   -0.004988\n",
-      "2017-03-20  140.400  141.46   4.952017    0.010446\n",
-      "2017-03-21  142.110  139.84   4.940499   -0.011518\n",
-      "2017-03-22  139.845  141.42   4.951734    0.011235\n",
-      "2017-03-23  141.260  140.92   4.948192   -0.003542\n",
-      "2017-03-24  141.500  140.64   4.946203   -0.001989\n",
-      "2017-03-27  139.390  140.88   4.947908    0.001705\n",
-      "2017-03-28  140.910  143.80   4.968423    0.020515\n",
-      "2017-03-29  143.680  144.12   4.970646    0.002223\n",
-      "2017-03-30  144.190  143.93   4.969327   -0.001319\n",
-      "2017-03-31  143.720  143.66   4.967449   -0.001878\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import quandl\n",
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "#get quandl data\n",
-    "aapl_table = quandl.get('WIKI/AAPL')\n",
-    "aapl = aapl_table.loc['2017-3',['Open','Close']]\n",
-    "#take log return\n",
-    "aapl['log_price'] = np.log(aapl.Close)\n",
-    "aapl['log_return'] = aapl.log_price.diff()\n",
-    "print(aapl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0273081001636184\n"
-     ]
-    }
-   ],
-   "source": [
-    "month_return = aapl.log_return.sum()\n",
-    "print(month_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.94597446550658\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.mean(aapl.log_price))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00014203280448152512\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.var(aapl.log_price))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb
deleted file mode 100644
index c870f22..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial09 Simple Linear Regression-checkpoint.ipynb	
+++ /dev/null
@@ -1,267 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import quandl\n",
-    "spy_table = quandl.get('LSE/SPY5')\n",
-    "amzn_table = quandl.get('WIKI/AMZN')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spy = spy_table.loc['2016',['Last Close']]\n",
-    "amzn = amzn_table.loc['2016',['Close']]\n",
-    "spy_log = np.log(spy['Last Close']).diff().dropna()\n",
-    "amzn_log = np.log(amzn.Close).diff().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>spy</th>\n",
-       "      <th>amzn</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-12-22</th>\n",
-       "      <td>-0.004462</td>\n",
-       "      <td>-0.005543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-23</th>\n",
-       "      <td>0.001372</td>\n",
-       "      <td>-0.007531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-28</th>\n",
-       "      <td>0.000928</td>\n",
-       "      <td>0.000946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-29</th>\n",
-       "      <td>-0.005671</td>\n",
-       "      <td>-0.009081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-30</th>\n",
-       "      <td>0.002086</td>\n",
-       "      <td>-0.020172</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 spy      amzn\n",
-       "Date                          \n",
-       "2016-12-22 -0.004462 -0.005543\n",
-       "2016-12-23  0.001372 -0.007531\n",
-       "2016-12-28  0.000928  0.000946\n",
-       "2016-12-29 -0.005671 -0.009081\n",
-       "2016-12-30  0.002086 -0.020172"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.concat([spy_log,amzn_log],axis = 1).dropna()\n",
-    "df.columns = ['spy','amzn']\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0,0.5,'amzn_return')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b031b70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize = (15,10))\n",
-    "plt.scatter(df.spy,df.amzn)\n",
-    "plt.xlabel('spx_return')\n",
-    "plt.ylabel('amzn_return')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.044\n",
-      "Model:                            OLS   Adj. R-squared:                  0.040\n",
-      "Method:                 Least Squares   F-statistic:                     10.63\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
-      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
-      "No. Observations:                 235   AIC:                            -1214.\n",
-      "Df Residuals:                     233   BIC:                            -1207.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept   1.234e-05      0.001      0.010      0.992      -0.002       0.002\n",
-      "spy            0.4921      0.151      3.261      0.001       0.195       0.789\n",
-      "==============================================================================\n",
-      "Omnibus:                       51.597   Durbin-Watson:                   2.255\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              570.651\n",
-      "Skew:                           0.405   Prob(JB):                    1.22e-124\n",
-      "Kurtosis:                      10.591   Cond. No.                         127.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import statsmodels.formula.api as sm\n",
-    "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pamameters:  Intercept    0.000012\n",
-      "spy          0.492112\n",
-      "dtype: float64\n",
-      "residual:  Date\n",
-      "2016-12-22   -0.003360\n",
-      "2016-12-23   -0.008219\n",
-      "2016-12-28    0.000477\n",
-      "2016-12-29   -0.006303\n",
-      "2016-12-30   -0.021211\n",
-      "dtype: float64\n",
-      "fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('pamameters: ',model.params)\n",
-    "print('residual: ', model.resid.tail())\n",
-    "print('fitted values: ',model.predict()[-6:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1d630588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (15,10))\n",
-    "plt.scatter(df.spy,df.amzn)\n",
-    "plt.xlabel('spx_return')\n",
-    "plt.ylabel('amzn_return')\n",
-    "plt.plot(df.spy,model.predict(),color = 'red')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb
deleted file mode 100644
index 3ae3a01..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial10 Multiple Linear Regression-checkpoint.ipynb	
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import quandl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import statsmodels.formula.api as sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "goog_table = quandl.get('WIKI/GOOG')\n",
-    "amzn_table = quandl.get('WIKI/AMZN')\n",
-    "ebay_table = quandl.get('WIKI/EBAY')\n",
-    "wal_table = quandl.get('WIKI/WMT')\n",
-    "aapl_table = quandl.get('WIKI/AAPL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>goog</th>\n",
-       "      <th>amzn</th>\n",
-       "      <th>ebay</th>\n",
-       "      <th>wal</th>\n",
-       "      <th>aapl</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-12-23</th>\n",
-       "      <td>-0.001708</td>\n",
-       "      <td>-0.007531</td>\n",
-       "      <td>0.008427</td>\n",
-       "      <td>-0.000719</td>\n",
-       "      <td>0.001976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-27</th>\n",
-       "      <td>0.002074</td>\n",
-       "      <td>0.014113</td>\n",
-       "      <td>0.014993</td>\n",
-       "      <td>0.002298</td>\n",
-       "      <td>0.006331</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-28</th>\n",
-       "      <td>-0.008246</td>\n",
-       "      <td>0.000946</td>\n",
-       "      <td>-0.007635</td>\n",
-       "      <td>-0.005611</td>\n",
-       "      <td>-0.004273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-29</th>\n",
-       "      <td>-0.002883</td>\n",
-       "      <td>-0.009081</td>\n",
-       "      <td>-0.001000</td>\n",
-       "      <td>-0.000722</td>\n",
-       "      <td>-0.000257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-30</th>\n",
-       "      <td>-0.014113</td>\n",
-       "      <td>-0.020172</td>\n",
-       "      <td>-0.009720</td>\n",
-       "      <td>-0.002023</td>\n",
-       "      <td>-0.007826</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                goog      amzn      ebay       wal      aapl\n",
-       "Date                                                        \n",
-       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
-       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
-       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
-       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
-       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goog = goog_table.loc['2016',['Close']]\n",
-    "amzn = amzn_table.loc['2016',['Close']]\n",
-    "ebay = ebay_table.loc['2016',['Close']]\n",
-    "wal = wal_table.loc['2016',['Close']]\n",
-    "aapl = aapl_table.loc['2016',['Close']]\n",
-    "goog_log = np.log(goog.Close).diff().dropna()\n",
-    "amzn_log = np.log(amzn.Close).diff().dropna()\n",
-    "ebay_log = np.log(ebay.Close).diff().dropna()\n",
-    "wal_log = np.log(wal.Close).diff().dropna()\n",
-    "aapl_log = np.log(aapl.Close).diff().dropna()\n",
-    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
-    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  Close   R-squared:                       0.346\n",
-      "Model:                            OLS   Adj. R-squared:                  0.319\n",
-      "Method:                 Least Squares   F-statistic:                     12.51\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
-      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
-      "No. Observations:                 124   AIC:                            -827.3\n",
-      "Df Residuals:                     118   BIC:                            -810.4\n",
-      "Df Model:                           5                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
-      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
-      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
-      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
-      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
-      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
-      "==============================================================================\n",
-      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
-      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
-      "Kurtosis:                       7.142   Cond. No.                         410.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
-    "print(model2.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
-      "Model:                            OLS   Adj. R-squared:                  0.348\n",
-      "Method:                 Least Squares   F-statistic:                     134.7\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
-      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
-      "No. Observations:                 251   AIC:                            -1401.\n",
-      "Df Residuals:                     249   BIC:                            -1394.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
-      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
-      "==============================================================================\n",
-      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
-      "Skew:                          -0.374   Prob(JB):                         0.00\n",
-      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
-    "print(simple.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
-    "fama_table = pd.read_csv(url)\n",
-    "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
-    "fama_table.index = index\n",
-    "fama_table = fama_table.iloc[:,1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fama = fama_table['2016']\n",
-    "fama = fama.rename(columns = {'Mkt-RF':'MKT'})\n",
-    "fama = fama.apply(lambda x: x/100)\n",
-    "fama_df = pd.concat([fama,amzn_log],axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  Close   R-squared:                       0.387\n",
-      "Model:                            OLS   Adj. R-squared:                  0.375\n",
-      "Method:                 Least Squares   F-statistic:                     30.97\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
-      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
-      "No. Observations:                 251   AIC:                            -1407.\n",
-      "Df Residuals:                     245   BIC:                            -1386.\n",
-      "Df Model:                           5                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0010      0.001      1.028      0.305      -0.001       0.003\n",
-      "MKT            0.9612      0.125      7.691      0.000       0.715       1.207\n",
-      "SMB           -0.5890      0.182     -3.235      0.001      -0.948      -0.230\n",
-      "HML           -0.1335      0.211     -0.632      0.528      -0.549       0.282\n",
-      "RMW           -0.4851      0.264     -1.840      0.067      -1.005       0.034\n",
-      "CMA           -1.5555      0.324     -4.801      0.000      -2.194      -0.917\n",
-      "==============================================================================\n",
-      "Omnibus:                       69.457   Durbin-Watson:                   1.937\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2012.675\n",
-      "Skew:                           0.241   Prob(JB):                         0.00\n",
-      "Kurtosis:                      16.864   Cond. No.                         399.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
-    "print(fama_model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a160d0128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "result = pd.DataFrame({'simple regression':simple.predict(),'fama_french':fama_model.predict(),'sample':df.amzn},index = df.index)\n",
-    "plt.figure(figsize = (15,7.5))\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103c271d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "simple.resid.plot.density()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 'residual mean: ', np.mean(fama_model.resid)\n",
-    "print 'residual variance: ', np.var(fama_model.resid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df.goog,simple.resid)\n",
-    "plt.axhline(0.05,color = 'r')\n",
-    "plt.axhline(-0.05,color = 'r')\n",
-    "plt.axhline(0,color = 'black')\n",
-    "plt.xlabel('x value')\n",
-    "plt.ylabel('residual')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.core import datetools\n",
-    "from statsmodels.stats import diagnostic as dia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "p-value of Heteroskedasticity:  0.14407584284381386\n"
-     ]
-    }
-   ],
-   "source": [
-    "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
-    "print('p-value of Heteroskedasticity: ', het[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2668279319999998\n"
-     ]
-    }
-   ],
-   "source": [
-    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb
deleted file mode 100644
index 06e6da5..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial11 Linear Algebra-checkpoint.ipynb	
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3])\n",
-    "b = np.array([2,2,2])\n",
-    "c = np.array([3,1,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 1]\n",
-      " [3 2 1]]\n",
-      "<class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "matrix = np.column_stack((a,b,c))\n",
-    "print(matrix)\n",
-    "print(type(matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 2]\n",
-      " [3 1 1]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "matrix2 = np.array([a,b,c])\n",
-    "print(matrix2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = np.array([[2,3],[4,2],[2,2]])\n",
-    "B = np.array([[4,2],[4,6]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[20 22]\n",
-      " [24 20]\n",
-      " [16 16]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = np.dot(A,B)\n",
-    "print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-ac89428c1137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)"
-     ]
-    }
-   ],
-   "source": [
-    "x = np.dot(B,A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 1]\n",
-      " [3 2 1]]\n",
-      "\n",
-      "-------------seperation line------------\n",
-      "\n",
-      "[[ 3.70074342e-17 -1.00000000e+00  1.00000000e+00]\n",
-      " [-2.50000000e-01  2.00000000e+00 -1.25000000e+00]\n",
-      " [ 5.00000000e-01 -1.00000000e+00  5.00000000e-01]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(matrix)\n",
-    "print('\\n-------------seperation line------------\\n')\n",
-    "print(np.linalg.inv(matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.00000000e+00 -6.66133815e-16  3.33066907e-16]\n",
-      " [ 0.00000000e+00  1.00000000e+00  1.11022302e-16]\n",
-      " [ 0.00000000e+00 -2.22044605e-16  1.00000000e+00]]\n",
-      "\n",
-      "-------------seperation line------------\n",
-      "\n",
-      "[[ 1.00000000e+00  0.00000000e+00  1.11022302e-16]\n",
-      " [ 0.00000000e+00  1.00000000e+00 -4.44089210e-16]\n",
-      " [-1.11022302e-16  0.00000000e+00  1.00000000e+00]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "inverse = np.linalg.inv(matrix)\n",
-    "print(np.dot(matrix,inverse))\n",
-    "print('\\n-------------seperation line------------\\n')\n",
-    "print(np.dot(inverse,matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 2.]\n",
-      " [ 3.]\n",
-      " [-1.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])\n",
-    "B = np.array([[8],[-11],[-3]])\n",
-    "inv_A = np.linalg.inv(A)\n",
-    "print(np.dot(inv_A,B))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 2.]\n",
-      " [ 3.]\n",
-      " [-1.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.linalg.solve(A,B))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb
deleted file mode 100644
index 6999639..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial12 Modern Portfolio Theory-checkpoint.ipynb	
+++ /dev/null
@@ -1,417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import quandl\n",
-    "from cvxopt import solvers\n",
-    "from cvxopt import matrix\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class stock(object):\n",
-    "    def __init__(self,ticker):\n",
-    "        self.ticker = ticker"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tickers = ['KO','JNJ','PFE','NKE','PG','WMT','MMM','IBM']\n",
-    "stocks = []\n",
-    "leng = len(tickers)\n",
-    "for i in tickers:\n",
-    "    vars()[i] = stock(i)\n",
-    "    stocks.append(vars()[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf = quandl.get('USTREASURY/LONGTERMRATES')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf = rf['LT Composite > 10 Yrs']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    table = quandl.get('WIKI/%s'%i.ticker)\n",
-    "    i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
-    "    i.mean = np.mean(i.rate)*252\n",
-    "    i.std = np.std(i.rate)*np.sqrt(252)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'float' object has no attribute 'shape'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-265a85598a64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmean_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mstd_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstocks\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcov_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrate_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mmean_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstd_list\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstock_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m   3083\u001b[0m             \u001b[0mw\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0maweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3085\u001b[0;31m     \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3086\u001b[0m     \u001b[0mw_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw_sum\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36maverage\u001b[0;34m(a, axis, weights, returned)\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1162\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreturned\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1163\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1164\u001b[0m             \u001b[0mscl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mavg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'shape'"
-     ]
-    }
-   ],
-   "source": [
-    "stock_list = [x.ticker for x in stocks]\n",
-    "rate_list = [x.rate for x in stocks]\n",
-    "mean_list = [x.mean for x in stocks]\n",
-    "std_list = [x.std for x in stocks]\n",
-    "cov_matrix = np.cov(rate_list)\n",
-    "df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def min_var_generator(rate):\n",
-    "    def target(x, sigma, mean,r):\n",
-    "        sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
-    "        return sr_inv\n",
-    "\n",
-    "    x = np.ones(leng)/leng\n",
-    "    mean = mean_list\n",
-    "    sigma = cov_matrix\n",
-    "    r = rf\n",
-    "    c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
-    "        {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
-    "    bounds = [(-1,1) for i in range(leng)]\n",
-    "    res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
-    "#     return res['x']\n",
-    "    return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 0.20992676,  0.27623188, -0.00295919, -0.10248824,  0.31125431,\n",
-       "         0.23690646, -0.10606759,  0.1771956 ]), 0.1455789711354554)"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_var_generator(0.06)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
-    "simu_var = []\n",
-    "for i in simu_rate:\n",
-    "    try:\n",
-    "        res = min_var_generator(i)\n",
-    "        simu_var.append(res[1])\n",
-    "    except:\n",
-    "        print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rate      0.179200\n",
-       "std       0.240626\n",
-       "sharpe    0.630854\n",
-       "Name: 1518, dtype: float64"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
-    "port_df.head()\n",
-    "port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
-    "opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
-    "opt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Simulation#\n",
-    "monte_rate, monte_std = [],[]\n",
-    "for i in range(100000):\n",
-    "    w = np.random.dirichlet(np.ones(leng),size=1)\n",
-    "    monte_rate.append(np.dot(w,mean_list))\n",
-    "    monte_std.append(np.sqrt(np.dot(np.dot(w,cov_matrix),w.reshape(8,1)))*np.sqrt(252))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simu_df = pd.DataFrame({'x':monte_std,'y':monte_rate})\n",
-    "simu_df['sharpe'] = (simu_df['y'] - rf)/simu_df['x']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Sy+XSwoULFRUVqod7NVC/nvVVp35bKaKnHM6oQMcEUM5RQAEAAABAJeTxeDRv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y7oq2cekW6qrqSUiJN13RDAaDwfCDYwQog8Fg+BGpq6vj1VdfZeLEiWzatIkOHTrw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\nERERERGRZKUClIiIiIiIiIiIJKv/A907XhlB7lZHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x118f8a990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
-    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
-    "plt.scatter(0,rf,color = 'r',s = 30)\n",
-    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
-    "plt.annotate('risk-free',(0,rf),size = 15)\n",
-    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
-    "for i in stocks:\n",
-    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
-    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
-    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
-    "plt.xlim(0)\n",
-    "plt.xlabel('Standard Deviation')\n",
-    "plt.ylabel('Expected Return')\n",
-    "plt.axhline(rf,ls = '--')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "###three stocks version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def efficient_frontier(tickers):\n",
-    "    stocks = []\n",
-    "    leng = len(tickers)\n",
-    "    for i in tickers:\n",
-    "        vars()[i] = stock(i)\n",
-    "        stocks.append(vars()[i])\n",
-    "    \n",
-    "    rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
-    "    rf = (rf.ix[-1][0]/100)\n",
-    "    for i in stocks:\n",
-    "        table = quandl.get('WIKI/%s'%i.ticker)\n",
-    "        i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
-    "        i.mean = np.mean(i.rate)*252\n",
-    "        i.std = np.std(i.rate)*np.sqrt(252)\n",
-    "        \n",
-    "    stock_list = [x.ticker for x in stocks]\n",
-    "    rate_list = [x.rate for x in stocks]\n",
-    "    mean_list = [x.mean for x in stocks]\n",
-    "    std_list = [x.std for x in stocks]\n",
-    "    cov_matrix = np.cov(rate_list)\n",
-    "    df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
-    "    print df\n",
-    "    \n",
-    "    def min_var_generator(rate):\n",
-    "        def target(x, sigma, mean,r):\n",
-    "            sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
-    "            return sr_inv\n",
-    "\n",
-    "        x = np.ones(leng)/leng\n",
-    "        mean = mean_list\n",
-    "        sigma = cov_matrix\n",
-    "        r = rf\n",
-    "        c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
-    "            {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
-    "        bounds = [(-1,1) for i in range(leng)]\n",
-    "        res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
-    "        return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))\n",
-    "    \n",
-    "    simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
-    "    simu_var = []\n",
-    "    for i in simu_rate:\n",
-    "        try:\n",
-    "            res = min_var_generator(i)\n",
-    "            simu_var.append(res[1])\n",
-    "        except:\n",
-    "            print i\n",
-    "    \n",
-    "    port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
-    "    port_df.head()\n",
-    "    port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
-    "    opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
-    "    return port_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         mean       std\n",
-      "PG   0.052920  0.177927\n",
-      "IBM  0.053190  0.222997\n",
-      "KO   0.072506  0.188207\n"
-     ]
-    }
-   ],
-   "source": [
-    "new = efficient_frontier(['PG','IBM','KO'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
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Wac+fr/u7XaCISnfIRHSWMcx4AnB6KKAAAAAAoBw6cOCAJkyYoNTUVP3222+6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RAAAg\nAElEQVRZfArx1DlwVRpPPIBY9lHMGHYV7bnnWJv9b0QEV4+gYLpRQCDRxDiAHcqPYUX2z9FZSiM4\nhGJQCL5EvibiaxocjED0o3zkcDLxPwEi55NGq8Ftka9JUEPT5kQgEAdBMaZsV9bnF6MUNLg70O6t\nQ4miuXwSnaUusmEvFU4N+TCPqzLkTZaUSrNvwxE83H5/lCsVi0wVTjU9QT9jM+Po9fvoC3oBSOk0\n02v2oCk9gsmVu+CqBDet+TUthTZEojprk3WcOOp4lgws5amO5yhzy+kPcoTGwVEuI9P1fHfXr7O5\n1M2wVD29Xj8XLvgxVW4lvX6W2kQlt+z5s622Fgf51Yo/8fv1j3PdjK8wpcbObbJYLB9+3o4A9Za/\nDlRKzRaRR0XkEhH5uog8qpSa/e7LtFgsFovFYnnnLFy4kNNPP50JEyZwww03cNJJJ7F48WIeeOAB\nKz5tBfq9Fh5Y+yUKYR/7D7/0fSc+AUjYggQrMP6CN9wf+Kvwwo10dZ1GGG5kIPs7Sv5KtB6J769h\n/cZ9QNWQTM7AcZrpyz9AIjGFkcPuoTz9carKjiadnEpj9bfRzi7kwxzZ0nwGinPJ+1Go9vqB68mF\nrYSi8EwBpUcBGt/082zrkSzrvoKyxC6Mrfsu6IpYfEoS4FKR2htDLAihgQQJZxiiEoS4+KTIhr2E\nEpmNDMQCjGZDfk48yc6lZDS+EULRJHUzIcl46pyLbxx8owlj0alkwDMJjKQIxCEQHU22MxpfNIFA\n0WgUFRijKRlNSZIEEk26W51bgkiSYujS4rXgiaaIw6rcarJBgYRUUzIapcrpC7KI0YzJ7EaCDIID\nsRjmqDTjyiYDinWFdQwEA4jAJZO+y171B/HKwCq6vRy3r7ubpsxwLtvtJxzSeASecXBUip9M+RFp\np4ITmo/nhhnXUpcYTmg0E8rHMTzdyOzdv89AkGfWgtmc+9IP+PmyW9mlckfO3OEU9qufzqU7n7tN\n1uSYsmE0pWupTVZsk+sP0ufleah14esm9lksFsv7mS0JIT8U+Mb/2XbEG2yzWCwWi8Vi2aaICE88\n8QSzZ8/moYceory8nK997WtceOGFjB49enuX96GhLTeXf2y8DIXmiFHXUJeesL1LekOc1AGkhj2F\n0q+fZlgqPU2h9L9oZzy+vxDfX0Nf/1WIdDGs6QU8fzl+sJZs8e8oXU9v7j4291+OwaG+4ouUl/8H\nS9pPoSz1EfpLLyISMOCvZHPuT7i6FiNZAlG4OoknJQSH8tRejKw4jUWdF0OcmQSKfm8NG3qujlvI\nXKoTu9HlLaSt8BJl7o5knCpGVxxNn7+eZ9q/STHsREmGEgFRDpSiIjEO3+vEIwe4BKYbE4tR0Z0i\n51Kd20h/2Ani4InBiBNlQYki7dQShr1oJSiSTKzYj9XZhXiSwwiEQ6lOmqpEM63huiGn1KB7ChRF\nMQgaZ9AhhUZE8CSgSI4hmxaaosCCgQW0FttwcKlLNJANS/SHfQyEBRRpRqaHc8iww1if38CIslFs\nan+Q9kInxWAOvUEP+SDPlauu5+X+JQiKgxoP4va1/83fO55mStVOfGfXCzh17HEs7H2F9fl21vYt\nIxcUaEjWMqFiLCuz68nmWxhfPpp9G6azb8P0bbQi4ZPNe/HJ5neX+7Ql/GbFP7hjzbP8p5zAUaNs\nKLnFYvlg8C8FKKXUOcC5wASl1MJ/2lUJPPPGZ1ksFovFYrFsfYwx3H///Vx++eU8//zzNDY28uMf\n/5hzzjmHurq67V3eh4rl/Y/w/KZfkNQVHD3mesoSDdu1HhO04HWfilP+JRLln3vdfu00v+F5rjuB\nRHIvysq/hJvYEaVr8EwXAEplKMscwqjhj7O2/cBIctHVVGWOo7/4CFqlWNf1dYQ8BW8lIgGaCkJy\nCIqiyaFVOQbImQIKBwS6ii9gSBHJM1X4ZEFgXfbPpHQjBelCoej2FhOKg6iAbLAOrSbwfMeVGEzc\n/KZfnUKnFKHAplLrUKg2KEJSaByUymOMIsrL1vSUeqlJ7EJ7cQ1KBYREgeYigmOEwETtfyEBhdBQ\nCEPC2F2FUhhRhOKwvrAOEyU9DWVB1egGesOOaEKdcvCMEIrC0eAbB4YELKh3h7HJ74hqjgPBp9V+\njKe6niDyfCmWZpcAimk1M2kpbOLB9v/lofZ/YAhRaLr93vhIzfKBFbjKpTpRw7zeJbQW2wEYXRZ9\n/rtVT2a36slcs/x3pJ00WmlSTpKfTb2EZf2rGZ5uoNz94E5t/L8cP2YmvjHs2zhpe5disVgsW8yb\nteDdCRwN/DH+c/C/GSJy2ntQm8VisVgsln9zSqUSN998MzvvvDMnnHACHR0d3HDDDaxbt45LL73U\nik9bmfldd/N0+88pTwzj+PG/2+bikxm4EtP9WSTOQXpDpD9utVv1tq7tOMOpb7yPTNknSSQm4TqN\n1FR+g4qy0yn66wBIuhNpqLkGT+ro91ZSU3E6tZUX0Vl4HkePIxCFJyXQ9UxtforqzLE4ejyOrieQ\ngMkN1wCQ0A2UJWcQiqazuATPaErG4BuHkrgMS3+M6cOup2gS5I2Lo0bgkcAzGs8k2FSKxJ5QNAYX\nT1wCSRKKIjBR/pKJc52MQFE0vgQUTRHPJAlIUhIHz2j6w046SpsoisEzLkJmKAcqF+bwJEEgSUCz\nPPsiBePjiYsft/J54kTZ4hKJUb4kCHBQKLrCLgKi9sBQwBeHEIeSifKhIAo1V2hyYRC9H3HwQ830\nmn14svMJRKBMV1LjDsM3Lgc3HsHRzceSDaNsLEc5iCi8UBibGcdOlZO5Z8ODVLtN5ANDfaqBU8Yc\nQ1O6Cc9Aykm/5nO/YMcv8JuZP+G3q+/ngdbHcZRml+qJ1KVqSDlvPgXz/g1P8aUXrqLHG3jT47pL\nA7QVut/WetzaTKgcxrenHEVdqny71mGxWCxvh38pQIlIn4isBa4FukVknYisAwKl1Lb3lVosFovF\nYvm3pa+vj9mzZzNu3DjOPPNMKioq+P3vf8/y5cs555xzyGQy27vEDx3Pbf4VcztvpS41iU+NvYmE\nfg+ecelJ8J4DKfzLQ0TX4tbeRqLqh+/oFpv7rmZDx5cwJkdd9QUUTC+rO45lY+/PmbthHBv6rqLE\nAPlwDb35R+krPEnOW4Bol0A0BZNHq2oWd3ydDbmH6AtbGQh7qUwdyryO8wkkzfCqL7G5tIKSJCiZ\nAQwZPEJCXASHdfmnaMs/jS8OIi7aqYVYFArQKNJUJHaA2O0UiiYUoSgOBUlEAhSKwCgKkoiFHQhw\n8UTiYXSaAI2PJi95JH7tmSDKgJLId4RS+KIoGYVvIlFLiHKgBCdu6dOEokirWgTwjcIzKjrOEIli\nxmFEeixR7LkGHAIBI4qi0fSFA2hSkYiGZnrdXlGOlNH0+HkmVEwG4MXuuVw8/3s8uulJQhRNqRGx\nsOWyNNvCgt7VzOtdzMbiZgya8eXj2b1mVzbkO6lwKjlx1JGv+8xzQYGH25/lT61PvK21srhvLauy\nbfR62Tc97ksvXM9JT82mGL6JcGqxWCyW1/GWU/CUUvOA6RIfqJTSwBwR2XbN01sZOwXPYrFYLJYP\nBhs3buSaa67h17/+Nf39/Rx66KHMmjWLQw45BKXUW1/A8o74W9tPWTvwBKPLZnL46B+9Z/cVkwPJ\no5zG124XA5RQKkPv5k8QBoupGfYsjvvGOV8iIf25O0klp5FOTkHEEJpeSsFa1m0+DpEApccwsu4H\ntPVejRe8jKPH4JsORCURCUk49QSmmqJZTyAlFCkCKeEoRXlyGjm/jZLpiqbViUJRRcAAGbeBTGIq\nmwtPRqKQRO1wSkCRJCCIHUhpAvERohY4BDxROEpFIo4oiIPCfTRaRaKRQgESO6CinCYF+KLQKEI0\nEktAVYlRbC5tRBO1zDnxn6AIzWAiFXHgucKIDE3fi/ZFrqe65Ag6SpsIRcXRT/FkPFHR1DwTtQaO\nL9uRNbmVKKXxxAwdF4WmR8JUuVOBoxQj06NZml2KkSgsPaPTnDXhc1y1/MbIM6UUZ47/D25cfTcO\nCk/8SDBD8f2dzwOl+MmSG/jMmGM4pvnjLOxdSrlbxoSKMW+4JlZnW6lKlNGQqv2X6+/RjS+RC4p8\navR+AAQmpM/PUZ+qetN1+5sVf6W92Mt3dzvFfl+yWCz/9rydKXhbIkDNF5Fp/2fbQhGZ+i5qfE+x\nApTFYrFYLO9vli1bxhVXXMHtt99OEAScdNJJzJo1i+nTPzC/7/pAIiI8uOE7tBXmMbHyYA4e+fX3\nvAYTbsKEbbjJjwxt6+s+E6/4V+qanscvPUtQepbymp+iVIJc/iG6e7/NsIbfkUpOIwg309l3Ob25\nO1HAqMaHWNdxNKh6fNNBZeoAIGCg9Bwh1YRkURiGV57J8OoLWbjxdMoSE9mh7lKeatkbROI2sBAP\n0GigAl9yNKYPoKv0FCWjAY1S4DCcomyOH6gmQAhIICKx0BMJPiIGPSg+KQfPqKH9CjMk2AwGiiuI\nA8AVHgoRF4F4ah6IROcKKp6QF4lKIoogFm6UGJSKRK1IzInCwgdzpUycXeWjaUw24agE7aVWQqMx\n0dVi+Qtm1OyLo9I80/0PQnHiTyq6tlagcSgZie8TnT3YbJFWGQbCEhKLWABKKcp0Fdkwi4hwaNOB\nPN01lwllo9HaYVV2HaXQ4/PjT+DQ4Qdw7bL/4pFNz/H5cUcjSnPE8H2oTLx1+1l3qZ+fvnIXJ435\nGHvW7zS0fUn/Bi566VcUjMffDpqNq503uYrFYrFY/hVvR4Dakil4q5VSXwN+Fb8+F1j9TouzWCwW\ni8ViGeT5559n9uzZ3H///aRSKc444wwuvvhiJkx4f05d+zBhTMh96y+ms7SCqbWfYp9hX9429/Hm\nonQTyn01LNyE3YTBy7jJ/cl1f5HQX4hbfgaJ1KGUin8jDHvQTjNKpUmXnYBkPsWgmBGELYSmne6B\nW0glpxCG3fTm7mTwV6prNn8Gg6I8MYnA6yOTnEF95ReY3zodGECrMkbX/oi2gbtIJZ5iwH+Zfm8x\nStfGYoqiJGEU3A2AQ1LV4ItHS2EOJm6rM0QuJ186EBKAQURHEeKxqymSlgb/dAjiMHBfHECh4zsE\nogjERasordsgGNFRALfS8bQ7htrdRAStVdx6F10/lOgekfijkTijKQoy1xgxsT/JwZjI7yTEIpdS\nbC5tJhRnyF0FClcpjIQopfiP8eewYmA5SwYWk/ULFKQ09HkGonBIEoqPUgxdJ5r6ByZ2gbnKxY9d\nV2Eo9IVZlIJTmo/hvra/kzdF9mvck2tX3MEuVRP46dQLh+6xsHclgmJe7wrm9S4nrZMc0/zRt1x/\nq3MbmdOznKZ07ZAAtaBnNefN/RUz6yZx+tiDrPhksVgs7xFbIkCdDfwC+A7R3yH/C2ybn1AsFovF\nYrF86BERHnroIS6//HIef/xxamtrufTSSznvvPMYNmzY9i7v34LQBNy79jz6vBZmNJzOzIZTt8l9\nJNyE33Uiyp1IsvGRoe2Fvm/gF/9KRf29JMvPwC/8hULuJkqlZ/D9xWg9jGEj5gMQhB1s2LgHZZlP\n0FB7BWVlR1OeOZ5VG6cg+T8gupl0Yl+K/ssYCoT0AYqm2m/S1ncTHoaW/puGRKLhVeezuudqfLOZ\njQP34kuUgbR+4I44CDxy6EjsKAIohDn8oVY3Z8iZNNiOh9KIRO4lIxpEUCr6Opog5xAiuMSB4oMO\nJ0UsFLkoFWUo+QLgRJPjohvgi0JwBv1TkThmBBFBcPCHYl2jCXYJyl4jELkkMOJjVCRaebEANuhg\n2qvmYzzT/WTcjhdN2Usol5TOkA0HOHb4yXxr4bfo9DqYWbMHi/1lZHQ5NYkKmjPNzO2dRz4MUCh2\nrJjMy/3LUEAoCoWiKDAs0UB/WGBEuppckCcfhORMIXqT2iVvCoCiMVXLtJqdeal7Cdcuu4vmsiaG\npWvZqWo8bR3dzOtdzpfGH8vBTVv0y3Zm1O7IldPO5r6W53ipeyUz6iYyrqKJvet34lPN+zC9zk6R\ns1gslveKtxSgRGQz8On3oBaLxWKxWCwfYnzf5/e//z2XX345ixYtYtSoUVx99dVDIeOW9wY/LPL7\ndeeR9Taxb9OXmVp79La7mW5Al/0HKrH7azYnyz8HqhwnsStuam+SmeNwi3+iVFpI3l9EVdnJhGE3\nA/kHKXnzQZWhdSVrNn2SUrAKIQVEDpswbKcYtiMoypK745oihWAZC9tPRIsQoOLJbApDOWv6/x/G\ndJPUYzGUvzq9TcAohyAO4wY/FocSaIqvtrKpWgIKgI+SeOqbIRKfiM6VOOdIIYSDGUwS1UHsODII\nvtFRxpPE4eO4IIKg8ESjCQkkMfTcjGhECSKaAAhEAy4munLkOFKaEqUhd5QAWWMAB22iFsFI3IrC\nwRHhya4noyek4nMEkrqCbr8f0NzT+j8MJkg92/0iEDmcisZQnRiBkTLGZeppL3XSVuihym2gx+8l\no5N4BkLxafd7AVib38xuVTvykdpdeK5rAdNrd+GYkQczuXIHckGBKTWTealnOS90LeOvm55lUAT8\n7s5nsGRgHceMOIBPjT6Qi+fdwKZiD7fu9U2cN3EwRRlNin9sXoSjNDPqJlKdKOfyaWe801VtsVgs\nlnfIWwpQSqkdidrvmkRkN6XUVOAYEfnxNq/OYrFYLBbLB55cLsfNN9/M1Vdfzfr169l111259dZb\nOfXUU0kkEm99ActWoxTkuGvNVymYXg4ecQGTqw/epvdTyiFR/er0OmNydHcej1LNeP488v7pNDTe\ni1JJ0pljcBNT8c1myspOpa3rPHKlx2JvTxV11d/Fl59QCFYBHpAiIIzzj6DcnUpZcl82Zv+CZxJA\nJD4NNrqFUgbKx5g8oVEUTBs9QQ+KMkI8DA6OhAwKHqFJ4MdCSyAS/+kgkotcRCQGU5UIxInDwIl9\nUlGrnB+HdodolBi0UniAEgWx2OTHjipFJJYEqCjfKc5pGqynJJEzSYuJAskVsaglgDPU6hftJ5ak\nNOWqHF8KgFCKtxkjiFKERv9TypPEzi4hNIouMxAdK68mQan4XnFqFf1+jhd6FgDQ5+eIcrGKhAIj\nUg1cOe17fPq5CwFFQ6KWnap2YFn/WhpStRw/6jCOH3XY0Nq4r/Vxnu1axHUf+Tob8h0YNLtXTaDM\nTXHQsBns2ziVfRtfjaAtGo9iWBpqvXwzptdO4NrpX2ZS5cgtONpisVgs24otacG7CbgEuBFARBYq\npe4ErABlsVgsFovlX9LZ2cl1113H9ddfT3d3N/vvvz+//OUvOfLII9Fav/UFLFuVYjDAnWu+SinM\nctjIS5hQte9Wu3ZQeDCaJFd2DMXsbyllb6S8/m5M2Ip2GnETUfaOSJbAX4QnS9D4iL8JkTzg0Jv9\nL7oHbgA0fcVTMKaPEPBRiAyQLc5lZN3PqK04jdaeK8l6a3DdGor+EjwJSalK1vb/DsEgaAYTj9J6\nBKVwIyE+WiTKcFIBIhDiA8SCjqJoXAJchChAXCOxyOOgJIzzlyJxJ4iDuF8VcKI1HcYOpkGz0+C1\nA3GGhCkwKDFD7qihz8gMhp7H0/BMAhQYGZqdR0EcQHDiEPHB3KoIRclEuU8GAE0vRaZV7cVLfS+i\n4hY8lwoKJh+LcxLnS0X/LDAmOk8Y9DxF165PNrCp1ENSadI6RX+Qj9v1oi66qN0uOqc2UcOXJ/wH\nt697gH3qP0JjqpYvjD+BK5bexpiycZy/4+det4YyOoNDkpLxacn3UJ+o4aTRh1Lmptm1etzrjr9+\n+vlRS6R66+8lSilm1E18y+MsFovFsm3Zkp/+ykTkhf+zLdgWxVgsFovFYvngs2bNGs477zzGjBnD\nZZddxgEHHMDTTz/Nk08+yVFHHWXFp+1APujjttXnUgxzHDHq21tVfAIo9Z6P1/e1aPJbuA4xGxno\nvZi+7k/T03H80HGO00RD0xyMSuEBoRqF1jV09P4nm3u/iReupxRuoBC2I3o8RioZzCpa23U+udIK\nMskpGFVByWxgwFuEH7eu9XgvEYhEgg+KaNJzkilNv8ao2ljsaQCq8UXjSRQy7hmFR4IpdT+kRDI+\nN5pyVzIufhxGHohDYBQBLmGcGxW16gE4hAKBiZxKnmhKRhOIxhfwDQSDbiIB32hyJkXRJCgYh6Jx\nyJoEviQpGRfPqChAPK7FiEMgLp64sQjm4IuLbzS+RBP1/FBTCjWhOASiCMWN8qlEMafvRQRFYKKA\n8LwpIjjUJRoxovBMdOzgJD4zOE1PNIEofKOodofFxyqObT6OMqeWwMBOlTtjxCUUByNJtKT51k5f\nwdUuf2r7B4XQ4wvjTwBgXs9S5vYuxRjDNct+z29W3T+0NpJOkpIJaMl3sTLbysTKMVz28h189aXr\nCEz4ujWnlHqd+JQLirzQtRIjZussbIvFYrFsVbbEAdWplJoA8dRYpU4ENm7TqiwWi8VisXzgmD9/\nPpdffjn33HMPWmtOP/10LrnkEnbeeeftXdq/NTm/hzvWfI3AFDlm9PcYXT71rU96E0zQSmngKpIV\n5+IkIldJqu4WiKelZap+SKZyFrnsLwmDVZRVnhtNfguW09X7Azx/NUayuM5ohjf8gSDsJpASQiXR\n7ziLGIH+4JVYQHFI67EUzAYWbTyM4VXnUAyKeKIJRcetZoNNZG4cDO5Ql9qbTHIiz7VfTCHMAy5F\nBnCoISSBiBBICiOGQBRPd/wURymCuKcrjMPD9VD7XeyEMoYwbkNTgMeg4ygSjBQKI4KouNUtnpgX\nZULFP1APtbINTomLBB/iHKZQdJRJFbfmhbEYpZSGWFwJxInFtqgFMSRyT0VupKi1LhhqndMYkbgV\nMLpnfbKWusRwWovdGHR0L1FUOJUMhP1Dzq1okp1maXYlCVLkTMBNa+5Fq+i9LexbPvQ+QgyBMSil\nqdAVfHOnM5lUOWZo7fxmj+9EwekKHtn0PGVOmi9P+BQA5006gTPGH0VFIsOosgbGlg3n75vmkQ0K\nWzyl7hfL/sIfW17kio98lgOG2e87FovF8n5jSwSorwC/AXZSSrUCa4DTtmlVFovFYrFYPhCICI89\n9hizZ8/mkUceobKykgsvvJALLriA5ubm7V3evz0Dfid3rL6QQDyOH3MZzWXv/h/lQekx/MIfUM4Y\nnMQFALipjw7tV0qBqsBJfZRMYiZFbwHtrc2kU4eQK/4DR0FDzdWUl32KtZtPI++/jEgOA4ys+R4b\nen8UO4scUCFGIBfm4jwmQ3v2z+TDdlAukS/m1XaxwUAgg6G9+Cx+4QUiw7+O584pAvrjkG8H808h\n4QgEQiwhOQTxzLuQKFBcJHpvUWaTZrDrzcSteKHo+N5EQo+RKFQ8Fn8kFnkGQ8+RSHgy/zTBziBo\nidr2AnEHNyNELXVKwDeD4lfk9ora6mQoMypqp4vaDwcD0VW8L3JygcKhzyuxa1UT0r8kbrWLRKy+\nMBs7wKA+UUuX10coAqI4tGl/Htz0BAAza6biKIdDh+9HU7qBr700m5KUGJtp5gcv30RHqYdPjzqc\nhb1r+OIOR5HQLhVu2dA6+d2e38GJHUw3LH+AgaDArJ1PBmDX6vHcvuYxqhIZPjPukC1em0eOnE7W\nL7JbzegtPsdisVgs7x1bMgVvNfBxpVQ5oEVkYNuXZbFYLBaL5f1MGIbcd999zJ49mzlz5tDU1MRP\nf/pTzj77bGpqarZ3eRZgwOvittUXYvA5YfQPGVm201a5rk7uA8n9cTOfeMP9YdiN1rVs7Pg04BMi\nOChK/jqUGkYgvazv+TYjRJHzXkIwQ+6iDX3XAo0Y6aah7PN05u9EM4w8bRAHaJfCDpSqiEWrSMCI\nQsZfdSENZjUNBYpL1NIGJmq7iwPEJRaliDObjBkM746Oj5KkFB4OiETCU5wPJRLlJ4koREVuocF8\nKNBD1xGJ3FMaFQtmDkgYuakG90dPFogEomCwJnk1IcpINPVOxaJT8BqHlAaJnFeowVwmwVEuPgEq\nFqVCGcygEkomoL3QM1SHIQo3N7H49NmxJ6KVy82r74kH9ylKJiA00JCs5fnupfgm5OwJp1KVrMDR\nKQLfpykznHFOEhD+a/3DCJqXelZwSNMMThnzauh9U7pu6OuH218iGxQ4fMQeVCXKGF3WwI2rHqLS\nzXDsqL23eG3uXjuO3WvHbfHxFovFYnlvUVF//JscoFQ98H1gf6K/358CLhORrm1f3tZh5syZMmfO\nnO1dhsVisVgsH3iKxSK33XYbV155JStWrGDixIlccsklfPaznyWdTm/v8iwxOb+HW1ZfgDElThr7\nQ0aWTX7X1wzDTpAipeJD5Pp/QKbiQvpz95JK7UdN9U/p6DkbgHzxr6RTR5Iv/Z2q8lMxJkt//lGU\nztBU9ws29l5BwZ+LUXWEpgcYdOxEkpAvKQxBLCIJolIgfpShhCGlGiiYXgIcksolqZoYCFsxsWNJ\nqyg3yRcdXyVyHgVx21zUoqfilrF4bl3sDvIkGUsxDAVsh0Lc+iaEOHHYtuAoKMXh2xDthyiPylEm\ndhxFrXAmbhc08buMZK3opiEOoZG4biFU7pBrKTDEYegmEr0Gn0l8nkjUsqeIbu/JYAtg5JjSCAmd\noGii6X6heTWwfL/aPXm8aw5KCWmVpmTAkwClIonqsKYD+Uv7E5FrS0ArUCTwTPR0xpU1syrXRlKn\nuHDHU/lj6xOMyNTz980vUeGUsVv1JLq9PgyGpf0bcJXL9TMuYFLl652RXaV+skGRzzxzFXXJSv70\nse+wuHcdKSfxjifX5QOP2S8/yOEjp7Bvow0gt1gslm2FUuolEZm5JcduSQve3cATwAnx69OA3wMf\nf2flWSwWi8Vi+aDR29vLr371K6699lo2bdrEzJkzuffeeznuuONwnC3LZ7G8N2T9Xn636gICKXHy\nmHcmPhnTQyF7I+myT+O44wDo6jicMGhHuTNx3Bmk0kfj9V+Fl19PtjSfIFwCVPESLnsAACAASURB\nVBGKS2gGCKVET+EJAtNDwp1A1l9ArvMsQhmIXTZ9aF2DF75qri/gRJHfsUAU4IAEUauaaAJcPOnH\nkIjkGD2CrPEo4cbnKMQowjj3SFCEsVsoIHI0GQHQoCJnkqMisSYYPB83blOLPEkhCkfAH3IPRR4o\nXwZdSnqorc4TB4UQGh0JS/EVTSwMhSgMCodIuArir6P3GQllkeCjIkGKSMQKhpxZg9PqZOg9mFj4\nCmIhTP5JZDNAMYzEKx1P5QuMRsTwXO8SRqSG01baTMFASQJEFFOqJ3Lm+FO4cMHlBCa6ZpRhJRgJ\nSWqXhmQNX5l0Mpcu/A15U+RPrU+zqHcdC3rX4iiH7rDI3J7luMrhvgN+zI0r/8xd6x5jfW7zGwpQ\n9akq6pKVfHGHjzMsVQ3AbjVjX3NMPigx4BdpylRv0RpeObCJ+zfMpbM4YAUoi8VieZ+wJQ6oxSKy\n2//ZtkhEpmzTyrYi1gFlsVgsFss7o7W1lZ///OfceOONZLNZDj/8cL7xjW9w4IEHRlk/lvcV+aCf\nm1eej2+KnDz2u4wu3+UdXaeYv4dc70Wky8+ivPq7APR0X0Jf/o4hUWR4/a10dM/CyEaMM4kw7CLp\njqHkLyCZ3IeaijNY33VOJLvo8RTC9YOyyVCmkScKcAhRQyKOEUUQ5zYpZKhFTYgmvKlYeNG4GDxK\nJhJAVRyY7YuLxHcK4pY4EQfNq/lMmlhQwUWpyOITDDqV0LGzSKHExOfrKOQ7ziwyg44pE01ii9xN\nsVgjQ1FUmDhoPBKJ4pa92PFk0MQmqDi8PPozGAwjR/Bj4Wow5yo0oHUcch4LTOafBLDBkPPQAEpH\nolbcJhg9N4ldVZHAZQT2qN2d57oWDj0/UKRUmkJYQFA4ysGT6BkJiqunfY1dq3dgVXYjf2l7mnk9\nK1lf6CCtU+QCDwBXaQ4bsQfnTjqaCjeDiLCx2M2IdN2bft8Y8Auc8MRV7NkwkR/v/unX7Pvs079m\ncV8LEyuauHXfs8i4yTddwyLCc52r2LFqOPWpijc9dlFPG6UwYGbDmDc9zmKxWCyvZ2s7oB5RSn0a\nuCd+fSLw8DstzmKxWCwWy/ufJUuWcMUVV3DHHXdgjOGUU07hkksuYdq0adu7NMu/oBjkuXnlRXim\nyIljvvmOxCffW0Q+fw/GFPAFSrmH6MreTVXl16ioOp++/B3xRDVDT/ZOmhp/S7bwNJv7ZwNgTAk3\nuR8hZazpOnsol4mwBSNRaHhj2bFszj8YC01Ra5gCUAp/0J0UO4VEIqEmEAcTx4MPtppFexKRm0oB\nJgoLR6k4c4loAp2oeBKdiYPC40l3KpKkjIFQXDx0dIe4p21w+psvGoja4koCg1cOTJw3FatNcSw4\nQSx2RdPt4udqFFpF7qPB9zbocPLiUHBB4kynOMMKQeK2PaK3F9UfRjJTGAtYqEGJysE3IaE4EHu1\novBzB4mFtDRJTCznGYHAKJ7pXBy959h55ShNzhRj8UyxR+2uPNf9cuxCUzzZsZC/bVrAH1uf5rhR\n+zOlZhIjMo2sy21mIOjBQRGIUDIhFW4mejZKMTJTD0B3KcuXnr+BT4yYxpcnHfaa9ReJeiG+CV+3\nNvcftiPrcp2szG6iEHpvKUAppdhnC51Pn3vyNnKBx8uf+g6u1m99gsVisVjeEVsiQJ0JXADcHr92\ngJxS6ixARKRqWxVnsVgsFovlveWZZ55h9uzZPPDAA2QyGc466ywuuugixo8fv71Ls7wJvilx06qL\nKIQ5jh99MeMqdt+i80R8+nq+RiK5J+UVXyCfu4Vc7m4Sqf0IMfhmDQbo7v8BmdR+jG1uARTLWneh\nVHyYdPqjbM7eRCAGQeEHK5FgFdWpgxCpwpMcxFKRiaWb9vwDsaAkCG7cohZnKSmNFomntQmGBMTC\nh1KRkGOIXUpErXaO+mdRSiMmdgspTRgLWiJQwmWwWdSPQ8UhgS+xUBVLP8q82h3giSaMayAWw3T0\nZSzxRAxO0vNN1MIXZUxFmVRRPpMQSOTqCiWqOTASu6aIhSwVTdsjymkKTRSYjiiUjkWzeKrd0OQ+\n4jZBI3jx+4jfxVDbXhRS7qCVojZZS0uhA+JjTOy2MrErSlD4sVsrrdMUwhLPdL085IwCWNbfyrze\nNVQlyvFCnz+3vcAxzfvwVOcydHz+aWMP5OyJR/I/G57jrnVPct2MMxmeiYYTFEOPtkIPG/Ldr1uP\n+aDEiHQDL3SuZUlvKzvXvNqu9+VJB/OFCR+lEPpUJTJbtL63lG9NOYxsULLik8VisWxj3vK7rIhU\niogWkUT8n463VVrxyWKxWCyWDz7GGP785z9zwAEHsN9++/HUU0/xve99j3Xr1nHddddZ8el9TmgC\nblx5MQN+L58ceS6TqrbIBQ+AMV0UC3+kkLsTgIqqS/FIki09jS9CwtkJV49G6zGs3Xwo2cITrO38\nKq4zloQzmlRiGk3V36ap+vvUV55P0pkAKHpLy8lLEcFFq2oi8SLBzT/v5pjpawiBtpaQw3dYypGT\nl9HSKgSSwDeaoiR4/G9Fjp34Ci0bAkriEOBw9sde4dafteHjRgHccctc0WhKxsWXBJ44+OLgiYNn\nNIFEIpQnGiMOgTh4xolDweNQ8jhvChSBcShKAk8SFI2LJ2kkdk2FaAJR+CEUjYsvijCecBeawa/d\nIWEqcl8N1uAQiU/RMSUT1WhEYyQKBw9EDYlAoYmynkJx8XGj3KY4CyqIz4t8Uy5+CJ4k4vqie/hm\nMKRcE5pI+ApCWJPvxBeHUBymV++CE/8uOqXTQ/lWjckGAqP56sQTGZcZTRAqmtNNuCSZVDGOjFtO\nmZOhzy9wypiD+MbOJ3PWhCP5/LjDKYWa08cewpcnHkE2KPKLpX+hJd9Fn58bWnMjy+r47V5fYWSm\ngULcsjfI852rWNq/kT6/wHcW/Pfr1mtCu28qPokIj7evYGO+b4v/HwA4afx0vjBpn7d1jsVisVje\nPm/pgFJKnSEiv/2n1w7wHRH54TatzGKxWCwWyzbF8zzuuusurrjiCl5++WXGjBnDtddeyxlnnEF5\nefn2Ls+yBYgIN636Jn1eB58YeSa71R6wxecG4UZM2E39sL+jdQNFbyE9A78ZavkaPXweBX85A8Un\nMWEbxsvQU3iYnsKfUKSY0ryAl1p2BRKEhIBQmfwYRX890BtNalORKCIkSTsTCIncNwFJhEJUhw9/\nuLGTM384JnYsqbg97lURR8WpTtG26BjfQDAUyB210cFg0hJx61kUHo6KWgGj7CSNUooQCCRElENo\n9FArn8Rh4aGY+DwwJpqAFwVyy2tylwZdTMhghhVx6DhDDiajwAyOwCNyUJmhTChFaDRqaJCeiift\nqfg4YndU7HBSCmMEbyg3KjF0zcFsLWMGv46OGHSFQdSaVu6WszLXScHA1bufz6pcG7eteYhsUGRj\nsQcjijvW/i/r8p2Aw74NU7hz/eMsH2jDC1sQ0Rw3ah9GlzcyuryR9blO7ln3LGU6xaTKkThK0+Pl\nyIc+oOgsZskHa/lI3TgA7l77HA+2zWeX6mY+1rQzAL4J+PmSRxmTaaC5vJb9hu24xWt5kCV97Zz1\nzF3s2TCW2z76ubd9vsVisVi2LVvSgneIUuoE4AygHvgd8Pg2rcpisVgsFss2I5vNctNNN3H11VfT\n0tLClClTuP322znllFNIJP4/e+cdZldVr//P2nufKZkkM+m9E0hCgBBCEUJvASmCIEUELFcUvKAC\nYvdy5QcmFFHKRZoUFREVEKQJEnoiAQIkQBLSJsmkzWQyk5k5Ze+1vr8/1tr7nChdYwTW53nmmZlz\ndll7Z4cM77zv+81t7eV53gc3Lf4BawsrOXDAyezS+90HFHflH6FQeJSG+h/RtPYwjFnLsEHzCMM+\nrGr+LMX4VUAY2OsaYrOBZc0no10vURQOQRdmoUVRFQ2jvfAy1kGUWKFGYH3xGURCFAniCrm15EkI\nKchq121ky7sTYzt8tt+9O4/9vpVPf20QPftWkwiud8lG7grOVSTYiFre5BAMSiKUstKUoVzmHTtx\nKHUTmaynSTASZhE6W+AdoVDEEhIq6xwCF/cjQhkQFThxycpFsbGCVuCmzRlxpeCpuoQVgKxLy55X\nTGDjcFIWnQA3sc8WsWPIeqDSiJyIFYzS+2EAZcoT74IAxNj4ooCL26WT61zle8XatNhztugukDxK\nwfQ3fsvq/EZMJj3aYvUB1X1Y2tkMwB59JzC4th8Pr34JBbzQuow/rPwb544/BqUUDza9xMa4i5CA\nbd2Uu+F1fbl+ty9TFVbxuWevpUdUw8yDbaH9YUN2ZEOpk516VU66U9SGOQbU1nPt7h9MPNqmRz9O\nGb0r+w96/+KVx+PxeLY87ypAicjJSqkTgFeBTuBkEXlmi6/M4/F4PB7Pv5R169bx85//nGuvvZbW\n1lb23Xdfrr/+eqZNm+Yn2n0I+dXS6azMv8kn+h7O1P5Hv6d9NnVcQ774NzZ23u4KrEOCoIE1rRfT\nFTfRq9up9Ox2HEXTzNqNVwA9MHSCCF3JGisESY5C3Mjr67/oxBMrwCQSum4mO0UucWKTuLLskpSc\ncGJjZAWxRdMHndSXxoUF7rlxPSd+a1gWrwNI0pJxJ8ZoAowrBBcgECGWgJicbVsSK0ahbCwu7Y1S\n2fQ65frKlROtArSx4o0Vcaz7yDjBR1wxty0Ut86jNKqWuC6mECuYBc7CFLtoX/o3ygjOBYYTg1J3\nV3lqnlJ2cp0VnayAlk7yEyPZPVbKTvaz91gw2r4nrudqQHU/EolZW2xDi+2rUqisYystPseJZyLQ\nlG8hMcqN0IswCEqEZzcsInCOq37VPblk/h9p7GpmbI9B/HD740kwKKV4o63JTbqDWAwdSSF73rbr\nOYRPP3kV43sM5bQxU7PX71/5Ck+ve5O5rY3sN2AcALkg5MEDz31Pz/HbURVGfH/SYf/UMTwej8ez\n5XjXDiil1FjgHOAPwHLgc0qpblt6YR6Px+PxeP41LFmyhDPPPJMRI0Zw8cUXs//++zNr1ixmzpzJ\nYYcd5sWnDyF3r/gFCza9yE71e3PooFPfdrtSvIB1zV8kTpYC0Kf3dURVU4FqCPpTMoamDReybtN1\nxNLKus7fUlU1lmUt59BeeIQu04VGkctNsR1EAgmBi4/VUBNNIJaIglQTU4UmwhCSYHuHcCKLpC6f\nitiaOJEnqM1x8GmDePSOZja0Bmg3uQ5sfC4hJHH7KScypeJNUXJoqSIQVV6bhMTGimElk0MTUZKI\nkutjyuvAln1LYJ1YhMTuvZIJskl5WgJi49ZD4CbS5RAiEglJJAduXwhtEbgJrVtKrNsoMfbDilr2\nOMZ1MBkJyh1Solx/U+SKyAN3Dnv+tN8pEVscnkYLtYRoUdSoGmJRLM9voEsLu/SagBAQG0VRByTG\nxgcTA4FETuyCnKpCmzSeGDoPlGLn3tvy1W0O5/sTTuBPe/+AvI5p7Gpm737juXCHk+ieq+OIwVPY\nWOris89ew12Nf+Opg39MqKr53ty7aOrayNl/+xWvtq6gtdRJXVTDwYN2yJ7Lc8YdwkWTjmVqv7H/\nmr8QHo/H4/lQ8F4iePcBZ4nIY8r+hPpN4Hlg+y26Mo/H4/F4PP8UL774IjNmzOCuu+4iiiJOPfVU\nzjvvPLbbbrutvTTPP8Gja37PC61PsE3dJI4ZftY7btuV/wtdhQeoKexNrvsogqAX7cVnAMWw3j+h\nsfksmjt/iaIWkS5i0cxffRyJVJNI0Yob4Sh6151M+8YmEtlgXTQCRTppj5cgkgo0igBt+5XEemzS\nOFxCBGJInHhSkJDYyR1aQg48ZSAP3tTEI7eu5pivj7BCDpCWhMeZcwpXyi1ZLC/Axe/ETsdLnUx2\nX4OoyFp9VIh2Ry2mbiMi62ySsjBmv0gn94EyKjumqTg2CLi+p4TARQ/TKCCueNwiksbjnCinFNoI\nxvVUKWwnlUi5JMo4J5YghIAohTFWaKsNulGSQhYB3GhKBMpO3GuNO5ncawIvblhCXmwHk8I6rCAg\nwQpiVUR8f4dT+PXSv1ITduP5DQsRgWoVMbS2L58duR8dcYFL5t/DovbVxFpx0IBJ/ODlu3i9vYn9\n+o3n3Amf5KghuzChfjBhELB9w1AG1tQzt3U5T6xbwJge/XnqkO8Rqs1/5z2gtidHDt35PT3vHo/H\n4/no8F4EqN1EpB1ARAS4XCl135Zdlsfj8Xg8ng+CiPDYY48xffp0Hn30UXr27Ml5553HOeecw+DB\ng7f28jz/JM+3PM7j6+5mYM1wTh/znXfdvr7Hf1FdNZGa6r0ACFQNPWoOQ8RQUz2FgmjbiUREIhGG\niFL8JoYciXMsdSWNLNjwPdeplBZ7C5oIXOm1LRYPAesA0q74O85cUHZf7Y6pJaTkIngikOsesd9n\nB/HXX69h/y8Md8eCooTkpYq0E8lUTHpD2VLx2AksWczNdS8pIJbIikJihSyNsqXgVtKxwpMKsvYj\nWxRu3UoiglJpXM9FDSuidbG4qKDYfcAVi7toHu6+iJgstmd7p6w3LHU6mbQdHNv3ZEvMg4rpfNZ5\nZsRF8oAuU3LXk/ZIBYRKgW2a4qqF9yEIdWEtORXREnchQETIiLqBLOpYTUEgIGS3Pttz/ZuPcvDA\nSSxoX82yzhb26W/dSg82zeWh1a8AUKNydI9qaCl2IgIvtTZy+ONXsHOv4fxox2MBuHGPL9n7IIZ+\n1T3ZoddQoqAsxHk8Ho/n483bRvCUUt8CEJF2pdTxf/f26VtyUR6Px+PxeN4fSZJw5513MmXKFA4+\n+GDmz5/P9OnTaWxsZPr06V58+giweNN8/rTyFnrm+vDVbS76h/cTvY7GtdNo67wze02pampr9kMp\nWy7fVniaTtPJmvzjvNp0FAkRidSSiyZSFe5ILBATEiPEElGUwMbXCIkJiCUkLyFFqbadT64jqWjS\naJh1P5WLv90kN7HupdRhVJKQWOyaYgkpSo6pnxuOTgxP/Hp11pNko2G46XhWmCnoiJI7tyEkMQFF\nEzlxLCQxUNLQpUNiiYhNSCz2ay0hBRNlsbiSCSlphTFQ0oFdlwmJjbLXb+y5EhNSNPb6i8bG+BIT\noI29Jm3sNWoDibH3wYgrOzfOleXcUiIhJWOjimn6NXVXGVOeoicV984KV6kgZY+bGEWilXOV2c6r\nklundg1Sm3SJ/Qfu4hxedh2LOtYQEdIz143blzzJ2nw7APPbmjh66O5EKuDqBY/QWurk4EE7MKim\nF6D4zg7HIASszrczpddofj31TKb0HvmW0+oCFbBr31HUhB9sqEFiDA+ueI0Nxa4PtL/H4/F4/jN5\nJwfUicAM9/V3gLsq3psGfHdLLcrj8Xg8Hs97I5/Pc8stt3DZZZexZMkStt12W2644QY+97nPUV1d\nvbWX5/kX0VxYyy1LryCKajl77E+Ign/8ES7RayjGr5AvPk193Qmbvbdi43Q2FWZRkxvLpuIscuFA\nSnoNSlUTm5iO4ktoFCEQKJV1DwFoBC2B7QpTEJuQQFnRJUmdREQoUleUuAhZgMYWjgsVsThAkwMX\niBMCtITU9Ir4xPFD+OutKznm+zYmarAuKnHF5KYizmfAubFsv5EyGq0URiJnSlJZiXiCc005YSvt\nXbIrUpREEAkRhMj9elbc9gJZ8bfJjpFejxCbgECJE45CxB1Xi4vooVAElNLYoHMyBcrY41bE+4yk\n20CgAOf6MiJoY9WqQEFiymtIJ+Vpezqo6MlSKNpLBbRb46SGkbzUupTLpnyRvtU9OeHpK3m9rYmc\nyjGpYSTXLXqMohFeb2/ijbYmPtFvLL/b+2yWdzYzvn4IWgz/b9LxTOkzigE1PbnxE1/8wM/0O/FY\n0wL++7k/ctyonfjJrkdukXN4PB6P59/POwlQ6m2+fqvvPR6Px+Px/BtpbW3lmmuu4ec//znr169n\n991357LLLuPoo48mCN51xojnQ0QxKXDVogsRga+O+RG10VvPgqmp2pGRA/9GFPZH6y7y8Rt0q96e\nheu+TD5eSD5ZQ09Tz4heP0UF3Vnd/jtai3PRstGKOohzLkk2Jc64uJpGIUZhECAkgkzUSbLJdxAo\nQYtCS85F9sBoxZonq9j4eo6NS+2PnqlDCJxoomw0burnR/HMHat47s4mALQOEKPSXu+KiXQBxqgs\n2iZINjEvC8o5R1Ia+8v6mdJjuM82vmdFLAQKOl2fFYqUu6a0UN1G9+zabTdU4Kbj2atJY3m26Dyd\nAmjlIHHCV6AUsQnsa9i+qUDK12UAY1zVukq7pwK0CEpUdjUhATHGCW32XGlsUEQRqIA/N80FAr4z\n/lieaV5o3Vw6oXtUy/Y9hvNa+0piY9i2x2BebW2irZTnBzscxR59twGgW1TNNj0GAhCqgE8O2emf\nfKLfnbZS0XWKlf9bdvYz9xAqxU/3fG8THz0ej8fzn8c7CVDyNl+/1fcej8fj8Xj+DaxYsYKf/vSn\nXH/99XR2dnLYYYdxwQUXsM8++/hpdh9BRISfLvwhed3F50d9nf41g95x+1w0FBHhpVVTMNJOfe00\n2gpPEqgeoBQbSk/R0vIsQFYQnooqkglKihx1FKXkBBAQA2kHkhASm7RjKUBU6oJSlIx1DEWpCKID\nnvxSPRteyaHziqawhkQCinE5gqclsDE4Aur61bHLMUOYfecKAErkbAdU+pOnE2MSN0FPXD+Trpi6\nlxgr61jnVfm3pgLERmGIAEOgrBsp7XJSbisjad9TSKBs95IVhZTVyUShncvKkDZcWbHJClWSdUZp\nA2Fg15w6rgxpJ5W9p9p9jp2TyfZRpeKVjeCVr8O6sKzDyopMSoXWfSYwpm4gyzrXo42hT1U9G+JN\nTO03jt37bMthQyZTHVXz7Po3OeeF2zlyyGRe2rjSHTnkV8tm0ZXEtBQ7uG3pc+w9cDwAv18+hwtf\nvo8vbbM3oQr5zZK/MWPKsUwd8O4T7IwIJZO87yjebv2Gs1u/4Rwzcgf35yzMbHqTyIvrHo/H86Hm\nnQSonZRS7dh/lWrd17jva7b4yjwej8fj8WTMnz+fSy+9lF//+teICCeddBLnn38+O+6449ZemmcL\ncv2bl7K+uIYjBp/A+PpJb7lNIV5CbDbQo3oKAB2luWjZBMCG/EwSUSCdkLmHbFW1BhAbo0tdOjG2\ntLtLSk6Use6fkoSu9NuAQBAEJG6SXGJsLE6ciGXjXzZetubJKja8UoXuclPsYg0qYPVT1eTGFgEb\noStRg4ghAPb6wjbM+cNKdCKIIZsEFxc0RAEFEzkHEVmZeKgUsXbxNLEl4lYwsiKTpM4lF2kTCVxE\nzhWTZ0JQaF1eYuOG4rqibAQxQIlkU+6yjiuFc0SpbBqfEQgCG5EraVwqLnBCkrgpeYF1Nxkyh1WF\nzubEQXtspez9BuhbVU9zaZOL3uH6p1xpu4GiBghYX9zEuJ5D2Vgqccn8+xndfQAHDpzIxa/ehzEl\nZq1byrDa3qwtbqKoE7qF1fzPDsfwo1fuZnLvkdnz1T2qpjqIuG7BU9RF1XTpEo+vWfCeBKhjH72R\n19rW8OeDv8LY+n7vun3KyB69ueOAz2XfK6V48qh3nvjo8Xg8nv983laAEhE/ssLj8Xg8nq3M008/\nzfTp07n//vvp1q0bZ555Jt/85jcZMWLE1l6aZwtzz4rf8EbHPHZp2IP9B3zybbdbsO5USnolEwc9\nwdINP6F/92PQEqERYoxz56TiU0Tsup2MlCex2a6jxEbpXCTMdg/Z6XYKQbnpbDGhjcVl/UYWkQBR\nVugpiZ2W1/JaNTpfXmueTmqkG+0LQkbu253/fXUaoDBGSIgwoug+KOL7Lx1ByTiXEwFd7SXybTF1\nA3s4h5KNq6Ul59p9ryVwIlO2KMiEHIV2Dq0gUJmwZK/fYCSdhufidq7oO+2BUlnMzU7xE7H+pzTa\nl4pYxt1vtGS9V4i4pWxeNh6nIpnb3x6z7KZKe6ZCJ3ApAnIqZ8Un7NS+dMKfATaVChhRBAqGdevD\n2eOmcem8PwOKi179E0ZgQ7FApEJWF9oyEc0YRTERtq0fyJ8P+Caz1i9lRWcrw+p6MW3IDgzt1ofj\nZ95Afa4b/7vzURw8eHz2Z5oYzbWvP8WUviPYc8CozZ7NjaU8IvBmezPDu/eiOnwvA7jfmobq2g+8\nr8fj8Xj+M/jg/wp4PB6Px+PZIhhjuO+++5gxYwbPPvssffv25cILL+Sss86iT58+W3t5nn8DL274\nGzPX/4WhtaM4ZdSZ77jtkPpzyMeLyccraM7/hUBVEYRjKOkliED33L60xs8BEYlo56gpp9oMtmRb\nU41yQkwsOG+R+1CKxEBMROjKvbUolLLRsdTRo5w4Y3uVArqPM4S10NbZRivraWY1Y3ITqN3WUHRF\n2olErqScLP6G0kBEXNSsmNvMK79fRhAoRkwdQiyhK+RWKBG0E4HSWJ6Nydn1WPNRuUMpRRt7naSd\nV068UghKKVfyXRaWbMeTyvZBKee+UtmUusCJYYK9wdooF4sVJ36pin6mwN1bt1jj3EwS2mMbFwpU\nipqginxiqFIBBUlY0dVKn6rurC92IhLw5bEH8vvGWbSUulxZuuKEYXuysGMdfap6UBVWow0s6Wi2\nwhiKU0dP5eXWRl7YsJztew6mYAwL29exsG0tg2ob+PzTt7Fdz/7cc+BXAdi+YRCX7fpp+lbX0V4q\nEri4r4hw0ENX09TVzoSGRXxm02TGNfRnct9hANx38Bk8uWYxX3v2j3xqxEQu38P3N3k8Hs/HmS0a\npFZKTVNKLVBKvamU+vZbvL+PUupFpVSilDru7947TSm1yH2cVvH6LkqpV90xf6584YXH4/F4PiKU\nSiV++ctfMnHiRD71qU/R1NTE1VdfzfLly/nhD3/oxaePCesKa/hV443U5er5xnY/fMttWvNPs6n4\nKgB9ux/P0IYLmN98PgS9GdzzLNriRjpNRJdUsbb0PLEoSsZQkJACObpMDg5y7gAAIABJREFUjqKJ\nyEtE0UTWsURAIhExOYSIItUUpZq8hMQSUqDKuaLIYnlFE1CSKopuv8RNgStIREJA76manjtoFgRz\nWcYChkfbMHHSGLrvFaLJufNaUSQR67pCWVEqMQFt60v88axn2LBsE0f8dCp1A7o5p1KEwbqxDDli\nyblJeSFGQkomQhMiJiAxYYXYZD8SiaxAJYpYArSxLjAtVnwq9zNZ4U0TOXeVXZ84Zc5kHzYiFxtF\nIiGJtu6kxECsA0o6IJGQOFsLpFXi2tiJg/a9cjdU6pQqajAIBaOzEvO1xTzaxQyvW/Q49bke7Ngw\nguZiJwOq67lz+fP8rWUJL7euoFpVERBy5jYHoZx4F6mIX+71JV458sd8edsDWNK+ga+PO4hd+oxg\nQG1PTh+zByeP3o3d7ruUH899EKUUnxw6kQdWvM5Zz93FfY3zsmexNqxiSLd6vjlxf3744oN8f84D\n2Xufm/kbzp99P5N6D2a/Qdtw+8IXuOLlJ/71f2k8Ho/H86FgizmglFIhcA1wMLASeF4p9ScRea1i\ns0bgdOC8v9u3N/AjYAr2l08vuH1bgf8D/guYDTwATAMe3FLX4fF4PB7Plqa9vZ0bbriBn/70p6xa\ntYqddtqJ3/zmNxx//PFEkTcrf5xITMIVb1wMAt/c9nuEwT82ImjTxby1pxOp3ozo8wOWtd5MrNsx\nFElMJ7NXf57YxcC09fSgpcoVdaeozC1Tcj8OarGiiVLWTWSn25Wn4qXT6gpG0ORIo2o2ApfDuAlw\n2oSEtgqKRCm2/b+YQU/vwaYFATXbKRr2EgjSiXBpLxLY6JmQGEWMIlKKnoO7c+as4504pigasU6j\nrDZcEBG0CTLnUyz2WLhJdmn/Ey4Cl7jCc+UqxIUQMCSa8lQ9Zw8zhKlByU6YE3ET+VLnU2jPv5l7\nysYRs3heNrrH3hRDOmGvcqy0AglIRLLXUjdY4tYZoIhUyKReQ3mhtdE1bgX0re7BKaP2ornYyeL2\nZlZ2biIIYPv6IRw9bGdealnBko5mnlz3JrGxa3xk9et8bfwB9pkTTVFrDML9K+ax78CxXLDjoazp\naufHLz9EQSfZKo8ftTPLOjZw3ux76UpiTh6zCw8eal1SN70xm8+M3JkTxpS7ykZ070VsNHcccCpV\nYciUP1zJhmIXZ03c65+K43k8Ho/nw8mW/C//bsCbIrIEQCn1W+BoIBOgRGSZe8/83b6HAn8RkQ3u\n/b8A05RSM4GeIjLLvX4b8Cm8AOXxeDyeDyFr167lZz/7Gddeey1tbW3sv//+3HTTTRxyyCF+ot3H\nlMveuIRO3cnpI79Mn+q3Lm0Og26M6vU9WguvMm/9t6wYQhpBqyYxmxAiJ3ZY145NgqWCkbhpdm4f\nZcWUWEAToMSKKoFSrk+pPGVOm/LEN7CaTkkCF8uzkTFBERtb8m0IUWFAz32F7ntXvC9pvC20BeOS\nCkbK6USKgrYNU5owm3aXnjP9rN2kO03oHEnl4u7UwaRUYIUoCRBMFp1LS9mR9Dh233L3kiF0Ebo0\nXmjPWZ6UZx1M9rq0gHIOqxQR64IKnOikUS6+ZsXAWFsZSSq7qoDUHyUiKAUhAV8deyivtTXxxLqF\nDKvpx9KOFnbuM5Jdeo3gN0vmMKS2ntZiEUVAv6ru3LDH52kpdjGlz2i+MeFgLphzN0O79WZdfhN7\n99suW+NfmxYhKG5a+BwbS0XO2G4vzp14AAO79eTlo7+bxe0AJvYaxDkT9mPhxmYG1vbMXi8kMRfP\nfYxeVbVcvFu5r+xne35qs2f3joM+S1cce/HJ4/F4PqZsyf/6DwFWVHy/Etj9n9h3iPtY+Rav/wNK\nqS8DXwYYPnz4ezytx+PxeDxbnkWLFnHZZZdx6623UiqV+PSnP823vvUtdt111629NM9W5M7GX7O8\nayn79juAXfq8849MQ+s/T6f+BSV5DNCIVKOdZymNtAmKkkQoJQSuUFsTZNPhtHMIaVHo7EfCABFj\nBR3XQxS7Qu6Y0PmOjBWMXGTNvmIzaUUduYiZoJTrUXJl5QaFNoBSxMYKRzklJGJja0jqanKT7CrW\nZKicVCfuOO4aCMkycc4dZSQgsD3gTnizko42ka1ld11M2kXeFK6fCZwgBKiIkjYIIUpZMSg2gCtx\nV1ihS1Ao1zmVvq6lUiwL7X4uupe6n2znVbiZ2Cxu+p49TkAi9spzQTW3LHmalmIXAMs6NmCA51uW\nMXv9Mue8MvSprqOl0ElnbJi1fhl/Xf0Gf2ycy3+P24+n1i1mr/5jWNHRRldSys756KoFiIGdew9j\nSF0vjh2xU/Ze8BZC+OS+Q3nuqK9v9lpNlOP2/U+me676Hx/WCt7PJDyPx+PxfPT4yP76QUSuB64H\nmDJlirzL5h6Px+PxbHHmzJnD9OnT+cMf/kBVVRWnn3465557LmPHvvs4c89Hm7mtc5m57gmG1I7k\nxBGnvO12ie7ileYfUx32Z1HbLdiIVoQS7abb2Yl1IobYuZZCrHBio3bObZMKOa73yRCQzoCzfUeC\nkQhRVviJxbp4UIqSCYEApdIQnI2dlSRyQo4iNsp6fJxwFQCxcxaJKU99K5oKIYi0tNtYN1RFMblx\njqDE4LqeAowR0gl16eQ4IXACj5tI51xZ2kA53KZIjFjJTALnIBPEiD12KhA5ZxTuOoyEWYzPbmMj\nczZaRzrsDnHl7OIm31n3lSIIypMH7YqzPwa0m4oXWGWMz42aypp8B4+sfgUt0GlKDOzeQHOhC1Du\nzwX65OpYW+hAjC1Wf+yQ83ho5Wuc+/wfOGvWnVy9+2eojXKcMmY3duozlAn1g1DAtIf/j2fWLuWR\naWfxpe325PJ5f+WgweM5flQ5Pvd+2XPAyA+8r8fj8Xg+HmxJAWoVMKzi+6Hutfe6735/t+9M9/rQ\nD3hMj8fj8Xj+7YgIjzzyCNOnT+fxxx+nvr6eb3/725x99tkMHDhway/P8x9AV9LFjUtuoCas4Vvj\nNp/Zsq7zKRa33cCk/tOpDvvyzOov0156xUXM0kltAQkKLRFpu1GJKhcPgxIhmjQyppwQFKIpT66z\n0+wCEgEIMSKEbpJbGkHT4HqbQtdtJK7DKXAF4nZbnBBUMkEm+lhRBicaSTb1TtzEvVQ4A0iMnUgX\nKCtMZW4iF+kjE6ZsbE8p2SwSaB1XqSAFyk39M67wwTiRyDiXWABu7QGJsZ/T3iuVupacN8tOsQPj\nJtqlXU9GFIjtaiJ1jOmAssyk0CZdpD1vVVhFiKJDF+x607WLoqgNDzbNAxSDqnuzMr+R7mE3kAgt\nBu2OedjIHXlizZss3rSB9lLC5Ht/wgMHn8WBg7YjMYZHmt7gnsZXOXHUruzcexif+estDK2rp6XY\nRV7HiAhnjNuLE0dPpr6q9j09r79Z+BJ/WvYa1+/3aXpW1bynfTwej8fjgS07Be95YKxSapRSqgo4\nEfjTe9z3YeAQpVQvpVQv4BDgYRFZDbQrpfZw0+9OBe7dEov3eDwej+efIUkS7rjjDiZPnsy0adNY\nsGABl156KY2NjVx88cVefPJkXDT/YhLRnD32G1SFVZu9t77wNK3FF+koLebl9VfQUpxnJ7dhxaIu\niUgkpOQmoiUS0ik5YhNQMCFFciTk7NQ4CZyQFDmnUAhEtnDcKDcVzrqbhJCiidBExJIjoYpE7LS8\nWCIKElGQakoSUZLIxeAUIiEiIbEJsolzRpRz/tjOqaLJUTARRVNFKTunnT6XT+wkvTReV44T2vWa\n1EXlRCTBdjDZ0vTITp0TO6kPF7MrmYhYK1tuLuVttaRF7fa+lDSk+TfJzpkWudv7HWtFrEO0RMQm\nwLhjABgT2ol2WhFrlYlvdrKdYEx6P9y6dELJ2AJ1Y5y4ZxTGKG5fMht0QF1Yzcp8GyKKea2r0VlX\nlOLbEw/l5FG787Vx+9O/pge1UY66XDWJaK7e4wS+vN3ebCjmGVbXQF1UzWcfv50FbetoLeS5ZJcj\n0Vpx0uO3A7xn8Qng0VWLmL2ukXX5jg/+0Hs8Ho/nY8kWc0CJSKKU+hpWTAqBm0VkvlLqf4E5IvIn\npdSuwN1AL+BIpdSFIrK9iGxQSv0YK2IB/G9aSA6cCdwC1GLLx30Bucfj8Xj+Y+jq6uLmm2/m8ssv\nZ9myZYwbN46bb76Zk08+merqd+5H8Xz8uGnxrawpruOIwYcxqvuozd4zErNNw1kIDcxsugBNESMR\nmpBI5ShJAlkDUzlylhBhXTf294xaIHEOo6yzyL1uPUtWXNFEICYTZWy3kaCdk8gQkhiFqMB2TTmn\nUVqWHZsAUaF1AqnAiSVBNkUu7YyyQpdx67B9VIkRYgkJnBMrcNcSgHNeObeWczLFEqDEXkfirjPE\nuqdEBQRKKOlUHBIMke16cp1XgQKw5e1ap6XlEUZcDLGyN8o5qEQCjHEOJ+eMst1L4s5T2ZeUurbS\niXlWZAqCtBdKUdSCUrFdh7HH0sa+J6IQBRPqh7Ndz/78Yflc2uMCu/cdycGDxvPKhtVcNPcvXKQe\nBYEeuWrWdHXQu6obBz14LQcN2paF7etp7GzlxFGTufilR3llwxoCpbhi909xxEM3IUDD+xCeUq7e\n+1Osy3cwskfv972vx+PxeD7ebNEOKBF5AHjg7177YcXXz7N5pK5yu5uBm9/i9TnAxH/tSj0ej8fj\n+edoaWnhmmuu4aqrrqK5uZlPfOITXHnllRx55JEEwZY0HHs+rLy68TWeaZ7FiLqRHDv0mH94/y8r\nTqEjbiRQPUgogXPviAoomQRx0+20hGj3nlLKuXZsjEyhKBFkQTNNzokszo1D4PqZcFPvbKQsNoEt\nIneURBEiJBKhELQIJQkIVAAiJBKiJSBywlfJBFm5t3ECDJgsGmfIuSNb108sdjqcNuI6maxSU5Jy\nzFAbIUyLxZWNEJK6obAl69ZhZGw0TrmC9HTanTuGISAx9qj2+m0Zu3VW2X6p8qw9ex8Tbb9WKo3S\nOYFKpffRlaS791yjVeZYSh1bRquKYyu0SSfgBYgpv55OK1zS3sxNe51Kv5p6pr/6CM+ubaSUwJwN\nKxjRvRfLOzYCMHXAaB5c+TobS3lE4C9NC6nP1SACD614gw2lPIcPHc/2vQbSLaqiJsqxW+/hXDf1\n+Pf93HaLqrz45PF4PJ4PxEe2hNzj8Xg8nn8Hy5cv54orruDGG2+kq6uLI444ggsuuICpU6du7aV5\n/oMp6Zhr3ryemqia7044P3t9XsuNrOmazcgeR7OxtAqNAbPJdQSpcmm1E1iMDond5LeECIxBCKxQ\nQ+Cm1aWxMhs1MxK5aW/YCXHKiiDaiUGGAC0QqrK4I+RIEOf8MTYmR2jFJKOywvOiKTt8ACeE4YrJ\n0wl5qfPKRvKy8nBjnUZKWTEpSdcu6bQ5W5NuhTXrhBInSNkyccAdR5yApZRk5eVpYXrZqVR2Z5n0\nPmC7n9wNyyJ/laIQm31Pdmyd9XKpbNJdVo4ulYKVOx+SCVhKle+ZOMdVr1wdzYUCu993Ods3DMQY\ne+8Wtq/nOzsczIMrXqc9V6Q6ynH62D14cX0Tq/NtbF8/kKZ8O/8zeRqje/alOoho6mrjzCf/yJ+X\nv8G8ljXcddBpDO1e/0EfX4/H4/F4PhBegPJ4PB6P5wPw6quvMmPGDO644w6UUpx88smcf/75TJzo\nTbqed+ei1y8nr4ucP+5sckEue/31jb/ESIl80kpsYhv9opailKzYQYBCSFy/kpVXAheTc3PsJHRl\n4k6IsXINiYQYCZ2gZIWRkigQ6/hRmTCSupbSonB7JFv8bfeNJbCRNMpT3+xWNqqXTpJLJ9jZ8vGy\noyqpEKqs88gJbMaQiC0CT49pp8vZ7iREUCpEa0GUciXgdnWpyCNuXxFciboVsdIuJxGTiTxGFMZY\n4SkIsA4lpVxRefk44trM0/sTuDgjznGWilEqm4RnBTE31G6zyXhpjC9SEQgkYsrt6UrZYnUUzUXr\nZioazZz1jQBUqZC2UoGjhu/AVfOfpi6q4slPng1AVxIjorhx7xPp160HAA+vWMDPX32G/9v7GI4f\nsxMPNL7O46sW89DyhVSHOfYZNJrjttmBHfoMpH9t9/f1DM9es4InVy7lnJ33oioM33X7x5cv4dXm\nNXxt8icI0j9bj8fj8Xys8AKUx+PxeDzvERHhySefZMaMGTzwwAPU1dVx9tln8/Wvf53hw4dv7eV5\nPiT8edWjLO5YxtQ+uzOxfjwAD634L7qSZnbr90NWd81mdf5NiuTcNLbYdh2JclPgbF+SjcK5niKV\nCgBhJkZZEcr+j34iVU6skoruJ7u9EedUwvUcOUdViHUHxSZyHVNWkAohE1/EncOkPU9i3T+JKIQI\nkwbaxMpehtQBFIAKXFxOuZlu5al6oNDaSVQKF5GzHVJK7L3ABFn0jewcqaNIXNm3E9zENmWFCnDl\n4sZAGJSn2WntHFLuGpS7Lkivrezm0gKJ/kcRRbJ7bkU3K9qV12hMuZy8JIaeYQ1FKbo1pPfTTgsM\nKl6LlXDmuL3Zve8ICOAzj96OkoBjhu/IxmKex1YtYkhtA62FAusKnfTr1gNtDL9dNJfXNqxlecdG\nvj/5IP66YgnrkwKHDR/HA8sX8GCj/Tho6DbceMBx7+s5vvzFp5i9ZiXTRm7LDn3ffajCxbOeYNHG\nFo7ddnuG9vDuK4/H4/k44gUoj8fj8XjeBWMM9957L9OnT2f27Nn069ePH//4x5x55pn07u27UDzv\nndZiO79bcT+9c735yjanY0SjTcy6/DxAmL/xftYUXgKqsuJtjRWerAhkXUepy0icIKWMoJ07SlBo\nwkycSUzagQSGCCunWNeULfm2jiPjxBUx1gVUdJPxAhf9E1f2HYuQSEDgjpFOucNtZ5yjKgB7HG2P\nnzqxtJtSFwaQmDBzB6VijwInaIU2EmdSB5GLu6myw0pcMXlleTe44m/jSsvTKJ2kgpUGd/+064LS\nhkxcyvYX26dOhbvL3huciymgHMvDrafCyJQJT6l7K11jdki3IZnwhBOcqoKQb+90MKUk5o4lc1m2\naSNXzXuWq3iWB6d9iU1xgdZigYdXLiQR4brXnuOcHabyuW2nMKHXAACeXbucmU1L2GfQKPYeNIqC\nTli6qRWAH+x6EPsOHsNVrzxDSz7PLv3espL1Hbl06mHMa1nLxD4D3tP21x5yJMvb2rz45PF4PB9j\nVOU/mh9VpkyZInPmzNnay/B4PB7Ph4xiscjtt9/OpZdeysKFCxk9ejTnnXcep59+OrW17396lMfz\n9Rf+h+ZSC5fu9AMUq/nzirPonhtGe7wCMOTCQXQmLVnczRCilEKJLRqKs5JsK8YEyoo5doJcGvey\nIpTtdrLuJi124lsgisSJTQaVtSEZwfU6KReBs9tqq7e4c9m4WUkCEEUYQOymt1mxRzn3VVl0MZJO\nuitPryt3RJU7j7TreVLYCXWZs0vICskTN/UuyESloOxUorx92ruUHjvtuEqnAhqTdlmVr12yLiyT\n9TFZcWrzAQLGpNunrwtpUblkHVCSRfHK7ifXHSVp95bd+9DB43hk9QL6VtWxtquT4d160r9bPXNa\nVgHwo50P4frXZ7E6v8n+maB47IivMLSugVUdG/ntmy/TLcqxMc5zxoRP0LemLltrV1LiZ688zSdH\njGfHPoMAWNXRhlIwuK6eY/98Oy+ubwLgc9vtzIZCge/suq/vhvJ4PB7P+0Ip9YKITHkv23oHlMfj\n8Xg8f0dbWxu/+MUvuPLKK1m9ejU777wzv/3tb/n0pz9NFPl/Oj0fjN8uv4/VxfUcPnB/BnXrz7r8\neiDHxrgJLSFCjlhvwIpAIOmPaW7iXGLsBLpAuTiehCC4onGIELQoYrHdQrEL0VnPky0Xj9NeI9JI\nG5SM7UbKVQg7gqAJ0SYt4saVhIeZgyfWkKBQ4lxAYgWgMHBCDW76nRJEYzubsngaBIEVtkxaNO4K\nxFMnla6IrmVCD0LJTaTLStlRIMa5m1yxeIXbiExgwp0nXbMTulQqZIE2QcU5s9ufOZgki/il4ln5\ns2SCnnKuLbe21O6Ufl+xtgdXLgQUE3sNZW3XmzR2dbLP4G15fv0qaqKIN9taaOraBAKX7nEEhw7b\njvOeuZ8nmpZQG1WxodhF75paXjz+6//wvHWLqvjO5AM2e21Ihbh05KjxNFTX8L0pB3Dv4te47fW5\n7DNkJCdsu+M7P8jvkRXtbdy/aAG/f2M+I+rrufmIY/8lx/V4PB7Phxf/U7TH4/F4PI7Vq1dz5ZVX\nct1119He3s5BBx3EbbfdxoEHHlguRPZ4PgCtxXbuW/UYfav6cNpo27VTE/Zml75nMrv5agyBjbWJ\nIXZxuxArYpQIXRzNTo1DJCtx1gKJRNgYmWT7pvE7I25Knot5iet6MhiUBCQV8buiE2fSuJuSdAKe\nymJzAZSn0mWuKfeiO1ZibKQtdgKSInUgpVE22wWVmCATigASbUWowL1UFnpscXdWGi4BSlnpSRvr\niNLm70uw0+l6dqIeKJSzPKXfp4qU8yxlYlLa36RUepzN3U2ZsEU5cpe6rCQVmLLLVZlwZbSb8ke5\nNF0BO/QazEFDtuXxpsUYEXbqPYSn65axZFMrvapqQaBnroYdeg3i4D/dyKZSgbzW5HWeSX0Hc8ke\nh2FE6EpKLGtvZWKfch+TNobD/3Qr/Wq68atpJ2x2hz4/YQqfn2B/Yf3VnfZg5/5D2HvIyLd/iN8n\nN7w0h9vmzaU2iqjN5d59B4/H4/F85PEClMfj8Xg+9ixcuJBLL72U2267jSRJOO644/jWt77FLrvs\nsrWX5vmI8D+v/pwE4fsTvoaIsHjT08xccwUF08ZnR/2GlZ1zeXTtT4klZwvBRShkE+sgUmlUzTYy\nxaYcxwud5pEQZGJUNnUNAOVcUjYWFyJoIitiiXUrJS5qFigXZxMomoAsUmZwXVTp1LegHLeriMfZ\ntQXWC1TRp5QKWYmLrxmxTi7c+rRUiGYY272UCmfunOl5wbgy7wARha6I1yUmPSKbTehLj2PFJCdI\nOZFLp1E8UxEfNJWCc/kYxk0fVFT0NqE2+5zuK24tNWGOCEWHxM7HlV6LYERY1bGJ3735Mgs/822e\nXL2E/376Xib2HsCS9o0EKuSpo8+iJd9FIoZ1+U72GTiSjqTE82tXMq95La+1rOOwP/2S0T16saS9\nlT8efgqT+w8B4IZ5z7N4YwvUv3PlRm2UY/9ho99xm/fLGZN3ZXh9A58ZP5HuVVX/0mN7PB6P58OJ\nF6A8Ho/H87Fl9uzZzJgxg7vvvpvq6mq++MUvcu655zJmzJitvTTPR4j7Vj5OY3Eth/bbk4G1/Zi3\n8SH+suZybCV2wI2LP09t2I+S5EAMStmeJRvBs+6bQjr9jcjF01I3k+tsEmNFIOVibyjEWKdN4mJm\nOIdOLIGbaCcukgY4camo0wlwASpIO5OUczMFabYNXPF3EJQFIOs2ksxlFSBZQXkiKq1Lt7qUKLRJ\n97Xr1oYK15ETc8RF8VzZOLhJea7oXCo6oqyeZsUjI2KvGUG5qXb2WsLMVSVOfMvcTKQCl/te0kr3\ndCJf4OxLLsJY4d6iQt8Z1q2eFV3t2XHz1trF8O4NtMYF2gvlqXeg2FQqsL6rizOfuJsoCGgvFpnf\nso7uYTW79x+GNsJRD9zK2Pq+aG3YVCoRGwOiSIyQ1zEAS9pa2XvISEbXlwcj3Dj/eWJjOGnbnd72\n+bx30es8vGwRl+932L/UqTSkR0++NMmL+B6Px+Mp4wUoj8fj8XysEBEeeughpk+fzhNPPEFDQwPf\n/e53Ofvss+nfv//WXp7nI0ZRx/x6+QM0RPV8ZduTWNU1n4dWX4lSoe1aEoWogE261TmJAhIToCVw\nridFkSgr6E7jW7YzSjknkSIxIYbAlpU7jIDGuqSMsZPrTBYps+4m445htZLAxfcAFMqIm4xnS8dF\nBWixApN1TqXijsoSZ7E7biY+uaid7a1Kp9i5fUSA0GX9rKOKVHgyqabjWpWUQhvBOIeXUna6X1rv\nXdm7VCkk2ShdKkq5KXeuE8oYQJV7pypyc9n9Q8q9VZCerBzDs84oQYxiZI8GVnS0saqz0x0zyFYn\noli+qZ26MAcoeuaq6SiVSAwM6t7AkvZWlm9qc2dWFLWmECcUtWZAt+4cPWoCo3r05pr255izfhX/\nNWFXionmjdZ1HDJsLHGi+dOSN9hzwEgaqssDEi7e8xB+POtxJvUf/JbP53OrGvm/ubN5Y0Mz35iy\nJ2N79X3XZ9rj8Xg8ng9K8O6beDwej8fz4SeOY371q1+x0047cfjhh7N48WKuuOIKGhsbueiii7z4\n5NkiXDjvOgqmxPnbfQGAvtUj6Ve9jetPUhQlR5eJKOqQLlNF0VSTSA5NjoLkKBI5sSh0XUyKgrEx\nPVyxeEkHlCQkkZBEIrpMRN6ExBISm4CShqKJXB15gJEIQwjOdZRISGwiYhNijO17sh8h2gTEWhEL\nFTE2K/5Yp5Pd1mDPr9y6SiagZELb82SsWyqdKmecqCOEaAlIdGUXVBqXC5FUoBNlC88lxGCdUyWt\n3BS8ymJzVRb1BLQGYwL7WZS9h855ZfcLNnMxpddmxSnlxDC7fmPs61qnH2URS4xCjGJpWzuJtq4k\nMemP2Io+UTdXhq7oiBNEB3QWNT2ibgx34lOkAib3HczkPoNB4Bf7HEvv6m58deY95IKQn+19FF/b\ncU9uPfAzDKzpwfWvPM9OfQcyoVd/uuVyfHbczry4ronr5/0NgFfWr+HUB+5ibEM/njrhDCb1G/SW\nz+cXHvwjb7Q0c88xn/Xik8fj8Xi2ON4B5fF4PJ6PNJ2dndx0001cfvnlNDY2MmHCBG655RZOOukk\nqnwviWcL8trGJcxvW8yEntswocH261SHdRw3/CJufPNrdOk2NEHWuyRSFmLsp1SMsc69zXqS3AS3\ntITcRrrEikDKRuBigdikvUrW+WPDYwGxto4qIXBnSzuiJDuHTuMDvekMAAAgAElEQVRpyq7LILa0\nXAInXlkZSSRwgpiVpBKxPUcot04UyrhJeu760iRf6lZKdBpps2KRUraryWSOJpUNk0un3YF1OKU9\nVLKZgIUrLleIGCDIYm9pgXp5Ip37mvR6XTxO0kigFZjSDimXISRzTUnZNQUg2r5lEvt9qy66e10m\nNobWQp7WQgGA7tXV1AXVzGteQ06FdK+qZr8ho8nrOPNk3Tx/Dv/v+ZnsMWAoqzs6eHX9Wk7abhJX\nvzCL83fbm0eO+TxREHDcPb+hK4l5bcM6nl+zklH1vd72GZ2x3zRKWjOp/1sLVB6Px+Px/CvxApTH\n4/F4PpI0Nzdz9dVXc/XVV9PS0sLUqVO55pprOPzwwwkCbwD2bHl+8vqtREEVF+7wFQAKuovfLb+U\nxq4FlKQTwfbtJMZkHUviSra1m/qGCijqwLqXBLTYLRLniBJJJ7kJsbGvKaeRaFPZaeREJUlFoNAJ\nOZKVfCtlJ9dJGodz+9ry7fLfGeX6nxITuMl0trxcBEpiC8hjABd/s0Xf5VJ0k4k5ZL1Oxig3SY4K\nIarcGaWNLfWWCrFHKbFrcN/bY5SjcXbt9nqUUiQ6FY3KpeFpos7VRdl9UZkSZYyACWxMMIvhOeFK\nWWEKd4wosO6ntH8rm+5ncEKX/b5/t+6sy3dk13HC2J14rmk5T61elglic9ev5ke7HcT/zvorL6xr\nYsqAIewxaBh7DBzGj/Y4kNtee5ERPXrxg6cfBeDkCTuxXe9+3PH6K8xZ20S/2m78/qiT2Pltoncp\nR24z7h3f93g8Ho/nX4kXoDwej8fzkWLZsmVcfvnl3HTTTeTzeY466iguuOAC9txzz629NM/HiF8v\nfZjmuI2Thx9KVWiFpjktj7CwYy4GMIRZT1IiObQEtpFJWWFHVIA2YCRyWkfqJgqdC0nKhd4ExKY8\nbc4KUW4fY2WWxPU0iQTZdnZSnG2WSpzbyRBWXEVatl3Ri2S7r0mMKzoXK3QZZZ1V2vUypesyJt3W\nkk6SczJPuWQ8K01P3UyQeppQoHV5Ap4tF4c4Sd1gFWJW1i1lRSs7rS5df+AEqrIXKa3MEu2uTytU\nYGN1aal5OgEPU77nIsrt47YXIU7cYtMRfOWCKspCoDC5z2AealyUvSeJoVrl6FVVy6S+g6iLqnlm\n5XKaO7v4/ZvzEIQpA4awY99B3Hn4SQAcMXIcN7w8B0TRt7Ybw3s2ADCiZwMD67ozY59DqQoipt15\nKxfufQB7Dh3Bcysb+fID93LZgdM4dMzYt3lytyzFJGFVWzuj+/R+9409Ho/H85HDC1Aej8fj+Ujw\n8ssvM2PGDO68806CIOCUU07h/PPPZ/z48Vt7aZ6PGbFJuGvF4/QK6zll5GGICE+tv5/7V98OhJlA\nUzJCLBGpGJMQIkYwElpBCEWgrJaRupkUVgQyBE5gEhc5C1wvU5iFvewEOutmMigb9XOCT2DNQtmx\n7HS5cql26payvVMhdhXOoWRwTq2KKXQmPV7ayWSVHTt1rtKRlN4lhaYsSAXOSZVOt0sjdzoViFw3\nk7hZesZAEASbCVpWlKtwIIlC0tweViRSWJEJ0kLyNMJXdkalopSxN8S5pOz57TWmF5EKZ0KtypHP\nVKzyNeIcUSioCUK65XIcOWo8zYUu5qxdhYjikcbFtBZtFK9bWM19SxaAQCFJOHToNuw5cDi/ff0V\n5qxZxU/2PZQoCDj1z3+wk/0UfHL0dgAUkph8nHDUqPHs2H8gjy1bwqINLXz2nt9z7u57Mb5vPzpK\nJdZ1lt1X/26+99BfuGf+G9x1yonsPMTH/jwej+fjhhegPB6Px/OhRUSYOXMm06dP5+GHH6Z79+58\n4xvf4JxzzmHo0KFbe3mejykXz/8VRRNz2qhdeHD1r1jasYBV+SUEhBiEkGq6dMlNibNRuACTRd1E\nOfFIhWgjWTRNKYiNIESIWBFGE9l4mutyUipAG7G9S07tSSQgCHA9TsoJNnb/OI3DOUEpdG4o5abD\n6SwaaEUy7UQtdwQq42zaTZVDyKbVCW7aHGRxvc27nQACG1NDMneRNjhRSmV9TGkJVCpylYUgQKQs\nRGXOI5UJZnaTsgiWuprEpOKV+yRSdjxJ+pnsOsvdUena7Y55owmVQiP0r+7OukKnFeUInIAl5BND\nPi5y+QvPsKytlZE9erGys50vTpzCba+9xI/2OBAReHLVMtqLRY7dZgLnzXyIvy5fypiGPixoaebT\n207gV/Nf5qDho2nc1MaM/aaxY/+BAJz/14e5b9ECAAbUdecLO02md00t3/jLgzTU1HDgqDGM69WX\nC598nGPGbU/3rdCBt9+Y0azY2M6whp7/9nN7PB6PZ+vjBSiPx+PxfOjQWnP33XczY8YMnn/+eQYM\nGMDFF1/MV7/6VRoaGrb28jwfY1pLm3iu+TWG1Q5gQcdjbIybqaIOAQrGCh15YgIV2viaK8eOTWid\nRE58Ma7fKXQyTeqI0hISuH4nUS4GB9jpcja7ZgvBA4yL+Ilz6RhJt3NCl4jtdlKp+0dcFE+52Fzg\nJCT7fqxVWQQCjMk5QcY4h5PKHE6ZmAbu9VTMkawsPN0u/ZzG91IxzLgIX0rWtwTgRKwso0elOCTZ\nWuQtnE3p16mzKXtNU+F4ch+VA6OVO0m2jxOi3PkSoEeuhsn9hvLQ8gUIsG1DX8b37susplWszXcg\nRljS2oogLN24EVAcNXo8/z1pT1ryXfy/52Zy1X5HkAtD9hg0jO8++SglrelX040FWJFpZUc7VSrk\npS+cRV2FiHTgiDHMXrWC2lwVx2w3gTvmvcKzKxuZ/fkzqI7sj/y7DhlCj+pqqsPKqOW/jyPGb8cR\n47fbKuf2eDwez9ZHVebgP6pMmTJF5syZs7WX4fF4PJ5/kkKhwG233cZll13GokWL2GabbTjvvPM4\n7bTTqKmp2drL83g46/mrWNTRyK27X8Ca4hvcueIaColx5dyBm3YHkbK9TGlptkmn0BlIiFAYhABj\nBJ3FzsTGzig7icC6krQELr4G6fi2knGz10ShCazoolyXkrNUxRoit5s2oMWuIx30luor9pzKOnoE\nElMWdtK4Hu7aSIvOVTphL+2fqtimQqzS2bFS69LmgpI2QuCcUajNy8jTY2SupbRY3bg4n3L3w1S+\nX0n6eurHqhSrKgWviuWlPVVum7RI3a5HESlbRt4jqmJTqUR9VTVtpRK5MKChqobmfBeCUKNyFLVm\nu159+cWhR/PwkkVcMvtJDhs1lr61dXxzt734W9NKznjo3iyiWBuElLTBCJw0YQdGNvRi5rKlXHLA\nIYyoEN83FvKces/vmbduHWdN2YPTJu1Mz+pq5q5ezeTBg4kqBjEsbtnAo4vf5LSdd6Yml9vs7mhj\nWL1pE0Pr6/F4PB6P561QSr0gIlPey7Z+DJDH4/F4/uPZuHEjl1xyCSNHjuSMM86gvr6eu+66izfe\neIMzzjjDi0+e/whWdqxnwaYVTOgxkkHd+hKpOnbvfTTD6ra3k+Ww0+ASIvImR9HkKOiIgq6ipCOK\nWlEy1hlVMgEloyhJhJaQ2IQkUkVsQmIdEOuQxITEJiKREC2hM+0oYhNR0JHrkgqdgGWLzVPnk65w\nWhV1SMEdT1xRd6wViSi0CYiTgDiJ0DogTiDWQdbJZIvS0ztg432JCcE5vIwotFFo7bbXijhRJElA\nohVx4pxWUnYsJRoSbQUxYwAJMcZtY5SddpcJVlZ4Mxr3HohxYpwrChetylG7rB/Krle0+9rYbez2\nkE26s43x7j1lt0u3cYhx59AKNDTkakEUm4olqlRIe6EEBpLYkC/E1KgIpQN65qqtIy4pceK9d3LJ\nrCf5/h77MnP5Um6fN5ddbr6Wlnye333qRL4yaXdqgxyFxPClSVNoqK5hn+EjuXfB6zy3cgXH/f6O\nbD3txSJTrr+ORAtf2WU3rpk9mztefYWbX3yBE+/6HXfNm7fZc3v1rFnMeOppnm1c8Q/P9OVPPsN+\nv7iZp5Yu/+f/gng8Ho/nY48XoDwej8fzH8uqVas4//zzGT58ON/97neZNGkSjz32/9k77zgrqruN\nf8/cu72y9LqA9CZdsIGCvSuCDSsSQzTG2GJeYzSJMYKxd0IUC4JgwYIdUZQm0osgLLB02IXt5d6Z\nc94/Zs7M3AU0TZeY830/N7t35pwzZ2bXfdlnn9/z+4RFixYxYsQIIvVURmIwHIzfrXwBiwi/63Ep\n26oKeXvnDD7a8w49s/u6OoaKIFUEV6hxhZu4covsJIJaGSWuIsSlJ/zIqO9qksoN7XakIK6i2CpK\nrYxQKyOeaOU6nmJSeBnYnpPHE4Uc6Qo6bnmd8MSrCEpFvOwkC1tZ2NIi7rhh37ZjucIRlv/SDikJ\nOAocFcGRFlIK4rbuVueFm0vh5T25wo9tCxwZAVyhS+m1pLc/x8JxXEEKIq6wJIMyPukLWa4g5The\n5zyJPx4pkJ4Y5ApNnhAFvsCECsSmIM/JCn1094UUfvi4G8zlrekFnCO9hHhPzEqxIqAERdXVWF7o\ne1zqr4YraFXGbWrcdnk0y8yiVVY2jw87m/KaGCh4ZslXVMdtWmflIFE4UjKwRSuuPrIf/Zu0YEyv\nvtxxzBCWXvsLSmtqGdq6LUIJspJSOPpvz/LWN2tJjUbp06w5R7VszYDmLREKYnGb4/PbMrRtO45q\n3Trh+/amY47mrhNO4LWVq7n7o9ms2LmLtXv2AtC3ZXM6N25E6xyT2WQwGAyGfx+TAWUwGAyGw461\na9cyYcIEXnrpJRzHYdSoUdx222307t27vrdmMByUjeU72Fi1m4ENOjJzxyss3DeP4xsNY1v1Xmbu\nmEWtE/VDtR0FiCSkcsvsar1yMh3urcPC/U5x0g0Xd8vu3Ospb7zllYXFQ+Vw0m2Ohu0It1seynUf\nIVAqyHXS2VE60FwhEEJgOwJLKC+AXJe6KV98cqT0cpqEXwoHIGXQmU+7otztBeN0mHq4DC/IaXJL\n7KQuuZN6nhtOrrvXBQsH+VaE15ReSZ5XVueHmPsTCR6iwBWRhAo69Gnnkzdc+HlRBPlPfjkeIARJ\nwqI2LhnYtAV5GRl0yG7IE0sXhEr3FO1zG7C5pNR9NgoaJKfx9gWjeXTRfCrjcVCC6rjNjQMGI5Tg\nlPYdaJyRyb2ffcpzy5YiUXy5rZBfDz6WvZWV3PHRRwgLhuS3pX+LFvx13jwKS0tJjkSYPvIiANbs\n2UOD1FTa5ObSrUkT/n7eeQd877bJzeWSI3tx3+zPyUtL5aUly8lITmb5Tb9geMcODO/Y4YA5BoPB\nYDD8KxgBymAwGAyHDfPnz+f+++9n5syZpKWlMXbsWG6++WbatWtX31szGL6Tu1e8QhJJXNC6O5O2\nPEsSqSwpWUWlHQccFFG3g5zXoc7y3ENa9PHFGFwBys11EkQsV0BxhSm3PM3xDOyWcLvYyVBWkqun\n6Mwod+24F6YtdFWZtHzBRuceSSWwFUSEK/rElT4e5C0JoV1VEV9IQs/3BKxwxzgQXnc7L1pciIRQ\ncQjEJ53DhJeLpd1aQUe6xHUTSuD0Glp504HnoZwpv9NdeBnlOpiUFqHAcz3hmrC8Jn/JIkJt6IJC\nz8MT0xywlRvE/tXOnWQlJXPh8O5BGZ9y5xTsKyUzKQmpFFWxOBuKivnNJx/Qv3lLkNAsPYP0aBK7\nyyqYtmYVjyycT6PUdIqrq9ACXafcPG5+7z0+3lTg3TvccezxNErPoH1uHm3qZDV1a9KExePGAbCw\ncCtpSUn0at7MP//Zxk386q1ZPHrOGXx+3TXsr6ph8uKldGnaiJhtU1hSSodGDTEYDAaD4T+BEaAM\nBoPBUK9IKZk1axbjx49n7ty55OXlcdddd3H99dfTuHHj+t6ewfC9bK7Yw5aqIgbkdcDywp0doCi2\nP9HV5JXcoSykAEe7fpTjBXcLJBFXfPFUkphDqA+dwlYWCC/HSYs7whWftKAVFa6ApbvqSQWWcF1O\nuhMeSiKVWybmeOKL8kQoLQK5B4OAb/elvNI55WU0JYaRg/LEKU8Y00Hr0lvbG6eUCs3Txzz3krt8\nIA55Ahfe5263Om+a1KKYd33fTRVqspPggCIQtMLB4vpZ6mNab5JQqxxXdNJLe89L6I8iNFdBeSzG\njNWr3TUEWAjfEZZkRZh7+bXMXLeWO+d8zIy1q1m6YwdDW7dl4/59bC4tYXNpCace0YEPN26kuLqa\nfs1asHzXLmxHsr54H+uL92EJQbvcXK7q05fLp89gf00NtiNpnpXFFz+7FoD91dU0SEtzv0f37efS\nV2YAsPrmG/yueJWxGBW1MSpiMZpkZvKrN2axeOt23hkzmrvfn82M5at5efSFDGjTCoPBYDAY/l2M\nAGUwGAyGeiEej/PKK68wfvx4Vq9eTZs2bXj44Ye55ppryMzMrO/tGQz/ML9fPhWBxR+OvJSMaCpd\nsrrys69vxhJumZstrVDJmqdfeK4mpRS2jHpOKL2idgW5ZW9aD7Gl62wSCi9LylvL614nEFgWxBzl\nXk+XqHmOKncPyisFdLOVHB3AjTtGl8x5VWN++Zrj5SUpL9tIes4r7d4KSu5CwhOeQ0jii1KBI8kK\nygk9ccm9poWyPRVIuWKUFr9EOED8gJI6LZgpEMo/ryT6btz3+qF5TilffBIi5IzyVlSJ7/2WgP61\ngu+B1tk5bC0t9d9/tnWLe13tlAJSLEGKiJCRlERFbYwT2rRjf3U1y/fswlaK4soqfz9VtXEWXfMz\nVu7dw5rde1iyfScg6NW0KdtKy/j9CSdwVpcudH3oEWKOw3Ft8xnUujXtGjQA4DezPmTGytWc1bUz\nD519Og0z0mmVk03L7GySQ9l5p3ftzEmdOpDkHbvmqH60b5hHfl4uQzu0o6B4P20aBN31DAaDwWD4\ndzAClMFgMBh+VCoqKpg4cSIPPfQQW7dupWfPnrz44ouMGjWKpDotwA2Gw53imnI2Vuyia3ZrMqKp\nzNkzj0XFS73wby2eRAHll9dpB1OtFAgsV9BRYCmwFZ5byA0C1xYdqdwSunAJm8JzLwk3PNsNGtfJ\nR65YpVTI9QSA8F1VlnDHuwHeCuE5qfQ46YknSrlraQHN0Y4sL6/Js0kFc7QDyltLeKV3vqkqVCIn\ntBtK34e3V9elpFC2Oy7IeMLfX+hB4JfvaaeS8oQtQGlBStUVjtxFfWErdDiqn4XvlPLWdQKhKj2a\nRJVtkwRMP/ci7pj9EXO3bqZv0+bsKC9ne7ycY1vns2D7VmxHEnMUESyuf/dt3tuwAYCGaWlEsNi8\nr8S7Z/fZzt1UyMCnn2HqqJGM6dePDcXFfLZxE0c2acabl1zqb/XhM04nLiVndunsH6uJ28xYvto1\nunk3m5WSwpzrruFgJIUEqWGdjmBYpyMAOLlLR07u0vGgcwwGg8Fg+FcwApTBYDAYfhT27NnDY489\nxhNPPMH+/fsZMmQITz/9NKeddpqfLWMw/Lfxu+VTUUT445EXo5RiauFM9sdLESqJaikRRHyhRhFB\nIUFCXEU9R5D0RZ1aqTveBWIRSs9TONLrSOeV6/neGr80LuK7bQBsKfxSPr9iTYEjXTHL8YQgNxTc\nc2dJy8+lkp4opffiRiy5e1MoTwyzEsWehOwlb6+Ot1crVP5GyDmlRBBMrsUpFf6ZEJS3uTfhXcsS\nrtnJP16n9C7sVPJymJQ/2HsuXp5TwlNSCkd46wkSrm0JCyklTTMy2F1RCQjiwOC/P+sv+9WOHf4O\nCvYVM2PExcxYs4qUSIS/L1nCzvIKf9mODRqyYOs2WmRnclmvI3lo3nz32SpXPJy2YiUDWrXi/4YM\n5e3V37CxeB9hTjiifYKjCSA1KUrPZk1ZuWs3A1v966Vzu8sqaJKVYX4+GwwGg+E/hvX9QwwGg8Fg\n+NcpKChg3Lhx5Ofnc++99zJ06FAWLFjAnDlzOP30080vN4b/Wmxps3L/VlqkNqBaljN64W3sri2n\nWXJzsiJ5QBRHut3oHOWW2cUdQY2MopTrCopLi2onQsyJuOVnyvJCyC2kcjvRSQVxJ4IjLb8cDbzM\nJsfNYXJk1M2Zkq7wFHMEjidoKWVhS4uYbWE7Ufy8JeXuLW5b2A7YjpXQrU6PUd66Slq+A0tKcdBX\nWHRy5+A7k6QtUI6FlJZ73saNv1K4n8u6Lie3bBFJQki4K6pZ4Ljz/eBwX5ATrlMJgrkJrieBkF6G\nk0OQG+VnQwnvuLd2qOxPOgqkYH9ltevfUqFSPSBZWCQJd/2oEghpcfmMGQxp3ZYVO3bTNa8JZ3fq\nwuDmrWiSms7CrdsQCsqrY/Rq1hyAUzt0pE+z5lhCcFW/vvzpkzlc+NJU5oy9hr+POJ/d5RWs2rmb\nyV8tofv4R1m4ZesB35sFRcUIBSd16sDusgpuev1dVu/cnTBmV1k5X2zcEhLlAmav28iQhyby7Jdf\nHXDOYDAYDIZ/FeOAMhgMBsMPwtKlS7n//vuZPn060WiU0aNHc+utt9K5c+fvn2ww/BfwwJp3qVUO\nt3Y7i5J4ORV2FY6KUFBVRJA5ZOEor2OdI3BUEkIIbKnckHBdquemJwE630m5ghM6u0l4LicvB9vr\nkie8jnUWrqMp7CxyhSALIQJflKvTuO4mtyxO/y3SLfdTKJQkMaBcu5q8Xdpxdz3XwaX8bnWuWwuU\nI9wyQ8e7rgjlN4Uzm/zSOe99XaFIX1EPccJTdcmdd0y7lSAQqxwSXUwinOskgnGacOaTn4flleiF\ncqEEENfleQJyU1KpqIkhUcQd5QeaOxJ2lJWDgLEz3/K31zY3h/lbtyGAbo0bUVhaSkUsxhur13B5\nr97MXPMN+6qqUSgykpIpLClha0kp42fP5a6TT2TMq2/wze4iUiIRMpKTsB1JvweepEVOFm9fOxqA\nR847k6pYjMaZGby1ci2zVq+neXYW3Zs39W/3ltff46vC7cwcexldmiU2fGiZm03bhrl0btIIg8Fg\nMBj+UxgBymAwGAz/MZRSfPLJJ4wfP56PPvqI7OxsbrnlFm688UZatGhR39szGP6jfLhjJTlJGQxs\n7ObkdMpoz9qKzX4ZWkwGodxSCRBu1pKUboc6L2oc6Qk4toz4bhSlIiFBySux0zOUdgEF5WSO54YS\ngGW57iVHl8gBIL29BAHnYbeTlF7rOaEDyAW2517SuduBIOWWBCrtEBK6lE8g4+5+3G17WU7SGytC\nxntfaLJCn7vXD4LGhRtC7m7ffxaBU0nfvXcfduiLo8UmX6RSiWV9dYQuLX4JREIZo/CEMi1c5aak\nUFJT689EQWlVjb+25eV0pSRFyEtNo6iqirgj/Y55KRGLDrl5XHpkLxZs3sbaXUUoARnRZN5YtZbk\nSIQx/fsxe0MB64uL+Wjdtzx9/jnc/cFsXlmyguPat6VvixZUVNewvbSCiaPOpVFGOuU1tWyMxdhX\nVc32klI6N25E85wspFJ0bdqYZy46hwH5ieV41x7Tn45NGtKuUQNmrVrHZ+s28Yezh5OSFKVz08a8\nf/1VfB+FxSUU7N3H0C7tv3eswWAw1Ce2rCQi0kJ/eDHUB+bpGwwGg+HfxnEcXn31VQYMGMBJJ53E\nypUr+ctf/kJhYSH333+/EZ8MPznm7l5HiV3D6c37ALA/Vs7odufSN7cHMWlRK6NIFSXmRInLCI6K\nuFlPXpmaUK4YFHMspLKCUj0ZxZZRV/zxBSdwxRy3m54WomzHLemL28LrUueetx1dYueVsOEKTFIJ\nv/RPSsv73L2+Hi8dC9t2S+Tw5wuUFDi2hZLeMeWedzOp3PPSEYFIJgVKamHHAqygHE47j5RAOcov\nvVOOcEUkv2zPc1M5uCV1umTOIXBLyeC4UAL//xxBQui4I8AmeOnSOx2ALgVChkrqVOJ19LqlNbXu\nOG9OxCsvFLjzpHS9ZLG4w66yChomp9GrSRP3H9wSauOSJ+Z/xdQlKyko3g9KEFWCWNwGCQNbtuSp\neYso3FeCiismfPolNXGbNjk5CAVPzl3IK1+vYPv+CoSEsupammRmcuPxg3AcxeOfzWfEpFe4espr\nAEyat5iznnqRipoYGcnJAFw9+TUuf246Qzq25/enDyMlGuXlhcuYuWIt20pK2VteycinpnDaQ8+x\nYGMhMdumrLrmoP8d3DJtFuNemEnBnn0HPW8wGAyHAzG7lE+2DGT+jovqeyv/8xgHlMFgMBj+Zaqr\nq3n++ed54IEHKCgooFOnTkycOJHRo0eTkpJS39szGH4wHl7zPkkk8cuuJ1NcW8qvljzKrtp9tEhp\niu1lLzleiZsuxovr/CJHedqIwFEWSmq/jeX6bxRIIp4QEuRAuWVvIB1t4NEB5/q95+bxcqTcvzN6\nziktxniB4zrgXHrOJX1Sd8PT2U9BILhCi2GJpXTeetompcvlAKQVlL9phCsYKYEb/i0855VXhujb\nj/Q1hHYghUsI/VsJyvJCt09YQPKPuye1W0qvoZQrOvmXkqG1cedZUncvDM4IBElCEHdUQgaUCBmt\nUiIR9lRUUR2zyUxKoby21p8vneBxKAXJ0QjPjzyXtg0a8OLXS2mamcGfPvyMFCtC3/GPc1LnI0BB\nSVUNKFfkystI55cz3qF9wwaMHtCHa4/uzwVHdseWkuyUZHr84RH657ekW/MmdPRK6Spqalm9fTeR\niOV/rQEeGXUmW/eXckTjhizZsp1V2928qIUFW3n4wy9ZtW038+68juy0VMLcMPxoFhVspU3DXAwG\ng+FwpSa+F0cpSmLr6nsr//MYAcpgMBgM/zT79+/nySef5NFHH2XPnj0MHDiQCRMmcM455xCp05HJ\nYPipUWPH2FK5j07ZzaiVcUZ8eQ8gGJTXhfnF61HKwvHEn4gnyNjS/YXfsgRx6flphPBzmKSfLeTm\nRllC4XgOJB38rXE8p49S0hWLCJfqBWKVUiIQXixvHc+lI4TyhS+F61ZyqxLccHBftBF447yxkpA6\nEwruDpfShUUn5WVH+WsROJwIZTn5opOeF4xBBKKRUEAdAcOwpZsAACAASURBVCwxuynx+sLLllL6\nc413HzrTSekxIZEJ71r+knXEqWgkQlzawXXAdX1ZbinevScP59ZZH+JIiZKuiKYkZCYlUenEQUBE\nCEb3O5LL+vchv4Er4tx6wnEAHNWmNRf+/RWUgjGDB3DrsOMZ9+pb1No2r17tdl384/ufMmv1et5Y\nvobpYy4G4A9nDOe+9+dgS0nMtnl97KX+bb+4YBll1bXccdqQhAYQjTIzaJSZAUDf/JbMvH40NbZN\nl+aNKa+uRUpFSvTAXxuO69SW4zq1PeC4wWAwHE4o4f3/ZfX9Yw0/LEaAMhgMBsM/zNatW3n44Yd5\n9tlnqaio4LTTTuP222/n+OOPN93sDP8zPLDqfeJKckePM0i2krCwcJRiVclOQLjVXUq4pXW4WUuW\ncLvTIbUrCVButzvlKJQIyu2UEkhPIdHjXLFK+RlOCLwud54jR3guHS9rSYXEHOVdQ4aEJOmLQK44\nlZC9BL7444pRwnfq1M1fcqnz377C7U6ns6B0jhR6noUv6+gyOu1OEuFxWggKKVthkUoGh7WjS+jS\nPeWJSd5UKzTHF6PCzqXQ3hGAnaiz+eJXyH1V49jepb2H44XC48Do/r1Zu2svLTOy2Fla7hvEhIRm\nWRlsLSnznq9k8sJlXNK3N3X58JtvqbUdGqSl0qtlM4QQvHPd5cHtS8Utw47jqkH9uGX6LM54bDLv\n3nAFADcMHUyLnGzaNWyQsOYZvTqzt7ySU3p0OuB6YTo2C8LH7zz7xEOOsx3Jwg2F9G3XkqraOA2z\n0r9z3brMmLeCZZt3cveok4hGTDKIwWD4YZASbBVBGPmj3jE/6Q0Gg8HwvaxZs4Yrr7yS9u3b88gj\nj3DOOeewfPlyZs2axZAhQ4z4ZPif4pOd68iKZNAzrw1RK8KvOo0kLqMUx6qosQW2E8GR+BlLjhTE\nHTcHyfFyoJTSx4QrGCmBlDq7SaBUBCnB8TKipJcRBcFYV4xy3zuOm8/k5jl5FWhOKPPJsTxRSYs6\nuoudcMvBVOB0UlJ4HfQIXEA6e8kXYTx3lV/OJ/wsKOUEx9zsJu+jstzPtRrjiVTC24+fD+XnM3nv\nbeG9CLKfwi8pEucovIwm73PHu2Qoz8m/TjiXyrtHESehrK5perpbLui9d+e795csLH559CBSrKi3\ntlse1ywzk+cXLWVnabl/XLutCopKSLWi2HGJoyA3NYUHPp7Liu27/Gte89LrvLPiGzKTk7n2mAEH\n/IytjsW5662PGP7gJHbuLyM5GsWWkltencWOkjIyU1N47atVXDf5TUY+OYWiikoA2uTlctdZJ9I0\nO/Of+I4/NDMXr+a6iW9w20vvMvSeZ5i19Jt/av6Uuct466s17K+o+t6xxWWVFJVV/qtbNRgM/8MI\nobCVhf39Qw0/MEaAMhgMBsMh+eKLLzj77LPp3r0706dPZ9y4cWzcuJGXXnqJXr161ff2DIYfnb3V\nZRTFKunbMB+A93cs4c9rXnMFGyWQKgpYvvDkKLe7naNFHWUhsbw8qCB8XAeHO14gtyNdh5N2RMUd\nC6mi3nFPXJIWjhMWmtwgcMd2P4LlClN+yLgrNknbFYmkA0gvQNwWKMd1PCkJyrHww8M9oUsHkus5\ngeDkCVQ2nptJBIISoY+6rM4REHdFImHjCUZ6jgiEJC0QCZEwXzjeS4eOa4HHE5KE131QSB1G7q0v\nw+t4IpMTmhMu8QsJUnvKqt21bVfM0s4rAcQcxWOfLyAWc9wxnnD3wMdf0D4vl6PatKJVbjYAg/Jb\n0bVJIyJCUF4bAwVNMtK569QT+XjdRt5eGYg3O0vL2bqvjMrqGMXlB4ozT81ZwGtfrwYFUxYt553r\nL+fM7p2ZtWId8zcUAvCLEwfTtmEuq7btZtu+0oT5Kwp3cvaDk1m6Zcc//d9AmKM6tOGknh04vlt7\nWjTIpkWD7H9q/tPXnc+0my+lcc73C2Ln/nEyZ979d79TpMFgMPzjCBwsHGUcUPWNEaAMBoPBkICU\nkrfeeotjjjmG4447jnnz5nHPPfdQWFjII488Qn5+fn1v0WCoN/64bBYK+F2vM7wjrqghldtZDlyB\nyPYEH8dxnUq2tIg5oRI7aWHbuvNdBKXc7nWOY2E7FlJGPBHLPS49MUrKCKiI1xFP+IHirntKB5pr\n0Un5Hd784HDpOZF81xIJLiaUJzxpG5Xfoc51SCnbvWZQwua5j7RwpIUofSzkTBIy9CJ0TScQhfw1\n6jiadBC5dib55XAhB5MWonQ5HoTWDDuebHyxSAtXgmC9QNgSnnAV7Fd49yu0WyrktNJuK73HbcVl\nHNW6FfecOoyvb/453Zs0YUdJGdILLk+ORnj92ks5vXtnJl16Pr868Wj/++zEDu2IIEiNRslNTaWy\nNpbwfXhSt44c37EtbfJy6J/fEoB+bVuSbFlY3lPo1qIxW/aW0C+/Bb3bJHYi3bC7mI179vHtrqLv\n/H7/Plo1zOHBK87iwkG9+OD/rqF323+u42mTnEy6tmryD409qU9HTu7TyThufwDuvvtuhBD+q0WL\nFlxwwQVs3LjxoOf1a/jw4f4abdu2PeiY6EGywwyGHxuFwFZRbGXkj/rG/EQwGAwGAwCxWIwpU6Yw\nYcIE1qxZQ9u2bXnssce4+uqrSU//53I9DIafKouLCsmJZtA4LRulFKe26EOqlcQLBZ+xqnQHjlQ4\nEresDnBCWUZSuqKRlBLtKFLSNfjocrggQVt4SVGhvCTcTxUhfcUvndNDgpK6cDC4UjrjKTwWVyTy\nL2n55XF+hpM/2OuOByC9DKtQOZ+fjeSJA35gtwiJSHXC1IMMJm8dqS1IIsgRlyKhHA4v20n5a6tE\nh5Ve1x/kre6JTMoTqvTzC7riBd3shH6+WtBSwRrgCV3epX3hSkGjzHSKKquC+cBjcxaAgIGtW/JV\n4Xaa52TRqXEjFm/ZwdndOvPaktXsLa/kgr7dyUhOBqCyNsaizduwHYmSioc//JJP1mxg2nWXAFBS\nVc2dr31Ez1ZNGX/haXy6ZiM1cRtLCGK2xPZa7OWmp9GrdTNO7HoEdTmvf3c6NWv0TzmWlm3awc6S\nck7r0/kfnvOf5K5LTvqn59iOZMXGHfRs35ykqGmQ8V3k5OTw/vvvA1BQUMDvfvc7hg0bxurVqw84\nH54T5pJLLuGGG25IOGYEQ8Phgq0EQpifA/WNEaAMBoPhf5zy8nKeffZZHnroIbZv386RRx7Jyy+/\nzMiRI81fLg2GEMU1lZTaNZzQrDPvbV/O/y2fzrmt+nFHj7N4fsM8ah3LCwN3+9JJL+9J//qlRRtF\nxBcpXOFJeYYjN8ha4ZXB+X+pDX6BUyr0uaNPa7dTSEzSXebCAd7+RP3SApUKMr+1mFW3A50U7qGw\nMCXwhB0rUGNCzqKEoHI/MNyb69RRlTw3lAgFiwtfBAoLVJ7HSSrfgeUHpIfv0iHhuG62h5MoXiUI\nXeHb1q6mSPCshb4/b70+LZuyubiEkppaAIrLXfEpKckiHpPYSmIJd9ul1TUg4dye3bCE4uvNO3ht\nyRr/GX206luKKqvo0rQRx3dqx8qtuzn3yK40yEjl1cWraJ7jCp5CCD5du5FvdxexdV8JTbIyeObT\nRcRsh5FH9WLO7ddyyZNTKdxbws1nHM8r49zOeMs272B/ZTUndA/EqDFPzyBiWTw95nxmLf2GG047\nmvSUZA7F7S/NYuf+cgZ1bEODzLRDjjuceHPuSu57eTY3jjiOy0/pX9/bOayJRqMMGjQIgEGDBtGm\nTRuOO+44Zs2adcD5Q9G8efPvHWMw1AduxXUEYRxQ9Y75zcJgMBj+R9m9ezePPvooTz75JCUlJZxw\nwglMmjSJk08+2fzF0mA4CBNWfoRU8JueJ/HJ7tUoBa8Xfs2bW5Zie0qCbaP7uXlChxtIDq6AIaWb\nFRW0twO84G89RnnlXkoF4orvXvKUHfe95Tt8lCO8zwVKizNaUfFFGL1uqDzNm+NnLiGCgIZQ+Zt+\nL/QeQkKMfx19XStwa4XnKV8cc9WgA37K1BGclFQkynci5KRynVFarPPFIRGs4z9iX4MTCee12Cbq\njEtwXNnw5rWXcN7EKSjl3pp+fFnJKZRW1/rjoxGB4ygyI8mUqBp31wpSIhG2FJcA8NRnC+nUOM/f\nQ5IQ2EpRVFkFCr7ZWcTPhx7FuX268e6StdhS0bZhLh+t+JY3Oqzm/AE9OL5Te64+th+n9uxEdnoq\n1TGbvvkteGvxGh6c9Tl7K6vZUVIOQGVNjHeXfsNj78+jpKKa0cf34bZzhiKEYGj3I4hYgufnLObD\n5d9yXNd2HN05v+5Xxad3mxa0zK0kJz3lkGMON/p1bs3RPdoyqNuh78twcPr16wfA5s2b63cjBsN/\nAInwyu+MA6q+MRKgwWAw/I+xYcMGrrvuOvLz87nvvvsYPnw4ixYtYvbs2ZxyyilGfDIYDsH8PZvJ\niKTQKjOPU1r0YmTro1AI4koFXeyIABGkiiBlBMfxRCfp5UFJL9hb90tTOjDcy4yyLaQdQTraBYX3\neSi3ybFAaWuO7lYnXJeSI0DqHKeg852Sbj6UL2Tpc44nXtUND/eDuL3cI92JLtyFTq/lH/Ou7WdA\nEZxDr0dCcHhi5ztPfNLjnJCQ5QTHw2taynMrOV52kx8o7oWQ+84qkbAn4bhz/eworywvXIYH7vsr\nnp9BbkpK6Dru9dfu3JuQA+XEXG9bWiTq329eWirxmEO7vAbcedoJNMvKJCstFUu53wW2rTilWwem\nX3cxvzxxMFEl+O2rH3D7qccT8coIy6tdMat5bhYAv5g8k+c++5pLn5hKRU2M288cwqsLVvLbaR+Q\nkpREr5ZNmXDx6QC8tXgNf5zxCUd3bENKNMLLc5exdW8JQ373NO0b53HvRady+7lDmXD56RzVsfV3\nfv8vWLeFpRu3I9Whx1TXxvn7h4so3FPynWvF4j9OL6p2zfN47Mbz6NS68Y9yvZ8SWnhq1qyZf8y2\n7YRX3UB4pdQBYxzHwWCodxTYysIxDqh6x3wFDAaD4X+ExYsXc+GFF9KpUyeef/55rrjiCtatW8f0\n6dMZMGBAfW/PYDisUUpRVFNJ24yGAFz0+TO8vGkxcdsNCnekwHaCEHLpldJJZaHwQsN1NzrdxU65\nH6UjcLyQb3SoueMKRtLrRpfghAI/zFzp8G8t3ISCu5XjCmNKh3yHM53CpXlanNHiii1c4UmKoPub\nF8rtjvVeul5NC0JS794L8A69tMPI7zzn3YuAIPQ7JBL5pXiemCUkvmhjQdCNTq+r9+B1x/Pvx8bv\nbGd57y0nWD8cZi5CH/PS03yxqqwmRmllbaJDyoHUSOQAwcqxFTtLK/y5L189klN7dMK2Jfe/+xlv\njLsMK7w/wI5JmmVn8fMTB9G9ZVOqYzaPfPAFMVuSHLHYV1HDU1ecy1FHtGbyZ1/Tu00zGmWmYzuK\nK596FYBRg3vRr21LGqSmMXHsBbzz9VqO/r8nadMolzHDBvKrM47ltVtG88ato4lLh/2V1ZRUVgMw\na/E37NlXQcT67l8Lpt5yKW/deSXRSDBuyqdLOfOuSezcVwbAF6s38djML3n+o68A2F9RzbfbE4PO\n/zZrIYNueIwVBTu/83qGHx8tGq1fv55x48aRlZXlB40XFxeTlJSU8Prkk08S5j/44IMHjBk2bFh9\n3IrBkEByJA2HCJL/HgfnTxVTgmcwGAw/YZRSfPTRR9x///3Mnj2bnJwcbr/9dm688caEv2oaDIbv\n5t0tq4krycVHuGLthfn9WbNvJ5/u/jYUCi68cjtd5mb5pXU6vsgtF/PEJ1sLTu4CMqHkTXhFZ25J\nne8z0KKPFpSUP911NRE+HlofAoGKxLkQ7CmhZO2A6+n9aTWMoKRNHqKkLvS5my+u54rvGOPtyS/J\nI/SMQ0KWnqOflUpcR7hfAj94nGDphEByfx/6pIRhHY5gV1kZczcUuuV9IReXUtAmL4fTe3Timc+/\nckvpLMhv2ICCvfuxhCtCDu7QhnW7ishJTqFXi6bUxmzmrC3gnnOGMfqZV9lfVQMKPl1bQOUr7/Hc\n2BE8ccU5THj3M/q3a0VyJMoJ3duTFInQt21LZixcxQNvf07DzDTOG9CdT1Zt5Mz+XXnwnbmsKdxN\nSjTCkoLtlFTWUFpdQ3l1LcnRCGNOHEBachKWFXyFvh7/S6IRNzvs0Xe+IGJZXH5iv7pfwQSa5x0Y\nWL5pVzHbi8soq6qleR4c16M9t104lCE93aypGx9/k1Wbd/HOn66mRSM3sDo3M5Xs9FRSk82vIYcT\nWmDStGnThmnTptG8eXPADRz/+OOPE+Z07pwYSH/ZZZdx4403JhzLysr6gXZsMPzjxGQttoogDuiG\nYfixMT/5DQaD4SeIbdtMnz6d8ePHs2zZMlq0aMGECRMYO3Ys2dn/eNcjg8Hg8tKGxUSVxXn5PQH4\nRZcTGfj2eBxPYOqY0YT1FXt9ZxJYfmg06HI6V8lwxRTh5Wmr4JwvGgV5R1rY8gUQEQoO12KQzn4K\nqygJgpG+i5BI5K+r/EynQIw5iDgU+je7u0OR0BkOgV9e5/fIC187HJAeDg4PYq2CtUL5UsJNdSeU\n1hScC+0x4b48sU+vlSCMhYQpIRPnhO93xtJV/jVyUlMoraoN1lawe3858zcU0jgjjeKKahwbthWX\nggyiqJZv2s7C9YUAnjsplTtnfEhOWirn9u/G5M+WANA7vxmXHdMbgNz0VN5bsp73l6zDloquLZqQ\nl5mOEIIhXdvRMi+b8wZ2562v1rK1qITRx/bh5D/8jbLqWh6+6ixGDOzJxl3FjD6+LxcdfSQfLvuW\nMY8/wWl9O1NcVkWnlo249byhCR3hXv71xQml15MmTWLMmDFs3bqVVq1a+cdvv/12xo8fz4svvshl\nl10GwB2jhtEjs5YurZvw5Zdf8uyzzzJ58mSGDRvGxx9/zLnH9KBZgywa5mRQXV1Nk6ZNqSgv5/rf\n/JFkuxIhmvB9bNq0ibZt237vOMO/hxaYhBA0a9aMFi1aJHxfRKNR+vf/7iD3pk2bfu8Yg6E+UErh\nKOE2zTDUK0aAMhgMhp8QVVVVPPfcc/z1r39l06ZNdOnShUmTJnHppZeSkmJsxwbDv8qGsmKyktKw\nvDIlW0qu7DCIh1fPQSnBN2VFfoc6yxJIT1RSvrAifFVEetlESim3k50ICzIisWNcSDxS2nUEJIaL\ngxKqThe8YF5dZxHgloAJgjnhPwr7Y70wdR0aHnZMgVsLlyBMedJUqDSvrvijtDiku8qpOmv4QltQ\nSuiHkofmhh6nX8qXgHZOhZ5F2P1kedlcCc3+dBlfeDEJGdEkSFaUVcf8w7Yj+XbXXmrjkrTkKNW1\nNnEp/WtZAmpiDpkpUSpqbb4u2E5mShIoKK2soV9+S8r61/Dm4jWc378H7y35hr/O/Jy/XHoaFwzs\nQUVNLU1zMnny/fnsLCnns3t+RpOcTN7/7TUAXHR0b6pr46SnJHPTWcfy3OyvyctMY8zjM4jbknd/\ndxUt8nJ48r15oCAnPZUPlqxnX0VV3SdF19ZNE973HzAQgNv/OpGXH7rHPz5v3jzS09OZN2+eL0BZ\nlmDZksWkpKT4odWZmZnMmTOH3bt3c/5xPTn/OFe0nf7mGyjpfrE37iiiefPmzJ8/31+/oKCASy+9\nlCeeeIK+ffv6x7UDx/DD8o8ITAbDfy1CuQ4ok3Na7xgBymAwGH4CFBcX88QTT/DYY49RVFTE4MGD\neeihhzjrrLP8X5gNBsO/hpSSiniM3o1a+McunfMCy4t30D6zERsrihNK2KQTEj6E+99f8N7NUXLN\nUME/hHW2k9+JDuGKU/qt/kRSR5giMehbCzYquFxQNpc4zm9Lp7OUhPBdW/5+tQsptJYud1M6X0nv\nR9S5TsiF5N+BxBWuQsKT0DYolXgc/fi8Y34ZnRUaqxKFpbr3qPfsm6+860vtvgoLVN5C2cnJlNXE\n/HmxWpvy6hiW1v88sWpIx/Z0bdmERz6YF+zXW2vEwB5MX7iKimo3bDsioKI6TlZqMrUxmz+99gmz\n7xrLFcf3o6Y2zt2vuqVN974+m4KdxeRkpPLx78dSWVXLrCXrKK2sJi8z3b/Ozc+9Q9y2ef6Xo7hg\nUC8uGNSLS/86hXhc0rVVE874w3OMOXkgf7n8dLbs2c8Z/bsw9pRBpH1H2ZtSirjt0O6IDkRS0lny\n1SL/XDweZ/HixVx55ZXMmzcvYd68efPo16+f/0eOzp07U15ezvTp07n++uv9cVOnTuWcc85hypQp\nnHd8T1JSUhg0aJB/PjMzE4Bu3bolHDf8MDiOw1fvLWPD0k1sW7+jvrdjMPygKC+AXJguePWOEaAM\nBoPhv5jCwkIefPBBJk6cSFVVFWeeeSa33XYbxx57rPkrj8HwH+LT7RuIK8V5+b0AuGn+mywv2k5c\nweaKUhxbkBFNplLG0IHgUoJlkRAajgyX0pFY6uaX7rmfJ4gshFQUWUd88oSkhBI0L+tHHETQ8cvu\nfBXJdTYpfV0VzNWuLJ2XRN0x4WtCQii4fzxUHpi4n2ANFdpjOKtJQdAdr44rKXz/vnspfCxkFvMF\nJn28TgO2BCeWgvKqWKIJSiq3W2BoXwKYvWYjA9u1Ji89lf1VNYwY0IOlm3dQWFxC12aNQUJSxMJ2\nJNIT0LJTUjgivwVNsjNRCu6dMZuvC7bzuxEn8uU3W+jZphmPbt3DntJKamI2OelpVNXGqayNJ+x5\ne3EptXU6yV0wuCc795VzzUkDeGLWPLq1akLP/Gb0zHfz/hpmpfNdPPz6XF786Gum/W40w08YQnHR\nXuat3sxDMz7nkv6uC2ncuHFMnDiR8vJysrKykFKycOFCxo4dm7DWqFGjmDp1qi9AlZeXM2vWLF59\n9VWmTJlCUsT8ElifOI7DHaf8ibWLNlBbWcvm6DdUqSocxyHyb3xtdu7cyYIFCw443rdvX5KTk/+d\nLRsM/xYStwueKcGrf4wAZTAYDP+FrFy5kgkTJvDKK68AcMkll3DrrbfSo0ePet6ZwfDTY8qGpUSU\n4Px2rgClUKRGkhmZ34OGKRk8suoLKuM2KtzeWVpIqdUnV5TyJpOQ6eR9VDo4yBOIlO+EIrQGXlmb\nJ+jUKdHzU7pD7qBwnlIYod1U+irSN2sRDhkPBKHApaRFI73dA7Kb9Hi3NtB9r0v9ABESc9xzCRsL\nwsfrCm0E1/c3LuscD805wBGlj4f3jvfsVR13loAmWRmUVFa7HfBCa+syQMdR/PnNT0HA82PPZ/76\nrVRXxXhu7AiqYzY5qcn0atWMmJQ0y8mkqKyK+esLOb13Fypqaul/26P8/NRBZKYmc2a/rsxevpGV\nm3dy7bCBvD5/FasLd/HLM45h7MlHkZacxOSPF1NUXkVtLM41wwZwzlHd/T80TJ2zjIbZ6cy+92cA\nnNSnE/8IVTUxfvfc+wzv14kGmWnkZqaRkhTl+OOO5e677+aLZd+ycXsxn1Ssp1+/fvTo0YOcnBwW\nLlzI8OHDuXn8JEpLSxkw8CgAauM2ZZU1jBw1ij/96U8UFhbSpk0b3njjDRo0aMCQIUP+oX0Zfli+\nem8ZaxdtoKaiBgA7ZmMLyVfvLWPQmd8dRv9dTJkyhSlTphxwvG6emMHw46M8B5QRoOobI0AZDAbD\nfwlKKebOncv999/PrFmzyMjI4Prrr+emm26iTZs29b09g+Eny4aSvaRYUT7cto5bF7zDxCEX8vDg\n8/zzT66aR0xKzyUUEppExK31CmclKeGXxmnhwy2t81QPi8QytlCZni+CCBG4ibSzSisqDiG0hUnU\nEWfEAWVpAhLnChHkRIVK6oQMfdQqUiiIPKEXnhMq3ZOB6UrfS13BCEgUxfT5sGvME8qCZ5cg0wX3\npEWmcEh6nespJyRGeUOilkXckQgHiksqSY5EiIcdXkB+w1wKi0oS1vv15FmUVLm/zE/65CuuHTaQ\nyqo4X64rpFurJvz556cx9YtlLFhfyNGd8/nim02kpyZxVr9uXDv8KKRUrNqyi7SUJIb16sC+8iq2\n7yulX4dWpCUnYTuSh2bO9feekZJEg/Q0+nVsRWpyEvdP/5TcjFRO6vuPCU+aHcVlfLpsI1W1cZ76\n1QVceYrb5fGYY44hHo8zb/4ChMpkw9qVDB48GKVg0KBBzJs3j+HDh7NwwUIA+vV3c6PWbtrN9r2l\nFMdS6dmzJ9OmTePWW29l6tSpjBw50pSEHyZsWLqJ2spa//0RojsdRA82Ltt8UAHq7rvv5u677/7O\nNTdv3vwf3qXB8J8kgk0ES5mfQfWNEaAMBoPhMEdKycyZMxk/fjwLFiygcePG/PGPf2TcuHHk5eXV\n9/YMhp88xTXVNExJJy4dahwbW8qE8ye37Mrbm9cSlLcF9V9KeeKSzlECr9wOr+RLBBlKCHACF5NQ\nVlh1ctcJC1O+2oJ/PCGwG3wxy3ct+WV1Xt6Tvm4doemg5XQKNzw8Eqzhqz2458Jimy8MecKWFVaK\nwtcK7ytsjfLvKXTOe26+S0rrfd5x/buFkEHpoB/wrpcOlfv5pjNPWLNj0s96UgJqbScQ57wGhIV7\nSwgjgJJKV3w6sm1zLjrmSC57eJp/sk3DHLYVl3LRsb0ZdcyRSKXYtGsff7tuBIvWFbJjXxlZaSk0\nzc7kwWvPIr9xA47p2i6hZG7V5p3kpafR64jmNM/N5pXPlvHQG3PZXlTKszeO4IlfnEdW2j/faKJD\ny0Y8c9MFPP3mPN6dv4YzBncDYMCAAUSjUVokVTDgqME8dceDnHzqmRw15iHaN873c6COyIqxp0MH\n2rdtDUC7FnkU7Uqnb+eWXHTRRUydOpWrr76ajz/+mLvuuuuf3p/hh6FDn3akZKT4DiiAlIxkjujd\ntv42ZTD8gCjPASWNA6reMV8Bg8FgOEypra1l0qRJdOvWjfPPP5/du3fzxBNPsGXLFu68804jPhkM\nPwJKKWodh/zsPM5t15MNF9/BkBZHAFARr+WkmZOIDdheMwAAIABJREFUCotjm+cDyhVBpEWiL8dy\n3UA2KEeALdz3Os9JKZAC4YBwhPsKu6CkgDiJc3A/F0qP1S+CcrKQIKVL93DcEjh3jAg61tm457y5\n+kXoJSTueNsrqXPw85yEcjv7CccdZ+nxek1fcAs++qV/WtjS6znufvQ+Cc8H/7r+/rw9W3jXtEP3\nH3oeVvjew6V8EpKt0PMm2BsKkrWDTIKloHFWun9PCU40oE1eDt1aNSVquYUeyZbgwyXfMulDN9Bb\nCMHarXv48/TZXPXIq9z18oc8/d4CZny5ko07ivnNpHeZv2bLAXlN67cXsb+imoJtxUydvYze7Zpz\n5Un9aZGXTV52OinRKK0a5Xz3N/MhyEpPZfmGHcxZusE/lp6eTu/evdm2cS1XnNCF7du30W/AQFKT\no3Tt2YcFCxaglGLBgvkcd+yx/ryczDSa5WWRlZ7KRRddxJIlS7jvvvto2bLlYRMsrpTi2017iMed\n7x/8E2XAab3pOrADqZkpCCFIzUyh68CODDitd31vzWD4QRCAoywcI3/UO+YrYDAYDIcZZWVljB8/\nnnbt2jFmzBjS09OZOnUq69evZ9y4caSlpdX3Fg2G/xmW7tmOreAET3SyQmnYuyrL+bakiDcK1tA6\nowE4wsuBcrvIKYknPHl5S0oLU55Yo4QrAkkROJHwhCRb+EIMNr4g5buOQi4gtNAjXQELx7uCdF1T\nrujizXPc9bUwpOdaCiwlQuKPno+3T090wt2nFnC0yIT3Ep5IE3Yv1RWRRB1hKCwI6XOWN0+ExCjL\ne28RjNeClb+eHqf3pcfoF4Hg5QtrCmzbFQ8jQFSEzjkQj6tg/w4UlVYBcM2J/Tm7X1fSk6L88sxj\nEBI+XVVAbnoaVwztR3ZaCmf17wYKrJD9q2vrJvxmxAk0y3G7vrVr2oARR/cABd9s3cutf3v7gO/D\nC4/rxTmDurN1bylJEYsVG3Zy9qBuvPuna6ipjXPtA9M5845J1MSCYPLlG3Zw6s3P8MWKgkN+fwM0\nyEzjld+P5vdXnsKCd77mpT/OYME7X3P00Uczf/585s2bR9u2bRly1JHMffqX/PraUZSXlzNnzhw2\nbNjA0UcffdB127Vrx8CBA3nooYcYNWrUd+7hx+TLrzZy1c0v8MzLc79/8E+USCTCfR/cyf9NuYkr\n7hnF/025ifs+uPPfCiA3GA5npBLYysKR4vsHG35QTAmewWAwHCbs3LmTRx55hKeeeoqysjKGDRvG\n5MmTGT58uOloZzDUE28VrAEFZ7Xt5h/7tqSYNzas5rpeA3n42DMpKN3PI0vngRIkRyxiUgalb1Jn\nPrkfSWhcFsp+Sqzq88+7FWquvUZINyNKyKCsDkLlcO47V6xyQiuEytSE4IAOcUFZnkpwXrnlafq9\nV0IYzn7SJXD+Yu7n4So6f44+FnIYKU9oUuFyvNA5X7DSl9eClQjW8kvtDvb4QuP1+3B5oJCJzyEt\nGqHWdsjLziApEmHH/rJER1RoLAqe+3AxALN+fzWllTU8Lr/EidnM+GI5z33snrNtSX7jXI7v3p5R\nf36R9OQkCveWcP3ZxzD1tssoqaymcU4mT709j+y0JBwJlw3r61/KkZLVm3aRkhSleF8Fv7/sJBzp\nUFpZS3JSlLjtkJ6SRF5WGpXVMZxQeei+skqKSivZva/8YE8HgI3bi7jozhc4sd8RqDeX+V3RUjJS\nEPk1FBUVMXnyZAYPHuzPyc7Opnv37jzwwAOAmxd1KG6++WamTJnC5ZdffsgxPzZHtG1Mr64tOapP\nu/reSr0SiUQYdGa/fyt03GD4b0EJU4J3uGAEKIPBYKhn1q9fzwMPPMDkyZOxbZsRI0Zw22230a+f\n+UehwVDfrNy3myQRoVF6pn/sN3PfY/GeHSzZs4Opp1/EnK0FvggSsz0BwMt3UiGhSCjcIKRwORrC\nV4D8rnae6KG8//HL5ASu20erPq6yFeQm6XI9Pa/OvWhHkPL2568jPOdUeJ96azZ+rpMvGoUzlZT3\nud5bMDUhO9zfU0KnvJAAJOuME4lzRahaKqE7XvhYeEG9P0liTlTd86HPa72SrNP7dGbynCXBuLrX\nC+0P4A+vfEROeipZqclUVMW499VPaZCZSsOsdN5ZuBYUtGuSx4YdxUjpLvb0uwv4YuUmPl2+kRHH\n9WLG5ysAGNanI9ed4TqKNu/ax/hXPmXh2kKOP7I981Zv4die7Rl1YlAm9YfnP+C9Bd/w4p2X0qFV\nI5KigYPlhL4dmf3wOCKRQ//C1TA7gy75TWhUGuPzUFe0mooa1Cb3ebz33ns88sgjCfMGDx7MxIkT\nadCgAV27dj3k+iNHjmTkyJGHPF8fNG+Sw5P3Xlzf2zAYDD8iQlk4Xqm4oX4xEqDBYDDUE4sWLeKC\nCy6gS5cuvPDCC1x99dWsW7eOadOmGfHJYDhM2FtdSbKVWJbymwFDyc/KZWSnHqwt3sOY919HOYKI\ntMAWJDmRoLROhvOZhJ+1pEvb0G4lKQJRSmcW6XI1/U9mLeDYBHlQuvzNK1Pzs4kI1sIG4vhldJbO\nftLOJk+08kvg9EdPULJC3eJ0aZxfwqZdSHVK6PyyulCZYDj7yS8ZlHXme+/9vKbwvPA6oeN4+U/h\nbKjwuuH5Qrl/fe3cvBECSEuK+B359Gvpxu2kWBbCgWTLwi+cDO2zQ7OGCCA7NYXF325j6cYdTL3t\nMhrnZJCVmkxVVZw/X3EaZw7owuXD+tG6cQ5v3X0VzRpkcXT3fP50xansK6+mffM8hhzZnsy0ZBpm\npdGzfTP/+2zGnBUsXFtIu2YNuOb0gTz96xF0aNmQ48Y9xmufLuep17+kXbM8urRpQuMGmQnik+Yv\nL3zMCT9/nKvumcL4Fz454HxuVhov3n0ZzaVI6IoGYFVHaZjTCKVUggMK8DriuceNQ9dgMBzuSBS2\nspCmC169YxxQBoPB8COilOL9999n/PjxzJkzh9zcXH77299yww030LRp0/rensFgqENZbQ2pIvGf\nSwOateLzkWMB+LSwAFsphPSa3SmBjQoyl+q6cpRIcM/4wlLYpeN4Z4S3jlBe4Hhoju42p4L5fmc9\nvPX8crM6AoFKPO8f8/eYuG/tmPIVmrDIpV1VMlgHiwQ3VEJNXmjcAbKFvnftfhKJ5xKelcLvcOe7\nmersO6HLXUgMk8C23fvBgRrHr1X0563astt3ZNlx6Tq+dLmegsyUJK4+YQB/evVjUiIRnrxpFJff\nP5U/vvwRH907lgE3PILtSNKSk/jZ6YO57+VPGPvX6dTGHd665yqSohHe/GIVyzfs4KrTBnBM93Z8\n/tAvUEohtbUMuOaMgWzeVczClVv4w98/5KW7LmXFxh1U18ZZWbCTd79cw0Un9eGluy6r+yR92jbP\no2XjHNZu2kVNLH7IcYfqivbOS+8ftETryiuv5Morrzzg+PPPP3/IawBkZmaiQvcYpkePHoc8ZzAY\nDP8O7s9XC2X8N/WOEaAMBoPhR8C2baZNm8b48eNZsWIFrVq14sEHH2TMmDFkZWXV9/YMBsMhiNkO\neenpBz23rriIrnmN6de4Bct27fLMOCoha8gXncJiDgSCjgq9V4FY5Ja7icQSvLolZL7YJMBWQWaT\nt164vOyAuQSOJb8SkKC0LiHkXJ/T16uTq1S3dE5pd5MViGL680NlMmkS9nwQsUqE7qPu/fn6WDhz\nSgtzoTlKQU3MSZwfugdNUsQi7kiUEzyDlKQIlVVx7p3yETVxh5oam18/+RYCkLbiwnteIDMpmSaN\nM7h6/Kucd1wPFqwp9Pcfsx1qYzZrN+1k3NmDGXVCUE434s7n2banhHcnjKVRbgYNstLJSEoCCQXb\ni1mxYQf9u7Zh4cRfEbcdeh3RgiF9juBgFGwrYv6KzVx11lGMPe9oSiqqSUk69D/7dVe0tYu+pbYy\nRkpGsumKZjAYfjJYWNjKwjICVL1jBCiDwWD4AamsrOTvf/87f/3rX9myZQvdunXj+eef5+KLLyY5\nObm+t2cwGL6HuFTkph4oQK3au5szp71EZnIylbEYjTPSaZyWzuq9e12xJeRYSgjs1gKMFnj0MSUS\n9Cilx0CCeCS01cnBS/AOzddii85LCrmktBijP1d1xaVQ2ZwmwcGk38sDx0HovQyELd8FpUUoHSAe\n3mPCIqG91hXORMjtFHY6hY75Dil9j6HnnOgISxTbfKdV6J6a5WYy+ZaLmTRrITO+WIElICc9jf1l\n1VgEAtYFx/Xitc9WgIDtRSXs2lcBCqpqY8RtSfe2TbnilH688MHXpCdHsW2HlQU7ee2zlZw2qCtZ\n6an+Ncsqa3CkYm9pBY1yMwCoron7z3XrnhL6d21DxLKIJFucP7QXW3fv5zePvs11I46hX9fW/lpP\nTf+Sz5dspFN+EwZ0b0Nu5nd3T9Vd0b56bxkbl23miN5tGXBab9MVzWAw/CRQ6BByUzJc3xgBymAw\nGH4AioqKePzxx3n88ccpLi7mmGOO4bHHHuOMM87AssxfXwyG/waUUthS0jg144BzrTKzSbMiVNbG\naJyezt7yKvaWV9E6K5tt5eW+AwjlOX9ClV5+hzxffMJXe3xRygm5kxRBzo5UvqNK2e45y1sSSOw4\nV7e6TAs/kYO4h8Ld5fS5uqVzIlgvYYw+pu8lErquPq8zm6izJ33PddxZYfy1D3Y+HHweFqU84Ska\nASfuHQ4LWHXvo8497tlXwZtfrGJFwQ6UgrSkJPaXVwNgCUGX1o3ZtHMfQ3u24/SBXXjs9S9Y9u12\nhIL01GQm3nohbZo2wBKCAZ3aMHvxt2zbU8otT7zF07deyF+uO5O+nVrx7da9FJdW0rlNE1679yr2\n7K+gQ6tG/u1V19ggYcIvz2Jo3w4HPJv1W/ayYv12Xn53MZlpyViWheNIbrjoeAb2yKdP55YHPtBD\nYLqiGQyGnypKCaSyTAj5YYARoAwGg+E/yObNm3nwwQf529/+RnV1NWeffTa33Xbbd7apNhgMhydF\nlZUoJWiXlXfAuQfmf0GN4xC1LF4+90JueP9d1hcVU1JdEwgqDkFZXZ0SsARnkKqjq/h5S0H4eMJa\nnoXHPxd2S4WVHYLPw93n/Jwl7WSqWxanArFGO6N0DlJYrDnAOeStp+w66xPcu78tmbj/BKdX3XvQ\na4fvKSS0+TpZKD9Kn3TCsUfheaGJKiQGJidbxOJu4NO7C1azbW8Zv7n4BPp3asV1D86guKQKhKJ4\nfyU11XE+X7GJ/p1aM7hLPivWbwegpiZGanKU9NRkzrntb+wvr2bm+Gu489n3GNrnCCKWxfD+nQC4\n8p6X2VVcDgpOP6Yb781bw6jhfeic34TTj+3G1WcfxdylGzm6Z7uDhn1//u7LfPP2owh5J/tKKtiy\nYz/VtbW0V8t4/fXXmDlzJqeccgqVlZVMmDCBV155hS1btpCVlcXQoUP5/e9/T48ePQ5Y12AwGH5K\nCAS2FESE+SNwffODClBCiFOBR3D/FvY3pdRf6pxPAV4A+gHFwCil1GYhxKXAraGhvYC+SqllQog5\nQHOg2jt3slJqzw95HwaDwfB9LF++nPHjxzNt2jQsy+Kyyy7jlltuoVu3bvW9NYPB8C+yubwEFLTL\nyT3g3O7KCiLA1PNG0rFhIx479UyeWfwVr69dQ7ucXDYVlyaWh4Uzkw4oL9NCU8jdpAUjEXInhR08\nYYFGl/N54d8JJXdh0UiEBJs6+Up+gHkdoScc5C0Odu3wWoTK3+qUvdUVliy9P0hwRh0gcB3MGSUS\n1xbhZxXanwqJcxYJj8GtTtTzQ4JgvFb674v2V9I4O4NT+nfmtc+Ws6+0ivTkKDW1Nnv3V5ASjfDa\n7OW89slyAPp2asmS9dtRAtZs2eN+OaWiY6vG5Gam8cTNFwDw1epCKqpqWV2wk5aNchnaryOff72B\n1k1zSU1O4rOvNzDtg6V0ym/C4J5tGfz/7J13eFRl3obvMy29FxJSSAi9hl6lKSKCIkVEUUCxgK4K\ngqBrWfVTV0AQsSEK2BBFQQHBglSl9x5KgEB6L5My7bzfH9NOAri66mJ57+vanZlz3nbOzGWGZ57f\n87ZO4lKYqyyUmWswGvQ8MqYvzRvGcPhkNvNfeY7Pt3zDihUrGDBgAGazmb59+5Kens7jjz9Op06d\nyM/PZ968eXTu3Jk1a9bQt2/fS84hkUgkfwWEAio6Z7ai5IryuwlQiqLogTeA/kAmsFtRlFVCiGOa\nZuOBEiFEI0VRRgEzcIpQS4AlrnFaA18KIQ5o+o0WQuz5vdYukUgkPwchBJs2bWLGjBl8++23BAYG\nMmnSJCZNmkR8fPyVXp5EIvmVZJU7Bai44JCLzr09+CasDju+BiP/3rwZs9XKywOuY1xqOx5Yucpb\nNlZXZFHd5XQaRcYtUqlKbfFIaMQVldqZT9Rph9ehpHVXubnIeVV3HvCW7Lmzo+qUrAG1c620LiT3\nfDpNO014t7a91m1VK0uq1j1yNanrfNLOr3GVacv9tCKX22mlLTlUVOc/Rjzrcp0L9DNhrrI62yhg\nqbHjsFcy6NEF2O0OUKHGYve0t1odtS4nt6iCYF8T0WGBXN+1Oe98uZ3cwgqG9W7Di4vW0a5JHIpO\nYc5HGymvtBAW7E+ZuZqXH76Rybf2RlEURlzdllGPLibYz0RVtQUta7YcYc57G3j18RG0alyf6bNX\nsvLb/aiqYOSA9gDMn/sC2zd/zSeffMLgwYMBePLJJzl48CB79+6ldevWnvGGDh1K3759GT16NOnp\n6fj5/XROlEQikfxZUYWCQ9Whu4STVPK/5fd0QHUGTgshzgAoivIJMATQClBDgGdczz8HXlcURRG1\n92C9Ffjkd1ynRCKR/CIcDgdffvklM2bMYPfu3URHR/PCCy8wceJEwsLCrvTyJBLJb0RBdRWKUKjn\nF3jROZ2i4GswArDs8BHMFisPdO7Cot17ySytcDbSOIYUze50znPO1xeV57lO18py0jh5POVmbhzU\nEoi0gpfiLptzl5m559G6njTlb55j7hK6Oo4orbNJuEPFda7n7uOacPFaZXXuuRUuKr9z36uLxC2t\nYIa3PwJ6t05m88GznuNCM1ctt1kdd5SiaTuke3NW/njMs4SkyBCOnssHnYKvQU+Nw45QwWJ3gIAn\nx11DemYhzRKiWPzVbooqKmlQL4xjZ/JBgQCTkdz8csyVxXy4Zhc/7jtDXEQwby3bCgqs2nQEgO5t\nkxjQvTkdmieQdjaPq+95nbE3dmbiLVdhtTkorahGCNiyN522Tb0/ZlTX2KiqsWG1OW/yNd2asHdz\nKKdznVfwyCOPMH/+fD766CMGDb6Rl978lm7tEnn33Xe5/fbba4lPAEajkRdeeIE+ffrw2WefMWbM\nGCQSieSviAI4hNv/KrmS/J4CVBxwQfM6E+hyuTZCCLuiKGVABFCoaXMLTqFKy2JFURzAcuD5OoIV\nAIqi3AvcC5CYmPgrLkMikUic1NTU8OGHHzJr1ixOnTpFSkoK8+fPZ8yYMfKXY4nkL0hpdTUICDH5\nXLZNUVUVUT4BVFRa+GD/AbrEJ7Dq8AmEIjxhp7VK19zh5G7qOow0x2vlNrnPa11VCs5wc11t15NW\nyKlbaqedSNGKVLjG0TqnNAKR+xo8U7sFJU0IeK2d/S6BR+gS3ke3GOQObEfBKYCBZ1c/j7ilKc/b\nffQ8egGqVrVzO6fstecUzn95EB8VQk5xBQ6HSoCfkY17Ttca+9i5fBSgQWQIgX4+HD2TR4BJj8Wu\nYrWrRIUE8tLi79EpCg5VoABpZ519wgJ8Sb9QBAqMHdSJ91bvxlxpAQUMeoXQQD8KS6sAyC0op0/H\nRvgYDSTEhBIa5EdEaACbdp5kw/aTPDjqKpqlxNIiJabW/RtxbTuGXtMWvWsji6HXpHLwxxa8fmAD\nTzzxBHPnzmXhwoUMG34z6RkFrN5wmH37dlFZWclNN910yfekd+/ehIaGsmXLFilASSSSvy6KM4Rc\nXO4PlOR/xh86hFxRlC5AlRDiiObwaCFElqIoQTgFqDtw5kjVQgixAFgA0LFjx4sEKolEIvm5lJaW\nMn/+fF599VVyc3Pp0KEDy5YtY9iwYXKLaonkL0y1w44QYHI5nS7FyYIizhSX0KJeNGM7tGNnxgVn\nyZdQuCo5kS3p571lbhqhxEOdkrKLHD/uZsIrGLnDwT2lZHV2vvOIOW43ks47tscp5B7YJSApivO5\n0LSrtRaV2l/bNY4m9/o8x7RuKrcjSSucufu7r9ftpnIJQTqoVXrnXodOI1jVWDRb/KnePto5nNfh\n7GTQKWQXlHnGr66yedYK0DQuipOZBSDgQl6px6VVVWNHFRAa6Et0aCDYQdFDYnQoT98zgE/X7WfD\n7lOUVNQAcHO/tnRvncxVqSms/fEoX2w4zJSx/WgQG876nScZfX1HzFUW+o6dx6DeLXlywnXcdl0H\njqRls/PgOSoqLWzYcYIfP5nCpdBfYhfVoqIiXnzxRSZPnkzztn24etRcHrnnauY8OYJDezbx2UJo\n0KDBJccD57msrKzLnpdIJJI/O6oQOISCTgpQV5zfU4DKAhI0r+Ndxy7VJlNRFAMQgjOM3M0oYKm2\ngxAiy/VYoSjKxzhL/S4SoCQSieTXkpWVxdy5c3n77bepqKjg2muvZcmSJfTt2/eSuxFJJJK/FqrD\nGQj+U1+WuibGs3DETTy5Zh3PfL2BRhFhzLrhOhbv2MsPp8+j04pNGiHGXa7mcUNpXDvakry6z90a\ni6J6xRvwCkja3CaPeKUti3MLRe6cJ+Ft48lKcs+haEQk10Ft1pJnfqEJ+XaPp53Lc4BauVHuY24R\nSAjnbntu95c2KsvjuPLsEOhu57opOgUF4bq/ivPyHM7fun1MemwW5wX6mQwIBDabA1VzX4b3ac1L\nH25w3mPN3MF+JhrGRTLy2nbc+3+fAuBwCC5kl/DBql28NGkIwT7radQgipv6teH0+QLG/fMjOrVK\nZMYjN7H38Hl+2HWaYY8PJ7VZHA/932fkFpSDcIaImystfP7NfgpLKpn92DAysoto3SSOX0JwcDDN\nmzdn4cKFdO91HcGBvoSF+NMlNYmzab6/aCyJRCL5K6IIBYdQpAPqD8DvWQS5G2isKEqyoigmnGLS\nqjptVgFjXc9HABvc5XSKouiAkWjynxRFMSiKEul6bgQGA0eQSCSS35C0tDTGjx9PcnIyc+bMYfDg\nwezbt49vv/2Wfv36SfFJIvmboMf5pdWuqpdtU2O3M+HTleRVVHIsN493tu/lWHYenRLiMLncKh5h\nx52VpHqf437uDst2HdcJzXFNG50Dp1ilalxRrvOecj6HqzRPdT13t3GNqVNdYzhc6xHeNXqea+bX\nudfv0Izn0LwWtedRNPO7hRwd3rF0aObVCFSe63CX9al11i4AIVAcAuzOR48Q5xCetTwy4iqCfEye\neWwut5SigqXajq3KgbBBkJ8Jg6LQNiWW+OhQEiJCuL5rUwJ9jM5rU6HcbOFAWhYnzuZTXWN1Bti6\n5t179AK5heV8seEQKzccRq/TkRgTzoCezbl5QDuMRj1lFdXkFpWz9/B5Vq8/zKG0LHLyy4kKDWD8\nsG68v2IHhUWV3H/bVXRrl8yoQR1p2TjW8/kqLDbzxnubyM0vI6+gvNZnz+5Q+Wz1Xqw2lTVr1lC/\nfn0enDiW15+5nr7dmwIQF+cUszIyMi77Gc7IyPC002Kx2jl6LItLJF1IJBLJnwqhgF3V4VBlBtSV\n5ndzQLkynf4BfIvzO9wiIcRRRVGeA/YIIVYBC4EPFUU5DRTjFKnc9AIuuEPMXfgA37rEJz3wPfDO\n73UNEonk78X27duZOXMmK1euxMfHh3vvvZcpU6aQnJx8pZcmkUiuAD4GI0KA7ScEKINOh0MIj4By\nY8tmvL99PzHBgdjtai0XkNZhpC1tc79W3CVw7twlzU50tcbRijbKxeN4cqa0uoErK8rdzvMVXNR5\n1JTKeRxIbteWOy9JO7drbR7Hk2sMj3nJXVrnnkPj0PL01QhgwqGxW7kFJ3c7bZlinfm0Y7/1+Y9Y\nbM4LNeoU7O4xXQ4rt8MpyMeHr+bcy6Ivt/PQjBUAFBSZAeEtI3Q428ZHh1IvNJCCYjM6wNfHyPU9\nW7Do8+2E+voyZUxfHnxuGYdPZPPKE8NpkhSNQa9jzfyJKIrCyH+8S15hBYlxYRQWm5nx6E00TopG\nr9NRYbYwsFdLLsXGrSf4ZOUezp4vZOe+szw/fQg9O6ew++sDnNybjq3EjACCgkP45NMVDLq+PwMG\nDGDr1q1ER0fToUMHAgICWLVqFTfeeONF42/atJnS0lISGrS46Nyi93/g0+W7eOaJIfS5qtkl1yeR\nSCR/FlSheLIZJVeO3zUDSgixFlhb59jTmuc1wM2X6bsJ6FrnWCXQ4TdfqEQi+dsihGDt2rXMmDGD\nH374gbCwMJ588kkefPBBoqKirvTyJBLJFSTYxwcElFtqiAq8eCc8AKNez9p7x7Dt7HlS42PZcvIs\nCpBbasbPpMduczir69xCDnhFGDTOIrgoK0nRCiuarrUyojQCDq6yPh2usjulTts6Qg3usdxrUTRj\ngjf3SWj6uR1PdcaoJZZRRyTS7GgnFM2YqnDO6ar181yqS3jCdW8UfZ31a8fXlPW5z1msqqd8MCYi\nmKz8Mnq2Saag1MyJswUex1R+QTmTZy333iCXU8tS4/DcC53r+Obdp3jivgFMenE5AX4mKiutrP7+\nMHa78yY99OxnKDoFq83BkpW72b73DC8+OoRu7ZP5x1Of0KJhPRonR/HjrnQAgvydwfYNEyN5bOK1\nPPbCF+zYd4Y3X7qNFhoH1KBrWuPvZyIwwIecvDJio4J5fMDzHN91GkulBQxF6ISDJ/5vBbv2Z/DR\nks8YNnQQAwcOZNOmTQQFBXH33Xfz5ptvMmnSJFq1auUZ2263M33645h8grmQG0xdenRrRPrZfJo1\nib3onETyv6DEWoCv3h8/fcCVXorkz47qFKC1On5kAAAgAElEQVScHhbJleQPHUIukUgkvxc2m42l\nS5cya9Ysjhw5QkJCAnPnzmX8+PEEXuYfmhKJ5O9FhK8fClBcXU3KT7RrFBVBo6gIAJrVi+JQZi5b\nTpzDYnEQ7udLUVVN7VwmraCjdTHZvUKSp737uVtQ8gQ7cbEjCq845BG2NOKS0LiZtP08biWNSOZe\np0dn0oR9Q22By+0W8iC82lTd8HJ3JpZ7PkUrNtW9H27HlcN7TSaDDqtd9QptaO5RLTeXM+njxfsH\nU1hWSc+2yVTVWLnzXx+TX2ymutoKwKET2R7hqX69EPIKKjw5VwoQGuRHaWk1GVnFRAT5c9v17Tl4\nLIuMrGJ6d2nM2s3HCA30o8Jcg8OuMvufw7Db7JxMzyM8xB+L1c7x07nYk6J4+cnhfNX4MA0TIgkM\nqL2z4rFT2ThUQUFRBWgEqJVr93MkLZtnpw2iV5dIdqz8muM7T1BT6QxRt1vt2BWVCJuNpo1i8A+s\nx0dLljFi+I0MHTqUtWvX8vzzz7N161Z69+7N448/TqdOncjPz2fevHkcPnyAGbPmM3zoYOrSplUC\nL794y0XH/1vM1d+RV/IYidGrMBrif7NxJX9dXjv1OAn+KdyZ/PiVXorkT06IXyAqOur7RFzppfzt\nkQKURCL5W2E2m3n33XeZM2cOFy5coFWrVnzwwQeMGjUKo/HyO11JJJK/H/FBQSAgs6KMTrX2Vbk8\nJr2exJAQ545tAlrHxbDlxDnnSZVau7u5j9XaAQ9quaUUFU9mOMIlxriVF62g5cbtCHKd12lL5ty5\nU3UjfbRlbdrx3KKTq0/dabTlfkK7FlWgc0aCa9oKb3+taKVortu9PpdY5hHTNIKd6goaV7T3TCOa\nCeGa23Xuzic/YsLNPTiZnsu7n2+nUWIk699+gEET3qK0osZzUU0bRjHt7mu576mPwQF6AVd1TqFd\niwRefX8TQ/q2YeyjHxIRFkBRsRmA9T+mcfOAVEZe356vNx2jVdP6nM0oZPigdqx6d6LnXn321j3c\nOuFdpjzzGafOFnjuwYev30VSQgTVNVZKSqoICfSlV5eGqGoNim0vwlFGcfYn3DngBJbCdfgpWzm9\nox6WqmBqfYqEICHAl9EP9OWOe96lRbP6LFu2jKFDh3LHHXewdOlSNm3axMyZM3nnnXd44oknCA4O\npk+fPuzcuZPWrVvzv0Cvi8ZkaIxO8f+fzCf58zM07m5CTdKNLvn1hJkC+azH44Sbgq70Uv72SAFK\nIpH8LSgoKGDevHm88cYblJSU0KtXL+bPn8/AgQNlqLhEIrkkSWFhKELhXEnZL+oXHxZCkK8PJr2e\nMnM1AP5GA1U1dqeYA7VdTsJrAqolLOEUWtyOHM9xt7OpbmkerswiUWv4WvN4drJzjeP+z59wiz/u\n6oQ6sVe18qvqlgaqoLjDq1ThugzNXkOutjqXcKTdZQ61tt7lHQ9vjpXwHlcdqick3e1+8jXosThU\nhEO4sqKcczeIDSUrr5S3P93q6X/6fCGfrztAeXmNU8dz3avbBnUiN6+U6JAAcguduUpZuaXUVNkY\nfk1brruqOT/sOk1EqD9VVVb2HD6PtcbB1l3pfL3+CNXVNoxGPTabg+SECI6eyCY+NowBfVsSHhJA\nUkIESQkRVFRZqKqyEhrkh15XyZEjabRIzmDRvwOICd6MKFwFuggc1oNk5kUx4lqVmhpf/IK6Aa1I\n6RaHT8Bn1JgtAKQoLWkZ2J6U1CTqRQdz/bWt6dg+iX69m2Oz2TzvX0BAAM8++yzPPvssVwo/n1QS\noj+9YvNL/ny0Du36nxtJJD+TWL/wK70ECVKAkkgkf3HOnDnD7NmzWbRoERaLhSFDhjB9+nS6dpVf\naiQSyU8TFRCITsC54pJf1G9czw6M69mBCe99wQ/HzwEQ7O9DdY3dK6a4cp/c4oynHM7t/nEJNJ6A\nblFHLLpU2ZlSuxxOK1bVCiV3O5c0OU46d3C4vfY6amVGedxVzk6erCi3uOUK7wa8geFaoQ3No2ss\nRVfb9VQr48nunlrUckK5ywDdwpzF5kARkFAvhMycMo94dj6zVKO24TkuHCpCCNd9dU5sNOh48uXV\nALRvmcD5rCIqK2o4e7aQsxcKueHqVhQXmjl2PJsRg9sRHuLPtt3p5OWVuYQwhY5tG9A5NYkmDesx\n7bnl1IsKYkDflhgMNt59+QYU6w8INRjsx8GygXNZn5McloFarielXldQi8DnWjA0Ye2WdrzxgZ0Z\nz95Matsmns9WlxscNO98gOO7TmGptOITYKJZ58YkpCZjNOqZNnkgEolEIpH8UZEClEQi+Uuyf/9+\nZs6cybJly9Dr9YwZM4apU6fSrJncyUcikfw8DHo9OqGQazb/V/2fG9afUa8vJa/UTGF5pTfHidou\npVph39R2B+nw9kH1HvM4pvAKM4r2uPY8ePOltAKPRpipVQ7nEZNcqpBbnPKIRt7jOurMi1cwE+7S\nOLc4pr0Onffa3evo0z6FLXtPO4PKNeV4ChqHlsb1ZTLocNhUHK55MrPKvO2FQHGV4nnFL4FR0bH0\ni93O8d03SsCTs1aTEBvKhZxShApFRZWgKESE+dOqUSzjp3wIinP/pISYUDq2TGTfgQxUH5VHJvTn\nqs6NCfA3IoQdxbab92ZFE+qfg/nC1eTkWUms78CoKwVDa1BLwdAM/Lrx9a5MrhtwBwHB8YCzDHzV\nmgO889E5npo2lNQ2XvHJ4VCZO28d3f4xiGEGhfQD50hJTeJwXjl33LmAEcM7cv+Ea5BIJBKJ5I+K\nFKAkEslfBiEEGzZsYMaMGaxbt46goCCmTJnCpEmTqF+//pVenkQi+RNiVHSUVVX/V32jgwP5euqd\n3LdwBXtPZ6ETEOBjpNLiKo1yCS1aUaiWIMXF5XmKznu8VmC4UrufZzzN2FojE9TOo3KKPMJrfXKX\n1V2iP6rwrFvRadahCQz3uLE8k3mvEwC7cIlr3hrDykoLCgqKKrzXrNYZS+AU8nRgr1Fru8dca0OB\nIF8T5korjZIiSc8oRAH0ioLDqlJYVIle71qSCq2bxWKxOoiNDiEzs4QAX4PrmgSPjL8ak8lAhbmG\nYde346mXVjFv/gbiYkMJ8slm3NAUTh6YSUpQDvH1o1AcZzEZy0gKTwa1muIyPV//kERUZCx7D+no\n3HUwiQkxdO3ciG8+38CabysorjjPlq3fYzLqqaqyMmhgW8yVFl586SsWzr+L2NhQACoqavjq64PE\nx4fz4aJ76Dq4Azt2nMZ82pkrVV3tLbmTSCQSieSPiBSgJBLJnx6Hw8Hy5cuZOXMme/fuJSYmhpde\neokJEyYQEhJypZcnkUj+xJj0BsprLP91fx+TgYev68GY15YBUFmjEQmES4ByUatcrW7pmrud42Ih\nScGV/aQtN9OUq9V1J7kbeeZ2iU5ul5C3pE7jPtK6l4Qm3NxVSuh2dCla4ciNQ9Rem2btQnWe69+1\nKRNG9mDsPz/EXGVDcWiENfejW/Ryja9zRU4pAvR6BewqqmveSrMVHZCVWcLbz9/KtOdXIBBUVlqd\nYhQKz04ZzFMzV5GXW0azlFhiw4PQCejaLpljx7IpK6/hrfc3s3T+GLq0C6A49zvuH76DQH8rXduc\nJyzITnZBCNemllBU5s+X3wZjscZTWNaDKVOeoKTEweaj5xg6Mpmx9yxCFYK9R7ehABvWTsNqc2Cx\n2Ek/W0BWZgmBgT6EhPgzelQ3iosrWblqH4VFFeTmlmG12unSJYUFb44jJNgPgAP7z/HEPz8jJNSf\nJR9MoF69y/+9O3L4AlMnLeGhSQO4/oZ2l20nkUgkEsnviRSgJBLJn5bq6mref/99Xn75ZdLT02nc\nuDELFizgjjvuwNfX90ovTyKR/AUINpko/RUCFEDLhHrc1bcjW9POcTK7EL1ewWFziUDUyT7SZi1p\nHj0OJK3rCa+IVNcx5c18Et5cKOEVrjzZUgoIh2stCt5sKhdCoZYAphPak948Jk8WlKutJ4TcLYxd\nJmzcXc23/8gFbv5xkVPswlk+51yw1tPlnCQ6LICCwkrvtehA5wCHxtHlPpkQG8r9j38MKHROTeLI\niWyqKi04VMHSFXvAAbYaB1t3nuKJSYMYf2tHGieW0aLBCbq0yeS6nlWIwq+oNF8gxKeGG/tFc/Qk\nnD4fRW5pZ778VsVm9yMr1/s3JzI8gNNnLHz1zQFWfXWQ8KX+zHnpFnbuSufI0SyG3OgUgPx9DBiE\nYOSwjhw8eJ42rRJ44bnhAPTo2oiVX+xlxYo97N6ZTnW1je/WTadxo3qeeVatOgACbryhnccldTmE\nAFUVHoFOIpFIJJIrgSK0P4v9RenYsaPYs2fPlV6GRCL5jSgpKeGtt97i1VdfJT8/n86dOzN9+nSG\nDBmCXq//zwNIJBLJz2TY+x9zPD+f449O+tVjmautfLx5H9EhAfzr4+9rn9QKUO7XbgeUXpOBpAnx\nFhphyd3HK8DU+X6nOSfwjuUaqlapnidPSrueuqjCW4KnLa9TlFqle+6xFJcAVSucXHWOowOEJ3Xd\nPYziCQoXmowsAZgMCnaH8AhQiXGhnL9QUscyBQ+M601uXhkr1u4HYM6/bqZJSjTPv7KGnfvOgQqt\nGpcSE5ZPfFQ5PTvmEh+dT3WND76+/uh1pWTmRhIQNoD3PknnQm4YM158jhtGvuFdG9AhtQH792d4\nLrld20QO7M9Ap1MwGPVYrQ6GDmlP2okcjqflsOT9+/D3MzFs+DxQBNERQUx/bDCpqQ08u7KWl1Ux\ndepSrh/UFh8fAyfTcnngwf4YDN6/ccXFZg4fzuSqq5qi09UV6iQSiUQi+d+gKMpeIUTHn9NWOqAk\nEsmfhszMTF555RUWLFiA2WzmuuuuY/r06fTu3dvzpV0ikUh+SxpHRnAkN48Ki4UgH59fNVZ6biFv\nrtlOYlRIbdFHm+ekA2F3OoPcCLvXaeRxRQGK6hpAW7bn6aQ5ru3r8AabC21+Uh1xCwGKw2lxUrTJ\nUm5xSq29bsCTm1RLwPJ0c75yuqK8i/I4voSovVYEisP5qFNctXaATlGw253P9TpQHQJ/k8lb/qcI\n/P1MKAIqyqrJyS4lyNeIucLCS7MX0qNDCY+PD+dgx620bpJPSICNCrOJMrMJgcKG7SnoA6+mXWpP\nHn5sC/lFDoQqCAxsg7nCwvsfbUevA4drd8HoyED+/dxw7rp3EdnZJZgMetq2TuDQgQyEKnh97h2s\n+mo/gwa2ZczoHhQUVlA/NhSHQ+WGG1KxWux89+1hsrJKaNcuyXP1ubllpJ/KY8umNJKTolj95T7a\ntEmg79UtPW3CwwPp3VturCGRSCSSPw9SgJJIJH94jh07xqxZs1iyZAmqqjJq1CimTZtGmzZtrvTS\nJBLJX5zrmzbhi8PH+C7tNMPbtvzPHX6CpRv242sw8ODgnkxftAYhYMLAriz4aoez0s4V4q2DWgKS\nM8fJ9UTVlNnV0d0VtXawONQpSasjNrmPa3eD07lfudUxB6ATRIcH0Tolhg07TnlDwdGITGqdcXGW\n0tWuuxPeHfW0gpfrUQeoKhj0ToFHUZ2ylbvuTwHCQvxIToikfnQwu/aepaCokpMn82jfJoHGydF8\n9uUe2qTGse/gCc6c/JwmCXn0b19CSnwRsdHlKECpOZSIEANHTkZT4+jCh8sFRWX+VFt86NIxiZn/\nN5K3F24kL99OcoNIQsP82b//PIoQLF+xh1devpUn/rWcqmobhTnlXH/9bBYvupsdu84w/+0NHNh3\nDn+TkcpKKw8+8D6tWyewd9cZ1ny1H1UIXntjLKGhAUyedB1CCApyy1j/7REGDUr1/JjSuEkM//fi\nzaSkRFNV6SwBbdO2wc/4lP0yzp3Ox2q106SF3KhDIpFIJL8/UoCSSCR/WLZu3cqMGTNYvXo1/v7+\nTJw4kcmTJ5OUlHSllyaRSP4mdE9KQCfg+xOnfrUAZXOo6AQ8vehrsDsFlTPZRXXEIbfYohGChMsR\npboyoNzZS+4d6BRqiUue8jbqnNeIVoriFrJcAeGumj4hBIonYdwlKtmhMK+CzQUV3pI/lzClOLxz\ne2xbbjeTK+DJ7bQK9DNSU23DrnrFqZjIIAqKKlAdLkeWoqC6xCf3dYeHBHD9Na0wGPR8tmIP+/dn\nsB/NnKqgtOAARaYCHhpdSP+e6zGIs+j0gsoqE4UlAeSXBLNhZ2P2HIsjN68epRVOMSww0AdzpcUl\nggnSjucAkJNViiLA38/Ik9MG8+DDH5GbWwYKNEiM5IOF9zJ69JvYcGZo3T1+IePH9ybQ10hYSADj\n7ryKpR9tw2ZXiYwMYuPG42RmlgBQUlzJu/M30vealnTomExeThkV5ppaoqKiKHTv3tjz2akXFcyo\nG+fyyvyxtGqT8LM+b6oq2PjdEVq0jic2LuySbabcuxhzRQ1rtj9Zq7xPIpFIJJLfAylASSSSPxSq\nqvLVV18xY8YMtm3bRkREBM888wwPPPAAkZGRV3p5Eonkb4bRYMBXb+BMUcmvHmv2vTdQVF5F/0ff\nBsDPx8jZrGJnGLcQ+Psaqa6xudxBolb4uHYHOLf7SNXsQOfeuc5tXNJ5h3CieoUtbf6ndxc+ZxYT\nqlNQUXSac+75PK+FM1zc4RVMhHsOtc6cKjRNjuLkuXyqzDbXnMLTJi+vAkVxrrd39yZYLXa27T7j\nWisYDTqKC8ws/XQnzRrHEB4agEIVsZFFtGuWRcdWF2iUWIivj53qGiMWi5GysmB2H2rMvmMJHEqL\nwWAMY8xtV3H87AnSTpwnNMQH1BoUwGy20KV9Env2nkN1CBwWOytW7CEqMggFGNC/NQsXbiE3pwyA\n9u0acOFCEW3aJDJx4tUsXrSFivIaHHYVo9EAqmDLxmNMmNCPN+eto1mz+kyefB1Wq52S0krCwwM5\nd6aAb9YepLS0ioT4cO68qxc9ev10jlNQsB+BQb74+hgv26Yuxw9fYMbTX9Cxawovzht9yTZ33NcH\nc0WNFJ8kEolE8j9BhpBLJJI/BFarlY8//phZs2Zx7NgxGjRowJQpU7jrrrsICAi40suTSCR/Y3q9\nuoAqi4090x74VeO8+cVWTmcVMu3WvqRnF9GmUX0em/8VO45mYFQUTAY9VVa7t8wOvHlMHpcRXpHH\nJUAJ1/87xSqviOHs6uqoukr8hFZ00jhucApMOkCnU0htFs++Ixdqh5m7s5guUf7nnEOgE85d6Twy\nlCq8a6/jxHKHi7uvM9jf5UYCfHz11FTbQYCv0UbLxrl0ap1J0wa5xESZ8fOxYDKp1NQYOHU+it2H\nEjif05iklPYcOlzCqfR8hBCEh/lTXFzl3G1Pk1Hl46PHalMZMjiVoqJKftx60iXyOe9jXFwYH3w4\nAYCcnFJmz/maW0d1Zc6steTmlrHkk/v59/+tpEvXRjRrEUdAgInEBpEMHTQbm11l1K3dOLD3HCfT\ncvDzN7Hy26me8johBHt3nyWlcT1mPreSPTvPMPftsYSFBfDxez8y+s6rPI6lHzYeZ+Eb63l21i00\nSI76qY/XRVitdpYu/oHOPRrTvFX8ReerqyycPp5Dq/YNZI6iRCKRSP5rZAi5RCL501BRUcE777zD\nK6+8QmZmJm3atGHJkiXcfPPNGI0//5deiUQi+b1oEBbK3vNZqKqKTqf7zx0uw4a9pziXW8y/7ryW\nbi2T0OkU7FYHigMcQmDyNRAc4EfD2HC2Hc5A0bqJ3FVxdq8DCpwle+7SLSFcIebakG93OZ67pM4l\nRHmEH4FHVHK7plRFsP/weddOdF4XllAFRoMOu11FuMrjhMObz+RZquoVvrTXIITAqFew25znOrZt\nwL5D5wnwN9K+VSLb9pwBITDoHLRKOkenVhdIbXmB+tFl6HUCq81IUXEApzISKSxvzYGjoRw7FUhq\n23jSTxVw/kIxffsm8fnyNB64ry89ezRl6vSl6IARQzuSdaGE/fvPUWO1Y6t2Cn3rvztKZaWFNm0S\nOHTIKbhFRwWTdaGYF/7vS04ez+HGmzrQrk0ibdokMu7OXmRkFCJUwdHDmZw5nc8HSycy8sZXCQgw\nERToi1DgZFoOJ9Oc5Xz+/iYAcrJLeGj8Im4a2ZnRd14FwOg7ryI5JZrGTWJZtXw33605SHJKNMNv\n7QrAmVO5ZGcWU5Bf/osFqJf++TlnTuRy67ieABTll/PKs19y87ietO3UkAWzv+Hr5Xt5dt5ouvRq\n6um3Z+spzqfnMfSOHlKYkkgkEslvihSgJBLJFSEvL4958+bx5ptvUlpaSt++fXnnnXcYMGCA/MIr\nkUj+UNzQshm7M7LYdPos/Zqk/NfjLHp8FOVVFoY/tpi4yBDe/9dobh/QgYjgAL7fcYKyshpSmkXw\n9PgBDHpogTcTSDjFH89Ob+ASh7zijycA3CHcmd3eXfbA6zwSAqE685a05+o+ugdQXOWA7gx0u8WZ\nku4Umbzra5Iczcmz+fj6GqipsqEIeHb6jRw6eoGNW0+AEJSWVONwCI/4lZVVDHaBEQeFeT8w5c5C\n2jbPwM+UhcmoogAlpQHsOdyYbXvrs/9IPL179wBVxw8/nqCouBKUSjauTwMBRh8D776zibAQf/Lz\nysnJLmHRgvE4HCr+/j7cMHg2lmobCEFMbCi5OWVUmi00bRbD1KkDeemFVbTvkMySj7ahKLB5/TFU\nVfD+4s1UVVj5bMl2Zr16O/0HtMbhUImJCSE0PAD/AB9atIrj2OFMmiZE8PCjAwmPCGTThmPMf+U7\nBgxsg6Io2Kx2ykqrKC+r8nwmWrVJ8GQ63TCsIzH1Q+nUtREA5aVVfLlkB917NKFjl1/+uSsvqaK0\npBLVVX55Oi2HPVtPExMfTttODek7sA2lRZU0aRlXq9+b/15N9vlieg9sS0RU0C+eVyKRSCSSyyFL\n8CQSyf+U06dP8/LLL/Pee+9htVoZNmwY06ZNo3Pnzld6aRKJRHJJLDYb7We8QbfkRN4dPexXjWV3\nqIx68n1iIoJ4feoIAB6bt5rN+0/jsAsUIWiVEkNOYTlFpVWeWjdnDpTmO5v7qYorkdyZyeQNJnfl\nMWnK7dy43VLe0j73lnaKU6ByNfLsYud2R2nmdjX3HPQzGbBU2zyldd7JtB3d/QWx9Sro1TmDNs1O\nkRxfTKCfFRA41CCqrE34eHkgp8815dQZg+dyGzeMIutCMTU1NkBB0Sv079+K77877BJZnPdq8qQB\nzJ3zNT4+Rv71zFCaNa/PY9M+4WRarseO5ednol2HJA7sOYvJZODxp4bw2JSl+PgYeGn2bQQH+/H4\n1KU0bFSPm4Z14PFHPkEIQcOUaBZ8cC8AlhobBoMevcH5BuTnljHr+ZUc3JtB156NGXRTe56a8glh\nEYF8uuYRAGw2BwaD7mf90FJeVsVdQ1+jU49GTP+/4f+xfWFeOWdP5tKxZ2NnmLuqsmfrKUqLzFx7\nUweEEBw/dIGGTWLw9TORlVFIcUEFrTsm1xrn1LFs8rJK6Nn/l4fulxaZ2bhqP/2HdyQw2O8X95dI\nJBLJnw9ZgieRSP5w7N27lxkzZrB8+XIMBgNjx45l6tSpNGnS5EovTSKRSH4SH6MRf4ORk3mFv3os\ng17H5/++k1PnC9hx6Bxd2yRxPrcERYXwYH+C/EwcPZ0LQKuUWI6eyqFFSj2aJkax++gFsvPKPFlN\nnjAlB86sJUVBOJzHFQce1xLu8jxcu+rpnEITKugUxR2L5MxFqra7XE3ClTHlFHaEZz6NC8sOMdFB\nmKstmM1WFAHBQb6Uldd4L9hV3tf7qkZEheyhQf09tGySSVhwFYpOYLXpOZ8ZwcEjzdmyI4YLOZHM\nmzOab777EAH06tOERinRLFq8hTMn8zziGEJgUnSsW3PQM9W/Z9yMTtEz9+U1KCpYqm38c/oyUlMT\nKSo0ozfoUG0OUts34OZbu9GpU0PuHvM2OVklPPHIUnSKwGTUk5tVQmVFNePu7MW1g9ricKjO+wvc\nPbEfADU1Nm4eMIsGDaN5ffHdAETHhFBTaQVgx+aTnDyaTcNG0eh1imu3QQWj8eKw7/07z7Dq0508\n9MQNhEUEeo4Hh/jz+YbpVFdZeGn6MnoPbE23Ps0v+9ma/dRy9u9I5+XFd5OTWUz3fi149dkvKcqv\noHu/FgQG+9GibaKn/dMTPyAro4ilmx8nLNI7b+MW9Wncov5l5/kpvvp4O0te+x6DUc8Nt3f/r8aQ\nSCQSyV8XKUBJJJLfDSEE33//PTNmzGD9+vUEBwczbdo0HnroIWJjY6/08iQSieRnEx8cTHph8W82\n3rTZX5JTWM438yfy/nOjcThUfH2MWG12Vm06QnFpJet/SMOAgt3iYOX3R5wdXdlLbjeU0aBgU91O\nJeEto8NjjHKW77mcUooA7NrcJuFxQ9kq7ZhMeqw2Z7q5TnW7oYQn9FwooNN5XU55eRX4mPQMvLol\nGzalYam2ERcTQlZWKfH1BS2bHqVvj2OkJBRiMDizo0rL/Nm8vSnpF7qwfksAwUHBmPR6iksqQbUy\n95VvMBh0OOwqrZvHERjoy9jRPfjggx8BeOSRAcyd8y1Wi52IyECKCs106dKQpk1iGTd6PmZzDUJR\nCA/zx2ZzcHBvBktXPEhRUQWTJ35Aelour7y4mpG3dePC2UJvraIAc0k1s55b6XmfUjskER0TwvBb\nupCTU4rBoGNE/1ncfk9v4hIiqJ8QXut9feWdO7Fa7GzdnEbm2QLWfrHPuQNeUSUZ6fmkdk6+yP30\n/ZoDbNtwnF1bTvDOigepnxjhXI4QVFVayD5fxKZvDmOuqKFbn+YU5ZeTn1NKc42YBHDznVcRlxjB\nySOZLHj5awr+UcrjM26huLCCyooa3v73V9xybx/iXVlSo+/vx5m0HELC/X/WZ3bXxuMU5JQy6LZu\nl20zcGQXjEYDfQan/qwxJRKJRPL3QgpQEonkN8dut/P5558zc+ZM9u/fT2xsLDNnzuS+++4jODj4\nSi9PIpFIfjGD2zTj5XU/su3UObo3Tgyp7b0AACAASURBVPrV4z00ujcZOcWEBPo5nTEGpzPGZDQw\non8q1457DXOVlYaJEYwa3IEZ89dhszmIjwkhM6/cIzIZ9TrCg3wpKavCbledopG7Gk3BWUrnCgLX\nluN5quLswttUBXuN3Tm2JiPKGTTuKgUUMPKmjny6fLer1A+sVgfHjmShUwXRMTV0brOVbvedIj6m\nAoPBgcWq43x2OBZ7XzZvT2HdejPYVQYPaouf/iQh/iZOn3G6y3RCcO5sgcuVJTh4IINtW0/RskV9\ndK7A8wBfE1OmDiQrs4Tbx/Rg4/pjvDprLSOHzEWoztDvqkoLLZvHkX46j4T64az+Yi+RUcEIIbBa\n7JjLa/h0yTZQBc1a1CftWDYKEBjsi1AFwSF+hIUHElXP+TdrzN29uHXgbHZuOI4K7PzxBC/NG01o\nuNc5BGAw6DEY9LRum8jLT60gPimCuYvv4ZnJH3NkbwZz3rublqm1haP7p12PalfZtuF4re0F33/9\nez55ZzMTpl9Pmw4NmDj9egCe/scHpB/PYfHaKcRqBLD23RrRvlsjigrKKcgro9/gdsS4dtNbvWQ7\n33+5j7ikSEbd1xeAfoNT6fcLhKJXn1xOcX45fW5IJSDo0uV1EfWCuWVC3589pkQikUj+XkgBSiKR\n/GZUVVWxePFiZs+ezdmzZ2natCkLFy5k9OjR+Pj4XOnlSSQSyX/NmK7teGXdVhZs3vWbCFB9O/90\n+fFtN3QiLT2XlPhIbDV2HBYHOiA7u4xH77uGBnFhHErL5sNl2ykqNqM6BIpewWRwOpicwpGzeE5o\nHD64RChFr3icP253k7vcTnE5qdwY9AoOl8sqMSGcFk3q46PXY1MdtGkZR4NEI3plOb16HqJeVAV6\nBewOHWmnYzh4sBObt9QnKDiSzMwSkpJMDLi6BZvWH2ftV87yuapKi6tUz/m/Zi3iOHkiB2EXXMgo\nAgFHD2d6nEOL3t3Mo48NJr5+OA/f+x6Bwb44bCqRUUEUFZqxVFkJCfLBarWRm1XK8JFdeOOVb4mI\nDOSLr6ei0yl8vXo/y97fSoOkSNp1TEbYVfoPTqXvta148+W1xCVEcMe9fTz3QFHAYVcx+RiIiAhg\n34+n+fLTnYybePUl37/N3x7Gx2TgtvG9CQr2I+NUHgoQUz/0orYBgb5Mf3HERcdj6ocRER3M/u2n\nObT7LPk5ZeReKCb9aDbNUxOJjLn0DzoRUcHcN21QrWMDbu5EeHQwHXo2vmSfn8OTr99BSWHFZcUn\niUQikUj+E1KAkkgkv5ri4mLeeOMN5s2bR2FhIV27dmXOnDnceOONv2rLcolEIvmjYDQYCPPz5WRe\n0f9kvnHDuzLivgX8uP2064hAr1NIbZVIv+5N8fM1snLtQaxWBwad4iyJc6hYcdqdmjWOIe1knrNk\nzyU66fSKK6xbOMvwBAQEmrBaHNjcdiZNGZ87n7xRUhRlpVUgIOd8Mc8+v5LRo9pz7twH3DjoA2Ji\nSzDowGbVc+ZcPOvXN2Pf/hZUVgqEQwVFwdfPgU6noNMpbNlwHJvNQYuWcRgNOo4dzaJd2wQO7MtA\nAWqqLAib8zpys0sYOrwjxw9ncuJ4NgDF+RVMuf9DYuNCyckqIbFBBAadQkVZFSEhfpSWVFFeVkOn\nzincPq4XTVvUJ6VxPYKC/fDzN1FabObIvgwK8ytI9PfBXFbFqWPZjL6rF0ajno3fHCEw0IfP3v+R\nee/fQ3hUEB+8uQEFga3KSrnOmZvVs2+Ly75/5ooarNU2GjaOAeD+xwZRkFdORPTPdwH/8M1hYuoF\n8+iLN3PmRC6tOyZx4lAmoaEBDBvTA6Ox9tf4f09ags3q4Ok3x1w0lslkoMd/ESqupXm7Br+qv0Qi\nkUgkUoCSSCT/NefPn2fOnDm8++67VFZWMmjQIKZPn07Pnj1/1g4/EolE8meiTVwMW06cpayympCA\n398FMvGOXvzr5dUEB/pw58juvLZwI3aLnblvfc/Z84WkZxSiAA6HioLicTcBGISOeqEB5BWa3cYm\nVIszvVxRFE+GVHWl1RlGDvj5G6mqsjkHUAUmg4569YI5ddLp3jEa9TRrVsGECUfx9XkNk8mBcOjI\nyQ5j3XetSUvrSE62lVat4qkoy/SUCaKqNG9Wnw8/GsLd497BUmNn1G1duee+fgghUB2CC5lFPPf0\ncvQ6HQ89ch3PP/UFRUVmQoP8WfnJLqb8czDt2jcg42wh4eEBrF11AEVRWLh0Ivfc+hZCFThsDqY/\nNYTwyCCWvvcDH7+7mbET+tGidTwtWsUx4db5RNULRlVV9u06y4Cb2nPbuKsIDQ+gd/9WtGnfAEVR\neO+Lh1izfDfLl2xDFYIDu86yetlumrWKI+1wJoEBPkRHB9GwSb3LvnfjH76WsfdfjcEVOt7v+raX\nbeuwO5g5bRnN2iYwdGxPtq47QmmhmeyMQqrMFvwDfWjTyblTXbO2CXyy9QkqSqu4f/Ar9LkhlZGu\nkrpDO89gqbF5As9/LTkZhegNeqJdZXw/hd3mYPKNc4hPiWb662N/9dwSiUQi+WsiBSiJRPKLOXLk\nCDNnzmTp0qUA3HrrrTz66KO0bt36Cq9MIpFIfj+mXteLzWlnmf3NDzw3/Nrffb7eXRtz9609WLfx\nGK+9sxEfXwOHj2biYzCg6BR6dm7I7j3nsNkdHkEJIKZeMHk5pRQVVaK4dq3TGxUcdlAcEBBopLLS\nCkIgbKDoID4+nEn/uJZXXv2GpAaR7Nl9BqvFQXFhBYE+eq7qe5Kbb9kHwulCqqnyYfOmlnz1ZUfK\nywPQ6RSEw0pYmD/Hj2YRExVMcVEF90zsx+J3NrF5/THiYkOYOn0Qa1fvp3v3xthsdh6btJSkhpE8\nOGUgiz6Y4LmG+YvHs/6bI4SE+TN/3jrCwgO5TpNXNObu3vj5mfAP8OHZmSPZtO4o+VmlPD9tGS++\nfgfmkirKS6tZ9Nr3DB7eEQGUFJsx+Ri4+Y7u5JwvoXO3FM6czOHovvOMn9QfgPVfHSClWSx3P3wt\n4x/qj6IoJCZF8di/h/PS9M/wMRkwGHRkZRRht6uYTJd3+RqMevKzSzlzIocufZpdVhSqKKtmy9eH\nyDidx9CxPXnt6S8oK65k2a6n8fEzXdJJXFFWzdkTOcQkhiOEYO5jy+gzuC2jH+xfa57ykkqCwwIu\n6i+EwGFXPQJZXRwOlXv6vohfgC+fHX7xstfoaW93cP5ULkLzOZRIJBKJpC5SgJJIJD8LIQQ//PAD\nM2fOZM2aNQQEBPCPf/yDyZMnk5iY+J8HkEgkkj85yVHhBJpMbD2Z8T+Zz2DQM+6W7qh2laNp2Uy8\nqw8ff7aT9RuPMebW7nTv0oht29MJ8DdRVWlFAP5+RvSKQl5xJQDuqCfVKjyOpMoKC35+RqorbegA\nYReU5lcQGxNCzoUScjNLEUJgUOyMGLWfjl124O9nw6GCjsa8NrsxpSUdaZBcj/qxZdRU5dOtR2NO\npOVQv34oB/dlUFJkJj4hnKjIEGqq7IBg6XvbKC2uokFiJJPue58hwzpw5OB5ysuqeOf1dWz49giv\nL7qbiKggSosrWTD3O9p3aciy1ZNxOBy17k1EZJDnedeeTbHW2Hnhsc/wD/QhKiYEo0mPosA9LmHJ\nYNDz6bdTUXQKp4/nkJtVwpx/raR+XCjpJ3K5YVRnqquszHpiOc3axDP3w/s8Qo7BqKfvdW3w8zVh\nMOpJbBjJK0+sYP+2U3Tp05yNXx3g8J6z/PjdUZ5+7XZadUjyrO2f4xeSda6QR2eMpN+N7S75PodG\nBLJgzWSCQpy70f3rzTGUl1Z5Xl+K+g0iWLrjaQKD/bDbHKxbvofQiEDue+JGT5uNK/cy8+GPGDP1\nelYs2MhtDw9g6PjeADw9dgEHt59iye7nCAq9eB69Xsf1o7vj6//z8ht9/Ex8evjfGAyXFrQkEolE\nIgEpQEkkkv+AqqqsWrWKGTNmsGPHDiIjI3nuued44IEHCA8P/88DSCQSyV+I5vWj2Xc2i0qLhYD/\n0eYKd43u6Xk++f7+7N9/jo+WbmPksE7ERAVRXFwJDqfAVC88CHNFted1dHgQI0Z0ZtG7m6i22klK\njuTcmUIsZqtntztFQFWVFbPZQmpqImfTs+k1YBP9BhzCZHQgVDi4vwFfftoHX0NDzp4rpHXbQJ55\ndhi3DH0Vm9XO9H/egI+PkTE3v46wOVAdKu3aJFJRVolid9CsVRxVlVaaNo+jQXIkAYE+rPx8N0kN\noxg4qC07fjhBUV4Fb7z8NU/PGElUvRCaNI9l3450nn7kY/ZsO834B69h5Niel7xHXXo2YeLU6+jW\nuxn1YkOZ8dZYHA61Vk6S3iWONGkZR1R0EJXmGv45cyR5OWXExofjcKjc9XB/Wl4i6+i9ud+yY0Ma\ns5fcR3paNgd2pBMRHUSXPs15/ZkvnUHqQGVFTa1+bTolk3W2gMh6ITjsDratO0pqt0YEhfpjtdgp\nKSinXnw4CQ2jAdi96Ti7N6Vx9+M3/MfPRWiEdwe+RRsfw2iq/bU+KjaUqNhQgsP8MZdXU1le7e0b\nGUhYZBB6w+UdXPf/38XB6D+Fr5/pF7WXSCQSyd8PxbMzyl+Yjh07ij179lzpZUgkfyosFgsfffQR\ns2bN4sSJEyQnJzN16lTGjRuHv//lf5WVSCSSvzInsgsYPu8jRnRsyTMjfv8yvLqsW3+UF2Z+RUxM\nMB++ey+qENjtDr5cuY/FH/xIrx5N2LT5OCaTAWuNHYB77u7Nu+9sIjjEj6ZNYtm96wwGo46WLeM4\nfTKXSrOFgCA/pk29nl0HX+Gqgd/g62sHFU4fbcY1vZby4+Zs5ry0BoC337+HejEhBAQ4RaS9u85g\nNOiY+uQQ5s1aw5b1zpBxhCAuIYysCyUAvDTvdtp3bui8jjUHmPXcSnxc6wwK9iMw1J/BwzowYnQ3\nAIoKKvjnQx/RrVdTVny4jQE3tuWBx2oLMxXl1agOlZCwAArzynny/vcpzC/HYXPw3tdTCLlE+Rk4\nM4tUITCZfvq32A2rD/DBq98RGRvC0b0ZzFk6gV0bjtO6S0NapDbA19/EqiXbeeu5lfS4thVPvnb7\nRWM47A4Kc8vYs/kEr/9rBX1uSKVt10Yc2HqKzV8d4LXVk2nUMg6AqSNf5+iec7y5dgrJzWJ/7sfi\nP+KwOzwCnEQikUgkvyWKouwVQnT8OW2lA0oikdSivLyct99+m7lz55KdnU1qaipLly5lxIgRGAzy\nPxkSieTvTdP6UYT4+LDp2JkrMn+zprGktk7gnrt6Y3Tl9/iYDIy+tRujb+1GXl4ZA/q3JCWlHsuW\n7SA6Mpi+/ZoTGRHE/Le+Z+/uM7z62h2YjHruv3cxgUG+KAIGDY6lKnAYfW+qQEFw5mQci2b1R6gh\n/Lj2Gw7tP899D11DSZGZTz/YSudujejeuymL3tpAdZUVgH7XtWbX1tPYrHZapSZyfP95cEDLNvH4\n+fkQHOINbu87oDUHdp0jNiGMlUt30qZDA56aeYvn/OG95/j4nc08N/tWAoN9+eTtTWxdn0ZBTjkn\njmSyePVkfP1N3DNkLtWVVr7Y8RQfvbWec6fy0Ol1+PmbfjKI+3LZR+B0/n7y1kYatazPhTP55GWV\n8PBzQ2ncKp55Ty7nh68PEREdTPvujQG44bauNGwaS3JT5453DrsDRaeQe76Ig9tPc/Wwjozv+xKK\nXqFLv+bknC/m/9m776gmz/eP4+8wEvYWZIuguBAX7u3XvbfWOuqqddUJtbWtrR2CUrfWvWetq446\nat24J24UlOFAQWRDkuf3h9ZfXVVbNSrX6xzPCcn9PPk8MecQrtz3de9c9wvt+tSicKAnTi7/vzNe\n8PhOxF6+9cqKT6cPRjGu/wIq1A2g7w/t/tO5stKzib9yC98Az2eOSb+XSXRkLMUrFZLNSIQQQjxB\n/poUQgBw/fp1Jk2axPTp00lJSaFOnTrMnz+f//3vf/IhUggh/iaooCc7zkSRkJyCm73tG31uTw8H\nJoz74In79XqF3FwtPbrNQqvTsXHzcFavOIyRkYqfJ24DoEoNf/oPqk/MlVssWXGILh9Vw9XNjuhb\nMyhZZRZGRgqpaRrmjWlImcCGZKcfR6fLpkKVQpiYGNO4eRn6dZtN3LU7/LnlNL0H1OX7nzry+2/H\nORpxmdzMXFKTM0ClIqCkJ2eOxGBqYkzHLlX5ctBSrCzUfP5jWxJik/i080xada7M/5oEsmjqDo7s\nufTI9RzYeZ7jBy5z6Ww8Vf9XnNFTPsTW3pJF0/5Am6NDQUFRFLwLumBiaoRKpaJ116rYO1nTvFMl\n7J4x8+lF3L6RwqKJW/Hyc+bnjUNo2aUKNvaWJN9OZc/mU9jYW1C7eZmH41UqFSXKFSAqMo4NS/ax\nZ+NJ3H3yYe9kzaEdZ3H1diKoVlEObItEl6Ol/zctObb3Iq161AD9JiaNWMnn07th8mDHub/vOnf5\nTBx7N56g/YB6/2qJ2+dtJ6HN1ZEQc+tfvx5/mTh0MX+uOkj4xmCKV/B76pjpny1l+7IIflg9mDK1\niv/n5xRCCPF+kQKUEHncxYsXGTduHAsWLECr1dK6dWuCg4MpV+6FZlEKIUSe82Wr2uyMjGLUim3M\n7PNyfXJel++/WcPuneeoUNmPu0kZ6PUK3XtUZ+6MnaCASgXW1mbkc7Zh4exdHDl0hbYfVOS28QiK\nFz6EAhzb7cvambXRaRWqVlRjampMIf/8tPugEu0+qETU+et8E9aOxJv32LvzHNVqF8XF1Y47t1LZ\ntu4EVy7dxNHJirSUTBo0L83503F0+KgaAWW8GTiiCWUr+rJt/XGWz97FvZRM7qVk4ORsg4WFGr1O\nj6IoD7/w6Nq/DtXqFce/hAcAQVULAzB6apeH4+JibnPywGX8irqhUqnw9MlH1/7/e+R1ycnOZcH4\nLZSpUohfZu0iqEYRWveo/o+vpbObPV//3BVXTwdUKtXDXeTuN/luikdBZyys/r//l6IoHNgWyZ/r\nj7Fn40kc89ti72RNl6ENKVLai8M7zlC9UQD12wbhF+CBU347/B5c197Np0iITqRZwaGMXz8Y/1KP\n9p9aOWUbu387TvGggpSrVQyAjLQszC01L/TlULcRzcjN1dG2X93njv0niqJw4fBlLK3McPd1eea4\nOu0rk34vk4IBsjmJEEKIJ0kPKCHyqEOHDhEWFsbq1atRq9V89NFHDB06FD+/p3+rKYQQ4v/VHz2b\n5PQMDo0ZaOgoAMyc/ge7/jyHSz4bTp+MZeLP3VAUPYM+XgCAo5MVYRM78c1nK2nSqhwaM1OsA0LR\nqY5ipCj8El6dK6cK0X94QwJKeeHh5YhWq8PIyAgjIxUXz8YzoMssAssVwNTEmF6D61PA937jbEVR\nuHA2gYKF7hcm9Dr9M2frfNZrHicOXUFRFGxszWnQsiwdP67FnZv3GP/lalp1q0LVuiVe6Jp1Oj1L\npv1B8TIFKFul0FPHnD95jcFtp1K8XAHOHImhRJAPY5f0edmX9x/FXLjOJ/XD8AvwoH3f/1GpXgDG\nxvebeycn3uODMl/iVsCJOXu+fOLY1LvprJ+3hxWTtxK+dhCFSj5auLkVn8TJfZeo3aocxibGRJ2O\nZUDdH2neqxZ9Rrd9bja9Xk981E08CuX/T7OZFUWhU7FhaMzUzDv+478+jxBCiPeP9IASQjyVoihs\n2bKF0NBQdu7ciZ2dHSNGjGDgwIG4uDz7G00hhBCP6lKzLKFrd7Ji30naVwk0dBx6f1KH3p/UIS42\niXNn4ilSzI2M9GwK+ecnOuomulwdt26mEHv1Dkvm7CawyV4qBFzAWDHi8C89uHHRiL6Da9GgaSlU\nKhU5WblMH/c7VesUJe1eJiWDfAiqUgh7Bwu2rj9B+aqFHhagVCoVRR400X6eL8I7cHjPBVKS0pk1\nbjMZ6dlYWGq4nJTG2RPX8Nrj/NwCVNKte/w4eCl1mpdmx+qj2NiYP7MA5V/Sk5FTOuPm7cgvM/6k\nUYeKL/fCvgCPgs607VOb0lULU7qq/yOP2eezIXTlABwe9Hk6fyyahOhEarcuD4C1nSWdBjeg0+AG\nTz23s7sDddtVePizpY05Tm72uHo7vVC2VVO2MnfUr4TM7EmtNhWeO/7K6WtMC15CnzEf4BfojU6n\nZ2noOopVLMSi02Ev9JxCCCHEs0gBSog8QKvVsnLlSsLCwjh58iTu7u6Eh4fTq1cvrK2tDR1PCCHe\nOZ2qlWbyxr3M3X7wrShA/cXD0wEPTwcArKzNmDavJyl3M+4vJbM1Z8n6QRw9Ekmu/zx0qPCzHsPW\n6FTu3bnCrPAt7N4SSeiMbsybup1Nvx7hjw0nyMnKBUVh4Mim1GlSCt/C+Wnwtx5Iz3PiwGW+6beI\nIT+0plr9AGrUD6Bp4JdY2ZjTf2QzAALK+TBz/afkf5D9n8TF3CbycDTWtubcjE8m7sqz+xupVCqq\n1CtBxLZI/lx7DBMTY0o82InvebIycwgftJhK9QOo3Sro4f07Vh/G2s6CoNr3exyZmBrT/bHd+f6u\nZKX/n1k8tv8CEqITKVm5ME6udi+U4+9cvZ1YdOz7fxxz93Yqc0atolnPWjjlt8PSSoPG4sX6R509\nGEXkvoucPXAJv0Bvrl+5xeIf1uIb6MW0faNfOq8QQgjxd1KAEuI9lp6ezty5cwkPD+fq1asULVqU\nefPm8cEHH6BWv3wzUyGEEP+vgp8nu89Ec/VmMt4u9s8/wEBs7Swe3s7nbINpwEyyc0y4fVPDhcXG\nnDh4hVoNA4g8dhW1xoTNvx7Bx88FOztzKtUswp+bTqHXK2jM1Wz+9Qg/h25EpYfmnSq90PPnZGvJ\nyswhJ1sLgLGJMS26VMHKxhyVSkXC1Tusmbeb9n1qo1Y/+dH0wqlY1i/YS6/Pm2LnaEXJ8gWZ9tsg\n3As4kZujxcLK7LkZytcuxudTOhNQwfcFXzVIjE9m76aT3EtOf1iAysnKZeyAhZiYGjNlSzDe/m4v\nfD6AT8d9QFzUTRzzv77m9af3XWDbkn2oNaZ4Fc5PekoG1y/ffKFjG35Uk8JlfPANvN+Lyt3Pha+W\nDsDzJa9TCCGEeBrpASXEe+j27dtMnTqVyZMnc+fOHapUqUJISAiNGzfGyMjI0PGEEOK9kJqRRe0v\nZ+Dvlo/FQ5/cme5ttSKqHEbouLdrALWqNWLxjJ207lyZ4qW82Ln5FGNCVuLt58zVqFuE/NiWWo3/\nf4ZXTNRN5vz0Oz2HNsT7wRK8F5GTo2Xq12vwLeZGs85VHnls6ZRtLJq4jb5ft6Dph5WfOHbs0KXs\nWHecTgPr8uHAev/+wv+FqNOxOHs4PGxEDjApeBmbF+2lc3ATPhjc8I3meRE6nZ5DW04SUMUfjbma\nk7vPU7KaP2qNqaGjCSGEeA9JDygh8qiYmBh++ukn5syZQ0ZGBs2aNSM4OJgqVao8/2AhhBAvxdrC\njIIujlyMTyQjOxsLjeb5B70FchQjVBjRo0d3AL4K7/DwsaCqhenQqwalKhTkyoUbVKpV9JFjC/i5\nMHpa16eed9KXq0m8fpdvZnZjzbw97Npwku/n98Ta1oLMtGy2rjqMR8F8TxSgmnethquXE5XrPb33\nU5mqhdmx+gjZaVn/5bL/Fb8Azyfu++S7tlSsF0BglcJvPM+LMDY2olKj0g9/Lve/F2vqLoQQQrxu\nUoAS4j1w6tQpQkNDWbFiBUZGRnTq1Inhw4dTrFgxQ0cTQoj32uhO9ekYtpgvFm5hfK9mho7zQnIV\nExRFIT03CUvTR3suWVqb0W1AXQBKlX/x5WoAJ/ZfIvFGClqtntOHrnApMo7UuxlY21pg62DJtN8G\nY/235YB/f85azUo/5Yz31W5RFi8/Fwr4u75UnudRFIUJgxfh7OlIp6GNATh76DJuBZ2xc3p2f0RT\ntQnlpagjhBBCvDRZiyPEO0pRFHbu3EnDhg0JDAxk/fr1fPrpp1y5coV58+ZJ8UkIId6Awu75cLWz\n5cDZGHQ6vaHjvBATlQe5igkbYke90vNOXvcpi/d8gVptwueTOrNk30jc/rZbm08RV5z+Re8jlUpF\noQBPTJ/SH+q/yM7MZeuyCH5ftBeA6DNxDG08ltCP57zS53lX3Yq9zYiG33Fy1xlDRxFCCPGekAKU\nEO8YnU7H6tWrqVixIrVq1eLYsWN8//33XLt2jfDwcDw8PAwdUQgh8pTPO9QmN1fH6CXbDB3lhdT3\n+J5sxZT47LMkZSW8svNaWplh63C/V5JaY4KDs80rO/frYGahZnbEN0z4PQQAVx9narYKoslH1V/o\neJ1Wx5yvfuHg7ydeZ0yDuXT0Cke2nGTf2kOGjiKEEOI9IU3IhXhHZGVlsWjRIsaNG8fFixfx9fVl\n2LBhdO3aFXNzc0PHE0KIPK3RyFmkpmezc1xfjI3f/u/3Fl4ewI3sC+jQMMR/JRrjV/N7RFEUDm6L\nxC/AEydXu1dyzrdVwpVbdC8Vgm9JL6bu/cbQcV45RVE4d/ASvoHeaMzfjf5mQggh3ryXaUL+9n9C\nEiKPS0lJYcyYMfj4+NC7d2+sra1ZuXIlFy5coE+fPlJ8EkKIt8DwNrXIys7lx2XbDR3lhXQuOAkj\n7MnV6xlzrgspOXdeyXnPHonmm+6zmBi8/JWc723mVtCZ0asG88WCvoaO8lqoVCqKVSwsxSchhBCv\njBSghHhLJSQkEBwcjKenJyNGjKBkyZJs376dw4cP07ZtW4yNjQ0dUQghxAO1SvnhYm/D7wfPo9Xp\nDB3nuVQqFYOKLEJt5ESOPpcfz3/MqbsH//N5fUt40LRbNdr2rfMKUr79guqVxM3XxdAxhBBCiHeC\nFKCEeMtcuHCBnj174uPjQ3h4OI0bN+bYsWNs2bKFOnXqoFKpDB1RCCHEU3zRsQ7ZuTq+mvu7oaO8\nEBMjNSFF5+NlURKdAvOiw5l0cTS5+tx/fU4zczV9v2tLyUqFXmFSIcSzjBo1Cien+83+Y2JiUKlU\nD/8ZGxvj5eVFr169SExMfOS4jSc4gAAAIABJREFUmjVrolKp6Nmz5xPnjI2NxcjICJVKxc6dO9/E\nZQgh8ggpQAnxljhw4AAtW7akaNGiLFmyhJ49e3Lp0iWWLVtG6dLP3p5aCCHE26FSiQJ4Otmx83gU\n6ZnZho7zQlQqFR/7fUtbjwGAKZfSzjL4RC+2XN9MXugT+qJGjRr1yB/2bm5utG7dmsuXLz8ybu3a\ntdSrVw9HR0fUajXu7u60adOG339/N4qS4v0wbtw4IiIi2L17N1999RXr16+nU6dOT4yzsrJi9erV\n5OY+WnRevnw5lpaWbyquECIPkQKUEAakKAobN26kRo0aVKpUiV27djFy5EiuXbvG1KlTKViwoKEj\nCiGEeAmhvRuj6GH41PWGjvJSyjvWILTkXPwsS5Kr1/JL/HIGHh9AxO2DUoh6wNbWloiICCIiIhg3\nbhwnTpygTp06pKenAzB48GBat26Nu7s7s2fPZvv27YwZM4bMzEwaNmz4RLFKiNfF39+fihUrUqVK\nFXr27MnXX3/N9u3bSUtLe2RcjRo10Ol0bNmy5ZH7ly9fTrNmzd5kZCFEHmFi6ABC5EW5ubksX76c\nsLAwIiMj8fT0ZPz48fTs2RMrKytDxxNCCPEvFfJypqhXPo5fjOfqjSS88zsYOtILUxtr+NR/BDey\nrjP10hRiM+OZdnkm82NW0NajBXVcquXpZeAmJiZUrFgRgIoVK+Ll5UW1atXYtGkTarWaCRMmMG/e\nPLp16/bIcZ07d+a3336TTUOEwVhbW6MoCrrH+tOZmZnRvHlzli9fTpMmTQC4dOkSx44dY9SoUSxd\nutQQcYUQ7zGZASXEG5SWlsaECRPw9fWlS5cuACxcuJDLly8zaNAgKT4JIcR7YPyAlpgYGTF80jpD\nR/lX8pu5Mjrge0YV/xZv8wLc06YyO3oxXQ4OYu6VVaTlZhg64luhbNmywP2+OxMmTCAoKOiJ4tNf\nmjZtipub2xtMJ/IyvV6PVqslOzubkydPMnbsWGrVqoWtre0TYzt27Mi6devIzMwEYNmyZVSoUAEf\nH583HVsIkQdIAUqINyAxMZGvvvoKb29vBg8eTIECBdiwYQOnTp2ic+fOmJqaGjqiEEKIV8TW2pz/\nlSvEtVt32RJx3tBx/rUCll58V/JLxgd+TynbQHL0Wn67voPOhz4j+OR4TiZfytPL82JiYgDInz8/\nERER1KtXz7CBhHigefPmmJqaYmZmRqlSpdDpdCxatOipY+vWrYtGo2HDhg0ArFixgg4dOrzJuEKI\nPEQKUEK8RleuXKF///54e3szevRoqlevzv79+9m9ezeNGzfO00sZhBDiffZVjwZYm2kIX7zjnS/S\n5Dd35rNi/VhYYTwdPZthY2zFuXtX+Pz0JNrt/4JJF1dxKzPZ0DHfCK1Wi1ar5eLFi/Tt2xdra2uq\nV69OdnY2np6ej4xVFOXheK1W+86/D8S7Y/z48Rw+fJhDhw6xZs0abGxsaNiw4RM9oOD+0tLWrVuz\nfPlyTp06xfnz52nXrp0BUgsh8gIpQAnxGhw/fpyOHTtSqFAhZs6cSceOHTl37hxr1qyhUqVKho4n\nhBDiNVOpVAxoV53UjGy+n7PV0HFeCY2xmnZeDVhQ8QfGlwomyCGAHL2WDQn76HTgOzru+4F5l7dy\nOyvF0FFfizt37mBqaoqpqSn+/v5cuXKFFStWYGZmBvDEl0rh4eEPx5uamjJ16lRDxBZ5kJ+fH+XK\nlSMoKIgWLVqwfv16zpw5w/z58586vkOHDmzatImZM2dSrVo1WS4qhHhtpAm5EK+Ioijs2LGDsLAw\ntm7dirW1NUOHDmXQoEHyi1wIIfKgZtVLsGzzUbZEnKNXy0q4ONoYOtIr42ftyagSvcnVazlw+ywr\nru3k4r0E5sdsY1HMDlzNnajjUorGbuVwMbczdNxXwtbWlu3bt6NSqcifPz9ubm6oVCq0Wi0ajYa4\nuLhHxnfu3JmaNWsCEBQUZIDEQtyXL18+nJycOHfu3FMfr1GjBvb29kyfPl0KpUKI10oKUEL8Rzqd\njtWrVxMaGsrRo0dxcXHhxx9/pE+fPtjZvR8fuoUQQvw7E4e3os3wuQwKXc2ysG6GjvPKmRqZUM25\nJNWcS5Kty2X3rVP8Fn+I0ymxzLmyndmX/8BRbUfNfMVo6lEWP5v8GKnejQn4Op2Ow5tPEHU8mriL\nCZiYmFCuXLknxpmYmFCpUiW2bt3Kt99++/B+FxcXXFxc3mRkkQf8/X3pV9oHvV7/3GNu3rzJ7du3\nn1gm+hcjIyM+//xztm/fTps2bV51ZCGEeEgKUEL8S1lZWSxYsIBx48YRFRX1cLld586dH07HF0II\nkbc5O1jTpGpx1v95iuWbj9Ch4ZMFjPeFxtiUuq5lqetallydlt23zrIp4Tgnk6+xIvYAy68dxNbE\nkrKOPtRzLUmQkw82puaGjv1UOp2OEfW/49yhKLLTs4kxOU+GkoFOp8PY2PiJ8YMGDaJFixYsWrSI\nzp07GyCxyAsef19qLDUkOcU+Me7ChQs4OTmhKArx8fGMHTsWa2trOnbs+Mxz9+/fn/79+7/O+EII\nIQUoIV5WcnIy06dPZ+LEidy6dYugoCBWrVpFixYtnvqhVAghRN4W/NH/2HfsMrN/3U/TmgFYmmsM\nHem1MzU2oY5rSeq4lkSn6DmbEs/WhFPsvXWJPbcusuPGedRGJniaO1LNxZ/a+Yvia5MPtdHb8dH0\n8OYTnDsURVZaFgDaHC1alZ7Dm09QsUnZJ8Y3b96cQYMG0a1bN/7880+aNm2Kk5MTd+7cYevW+z3A\nrKys3ug1iPfP4+/LrLQsErOT0Gq0j4wbNmzYw9suLi6UK1eOGTNm4O3t/UbzCiHE496O3/JCvAPi\n4uKYMGECM2bMIC0tjQYNGhASEkKNGjVkNzshhBD/6MdPm/LJNysY+N0vzPn+Q0PHeaOMVUYE2HkS\nYOfJ0GJwK+se+29dYkvCGSKTE5h9aS+zL+3HxsScYrZu1MhfiMrOBfGwtMfIQL9fo45Hk52e/eid\nisLlEzFPLUDB/Z3HqlevzrRp0+jRowepqanky5ePSpUqsWnTJho2bPgGkov32dPelwW0Rfj6y6/v\n3y5Q4IV3W9y5c+c/Pl6iRAnZuVEI8cpJAUqI5zh79ixjx45lyZIl6PV62rdvT3BwMIGBgYaOJoQQ\n4h1RzM+NGkGF+DPiAhv+OEWTOiUNHclgnM1saOFVlhZeZdHqdVy8d5M/b1xk9/UojiXFEpEYg5FK\nhZ2pOUVs81PbtTDlnLwpYO3wxjL6lfZBY6l5ONPEV1Wc4lZl8C1V4B+Pa9myJS1btnwDCUVe9Pj7\nEkBjqX7u+1IIId4WqrxQ2S5Xrpxy5MgRQ8cQ75h9+/YRGhrKb7/9hrm5OT179mTIkCEUKFDA0NGE\nEEK8g/R6PS37ziIrK4fV03vniaV4LystN5tzd6+z++ZlDibGEJt2l3s5WegUsDU1p6SDBzXyF6Ra\nfl/cLW0xMXo9Dc3/v9fOJbLTc9BYqilavhA/bhkpy+2Fwcj7UgjxNlKpVEcVRXmhJpdSgBLib/R6\nPRs3biQ0NJR9+/bh4ODAgAED6N+/P05OToaOJ4QQ4h13Nuo6/b5aQQFPR+aFSrPq58nQ5nA66Tp7\nbl5m7/UrXEy5Ta5ewVRlTH5za/xsnCjt6E5ZZ0/KOLm/0oLUX7uNXT4Rg2+pAgQ1LCV/5AuDk/el\nEOJtIwWox0gBSjxPTk4Oy5YtIywsjLNnz+Lt7c3QoUPp3r07lpaWho4nhBDiPTJm+hY27Yykd/sq\nfNiqoqHjvFN0ej3H78Rz4OZVjibGEZOaTGJmBlm6XEwwwt/WmWpuPpR19sDPxhFPaztDRxZCCCHe\na29NAUqlUjUAJgLGwGxFUcY89rgGWAiUBe4A7RVFiVGpVAWAc8CFB0MPKIrS58ExZYH5gDmwCfhU\nec5FSAFKPEtqaiqzZs1i/PjxxMXFUbJkSYKDg2nXrh2mpqaGjieEEOI91a7vLO6mZLBgfFdcnaVI\n8m/l6nXcSE8l4uZVdiVc5uTtG2Tk5gKQqdViZaIm0MmN2h6+BDjmx9PaFjuNuYFTCyGEEO+Pt6IA\npVKpjIGLQF0gDjgMdFQU5ezfxvQFSiqK0kelUnUAWiqK0v5BAWqDoiglnnLeQ8BA4CD3C1CTFEXZ\n/E9ZpAAlHnfz5k0mTZrEtGnTuHv3LjVr1iQkJIT69evLjnZCCCFeu1u379Fp4FzsbCxYOb2X/O55\nhW5lphGdkszRxDj+jLvC2Tu3UBsZY6IyJi03B1cLa6q4eVPZ1QtvG3sK2TtiaiRLmIQQQoh/420p\nQFUCRimKUv/BzyMAFEX58W9jtjwYE6FSqUyAG0A+wJunFKBUKpUr8KeiKEUe/NwRqKkoysf/lEUK\nUOIvly9fZty4ccybN4+cnBxatWpFcHAw5cuXN3Q0IYQQeczKDUeZvmAnNSoWZtTQpoaO897SKwrR\n95K4cjeJAzdi2RMXQ3x6ClamGjJzteTqdBR3cKG6RwF8bB3ws3ekiIOTFAWFEEKIF/AyBSiT15jD\nHYj9289xQIVnjVEURatSqVIAxweP+ahUquPAPWCkoih7HoyPe+yc7k97cpVK1RvoDeDl5fXfrkS8\n844ePUpYWBirVq3CxMSErl27MmzYMAoXLmzoaEIIIfKodk3Ksv/wZXZHXGDnvkLUrFLE0JHeS0Yq\nFb62jvjaOlLXuxBUgNScbK6np3IhKZEd1y5z/NZ11kadIykzk7ScHFwsrCiVLz/Wag3lXT2o5OGF\njVqDpVpt6MsRQggh3lmvswD1X1wHvBRFufOg59NalUpV/GVOoCjKTGAm3J8B9Royirecoihs376d\n0NBQ/vjjD2xsbBg+fDiffvoprq6uho4nhBBCMO7L1rTrNZOxU7cRWMILe1sLQ0fKE6zVGqzVGgrb\nO9HUtyhwvyh1Kz2NvXFXOZQQR4ZWy4GEOH45dwZnCyss1aaoFKjp5UN5dw/szcwp6ZIftexAJoQQ\nQryQ11mAigc8//azx4P7njYm7sESPFvgzoOm4tkAiqIcValUl4HCD8Z7POecIo/TarWsWrWKsLAw\njh8/jqurK2FhYXz88cfY2NgYOp4QQgjxkImJMWO/bkvf4EUMCFnKouk9ZOmXgfxVlPK1d6RrQBkA\n9Ho9x25eJ/ZeCrH3Uth6JYqVZyPZfS2G6LvJmKqMqOHtg7HKiABnF6p7F8DGzAw3a2sDX40QQgjx\n9nmdPaBMuN+EvA73i0SHgQ8URTnztzH9gIC/NSFvpShKO5VKlQ9IUhRFp1KpCgJ7HoxLekoT8smK\nomz6pyzSAypvyMzMZN68eYSHh3PlyhX8/f0ZPnw4H374IRqNxtDxhBBCiGdavvoQsxftoWpFP0aF\nNDd0HPEP0nNySMnK4vydRLZfuUxmrpaopCQu3L6Nu7U1OTo92VotjQsVxsHcHHcbW2r4FMDMxARr\n+TwihBDiPfNW9IB60NOpP7AFMAbmKopyRqVSfQscURRlPTAHWKRSqaKAJKDDg8OrA9+qVKpcQA/0\nURQl6cFjfYH5gDmw+cE/kYclJSUxdepUJk+eTGJiIhUrViQ8PJxmzZphZGRk6HhCCCHEc3VoVZ7j\nJ6+yLyKKtRuO06JJaUNHEs9gqVZjqVbjZmNDbR/fh/dfS7nLzbR04u/dY825s5y/fZu07Gyi7iTh\naWOLgoKRoqJl8WJYmJpS0MGBMu5umBoboTF5W7tiCCGEEK/Oa5sB9TaRGVDvp9jYWH766SdmzZpF\neno6jRo1IiQkhGrVqsnyBSGEEO8cRVHo1GMmyXfTmTa+Cz7eToaOJP4DRVHQKwonb9zgdno6N1LT\n+DXyDBojExLT07l+L42Sri7E3k2hoL09DYsUIlenp5ZfQWzNzLC3MDf0JQghhBDP9TIzoKQAJd45\nkZGRjB07lqVLlwLQsWNHhg8fTkBAgIGTCSGEEP9NUnI6XXvPRm1qzNJ5H6PRmBo6knjFcnU6tHo9\n+2KuodVpOXH9Jjujoilgb8eeK1exMDHFx8Geq0l3aR1YHBMjI3wc7alUwBMjlQpnaytDX4IQQgjx\nkBSgHiMFqHefoijs3buX0NBQNm7ciIWFBb169WLIkCF4eXkZOp4QQgjxyhw6eoWvvl2Dp7s9s6Z1\nN3Qc8Ybk6nTcTs/g3M1b6BWF5cdOoyiQnJ7B+Vu3CfJy50TsdQLd81PQyQEUaF26OLk6PcXzO2Nq\nbIyRkcwAF0II8WZJAeoxUoB6d+n1etavX09YWBgRERE4OTkxcOBA+vbti6Ojo6HjCSGEEK/F/IV7\nWLbyANWq+DNyRDNDxxEGFnn9JioFDlyN5XT8DazNNKw9eY5A9/ycvX4LM2MTyhfwICElla4Vy5CZ\nk0sZLzccrSywNTczdHwhhBDvMSlAPUYKUO+e7OxslixZwtixYzl//jw+Pj4MHTqUjz76CAsLC0PH\nE0IIIV67L75exbGj0XTrXI327SsaOo54y2Tk5JKSmcX1lHvsvhSDk6UFCw4ex8vejjMJN8nV6ijl\n6cqZ+Ft0r1yWy4l3aBxYBEdLC+wtzclvYy0zpoQQQvxnUoB6jBSg3h337t1j5syZjB8/noSEBEqV\nKkVISAht2rTBRHaIEUIIkYcoikLv3nNJuJ7M11+1pHx53+cfJPK8rFwt6dk5HI9NwFKtZmHEMTxs\nbdh5MZpcrQ4XWyvOJyTSqmwJDl6OpUvl0mTnainu7oKnox3W5hos1NJ7TAghxIuRAtRjpAD19rtx\n4wYTJ05k+vTppKSkULt2bUJCQqhbt67saCeEECLPyszMoWuXGeTmapk2/SNcXe0MHUm8w2KT7mJq\nZMRvJ8/jZmvDkgMnsDA1ITE1g7g7KQQV9ODg5Vi6Vy/HgahrdK8RRFZODkXcnHG0ssDOUnbmE0II\n8SgpQD1GClBvr0uXLjFu3DgWLFhAbm4urVu3Jjg4mHLlXuj9K4QQQrz34uOT6N93AWqNMfPmf4yF\nhcbQkcR7RKfXo9MrRMbdwMbcjKURJ/BzdmTFgVPYmmu4fS+DpNQMSvu4cTrmOr3qlOfg5Th61ynP\nzZQ0KhX2QtGDraX0mhJCiLxIClCPkQLU2+fw4cOEhoayevVq1Go13bp1Y9iwYfj5+Rk6mhBCCPHW\nOXL4Ct+OWo2Tkw2z5/XEyMjI0JFEHpCRnUOuTs/xmHgcrCyYu+MI5Qq6M2vHIcoUcGffhRgKONmj\n0yuYq00p7+dBamYOTcsV4U5qJhX9vdDrFSzN1Ia+FCGEEK+JFKAeIwWot4OiKGzZsoXQ0FB27tyJ\nnZ0dffv2ZeDAgbi4uBg6nhBCCPFWW7/2CDOm7cC/SH5+mtTF0HFEHpeamc3t1HRib6eg1elYf/gs\nAd75mb3tMM2CivJLxGk+qFqK5XtOMqxFDdYfOsPwljU4cimOdlVLkpCUSlFPZxRFkXYLQgjxDnuZ\nApR0dRavnVarZeXKlYSFhXHy5Enc3d0ZN24cvXv3xtra2tDxhBBCiHdCsxbluJFwlzW/HmbMt2v5\n7KsWho4k8jBrcw3W5hp8nB0AqB1wfxZ75xplMDEyplqxgng52ZGr0+FgbU5Gdi57IqNZvOsYCUn3\n+O3QOT6sUZpNR84zqHkVdp2OpnfDilyKv02tQF+S0zLJby+fE4UQ4n0iM6DEa5ORkcHcuXMJDw8n\nJiaGokWLEhwczAcffIBaLVOxhRBCiH/j+69Ws3/vRVq0CaJX3zqGjiPEC9Pp9SSnZZKr1XHm2k3U\nJsZsP3EJV3trNh+5QGkfN7Yev0iT8kXZcOgcA5tX5ZddJ/myU102HjxH32aVORGVQM1AX9Kzc7A2\nl35oQghhaDIDShjUnTt3mDJlCpMnT+bOnTtUrlyZiRMn0qRJE+lZIYQQQvxHX3zbimEDFrHu18PY\nO1jSpkNFQ0cS4oUYGxnhZGMJgKuDDQDVSxQE4JPGlUnPyqF7/fLkaHX4ezjjYG2Bi701567d5PfD\n5/FwsmHa+giGta1J+C87+aFHI+ZsPsTwdjU5ejGOmqV8UQF21uY4WFsAyPI+IYR4i0gBSrwyV69e\nJTw8nDlz5pCRkUHTpk0JCQmhSpUqho4mhBBCvFfCJnai70dzWDhrF44OVtSqV8LQkYT4zyzN1A8b\nlhdydwKgdqn7S/vaVg9Eq9dTxNMFXzdHrt5Kxj2fLdk5WlLSs5i3+RA6vZ7l249TupA72bk6crRa\nWlcvya4TUQxtX5Odxy/ToloJEu7cwyOfHUZGUpwSQog3SZbgif/s1KlThIWFsXz5clQqFR9++CHD\nhg2jePHiho4mhBBCvLdyc3T07vwzd++k8sV3bShXUXaSFXlXcmoGNpZmHDoXS34HK/ZHXiVXqyM7\nJ5dthy/SrnYgPy3fxeieDflq1mY+61KHldtPUKusH8521iSnZtC8egAnL8VTvbQvaRnZWFloZAaV\nEEI8h+yC9xgpQL16iqKwe/duQkND2bx5M1ZWVvTu3ZvBgwfj4eFh6HhCCCFEnpCRnk2vTtPJSs/h\n+/EfUKSE/A4W4mm0Oj0x15PwcrHjlx0nqV/Bn9BFf1C9lC/bj1wiKSWdGqX9mLv+AOGDWjBswjpG\n9W7A5n1nKe7rStkinpy9cp329ctw8kI8gf7uqFChUskyPyFE3iYFqMdIAerV0el0rFu3jtDQUA4d\nOkS+fPn49NNP6du3L/b29oaOJ4QQQuQ5SbfT6N91Jrm5OsJndMXLx9nQkYR4p+j1CgoKmdm5XLyW\niL+XM7PWRtC+bmnGzN9Ocd/83Lydyu5jl/miRz2+mLKBMQObMm7hDqqV8aWYjwv7TkQzslc91u+M\npGG1YuTkaFGbmmBjZWboyxNCiNdKClCPkQLUf5ednc3ChQsZN24cFy9exNfXl2HDhtG1a1fMzc0N\nHU8IIYTI024kJDOo+2zQw/h5PXB1dzB0JCHeK1qtjozsXNSmxuw8HEXNcn5MXbmXskU9uRB9k91H\noxjcpRaDxvzKt/0aM3nJLtycbWlaswSzftnPzFEdmPHLfhpXL4aDrSU376RSroQXKamZ2FiZySwq\nIcQ7SwpQj5EC1L+XkpLCzz//zIQJE7hx4wZlypQhJCSE1q1bY2xsbOh4QgghhHggNuY2Q3vOwdjE\niMkLe+PkbGvoSELkKYqiEB1/hwJujuw5GoWttTm5Wh2L1h9iZJ8GdA5eSOfm5Ym8kMCxs7FM/aod\nPb9Yysi+DTh1IZ6MjBwGdKnBknWH6dQ8iHtpWRgbG+HuYodOp8fYWHaTFkK8faQA9RgpQL28hIQE\nJkyYwM8//0xqaip169YlJCSE2rVryzc0QgghxFvq4rl4vui/CLWpCZOXfIyDo7WhIwkhHtDq9JgY\nG3E7OY2klAw88tsx79cDtK5fisXrDpORmUOjGsUY+v1qwr9oxU9zdqBRm9C/cw2Cx6xh8qh27Nh3\nAY3GhA+aB7FxRySNa5cgV6tDp1NwtLc09CUKIfIgKUA9RgpQL+7ChQuMHTuWRYsWodVqadu2LcHB\nwZQpU8bQ0YQQQgjxAs6euMbXg5ZiZmbKlGV9sJU/SoV4p9y9l4GdjQVnL13H2NgIW2szfl6yhwFd\naxH281bMNCY0qFmcEWPW8uNnLZi7fD8ZmTmMGdGSgV+u4PMBDUhNz+LcpRv07VqD3QcuUdzfDQc7\nS1LTsrC1kfYZQohXRwpQj5EC1PMdOHCAsLAw1q5di0ajoXv37gwdOpSCBQsaOpoQQgghXlLk8Ri+\nHbQUM3MNk5Z9jJ29laEjCSFeIUVRuBqfhLe7A8cjY8nO1VLYx4Vvx2+g/0e1+HXjcU6ciWX6jx1p\n1eNnOrYIwsJczYIVEayY1ZtxU7fi7elI+xblWLLqIB+0Ko9Op5Cckk6hgi5odXqMjVSy8kEI8VxS\ngHqMFKCeTlEUNm/eTGhoKLt378be3p5+/foxYMAAnJ1lBx0hhBDiXXb6aAzfDl6CuYWaScs+kSK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LQyZ3Tnafw2a4ehIwkhhBAvTn9/CZ5KluC9MSaGDmBIjRs3JiIiAgsLi5c67vPPP2fz5s0o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\nUwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115569390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
-    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
-    "plt.scatter(new['std'],new['rate'],c = new['sharpe'],s = 0.5)\n",
-    "plt.scatter(0,rf,color = 'r',s = 30)\n",
-    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
-    "plt.annotate('risk-free',(0,rf),size = 15)\n",
-    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
-    "for i in stocks:\n",
-    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
-    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
-    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
-    "plt.xlim(0)\n",
-    "plt.xlabel('Standard Deviation')\n",
-    "plt.ylabel('Expected Return')\n",
-    "plt.axhline(rf,ls = '--')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb
deleted file mode 100644
index 9990b04..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial13 Capital Asset Pricing Model-checkpoint.ipynb	
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from googlefinance import getQuotes\n",
-    "import json\n",
-    "import statsmodels.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats.mstats import normaltest\n",
-    "import time\n",
-    "from cvxopt import matrix\n",
-    "import seaborn as sns\n",
-    "import statsmodels.tsa.stattools as ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class stock(object):\n",
-    "    def __init__(self,ticker):\n",
-    "        self.ticker = ticker\n",
-    "tickers = [\"MMM\", \"AXP\", \"AAPL\", \"BA\", \"CAT\", \"CVX\", \"CSCO\",\"KO\",\n",
-    "                \"DIS\",\"DD\",\"XOM\",\"GE\",\"GS\",\"HD\",\"IBM\",\"INTC\",\"JPM\",\"MCD\",\n",
-    "                \"MRK\",\"MSFT\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"WMT\"]         \n",
-    "stocks = []\n",
-    "for i in tickers:\n",
-    "    vars()[i] = stock(i)\n",
-    "    stocks.append(vars()[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    table = quandl.get('WIKI/%s'%i.ticker,start_date = '2012-03-21',end_date = '2015-01-01')\n",
-    "    i.rate = np.log(table['Adj. Close']).diff().dropna()\n",
-    "    i.mean = np.mean(i.rate)*252\n",
-    "    i.std = np.std(i.rate)*np.sqrt(252)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "spy = quandl.get('LSE/SPY5')\n",
-    "spy = np.log(spy['Last Close']).diff().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class rolling(object):\n",
-    "    def __init__(self,ticker,series,spy_series):\n",
-    "        self.ticker = ticker\n",
-    "        self.prices = series\n",
-    "        self.spy = spy_series\n",
-    "        self.df = pd.concat([self.prices,self.spy],axis = 1).dropna()\n",
-    "        self.df.columns = ['SPY','%s'%self.ticker]\n",
-    "        self.prices = self.df['%s'%self.ticker]\n",
-    "        self.spy = self.df['SPY']\n",
-    "        \n",
-    "    def roll(self, length):\n",
-    "        df_leng = self.df.shape[0]\n",
-    "        beta, beta_p, inter, inter_p,resid = [],[],[],[],[]\n",
-    "        loop = df_leng - length\n",
-    "        for i in range(loop):\n",
-    "            x = sm.add_constant(self.spy[i:i+length])\n",
-    "            model = sm.OLS(self.prices[i:i+length],x).fit()\n",
-    "            beta.append(model.params[1])\n",
-    "            beta_p.append(model.pvalues[1])\n",
-    "            inter.append(model.params[0])\n",
-    "            inter_p.append(model.pvalues[0])\n",
-    "        beta_df = pd.DataFrame({'beta':beta,'beta_p':beta_p,'inter':inter,'inter_p':inter_p},index = self.df.index[length:])\n",
-    "        self.beta_df = beta_df\n",
-    "        self.mean_beta = np.mean(beta)\n",
-    "        self.std_beta = np.std(beta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    i.r = rolling(i.ticker, i.rate,spy)\n",
-    "    i.r.roll(21*6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      mean_beta   sd_beta  sd_beta_p\n",
-      "MMM    0.391424  0.106368   0.000174\n",
-      "AXP    0.260624  0.055665   0.000784\n",
-      "AAPL   0.095790  0.041038   0.095875\n",
-      "BA     0.190906  0.070859   0.023826\n",
-      "CAT    0.191012  0.063119   0.019710\n",
-      "CVX    0.280610  0.089215   0.011553\n",
-      "CSCO   0.132116  0.067679   0.115558\n",
-      "KO     0.181293  0.166132   0.309038\n",
-      "DIS    0.254268  0.063585   0.000340\n",
-      "DD     0.247352  0.057332   0.002610\n",
-      "XOM    0.292914  0.150067   0.026876\n",
-      "GE     0.256557  0.075137   0.010253\n",
-      "GS     0.237748  0.029856   0.000318\n",
-      "HD     0.219472  0.061830   0.012097\n",
-      "IBM    0.212634  0.112679   0.055888\n",
-      "INTC   0.152051  0.073508   0.057509\n",
-      "JPM    0.222741  0.057927   0.008795\n",
-      "MCD    0.243713  0.113179   0.189099\n",
-      "MRK    0.170320  0.062014   0.049013\n",
-      "MSFT   0.211149  0.104175   0.008382\n",
-      "NKE    0.115547  0.066427   0.252076\n",
-      "PFE    0.247528  0.092036   0.012432\n",
-      "PG     0.197096  0.159905   0.280305\n",
-      "UTX    0.318099  0.081374   0.000436\n",
-      "UNH    0.135254  0.032307   0.036826\n",
-      "VZ     0.170965  0.087304   0.082649\n",
-      "WMT    0.193440  0.062965   0.062210\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = [x.ticker for x in stocks]\n",
-    "mean_betas = [x.r.mean_beta for x in stocks]\n",
-    "sd_betas = [x.r.std_beta for x in stocks]\n",
-    "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n",
-    "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n",
-    "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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K2fj3Ofp0b6SfSQYba13nzc7WHG1K1cZgSP8ANv1dNYLkQWju5UBcWi4J6fk6e4hS07uR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Lc/ZtfJfI9dMYZGLHhMLRGpVeg0R94yYKB0N/CGzkgNLJmn2XDRPiJVUB4a09kcsEvJ2taevt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10Pej0eA8PwGTXC4L6+spK473+kOCkZM1sbOr70AlaurgAUp6Ry5aefuV5WhiAIBM9+B4W5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\nr6xEn5dLfWUFzhHmb4+cz/+DIJPhMWIE/rOv7XI2hAZtJQqP1nu18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yUilc8fEUy4uxOP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f9ifI14EzsZlM/v6wXuVDjV28W2MXWy6zcHkDu3i7CbsYqLGL914PoX9vDwDmLT7Ljn3x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\n1RqB+SuDWf+VeFzS9mNxxN5VMfP5zkTGZ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bLt0A1Wbw3XzvvNnvTq7AaA3NQIB1s5XZ/6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\nGW1fmMiVxb8gaDR49O+DpYc7sbv2YuPrjXNAJzwG9CVi9XrOfjwHYwtzO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qOt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2cAX3+9HkdHWwBefdWfF18corMMR9bu4PZ5TVmMmDoWp5aVy+L4xn2EHw2lMDef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\nwWAwGAwGt/N/S3w2JE4ATzkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11663fbd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cov_matrix = np.corrcoef(beta_list)\n",
-    "cov_df = pd.DataFrame(cov_matrix)\n",
-    "plt.figure(figsize = (20,10))\n",
-    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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v448vrefjt73Eaf/5MN97YAlrtjYYJkmSJEmSBpxh0gAaWpjgv99+DL95/8nc\ncO3xDCnI7ZfnnjdzBEWJGAW5MS6e3YPqmt2cOLGMdAg/e2IFY4cV8C8Xz2BVdT2f/cMrpNIhiXgO\nxTTSlFMIp348qkx65Fv7PmjbcuLpFpakx7GjvhVO+wQUDAMgJ3//YVJ3plQW81jqaIqqX4P6fQea\n90nd5p5XJrVJ5XT93/HUqRW8fc443nzUKB54vYrnVmxl2eZaquuaSfag7e3ulzcwe0wpP732BGaO\nKuGGR5dx5ncf4f1/2U6YE4dNCzLfuyRJkiRJfRDP9gYOR6dNrejX5xUkYnzivGmEQHFe7/6THjt+\nKLGcgB0NrVx+zGg+cvZUrj9jMg8u2gQEzH18OcUbGmmJFZE/6Uw4/j3wzI/hyCthzPEdD1r/IgCL\nw/Fsa2hhfPlQOOvzNP/1S6QLuq46ysTooQU8FzuOgDtgxSNw1NV9et4urY3QvLNnlUlDJ0AsD1LN\nhPsJk9q99fgx3Pb8Gt4x99ld9xKxHI4YXcot7z2RYUWJLt+7Yksdr63fyRcvPYKLZ4/k4tkj2biz\nkd+/sJYfPLiUzRXTGLFuXuZ7lyRJkiSpD6xMOkR88KwpfOisKb1+f3FenFmjopPMzp5RCUA8lsPF\ns0dx8eyRzB4zhOKggWR7q9qFX4fiEXD3xyDZ0vGgxffSUjiCReF4tje03T/5Q1ycM5dY0bBe7w+i\n+VCnnn4e28JiNr3wxz49aw+1VdG1J5VJOTGomA5AOqfrIKjdiRPL+Punz+RX7zuJH77zWL56+ZFc\nPWcsr6zdwbMr9l9lFQV6cOnRo3bdGzWkgE+dP50LZ43gkbpxhBvms3zTTp5efgCGk0uSJEmStBvD\nJO1y6pRyChMx3jR53wqiI0eXUkIjqfYwKX8IXPZ92Pw6PPWD6F5LAyx7iKYpFxOSw472MCkIWN9S\nQFEvq6Z29+Fzp/NI3jmMWHMvPHNDr55x+7y13N42BwqIhm9DzyqTACrbwqQgs3bFaSNKOHN6JVcc\nO4b3nDqR/3jLLHJjAa+2nfDXlZXV9ZQXJRg1pGCf1z5x3jSeb5lM0FLP92/7M9f/6sUDc2qcJEmS\nJEltDJO0yyfPn8b9nzyDwsS+oc+x44ZRHDQS5JV03JxxCcy+Ch77DmxeBMsfhmQjidmXk5+bw5NL\no4qb1lSalmSa4k6e21N58Rj1Z36Fv6dOIPzbF6G5tvs37aa6rpkv372AGx5d1nFzV2VST8OkmQCk\nY91XJnW/CG7zAAAgAElEQVQmLx5j5shSXl23Y7/r1mxrYFxZYaevzR4zhLxJbwKgaMvL1DUneWnN\n/p8nSZIkSVJfGCZpl8JEnAnlRZ2+NmNkCUeUBZSV7TXv6ZLvQKIQHvo6zP8VFJSRP/UsrjlpPH96\neT1rtzVQ35wE6JfKJICLjx7LbelzCcIUVO1/8PTPHl/BxT94nE///mVuemIF375vMU2taVZva6Cp\nNRUtaq9M6kmbG8DwWQC0xDoPejJx1NghvLpuJ2EYdrlmzbYGxncRJgG8/cKz2BEW8Z7cB5mSU8UT\nS7f0ej+SJEmSJHXHMEkZK0g3kJNfuufNogo4+cOw5F5Y+gCc8lGI5XL9mZOJBQE/fWw5dW1hUnF+\n/4RJw0vzSYw5Nvqm6tUu17Uk09z42HJ2Nrby9PJqvnHvIu6cv46K4gRhCMs210ULa6sgJw6FPRwQ\nPuMS/nfEN1idO7WXPwkcPWYItU1JVm9t6PT11lSaDTuamFDedZh03IQyHp36BabGt3Jrwfd4/I0u\nwqSaDbBjDewnuJIkSZIkqTuGScpccy3s3ubW7uQPQqIY8ofCSdcD0YDoq+eM5Q/z1rF8Sz3Q+5Pm\nOnPSUbOoDkupWz2/yzWPvbGFbfUtfPPK2Tz3b+fzwr+fz2/efzL/d90cAJZubmuRq9sERcMhp4f/\nHHJizM8/mdx47/8ZHTV2CAAvr+28NW3jjiZS6bDLNrd2/3DdJ0ic+3lGpTawYf0a1m3fK5xaPx/+\n5wj4wVHw/Nxe71eSJEmSJMMkZSad7jpMKiyDt/4Mrv457Fa59OGzppAKQ77/9zeA/mtzAzh1WgUL\n0xNoXfdyl2v+2FaFdMa06HS6ypI8TptawdFjh5AbC3hjUx0km6PKpJ4O326TTIXEc4JevRdgxogS\nRg/J59fPru601W3NtigU2l+b2y6jo2qt4+Kr+NZ9i/Z8bfXT0bWgDFY92ev9SpIkSZJkmKTMtNYD\nYedhEsDMN8PU8/e4Na6skH84dsyuqpvivFi/bWf68BKWxyZTUrMMki37vF7XnOShxZu57OjR5Mb2\n/GueG8thUkURU5bcBN8eB+te6Pm8pDYtqfQ+z++JeCyHD589hRdXb+eZ5Vv3eb1HYdLIo4GA903e\nyX2vVfHk0uqO1za8BKVjYPLZsKHrAE6SJEmSpO4YJikz7aemdRUmdeEj50whaCvcKc7L7bft5OQE\npEYcTZwk4aZ9h3A/tmQLLck0l8zuPCS6Lv9Jrt7+M0i3QnNNHyqT+hYmAbxtzjgqS/L49bOr93lt\n9bZ6ErEcRpTmd/+g/FIon8qJeWuYUF7If9yzgJZkOnpt48sw+rioemnnGqjfN7iSJEmSJCkThknK\nzNbl0bWHQ6qnVBbz5qNGAVDUj5VJACUzz6E1jFH7wu/2ee2vr1dRXpRgzsSyfd/Y2sSV23/BvPR0\nWi75fnSvl5VJyXRIPNb7NjeA/NwYp04p56U1e85NqmtO8uTSasaWFRDLtJVu9LHEql7hy5fNYvmW\nen759Cr+9Owi2LqM1MhjokAJYONLfdqzJEmSJOnwZZikzLx4C+QNgann9fit//bmI/jMBdMZM7Sg\nX7d0/KzpPJg+nsTC2/dodWtOpnhk8WbOP2JE5yHMi7+guGUL/518G0tGXg5nfBZmv7VXe2hJpon3\ndHB3J44dN5SqmiaqdjYBsLWumWvmPsviqlo+ff70zB80+nioWc95uQs4d+ZwfvjQUv7ywP0ArMqb\nDqOOidZtMEySJEmSJPWOYZK6V7cFFt4Nx7wTEkU9fvuYoQV8/LxpBEHfKnj2NnV4MX9NXEh+y3ZY\nct+u+6uqG6hrTnLq1E6qqOb/Gv72RRrGns4z6Vm8saUBzvsSVM7o1R6S6ZBEvO8/1zHjhgLRqW7r\ntjfwtp8+wxubapl73Qm85ZjRmT/o2HfBiNnw+2v55vG1tCTTzGp9HYBnGsZC/hAom+LcJEmSJElS\nrxkmqXuL7o5mC53w3mzvZA9BEBCfdh5VlBPO//Wu++t3REOrxw7bbWh1Og0PfhXu+RhMOpPEu35D\nIhbjjc21fdpDMtU/lUmzRpWSGwu4++X1XH3jM1TXNXPr+0/mvCN6OMupYChcdxeUjmHUve9h7rlp\nPlj8JPNzjuKJDW1rRsyCLYv7vGdJkiRJ0uHJMEndWzcPiiph+BHZ3sk+Tp02gt8nz4TlD8GOtQCs\n394IwNhhbW11rU1w5z/Bk/8Dx78H3nU78cKhTK4sYummuj59fmuq7zOTIJqbdMSoUu5fUEU6DPn9\nB0/hxM7mPWWieDi8+24oGMbZT72HoqYqXhrzTuat2s7STbUky6bDtpWQbO7zviVJkiRJhx/DJHVv\n3TwYMwf6uU2tP5w+rYI/pM6Ovnn5twCs29FIIpZDZXFedGrZr66A1/8I538F3vJDiEWnyk0bUcIb\nm/pWmdSaSpPo42lu7S49ahRHji7ljg+dyhGjSvv2sCFj4D33RCFg2WSKZl/K1voWLvj+4zyxsxzC\nVMdQdUmSJEmSesAwSfvXuAO2LoWxJ2R7J50aUZpPQeUkXs87Fl66FdJp1m9vZPTQfHJqN8DNF0XD\npq/+BZz+6T0CsenDi1m3vZH65uSuew0tSZ5eVk0Yhhl9fn+c5tbug2dN4d5PnMH48sLuF2di2ET4\nyNPwvge44MjRXHTkCOI5AcvDsdHr1Uv653MkSZIkSYcVwyTt34b50XXM4AyTAE6bWsHPG8+AnWtg\n5aOs39HIxKE5UUVSbVU0Q6iT09qmjywB4LX1OwFYu62Bt97wNO+66Tl++NDSXesWV9XwlXtepzWV\n3ucZrf00M+mAKRgGxcMpL87j/66bw9DCBKuDMUAAWwyTJEmSJEk9N4h/C9agsO7F6Dr6+OzuYz9O\nn1rBvS0n0JoYCvN/xfrtjVzbckdUUfWOX8HE07p8X2l+nF8+vYqnllXzlh8/yYYdjZw9o5IfPLiU\n21+IZjD99NHl3PL0Ku58cd0+z2hNpUnED55/RiX5cXa0xmDYBMMkSZIkSVKvxLO9AQ1yW5fBkHHR\nKWGD1MmTy0jlJHil/GJOWHQHU5pmcU7yt3D0O2HKuV2+rygvznWnTOCGR5fzt4WbmFxRxM/ePYcx\nwwp43y0v8K93vUZpQZy/LdwEwP8+tJQrjx9DXjy26xnJVEg8Z/DNkupKcV6cuqZWqJhhmCRJkiRJ\n6pWDp6RC2dG0I2qVGsRK8nM5btxQftp8EYQhN+d+lzAnFy74Wrfvfc+pEynNz+X8I4Zz10dPY2JF\nEbmxHG689gRmjCjhQ7fOp6ElxcfOmcqGnU387vm1u94bhmHbzKSD559RcV6cuuYkVE6PgsJ0Kttb\nkiRJkiQdZA6e34KVHY07BnVVUrvTplbw8MY8Noy7jIKghY1Hvh9KRnT7vuEl+Tz/7+fxf9fNoTiv\no1CvOC/OLf94ImOGFjBmaAGfvmA6J08q48ePLKOxJQpgkuloSHfuwVSZlB+ntikJlTMh1QzbV2V7\nS5IkSZKkg4xhkvavaSfkD8n2Lrp1+rQK0iF8fttl3JY6j4KzPp3xe3dvW9vd8NJ87v3E6fzhQ6cQ\nywn4zIUz2FLbzK+eWQWwayB37sE0M6m9MqliRnTDVjdJkiRJUg8dPL8FKzuadkD+4K9MOnbcUIoS\nMZ6sLuK5I79ERXl5vzx3aGGC0UMLADhpUhlnTq/kp48tp7apldZUVJl0UM1Myt+tzQ2g2jBJkiRJ\nktQzhknav4OkMik3lsPJk6MA6fozpxywz/nshdPZ3tDKzU+uItlemXSwzUxqShLmlULJKCuTJEmS\nJEk9dvD8FqyBl2yB1oaDYmYSwCfOm8ZXLz+SWaNLD9hnHD12KGdOr+TO+et2VSYdVGFSfpxkOqQ5\nmYZKT3STJEmSJPXcwfNbsAZe087oehC0uUHU6vaeUyce8M85d0Yla7Y1sLK6HoB47OBpcyvJzwWI\nhnBXzIDqNyAMs7wrSZIkSdLBxDDpcFK/tWfBQdOO6HqQhEkD5fRpFQA8+sZmAHIPpjCp7cS6aG7S\nDGipg5r1Wd6VJEmSJOlgYph0uNj4Cnx3Mrz6+8zf09gWJh0kbW4DZUplMSNK83h08RbgIGtzawuT\naptaozAJbHWTJEmSJPXIwfNbsPrm9bui65bFmb9nV5vb4B/APZCCIOC0qRUs2VQLQDzn4PlnVJzf\nVpnUlITKmdFNwyRJkiRJUg8cPL8Fq2/WPh9dS8dk/h7b3Lp0+tSKXV8fTG1uuyqTmpNQVAEFZVBt\nmCRJkiRJypxh0uEgnYZ1L0RfJ5szf9+uMMnKpL2dtluYFD+I2txKdq9Mgqg6ycokSZIkSVIPHDy/\nBav3Ni+EVEv0dbIp8/c1GiZ1ZURpPtOGFwMHZ2VSXXN7mDQ9an30RDdJkiRJUoYMkw4Ha57p+LpH\nlUk7IZ4Pufn9v6dDQHt10kE1gDt/7zBpJjRuh/rqLO5KkiRJknQwOXh+C1bvbVoQzT2K5fWsMqlp\nh/OS9uOCWSMIAqgozsv2VjKWF4+RiOVQ297mVjE9ujo3SZIkSZKUIcOkw0HVAhh5VFRh1N7ulonG\nHVBgmNSV06ZW8Py/nc+kiqJsb6VHivPj1DW3Rt+MOBKCGNz/Bdj4auYPWXgP3PE+WPGYLXKSJEmS\ndJiJZ3sDOsDSqWhm0vHviQYt96gyaafzkrpRWXLwVCW1K86LdwzgLhkJ77gV/vIp+Nk5MPp42LYc\n8kqhcgaMmB0FkSOPgpwYPPkDmHAa3P+5qD1uwZ3Rmjd9GI56O8QT2f3hJEmSJEkHnGHSoW7bSmht\ngJGzYfG9PT/NrXjkgdubsqI4L05Ne5gEMPPNMOEU+Pt/RC2RMy+F5lrYvBiW/h3C1J4PePEXUTXT\n9Y9GVW/P3gh3fxS2rYDzvjyQP4okSZIkKQsMkw51m16LriNmQzyv5wO4K2YcmH0payZXFvHi6u17\n3iwYBpf/776LW5tgy6IoNKrZALPfCs/PhbLJMPq46M9x18INp8DmRQPzA0iSJEmSsqpfwqQgCC4G\nfgjEgJvCMPzPLtZdBdwBnBiG4bz++Gx1Y/XTURVJ5cyeh0nOTDokzZkwjL+8upH1OxoZM7Rg/4tz\n8ztCo3Zv/u6ea4IAiiuhYVv/b1aSJEmSNOj0eQB3EAQx4CfAJcAs4JogCGZ1sq4E+CTwXF8/Uxl6\n6GtRFckRb4lCgXgPTnNLp6G5xplJh6A5E8sAmLeqH8OfwnJo2Np/z5MkSZIkDVr9cZrbScCyMAxX\nhGHYAvwOuKKTdV8H/gvowQRo9dq8X8AT/x0N3r7q59G9eA9Oc2uphTAN+VYmHWpmjiyhKBFj3qrt\n3S/OlGGSJEmSJB02+iNMGgOs3e37dW33dgmC4HhgXBiG9+7vQUEQXB8EwbwgCOZt2bKlH7Z2mFr1\nFNz3WZh6Plz2fYi1dTP2pDKpaWd0tc3tkBOP5XDc+GHM23tuUl8Ulkenu6VT3a+VJEmSJB3U+iNM\n2q8gCHKA/wE+093aMAznhmE4JwzDOZWVlQd6a4emHWvg9utg2KSoIikn1vFarAdhUuOO6Gqb2yFp\nzsRhLK6qoaaptX8eWFgOhB1/byRJkiRJh6z+CJPWA+N2+35s2712JcBs4NEgCFYBbwLuCYJgTj98\ntnbXUg+3vQtSSbjmtn2riuJ5kMywza2pPUyyMulQNGdCGWEIL63pp/CnsDy62uomSZIkSYe8/giT\nXgCmBUEwKQiCBPBO4J72F8Mw3BmGYUUYhhPDMJwIPAtc7mluB8CzN8KmBXD1zVAxbd/X4/k9b3Oz\nMumQdOz4oeQE8GJ/DeEuGBZdDZMkSZIk6ZDX5zApDMMk8DHgAWARcHsYhq8HQfC1IAgu7+vz1QPV\nb8CQcTDt/M5fjycg2ZzZs9rblZyZdEgqzosza3QpL/TXEO69K5PS6f55riRJkiRp0OmXmUlhGN4X\nhuH0MAynhGH4zbZ7Xw7D8J5O1p5tVdIBsmMtDB3X9etWJmk3cyaU8fLaHbSm+iH42T1M2r4avjUK\n1vnPXJIkSZIORQd8ALcG0M51MGRs16/H8yDVg5lJQQ4kSvpnbxp0TpgwjMbWFIs21vT9Ye1hUuM2\nWPdCFFquebbvz5UkSZIkDTqGSQeD5jp4+bdR5VFXUkmoWR+1uXWl/TS3MOz+M5t2RlVJOf4VOVTN\nmRjNOeqXVrdEIcQLosqkzQuje9Vv9P25kiRJkqRBx6TgYPDwN+BPH4YfHAVL/tr5mtqNEKa6b3ML\n05BOdv+ZjTtscTvEjRpSwJihBby4up+GcBeWQ8M22Lwo+n7rss7XrZ8Pr98FdVv653MlSZIkSQPK\nMGmw274KXrgJjnwrlE2Cx7/TeWXRzraqpe7a3CCzIdxNOyDf4duHujkThzFv1XbCTKrVulNY1n1l\nUhjC76+DP7wXbjy1YzbX7rathFuvhh1r+r4nSZIkSVK/M0wa7J78AeTE4KJvwps+AutfhLXPd7we\nhnD7u+HZG6Lvh4zv+lnx/OiaUZi008qkw8CcCcPYXNvMnfPXs3FnY98eVlgetWJuXx0FkfVbOk4F\nbFf9BtSsg2OvhfrN8MwNe77e2hT9fV7292j2kiRJkiRp0DFMGsySzVE70KwroHQ0HHNNFPA8+5OO\nNQ1bYeHdsOjP0ff7rUxKtD03gxPdGndAgZVJh7o5E8sA+OwfXuF7D/RxxtHQcbD5dSCEGW+O7u3d\n6rb8keh61ufgiLfAMz+GR77d0Rr3189D1avR1w391H4nSZIkSepXhkmD2bKHonazo94WfZ9XDCe8\nNwqOtq+O7lUv7VhfWBENQu5Ke2VSysokRWaOLOE7Vx3NhPJCNtdmEDLuzzn/3jEAftbl0XXvVrcV\nj8CwSTBsIlz4TRh9HDz2X3DDm+CHx8CLt8ApH4vWGiZJkiRJ0qBkmJRtzbXwp4/A4vv2fe3V30Wt\nQ5PP7rh30vVAAM/Pjb7f2hYmxfP3X5UEzkzSPoIg4O0njmNqZTHb6lv69rCSkXDdXXDul2Dq+dHp\ngX/9V7jjfdFphEsfhJVPwJRzovXDJsB7/wKfWQJv/l4URM26As7/KuSVQqNhkiRJkiQNRvFsb+Cw\n1toEv74ymg2z8B748JNRxQbAuheje6d9AmK5He8ZMhaO/AeY/ys4+wtRZVIsAW//9Z7rOhNrD5O6\nqUBpbYrW2OZ22BhWlGDhxpq+P6hiGpz52ejra26D1+6AZQ/Cgjuje0PGw0kf3PM9JSPgpA9Ef9oV\nDLMySZIkSZIGKcOkbFr+cBQknfsleOqH8KM5UDIKSkdBzQYoHgFnfHbf973pI9Ev5y/dGs2kKZsM\n0y/s/vMyrUxqP2HLNrfDRnlRgq31LYRhSBAE/fPQqedFf9Jp2PQabF4MMy+N2jW7U1gezQOTJEmS\nJA06hknZtOqJqD3t1I/D5HNg0T1QuzH6k1cShUz5pfu+b+wcGHsSPHtjdNLb8FmZfV6mp7ntCpOs\nTDpclBUlaEmmqW9JUZzXz/+3kJMDo46J/mSqsMwwSZIkSZIGKcOkbFr1BIw7KaoYGntC9CdTZ3wG\nbntH9PWsKzJ7T8ZhUttx7oZJh41hRdFJf9vrW/o/TOqNgrI9h8tLkiRJkgYNB3BnS8M2qFoAE8/o\n3ftnXAxHtJ2YVT41s/fEo8Cg25lJjW1hkjOTDhvlbWHS1r4O4e4vheXOTJIkSZKkQWoQlCAchsIQ\nXvkdEPY+TILoBKx4Pkw5L7P17ZVJqW4CA2cmHXbK2sKkbfUZnPQ3EArLoKUWki0dIagkSZIkaVAw\nTBoIW5fDfZ+Fxu3QXAfNtVBXFc09Gjun988tGQFX/Szz9fEMT3Ozze2w0xEmtWZ5J20KhkXXxu3R\n33NJkiRJ0qBhmDQQXrgJVj4Bk8+GYRMhUQQjj4E574PYAP4niPU0TLIy6XAxKCuTIBrCbZgkSZIk\nSYOKYdKBlk7Bgj/C9Ivgnb/J7l52VSZ10+bWuANyC20vOowU58VJxHIG18wkgEbnJkmSJEnSYOMA\n7gNt9VNRS9vsq7K9k91Oc+uuMmmnLW6HmSAIKCtKsH2whEkF7ZVJhkmSJEmSNNgYJh1or90BiWKY\nfnG2dwKx9tPcumllatphi9thaFhRgm2DJUzavc1NkiRJkjSoGCYdaGd+Dq76OSQKs70TyMmJAqXu\nKpMad0CBlUmHm/KixOBpc2uvTHryf+C2d0WhbEsDtNTDzy+CB7/af5+VTvXfsyRJkiTpMODMpANt\n6Ljoz2ARz4dUN4FB004oHT0w+9GgUVmSx8I3amhsSVGQiGV3M4lCGH08tDbAhpdgyb1Rhd+QcbBl\nEQRB/3zOznXw4xPhmt/B5LP655mSJEmSdIizMulwUzwcFtwJq5/pek3TDmcmHYauOWk82+pbuPGx\n5dneSuT6R+Cjz8GnX4f3/AVmvxUaqqF4ZBQC9YflD0eB1drn+ud5kiRJknQYMEw63Lztl5Aoglsu\nhWd+AmG475qmnba5HYZOmlTGW44Zzf89tpx12xuyvZ0OOTkw6Qy4/EfwuWVw3P+Dmg3905628ono\nWv1G358lSZIkSYcJw6TDzcjZcP2jMOMSeODf4PZ3Q1NNx+vpdPS9A7gPS/96yUyCAL5136Jsb6Vr\npWMgTEHdpr49JwxhlWGSJEmSJPWUYdLhKH8IvONWuPAbsPhemHs2bHo9eq25BghtcztMjR5awEfO\nnsp9r1Xx0KI+hjUHypC2GWSL/gxzz4GGbb17ztblULsx+rtevTQKUiE67fDR/4LvTIGXf9s/e5Yk\nSZKkQ4hh0uEqCODUj8N7/gwtdfCz82Dh3dG8JLAy6TB2/ZmTmTWqlE/9/mVWbKnL9nb2NWRMdH3m\nJ7BhPiy5r+u1YRi1w6WSkGyB1qboVLimnfDQV6I1x10bzU2q3QCN2+HXb4VHvxV9vfLxA/7jSJIk\nSdLBxjDpcDfxNPjgE1H7210fhqoF0X1nJh228nNjzH33CeTGcvjAr+ZR29Sa7S3tqbQtTNqxOrou\n+nPn6za8DHPPgq+VwdfL4RuV8M0R8K1R8J/jo/dd9C2YfnG0funf4ecXwrrn4a03wcTTbX+TJEmS\npE7Es70BDQIlI+Dqm+Enb4J7PxPdszLpsDZ2WCE/edfxXPvz5/j0719m7nVzyMkJsr2tSP4QSJRA\nSy0EMVj+CNRWQU4upFshnh8FTTdfFLWwnfkvEEtE1XhBTsd1xJEw9XyobWvn+8unomdfd1cUJK19\nDl69PapuCgbJzy5JkiRJg4BhkiJDx8Ol/w0PfiU6er18WrZ3pCw7ZUo5X75sFv9xz+t8/8E3+MyF\nM7K9pUgQRK1uWxbD8dfBi7fAf++2tyAnCpsKK6Jh88WV+39e8fDo73w8Af/vDqhse1bFdGjeCXWb\no9lKC+6E0z8NhWUH6AeTJEmSpIODYZI6HHtN9Edq8+5TJrBg/U5+9PAyxg0rZFNNE2fPGM5RY7Nc\nuVbaFiad+gkYc0I0AymWgJw47FwLKx6DS7/XfZAEUTj1/gejqqT80o77FW2B6sNfiwZxh2lY9hC8\n+09RACVJkiRJhynDJEldCoKAb1w5m6Wb6/iXO18F4EePLOOoMUMoTMS4+b0nkhvLwui1yplRmFQ2\nGcqn9P15Q8fte69ienR96dYosDrtU/DH6+GWS+Hd90DpqL5/riRJkiQdhBzALWm/8uIx5l53Au89\ndSK/ff/JnD29krqmJE8srea3z63JzqbO/Xf4wMMHdpZR6WjILYq+PvNzMOtyuPZOqNkAv7gEdqw9\ncJ8tSZIkSYNYEIZhtvfQqTlz5oTz5s3L9jYkdSIMQ679+XO8vqGGRz97NkMLE9ne0oHxs/OiFrqP\nPg85bdn72hfg1qugcBi8/2EoKs/uHiVJkiSpB4IgeDEMwzl9eYaVSZJ6LAgCvnTZLGoaW/nBg0uz\nvZ0D56qfwXV/7AiSAMad2FahtBH+8B5Ip7O3P0mSJEnKAsMkSb0yc2Qp15w0nl8/u5plm2uzvZ0D\no2xydNLh3sadCOf/B6x6ArYewmGaJEmSJHXCMElSr/3zBdMpTMT4xr2Lsr2VfpNKh9z/2kZaU91U\nHI07ObpuXX7gNyVJkiRJg4hhkqReKy/O45PnTePRJVt4ZMnmbG+nX9z2/Bo+/Jv53PLUqv0vLJsc\nXbetOOB7kiRJkqTBxDBJUp+8+5SJlBcl+PPLG7K9lT5rTqa44ZFlANz42HLqmpNdLy4sg/yhsM3K\nJEmSJEmHF8MkSf+fvfsOk6o83zj+PdO29w4svSOgoKIiltglaqKJLRpjS7MlJiYmMc10TfKLBZNY\nEhONxo69S2wIgtKrdNhdFnaX7WXa+f3xzmyBrbOz7M5yf66La3ZnzpzzrsDI3PM8z9srHpeDIekJ\nVNR7+3spvfb0J7sormrk+6ePp6LOy1NLd3b+hKwxqkwSEREREZFDjsIkEem19EQ3++p9/b2MXvH6\ng9y3YDNHDE/nupPHkp0cx/qSLgaLZ46GcoVJIiIiIiJyaFGYJCK9lpHooTLGK5Oe/mQXRZUNfOfU\n8ViWRWFmAjv31R9wnG3brC6qYl1JNWSOgaqd4GvshxWLiIiIiIj0D1d/L0BEYl9Gopt9dbEbJnn9\nQeYt2MQRw9M5YVw2AMMzE/l0x77mY8prm5i/vJinlu5k/e4ahqYn8OHZYwAbKrdDzoR+Wr2IiIiI\niMjBpTBJRHotPdFDdaMffyCIyxlbBY9vrytlVVEVRZUN/OaLh2FZFgCFGYm8tLIEfyDI4q0VXPnw\nErz+INOHpXHM6Ew+3lpBIH0UToDyTQqTRERERETkkKEwSUR6LSPRDUBVg4+s5Lh+Xk33bSyt4ep/\nLQXg8MJ0Thyf0/xYYWYCgaDNlrI6bn12JcPSE7jvshlMzE/lkUXbWbSlgvKk0eRaDihZARPn9teP\nISIiIiIiclApTBKRXktP9ACwrz62wqQte+sAuP7ksZw/Y2hzVRJAYWYiAL94YQ07Kxp47NpZTMxP\nBfz2H70AACAASURBVCAvxfyMpQ0ucnMmQdEnB3nlIiIiIiIi/Se2+lFEZEBKD1UmxdoQ7h0VJky6\n9oTRjM5JbvNYYYYJkxZuLue4MVkcNya7+bG81HgASqsbYegMKPoUbPsgrVpERERERKR/KUwSkV7L\naFWZFEu2l9eTnugmLcF9wGMFafE4HaZS6fwZw9o8Fg6T9tQ0wdCZ0FAB+7b1+XpFREREREQGAoVJ\nItJrLWFSrFUm1TM81M62P5fTwdD0BOLdDs48LL/NY9nJHiwrXJk009ypVjcRERERETlEaGaSiPRa\nelKstrnVM3VoWoePn3f4EByWRXJc25dKl9NBVlIce2oaIXcSuOKheBlM/VJfL1lERERERKTfKUwS\nkV5LiXPhclhUxlCbmz8QpGhfA5+fVtDhMd87fUKHj+WlxlFa3QRON2SOgYotfbHMFv4mePQCqCmB\nw78Cc25u+/hbv4BgAE7/Vd+uQ0REREREDnlqcxORXrMsi/REd0zNTCqpasQftDtsc+tKXmq8aXMD\nyBzV92FSdRFse9+ESm//Ej6a1+qxElh4Dyz9JwT8fbsOERERERE55ClMEpGoSE/0xFSb2/byegCG\nZyZF9PzmyiSAzNFQsRWCwWgt70Bes15Oux0mnwev/xiWP27uW/oQBP3grYGSFX23BhERERERERQm\niUiUZCS6Y2oA9/zlRTgsGJMbWZiUmxJPeV0T/kDQVCYFmqCmuGcnsW0TQtl218f6GsxtXAqc/wCM\nOgGevw4W/RU+fgAKZ5nHt77bszWIiIiIiIj0kMIkEYkKU5kUG21uL6wo5ulPdvHtk8aSmxIf0Tly\nUuJMFlTnNZVJ0PNWt4V3w92Hw7oXuj7WV2du3QngioOLH4P8qfDarWYA+Bf+CjmTTCuciIiIiIhI\nH1KYJCJRkZXkobyu88qkV1eVUF7bdJBW1L73Nu7l+0+uYOaIDG46dVzE5/G4zMunNxDsWZhUvhkW\n3w+PXQRv/szc99mbXT8v3ObmDs14ikuBrzwNR38DvvYyZI2BUXNgxyIzV0lERERERKSPKEwSkajI\nSvZQUeclGGy/Zeud9aV86z+f8pe3PjvIK2vx8dYKvv7IUsbkJvOPK47C7Yz8JdDlsAAIBG1IHQpO\nT+dhUl0Z/HU23DMDXr0F9q6HY6+Hsad1r5rIFwqTPK3a8pJz4Ow7IHus+X7MKea47R9G+FOJiIiI\niIh0TWGSiERFVlIcgaBNVcOBrW5N/gC3v7gWgBdXFuP19+Gg6g4s31nJVQ8vYWh6Ao9cfTRpie5e\nnc8ZCpP8QRscTsgYaeYfdeSV70PZRjjzD3DDp3DTCjjjNzD2FNi3DSp3dH7BcJjkTuj4mFEnmJa3\njW/06GcRERERERHpCYVJIhIVWckeAMrrDmyxmvfOJraV13PV7FFU1vv40bOruOO19aaqp498umMf\nNY0m2FpXUs0V//iYzCQP/7nmGLKT43p9fpfDvHw2/wz502DDq2Yg9v4Dtdc+D2uegxN/CMd807Sk\nhY2cY263dlGdFB7A7e5kYLgn0QRKG1/r3lBvERERERGRCChMEpGoCAc0ZbVt5yatLqpi3v82c/6M\nofz47IlkJ3t45tNd3Pe/zfz42VXUe/1RX8uji7Zz/n0L+e4TK9i8t5bLH1pMosfJf66ZRX5aZAO3\n9+dyhiqTAqHQ5uw7YeypZiD2E5dBwz5zf105vPw9KDgcZn/nwBPlToa4NCha2vkFva0GcHdm3Omw\nb6uZzSQiIiIiItIHXP29ABEZHMKVSWW1Tby4opjTJufhsCxueXolmUkefvb5ybicDh6+8mi8gSBv\nrytl3oLNvLK6hAtmDOOyY0YwNje51+t4+pNd3DZ/Nbkpcby1rpRPtlfgdFg8es0sCjMTe33+sDYz\nkwASM+GSx+GjefDWz+HvJ5gd1z74CzRUwlefB2c7L7kOh6lU6qxFDkKVSVbXYdLwY8zt7pUts5RE\nRERERESiSGGSiERFVpKpTHpzbSnPLy/mljMm4A/YrCup5v7LZ5KeaMKmw4amATBjeAYnTcjl0UXb\n+c/i7Ty8cBufn1bAXy46HFcEg7EbfQEe+mArf3pjA8ePzWbepTM4++73qW3y89jVsxiT0/ugqrWW\nmUmt5j9ZFhx3PRTOgie/Cv84E7y1cPJPIG9KxyfLHA27lnR+QV+92cnNsjo/LnMMYEFZ/w06FxER\nERGRwU1hkohERUaiG8uCRVvKAbj/vS3UNfk5d/oQTp+S3+5zjhqZyVEjM/np5yfzjw+2ct//NpOZ\n5OH28w7r9nVt2+b1Nbv5zSvr2FnRwJlT8vnzRdNJ9Lh46pvHYllQkNZFNU8EDpiZ1FrhUXDly/DP\nuabq6Pjvdn6yzFGw5lnwe8Hlaf8YX33XVUlg5ialF0LZhq6PFRERERERiYDCJBGJCpfTQUaih9Jq\nM4C7qsFHdrKHX5zbSUVOSHZyHD84cyL+oM39721hTE4yVxw3ssvn+QJBrvnXUt7duJcJeSn855pZ\nzB6b3fz4kPToh0hhbXZza0/maLhhKWCBs4ud4zJHgx2Eqp1th3O35q03QVF3ZI83O8eJiIiIiIj0\ngaiESZZlnQncBTiBB23b/v1+j38TuA4IALXA123bXhuNa4vIwJGV5KGizsvE/BTOnlrAMaOzyEzq\noNKmHT88cyJb9tbxyxfXMGt0JhPzUzs9fm1xNe9u3Mv1J4/lO6eOi6g9LlLhAdyd7kjn6WTntdYy\nRpnbii0dh0m+OtPm1h3ZE2DbhxAMmplMIiIiIiIiUdTrdxmWZTmBecBZwGTgEsuyJu932GO2bU+1\nbftw4A7gz729rogMPOEh3GNyk7nxlHEcPSqzR893Oix+dPZEgjasK6nu8vht5WaHs3MPH3JQgyTo\nRmVST2SONredDeH2NfQgTBoH/gao3tX7tYmIiIiIiOwnGu++jgY22ba9xbZtL/Bf4LzWB9i23fpd\nYRIQhXdfIjLQZCWbIdxjsrtZkdOOnBRzjrIab5fHbi+vB2B4FHdp666W3dyCXRzZDcm54E4ylUkd\n8dZ3v9Ipe7y5VaubiIiIiIj0gWiESUOBna2+3xW6rw3Lsq6zLGszpjLpxihcV0QGmOyklsqkSKXE\nufA4HZTVNnV57LbyOgrS4ol3OyO+XqTClUm+QBSyccsyQ7g7C5O6O4AbWoVJ2tFNRERERESi76D1\nhdi2Pc+27THAD4Hb2jvGsqyvW5a11LKspXv37j1YSxORKMkOVyblRB4mWZZFdrKHvd0Ik7aX1zMi\n6+BXJUEXu7lFIncSlKwAu4Pz+eq73+aWlA3x6apMEhERERGRPhGNMKkIKGz1/bDQfR35L/CF9h6w\nbft+27aPtG37yJycnCgsTUQOppkjM5g2LI2xvahMAshOiaO8tjttbnWMzIq8pa43ojozCWDEbKjd\nDeWb23+8JzOTLCu0o5sqk0REREREJPqiESYtAcZZljXKsiwPcDHwQusDLMsa1+rbuYDe4YgMQseN\nyeaF64/vddtZdnJcl21uNY0+ymq9jOinMCmqM5MARs4xt9veb/9xbx14elCFlT1elUkiIiIiItIn\neh0m2bbtB64HXgfWAU/atr3GsqzbLcs6N3TY9ZZlrbEsazlwM3BFb68rIoNXVpKnyzApPHx7ZD+1\nuTVXJkVjZhJA1hhIzoPtH7b/eE8qk8Ds6FZbCg2V0VmfiIiIiIhIiCsaJ7Ft+xXglf3u+1mrr2+K\nxnVE5NAQbnMLBm0codAGwBcIMn9ZERPyU9hZ0QDA8P6ameQMVyZFKUyyLBh5PGx9H4IBcLSq7goG\nwd/TMCk0hLt8Eww7MjprFBERERER4SAO4BYR6a7s5Dj8QZvqRh8Atm3z5tpSzvi/97jl6ZX8+c2N\nFFWayqTCzH6uTIpWmAQw6VwzN2nZI23v95mftUdtbjkTzK1a3UREREREJMoUJonIgJOd7AGgrLaJ\nNcVVfOXBxVz776VgwcT8FIr2NVBc2UhynIvUeHe/rDHqu7kBTD4Phh8Hb98ODfta7veZKqweVSal\njwCHW2GSiIiIiIhEncIkERlwspPjAPj1y+v4/D0fsK6kml+eO4XXv3MCx43JpqiygeLKBgrS4vtt\njX1SmWRZcNYfTJC04Hct9/vqzG1PwiSny8xh0o5uIjIANPoCrC6qYv6yInZW1EflnHVNfvyBKG2C\nICIiIj0SlZlJIiLRFA6T/rdhL6dNzuOPX5pOWqKpQBqSHk+9N8C63dWMzk7utzVGfTe3sIJpMPNK\nWPIgzLwC8qa0VCb1pM0NzBDuvRuiuz4ROST5A0E+3lrB5CGppCd6Oj3W6w9yy9MrsABfwGb97mq2\nldc3V3JOG5bG/G/PbjMTr6eeW7aL7z6xgjiXgxdvOJ7xeSk9PkdxZQNn3fU+j107iylD0iJei4iI\nyKFIlUkiMuCE29wcFtw2d1JzkAQwND0BgJ0VDQxJH2SVSWGfuw3iU+HVH4Jtgzf0KX5PKpPADOGu\n2AIBX/TXKCKHjA8+K2Pu3R9w6YOLOeGOBTzw3haa/IEOj39u2S6eX17Mh5vLWVVUxeicZL590hju\nvfQIfnL2JFbuquLFlcW9WtOqXdUANPmDbCuri+gcH2+toKrBx5/eUDuwiIhIT6kySUQGnIxED3Eu\nB6dOzmNEVlKbx4ZmJDR/XZCWsP9TDxq3MzQzKdAHYVJipgmUXv4erJ0Pidmhi0YQJgX9ULEVcsZH\nf50iMmjVe/1s2F3DvAWbeWtdKYWZCfzu/Km8vmY3v3llHf9etI0fnjmRuVMLsKyWCiN/IMi8BZuZ\nNiyN56+b3eYxgGDQZv7yIu54bQNnTMkn3u3c/9LdUlzZgNtp4QvY1Hs7DrY6kxxn/hn86Y59XRwp\nIiIi+1OYJCIDjsNh8fjXj2FMO21sQ9IT2v36YAt3Z/RJZRKYVrelD8Prt8EZvzb39ThMGmduyzYq\nTBKRTn2yvYJXV+3msz21bNpTS1Glaa9N8jj5wZkTuGr2KOLdTi45ejjvf7aX37y8jusfW8abh5fy\nmy9ObQ5m3lpXyo6Ken4yd+YBQRKY1/efnD2JSx9czMMLt/HNE8dEtN6iygbG5qawrqSa2iZ/ROdo\nDFVXVdb78PqDeFwq2BcREekuhUkiMiDNGJ7R7v1ZSaZqqckfZEg/DuC2LAuXw4rubm6tOZxw9h3w\nz7NMoASQkN6zc2S1CpNa++Rh2LcdTv15r5cpIrHv+eVFfO/JFTgdFmNykpk5IoOLjipkbG4yR4/K\nbJ5jFzZnXA4v35jNfQs28X9vbWTlriruvfQIpgxJ44UVxWQnezhlYm6H1ztubDanTMxl3jubuPDI\nQjKTOp/B1J7iygaOH5fNupJq6r0Rhkm+lpl3K3ZVctTIzIjOIyIicijSRzAiElMsy2qem1TQj5VJ\nYOYm9VllEsCI42DaxVC3Fz73U7M7W0/Ep0JKwYE7ui17FD79d/TWKSIDgm3bPQ64//nhVm7673KO\nHJnBkttO5ZWb5nD3JUdw4ynjOHtqwQFBUpjTYXHDKeN4/NpjqPf6+eJ9C3ngvS28vW4Pc6cW4HJ2\n/k/MH509kXpfgLvf7vmOk42+AOV1XsbkmOrVuqbI2twafS3PW7KtIqJziIiIHKpUmSQiMWdIegJb\nyuoo6MfKJDA7uvX5ttTn3Qtn/R4S2q/U6lL2+LaVSbZtdnhrqjaDvXu6Q5yIDCglVQ18uKmcDzeV\n8eGmMgA+vPVzzXPdOmLbNn96YyP3LtjEGVPyuOviIyKaXzRrdBav3DiH7z21gt+8sg6Ac6YP6fJ5\nY3NTuPioQh5dtJ1xeclcMGNYt69fHGrBG5aRQKLH2YvKpJYwaU91U0TnEBEROVQpTBKRmDMmJ4mt\nZXURD26Nlj6vTAJwuiMPkgDyp8Liv8GWd2H0iVBdZIIkgKqdkDMhOusUkYNm055a/rVwGx9uKmNL\naCezrCQPBenxrC6qpqSykeFZnQfFv39tPX9/dwuXHF3Ir78wtXmHykhkJcfxjyuO4h8fbmVtSXWH\nbcr7u/m08awuruYnz63mL299xpWzR/KVWSNIS3B3+rziykbA7O6Z6HFRF+EA7ia/+TAgNyWOynpv\nROcQERE5VClMEpGYc/PpE7j6+NH9vQxcTkffzUyKlhNugU1vwxOXw9WvmzAprFJhkkgs+tu7m5m/\nrIg547K5dNZwZo/NZkJeCou3VnDJA4vYua++0zDpXwu38fd3t3DZMcP51XmHtTsou6ccDotr5vTs\ndTkrOY753z6OhZvL+du7m7njtQ3ct2Azl84azlWzR5HfQfVpUWU9YKpUk+Kc1EU4gLvJF8CyIC81\nnn31vojOISIicqhSmCQiMSctwd3lJ9cHw0GpTOqthHT4ypPw4Knw6JfgsPNbHqva0X/rEpGIef1B\nhmcm8s8rj25zf2GmmSO3o6Ke2R0897XVu/nFi2s4bXIevzw3OkFSb1iWxeyx2cwem83qoiruf28L\nD76/hX9+uJU/fnk65x0+9IDnFFU2YlmQnxZvKpMinZnkDxLncpCR5FFlkoiISA9pALeISITMbm59\nPDMpGtKHw6VPQn0ZLLwHErPA4TKVSSIScwJBG0c7bWkFaQm4HBY7K+rbfd4n2/dx03+XMX1YOndf\nfESvWtv6wmFD07j7kiN495aTmT4snVufWcWmPbVtjgkGbdYWV5GXEo/b6SA5rnczk+LdTjIS3VQo\nTBIREekRVSaJiEQoJiqTwoYcDid8H975NeROhsod5peIxBx/MIirnSDI6bAYmpHAzn0NvLW2lG3l\ndXgDQbz+IKXVTby0opiCtHgeuuJIEjz9O3OuM4WZicz7ygzOuut9rn/sU+ZfN5t4t5OdFfV8/6kV\nLN5awdeOGwlAosdFZUNkLWqNvgDxLicZiR4q69TmJiIi0hMKk0REImQqk2IkTAI47iYzP2n8GbDx\ndTOAW0RiTiBod1hVNDwzkSVbK3hxRXGb+5M8Tk6akMutZ00kKznuYCyzV/JS4/nThdO58p9LuPnJ\n5Rw1MpM/vr4By7K480vT+NLMYQAkxTmbd3frqUZfkHi3g4xEDzVNfnyBYJe74ImIiIihMElEJEIx\nVZkE4PLAVa+Zr0vXwpb/9etyRCQygaDdbmUSwLCMRN7/rAyA174zh5FZSbidjgHX0tYdJ0/I5ebT\nxvOXtzbyyqrdHDs6izu/PI1hGS3DxRM9Luoj3M2tuc0tyczgq6z3kZMy8IM2ERGRgUBhkohIhFwO\nB4FADIVJraUXQk0J+L0mZBKRmOHvpDIpPIT7sKGpTMxPPZjL6hM3njKOi44qZMveOmaNyjxgVlSS\nx0ltpLu5hQZwpyea18DKeq/CJBERkW5SmCQiEqGYq0xqLX04YEN1EWSO6u/ViEgPdNXmBnDWYQUH\nc0l9Ki81nrzU+HYfS4pz9WoAd1xoADdARZ2GcIuIiHSXGsNFRCLkcsbIbm7tSSs0txrCLRJzOqtM\nmjUqi5Mm5HDBjGEHeVX9IynOhS9g4/X3/LW40R8M7eZmKpP21WsIt4iISHepMklEJEKxXZkUCpM0\nhFsk5gSDNh53+7ux5aTE8fCVRx/kFfWfxNCudPVeP54etuw2+QLEp8SRkdTS5iYiIiLdo8okEZEI\nxdxubq2lDgMsqFSYJBJrOqtMOtQkecznonURDOFuHsAdanNTZZKIiEj3KUwSEYlQTFcmuTyQUqDK\nJJEY1NnMpENNYpypTKqLYAh3oy9IvNtBgtuJx+VgnyqTREREuk1tbiIiEXI5HDT4ItuSekBIL9TM\nJJEYpMqkFs2VSZGESX5TmWRZFhmJbvbVeamo87KxtIbPSmvYUFrDxtJaDhuSxs/OmRztpYuIiMQ0\nhUkiIhFyOS38jTE6gBvMEO5dS/p7FSLSQ8GgjUthEmAGcAPUR9Dm1uQzA7gBMhI9PPPpLp76ZFfz\n4ylxLiwLdlXUK0wSERHZj8IkEZEIuWK5zQ1MZdLa+RAMgKP9Yb4iMvD4g0FVJoWEB3D3tDLJtm0a\n/QHiXGbiw7VzRrN4aznj81IYl5fC+Lxk8lPj+dGzq3hn/Z6or1tERCTWKUwSEYmQM5YHcAOkD4eg\nH2p2Q9rQ/l6NiHSTZia1iLQyyRsIYts0VyZdMHMYF8wcdsBxLmeMf2ggIiLSRzSAW0QkQi6HI7bf\nZKQNN7eamyQSUzQzqUVSqDKptoeVSY0+06Ic53LA9o+gvqLd41wOB/5ADLczi4iI9BGFSSIiEYr9\nyqRCc6sd3URiSkAzk5olhiqT9tY0Udfkx7bNa3KTP8Bji3ewYmdlu89rCm2ekOXbDf88E179YbvH\nxXw7s4iISB9Rm5uISITMm4wY/sQ6LdTSocokkZhi2tz0eSBAotuJx+ngrrc/4663P8OyWnZ4q23y\nM2dcNo9cPeuA54Urk8aVvmLu8De0e36XM8YrUEVERPqIwiQRkQg5HRaBQAy/yfAkQWKWKpNEYowJ\nk/p7FQODw2Hx6DWz+GxPDXVNfmob/dQ2BWj0B9iwu4Z1JTXtPq/JHwBsRha9YO6IT2/3OJfDUpub\niIhIOxQmiYhEaFAMZk0fDpUKk0RiiT9o41JlUrOjR2Vy9KjMA+5/8P0t/PrldeytaSInJa7NY42+\nIJOsHSTXbjd3NLUfOrmcFkEbgkEbh1oLRUREmulfIiIiEYr5mUkAaYVqcxOJMdrNrXsmFaQCsGH3\ngUFRoz9AgVXecoe3tt1zhGdTxfwHByIiIlGmMElEJEIxv5sbmMqkql1gx/jPIXII0QDu7pmYnwLA\n+t3VBzzW6AuQRp35Jm14J5VJ5p/KMT0fT0REpA8oTBIRidCgqUzyN0BdWX+vRES6KaCWq27JSo4j\nJyWu3blJjb4gaVYoTEov7DhMUmWSiIhIuxQmiYhEKOZ3cwNTmQRQpVY3kVjhDwZVmdRNE/NTOqxM\nSrdCrW2pQ7sOk2J5swUREZE+oDBJRCRCg6IyKb3Q3GpukkhMCAZtgjaamdRNkwtS+ay09oAd2cJt\nbkFPKiSkq81NRESkhxQmiYhEyFQmxXiYlBYOk7Sjm0gsCITmm6kyqXsmFqTgDQTZWlbX5v4mf5BU\nqw47Pg3iUkyY1M7sOFUmiYiItE9hkohIhJwOB3Zoy+iYlZAOcalQpTBJJBaEqyE1M6l7JuabHd3W\nlrRtdWv0BUinLvQamAJ2AHwNBzy/uTJJYZKIiEgbCpNERCLkcpo3c75Yb39IH67KJJEYEQ6TVJnU\nPWNyknE5LNbvbmlja/AGeG5ZEVnOeqzETBMmQbutbi0DuGP8dV5ERCTKFCaJiEQoPLMk5ucmpRWq\nMkkkRoRba50O/ROuOzwuB2Nzk1kfqkyybZsfPbuStSXVjEv140hIB08oTPLWHvD88IcGMd/SLCIi\nEmX6l4iISIQGzZbR6YUawC0SI1SZ1HOTClKbK5Me+mAr85cXc/Op40kK1kJ8eqvKpAN3fXM51OYm\nIiLSHoVJIiIRaq5MivU3GWmF5k1UQ2V/r0REuqCZST03MT+FkqpG7n9vM797dT1nTMnjupPGQMO+\nlplJoDY3ERGRHlCYJCISoZYto2M8TEofbm7V6iYy4Kkyqee+cMRQRmQl8ttX1jM6O4k/XXg4jkAj\nBLyQkNF5mKQ2NxERkXYpTBIRiZBrsMxMSi80txrCLTLghStknAqTui0vNZ5nvnUc1xw/igevOJLk\nOBc0hiox27S5tTMzSW1uIiIi7XL19wJERGKVc7C0P6SFKpM0N0lkwFNlUmSyk+O47fOTW+5o2Gdu\nE7qYmRSuTArE+Ou8iIhIlKkySUQkQoOmMikpG1wJanMTiQEtu7kpTOqV8Iy4rtrcBstGCyIiIlGm\nMElEJELOwfImw7IgbZgqk0RiQFBhUnS0bnNzxYPD1cHMpPBsPFUmiYiItKYwSUQkQuFZGjFfmQSQ\nWgC1e/p7FSLSBb/a3KKjuTIp3QTqcSngbW9mUrjNbRC8zouIiESRwiQRkQg5B9ObDHcS+Or6exUi\n0oVAc2WS/gnXK+GZSfHp5taTot3cREREekADuEVEIjRoZiYBuBPA19DfqxCRLrTMTOrnhcS6sg1m\nXlJ8mvk+LgW2L4T514Gv3rwe+uoZUV/LD1xD8QeP6N/1ioiIDDD6p4iISISczt7v5mbbNr6BsEuQ\nJ1FhkkgMUGVSlBQvh4LDTYsbwMjZZje3LQtg90qo3gX+Rtw1uzjf+b52cxMREdmPKpNERCLU28qk\ntcXVXP7QYsrrvNw2dxLXzBkdzeX1jDsRvGpzExnoApqZ1H3bP4LELMgZ3/Z+XyPsWQvH3dBy39l3\nml/7aXjuZhKWPz442plFRESiSB9riYhEqDe7uTV4A3z3ieU4HBb5qfF8tLk82svrGbcqk0RiQbgS\nUru5dcML18MbPznw/j1rIOg3lUldsDyJJNCkmUkiIiL7UWWSiEiEwru5dfSJ9aY9NWzZW0dGkoeM\nRDfxbieLt1Tw5tpS3vtsL/XeAP/82lE8u6yIZTv2HcylH8idCIEmCAbA4ezftYhIh1ra3BQmdamh\nEkpWHHh/8XJzO6TrOUiWJxGPFSDgb4ry4kRERGKbwiQRkQi1VCYF8QeC7KioZ3d1I3uqm1hTXMU/\nP9zW7qfZealxfPGIoZwzfQjHjM5iTXEVL64oprbJT3JcP70sexLNra/eDKIdJBp9AXyBICnx7v5e\nikhUKEzqgaYaE5LXlEJKXsv9JcvN8O304V2ewuFJMl/4GvtokSIiIrFJYZKISIRaz0y66b/LeXlV\nSZvHzzt8CFfOHkV1g4999V5qGv1MHZrG1KFpOFq9EZyQnwrAxtIaZgzPOHg/QGvuBHPrHVxh0pl/\neY9t5fVs+/3c/l6KSFRoZlI3+ZtMkARmoHbKaS2P7T98uxNWc9CumXIiIiKtKUwSEYlQuDJgb00T\nr6/ZzeenFXDp0cPJS4snPzWepG5WGU3IM+HNxt39GSa1qkyKAVvL6nhz7W6uPn50hxUaTf4A5pjc\nPwAAIABJREFU28pj4+cR6S6/KpO6p6m25euS5TAuFCa1N3y7E45QmGT5NVNORESkNYVJIiIRcjnN\nm7mnPtmFP2jzrZPGMGVIWo/PMywjgQS3kw2lNdFeYvfFWJj0o2dXsmhLBV5/kOs/N67dYxZtqTjI\nqxLpey2VSdpDpVNN1S1fl6xs+boHw7cBnHGmzc3qzQYF5ZthyUNw+q9Bv28iIjJI6P9oIiIRGpmV\nxMisRD7Zvo9xuclMLkiN6DwOh8X4vGRWF1VFeYU90BwmDfxP3z/aXM6iLRUMSYvnz29uZMm29kOj\nN9bsBiDOpf/VyeDRUpnUzwsZ6JpC4bwrwbS5hfVg+Da0VCY5ehO0b3gFFs2D2tLIzyEiIjLA6J8i\nIiIRinc7efpbx3H65DxuPGUcVjfmb3TktMl5LNm2jzXF/RQoeWKnMum+/20iNyWOF244nsLMRG58\nfBmvrS7hhRXFPLV0Jy+sKGZnRT0vrigGwNGL3xeRgSbYHCbpn3CdCodJhUfDvm3QGHpt7cHwbQAr\nNIC7V21uDaHdOtUqJyIig4j+JSIi0gvZyXHc/9UjOWf6kF6d5/JjR5Ic5+K+/22O0sp6qPUA7mAA\n3rgN9qzv++sG/D06fGdFPe9/VsZXZo0gOzmOey+ZQXmdl28++ik3Pr6MW55eyY2PL+OUP7+LP2gz\nd2oBvkCwjxYvcvD5NYC7e8Jh0sjjze3uVea2eFm3h28Dza+NjmiESdoRTkREBhGFSSIiA0Bagpsr\njhvByytLeHtdP7RCuMPbX9fDlgWw8B5YO79vr1mxBX6T33aeSReeXLoThwUXHjUMgKnD0nj/Byfz\n0g3H89bNJ/L+D07mnkuOICc5jl9/4TDG5SXjD9rN1RwisS4QNOGoBnB3Yf8wqWRlaPj2OhjSvXlJ\nAIQqkxz+XgRB9aFWXFUmiYjIIBKVMMmyrDMty9pgWdYmy7Jubefxmy3LWmtZ1krLst62LGtENK4r\nIjKY3PC5cUwZksp3nljOtrKDvA11uDLJVw/LHzNfVxf37TUrd0LQBzsXd+vwrWV1/GfxDk4cn0NB\nWkLz/Xmp8Rw2NI2xuckUZiZyzvQhfHjr5zh/xjDcocEyvqCqk2Rw0G5u3RQewJ05BpLzzdykHg7f\nBppfG52BXrQAqzJJREQGoV6HSZZlOYF5wFnAZOASy7Im73fYMuBI27anAU8Dd/T2uiIig02828nf\nLpuJ02HxzUc/od7bsxawXgl9+k51Cax7yXxdU9KnlwyE3lgF9mzo8titZXVcfP9HAPz47EndvoYn\nHCYFVJkkg0NQYVL3hCuT4lKgYJqpTOrh8G2geXMCZ6AXQVDrmUnPfavlNVZERCSGRaMy6Whgk23b\nW2zb9gL/Bc5rfYBt2wts2w5/pLMIGBaF64qIDDqFmYncffERbCit4UfPrsK2D1IIEq5M2vw2BJog\nMdsES31oW6lp/dizpfM2t3CQ5AvYPH7tMYzLS+n2NdxO84bb51dlkgwOmpnUTU01YDnNa1v+NNi7\nHnZ81KPh20BzmOTy96YyqdLc+hph5ROw7sXIzyUiIjJARCNMGgrsbPX9rtB9HbkaeDUK1xURGZRO\nGJ/D904bz/PLi3l44baDc1FXKEyq2GJuhx0J1UV9esmmRvNJv7tyU7uP7yg3O7K1DpIm5Hc/SAJw\nu8KVSQqTZHAIqDKpe5pqIC7ZDNoeczLYAVj1dM+GbwO44oEoVSY1VZt1VG6P/FwiIiIDxEEdwG1Z\n1mXAkcCdHTz+dcuyllqWtXTv3r0Hc2kiIgPKt08ay2mT8/jNy+tYtKW87y/ocJhAqTY0/Dt/GjRU\n9OmMj4DPDKPNDpazduuuto8FbS78+0fc8Pgy/BEGSUDzzCSvwqTBZfHf4Zlr+nsV/UIzk7qpqQbi\nUs3XI4+HMZ8D7J4N3wZwOGggDlcgwuHZAR94Qy134UHc+xQmiYhI7HNF4RxFQGGr74eF7mvDsqxT\ngZ8AJ9q23dTeiWzbvh+4H+DII4/UgAsROWQ5HBZ/unA6X7j3Qy6+fxGZSR6umTOKa+eMbg5Ios6d\nYGZ6JGa1tIHUlEDmqD65XMDbElS9/9FCJo+6sPn7RVvK2V3dyC/PncIFM4eRHBfZ/66GVHzMhc6P\n8AVO6u1ypa8F/FCyHErXgOUAhyv0yxn6Ffq+pgRe/SFgwzl3tcz7OkSoMqmbmqrNvKSw026H7R/B\nqBN7fiorDncwwmA9XJUEUB/6YKCmBPxN4IqL7JwiIiIDQDTCpCXAOMuyRmFCpIuBS1sfYFnWEcDf\ngTNt294ThWuKiAx6qfFu/nPtLF5YXsyiLeXc8doGXA6Lr58wpm8u6Eky1UjJeZA6xNzXl2GSr+Vz\nhe0bl9Pou4B4txOA+cuKSI5zcdFRhc33RWL85oe5ybWGusDPe71eiYJgEN75FSTnwqxvQskK2Pou\nbH3fzLPx1nbvPK5Q8LlnHax5DmZ9o2dzcGJYoHlm0kEtLo89TTVtw6T8qfCjneB09/xUvalMah0m\nNYQqk7DNbpbZYyM7p4iIyADQ6zDJtm2/ZVnXA68DTuAftm2vsSzrdmCpbdsvYNrakoGnLNOnvsO2\n7XN7e20RkcGuIC2Bb5w4hm+cOIYv/20hj3+8k2vnjCb0WkqjL9CrsKWN8BDu5NyWMKm6ODrnbkcw\n1EJnYzHMv5NXV5dw3vShPL5kB6+sKuHMwwp6/bMl126jHh/7NIC7/wX88ObPYNE88/1nb5qB7wDZ\n42HaRTBqjtlpy3KaLdyDATNjJuhv9StgKpQeOBmWPQKfPGzamU76Yb/9aAdTuM1NhUld8NaaYdut\nRRAkATQ54nEH2y2q71p7lUkAldsUJomISEyLRmUStm2/Aryy330/a/X1qdG4jojIoezio4bzvadW\ncM87myitbmTRlnK2lNXxl4sO5421pTgti7sv6cGW1/sL7VrkTcjBk1Jg7qvpux3d7FBlkp05mqlV\nu5m3ZCdef5CfPLeaifkpfOuk0b27gL+JhPpdBIjTAO7+tvV9eO1WKF0NM6+EnR+bIOn4m01VUUp+\nz87n95pAadXT5vuiT6K/5gEqEAzidFjNgbJ0oKkmatVq3qi1uVW0fK25SSIiEuOiEiaJiEjfO3tq\nAb98cQ1/fnMjyXEujhqZgdvp4LtPLCdUrMAVx41g5ojMyC4QCpP+uaKelMIqLnUnQnXfhUlBfxMB\n28KRO4nDGlaxaEsFpdVNjM1N5tWb5nT+ZrnsM9j4Ohx7Xcc7M1VsxbKDxOPFF9AYvoPGtk3IM+kc\n09bz2o9g7XxIGw4X/hsmnQu1e6BiM4w4LrJruDyQOQbKNpjvi5aa6x4CAYs/aGteUnfs3+bWm1NZ\n8XiiHSZpRzcREYlxCpNERGJEgsfJM986jgZfgMkFqbicDnaU1zP3nvc5dnQWS7fv4663N/Hvq46O\n7AIeEybttdP53fzVfDk/H3dN37W54W+iCQ+JORNI3/AqcZafrWV1/ODMCZ0HSf4meOJy2LsOxp4C\nuZPaP658EwBuK4DP5+2DH0DatWsJPHsNnPkHWPeiqRo66ccw+8aWVsqUPPOrN3LGmzDJnWTah/Zt\n67P5XgNJMGjjUpjUtda7ufWSzxFPcrA6sieHwySnp22bmyqTREQkxml6o4hIDBmXl8K0Yem4Qju6\nDc9K5IMffo6/XTaTb544mvc27uX55QdsqNk9oTf6e+00AHbbGX1amYS/EZ/lguzxWHaAC0f7sCz4\nwuFDO3/eu3eYIAlgwysdH1f+WfOXAW+Ew3Ol50pWmNtP/wXbP4Q5N5t5RuEgKVpyJprbI75ibg+R\nVjdVJnVDMGBmJkWpMsnr6GZl0nt3wn3HmkHzYfUVZnfCpNyWAdy5U2D9S/DWL8BbH5U1ioiIHGwK\nk0REYlxaghuHw+Kq2aOYOSKD255bzZ6aCFoyQm1ue0knM8lDSTAD+rIyKeDFh9sMXwZumGZz36Uz\nGJLeSehQsRUW3g3TLjaDmtd3FiZtanWpGA2TbBs+/TfM/7aZExQLSleb2z1rARsmn9c31xl2NDjc\nZlc4VwIs+isUfdo31xpAAgqTutYUqiKKUpjkc8QTZ3cxgNvXAB/NM3/ui5a23N9QAfHpJkwN+s19\n599vBs5/8H9w3zFmGL2IiEiMUZgkIjJIuJwOfvb5ydQ0+Vm8paLrJ+zP3dLmNm1YGtu8aVCz2wQa\nfcAKePFZnuYwKbdpG2dNLWh7UNln8MFfYMHvzE5gz1xtBi+f+nOYMNe8aaspbf8CZS1hUtAb4U5M\n/alyJzzyRXjhBlj+H1jzXH+vqHt2r4bEbPN1zkTImdA31xl3GtzyGWSNgTN/ZyrRHjgZHr3ADPge\npPxqc+va3o3mNiM6bY8mTOoioF/1VEtL27oXTJvdG7eZMDhnArjjW45NGwZfuA++9jK44uE/X4IV\nT0RlrSIiIgeLwiQRkUFkYkEKLofF+t0RzPcIhUl17kwm5KWwoT4ZAt62cz6iyBFowm95IC4ZUoea\nN4CN1bDwHvMp/5718NBp8NbP4d3fw6K/QflmOP1XkDoEJpxlTrTx1QNPXroGdi3Bn2zCqYAvhlpJ\nbBuW/sNULOz8GM7+owllFt7TZ8Fe1AQDpjJj6peg8Bg46pq+u5ZltWz9fuSV8J3VcMrPoXiZ+XOz\n6S1++9JKfvrssr5bQz8IBFSZ1KVwq2XB9KiczudI6Loyadmjpn1tzCkmGLr3KPN3dvolcNGjpnou\nLPRay8jj4ZsfQMZIWPNs1wvZvaptC52IiEg/0gBuEZFBJM7lZExOMutLanr+5IwR1LgyCVppjMlN\n5p1ABjiB6mJIyo76Wq1AEwGH23yTPR7KNpo5Im/cBsXLzbwdpweuX2p27nLs9/lH3hSz9feGV2Hm\n11ru9zXAK7dAfCo1R3+XjHd+gO2NcCemg62hEp78Kmx9F0adCOfeAxkjTPXCC9fD1vdg9In9vcqO\nVWwFXz3kT4Wz/nBwrx2fauYzzfoG3DMTPprH2VtK8AcCFJ38P4ZmJB3c9fSRgG3j2v/vQnu2LzSV\nhe5EUxWTnNfxsPrBpmQ5JOWY0DkK/M544mjqeMdAX6NpsTz2OsgaC5vfhoLDTYg07EhzTLgyyeEy\nuxGGuTww5nOw8kkI+MDpbn8RRZ/AA5+DI6+GuX86JHYuFBGRgU2VSSIig8zEghTWlURQmXTUNfx8\n5CMkxXsYl5tMqR2q+qjpmyHczqDPVCaBaQMp+wz2rjffr37ahEKXz4fscQcGSWDeTE04G7b8D7x1\nsHcDvPdHmDfLBFGn/Qor9GYy6IuRmUkrHjdB0tw/w1efN0ESwNQvm7krnzzcr8vr0u6V5jbvsD6/\nlG3bNPoC/Pi5VWwrq2t5wJMEM74Km9/hcHsdRzo2suql+/p8PQdLIGi3+9ehjbpyeHguPH0lPH4R\n/Ps8U+nWqvVzUCtZYaqSohS4+J3xOLDB30EovXslBH0w7Cg4/Ctw1etw7TstQRK0VCa52wk1R59s\nBobvWnrgY2Fb3ze3Sx8yba8iIiL9TGGSiMggM6kgleKqRqrqfT17osNJuddDcrybsbnJ7LYzzf3V\nfTOE2xlsIugIhUnZ48BXB5sXQOZoOPZ6+Op8yJvc+UkmnGXe4N09A+YdDe/8ylRgXPEizLgcp8e0\nk9gdvQkcaIqXQcoQOOrqtm+E3fEw/WJTuVXXN22HUbHxdYhPg9wuft966dcvrWXu3R/wwvJiHlu8\ng/n772A44wpsy8mi4CSWWxM5ftOfePPeG3hj8Qoq61sGmb+xZjdz7niHP72xAV+gbftQoy9AVYOP\nmkYftU1+6r1+Gn2BPv25usPMTOrin287F4MdhPMfMKHG+Q+a+w+FHe98DbBnnakMipKAMxQEVReb\n6sC9G9oeEA6Bhh1pgu/hx4DD2fYYV5y5bW9Xw1FzAMsE4x3Z8ZGp0EwbDpvejuTHEBERiSq1uYmI\nDDIT880ORut2V3PM6KwePbe2yU9KnIuUeDeOlDyCPgeOPguTvAQ9yeab7NCQ5t0rYdK5cMZvuneS\nEbNNNYA7EU74Pkyc26a1xRln3rjZvhgJk4o+NbvUtWfGFbD4b/DEZTDr66YqK/wGdSDwNcD6l2HK\nF9q28fTArn31rCupwbZtLMvCwrw3t7DAAodlsb28jgc/2ArAL15cA8DynZVtT5Q2lBen/5XbFwV4\n5PLD2PzKrZxS9gi+Vx7j+ZeOZ2HOhez1DKNm+woKk5K55516FmzYw2WzRrC6uIplOypZv7uGQPDA\nGVXZyXFMyE9mfF4K4/NSmFSQyvRhaVgHqe0oEAx2PTNpx0emRXTSuSaIzJ8Oz19n/n5Nv+igrLPf\nlK4FOwBDohcmFSVPwYcL971HmpAOzG6CR1wGh51vNgJIK4SU/I5PEg6RQgF3GwkZMHQmbHwNTv7R\ngY8Hg7BjEUw6x8yw27O29z+UiIhILylMEhEZZCYVpAKwviSCMKnRT06yCShG56ezryidrJo+CpNs\nH0FHKAxpveNXT3b/crrhmrc6fNjlMXNKrFioTGqsNjuSTevgzX7eZDjjt2b78ae+Ztrepn7ZvKGN\n4hvniH32Jnhr4LALuv2UynovH20u54NNZXy4qYxt5d0blD6pIBWP02LFririXA5W7KxsDqDC3moY\nT1z6PiZNmQZTXsG/ZyNV79zFFzc+xYUV/6OeBBLjGsAPW6Zdw5c2n8mtz64iOc7F4YXpfOvEMaQn\nmvk1QdvGtk1V0PbyOjaU1vLEkp3Ue02l0l0XH855hw/twX+syAW6s5vbjkUwZEbLnB6ny/z5KV3d\n9wvsbyWhgetRGr4NUJI8hYsdd/LMzLVQOAtqd8Onj8CLN8JrtwKW2V2wM67Q70V7bW4Ak8+DN39q\nNhnIGtP2sbIN0FgJI46D8k2mAtDfNLDCZBEROeQoTBIRGWRyU+LITPKwLoIh3LVNfpLjzf8axuQk\nU7wjnczqEvqi5sJl+2hyhipYknJMe1RjVUuVUhS440JtbrFQmRTegaqjyiQwA35nfdO0wyz/j9l2\nfMkDcMFDZge1/rT6GfP7OHJOtw5ftKWcyx9ajC9gkxzn4pjRmXz12JEcMTwdt9OBbYONTdA285Fs\nQrc2TBmSxvKdlfxk/irOmz6U/3trI9vL6xmRlcj/NuzlhRXFfLCpjJkjMpqv58odT+7F86Dudvjk\nHyRW7jSDjz97g9HLH+SDc4+haOiZjM5J7tZuacGgTVFlA+f/dSFvrC09qGGSo7MqKF+DaZc89rq2\n9+dPNZVjHQ2RHiyKl0NCpqkUihKXw8FnwaEw96qWO4+93rS3Lfs3rHvRVAp2JlyZ1F6bG8CUL5ow\nac1zpsoybN82eC1UrTT8GFNxZgfMhgX5UyP+mURERHpLYZKIyCBjWRYT81NYv7vnQ7hrGn0kx5n/\nNYzLS6YkmMHEyiI62F+oV9y2l8ZwO5RlmRBp18eQMz5q17DcMVCZtGe9GXK+6mnzfWdhEphZLGNP\nMb8a9sGd40zFSX+GSU01plriiMtMFUw3PPTBVtIS3Pz98plMG2YCpJ44dkwW73zvJNaVVPN/b21k\n3oJNrCqqYv3uGjIS3TT6gpw4PufAJyZlwQm3tHw/4Wwo30ziqzcy7pq3wDGlW9d3OCwKMxM5eUIO\nr67ejS8Q7PHPEAl/0Mbl7CQM2rXUDIMefkzb+/OmmvCxpiRqu5wNSFEevg3gclj49295tCwoPMr8\nOveebpwkVJnUXpsbQHqhqXpa+QQcd4O5b+E9ZlMBywFn3WnmyfmbzGN71ilMEhGRfqUB3CIig9DE\n/FQ2lLY/86Ujtm2bmUmhyqRxuSlmCHcf7OZm2zZufOBs1aaRMx6wIGtc9C4U2kHJCgzQMMlbB387\nHh75Aix/1LQmJfWgNTEhA1ILoKrVAOq6crj/ZHj3DvB7O35uNG14DfwNcNgF2LbNU0t30uDteFj1\nnppG3lm/hwtmDGPmiMxehTDj81JI9Dh56pNdBG2bP315Oh//5FTW3n4Glx0zousTuDxw4b8gLhUe\nPBWe/YYZshwMdv1c4HMTc6lp9PPIR9v5dMe+yH4Ib72pGOqGQNDuvHJq8ztm+/kRx7W9Pxw8FC+L\nbI2xwN9kQpYot326nA78ge6/lrbL3clubmGzbzIVR4980exK+c6vYPzpcP0SMycNIGssONxQuqZ3\n6xEREeklVSaJiAxCkwpSaPQF2VZex5ic5G49p8EXIGjTXJk0NjeZ/9kZuL1VpnWmo/aMCDT5g3jw\nt535cfQ3zKDgjj65j0To/Fb40/yBpr7CVJHM/g4ccTlkjur5OVKHtt1xb/2LUPyp+bX6GTjnbhg+\nK3prbs/qZ8w6CmexpriaW55eSVWDj2vmjG738Oc+LSIQtPnykb1vRXI6LOZ9ZQbYcNKEnMgGYafk\nw9deho/ugdXPwsr/QvpwyJlo/gw548ytK97MHhp+nHnM4eD4cTm4nRa3v2SGIs8Zl80PzpjI1GFp\n3bv2rqXwny+ZirSL/tPln39/oIuZSZveMsOh4/e7fsF0SMqFN39mWhHjU7u3vliyZ635+xTFeUkQ\nrkzqXrjY8UnCM5M6eR2dOBdOux3e/DkUHg1n33ngLCanG7LHh6rMdpuB3Rkje7c2ERGRCKgySURk\nEGoZwt39uUm1jX6A5plJmUke6jy55sEo7+jW6AsQhw+rdZhUMK3l0/docYcrkwZomNQUakUccjhk\njz1wO/HuSB0C1a0qk9a9BOkj4NInTeXTP86Al24286j6QsM+E2BM+SI4HOyuMlVgr6/Z3eFTnltW\nxBHD0xmb272gsysnT8jl5Im5vdtRLXssnHMXfG8DnP8g5EyC2j1QtskEc1veNaHZy9+Dvx4Ld46G\nxy8hedkD/PXiqdx76RHcNncSq4uqOOfeD7jusU/Zsre282tufR/+fZ4JqzYvgMcvMlVKnQjYncxM\nqt1jdmwbe8qBj3kS4cv/hIqtMP9b3a6EiinFy81tQbQrkyyCtpmTFbHOdnNrbfZN8ONiuPqNjod6\nn3iLCZvWvWgqmN69A2JhLpyIiAwqqkwSERmExuaaIcLrSqqZO62gW8+paQqFSXEt/2twZQyBCkyr\n2/47DHVmz3oz36ODLeIbfUEy8GGFP63vK04PQSwcAzVMCgc8+1eR9ETqEBMg2baZXbT1XTj66zD+\nDBgxGxb8Bhb/DTa8YiodJp0TnbWHrXvJVIOEdnErrTFvapdu38femiZyUtruOLWxtIb1u2v4xTmT\no7uOaPEkwrQvm1/7s20zEHn7QtixELZ/BBte4dTP1TUPTb7wqEIefG8LD36wlddW7+bCIwu56ZRx\n5Kft92d94+sEn/gq5Z4CrvL+hHOyNnLttj9iPXYhXPoEeNpvhwoEbeLdDmiohKpdJkgM34bDlPbC\nJICRx5vKlzd+Ah/eBcd/J8L/SANUyXLzdynKlTrhNkx/0MbTjeHs7QoH5521uYV1FThN+aL5VVVk\nfi8X/AaWPwZf+gcMnRHZ+kRERHpIlUkiIoNQvNvJ6OykHg3hDlcmhWcmAaTkDAfA7kllUsUWU7mx\naF6HhzR4/cRZfhzuPt7a2rLw4sYxUGcmNYZ+f+J6EyYNg0AT1JebwCjghYmfD503Gc78HVzzFiRm\nwxOXwWs/jm5VyupnIGNU8+DwPdUmuLNteHNt6QGHv7C8GIcFc6fF4BBoyzKtiEd8Bc6bBzd+CqNO\nhE8ehqCZEZUa7+bm0yfw7i0nc/kxI3j6k52ceOcCfvXSWt5aW8q2sjqWvPwQ/scuYY0vn7OrbiWr\nYAR3lx3J9/3fJrjtQ4KPfgma2q9q8gdtLqz+F/xhBPxtNjx2Ibx8M3zwF9i7wWwxn99Jm9ex15kg\n4u1fmmqrwaQPhm8DzTOqetXq5upiN7dIpA2FLz8Ml8+Hxkp4/0/RO7eIiEgXVJkkIjJITSxI5dPt\n3R8IXNtcmdSyd1vOkJGwAerKdtLthqTlj4MdhLXPw/HfbfeQxgbTyuNw93FlEuDFg3OgViaF29x6\nM78mvDNX1U5TbZIz0ewK1drQmfD1BfD6j03Il5zbcVWKrwFW/Nd8nZRtQqikbEjMgvh0cDhg1yem\nva1guqmEOv7m5jfwe2qayE72kBLvZv7yIi6dNbz51NWNPv67ZCezx2YfULEUs466Gp78qgnyWlV9\n5aTE8Yu54/n6NBfPLFjEho8W8tZH9Wyw9vBN54uscU5k8ey/8vKsSeSmxlNS1cAvX8jmpnXwlx33\nUfvQeSRfPR/iUtpcLsVfztnVT8G402H6JZA2zMyrSsnvXpukZcG590LpWrPuU34KR3y1wyrCdu3b\nBgt+a9obs8aZ4d75U02rau7ktrPQ+srW9+C9O+GiR001kt9rhlLP+mbUL+VqDpMiC2Ft26YRNwkQ\n3ZlwYWNOhswx4Ou8RVJERCSaFCaJiAxSkwpSeHFFMVUNPtIS3F0eX9N4YJvbiCEF1NrxVO/Z0b0w\nKRiEFY+D5TS7RlXtMm9299PkbQAOTpjkszw4ggM0TIpKm9tQc7vkQTOA+PwHTOCzP6cbzvwDVO40\nFQxHXWMql1orWQnPXgt717d/LcsJCemmCspymPkudrC5xQ1gT3UjOSnxfH5aAXe+voFtZXWMzE6i\n0RfgjtfWU17XxC1nHBn5zzvQTDjbtFU9+3WYdhHUl5n2o+piqC1lCDY3ALT6K1gx5EQO++pjTItv\n+e9fkJbA3y6fyZtrh/GzZ+P5Zemf2X7XmaR//UXS0jObj5vb8BJO/HDm73vWetpaXDJc8jjM/7aZ\nAfXhXXDirWb9zk7+aVi7F97/Iyx5yARXk84xf8dX/BeWPGCOScox87r6ut1q+WMmUFrwWzjrD+bP\nbMAb9eHb0BImzfnDAh69elb3h6uH/N9bn7FywVoe9tC9NrdIuOIP3u6NIiIiKEwSERm0JuWHh3BX\nM2t019vNhyuTWre5jctLptTOwLmvqKOntbX0IajaSeCEW3G+93vm//dBzrzqp8S721acyar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8rx/ApMeh2v3DyKi/qHdehaAyJVCHbdmDgGRbu3ukqInqQ+THJLZZLMTBJCCOEeEiYJIYRoV6Up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\n7f8DDi7hGT7PuDIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1164b3110>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n",
-    "plt.plot(KO.r.beta_df['beta'])\n",
-    "plt.plot(PG.r.beta_df['beta'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   beta   R-squared:                       0.870\n",
-      "Model:                            OLS   Adj. R-squared:                  0.870\n",
-      "Method:                 Least Squares   F-statistic:                     3363.\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          6.92e-225\n",
-      "Time:                        12:43:08   Log-Likelihood:                 704.82\n",
-      "No. Observations:                 505   AIC:                            -1406.\n",
-      "Df Residuals:                     503   BIC:                            -1397.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "const         -0.0097      0.004     -2.285      0.023      -0.018      -0.001\n",
-      "beta           0.9690      0.017     57.988      0.000       0.936       1.002\n",
-      "==============================================================================\n",
-      "Omnibus:                       11.655   Durbin-Watson:                   0.068\n",
-      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               11.842\n",
-      "Skew:                           0.369   Prob(JB):                      0.00268\n",
-      "Kurtosis:                       3.133   Cond. No.                         6.50\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "-3.19434626171\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = sm.add_constant(PG.r.beta_df['beta'])\n",
-    "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n",
-    "print(coint.summary())\n",
-    "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n",
-    "print(adf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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nFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116b40890>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df['sd_beta'],df['sd_beta_p'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GWZdSzCwCAABgLtq2CxUWQQYzi8ZtaG17TAEAADjM2rYPFRax0CYfx3Fl0XzWAgAAwOL4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\nRERERCRPxSIREREREREREcn7/0SUm9TtupAjAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e3db810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "GE.r.beta_df['beta_p'].plot()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb b/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb
deleted file mode 100644
index 951f57e..0000000
--- a/Tutorial Series/Introduction to Financial Python/.ipynb_checkpoints/Tutorial14 Fama-French Multi-Factor Models-checkpoint.ipynb	
+++ /dev/null
@@ -1,320 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import statsmodels.formula.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats.mstats import normaltest\n",
-    "import time\n",
-    "from cvxopt import matrix\n",
-    "import seaborn as sns\n",
-    "import statsmodels.tsa.stattools as ts\n",
-    "from scipy import stats\n",
-    "from scipy.optimize import minimize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "fama_5 = quandl.get('KFRENCH/FACTORS5_D')\n",
-    "fama_5 = fama_5/100\n",
-    "fama_5 = fama_5.rename(columns = {'Mkt-RF':'mkt'})\n",
-    "spy = quandl.get('LSE/SPY5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "spy = np.log(spy['Last Close']).diff().dropna()\n",
-    "spy.name = 'spy'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mkt</th>\n",
-       "      <th>SMB</th>\n",
-       "      <th>HML</th>\n",
-       "      <th>RMW</th>\n",
-       "      <th>CMA</th>\n",
-       "      <th>RF</th>\n",
-       "      <th>spy</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2017-06-23</th>\n",
-       "      <td>0.0024</td>\n",
-       "      <td>0.0077</td>\n",
-       "      <td>-0.0050</td>\n",
-       "      <td>-0.0035</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-26</th>\n",
-       "      <td>0.0004</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>0.0069</td>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>0.0017</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>0.000738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-27</th>\n",
-       "      <td>-0.0084</td>\n",
-       "      <td>-0.0025</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>0.0032</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-28</th>\n",
-       "      <td>0.0102</td>\n",
-       "      <td>0.0082</td>\n",
-       "      <td>0.0021</td>\n",
-       "      <td>-0.0018</td>\n",
-       "      <td>-0.0007</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-29</th>\n",
-       "      <td>-0.0083</td>\n",
-       "      <td>0.0007</td>\n",
-       "      <td>0.0135</td>\n",
-       "      <td>-0.0025</td>\n",
-       "      <td>0.0036</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.005262</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               mkt     SMB     HML     RMW     CMA       RF       spy\n",
-       "Date                                                                 \n",
-       "2017-06-23  0.0024  0.0077 -0.0050 -0.0035  0.0005  0.00003 -0.000615\n",
-       "2017-06-26  0.0004  0.0005  0.0069  0.0025  0.0017  0.00003  0.000738\n",
-       "2017-06-27 -0.0084 -0.0025  0.0130  0.0031  0.0032  0.00003 -0.000779\n",
-       "2017-06-28  0.0102  0.0082  0.0021 -0.0018 -0.0007  0.00003 -0.000205\n",
-       "2017-06-29 -0.0083  0.0007  0.0135 -0.0025  0.0036  0.00003 -0.005262"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.concat([fama_5,spy],axis = 1).dropna()\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                    spy   R-squared:                       0.340\n",
-      "Model:                            OLS   Adj. R-squared:                  0.339\n",
-      "Method:                 Least Squares   F-statistic:                     210.6\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          3.73e-110\n",
-      "Time:                        16:03:03   Log-Likelihood:                 4457.8\n",
-      "No. Observations:                1229   AIC:                            -8908.\n",
-      "Df Residuals:                    1225   BIC:                            -8887.\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept   9.267e-05      0.000      0.503      0.615      -0.000       0.000\n",
-      "mkt            0.5700      0.023     24.636      0.000       0.525       0.615\n",
-      "SMB           -0.0753      0.039     -1.942      0.052      -0.151       0.001\n",
-      "HML            0.0658      0.039      1.670      0.095      -0.011       0.143\n",
-      "==============================================================================\n",
-      "Omnibus:                      142.776   Durbin-Watson:                   2.657\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              609.967\n",
-      "Skew:                          -0.472   Prob(JB):                    3.53e-133\n",
-      "Kurtosis:                       6.319   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = sm.ols(formula = 'spy ~ mkt+SMB+HML',data = df).fit()\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  small   R-squared:                       0.995\n",
-      "Model:                            OLS   Adj. R-squared:                  0.995\n",
-      "Method:                 Least Squares   F-statistic:                 9.912e+04\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
-      "Time:                        17:13:18   Log-Likelihood:                 8506.2\n",
-      "No. Observations:                1517   AIC:                        -1.700e+04\n",
-      "Df Residuals:                    1513   BIC:                        -1.698e+04\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept     -0.0001   2.29e-05     -6.254      0.000      -0.000   -9.82e-05\n",
-      "mkt            1.0407      0.003    403.477      0.000       1.036       1.046\n",
-      "SMB            0.8707      0.005    181.700      0.000       0.861       0.880\n",
-      "HML            0.1050      0.005     21.744      0.000       0.095       0.114\n",
-      "==============================================================================\n",
-      "Omnibus:                       84.277   Durbin-Watson:                   1.984\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              260.380\n",
-      "Skew:                          -0.213   Prob(JB):                     2.88e-57\n",
-      "Kurtosis:                       4.985   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "small_table = quandl.get('NASDAQOMX/NQUSS')\n",
-    "small = np.log(small_table['Index Value']).diff().dropna()\n",
-    "small.name = 'small'\n",
-    "small_df = pd.concat([fama_5,small],axis = 1).dropna()\n",
-    "small_model = sm.ols(formula = 'small~mkt+SMB+HML',data = small_df).fit()\n",
-    "print(small_model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  large   R-squared:                       0.998\n",
-      "Model:                            OLS   Adj. R-squared:                  0.998\n",
-      "Method:                 Least Squares   F-statistic:                 2.425e+05\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
-      "Time:                        17:12:41   Log-Likelihood:                 9634.6\n",
-      "No. Observations:                1517   AIC:                        -1.926e+04\n",
-      "Df Residuals:                    1513   BIC:                        -1.924e+04\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept     -0.0001   1.09e-05    -11.144      0.000      -0.000   -9.98e-05\n",
-      "mkt            0.9891      0.001    806.811      0.000       0.987       0.992\n",
-      "SMB           -0.1468      0.002    -64.463      0.000      -0.151      -0.142\n",
-      "HML           -0.0076      0.002     -3.305      0.001      -0.012      -0.003\n",
-      "==============================================================================\n",
-      "Omnibus:                       97.595   Durbin-Watson:                   1.959\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              365.063\n",
-      "Skew:                          -0.190   Prob(JB):                     5.34e-80\n",
-      "Kurtosis:                       5.373   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "large_table = quandl.get('NASDAQOMX/NQUSL')\n",
-    "large = np.log(large_table['Index Value']).diff().dropna()\n",
-    "large.name = 'large'\n",
-    "large_df = pd.concat([fama_5,large],axis = 1).dropna()\n",
-    "large_model = sm.ols(formula = 'large~mkt+SMB+HML',data = large_df).fit()\n",
-    "print(large_model.summary())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial00 Introduction to Financial Python.html b/Tutorial Series/Introduction to Financial Python/Tutorial00 Introduction to Financial Python.html
deleted file mode 100644
index fc6801a..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial00 Introduction to Financial Python.html	
+++ /dev/null
@@ -1,119 +0,0 @@
-<h2>About</h2>
-<p>This tutorial series introduces basic Python applied to financial concepts. If you have great investment ideas but don't know how to write them, or if you think you need to learn some basic skills in quantitative finance, then this is a good starting point. The series is broken into four parts: python, math and statistics, basic financial concepts related to investment and financial time series analysis.</p>
-
-<p>We not only introduce the concepts but also show you how to apply the introduced techniques step by step using Python code snippets. We use real financial datasets as examples and after each chapter we design a QuantConnect algorithm applying what we learned.</p>
-
-<div class="row tutorials-page-about">
-	<div class="col-xs-12 col-md-4">
-	&nbsp;
-	<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-	<h3>14 Tutorials</h3>
-	</div>
-	<div class="col-xs-12 col-md-4">
-	&nbsp;
-	<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-	<h3>8 Backtests</h3>
-	</div>
-	<div class="col-xs-12 col-md-4">
-	&nbsp;
-	<p><i class="fa fa-code" aria-hidden="true"></i></p>
-	<h3>167 Code Snippets</h3>
-	</div>
-</div>
-<h2>What Will I Learn ?</h2>
-<div class="row tutorials-learn">
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Python</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Statistics</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Linear Algebra</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Modern Portfolio Theory</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Multi-factor Models</div>
-</div>
-<h2>Tutorials</h2>
-<table class="table qc-table tutorials-table">
-<tbody>
-<tr onclick="window.document.location='/tutorials/introduction-python-data-types-data-structures/';"  class="clickable" ><td>1</td>
-<td>
-<h2>Python: Data Types and Data Structures</h2>
-First glimpse of Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-logical-operation-loop/';"  class="clickable" ><td>2</td>
-<td>
-<h2>Python: Logical Operations and Loop</h2>
-The essential of programming.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-functions-object-oriented-programming/';"  class="clickable" ><td>3</td>
-<td>
-<h2>Python: Functions and Object-Oriented Programming</h2>
-The Python magic.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-numpy-basic-pandas/';"  class="clickable" ><td>4</td>
-<td>
-<h2>NumPy and Basic Pandas</h2>
-The power scientific calculation package for Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-pandas-resampling-dataframe/';"  class="clickable" ><td>5</td>
-<td>
-<h2>Pandas: Resampling and DataFrame</h2>
-The magical Data manipulation tool for Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-rate-return-mean-variance/';"  class="clickable" ><td>6</td>
-<td>
-<h2>Rate of Return, Mean and Variance</h2>
-The basic mathematical concepts for quantitative finance.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-random-variable-distributions/';"  class="clickable" ><td>7</td>
-<td>
-<h2>Random Variable and Distributions</h2>
-Point estimation vs interval estimation
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-confidence-interval-hypothesis-testing/';"  class="clickable" ><td>8</td>
-<td>
-<h2>Confidence Interval and Hypothesis Testing</h2>
-Test your ideas rigorously.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-simple-linear-regression/';"  class="clickable" ><td>9</td>
-<td>
-<h2>Simple Linear Regression</h2>
-Find the relationship between two random variables.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-multiple-linear-regression-residual-analysis/';"  class="clickable" ><td>10</td>
-<td>
-<h2>Multiple Linear Regression and residual analysis</h2>
-Explain a random variable using the power of multi-variables.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-linear-algebra/';"  class="clickable" ><td>11</td>
-<td>
-<h2>Linear Algebra</h2>
-Mathematic tool for large scale calculation
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-modern-portfolio-theory/';"  class="clickable" ><td>12</td>
-<td>
-<h2>Modern Portfolio Theory</h2>
-Don't put all the eggs in one basket.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/capital-asset-pricing-model/';"  class="clickable" ><td>13</td>
-<td>
-<h2>Market Risk</h2>
-Beta and Alpha.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/fama-french-multi-factor-models/';"  class="clickable" ><td>14</td>
-<td>
-<h2>Fama-French Multi-factor Model</h2>
-The most popular asset pricing model since 1992.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-</tbody>
-</table>
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html
deleted file mode 100644
index 5e777fb..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.html	
+++ /dev/null
@@ -1,220 +0,0 @@
-This tutorial provides a basic introduction to the Python programming language. If you are new to Python, you should run the code snippets while reading this tutorial. If you are an advanced Python user, please feel free to skip this chapter.
-
-<h2>Basic Variable Types</h2>
-
-The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
-
-Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
-<pre class="prettyprint linenums">my_string1 = 'Welcome to'
-my_string2 = "QuantConnect"
-print(my_string1 + ' ' + my_string2)
-[out]: Welcome to QuantConnect
-</pre>
-
-An integer is a round number with no values after the decimal point.
-<pre class="prettyprint linenums">my_int = 10
-print(my_int)
-[out]: 10
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-The built-in function int() can convert a string into an integer.
-<pre class="prettyprint linenums">my_string = "100"
-print(type(my_string))
-[out]: type 'str'
-my_int = int(my_string)
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
-<pre class="prettyprint linenums">my_float = 1.0
-print(type(my_float))
-[out]: type 'float'
-my_int = 1
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
-<pre class="prettyprint linenums">my_string = "100"
-my_float = float(my_string)
-print(type(my_float))
-[out]: type 'float'
-</pre>
-
-A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
-<pre class="prettyprint linenums">my_bool = False
-print(my_bool)
-[out]: False
-print(type(my_bool))
-[out]: type 'bool'
-</pre>
-
-<h2>Basic Math Operations</h2>
-
-The basic math operators in python are demonstrated below:
-<pre class="prettyprint linenums">
-print("Addition ", 1+1)
-print("Subtraction ", 5-2)
-print("Multiplication ", 2*3)
-print("Division", 10/2)
-print("exponent", 2**3)
-[out]:
-Addition  2
-Subtraction  3
-Multiplication  6
-Division  5
-exponent 8
-</pre>
-
-<h2>Data Collections</h2>
-
-<h3>List</h3>
-
-A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
-<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print(my_list)
-[out]: ['Quant', 'Connect', 1, 2, 3]
-</pre>
-
-The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
-<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print(len(my_list))
-[out]: 5
-print(my_list[0])
-[out]: Quant
-print(my_list[len(my_list) - 1])
-[out]: 3
-</pre>
-
-You can also change the elements in the list by accessing an index and assigning a new value.
-<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
-my_list[2] = 'go'
-print(my_list)
-[out]: ['Quant', 'Connect', 'go', 2, 3]
-</pre>
-
-A list can also be sliced with a colon:
-<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
-print(my_list[1:3])
-[out]: ['Connect', 1]
-</pre>
-
-The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
-<pre class="prettyprint linenums">print(my_list[1:])
-[out]: ['Connect', 1, 2, 3]
-</pre>
-
-And all elements up to but excluding index 3:
-<pre class="prettyprint linenums">print(my_list[:3])
-[out]: ['Quant', 'Connect', 1]
-</pre>
-
-If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
-<pre class="prettyprint linenums">my_list = ['Hello', 'Quant']
-my_list.append('Hello')
-print(my_list)
-[out]: ['Hello', 'Quant', 'Hello']
-my_list.remove('Hello')
-print(my_list)
-[out]: ['Quant', 'Hello']
-</pre>
-
-When there are repeated instances of "Hello", the first one is removed.
-
-<h3>Tuple</h3>
-
-A tuple is a data structure type similar to a list. The difference is that a tuple is immutable, which means you can't change the elements in it once it's defined. We create a tuple by putting comma-separated values between parentheses.
-<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
-</pre>
-
-Just like a list, a tuple can be sliced by using index.
-<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
-print(my_tuple[1:])
-[out]: ('to', 'QuantConnect')
-</pre>
-
-<h3>Set</h3>
-
-A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
-<pre class="prettyprint linenums">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
-stock_set = set(stock_list)
-print(stock_set)
-[out]: set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])
-</pre>
-Set is an easy way to remove duplicate elements from a list.
-
-<h3>Dictionary</h3>
-
-A dictionary is one of the most important data structures in Python. Unlike sequences which are indexed by integers, dictionaries are indexed by keys which can be either strings or floats.
-
-A dictionary is an <strong>unordered</strong> collection of key : value pairs, with the requirement that the keys are unique. We create a dictionary by placing a comma-separated list of key : value pairs within the braces.
-<pre class="prettyprint linenums">my_dic = {'AAPL': 'Apple', 'FB': 'FaceBook', 'GOOG': 'Alphabet'}
-</pre>
-
-After defining a dictionary, we can access any value by indicating its key in brackets.
-<pre class="prettyprint linenums">print(my_dic['GOOG'])
-[out]: Alphabet
-</pre>
-
-We can also change the value associated with a specified key:
-<pre class="prettyprint linenums">my_dic['GOOG'] = 'Alphabet Company'
-print(my_dic['GOOG'])
-[out]: Alphabet Company
-</pre>
-
-The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
-<pre class="prettyprint linenums">print(my_dic.keys())
-[out]: ['GOOG', 'AAPL', 'FB']
-</pre>
-
-<h2>Common String Operations</h2>
-
-A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
-<pre class="prettyprint linenums">my_str = 'Welcome to QuantConnect'
-print(my_str[8:])
-[out]: to QuantConnect
-</pre>
-
-There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
-<pre class="prettyprint linenums">
-print("Counting the number of e's in this sentence".count('e'))
-[out]: 6
-print('The first time e appears in this sentence'.find('e'))
-[out]: 2
-print('all the a in this sentence now becomes e'.replace('a','e'))
-[out]: ell the e in this sentence now becomes e
-</pre>
-
-The most commonly used method for strings is string.split(). This method will split the string by the indicated character and return a list:
-<pre class="prettyprint linenums">Time = '2016-04-01 09:43:00'
-splited_list = Time.split(' ')
-date = splited_list[0]
-time = splited_list[1]
-print(date, time)
-[out]: 2016-04-01 09:43:00
-hour = time.split(':')[0]
-print(hour)
-[out]: 09
-</pre>
-
-We can replace parts of a string by our variable. This is called string formatting.
-<pre class="prettyprint linenums">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
-print(my_time)
-[out]: Hour: 9, Minute: 43
-</pre>
-
-Another way to format a string is to use the % symbol.
-<pre class="prettyprint linenums">print 'pi is %f' % 3.14
-[out]: pi is 3.140000
-print('%s to %s' % ('Welcome', 'QuantConnect'))
-[out]: Welcome to QuantConnect
-</pre>
-
-%s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
-
-<h2>Summary</h2>
-
-We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb
deleted file mode 100644
index e4566f2..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial01 Data Types and Data Structures.ipynb	
+++ /dev/null
@@ -1,512 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Welcome to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string1 = 'Welcome to'\n",
-    "my_string2 = \"QuantConnect\"\n",
-    "print(my_string1 + ' ' + my_string2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10\n",
-      "<class 'int'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_int = 10\n",
-    "print(my_int)\n",
-    "print(type(my_int))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'str'>\n",
-      "<class 'int'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string = \"100\"\n",
-    "print(type(my_string))\n",
-    "my_int = int(my_string)\n",
-    "print(type(my_int))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'float'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_string = \"100\"\n",
-    "my_float = float(my_string)\n",
-    "print(type(my_float))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "<class 'bool'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_bool = False\n",
-    "print(my_bool)\n",
-    "print(type(my_bool))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Addition  2\n",
-      "Subtraction  3\n",
-      "Multiplication  6\n",
-      "Division  5.0\n",
-      "exponent 8\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Addition \", 1+1)\n",
-    "print(\"Subtraction \", 5-2)\n",
-    "print(\"Multiplication \", 2*3)\n",
-    "print(\"Division \", 10/2)\n",
-    "print('exponent', 2**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.3333333333333333\n",
-      "0.3333333333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(1/3)\n",
-    "print(1.0/3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 1, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n",
-      "Quant\n",
-      "3\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant', 'Connect', 1,2,3]\n",
-    "print(len(my_list))\n",
-    "print(my_list[0])\n",
-    "print(my_list[len(my_list) -1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 'go', 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant','Connect',1,2,3]\n",
-    "my_list[2] = 'go'\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect']\n",
-      "['Connect']\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant']\n",
-    "my_list.append('Connect')\n",
-    "print(my_list)\n",
-    "my_list.remove('Quant')\n",
-    "print(my_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Connect', 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_list = ['Quant','Connect',1,2,3]\n",
-    "print(my_list[1:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Connect', 1, 2, 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_list[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quant', 'Connect', 1]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_list[:3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "my_tuple = ('Welcome','to','QuantConnect')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('to', 'QuantConnect')\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_tuple = ('Welcome','to','QuantConnect')\n",
-    "print(my_tuple[1:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])\n"
-     ]
-    }
-   ],
-   "source": [
-    "stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']\n",
-    "stock_set = set(stock_list)\n",
-    "print(stock_set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "my_dic = {'AAPL':'AAPLE', 'FB':'FaceBook', 'GOOG':'Alphabet'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alphabet\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_dic['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Alphabet Company\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_dic['GOOG'] = 'Alphabet Company'\n",
-    "print(my_dic['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['GOOG', 'AAPL', 'FB']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(my_dic.keys())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_str = 'Welcome to QuantConnect'\n",
-    "print(my_str[8:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n",
-      "2\n",
-      "ell the e in this sentence now becomes e\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Counting the number of e appears in this sentence'.count('e'))\n",
-    "print('The first time e appears in this sentence'.find('e'))\n",
-    "print('all the a in this sentence now becomes e'.replace('a','e'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2016-04-01 09:43:00\n",
-      "09\n"
-     ]
-    }
-   ],
-   "source": [
-    "Time = '2016-04-01 09:43:00'\n",
-    "splited_list = Time.split(' ')\n",
-    "date = splited_list[0]\n",
-    "time = splited_list[1]\n",
-    "print(date, time)\n",
-    "hour = time.split(':')[0]\n",
-    "print(hour)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hour: 09, Minute:43\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_time = 'Hour: {}, Minute:{}'.format('09','43')\n",
-    "print(my_time)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the pi number is 3.140000\n",
-      "Welcome to Quantconnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('the pi number is %f'%3.14)\n",
-    "print('%s to %s'%('Welcome','Quantconnect'))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.html b/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.html
deleted file mode 100644
index abd7ba1..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.html	
+++ /dev/null
@@ -1,194 +0,0 @@
-We discussed the basic data types and data structures in Python in the last tutorial. This chapter covers logical operations and loops in Python, which are very common in programming.
-
-<h2>Logical Operations</h2>
-
-Like most programming languages, Python has comparison operators:
-<pre class="prettyprint linenums">print 1 == 0    # 1 equals 0
-print 1 == 1    # 1 equals 1
-print 1 != 0    # 1 is not equal to 0
-print 5 &gt;= 5    # 5 is greater than or equal to 5
-print 5 &gt;= 6    # 5 is greater than or equal to 6
-[out]:
-False
-True
-True
-True
-False
-</pre>
-
-Each statement above has a boolean value, which must be either True or False, but not both.
-
-We can combine simple statements P and Q to form complex statements using logical operators:
-<ul>
-    <li>The statement "P and Q" is true if both P and Q are true, otherwise it is false.</li>
-    <li>The statement "P or Q" is false if both P and Q are false, otherwise it is true.</li>
-    <li>The statement "not P" is true if P is false, and vice versa.</li>
-</ul>
-
-<pre class="prettyprint linenums">print 2 &gt; 1 and 3 &gt; 2
-print 2 &gt; 1 and 3 &lt; 2
-print 2 &gt; 1 or  3 &lt; 2
-print 2 &lt; 1 and 3 &lt; 2
-[out]:
-True
-False
-True
-False
-</pre>
-
-When dealing with a very complex logical statement that involves in several statements, we can use brackets to separate and combine them.
-<pre class="prettyprint linenums">print (3 &gt; 2 or 1 &lt; 3) and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
-print 3 &gt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
-[out]:
-False
-True
-</pre>
-
-Comparing the above two statements, we can see that it's wise to use brackets when we make a complex logical statement.
-
-<h2>If Statement</h2>
-
-An if statement executes a segment of code only if its condition is true. A standard if statement consists of 3 segments: if, elif and else.
-<pre class="prettyprint linenums">if condition1:
-    # if condition1 is true, execute the code here
-    # and ignore the rest of this if statement
-elif condition2:
-    # if condition1 is false, and condition2 is true, execute the code here
-    # and ignore the rest of this if statement
-else:
-    # if none of the above conditions is True, execute the code here
-</pre>
-
-An if statement doesn't necessarily has elif and else part. If it's not specified, the indented block of code will be executed when the condition is true, otherwise the whole if statement will be skipped.
-<pre class="prettyprint linenums">i = 0
-if i == 0: print 'i == 0 is True'
-[out]: i==0 is True
-</pre>
-
-As we mentioned above, we can write some complex statements here:
-<pre class="prettyprint linenums">p = 1 &gt; 0
-q = 2 &gt; 3
-if p and q:
-    print 'p and q is true'
-elif p and not q:
-    print 'q is false'
-elif q and not p:
-    print 'p is false'
-else:
-    print 'None of p and q is true'
-[out]: q is false
-</pre>
-
-<h2>Loop Structures</h2>
-
-Loops are an essential part of programming. The "for" and "while" loops run a block of code repeatedly.
-
-<h3>While Loop</h3>
-
-A "while" loop will run repeatedly until a certain condition has been met.
-<pre class="prettyprint linenums">i = 0
-while i &lt; 5:
-    print i
-    i += 1
-[out]:
-0
-1
-2
-3
-4
-</pre>
-
-When making a while loop, we need to ensure that something changes from iteration to iteration so that the while loop will terminate, otherwise it will run forever. Here we used i += 1 (short for i = i + 1) to make i larger after each iteration. This is the most commonly used method to control a while loop.
-
-<h3>For Loop</h3>
-
-A "for" loop will iterate over a sequence of value and terminate when the sequence has ended.
-<pre class="prettyprint linenums">for x in [1,2,3,4,5]: print x
-[out]:
-1
-2
-3
-4
-5
-</pre>
-
-We can also add if statements in a for loop. Here is a real example from our pairs trading algorithm:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-selected = ['AAPL','IBM']
-new_list = []
-for stock in stocks:
-    if stock not in selected:
-        new_list.append(stock)
-print new_list
-[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
-</pre>
-
-Here we iterated all the elements in the list 'stocks'. Later in this chapter we will introduce a smarter way to do this, which is just an one-line code.
-
-<h3>Break and continue</h3>
-
-These are two commonly used commands in a for loop. If "break" is triggered while a loop is executing, the loop will terminate immediately:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-for stock in stocks:
-    print stock
-    if stock == 'FB': break
-[out]:
-AAPL
-GOOG
-IBM
-FB
-</pre>
-
-The "continue" command tells the loop to end this iteration and skip to the next iteration:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-for stock in stocks:
-    if stock == 'FB': continue
-    print stock
-[out]:
-AAPL
-GOOG
-IBM
-F
-V
-G
-GE
-</pre>
-
-<h2>List Comprehension</h2>
-
-List comprehension is a Pythonic way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence. For example, if we want to create a list of squares using for loop:
-<pre class="prettyprint linenums">squares = []
-for i in [1,2,3,4,5]:
-    squares.append(i**2)
-print squares
-[out]: [1, 4, 9, 16, 25]
-</pre>
-
-Using list comprehension:
-<pre class="prettyprint linenums">foo = [1,2,3,4,5]
-squares = [x**2 for x in foo]
-print squares
-[out]: [1, 4, 9, 16, 25]
-</pre>
-
-Recall the example above where we used a for loop to select stocks. Here we use list comprehension:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-selected = ['AAPL','IBM']
-new_list = [x for x in stocks if x not in selected]
-print new_list
-[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
-</pre>
-
-A list comprehension consists of square brackets containing an expression followed by a "for" clause, and possibly "for" or "if" clauses. For example:
-<pre class="prettyprint linenums">print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
-print [str(x) + ' vs ' + str(y) for x in ['AAPL','GOOG','IBM','FB']
-                                for y in ['F','V','G','GE'] if x != y]
-[out]:
-[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
-['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']
-</pre>
-
-List comprehension is an elegant way to organize one or more for loops when creating a list.
-
-<h2>Summary</h2>
-This chapter has introduced logical operations, loops and list comprehension. In the next chapter we will introduce functions and object-oriented programming, which will enable us to make our codes clean and versatile.
\ No newline at end of file
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.ipynb
deleted file mode 100644
index 4270da9..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial02 Logical Operations and Loops.ipynb	
+++ /dev/null
@@ -1,372 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "True\n",
-      "True\n",
-      "True\n",
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 1 == 0\n",
-    "print 1 == 1\n",
-    "print 1 != 0\n",
-    "print 5 >= 5\n",
-    "print 5 >= 6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n",
-      "False\n",
-      "True\n",
-      "False\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 2 > 1 and 3 > 2\n",
-    "print 2 > 1 and 3 < 2 \n",
-    "print 2 > 1 or 3 < 2\n",
-    "print 2 < 1 and 3 < 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n",
-      "True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print (3 > 2 or 1 < 3) and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))\n",
-    "print 3 > 2 or 1 < 3 and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-5-91506eaa57cd>, line 5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-91506eaa57cd>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    elif statement2:\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
-     ]
-    }
-   ],
-   "source": [
-    "if statement1:\n",
-    "    # if the statement1 is true, execute the code here.\n",
-    "    # code.....\n",
-    "    # code.....\n",
-    "elif statement2:\n",
-    "    # if the statement 1 is false, skip the codes above to this part.\n",
-    "    # code......\n",
-    "    # code......\n",
-    "else:\n",
-    "    # if none of the above statements is True, skip to this part\n",
-    "    # code......"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "i==0 is True\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = 0\n",
-    "if i == 0:\n",
-    "    print 'i==0 is True'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "q is false\n"
-     ]
-    }
-   ],
-   "source": [
-    "p = 1 > 0\n",
-    "q = 2 > 3\n",
-    "if p and q:\n",
-    "    print 'p and q is true'\n",
-    "elif p and not q:\n",
-    "    print 'q is false'\n",
-    "elif q and not p:\n",
-    "    print 'p is false'\n",
-    "else:\n",
-    "    print 'None of p and q is true'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n"
-     ]
-    }
-   ],
-   "source": [
-    "i = 0\n",
-    "while i < 5:\n",
-    "    print i\n",
-    "    i += 1 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in [1,2,3,4,5]:\n",
-    "    print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['AAPL', 'GOOG', 'IBM', 'FB', 'F', 'V', 'G', 'GE']\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "selected = ['AAPL','IBM']\n",
-    "new_list = []\n",
-    "for i in stocks:\n",
-    "    if i not in selected:\n",
-    "        new_list.append(i)\n",
-    "print stocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "FB\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "for i in stocks:\n",
-    "    print i\n",
-    "    if i == 'FB':\n",
-    "        break"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "F\n",
-      "V\n",
-      "G\n",
-      "GE\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "for i in stocks:\n",
-    "    if i == 'FB':\n",
-    "        continue\n",
-    "    print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 4, 9, 16, 25]\n"
-     ]
-    }
-   ],
-   "source": [
-    "squares = []\n",
-    "for i in [1,2,3,4,5]:\n",
-    "    squares.append(i**2)\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 4, 9, 16, 25]\n"
-     ]
-    }
-   ],
-   "source": [
-    "list = [1,2,3,4,5]\n",
-    "squares = [x**2 for x in list]\n",
-    "print squares"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['AAPL', 'IBM']\n"
-     ]
-    }
-   ],
-   "source": [
-    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "selected = ['AAPL','IBM']\n",
-    "new_list = [x for x in stocks if x in selected]\n",
-    "print new_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]\n",
-      "['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]\n",
-    "print [str(x)+' vs '+str(y) for x in ['AAPL','GOOG','IBM','FB'] for y in ['F','V','G','GE'] if x!=y]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html
deleted file mode 100644
index 560e7f9..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.html	
+++ /dev/null
@@ -1,194 +0,0 @@
-In the last tutorial we introduced logical operations, loops and list comprehension. We will introduce functions and object-oriented programming in this chapter, which will enable us to build complex algorithms in more flexible ways.
-
-<h2>Functions</h2>
-
-A function is a reusable block of code. We can use a function to output a value, or do anything else we want. We can easily define our own function by using the keyword "def".
-
-<pre class="prettyprint linenums">def product(x,y):
-    return x*y
-print product(2,3)
-[out]: 6
-print product(5,10)
-[out]: 50
-</pre>
-
-The keyword "def" is followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The product() function above has "x" and "y" as its parameters. A function doesn't necessarily have parameters:
-
-<pre class="prettyprint linenums">def say_hi():
-    print "Welcome to QuantConnect'
-say_hi()
-[out]: Welcome to QuantConnect
-</pre>
-
-<h2>Built-in Function</h2>
-
-<strong>range()</strong> is a function that creates a list containing an arithmetic sequence. It's often used in for loops. The arguments must be integers. If the "step" argument is omitted, it defaults to 1.
-<pre class="prettyprint linenums">
-print range(10)
-[out]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-print range(1, 11)
-[out]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-print range(1, 11, 2)
-[out]: [1, 3, 5, 7, 9]
-</pre>
-
-<strong>len()</strong> is another function used together with range() to create a for loop. This function returns the length of an object. The argument must be a sequence or a collection.
-<pre class="prettyprint linenums">
-tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
-print "The number of tickers is {}".format(len(tickers))
-for k in range(len(tickers)):
-    print k + 1, tickers[k]
-[out]:
-The number of tickers is 8
-1 AAPL
-2 GOOGL
-3 IBM
-4 FB
-5 F
-6 V
-7 G
-8 GE
-</pre>
-
-Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
-
-<strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
-<pre class="prettyprint linenums">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-print(list(map(len,tickers)))
-[out]: [4, 5, 3, 2, 1, 1, 1, 2]
-</pre>
-
-The <strong>lambda operator</strong> is a way to create small anonymous functions. These functions are just needed where they have been created. For example:
-<pre class="prettyprint linenums">map(lambda x: x**2, range(10))
-[out]: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
-</pre>
-
-map() can be applied to more than one list. The lists have to have the same length.
-<pre class="prettyprint linenums">map(lambda x, y: x+y, [1,2,3,4,5], [5,4,3,2,1])
-[out]: [6, 6, 6, 6, 6]
-</pre>
-
-<strong>sorted()</strong> takes a list or set and returns a new sorted list:
-<pre class="prettyprint linenums">sorted([5,2,3,4,1])
-[out]: [1, 2, 3, 4, 5]
-</pre>
-
-We can add a "key" parameter to specify a function to be called on each list element prior to making comparisons. For example:
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-sorted(price_list, key = lambda x: x[1])
-[out]:
-[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
-</pre>
-
-By default the values are sorted by ascending order. We can change it to descending by adding an optional parameter "reverse'.
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-sorted(price_list, key = lambda x: x[1], reverse = True)
-[out]:
-[('GOOGL', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]
-</pre>
-
-Lists also have a function list.sort(). This function takes the same "key" and "reverse" arguments as sorted(), but it doesn't return a new list.
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-price_list.sort(key = lambda x: x[1])
-print price_list
-[out]:
-[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
-</pre>
-
-<h2>Object-Oriented Programming</h2>
-
-Python is an object-oriented programming language. It's important to understand the concept of "objects" because almost every kind of data from QuantConnect API is an object.
-
-<h3>Class</h3>
-A class is a type of data, just like a string, float, or list. When we create an object of that data type, we call it an <strong>instance</strong> of a class.
-
-In Python, everything is an object - everything is an instance of some class. The data stored inside an object are called <strong>attributes</strong>, and the functions which are associated with the object are called <strong>methods</strong>.
-
-For example, as mentioned above, a list is an object of the "list" class, and it has a method list.sort().
-
-We can create our own objects by defining a class. We would do this when it's helpful to group certain functions together. For example, we define a class named "Stock" here:
-<pre class="prettyprint linenums">class Stock:
-    def __init__(self, ticker, open, close, volume):
-        self.ticker = ticker
-        self.open = open
-        self.close = close
-        self.volume = volume
-        self.rate_return = float(close)/open - 1
-
-    def update(self, open, close):
-        self.open = open
-        self.close = close
-        self.rate_return = float(self.close)/self.open - 1
-
-    def print_return(self):
-        print self.rate_return
-</pre>
-
-The "Stock" class has attributes "ticker", "open", "close", "volume" and "rate_return". Inside the class body, the first method is called __init__, which is a special method. When we create a new instance of the class, the __init__ method is immediately executed with all the parameters that we pass to the "Stock" object. The purpose of this method is to set up a new "Stock" object using data we have provided.
-
-Here we create two Stock objects named "apple" and "google".
-<pre class="prettyprint linenums">apple  = Stock('AAPL', 143.69, 144.09, 20109375)
-google = Stock('GOOGL', 898.7, 911.7, 1561616)
-</pre>
-
-Stock objects also have two other methods: update() and print_return(). We can access the attribues of a Stock object and call its methods:
-<pre class="prettyprint linenums">apple.ticker
-[out]: 'AAPL'
-google.print_return()
-[out]: 0.0144653388227
-google.update(912.8,913.4)
-google.print_return()
-[out]: 0.000657318141981
-</pre>
-
-By calling the update() function, we updated the open and close prices of a stock. Please note that when we use the attributes or call the methods <strong>inside a class</strong>, we need to specify them as self.attribute or self.method(), otherwise Python will deem them as global variables and thus raise an error.
-
-We can add an attribute to an object anywhere:
-<pre class="prettyprint linenums">
-apple.ceo = 'Tim Cook'
-apple.ceo
-[out]: 'Tim Cook'
-</pre>
-
-We can check what names (i.e. attributes and methods) are defined on an object using the dir() function:
-<pre class="prettyprint linenums">dir(apple)
-[out]:
-['__doc__',
- '__init__',
- '__module__',
- 'ceo',
- 'close',
- 'open',
- 'print_return',
- 'rate_return',
- 'ticker',
- 'update',
- 'volume']
-</pre>
-
-<h3>Inheritance</h3>
-
-Inheritance is a way of arranging classes in a hierarchy from the most general to the most specific. A "child" class is a more specific type of a "parent" class because a child class will inherit all the attribues and methods of its parent. For example, we define a class named "Child" which inherits "Stock":
-<pre class="prettyprint linenums">class Child(Stock):
-    def __init__(self, name):
-        self.name = name
-</pre>
-
-Then we create an object:
-<pre class="prettyprint linenums">aa = Child('AA')
-print aa.name
-[out]: 'AA'
-aa.update(100, 102)
-print aa.open
-[out]: 100
-print aa.close
-[out]: 102
-print aa.print_return()
-[out]: 0.02</pre>
-As seen above, the new class Child has inherited the methods from Stock.
-
-<h2>Summary</h2>
-
-In this chapter we have introduced functions and classes. When we write a QuantConnect algorithm, we would define our algorithm as a class (QCAlgorithm). This means our algorithm inherited the QC API methods from QCAlgorithm class.
-
-In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb
deleted file mode 100644
index 261e947..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial03 Functions and Objective-Oriented Programming.ipynb	
+++ /dev/null
@@ -1,418 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n",
-      "50\n"
-     ]
-    }
-   ],
-   "source": [
-    "def product(x,y):\n",
-    "    return x*y\n",
-    "print(product(2,3))\n",
-    "print(product(5,10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Welcome to QuantConnect\n"
-     ]
-    }
-   ],
-   "source": [
-    "def say_hi():\n",
-    "    print('Welcome to QuantConnect')\n",
-    "say_hi()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "range(0, 10)\n",
-      "range(1, 11)\n",
-      "range(1, 11, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(range(10))\n",
-    "print(range(1,11))\n",
-    "print(range(1,11,2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The length of tickers is 8\n",
-      "AAPL\n",
-      "GOOG\n",
-      "IBM\n",
-      "FB\n",
-      "F\n",
-      "V\n",
-      "G\n",
-      "GE\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print('The length of tickers is {}'.format(len(tickers)))\n",
-    "for i in range(len(tickers)):\n",
-    "    print(tickers[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4, 4, 3, 2, 1, 1, 1, 2]\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
-    "print(list(map(len,tickers)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x: x**2, range(10)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[6, 6, 6, 6, 6]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted([5,2,3,4,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "sorted(price_list, key = lambda x: x[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('GOOG', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "sorted(price_list, key = lambda x: x[1],reverse = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
-    "price_list.sort(key = lambda x: x[1])\n",
-    "print(price_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class stock:\n",
-    "    def __init__(self, ticker, open, close, volume):\n",
-    "        self.ticker = ticker\n",
-    "        self.open = open\n",
-    "        self.close = close\n",
-    "        self.volume = volume\n",
-    "        self.rate_return = float(close)/open - 1\n",
-    " \n",
-    "    def update(self, open, close):\n",
-    "        self.open = open\n",
-    "        self.close = close\n",
-    "        self.rate_return = float(self.close)/self.open - 1\n",
-    " \n",
-    "    def print_return(self):\n",
-    "        print(self.rate_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "apple = stock('AAPL', 143.69, 144.09, 20109375)\n",
-    "google = stock('GOOG', 898.7, 911.7, 1561616)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.014465338822744034\n",
-      "0.0006573181419806673\n"
-     ]
-    }
-   ],
-   "source": [
-    "apple.ticker\n",
-    "google.print_return()\n",
-    "google.update(912.8,913.4)\n",
-    "google.print_return()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Tim Cook'"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "apple.ceo = 'Tim Cook'\n",
-    "apple.ceo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['__class__',\n",
-       " '__delattr__',\n",
-       " '__dict__',\n",
-       " '__dir__',\n",
-       " '__doc__',\n",
-       " '__eq__',\n",
-       " '__format__',\n",
-       " '__ge__',\n",
-       " '__getattribute__',\n",
-       " '__gt__',\n",
-       " '__hash__',\n",
-       " '__init__',\n",
-       " '__init_subclass__',\n",
-       " '__le__',\n",
-       " '__lt__',\n",
-       " '__module__',\n",
-       " '__ne__',\n",
-       " '__new__',\n",
-       " '__reduce__',\n",
-       " '__reduce_ex__',\n",
-       " '__repr__',\n",
-       " '__setattr__',\n",
-       " '__sizeof__',\n",
-       " '__str__',\n",
-       " '__subclasshook__',\n",
-       " '__weakref__',\n",
-       " 'ceo',\n",
-       " 'close',\n",
-       " 'open',\n",
-       " 'print_return',\n",
-       " 'rate_return',\n",
-       " 'ticker',\n",
-       " 'update',\n",
-       " 'volume']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(apple)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class child(stock):\n",
-    "    def __init__(self,name):\n",
-    "        self.name = name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "aa\n",
-      "100\n",
-      "102\n",
-      "0.020000000000000018\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "aa = child('aa')\n",
-    "print(aa.name)\n",
-    "aa.update(100,102)\n",
-    "print(aa.open)\n",
-    "print(aa.close)\n",
-    "print(aa.print_return())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.html b/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.html
deleted file mode 100644
index 31ef442..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.html	
+++ /dev/null
@@ -1,229 +0,0 @@
-Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas.
-
-NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data.
-
-Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package:
-<pre class="prettyprint linenums">import numpy as np</pre>
-
-<h2>NumPy</h2>
-
-<h3>Basic NumPy Arrays</h3>
-
-A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. Here we make an array by passing a list of Apple stock prices:
-<pre class="prettyprint linenums">price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
-price_array = np.array(price_list)
-print price_array, type(price_array)
-[out]: [ 143.73  145.83  143.68  144.02  143.5   142.62]
-&lt;class 'numpy.ndarray'&gt;</pre>
-
-Notice that the type of array is "ndarray" which is a multi-dimensional array. If we pass np.array() a list of lists, it will create a 2-dimensional array.
-<pre class="prettyprint linenums">Ar = np.array([[1,3], [2,4]])
-print Ar, type(Ar)
-[out]: [[1 3]
-        [2 4]]
-&lt;class 'numpy.ndarray'&gt;</pre>
-
-We get the dimensions of an ndarray using the .shape attribute:
-<pre class="prettyprint linenums">print Ar.shape
-[out]: (2, 2)</pre>
-
-If we create an 2-dimensional array (i.e. matrix), each row can be accessed by index:
-<pre class="prettyprint linenums">print Ar[0]
-[out]: [1 3]
-print Ar[1]
-[out]: [2 4]</pre>
-
-If we want to access the matrix by column instead:
-<pre class="prettyprint linenums">print 'First column:', Ar[:,0]
-[out]: First column: [1 2]
-print 'Second column:', Ar[:,1]
-[out]: Second column: [3 4]</pre>
-
-<h3>Array Functions</h3>
-
-Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array:
-<pre class="prettyprint linenums">np.log(price_array)
-[out]: [4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]</pre>
-
-Other functions return a single value:
-<pre class="prettyprint linenums">np.mean(price_array)
-[out]: 143.896666667
-print np.std(price_array)
-[out]: 0.967379047852
-print np.sum(price_array)
-[out]: 863.38
-print np.max(price_array)
-[out]: 145.83
-</pre>
-The functions above return the mean, standard deviation, total and maximum value of an array.
-
-<h2>Pandas</h2>
-
-Pandas is one of the most powerful tools for dealing with financial data. First we need to import Pandas:
-<pre class="prettyprint linenums">import pandas as pd</pre>
-
-<h3>Series</h3>
-
-Series is a one-dimensional labeled array capable of holding any data type (integers, strings, float, Python object, etc.)
-
-We create a Series by calling pd.Series(data), where data can be a dictionary, an array or just a scalar value.
-<pre class="prettyprint linenums">price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
-s = pd.Series(price)
-print s
-
-0    143.73
-1    145.83
-2    143.68
-3    144.02
-4    143.50
-5    142.62</pre>
-
-We can customize the indices of a new Series:
-<pre class="prettyprint linenums">s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f'])
-print s
-
-a    143.73
-b    145.83
-c    143.68
-d    144.02
-e    143.50
-f    142.62</pre>
-
-Or we can change the indices of an existing Series:
-<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
-print s
-
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62</pre>
-
-Series is like a list since it can be sliced by index:
-<pre class="prettyprint linenums">print s[1:]
-print s[:-2]
-
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62
-dtype: float64
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-dtype: float64</pre>
-
-Series is also like a dictionary whose values can be set or fetched by index label:
-<pre class="prettyprint linenums">print s[4]
-s[4] = 0
-print s
-
-143.68
-6    143.73
-5    145.83
-4      0.00
-3    144.02
-2    143.50
-1    142.62
-dtype: float64</pre>
-
-Series can also have a name attribute, which will be used when we make up a Pandas DataFrame using several series.
-<pre class="prettyprint linenums">s = pd.Series(price, name = 'Apple Prices')
-print s
-print s.name
-
-0    143.73
-1    145.83
-2    143.68
-3    144.02
-4    143.50
-5    142.62
-Name: Apple Prices, dtype: float64
-Apple Prices</pre>
-
-We can get the statistical summaries of a Series:
-<pre class="prettyprint linenums">print s.describe()
-
-count      6.000000
-mean     143.896667
-std        1.059711
-min      142.620000
-25%      143.545000
-50%      143.705000
-75%      143.947500
-max      145.830000</pre>
-
-<h3>Time Index</h3>
-
-Pandas has a built-in function specifically for creating date indices: pd.date_range(). We use it to create a new index for our Series:
-<pre class="prettyprint linenums">time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D')
-print time_index
-s.index = time_index
-print s
-
-DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
-               '2017-01-05', '2017-01-06'],
-              dtype='datetime64[ns]', freq='D')
-2017-01-01    143.73
-2017-01-02    145.83
-2017-01-03    143.68
-2017-01-04    144.02
-2017-01-05    143.50
-2017-01-06    142.62
-Freq: D, Name: Apple Prices, dtype: float64</pre>
-
-Series are usually accessed using the iloc[] and loc[] methods. iloc[] is used to access elements by integer index, and loc[] is used to access the index of the series.
-
-iloc[] is necessary when the index of a series are integers, take our previous defined series as example:
-<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
-print s
-print s[1]
-
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62
-Name: Apple Prices, dtype: float64
-142.62</pre>
-
-If we intended to take the second element of the series, we would make a mistake here, because the index are integers. In order to access to the element we want, we use iloc[] here:
-<pre class="prettyprint linenums">print s.iloc[1]
-[out]: 145.83
-</pre>
-
-While working with time series data, we often use time as the index. Pandas provides us with various methods to access the data by time index.
-<pre class="prettyprint linenums">s.index = time_index
-print s['2017-01-03']
-[out]: 143.68</pre>
-
-We can even access to a range of dates:
-<pre class="prettyprint linenums">print s['2017-01-02':'2017-01-05']
-
-2017-01-02    145.83
-2017-01-03    143.68
-2017-01-04    144.02
-2017-01-05    143.50
-Freq: D, Name: Apple Prices, dtype: float64</pre>
-
-Series[] provides us a very flexible way to index data. We can add any condition in the square brackets:
-<pre class="prettyprint linenums">print s[s &lt; np.mean(s)]
-print s[(s &gt; np.mean(s)) &amp; (s &lt; np.mean(s) + 1.64*np.std(s))]
-
-2017-01-01    143.73
-2017-01-03    143.68
-2017-01-05    143.50
-2017-01-06    142.62
-Name: Apple Prices, dtype: float64
-2017-01-04    144.02
-Freq: D, Name: Apple Price List, dtype: float64</pre>
-
-As demonstrated, we can use logical operators like &amp; (and), | (or) and ~ (not) to group multiple conditions.
-
-<h2>Summary</h2>
-
-Here we have introduced NumPy and Pandas for scientific computing in Python. In the next chapter, we will dive into Pandas to learn resampling and manipulating Pandas DataFrame, which are commonly used in financial data analysis.
\ No newline at end of file
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb
deleted file mode 100644
index 32fd4f9..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial04 NumPy and Basic Pandas.ipynb	
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[143.73 145.83 143.68 144.02 143.5  142.62] <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
-    "price_array = np.array(price_list)\n",
-    "print(price_array, type(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 3]\n",
-      " [2 4]] <class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "Ar = np.array([[1,3],[2,4]])\n",
-    "print(Ar, type(Ar))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Ar.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1 3]\n",
-      "[2 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(Ar[0])\n",
-    "print(Ar[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "the first column:  [1 2]\n",
-      "the second column:  [3 4]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('the first column: ', Ar[:,0])\n",
-    "print('the second column: ', Ar[:,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.log(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.89666666666668\n",
-      "0.9673790478515796\n",
-      "863.38\n",
-      "145.83\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.mean(price_array))\n",
-    "print(np.std(price_array))\n",
-    "print(np.sum(price_array))\n",
-    "print(np.max(price_array))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    143.73\n",
-       "1    145.83\n",
-       "2    143.68\n",
-       "3    144.02\n",
-       "4    143.50\n",
-       "5    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
-    "s = pd.Series(price)\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    143.73\n",
-       "b    145.83\n",
-       "c    143.68\n",
-       "d    144.02\n",
-       "e    143.50\n",
-       "f    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s = pd.Series(price,index = ['a','b','c','d','e','f'])\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6    143.73\n",
-       "5    145.83\n",
-       "4    143.68\n",
-       "3    144.02\n",
-       "2    143.50\n",
-       "1    142.62\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s.index = [6,5,4,3,2,1]\n",
-    "s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "dtype: float64\n",
-      "6    143.73\n",
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[1:])\n",
-    "print(s[:-2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.68\n",
-      "6    143.73\n",
-      "5    145.83\n",
-      "4      0.00\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[4])\n",
-    "s[4] = 0\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0    143.73\n",
-      "1    145.83\n",
-      "2    143.68\n",
-      "3    144.02\n",
-      "4    143.50\n",
-      "5    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "Apple Price List\n"
-     ]
-    }
-   ],
-   "source": [
-    "s = pd.Series(price, name = 'Apple Price List')\n",
-    "print(s)\n",
-    "print(s.name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
-      "               '2017-01-05', '2017-01-06'],\n",
-      "              dtype='datetime64[ns]', freq='D')\n",
-      "2017-01-01    143.73\n",
-      "2017-01-02    145.83\n",
-      "2017-01-03    143.68\n",
-      "2017-01-04    144.02\n",
-      "2017-01-05    143.50\n",
-      "2017-01-06    142.62\n",
-      "Freq: D, Name: Apple Price List, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "time_index = pd.date_range('2017-01-01',periods = len(s),freq = 'D')\n",
-    "print(time_index)\n",
-    "s.index = time_index\n",
-    "print(s)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6    143.73\n",
-      "5    145.83\n",
-      "4    143.68\n",
-      "3    144.02\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "142.62\n"
-     ]
-    }
-   ],
-   "source": [
-    "s.index = [6,5,4,3,2,1]\n",
-    "print(s)\n",
-    "print(s[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "145.83\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s.iloc[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "143.68\n"
-     ]
-    }
-   ],
-   "source": [
-    "s.index = time_index\n",
-    "print(s['2017-01-03'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2017-01-02    145.83\n",
-      "2017-01-03    143.68\n",
-      "2017-01-04    144.02\n",
-      "2017-01-05    143.50\n",
-      "Freq: D, Name: Apple Price List, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s['2017-01-02':'2017-01-05'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6    143.73\n",
-      "4    143.68\n",
-      "2    143.50\n",
-      "1    142.62\n",
-      "Name: Apple Price List, dtype: float64\n",
-      "[6    False\n",
-      "5    False\n",
-      "4    False\n",
-      "3     True\n",
-      "2    False\n",
-      "1    False\n",
-      "Name: Apple Price List, dtype: bool]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(s[s < np.mean(s)] )\n",
-    "print([(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.html b/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.html
deleted file mode 100644
index 7805251..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.html	
+++ /dev/null
@@ -1,678 +0,0 @@
-In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
-
-Here we use data from the yahoo_finance API.
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'</pre>
-
-We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
-<pre class="prettyprint linenums">aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table['Adj. Close']['2017']
-print aapl</pre>
-
-Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
-<pre class="prettyprint linenums">print aapl['2017-3']
-Date
-2017-03-01    138.657681
-2017-03-02    137.834404
-2017-03-03    138.647762
-2017-03-06    138.211326
-2017-03-07    138.389868
-2017-03-08    137.874080
-2017-03-09    137.556672
-2017-03-10    138.012946
-2017-03-13    138.072460
-2017-03-14    137.864161
-2017-03-15    139.322254
-2017-03-16    139.550391
-2017-03-17    138.856061
-2017-03-20    140.314154
-2017-03-21    138.707276
-2017-03-22    140.274478
-2017-03-23    139.778528
-2017-03-24    139.500796
-2017-03-27    139.738852
-2017-03-28    142.635200
-2017-03-29    142.952608
-2017-03-30    142.764147
-2017-03-31    142.496334</pre>
-
-Or in several consecutive months:
-<pre class="prettyprint linenums">aapl['2017-2':'2017-4']</pre>
-
-.head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
-<pre class="prettyprint linenums">print aapl.head()
-print aapl.tail(10)</pre>
-
-The output:
-<pre class="prettyprint linenums">
-Date
-2017-01-03    114.715378
-2017-01-04    114.586983
-2017-01-05    115.169696
-2017-01-06    116.453639
-2017-01-09    117.520300
-Name: Adj. Close, dtype: float64
-Date
-2017-08-08    159.433108
-2017-08-09    160.409148
-2017-08-10    155.270000
-2017-08-11    157.480000
-2017-08-14    159.850000
-2017-08-15    161.600000
-2017-08-16    160.950000
-2017-08-17    157.870000
-2017-08-18    157.500000
-2017-08-21    157.210000
-Name: Adj. Close, dtype: float64</pre>
-
-<h2>Resampling</h2>
-
-series.resample(freq) is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
-
-series.resample.mean() is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
-<pre class="prettyprint linenums">by_month = aapl.resample('M').mean()
-print by_month
-
-Date
-2017-01-31    118.093136
-2017-02-28    132.456268
-2017-03-31    139.478802
-2017-04-30    141.728436
-2017-05-31    151.386305
-2017-06-30    147.233064
-2017-07-31    147.706190
-2017-08-31    157.444303</pre>
-
-We can also aggregate the data by week:
-<pre class="prettyprint linenums">by_week = aapl.resample('W').mean()
-print by_week.head()
-
-Date
-2017-01-31    120.932434
-2017-02-28    136.551200
-2017-03-31    143.532630
-2017-04-30    144.179981
-2017-05-31    156.100000
-2017-06-30    155.450000
-2017-07-31    153.460000</pre>
-
-We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
-<pre class="prettyprint linenums">three_day = aapl.resample('3D').mean()
-two_week  = aapl.resample('2W').mean()
-two_month = aapl.resample('2M').mean()</pre>
-
-Besides the mean() method, other methods can also be used with the resampler:
-<pre class="prettyprint linenums">std = aapl.resample('W').std()    # standard deviation
-max = aapl.resample('W').max()    # maximum value
-min = aapl.resample('W').min()    # minimum value</pre>
-
-OFten we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
-<pre class="prettyprint linenums">last_day = aapl.resample('M').agg(lambda x: x[-1])
-print last_day
-
-Date
-2017-01-31    119.851150
-2017-02-28    135.880362
-2017-03-31    142.496334
-2017-04-30    142.486415
-2017-05-31    152.142689
-2017-06-30    143.438008
-2017-07-31    148.248489
-2017-08-31    157.210000</pre>
-
-Or directly calculate the monthly rates of return using the data for the first day and the last day:
-<pre class="prettyprint linenums">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
-print monthly_return
-
-Date
-2017-01-31    0.045940
-2017-02-28    0.070409
-2017-03-31    0.033823
-2017-04-30   -0.007736
-2017-05-31    0.039829
-2017-06-30   -0.073528
-2017-07-31    0.033035
-2017-08-31    0.004505</pre>
-
-Series object also provides us some convenient methods to do some quick calculation.
-<pre class="prettyprint linenums">print monthly_return.mean()
-print monthly_return.std()
-print monthly_return.max()
-[out]: 0.0208974076157
-       0.0476398315185
-       0.0704090212384</pre>
-
-Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
-<pre class="prettyprint linenums">print last_day.diff()
-print last_day.pct_change()
-
-Date
-2017-01-31          NaN
-2017-02-28    16.029211
-2017-03-31     6.615972
-2017-04-30    -0.009919
-2017-05-31     9.656274
-2017-06-30    -8.704681
-2017-07-31     4.810482
-2017-08-31     8.961511
-Freq: M, Name: Adj. Close, dtype: float64
-Date
-2017-01-31         NaN
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449</pre>
-
-Notice that we induced a NaN value while calculating percentage changes i.e. returns.
-
-When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
-<pre class="prettyprint linenums">daily_return = last_day.pct_change()
-print daily_return.fillna(0)
-
-Date
-2017-01-31    0.000000
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449</pre>
-
-Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
-<pre class="prettyprint linenums">daily_return = last_day.pct_change()
-print daily_return.fillna(method = 'bfill')
-
-Date
-2017-01-31    0.133743
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449</pre>
-
-As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
-
-We can also simply remove NaN values by <strong>.dropna()</strong>
-<pre class="prettyprint linenums">daily_return = last_day.pct_change().dropna()
-print daily_return
-
-Date
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.038050</pre>
-
-<h2>DataFrame</h2>
-
-The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
-
-More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
-
-<h3>Create DataFrames</h3>
-
-The most common method of creating a DataFrame is passing a dictionary:
-<pre class="prettyprint linenums">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
-        'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
-        'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
-dates = pd.date_range('2017-07-03', periods = 5, freq = 'D')
-df = pd.DataFrame(dict, index = dates)
-print df
-
-              AAPL    GOOG     IBM
-2017-07-03  143.50  898.70  155.58
-2017-07-04  144.09  911.71  153.67
-2017-07-05  142.73  906.69  152.36
-2017-07-06  144.18  918.59  152.94
-2017-07-07  143.77  926.99  153.49</pre>
-
-<h3>Manipulating DataFrames</h3>
-
-We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
-
-If a column name contains no spaces, then we can also use df.column_name to fetch a column:
-<pre class="prettyprint linenums">df = aapl_table
-print df.Close.tail(5)
-print df['Adj. Volume'].tail(5)
-
-Date
-2017-07-24    152.09
-2017-07-25    152.74
-2017-07-26    153.46
-2017-07-27    150.56
-2017-07-28    149.50
-Name: Close, dtype: float64
-Date
-2017-07-24    21122730.0
-2017-07-25    18612649.0
-2017-07-26    15172136.0
-2017-07-27    32175875.0
-2017-07-28    16832947.0
-Name: Adj. Volume, dtype: float64</pre>
-
-All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
-<pre class="prettyprint linenums">aapl_2016 = df['2016']
-aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
-print aapl_month</pre>
-
-<pre class="prettyprint linenums">
-              Open      High     Low   Close      Volume  Ex-Dividend  \
-Date
-2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0
-2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0
-2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0
-2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0
-2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0
-2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0
-2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0
-2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0
-2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0
-2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0
-2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0
-2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0
-
-            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \
-Date
-2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495
-2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856
-2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931
-2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748
-2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913
-2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775
-2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363
-2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460
-2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885
-2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287
-2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807
-2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583
-
-            Adj. Volume
-Date
-2016-01-31   64416504.0
-2016-02-29   35216277.0
-2016-03-31   25888449.0
-2016-04-30   68531478.0
-2016-05-31   42307212.0
-2016-06-30   35836356.0
-2016-07-31   27733688.0
-2016-08-31   29662406.0
-2016-09-30   36379106.0
-2016-10-31   26419398.0
-2016-11-30   36162258.0
-2016-12-31   30586265.0</pre>
-
-We may select certain columns of a DataFrame using their names:
-<pre class="prettyprint linenums">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
-print aapl_bar
-
-              Open      High     Low   Close
-Date
-2016-01-31   94.79   97.3400   94.35   97.34
-2016-02-29   96.86   98.2300   96.65   96.69
-2016-03-31  109.72  109.9000  108.88  108.99
-2016-04-30   93.99   94.7200   92.51   93.74
-2016-05-31   99.60  100.4000   98.82   99.86
-2016-06-30   94.44   95.7700   94.30   95.60
-2016-07-31  104.19  104.5500  103.68  104.21
-2016-08-31  105.66  106.5699  105.64  106.10
-2016-09-30  112.46  113.3700  111.80  113.05
-2016-10-31  113.65  114.2300  113.20  113.54
-2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82</pre>
-
-We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
-<pre class="prettyprint linenums">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
-
-              Open    High     Low   Close
-Date
-2016-03-31  109.72  109.90  108.88  108.99
-2016-04-30   93.99   94.72   92.51   93.74
-2016-05-31   99.60  100.40   98.82   99.86
-2016-06-30   94.44   95.77   94.30   95.60</pre>
-
-The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
-<pre class="prettyprint linenums">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
-print above
-
-              Open      High     Low   Close
-Date
-2016-03-31  109.72  109.9000  108.88  108.99
-2016-08-31  105.66  106.5699  105.64  106.10
-2016-09-30  112.46  113.3700  111.80  113.05
-2016-10-31  113.65  114.2300  113.20  113.54
-2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82</pre>
-
-<h3>Data Validation</h3>
-
-As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
-<pre class="prettyprint linenums">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
-print aapl_bar
-
-              Open      High     Low   Close  rate_return
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
-
-Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
-<pre class="prettyprint linenums">missing = aapl_bar.isnull()
-print missing
-print '---------------------------------------------'
-print missing.describe()
-
-             Open   High    Low  Close rate_return
-Date
-2016-01-31  False  False  False  False        True
-2016-02-29  False  False  False  False       False
-2016-03-31  False  False  False  False       False
-2016-04-30  False  False  False  False       False
-2016-05-31  False  False  False  False       False
-2016-06-30  False  False  False  False       False
-2016-07-31  False  False  False  False       False
-2016-08-31  False  False  False  False       False
-2016-09-30  False  False  False  False       False
-2016-10-31  False  False  False  False       False
-2016-11-30  False  False  False  False       False
-2016-12-31  False  False  False  False       False
-
----------------------------------------------
-
-         Open   High    Low  Close rate_return
-count      12     12     12     12          12
-unique      1      1      1      1           2
-top     False  False  False  False       False
-freq       12     12     12     12          11</pre>
-
-The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
-
-We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
-
-We can also find the rows with missing values easily:
-<pre class="prettyprint linenums">print missing[missing.rate_return == True]
-
-             Open   High    Low  Close rate_return
-Date
-2016-01-31  False  False  False  False        True</pre>
-
-Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
-<pre class="prettyprint linenums">drop = aapl_bar.dropna()
-print drop
-print '\n--------------------------------------------------\n'
-fill = aapl_bar.fillna(0)
-print fill
-
-              Open      High     Low   Close  rate_return
-Date
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955
-
---------------------------------------------------
-
-              Open      High     Low   Close  rate_return
-Date
-2016-01-31   94.79   97.3400   94.35   97.34     0.000000
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
-
-<h3>DataFrame Concat</h3>
-
-We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
-
-In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
-<pre class="prettyprint linenums">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
-s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
-data_frame = pd.concat([s1, s2], axis = 1)
-print data_frame
-
-     AAPL    GOOG
-0  143.50  898.70
-1  144.09  911.71
-2  142.73  906.69
-3  144.18  918.59
-4  143.77  926.99</pre>
-
-The "axis = 1" parameter will join two DataFrames by columns:
-<pre class="prettyprint linenums">log_price = np.log(aapl_bar.Close)
-log_price.name = 'log_price'
-print log_price
-print '\n--------------------------------------------\n'
-concat = pd.concat([aapl_bar, log_price], axis = 1)
-print concat
-
-Date
-2016-01-31    4.578210
-2016-02-29    4.571510
-2016-03-31    4.691256
-2016-04-30    4.540525
-2016-05-31    4.603769
-2016-06-30    4.560173
-2016-07-31    4.646408
-2016-08-31    4.664382
-2016-09-30    4.727830
-2016-10-31    4.732155
-2016-11-30    4.705197
-2016-12-31    4.752037
-Freq: M, Name: log_price, dtype: float64
-
---------------------------------------------
-
-              Open      High     Low   Close  rate_return  log_price
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037</pre>
-
-We can also join two DataFrames by rows. Consider these two DataFrames:
-<pre class="prettyprint linenums">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
-print df_volume
-print '\n-------------------------------------------\n'
-df_2017 = aapl_table.loc['2016-10':'2017-04', ['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
-print df_2017
-
-                Volume  Split Ratio
-Date
-2016-10-31  26419398.0          1.0
-2016-11-30  36162258.0          1.0
-2016-12-31  30586265.0          1.0
-2017-01-31  49200993.0          1.0
-2017-02-28  23482860.0          1.0
-2017-03-31  19661651.0          1.0
-2017-04-30  20247187.0          1.0
-
--------------------------------------------
-
-              Open     High     Low   Close
-Date
-2016-10-31  113.65  114.230  113.20  113.54
-2016-11-30  111.56  112.200  110.27  110.52
-2016-12-31  116.65  117.200  115.43  115.82
-2017-01-31  121.15  121.390  120.62  121.35
-2017-02-28  137.08  137.435  136.70  136.99
-2017-03-31  143.72  144.270  143.01  143.66
-2017-04-30  144.09  144.300  143.27  143.65</pre>
-
-Now we merge the DataFrames with our DataFrame 'aapl_bar'
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1)
-print concat
-
-              Open      High     Low   Close  rate_return      Volume  \
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0
-2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0
-2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0
-2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0
-2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0
-
-            Split Ratio
-Date
-2016-01-31          NaN
-2016-02-29          NaN
-2016-03-31          NaN
-2016-04-30          NaN
-2016-05-31          NaN
-2016-06-30          NaN
-2016-07-31          NaN
-2016-08-31          NaN
-2016-09-30          NaN
-2016-10-31          1.0
-2016-11-30          1.0
-2016-12-31          1.0
-2017-01-31          1.0
-2017-02-28          1.0
-2017-03-31          1.0
-2017-04-30          1.0</pre>
-
-By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
-print concat
-
-              Open    High     Low   Close  rate_return      Volume  \
-Date
-2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0
-2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0
-2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0
-
-            Split Ratio
-Date
-2016-10-31          1.0
-2016-11-30          1.0
-2016-12-31          1.0
-</pre>
-Only the intersection part was left if use 'inner join' method.
-Now let's try to append a DataFrame to another one:
-<pre class="prettyprint linenums">append = aapl_bar.append(df_2017)
-print append
-             Close      High     Low    Open  rate_return
-Date
-2016-01-31   97.34   97.3400   94.35   94.79          NaN
-2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.54  114.2300  113.20  113.65          NaN
-2016-11-30  110.52  112.2000  110.27  111.56          NaN
-2016-12-31  115.82  117.2000  115.43  116.65          NaN
-2017-01-31  121.35  121.3900  120.62  121.15          NaN
-2017-02-28  136.99  137.4350  136.70  137.08          NaN
-2017-03-31  143.66  144.2700  143.01  143.72          NaN
-2017-04-30  143.65  144.3000  143.27  144.09          NaN
-</pre>
-'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_2017], axis = 0)
-print concat
-             Close      High     Low    Open  rate_return
-Date
-2016-01-31   97.34   97.3400   94.35   94.79          NaN
-2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.54  114.2300  113.20  113.65          NaN
-2016-11-30  110.52  112.2000  110.27  111.56          NaN
-2016-12-31  115.82  117.2000  115.43  116.65          NaN
-2017-01-31  121.35  121.3900  120.62  121.15          NaN
-2017-02-28  136.99  137.4350  136.70  137.08          NaN
-2017-03-31  143.66  144.2700  143.01  143.72          NaN
-2017-04-30  143.65  144.3000  143.27  144.09          NaN
-</pre>
-Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
-<pre class="prettyprint linenums">df_2017.columns = ['Change', 'High', 'Low', 'Close']
-concat = pd.concat([aapl_bar, df_2017], axis = 0)
-print concat
-
-            Change   Close      High     Low    Open  rate_return
-Date
-2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN
-2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN
-2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN
-2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN
-2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN
-2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN
-2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN
-2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN</pre>
-
-Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
-
-<h2>Summary</h2>
-
-Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.
\ No newline at end of file
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb
deleted file mode 100644
index a67e05c..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial05 Pandas-Resampling and DataFrame.ipynb	
+++ /dev/null
@@ -1,1540 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "aapl_table = quandl.get('WIKI/AAPL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "aapl = aapl_table['Adj. Close']['2017']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-03    114.715378\n",
-      "2017-01-04    114.586983\n",
-      "2017-01-05    115.169696\n",
-      "2017-01-06    116.453639\n",
-      "2017-01-09    117.520300\n",
-      "2017-01-10    117.638817\n",
-      "2017-01-11    118.270913\n",
-      "2017-01-12    117.777088\n",
-      "2017-01-13    117.569682\n",
-      "2017-01-17    118.517825\n",
-      "2017-01-18    118.507948\n",
-      "2017-01-19    118.300542\n",
-      "2017-01-20    118.517825\n",
-      "2017-01-23    118.596837\n",
-      "2017-01-24    118.488195\n",
-      "2017-01-25    120.374604\n",
-      "2017-01-26    120.433863\n",
-      "2017-01-27    120.443739\n",
-      "2017-01-30    120.127692\n",
-      "2017-01-31    119.851150\n",
-      "2017-02-01    127.159749\n",
-      "2017-02-02    126.942467\n",
-      "2017-02-03    127.485673\n",
-      "2017-02-06    128.680728\n",
-      "2017-02-07    129.905412\n",
-      "2017-02-08    130.409113\n",
-      "2017-02-09    131.347379\n",
-      "2017-02-10    131.049809\n",
-      "2017-02-13    132.210332\n",
-      "2017-02-14    133.926319\n",
-      "                 ...    \n",
-      "2017-11-16    171.100000\n",
-      "2017-11-17    170.150000\n",
-      "2017-11-20    169.980000\n",
-      "2017-11-21    173.140000\n",
-      "2017-11-22    174.960000\n",
-      "2017-11-24    174.970000\n",
-      "2017-11-27    174.090000\n",
-      "2017-11-28    173.070000\n",
-      "2017-11-29    169.480000\n",
-      "2017-11-30    171.850000\n",
-      "2017-12-01    171.050000\n",
-      "2017-12-04    169.800000\n",
-      "2017-12-05    169.640000\n",
-      "2017-12-06    169.010000\n",
-      "2017-12-07    169.452000\n",
-      "2017-12-08    169.370000\n",
-      "2017-12-11    172.670000\n",
-      "2017-12-12    171.700000\n",
-      "2017-12-13    172.270000\n",
-      "2017-12-14    172.220000\n",
-      "2017-12-15    173.870000\n",
-      "2017-12-18    176.420000\n",
-      "2017-12-19    174.540000\n",
-      "2017-12-20    174.350000\n",
-      "2017-12-21    175.010000\n",
-      "2017-12-22    175.010000\n",
-      "2017-12-26    170.570000\n",
-      "2017-12-27    170.600000\n",
-      "2017-12-28    171.080000\n",
-      "2017-12-29    169.230000\n",
-      "Name: Adj. Close, Length: 249, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-03-01    138.657681\n",
-      "2017-03-02    137.834404\n",
-      "2017-03-03    138.647762\n",
-      "2017-03-06    138.211326\n",
-      "2017-03-07    138.389868\n",
-      "2017-03-08    137.874080\n",
-      "2017-03-09    137.556672\n",
-      "2017-03-10    138.012946\n",
-      "2017-03-13    138.072460\n",
-      "2017-03-14    137.864161\n",
-      "2017-03-15    139.322254\n",
-      "2017-03-16    139.550391\n",
-      "2017-03-17    138.856061\n",
-      "2017-03-20    140.314154\n",
-      "2017-03-21    138.707276\n",
-      "2017-03-22    140.274478\n",
-      "2017-03-23    139.778528\n",
-      "2017-03-24    139.500796\n",
-      "2017-03-27    139.738852\n",
-      "2017-03-28    142.635200\n",
-      "2017-03-29    142.952608\n",
-      "2017-03-30    142.764147\n",
-      "2017-03-31    142.496334\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl['2017-3'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-02-01    127.159749\n",
-       "2017-02-02    126.942467\n",
-       "2017-02-03    127.485673\n",
-       "2017-02-06    128.680728\n",
-       "2017-02-07    129.905412\n",
-       "2017-02-08    130.409113\n",
-       "2017-02-09    131.347379\n",
-       "2017-02-10    131.049809\n",
-       "2017-02-13    132.210332\n",
-       "2017-02-14    133.926319\n",
-       "2017-02-15    134.412350\n",
-       "2017-02-16    134.248686\n",
-       "2017-02-17    134.620649\n",
-       "2017-02-21    135.592711\n",
-       "2017-02-22    135.999390\n",
-       "2017-02-23    135.424088\n",
-       "2017-02-24    135.553035\n",
-       "2017-02-27    135.820848\n",
-       "2017-02-28    135.880362\n",
-       "2017-03-01    138.657681\n",
-       "2017-03-02    137.834404\n",
-       "2017-03-03    138.647762\n",
-       "2017-03-06    138.211326\n",
-       "2017-03-07    138.389868\n",
-       "2017-03-08    137.874080\n",
-       "2017-03-09    137.556672\n",
-       "2017-03-10    138.012946\n",
-       "2017-03-13    138.072460\n",
-       "2017-03-14    137.864161\n",
-       "2017-03-15    139.322254\n",
-       "                 ...    \n",
-       "2017-03-17    138.856061\n",
-       "2017-03-20    140.314154\n",
-       "2017-03-21    138.707276\n",
-       "2017-03-22    140.274478\n",
-       "2017-03-23    139.778528\n",
-       "2017-03-24    139.500796\n",
-       "2017-03-27    139.738852\n",
-       "2017-03-28    142.635200\n",
-       "2017-03-29    142.952608\n",
-       "2017-03-30    142.764147\n",
-       "2017-03-31    142.496334\n",
-       "2017-04-03    142.536010\n",
-       "2017-04-04    143.597342\n",
-       "2017-04-05    142.853418\n",
-       "2017-04-06    142.496334\n",
-       "2017-04-07    142.178926\n",
-       "2017-04-10    142.010303\n",
-       "2017-04-11    140.482777\n",
-       "2017-04-12    140.651400\n",
-       "2017-04-13    139.907475\n",
-       "2017-04-17    140.681157\n",
-       "2017-04-18    140.056260\n",
-       "2017-04-19    139.540472\n",
-       "2017-04-20    141.286216\n",
-       "2017-04-21    141.117593\n",
-       "2017-04-24    142.476496\n",
-       "2017-04-25    143.369205\n",
-       "2017-04-26    142.487208\n",
-       "2017-04-27    142.625281\n",
-       "2017-04-28    142.486415\n",
-       "Name: Adj. Close, Length: 61, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aapl['2017-2':'2017-4']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-03    114.715378\n",
-      "2017-01-04    114.586983\n",
-      "2017-01-05    115.169696\n",
-      "2017-01-06    116.453639\n",
-      "2017-01-09    117.520300\n",
-      "Name: Adj. Close, dtype: float64\n",
-      "Date\n",
-      "2017-12-15    173.87\n",
-      "2017-12-18    176.42\n",
-      "2017-12-19    174.54\n",
-      "2017-12-20    174.35\n",
-      "2017-12-21    175.01\n",
-      "2017-12-22    175.01\n",
-      "2017-12-26    170.57\n",
-      "2017-12-27    170.60\n",
-      "2017-12-28    171.08\n",
-      "2017-12-29    169.23\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl.head(5))\n",
-    "print(aapl.tail(10))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    118.093136\n",
-      "2017-02-28    132.456268\n",
-      "2017-03-31    139.478802\n",
-      "2017-04-30    141.728436\n",
-      "2017-05-31    151.386305\n",
-      "2017-06-30    147.233064\n",
-      "2017-07-31    147.706190\n",
-      "2017-08-31    158.856375\n",
-      "2017-09-30    157.606500\n",
-      "2017-10-31    157.811627\n",
-      "2017-11-30    172.214500\n",
-      "2017-12-31    171.893100\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "by_month = aapl.resample('M').mean()\n",
-    "print(by_month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-08    115.231424\n",
-      "2017-01-15    117.755360\n",
-      "2017-01-22    118.461035\n",
-      "2017-01-29    119.667448\n",
-      "2017-02-05    124.313346\n",
-      "Freq: W-SUN, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "by_week = aapl.resample('W').mean()\n",
-    "print(by_week.head())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    120.443739\n",
-       "2017-02-28    135.999390\n",
-       "2017-03-31    142.952608\n",
-       "2017-04-30    143.597342\n",
-       "2017-05-31    155.469192\n",
-       "2017-06-30    154.821818\n",
-       "2017-07-31    152.839860\n",
-       "2017-08-31    164.000000\n",
-       "2017-09-30    164.050000\n",
-       "2017-10-31    169.040000\n",
-       "2017-11-30    175.880000\n",
-       "2017-12-31    176.420000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "aapl.resample('M').max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "three_day = aapl.resample('3D').mean()\n",
-    "two_week = aapl.resample('2W').mean()\n",
-    "two_month = aapl.resample('2M').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "std = aapl.resample('W').std()\n",
-    "max = aapl.resample('W').max()\n",
-    "min = aapl.resample('W').min()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.045940\n",
-      "2017-02-28    0.070409\n",
-      "2017-03-31    0.033823\n",
-      "2017-04-30   -0.007736\n",
-      "2017-05-31    0.039829\n",
-      "2017-06-30   -0.073528\n",
-      "2017-07-31    0.033035\n",
-      "2017-08-31    0.047890\n",
-      "2017-09-30   -0.049112\n",
-      "2017-10-31    0.094252\n",
-      "2017-11-30    0.022247\n",
-      "2017-12-31   -0.003357\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
-    "print(monthly_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    119.851150\n",
-      "2017-02-28    135.880362\n",
-      "2017-03-31    142.496334\n",
-      "2017-04-30    142.486415\n",
-      "2017-05-31    152.142689\n",
-      "2017-06-30    143.438008\n",
-      "2017-07-31    148.248489\n",
-      "2017-08-31    164.000000\n",
-      "2017-09-30    154.120000\n",
-      "2017-10-31    169.040000\n",
-      "2017-11-30    171.850000\n",
-      "2017-12-31    169.230000\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
-    "print(last_day)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "by_week = aapl.resample('W').mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    1.781512\n",
-       "2017-03-31    4.323834\n",
-       "2017-05-31    5.454495\n",
-       "2017-07-31    3.865333\n",
-       "2017-09-30    3.634223\n",
-       "2017-11-30    8.011704\n",
-       "2018-01-31    2.291229\n",
-       "Freq: 2M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "twomon = aapl.resample('2M').std()\n",
-    "twomon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    119.851150\n",
-       "2017-02-28    135.880362\n",
-       "2017-03-31    142.496334\n",
-       "2017-04-30    142.486415\n",
-       "2017-05-31    152.142689\n",
-       "2017-06-30    143.438008\n",
-       "2017-07-31    148.248489\n",
-       "2017-08-31    164.000000\n",
-       "2017-09-30    154.120000\n",
-       "2017-10-31    169.040000\n",
-       "2017-11-30    171.850000\n",
-       "2017-12-31    169.230000\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
-    "last_day"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-01-31    0.045940\n",
-       "2017-02-28    0.070409\n",
-       "2017-03-31    0.033823\n",
-       "2017-04-30   -0.007736\n",
-       "2017-05-31    0.039829\n",
-       "2017-06-30   -0.073528\n",
-       "2017-07-31    0.033035\n",
-       "2017-08-31    0.047890\n",
-       "2017-09-30   -0.049112\n",
-       "2017-10-31    0.094252\n",
-       "2017-11-30    0.022247\n",
-       "2017-12-31   -0.003357\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
-    "monthly_return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.02114094011940022\n",
-      "0.04775652864223314\n",
-      "0.09425168306576914\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(monthly_return.mean())\n",
-    "print(monthly_return.std())\n",
-    "print(monthly_return.max())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31          NaN\n",
-      "2017-02-28    16.029211\n",
-      "2017-03-31     6.615972\n",
-      "2017-04-30    -0.009919\n",
-      "2017-05-31     9.656274\n",
-      "2017-06-30    -8.704681\n",
-      "2017-07-31     4.810482\n",
-      "2017-08-31    15.751511\n",
-      "2017-09-30    -9.880000\n",
-      "2017-10-31    14.920000\n",
-      "2017-11-30     2.810000\n",
-      "2017-12-31    -2.620000\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n",
-      "Date\n",
-      "2017-01-31         NaN\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(last_day.diff())\n",
-    "print(last_day.pct_change())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "count    249.000000\n",
-      "mean     149.815713\n",
-      "std       15.065681\n",
-      "min      114.586983\n",
-      "25%      140.651400\n",
-      "50%      151.890000\n",
-      "75%      159.780000\n",
-      "max      176.420000\n",
-      "Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.000000\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "print(daily_return.fillna(0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2017-01-31    0.133743\n",
-      "2017-02-28    0.133743\n",
-      "2017-03-31    0.048690\n",
-      "2017-04-30   -0.000070\n",
-      "2017-05-31    0.067770\n",
-      "2017-06-30   -0.057214\n",
-      "2017-07-31    0.033537\n",
-      "2017-08-31    0.106251\n",
-      "2017-09-30   -0.060244\n",
-      "2017-10-31    0.096808\n",
-      "2017-11-30    0.016623\n",
-      "2017-12-31   -0.015246\n",
-      "Freq: M, Name: Adj. Close, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "print(daily_return.fillna(method = 'bfill'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Date\n",
-       "2017-02-28    0.133743\n",
-       "2017-03-31    0.048690\n",
-       "2017-04-30   -0.000070\n",
-       "2017-05-31    0.067770\n",
-       "2017-06-30   -0.057214\n",
-       "2017-07-31    0.033537\n",
-       "2017-08-31    0.106251\n",
-       "2017-09-30   -0.060244\n",
-       "2017-10-31    0.096808\n",
-       "2017-11-30    0.016623\n",
-       "2017-12-31   -0.015246\n",
-       "Freq: M, Name: Adj. Close, dtype: float64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "daily_return = last_day.pct_change()\n",
-    "daily_return.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# aapl.resample('M').agg(lambda x: max(x) - min(x))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              AAPL    GOOG     IBM\n",
-      "2017-07-03  143.50  898.70  155.58\n",
-      "2017-07-04  144.09  911.71  153.67\n",
-      "2017-07-05  142.73  906.69  152.36\n",
-      "2017-07-06  144.18  918.59  152.94\n",
-      "2017-07-07  143.77  926.99  153.49\n"
-     ]
-    }
-   ],
-   "source": [
-    "dict = {'AAPL': [143.5, 144.09, 142.73, 144.18, 143.77],'GOOG':[898.7, 911.71, 906.69, 918.59, 926.99],\n",
-    "        'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}\n",
-    "data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')\n",
-    "df = pd.DataFrame(dict, index = data_index)\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     AAPL    GOOG\n",
-      "0  143.50  898.70\n",
-      "1  144.09  911.71\n",
-      "2  142.73  906.69\n",
-      "3  144.18  918.59\n",
-      "4  143.77  926.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
-    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
-    "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print(data_frame)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['AAPL', 'GOOG', 'IBM'], dtype='object')\n",
-      "2017-07-03    143.50\n",
-      "2017-07-04    144.09\n",
-      "2017-07-05    142.73\n",
-      "2017-07-06    144.18\n",
-      "2017-07-07    143.77\n",
-      "Freq: D, Name: AAPL, dtype: float64\n",
-      "2017-07-03    898.70\n",
-      "2017-07-04    911.71\n",
-      "2017-07-05    906.69\n",
-      "2017-07-06    918.59\n",
-      "2017-07-07    926.99\n",
-      "Freq: D, Name: GOOG, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df.columns)\n",
-    "print(df.AAPL)\n",
-    "print(df['GOOG'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2018-03-21    171.270\n",
-      "2018-03-22    168.845\n",
-      "2018-03-23    164.940\n",
-      "2018-03-26    172.770\n",
-      "2018-03-27    168.340\n",
-      "Name: Close, dtype: float64\n",
-      "Date\n",
-      "2018-03-21    35247358.0\n",
-      "2018-03-22    41051076.0\n",
-      "2018-03-23    40248954.0\n",
-      "2018-03-26    36272617.0\n",
-      "2018-03-27    38962839.0\n",
-      "Name: Adj. Volume, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "df = aapl_table\n",
-    "print(df.Close.tail(5))\n",
-    "print(df['Adj. Volume'].tail(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close      Volume  Ex-Dividend  \\\n",
-      "Date                                                                    \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0   \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0   \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0   \n",
-      "2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0   \n",
-      "2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0   \n",
-      "2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0   \n",
-      "2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0   \n",
-      "2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0   \n",
-      "2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0   \n",
-      "2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0   \n",
-      "2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0   \n",
-      "2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0   \n",
-      "\n",
-      "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
-      "Date                                                                      \n",
-      "2016-01-31          1.0   91.581233   94.044912   91.156128   94.044912   \n",
-      "2016-02-29          1.0   94.084911   95.415659   93.880927   93.919781   \n",
-      "2016-03-31          1.0  106.576465  106.751308  105.760531  105.867380   \n",
-      "2016-04-30          1.0   91.297138   92.006223   89.859540   91.054300   \n",
-      "2016-05-31          1.0   97.337844   98.119674   96.575559   97.591939   \n",
-      "2016-06-30          1.0   92.295040   93.594832   92.158220   93.428693   \n",
-      "2016-07-31          1.0  101.823594  102.175417  101.325177  101.843140   \n",
-      "2016-08-31          1.0  103.816156  104.710177  103.796505  104.248477   \n",
-      "2016-09-30          1.0  110.497491  111.391610  109.849008  111.077195   \n",
-      "2016-10-31          1.0  111.666724  112.236603  111.224577  111.558644   \n",
-      "2016-11-30          1.0  110.182071  110.814166  108.908004  109.154917   \n",
-      "2016-12-31          1.0  115.209202  115.752409  114.004271  114.389454   \n",
-      "\n",
-      "            Adj. Volume  \n",
-      "Date                     \n",
-      "2016-01-31   64416504.0  \n",
-      "2016-02-29   35216277.0  \n",
-      "2016-03-31   25888449.0  \n",
-      "2016-04-30   68531478.0  \n",
-      "2016-05-31   42307212.0  \n",
-      "2016-06-30   35836356.0  \n",
-      "2016-07-31   27733688.0  \n",
-      "2016-08-31   29662406.0  \n",
-      "2016-09-30   36379106.0  \n",
-      "2016-10-31   26419398.0  \n",
-      "2016-11-30   36162258.0  \n",
-      "2016-12-31   30586265.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_2016 = df['2016']\n",
-    "aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])\n",
-    "print(aapl_month)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close\n",
-      "Date                                        \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82\n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_bar = aapl_month[['Open', 'High', 'Low', 'Close']]\n",
-    "print(aapl_bar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open    High     Low   Close\n",
-      "Date                                      \n",
-      "2016-03-31  109.72  109.90  108.88  108.99\n",
-      "2016-04-30   93.99   94.72   92.51   93.74\n",
-      "2016-05-31   99.60  100.40   98.82   99.86\n",
-      "2016-06-30   94.44   95.77   94.30   95.60\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close\n",
-      "Date                                        \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82\n"
-     ]
-    }
-   ],
-   "source": [
-    "above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]\n",
-    "print(above)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
-      "  \"\"\"Entry point for launching an IPython kernel.\n"
-     ]
-    }
-   ],
-   "source": [
-    "aapl_bar['rate_return'] = aapl_bar.Close.pct_change()\n",
-    "print(aapl_bar)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Open   High    Low  Close  rate_return\n",
-      "Date                                               \n",
-      "2016-01-31  False  False  False  False         True\n",
-      "2016-02-29  False  False  False  False        False\n",
-      "2016-03-31  False  False  False  False        False\n",
-      "2016-04-30  False  False  False  False        False\n",
-      "2016-05-31  False  False  False  False        False\n",
-      "2016-06-30  False  False  False  False        False\n",
-      "2016-07-31  False  False  False  False        False\n",
-      "2016-08-31  False  False  False  False        False\n",
-      "2016-09-30  False  False  False  False        False\n",
-      "2016-10-31  False  False  False  False        False\n",
-      "2016-11-30  False  False  False  False        False\n",
-      "2016-12-31  False  False  False  False        False\n",
-      "\n",
-      "------------------ separate line -----------------\n",
-      "\n",
-      "         Open   High    Low  Close rate_return\n",
-      "count      12     12     12     12          12\n",
-      "unique      1      1      1      1           2\n",
-      "top     False  False  False  False       False\n",
-      "freq       12     12     12     12          11\n"
-     ]
-    }
-   ],
-   "source": [
-    "missing = aapl_bar.isnull()\n",
-    "print(missing)\n",
-    "print('\\n------------------ separate line -----------------\\n')\n",
-    "print(missing.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Open   High    Low  Close  rate_return\n",
-      "Date                                               \n",
-      "2016-01-31  False  False  False  False         True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(missing[missing.rate_return == True])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open      High     Low   Close  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34     0.000000\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
-     ]
-    }
-   ],
-   "source": [
-    "drop = aapl_bar.dropna()\n",
-    "print(drop)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "fill = aapl_bar.fillna(0)\n",
-    "print(fill)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     AAPL    GOOG\n",
-      "0  143.50  898.70\n",
-      "1  144.09  911.71\n",
-      "2  142.73  906.69\n",
-      "3  144.18  918.59\n",
-      "4  143.77  926.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
-    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
-    "data_frame = pd.concat([s1,s2], axis = 1)\n",
-    "print(data_frame)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date\n",
-      "2016-01-31    4.578210\n",
-      "2016-02-29    4.571510\n",
-      "2016-03-31    4.691256\n",
-      "2016-04-30    4.540525\n",
-      "2016-05-31    4.603769\n",
-      "2016-06-30    4.560173\n",
-      "2016-07-31    4.646408\n",
-      "2016-08-31    4.664382\n",
-      "2016-09-30    4.727830\n",
-      "2016-10-31    4.732155\n",
-      "2016-11-30    4.705197\n",
-      "2016-12-31    4.752037\n",
-      "Freq: M, Name: log_price, dtype: float64\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open      High     Low   Close  rate_return  log_price\n",
-      "Date                                                                \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210\n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510\n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256\n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525\n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769\n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173\n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408\n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382\n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830\n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155\n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197\n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037\n"
-     ]
-    }
-   ],
-   "source": [
-    "log_price = np.log(aapl_bar.Close)\n",
-    "log_price.name = 'log_price'\n",
-    "print(log_price)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "concat = pd.concat([aapl_bar, log_price], axis = 1)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                Volume  Split Ratio\n",
-      "Date                               \n",
-      "2016-10-31  26419398.0          1.0\n",
-      "2016-11-30  36162258.0          1.0\n",
-      "2016-12-31  30586265.0          1.0\n",
-      "2017-01-31  49200993.0          1.0\n",
-      "2017-02-28  23482860.0          1.0\n",
-      "2017-03-31  19661651.0          1.0\n",
-      "2017-04-30  20247187.0          1.0\n",
-      "\n",
-      "---------------------- separate line--------------------\n",
-      "\n",
-      "              Open     High     Low   Close\n",
-      "Date                                       \n",
-      "2016-10-31  113.65  114.230  113.20  113.54\n",
-      "2016-11-30  111.56  112.200  110.27  110.52\n",
-      "2016-12-31  116.65  117.200  115.43  115.82\n",
-      "2017-01-31  121.15  121.390  120.62  121.35\n",
-      "2017-02-28  137.08  137.435  136.70  136.99\n",
-      "2017-03-31  143.72  144.270  143.01  143.66\n",
-      "2017-04-30  144.09  144.300  143.27  143.65\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])\n",
-    "print(df_volume)\n",
-    "print('\\n---------------------- separate line--------------------\\n')\n",
-    "df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])\n",
-    "print(df_2017)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open      High     Low   Close  rate_return      Volume  \\\n",
-      "Date                                                                    \n",
-      "2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN   \n",
-      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN   \n",
-      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN   \n",
-      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN   \n",
-      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN   \n",
-      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN   \n",
-      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN   \n",
-      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN   \n",
-      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN   \n",
-      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0   \n",
-      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0   \n",
-      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0   \n",
-      "2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0   \n",
-      "2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0   \n",
-      "2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0   \n",
-      "2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0   \n",
-      "\n",
-      "            Split Ratio  \n",
-      "Date                     \n",
-      "2016-01-31          NaN  \n",
-      "2016-02-29          NaN  \n",
-      "2016-03-31          NaN  \n",
-      "2016-04-30          NaN  \n",
-      "2016-05-31          NaN  \n",
-      "2016-06-30          NaN  \n",
-      "2016-07-31          NaN  \n",
-      "2016-08-31          NaN  \n",
-      "2016-09-30          NaN  \n",
-      "2016-10-31          1.0  \n",
-      "2016-11-30          1.0  \n",
-      "2016-12-31          1.0  \n",
-      "2017-01-31          1.0  \n",
-      "2017-02-28          1.0  \n",
-      "2017-03-31          1.0  \n",
-      "2017-04-30          1.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar,df_volume],axis = 1)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              Open    High     Low   Close  rate_return      Volume  \\\n",
-      "Date                                                                  \n",
-      "2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0   \n",
-      "2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0   \n",
-      "2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0   \n",
-      "\n",
-      "            Split Ratio  \n",
-      "Date                     \n",
-      "2016-10-31          1.0  \n",
-      "2016-11-30          1.0  \n",
-      "2016-12-31          1.0  \n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Close      High     Low    Open  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
-      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
-      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
-      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
-      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "append = aapl_bar.append(df_2017)\n",
-    "print(append)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "             Close      High     Low    Open  rate_return\n",
-      "Date                                                     \n",
-      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
-      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
-      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
-      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
-      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
-      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
-      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            Change   Close      High     Low    Open  rate_return\n",
-      "Date                                                             \n",
-      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
-      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
-      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
-      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
-      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
-      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
-      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_2017.columns = ['Change', 'High','Low','Close']\n",
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "            Change   Close      High     Low    Open  rate_return\n",
-      "Date                                                             \n",
-      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
-      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
-      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
-      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
-      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
-      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
-      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
-      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
-      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
-      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
-      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
-      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
-      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
-      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
-      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
-      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
-      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
-      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
-      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
-     ]
-    }
-   ],
-   "source": [
-    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
-    "print(concat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.html b/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.html
deleted file mode 100644
index e1da695..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.html	
+++ /dev/null
@@ -1,122 +0,0 @@
-<h2>Introduction</h2>
-In this chapter we are going to introduce some basic concepts in quantitative finance. We start with rate of return, mean and variance. You may think it's simple to calculate these values, however, there are number of different methods to calculate them. It's important to choose the appropriate calculation methods case by case.
-<h2>Rate of Return</h2>
-<h3>Single-period Return</h3>
-The single-period rate of return can be calculated as following:
-\[r = \frac{p_t}{p_0} - 1 = \frac{p_t - p_0}{p_0}\]
-Where \(r\) is the rate of return, \(p_t\) is the asset price at time \(t\), and \(p_0\) is the asset price at time 0.
-<pre class="prettyprint linenums">
-import numpy as np
-rate_return = 102.0/100 - 1
-print rate_return
-[out]: 0.02
-</pre>
-Let's say we bought a stock at $100, and half a year later it will grow to $102. A year later the price will come to $104. How to calculate our total return?
-Well, we can either deem it as a single-period:
-\[r = 104/100 - 1 = 0.04\]
-or as a two-stage period:
-\[ r = (1+r_1)*(1+r_2) - 1 = \frac{102}{100} * \frac{104}{102} -1 = 0.04\]
-Here we make calculations twice a year. It's called semi-annual compounding. How about quarterly compounding?
-Let's assume the stock prices at the end of each quarter are \(p_1, p_2, p_3, p_4\) respectively.
-\[r = (1+r_1)*(1+r_2)*(1+r_3)*(1+r_4) -1\]
-The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
-Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now!
-\[(1+r)^3 = 1+0.65\]
-\[ r = \sqrt[3]{1.65} - 1\ = 0.18167\]
-Strategy A has an higher compounding annual return!
-<h3>Logarithm Return</h3>
-We introduced compounding annual return above, which is a kind of <strong>effective rate of return</strong>. You can regard it as a 'hypothetical return'. Strategy B might never have a 18.167% rate of rate annually during the 3-year backtesting period. However, if we assume that the strategy has a 18.167% rate of return every year, it has the same cumulative return over the 3 years.
-As we mentioned previously, if we assume a strategy is quarterly compounding, the relation between quarterly effective rate of return and annual return is:
-\[(1+\frac{r}{4})^4 = 1+r\]
-More generally, if the times of compounding in one year is \(n\) and the annual rate of return is \(r\), the relation is given by:
-\[(1+\frac{r}{n})^n = 1+r\]
-Now imagine the stock markets. The prices of your assets is changing every second, or even every millisecond. If the times of compounding, or n, approach to infinite, this is called <strong>continuous compounding</strong>. The calculation formula is given below:
-\[\lim_{n \to \infty }(1+\frac{r}{n})^n = e^r\]
-From the above limitation equation, we know that if we assume continuous compounding:
-\[e^r = 1 + r = \frac{p_t}{p_0}\]
-Then we take \(ln\) on both side of the equation:
-\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
-Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>.
-This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
-<pre class="prettyprint linenums">import quandl
-import numpy as np
-import quandl
-quandl.ApiConfig.api_key = 'zNXvSaz2oX5afVGKjf6o'
-#get quandl data
-aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table.loc['2017-3',['Open','Close']]
-#take log return
-aapl['log_price'] = np.log(aapl.Close)
-aapl['log_return'] = np.log_price.diff()
-print aapl
-</pre>
-The output is:
-<pre class="prettyprint linenums">
-Date          Open   Close  log_price  log_return
-2017-03-01  137.890  139.79   4.940141         NaN
-2017-03-02  140.000  138.96   4.934186   -0.005955
-2017-03-03  138.780  139.78   4.940070    0.005884
-2017-03-06  139.365  139.34   4.936917   -0.003153
-2017-03-07  139.060  139.52   4.938208    0.001291
-2017-03-08  138.950  139.00   4.934474   -0.003734
-2017-03-09  138.740  138.68   4.932169   -0.002305
-2017-03-10  139.250  139.14   4.935481    0.003311
-2017-03-13  138.850  139.20   4.935912    0.000431
-2017-03-14  139.300  138.99   4.934402   -0.001510
-2017-03-15  139.410  140.46   4.944923    0.010521
-2017-03-16  140.720  140.69   4.946559    0.001636
-2017-03-17  141.000  139.99   4.941571   -0.004988
-2017-03-20  140.400  141.46   4.952017    0.010446
-2017-03-21  142.110  139.84   4.940499   -0.011518
-2017-03-22  139.845  141.42   4.951734    0.011235
-2017-03-23  141.260  140.92   4.948192   -0.003542
-2017-03-24  141.500  140.64   4.946203   -0.001989
-2017-03-27  139.390  140.88   4.947908    0.001705
-2017-03-28  140.910  143.80   4.968423    0.020515
-2017-03-29  143.680  144.12   4.970646    0.002223
-2017-03-30  144.190  143.93   4.969327   -0.001319
-2017-03-31  143.720  143.66   4.967449   -0.001878
-</pre>
-
-Here we calculated the daily logarithmic return of Apple stock. Given that we know the daily logarithm return of in this month, we can calculate the monthly return by simply sum all the daily returns up.
-<pre class="prettyprint linenums">month_return = aapl.log_return.sum()
-print month_return
-[out]: 0.0273081001636
-</pre>
-It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
-\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
-According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
-<h2>Mean</h2>
-<h3>Arithmetic Mean</h3>
-Mean is a measure of the central tendency of a data series. It capture the key character of the distribution of the data series. When we talk about mean, by default it refers to <strong>arithmetic mean</strong>. It's defined as the sum of the values divided by the number of observations:
-\[\mu = \frac{\sum_{i = 1}^{n}x_i}{n}\]
-Where \((x_1,x_2,x_3.....x_n)\) is our data series.
-In python we can use NumPy.mean() to do the calculation:
-<pre class="prettyprint linenums">print np.mean(aapl.log_price)
-[out]: 4.94597446551
-</pre>
-<h3>Geometric Mean</h3>
-The geometric mean is an average that is useful for data series of positive numbers that are better interpreted according to their product, such as growth rate. It's calculated by:
-\[\bar{x} = \sqrt[n]{x_1x_2x_3...x_n}\]
-Let's calculate the geometric mean of a series of single-period return:
-\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\]
-\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\]
-Now the equation becomes the form which we are familiar with:
-\[(1+\bar{r})^n = \frac{p_t}{p_0}\]
-This is why we said it make sense when applied to growth rates.
-<h2>Variance and Standard Deviation</h2>
-<h3>Variance</h3>
-<strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by
-\[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]
-In python we can use NumPy.var to calculate it:
-<pre class="prettyprint linenums">print np.var(aapl.log_price)
-</pre>
-<h3>Standard Deviation</h3>
-The most commonly used measure of dispersion in finance is <strong>standard deviation</strong>. It's usually represented by \(\sigma\). It's obvious to see the relation between standard deviation and variance:
-\[\sigma = \sqrt{\sigma^2} = \sqrt{\frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}}\]
-NumPy also provides us a method to calculate standard deviation.
-<pre class="prettyprint linenums">print np.std(aapl.log_price)
-[out]: 0.000142032804482
-</pre>
-<h2>Summary</h2>
-We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those comcepts intensively in our later chapter.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb
deleted file mode 100644
index 8c4c7b6..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial06 Rate of Return, Mean and Variance.ipynb	
+++ /dev/null
@@ -1,146 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.020000000000000018\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "rate_return = 102.0/100 - 1\n",
-    "print(rate_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "               Open   Close  log_price  log_return\n",
-      "Date                                              \n",
-      "2017-03-01  137.890  139.79   4.940141         NaN\n",
-      "2017-03-02  140.000  138.96   4.934186   -0.005955\n",
-      "2017-03-03  138.780  139.78   4.940070    0.005884\n",
-      "2017-03-06  139.365  139.34   4.936917   -0.003153\n",
-      "2017-03-07  139.060  139.52   4.938208    0.001291\n",
-      "2017-03-08  138.950  139.00   4.934474   -0.003734\n",
-      "2017-03-09  138.740  138.68   4.932169   -0.002305\n",
-      "2017-03-10  139.250  139.14   4.935481    0.003311\n",
-      "2017-03-13  138.850  139.20   4.935912    0.000431\n",
-      "2017-03-14  139.300  138.99   4.934402   -0.001510\n",
-      "2017-03-15  139.410  140.46   4.944923    0.010521\n",
-      "2017-03-16  140.720  140.69   4.946559    0.001636\n",
-      "2017-03-17  141.000  139.99   4.941571   -0.004988\n",
-      "2017-03-20  140.400  141.46   4.952017    0.010446\n",
-      "2017-03-21  142.110  139.84   4.940499   -0.011518\n",
-      "2017-03-22  139.845  141.42   4.951734    0.011235\n",
-      "2017-03-23  141.260  140.92   4.948192   -0.003542\n",
-      "2017-03-24  141.500  140.64   4.946203   -0.001989\n",
-      "2017-03-27  139.390  140.88   4.947908    0.001705\n",
-      "2017-03-28  140.910  143.80   4.968423    0.020515\n",
-      "2017-03-29  143.680  144.12   4.970646    0.002223\n",
-      "2017-03-30  144.190  143.93   4.969327   -0.001319\n",
-      "2017-03-31  143.720  143.66   4.967449   -0.001878\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import quandl\n",
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "#get quandl data\n",
-    "aapl_table = quandl.get('WIKI/AAPL')\n",
-    "aapl = aapl_table.loc['2017-3',['Open','Close']]\n",
-    "#take log return\n",
-    "aapl['log_price'] = np.log(aapl.Close)\n",
-    "aapl['log_return'] = aapl.log_price.diff()\n",
-    "print(aapl)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.0273081001636184\n"
-     ]
-    }
-   ],
-   "source": [
-    "month_return = aapl.log_return.sum()\n",
-    "print(month_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.94597446550658\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.mean(aapl.log_price))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.00014203280448152512\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.var(aapl.log_price))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variable and Distributions.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variable and Distributions.ipynb
deleted file mode 100644
index d8f5a7d..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variable and Distributions.ipynb	
+++ /dev/null
@@ -1,364 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4 3 5 ..., 2 3 6]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import random\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "def dice():\n",
-    "    number=  [1,2,3,4,5,6]\n",
-    "    return random.choice(number)\n",
-    " \n",
-    "series = np.array([dice() for x in range(10000)])\n",
-    "print series\n",
-    "from math import factorial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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gBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4\nAQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwl\nOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAM\nNdfgVFVXV9Xhqrpzw7Fvqqqbq+qz05/Pno5XVb2zqg5U1Ser6gUbnvPa6fGfrarXznNmAAAAAJ6c\neb/D6Zokux9x7PIkt3T3OUlume4nyUuTnDN97Uny7uRooEry1iQvTHJhkrcei1QAAAAArJ65Bqfu\n/liS+x5x+BVJ3jvdfm+SV244/r4+6rYkz6qq05J8f5Kbu/u+7v5Skpvz9RELAAAAgBVR3T3fH1B1\nVpIbuvv50/0/7u5nTbcryZe6+1lVdUOSd3T3b03fuyXJTyRZT/KM7v4X0/F/luSB7v7p4/ysPTn6\n7qjs3Lnzgr179871d1uEw/cdyb0PLHuKE8t5p5+y7BFOKPsPHRnyOjtPjr0yA+t7sUat75G28l6x\nvhdvFdf4KKu4V6zxxdrK63ukUXvF+l48a3yxzj5lW3bs2LHsMYbYtWvXvu5ee7zHnbSIYR5Nd3dV\nDSte3X1lkiuTZG1trdfX10e99NK869rrcsX+pf41nXAOvmp92SOcUC65/CNDXuey8x6yV2ZgfS/W\nqPU90lbeK9b34q3iGh9lFfeKNb5YW3l9jzRqr1jfi2eNL9Y1u7dnKzSKJ2IZV6m7d/qoXKY/D0/H\nDyU5c8PjzpiOPdpxAAAAAFbQEw5OVfWUqnrmk/iZ1yc5dqW51ya5bsPx10xXq3tRkiPd/cUkH03y\nfVX17Olk4d83HQMAAABgBc0UnKrql6rqmVW1PcmdST5VVf9ohue9P8l/TvLcqrqnqn4kyTuSfG9V\nfTbJ90z3k+TGJJ9PciDJLyR5Y5J0931JfjLJx6evfz4dAwAAAGAFzfph23O7+/6qelWSm5JcnmRf\nkp96rCd198WP8q2XHOexneTSR3mdq5NcPeOsAAAAACzRrB+pe2pVPTXJK5Nc391fTTLfy9sBAAAA\nsCnNGpx+PsnBJNuTfKyqvjXJ/fMaCgAAAIDNa6aP1HX3O5O8c8OhP6iqXfMZCQAAAIDNbNaThu+s\nqquq6qbp/rn5syvNAQAAAMD/MutH6q5J8tEk3zLd/70kb57HQAAAAABsbrMGp1O7+wNJvpYk3f1Q\nkofnNhUAAAAAm9aswekrVfXNma5MV1UvSnJkblMBAAAAsGnNdNLwJD+e5Pokf7mqfjvJc5L80Nym\nAgAAAGDTmvUqdZ+oqr+e5LlJKsnd3f3VuU4GAAAAwKY061XqLk2yo7vv6u47k+yoqjfOdzQAAAAA\nNqNZz+H0+u7+42N3uvtLSV4/n5EAAAAA2MxmDU7bqqqO3amqbUmeNp+RAAAAANjMZj1p+K8m+eWq\n+vnp/humYwAAAADw58wanH4iRyPT35/u35zkPXOZCAAAAIBNbdar1H0tybunLwAAAAB4VDMFp6r6\nziRvS/Kt03MqSXf3t81vNAAAAAA2o1k/UndVkn+YZF+Sh+c3DgAAAACb3azB6Uh33zTXSQAAAADY\nEmYNTr9ZVT+V5FeSPHjsYHd/Yi5TAQAAALBpzRqcXjj9ubbhWCf57rHjAAAAALDZzXqVul3zHgQA\nAACAreEpszyoqnZW1VVVddN0/9yq+pH5jgYAAADAZjRTcEpyTZKPJvmW6f7vJXnzPAYCAAAAYHOb\nNTid2t0fSPK1JOnuh5I8PLepAAAAANi0Zg1OX6mqb87RE4Wnql6U5MjcpgIAAABg05r1KnU/nuT6\nJH+5qn47yXOS/NDcpgIAAABg03rc4FRVT0nyjCR/Pclzk1SSu7v7q3OeDQAAAIBN6HGDU3d/rap+\nrru/I8ldC5gJAAAAgE1s1nM43VJVf6uqaq7TAAAAALDpzRqc3pDk3yd5sKrur6o/qar75zgXAAAA\nAJvUTCcN7+5vnPcgAAAAAGwNMwWnqnrx8Y5398fGjgMAAADAZjdTcEryjzbcfkaSC5PsS/LdwycC\nAAAAYFOb9SN1//fG+1V1ZpJ/M5eJAAAAANjUZj1p+CPdk+SvjhwEAAAAgK1h1nM4vStJT3efkuT8\nJJ+Y11AAAAAAbF6znsPp9g23H0ry/u7+7TnMAwAAAMAmN2tw+mCS/6+7H06SqtpWVd/Q3X86v9EA\nAAAA2IxmPYfTLUlO3nD/5CS/Pn4cAAAAADa7WYPTM7r7y8fuTLe/YT4jAQAAALCZzRqcvlJVLzh2\np6ouSPLAfEYCAAAAYDOb9RxOb07y76vqvyWpJH8xyd+Z21QAAAAAbFozBafu/nhV/ZUkz50O3d3d\nX53fWAAAAABsVjN9pK6qLk2yvbvv7O47k+yoqjfOdzQAAAAANqNZz+H0+u7+42N3uvtLSV4/n5EA\nAAAA2MxmDU7bqqqO3amqbUmeNp+RAAAAANjMZj1p+EeT/HJV/fx0/0eT/Op8RgIAAABgM5s1OP2z\nHP0I3bHzNn00yVVzmQgAAACATe0xg1NVnZTkXyb54SRfmA7/pSSfz9GP4z081+kAAAAA2HQe7xxO\nP5Xkm5J8W3e/oLtfkOTsJKck+el5DwcAAADA5vN4wenlOXqFuj85dmC6/feTvGyegwEAAACwOT1e\ncOru7uMcfDjJ1x0HAAAAgMcLTp+qqtc88mBV/d0kn5nPSAAAAABsZo93lbpLk/xKVb0uyb7p2FqS\nk5P84DwHAwAAAGBzeszg1N2Hkrywqr47yfOmwzd29y1znwwAAACATenx3uGUJOnu30jyG3OeBQAA\nAIAt4PHO4QQAAAAAT4jgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABD\nCU4AAAAADCU4AQAAADCU4AQAAADAUAsPTlX13Kq6Y8PX/VX15qp6W1Ud2nD8ZRue85aqOlBVd1fV\n9y96ZgAAAABmd9Kif2B3353k/CSpqm1JDiX5cJIfTvIz3f3TGx9fVecmuSjJ85J8S5Jfr6pv7+6H\nFzo4AAAAADNZ9kfqXpLkc939B4/xmFck2dvdD3b37yc5kOTChUwHAAAAwBO27OB0UZL3b7j/pqr6\nZFVdXVXPno6dnuQLGx5zz3QMAAAAgBVU3b2cH1z1tCT/LcnzuvveqtqZ5A+TdJKfTHJad7+uqn42\nyW3d/YvT865KclN3f/A4r7knyZ4k2blz5wV79+5d0G8zP4fvO5J7H1j2FCeW804/ZdkjnFD2Hzoy\n5HV2nhx7ZQbW92KNWt8jbeW9Yn0v3iqu8VFWca9Y44u1ldf3SKP2ivW9eNb4Yp19yrbs2LFj2WMM\nsWvXrn3dvfZ4j1v4OZw2eGmST3T3vUly7M8kqapfSHLDdPdQkjM3PO+M6djX6e4rk1yZJGtra72+\nvj5+6gV717XX5Yr9y/xrOvEcfNX6skc4oVxy+UeGvM5l5z1kr8zA+l6sUet7pK28V6zvxVvFNT7K\nKu4Va3yxtvL6HmnUXrG+F88aX6xrdm/PVmgUT8QyP1J3cTZ8nK6qTtvwvR9Mcud0+/okF1XV06vq\n7CTnJPmdhU0JAAAAwBOylH+2qartSb43yRs2HP5XVXV+jn6k7uCx73X3XVX1gSSfSvJQkktdoQ4A\nAABgdS0lOHX3V5J88yOOvfoxHv/2JG+f91wAAAAAPHnLvkodAAAAAFuM4AQAAADAUIITAAAAAEMJ\nTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABD\nCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAA\nQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAA\nAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAA\nAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMA\nAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIIT\nAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCC\nEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQ\nSwtOVXWwqvZX1R1Vdft07Juq6uaq+uz057On41VV76yqA1X1yap6wbLmBgAAAOCxLfsdTru6+/zu\nXpvuX57klu4+J8kt0/0keWmSc6avPUnevfBJAQAAAJjJsoPTI70iyXun2+9N8soNx9/XR92W5FlV\nddoyBgQAAADgsS0zOHWSX6uqfVW1Zzq2s7u/ON3+70l2TrdPT/KFDc+9ZzoGAAAAwIqp7l7OD646\nvbsPVdVfSHJzkn+Q5PruftaGx3ypu59dVTckeUd3/9Z0/JYkP9Hdtz/iNffk6EfusnPnzgv27t27\nqF9nbg7fdyT3PrDsKU4s551+yrJHOKHsP3RkyOvsPDn2ygys78Uatb5H2sp7xfpevFVc46Os4l6x\nxhdrK6/vkUbtFet78azxxTr7lG3ZsWPHsscYYteuXfs2nBrpUZ20iGGOp7sPTX8erqoPJ7kwyb1V\ndVp3f3H6yNzh6eGHkpy54elnTMce+ZpXJrkySdbW1np9fX2Ov8FivOva63LF/qX9NZ2QDr5qfdkj\nnFAuufwjQ17nsvMesldmYH0v1qj1PdJW3ivW9+Kt4hofZRX3ijW+WFt5fY80aq9Y34tnjS/WNbu3\nZys0iidiKR+pq6rtVfWNx24n+b4kdya5Pslrp4e9Nsl10+3rk7xmulrdi5Ic2fDROwAAAABWyLL+\n2WZnkg9X1bEZfqm7f7WqPp7kA1X1I0n+IMnfnh5/Y5KXJTmQ5E+T/PDiRwYAAABgFksJTt39+ST/\n53GO/1GSlxzneCe5dAGjAQAAAPAkLfMqdQAAAABsQYITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAA\nAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQA\nAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAE\nAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTg\nBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU\n4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAw\nlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAA\nMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAA\nADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAy18OBU\nVWdW1W9W1aeq6q6q+rHp+Nuq6lBV3TF9vWzDc95SVQeq6u6q+v5FzwwAAADA7E5aws98KMll3f2J\nqvrGJPuq6ubpez/T3T+98cFVdW6Si5I8L8m3JPn1qvr27n54oVMDAAAAMJOFv8Opu7/Y3Z+Ybv9J\nkk8nOf0xnvKKJHu7+8Hu/v0kB5JcOP9JAQAAAPjfUd29vB9edVaSjyV5fpIfT3JJkvuT3J6j74L6\nUlX9bJLbuvsXp+dcleSm7v7gcV5vT5I9SbJz584L9u7du4DfYr4O33ck9z6w7ClOLOedfsqyRzih\n7D90ZMjSVGwGAAAKrElEQVTr7Dw59soMrO/FGrW+R9rKe8X6XrxVXOOjrOJescYXayuv75FG7RXr\ne/Gs8cU6+5Rt2bFjx7LHGGLXrl37unvt8R63jI/UJUmqakeSDyV5c3ffX1XvTvKTSXr684okr3si\nr9ndVya5MknW1tZ6fX196MzL8K5rr8sV+5f213RCOviq9WWPcEK55PKPDHmdy857yF6ZgfW9WKPW\n90hbea9Y34u3imt8lFXcK9b4Ym3l9T3SqL1ifS+eNb5Y1+zenq3QKJ6IpVylrqqemqOx6dru/pUk\n6e57u/vh7v5akl/In31s7lCSMzc8/YzpGAAAAAAraBlXqaskVyX5dHf/6w3HT9vwsB9Mcud0+/ok\nF1XV06vq7CTnJPmdRc0LAAAAwBOzjPcJf2eSVyfZX1V3TMf+SZKLq+r8HP1I3cEkb0iS7r6rqj6Q\n5FM5eoW7S12hDgAAAGB1LTw4dfdvJanjfOvGx3jO25O8fW5DAQAAADDMUs7hBAAAAMDWJTgBAAAA\nMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAA\nADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEA\nAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgB\nAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4\nAQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwl\nOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAM\nJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAA\nDCU4AQAAADCU4AQAAADAUIITAAAAAEMJTgAAAAAMJTgBAAAAMJTgBAAAAMBQghMAAAAAQwlOAAAA\nAAwlOAEAAAAw1KYJTlW1u6rurqoDVXX5sucBAAAA4Pg2RXCqqm1Jfi7JS5Ocm+Tiqjp3uVMBAAAA\ncDybIjgluTDJge7+fHf/zyR7k7xiyTMBAAAAcBybJTidnuQLG+7fMx0DAAAAYMVUdy97hsdVVT+U\nZHd3/73p/quTvLC73/SIx+1Jsme6+9wkdy900Pk4NckfLnsI2ATsFZiNvQKzsVdgNvYKzGYr7ZVv\n7e7nPN6DTlrEJAMcSnLmhvtnTMf+nO6+MsmVixpqEarq9u5eW/YcsOrsFZiNvQKzsVdgNvYKzOZE\n3Cub5SN1H09yTlWdXVVPS3JRkuuXPBMAAAAAx7Ep3uHU3Q9V1ZuSfDTJtiRXd/ddSx4LAAAAgOPY\nFMEpSbr7xiQ3LnuOJdhSHxGEObJXYDb2CszGXoHZ2CswmxNur2yKk4YDAAAAsHlslnM4AQAAALBJ\nCE4rqqqurqrDVXXnsmeBVVZVZ1bVb1bVp6rqrqr6sWXPBKuoqp5RVb9TVf912iv/z7JnglVVVduq\n6ner6oZlzwKrrKoOVtX+qrqjqm5f9jywiqrqWVX1war6TFV9uqr+r2XPtCg+UreiqurFSb6c5H3d\n/fxlzwOrqqpOS3Jad3+iqr4xyb4kr+zuTy15NFgpVVVJtnf3l6vqqUl+K8mPdfdtSx4NVk5V/XiS\ntSTP7O6XL3seWFVVdTDJWnf/4bJngVVVVe9N8p+6+z1V9bQk39Ddf7zsuRbBO5xWVHd/LMl9y54D\nVl13f7G7PzHd/pMkn05y+nKngtXTR315uvvU6cu/OsEjVNUZSX4gyXuWPQsAm1tVnZLkxUmuSpLu\n/p8nSmxKBCdgC6mqs5J8R5L/stxJYDVNHxO6I8nhJDd3t70CX+/fJPnHSb627EFgE+gkv1ZV+6pq\nz7KHgRV0dpL/keT/nT6q/Z6q2r7soRZFcAK2hKrakeRDSd7c3fcvex5YRd39cHefn+SMJBdWlY9s\nwwZV9fIkh7t737JngU3ir3X3C5K8NMml02lBgD9zUpIXJHl3d39Hkq8kuXy5Iy2O4ARsetP5aD6U\n5Nru/pVlzwOrbnor928m2b3sWWDFfGeSvzGdl2Zvku+uql9c7kiwurr70PTn4SQfTnLhcieClXNP\nkns2vKv8gzkaoE4IghOwqU0nQr4qyae7+18vex5YVVX1nKp61nT75CTfm+Qzy50KVkt3v6W7z+ju\ns5JclOQ3uvvvLnksWElVtX26YEumjwh9XxJX2IYNuvu/J/lCVT13OvSSJCfMxY1OWvYAHF9VvT/J\nepJTq+qeJG/t7quWOxWspO9M8uok+6dz0yTJP+nuG5c4E6yi05K8t6q25eg/OH2gu13yHYD/XTuT\nfPjov/3lpCS/1N2/utyRYCX9gyTXTleo+3ySH17yPAtT3S5QAwAAAMA4PlIHAAAAwFCCEwAAAABD\nCU4AAAAADCU4AQAAADCU4AQAAADAUIITAHDCq6qHq+qOqrqrqv5rVV1WVU+ZvrdWVe8c8DOuqapD\nVfX06f6pVXXwyb7u9FrrVXXDiNcC4P9v535CrCrDOI5/fyiVOIhCudckTDBkFiLkJpRWrfpjuQjE\nRQTWInFZRFCIOxcDustVVi6iYBY14kbDqGHKf4hI/3Q3IRFDlJA+Le6ZOHPVmXtvB4z4fjbnvOec\n+7zPvavLc573ldSF5fc7AUmSpP+AP6pqC0CStcAHwCrg7aqaBqY7mucWsBc40lG8TiRZVlW37nce\nkiTp/8MOJ0mSpJaqmgVeAV5Lzz/dQ0nGkryf5EKS80mea64/neRskpkkJ5KM3SP8YeCNJAte+vV3\nKCWZSLKnOf8pycGmA2s6yXiSz5N8n+TVVphVSSaTXElytNWhddfcmriHkswAL3Ty40mSJDUsOEmS\nJPWpqh+AZcDavltvAb9V1eaqegI4leRh4E1gZ1WN0+uG2n+P0NeAM8DLQ6Z0renAOg0cA54HtgHv\ntJ7ZCrwObAIeBZ4dILcbVTVeVR8OmY8kSdKiXFInSZI0uJ3AS/ODqvo1yTP0ijxfJgF4ADi7SIyD\nwKfA5BDzftYcLwBjVTUHzCW5mWR1c+/rplBGkuPAduDPJXL7aIgcJEmSBmbBSZIkqU+S9fT2W5oF\nHl/qcWCqqnYPEruqrib5DtjVuvwXCzvPH+r72M3meLt1Pj+e/z9X/VMNkNvvg+QsSZI0LJfUSZIk\ntSR5BDgKTFRVfxFnCtjXenYN8BXwZJINzbWVSR5bYpr3gAOt8c/ApiQPNh1LO0ZIfWuSdc3eTS/S\nW7o3Sm6SJEn/mgUnSZIkWNFsyn0JOAl8wcL9kea9C6xJcjHJOeCpqvoF2AMcT3Ke3pK1jYtNVlWX\ngJnW+DrwMXCxOX47wnf4BpgALgM/Ap+MkpskSVIXcueLO0mSJEmSJGl0djhJkiRJkiSpUxacJEmS\nJEmS1CkLTpIkSZIkSeqUBSdJkiRJkiR1yoKTJEmSJEmSOmXBSZIkSZIkSZ2y4CRJkiRJkqROWXCS\nJEmSJElSp/4GZuC9GNWuBlIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x109d98910>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.hist(series,bins = 11,align = 'mid')\n",
-    "plt.xlabel('Dice Number')\n",
-    "plt.ylabel('Occurences')\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.4956\n",
-      "3.5103\n"
-     ]
-    }
-   ],
-   "source": [
-    "print len([x for x in series if x <= 3])/float(len(series))\n",
-    "print np.mean(series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.4956\n",
-      "3.5103\n"
-     ]
-    }
-   ],
-   "source": [
-    "print len([x for x in series if x <= 3])/float(len(series))\n",
-    "print np.mean(series)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def trial():\n",
-    "    number = [1,2,3,4,5,6,7,8,9,10]\n",
-    "    a = random.choice(number)\n",
-    "    if a<= 7:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7\n"
-     ]
-    }
-   ],
-   "source": [
-    "res = [trial() for x in range(10)]\n",
-    "print sum(res)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2367\n"
-     ]
-    }
-   ],
-   "source": [
-    "def binomial(number):\n",
-    "    l = []\n",
-    "    for i in range(10000):\n",
-    "        res = [trial() for x in range(10)]\n",
-    "        l.append(sum(res))\n",
-    "    return len([x for x in l if x == number])/float(len(l))\n",
-    " \n",
-    "print binomial(8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1     0.0002\n",
-      "2     0.0013\n",
-      "3     0.0087\n",
-      "4     0.0373\n",
-      "5     0.1041\n",
-      "6     0.2000\n",
-      "7     0.2674\n",
-      "8     0.2342\n",
-      "9     0.1153\n",
-      "10    0.0283\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "prob = []\n",
-    "for i in range(1,11):\n",
-    "    prob.append(binomial(i))\n",
-    "prob_s = pd.Series(prob,index = range(1,11))\n",
-    "print prob_s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.266827932\n",
-      "0.2334744405\n"
-     ]
-    }
-   ],
-   "source": [
-    "print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)\n",
-    "print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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1fjrJoU37Did5b3dfl+S9i212AbEIdqHu/p3u/uDi8R9l/Q37VcudCna/qro6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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x109eca950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.bar(range(1,11),prob)\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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prSOyuPS7nZIpJbXWhRiXAwAgS1AyAQCAtNYZiaqiJF8N5UV+R8FttNSFdO7G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\n1Tm3P7rui5I+Likk6TPOuWfP9GdRMgFA4giHndp7Qmrr7NG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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x109e0d350>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "norm = pd.Series(np.random.normal(0,1,100000))\n",
-    "type(norm)\n",
-    "plt.figure(figsize = (20,10))\n",
-    "norm.plot.density()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "spy_table = quandl.get('LSE/SPY5')\n",
-    "spy = spy_table.loc['2009':'2017',['Last Close']]\n",
-    "spy['log_return'] = np.log(spy['Last Close']).diff()\n",
-    "spy = spy.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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64YlDlcvkFVD5MyL6sddfGvubM2mkTGqX6j8N13JYML/FaMoA6AZwJlVgdnlA\n/+fHHqXrNu3V/u75rES4mt2wsWgwXleYW/DZpg5PXb2klnPEzlfjidYIUc4kvTZpLTgIRM4kKJNK\nwyhUhLaoPQLdpQ1hbsUX8QAgUpCr4ZJFcmqpq7l5DU+iVc3/xffneY4+98iB8scSjqki94/Ray9e\nr/1tciJ/NTfqyOSI7XayyVBKAEB14EyqwPzqkIiIHnvlrPb3oedTUR+OfWVSOBivazW38LBtUiY1\nkTy1RZc7FrgpoYn8NejCiiZViXImwZlUFhHWW9AA//aOE3T1NZtofmVYR7FAR0gkG25BQ2+qNGow\n+gh0CKFIKlExGLGYraOGvNWNLWWSUNVb6FsL5UyieFoI+ew8AXdezqQuLD7C64WtSZz2XzEAQAbO\npApMKDM/Kl6JnEl8a1sJuPlh6lvhKIwjb1GHF4W51XcO2TBDWEF1+OxbMsyN16/2z85Vhddb+JLK\nU7a9u+WpV4mI6PiFFdtFAh0i6cwepTKp3KQIVnMDMRSVR6EwN0WZJE+ONqJMsuRM4lTpWvmhiobc\nT6bErU9M5B/PZ90I8Y8mcO0cDzY1AN0CzqQK8CW804y9oVcmzC0gzcO/+8Q8feZhc6mu6ABt5EzS\nXIsIc2tR4y8ksnWehGn/C0oSJeCO303+11pQJvF6i5xJ5eH1p2gSbjUkBKxNkjmTRuhMCj+LFgF1\nGBAlVTnqp9ExKCPMrYF6Vj0Bd2hX2JiL4hNbRXImsbjSWL6aKGdSVgJuVluKCptEi97YqRQd8J8B\nACTgTKpA3gDE8wOJcBEvO982zTnzwEtn6NMP7yffZ3T/ntN08+bDOccLPm0MxsUR5M7RsrzVBqIs\ntSqTpP+34NKf3D9DV1+zifadWRh1UUqRZjDVnfOrTdiU4q9VhDKprDMJruE1TatWcysYQi5yBaIK\nAw1l67Js2sXC3KoWyICq5xBpI8RSxBUPSMVzJqV159zJlHU8RkReBybSRJibpaK2IbwYAGAOnEkl\nObvQo5/9xGOZ2/ABcNEYa6L0DoY3sgPPp3t2nqY7nj0W+/0b247RPTtPJfazEeama9/LSn/rhBsO\ndQ4MY6uatGAAev9LZ4iIaNuRCyMuSTmEs1MNcws/2xRGWReRMgnOpLLwe1g0aSlXmcKGXduoj3+U\n0bW8XymqjkIVBkSSEkmRJBWb3IxvL08aNhGKVNWpEDk5gs9RhLml9edTJqu5MaJhBybShDLJ1mpu\naMQA6BRlPaNXAAAgAElEQVRToy5AV9m0+3TuNm6JOOK82Uj+7cDziVE8jG52eUB/9u3dRET07//3\nfxQqHEJprg1lkqZMbUzA3XTOJFAd7nhV72rZhMpdhNdXhLmVp6oyCaxt2hTmxjEdvEbOg9GXGbQH\nEeol/i62N69+86vDxvthWzmTbCrniziTGMX781gC7vCHYc7xujCRZnv1NbRhAHQLKJNKIrf/aQ0f\n7wSKOFr4lmlGLD9Uf+gnkiPKoUB9N57Q2Mbyoroy8fPbMLpfObNY+RhEkeHQ2GpuLej3LKq4R4LI\nmaQqk1j89yKsDjz6f+98gc4u9KoWrxH4O5SWsLOLuJ5Pfddr7Hy8lvQLOpPU/cFapX1hbkXzHaIO\nA6Jk2GOZAbpsY/7EXz9IH/jmzthvtWPpHHzStUoIuQiZK6IUYuk5kyac7JxJYvERn7XeucKbKFvt\nZZvSZgAA8oEzqSQx6W9Ku1cmzI2T6kziCWY9n3zG4qtrSLv0hl7sOxvOpKzLqOpMunfXKfrFv3+S\n7t+TrvgauD6997btdGA62+nURALulvmSBF1VWPDZvmQCbm5QFa+/9+w6RXfvPEWfeOCV6gVsgDap\n+2xx3aa99Hu3bmvsfLxdLh7mFt8frE3apEwqG1YDwLV3v0T37gpsKTXcrdBqbhQPddt84Jz4rYn+\nqnICbuEA4s6kqiUqmDOJiCZSJoemJrNzJsmvfRsUkllECbjt0PLLBQAowJlkgbSQJ95JFFnpLC8B\ntxzGwVi8k5F3WeXOJOKDK3thbk7su+CzamfHVUmvnFlK3ebF43P04MvT9Off3Z15rEbC3Bpe1WTc\n4bPvacqktbCaWxTm1lGPoIYTF1bp+IWVxs5XNsyNjzLGv5aBLNTnP8pVSouqMss4C0D38H1Gf/fA\nPjq7mK64/fIzR8T/1VXdirRyjLHwn+4348OUpuop1PCrajmTIqVQEdKExpEyKc3Wl6MO2v1Sq7mp\nbB0PANAN4EwqSSwBs7ajZZHaooQoKE2IwTuYvusllm2VnVqrg2aUSSLMrWLjL5QBBuZD3qma6Iji\nyqQ2dHxtKEN50pxFZUM9dMdoO/zdHqMoNxp4PvWGzeXZ4I+6qPMxUiZZLY41HnvlbGfCNbtMYjW3\nkQ7iiqkyyzgLQPc4cWGVrn/sED3xyozR9iJnUolqEdiYepuqCbunsjIp/LSp7CliizDGUsPW+fdp\njiL527Y7k6IE3HaOVyU0EwDQPHAmlYTlmG5y51VMmZTcX/d7oExisc5W3iVSJgXYcSZpDIrwq8pG\nt4Eaw1Sw0ciKIznOxKaJZt666YlIS8BddnUuou7lj+LVNk0W30WGrk/9YYM5k0TC9mLnjNqW+l7m\nTbtO0/Yjs4X3Y4zR7926jX79xi01lArItCrMraAyie/Qhv4I1IfoKws+57JJkn2mdxs1okyqeA5V\n6W8jZ1LRBNxp55zMWc1NvvYqk2lNIFZQtlQp2jFBCwAwBc6kkuQl4JY7nCIGaZ6Uln/b14a5Rf9P\n5kyq3jhHHXKyvE32dXlXIjvv6vMltbOz62qEVJSAO35fyypN4sdg9MT+GTo8kx5C2QZ8CwZv2xh4\nPvVKJsMuA28OC6/mFn7WOUB63x3P0ztuKO4Q4td05Hxz4YJ18+i+aTq31B91MRKo7U8bwi2KqhJG\nX+IkfgeSCHeFtPyCqbDMP7N3ZVnKpPqpWmf43roUDWUpuhhIWtj6pJOnTIq+b2uY/6P7punIuWVp\n0Rs7x225EAsAoABnUk3IHUQZgzRtH1mZ5DOmOLWi/68O4oMpG8okXYfMz19ohYvMc6T/ZmoIyMeo\nazAQC3Os5QxrC14/E/dSzAaWUCZJRtwH79pJtzz1asnSNYNQJo2PL4kGrh+0VQ1Zh/wsg6Jhbi3O\nmVSm7reZgevTH3xlO921/cSoi5JAff6jFASUDfEt0+VdWB7Q1sPni+9owOrAo3/x3x+iR/aereX4\na420lU/TUB0qRcjKmdREY1n1FEKZZOE95mUppExiyf68N/ToA9/cSRdWBmHZ0lZzi/5vMiE9vdBr\nXMH0wbt20a1PvyrKh5xJAKxN4EwqSV7OJE8azBRpGPmms8sD+t0vPZdIssiPNXD9IJ7dOAG3hTA3\n3SG4M8lazqR88oyimFqrfJFyymBenrr44ztfoN/90nOJ8nQR8cwSs6ihUq/izFzf9SsfwzaPv3KW\nthyKBnCRs3Z8vEm83ek3pU6S2scitDlnUttX8ilK3/XIZ3b6JOsot3qkCbh53234/IXToESv99u3\nPEvvvmlrLU7fxd6Q5leHdHJu1fqx1yJFV87i/UrkVDI/F6OMic0u5EwSk532ylpk4pQRSyiTDkwv\n0befP0EvHp8jIjPnVF5budgb0r/8m0foI3e/ZFw2G7g+o344sU1kP2cSAKAbwJlUkrycSUOpwykU\n5iZt+sT+GbrpicPa7fquF0qQ5XJE/+fOJP6VDZlsVhLGvHHBk/tnaGfYeepwDEZzfJu8K4ndk5o6\nJZby/ya5Z+cpemL/TFgGezLuUTBMke7bCtP0fDbSgaGOTz98gL7w+EHxN6+346ZMIgraqyYoHebW\n4nvO+49xqRfDgsqKJmlTAu6yA+Ey93VfuJrqoAYHn2tZtbDWKZozKUpmHH4WsVhYRphbE8qkiufg\nu0ch5NXLUmRSKlAmxU/KxwZcDeulHE++53ltwEq44M6DL08bl80GjDEaeL6w/21NrI524QM7bD4w\nk7vydJt44KUz9MG7do66GKCjwJlUkjxlitz4F4lSyOvoo9XcgtmAtPxAvUEdCbjTv8szFP/mB3vp\nhicOpf7O1Rg2upA0BxsR0V3bj9Nib1j5HLFn3qJ+r82D4izS5NlVEnALQhVE2wyU/tBTcp4Fn119\nhjq4IqmpFd3SlJirA4+eOnAuf/8WDnh5HRmXXFpRSGv77rVaopEm4A4/Tdu+yFlQnKnQU1mHMykK\ngbF+6DVJ0ZCiKoo1rkzSnYoR0fzqUKwcXAdVqwwztE/NyhKqpAtWZHUSYBj2ifzrNOVhLAF3ziBi\nVD0DY4FzjY9DkDMp4j/d8hzd8eyxURfDmD+8fQfdtaN9oeegG6wZZ9LMYp/+j795hA6eXbRyPHnQ\noWv4ZAOwzGpuqb+Hn3zmXe5j5A5ThLnZGIxrji/KE36XN1vTd/3GljeV74lc5BePz9EHv7WL/uK7\neyqfYwz6utbg+1HuL7WK8T9L5UyS/u/5rHUz40PPj5VpHAwoFT447TW0opucU07mnl2n6LdveZbO\npyR9tunMtg1vN8fDlST1XS282WoTMdIwtxJKiGC/4mVeNxmYgkUVfSaIhNEjuJcLvSGtDNzGz1sn\nUQJuMxLKpCLCJMaImP5cjDH6ib9+kP7tZzebH7AgVSeAeP8aOYWrt6JFbBFGSWWS+vxScyZJ/89V\nZnPVfsOvGKP4RJ2tCQIRNmflaOON6/l053PHxi4cHnSLNeNMevDlM3RmoWctCW8hZVKFFl6djObH\n4qu5xVU4EWI1t/BvG/lidKtNqTLiNAZheathZgiklYUbldMLPe3vMgfPLtGPX/sAnbigX0EpLkwa\nfSPeMj9JIeSQ0IQzKfy72mpugQHXspRJNPB8xfFZvoDTC71aZ4jLwp3YvYbC3IQzSXGez68Mtd8L\nRmSMm8AN9bRVgbrGIIqJGG1BNKht+UjD3EQIef1lmJoMlUl1OJO4Em0Et/KPbt9B1zacR6ZuvLSZ\nlxyEQqmIM4koXOhFM5EYfr56brlQOZokUiYFn5WaUO7cLfI+MqIJZZQ1EO9DttJJtgfy2gAxGdLw\nS8YYo6HnW1cf8uto2wRgG3nu1Vn60Hd20/PHLoy6KGANs2acSbaT2uY1cfLgt4hBmrelPPOeFeYW\nKZPC7WsOc8vr7IJBZfo2ToHBXN428j2RO6MideDO547RYs+l+3afSSuFcXmaIJp3696AU3Z0qrcy\ncoaWr79tzdkxcH1tXS1TzF/9wjP0RSn/UlsQOZMaCnOTFyiQ4Tkl0pqpaAGAdtURImmw0b1XWwt3\nMLZxIrVNyiTO0FAJwetumSJPTdSvTBpF+3tuqU9nF/VqxK7C7+eTB87Rv/7Yo7mqz6helLv/PtPX\nqSYeZ+UE3GS/7jFm7uBVE3AzFoW5Cds5ZZYrrkzKCXMzzCdqm0CZFI1DrOVMUpyAIB1u29TRdgNg\nyppxJtkmL2WO3NkUMkiVTdU8GfxnrkwK/iUb8tWh/ZxJ2o6Cy4hzrtGVQpl0mAzmTDvMuGosZ+Oc\nY6TNZNk4Ry10cMAZcyalSJNMVzSS4c+OO6LaljNp6DGlnQg+i5aSMUan5ldpNlxquE0M3GbD3MR5\nlfZuZRioEtPqQLQAQJ2lKofImTTicthi6Ib9VQtvtloitb4cn12h47N6tWpdhSkyeA12K35f13Fl\n0ohyJm07MluLstLzWW2rBp5f6tNXnjnSuBqEh0U99PI0nZxbpaPns+tjIsytQP3g6nfdNTZx1VVv\nLd/fRhsqF6VIqFtamBt3cKUrk5L75JaxcWWSEuZm6fS+ZkzTVeq+Bt5m20ojMg73HDTPmnMm2XpP\nYqu5aY4Zy5lk0RBUlUlEkZEm79lTDDObYW6x8oSfabMrnKHrZzZSJsokU0MgltTYcB+V3OeQ8n9Q\nHNkwSwzmwi+GFWZduEqwbTHl8jtMVF6ZtDzwRCLMNuH7TBg4vYZmzVhKfeED1bR724WcSeMT5pb9\nLEaJ2kepbcaff3c3/eX3q+fcMyqL9P9CeVpK3Ndaw9xylElzKwP69Ru30D07T1k/t88i56Vt/r9v\n7qSP3P0S7T1tJw+nKWo7b+ocKpUziZiYtJSZnHAaeYErK5O4wkUsYlC1RAHGyiQWT8DtOJIy00/a\nJTOLfZGOQW6L8vr2yFHYLIzCMDfLYWn8KC0z2UpR9zXwlXLTcm8VpW12MugGa86ZZAv5fcvLmWTa\nvl7/2EHadiQe95rs+4KDDTxPSuCXHITWkYBbdwiR4DAvZ5LnN9YxxPJIlZxRi5RJeuuD5Tz/pmlB\nEQpzfqlP55f6sRmVhDCp5AoqMryTbVsfOVAScAslQkGTkK9O2FSCe1NklUO/IWWSCHNTlUkizE1/\nj4qE2TaNJ5xJIy6IJQZuvlJlVCTD3OJ/L/VdmlupvhqoWVnMB5PB9uXPtS4Mc+vX4EzKG+Qs9V1i\nLLJZbOIzRn1LyqQD04u0IK0EO7schM+VWRzChH/yF/fRjZoVcNV2Pu+5C+V6mdXcWKhMUr6fdJxm\nlEmV94+3NVXSAMjvo2kOR0ZJG5Lvyw8n15/f+Iet9JlHDsR+V7fRn6fcRFRVAmUSq02Z1LbUBGWo\n+xp4CgFbk4ltCO0G3WPNOZOsTe7KDgvNz3Ljr/P0rg48uubbu0RiWCKivacXck/LD9sf+uLEuoZ3\ndciXXw6wkzMpnN2RvlNnftIYen5jsyZpScnLPPu0XdoWosHL0/R48+xCj/acnC+1709d9zD91HUP\nZzo6hdJkzHImMcaCnEma1RiLFnOxF8xkVskrVQdym9OYMomfW1UmDQ2dSS17r4mkEI0xUSbxd7mN\n91otkdqv+T5rbmVC6f9FBgplmrnJiToTcIftb4qNwM9Zx4y457NKqlaZX79xC9361BHxt5hwqqHX\nZYzRwPPpb+/bl/it6KQB37pMvWCkT8A9OeE04riwFuZmubBF6uqkNAvAw8LkMskO3AvLAzq/FISr\nF3r/+URU02FuRDUl4A4+25aaoAx1253czrLVftbkGwdjzppzJtkLc8s+ptz46zqyO547Rl/fdpw+\n9+gB8Z220VHsFG6Ay6oG3SBU5B/gsx8WvNZalY9BZ+2F+ZKyw9wKhJnkPMT4ClnJ/5uYfrysRjmT\nDI7XFE0POD/10H76v2/bXukY8bqpn3UtU3/5rahzsFKWoWaAVXY5XOFMatH1EcVDzRobgKcok1Zz\nE3C311HDJybaW8JiCGdSu6orEWnC3DR/16He0Zcl+r+J+qVKqMvUZJiAewQ5k/g56xh0+RZzJi31\nXVrqR5N/kXrZyuFjZDXlqtIrX5kUfhpuH99Zn4B7csIx6k/PL/Vpue8WOKFy+qphbuGnjTC32Pto\nWKcYYwlFqavUd9mB6zMmtY/RCfP6dl2qi0ZggW0m8j1aeofTlE7Hzq/QUoX6NArq7ueEMsmS/Qdl\nEijDmnEm2e7w486E5Msnv9g677poLGPfmZ+3P4yUPiLMTTpaTyTgDr6zYVDpV3MLvsxqyIoMHmy0\nY/EV7jQOMINj5G2T50wcBx5+eZru35O2ml3A4ZllulAx8XMsZ5JyL5lmm+LHjztd24A6O0kkvV8F\nyynC3FqWM2kUYW781iVXcwsTcOfc2xZVEQGv+mMiTBLPpg3hwSpqidQBs+uxWhJF52EyUIiaj+L3\ndV0DOZOWBy79YPfpxO9RfbB+avKYPWeSzxS7LmfCqQpZ/V3VnElFEDmTlHNMTjhG9/U9t26jT9yf\nVFeZn78a6oSrrUdlnBCbkrnuBkqYm+yclhPGy2fIc16J59Nwk8qdX7rxTLXjRseX+fUbt9D1j7Vv\n1dosuqZMyst/C4COzjqTPv/oAbrpyUP06L5peteNWyrLIc/M9+iff+QB2ncmP9SMKN65apVJMaMj\n+TtvYORZCxOPMN9i4EUJrcWshLR7lDMp2r4qWcZT1v03CWswCTNhymfqdkz/bIoYfXy/tKS3cYN9\ntI0vC6w96/zBbdvpj766I3ObY7Mr1Bv6lUKs5PwD6mVESpPiF8jVJupMYBvgAyj5vYlyWxQjUia1\nS58sD0wbU3Nozk0kK5P0d3dUSyubIJRJY+JNGrRamRT/OxHmxhj13ObD3Io4RMrc1nWhMqmOlc/4\nIOemJw/Tf/na87T9yGzsd9EW1lAhPN+eg8xnTGvn1KFqzJoYUAeNucokkU8nOeloAmMscY4pQ2fS\n+aU+nV3sFzqfjK0E3DYG2nHnjrkDT2635QTcOmcSY1FOOfnS81azraJKrAKjoD23nYBbXVyIc2Fl\nQCcvrFo5R1PUnoA7HOtBmQRGSWedSQ+9PE2P7ZuhPScX6NlXZ2nRUPqY9p489PIZWuq7dPuWo0bH\niSXg1vwuD651HRn/RnZW6GYUVUNFnnkX3ntdAm5l9SIryiTddRh01lHOhGrnFx1mTluX92yMzsVz\nEBnYiaNue33Jl9TkcLM39OjMQo+IghXFyiIbZgllEuPbFK88avLuKgblJx98hR575Wzp/VWGGgeX\nzilsghzm9tlHDtAffKVa2KEt5Dan6TA3tb0TCbhzqlGbHI6cKGfSiAtiCRHi2bJbrV1IQ/nObTJn\nknRuk7aL5YwoP/DNnfT9F09qf5uqM2eSUvaTc/HBoAhD1tz/m548RN/cfrz0uX3GSk1E6GBMVT0H\nn2Xey8dfOZtZj7IGhoVzJhk6GlYGrtZR5WucSRMTjlFdqRoWWr05jjs5bDnkdRM3R88v082bDye+\nlyeMGdOFuUX1IKivyclXzw8mj9PsIDHR2nD/FZSJ1ZAzKbTZlOsZej6dXy7vnBwFtSfgFmkc1u5q\nbkPPp7/6/h46G45HQD59y5NinXUm9YZB0jf+ni6sZq+wkteFiMG4YV+TJ0yR1Ra6xkR8JZ3P5B3m\njWzfjVZzi3KtBJ8XrZtMhLnZCIHRrYghVnOrqEwyw2z/+Ep6mn2M7nPwmZqAu0XtbR2zySackGaI\nquRFiIW5qTmTCtTfuZUBvf36p+no+eX48T3uYGD0qQdfEWFhpjDG6MYnD9NDL08X2i+LvmYAJWaO\nK4S57Z9epBeOXcjZoxn6sZxJDSuTCq/mFuZsa9OLHRKt5jYe3iQR1tQyHZju0esTcPuN1BP5DKar\nRwX76bf99vMn6L9+/UXtb1yZVE8C7vgxLyzHw6L7GUq1775wijbtSobGmSKHDVVBDG7lfIwFJpxk\nTs+v0ntu3Ub37Um/rqwVHIsO9lRHQ1o9f9tfPUAf/t6exL4+S7abUxOO1kl3+9aj9MT+mVhZqzhf\nbSmTbIyP5aLonsFv/sOzdN2mvTSvjEXSwtx0Cbh9FuUajCmTPEbfef4k/auPPao9t+0wM1MYUTzM\nzZoyiRLHc8MVoXmC8q7AajZ9eP2xpUxqYkJtZrFPV1+ziR7dZ8emfnL/DN225Sj9hdJ+AT37zizQ\n//rh++k+Tdh5WbrrTHI9cn1fdOgLBQeIaZhKlmNhbpomXG7w9cokljifbjvVUOFb9F0/4b3nu1+y\nYTKxzK7r6yXaRdA6xcLPLGmkSViDuA8Z25gWP3U1N7Pd4/ulhbmR/hyjQJfHoSo8x0wWxy+siP9X\nSYqY1QkKZZ3BrMs9u07TzuNzdOOTweygCHML991x9AJ99tGD9In7XylUvgsrQxq4vlUHokg6G1vN\nLfgsehp+710/KOPc6rAVq6AMRpCAW5fUlCgZ9qvC3/KWRQoSUfbAsou0NQG3rji6BNxEDYVtSqcu\nEsJa5r5OhTmT+jVMTKjt+4WVuK2mC/nlVF09z/eZFQcZL5qN2f+58PqX+1nKpOA8Ogeyej9zw9yU\nfkW3+XLY33/7+RPKvsFKbuo+E44TU9RwbnwiriSrquSr2kZEz81uY6Nz7ooxiOIImVAablcNc5Mm\nWuQcX6rz6uTcKs0s9jMVBU23qYzFw9xsnV8X5sbv+exyt5xJzSmT7JyniYVcdp+cIyKi2wwjgfLw\nLDszx53dJ4IVuB/aa2+CvLPOpP7QDxwkYd1ZWK2W4b9wHZS21717svGnc7ToJNImjQ6TjFkx6+LH\nf7to/aR28GQyIM9Cd53RjF162YdefkdjkrPE9BnFnEkl25YiyqRRt1+ulD/LVllMZn+Oz5Z3Jsmz\nxfL/E2Fu4aeJMokrdC7bOBX7Xu0cVUdrHtOhdNZmRyUScFtwBIowNy8w/D2fie9GyUhyJoW3UJ01\nz0vAzdufNoW5uZ5P39pxQsqXMR7eJN1qRW1AG+amVFuenLTfgNJOHsIXURaXuatTE/Upk3gbtz5U\nP80pCzZkhbl5rJozwlYC7kiBHX1nGnavwtuirHLx561zJhV3aOXbBry/uHj9ZGJPxpLvxtSko3Wo\nuB6LLbbgeaySKrVqExHlFQ0nb1Oa0C2HzmtD1GLHioWdaSZ+U/ZTJwHU5y47h5jsTIq9/9HqzUM3\nfTwxiv7L9aKJans5k4JPT3IG8wm4CyuD1vUdWdTvTApzJlkK521iIjJvfFWe8bCR6sapIUloZ51J\nPdcLXp6wVuaFruQleM5bCj6xfc7v8TwwusY/Odusa3PU4vBNBm6kylKXFL943VSUMymlTGXQXwfF\nyqDDNQhz49eZ1Umo4Xyp28UMPk2Zjd6g7PoQcyaNWJvk+lEJbHVc55by49KPnY+cSUXD3GSHTuxd\n4Z+M0StnFlNz4OjgBvFlGwJnEn92w4oDJJ4XyqZRoBtAlR2c8BnRwLke7Fx1hT0byION5vLMBJ8D\n16e5lQEt9V0RmkSU4UwKP1sg6BLctPkw/eldO+mucKZ/TKLcpJwg7UJXHrW+8Pe1iSTc8qmLzBbr\nqvi9u05l7tPEam6hvypdmaQpt/zulsHzmVVVtk71XLRf4IqkrD4tK0+a6sTJsz8SyiRNeXkfcsn6\n+EQMY/qcSZMTjnZRF9f3Y8/L9Vml3BxVbSu+d17eud/4h6103aa92ceSimIyMRsNmOMnVZ+ffB99\nllztje/D67DuvufXAUZ/98A+OjyzlFtuU2TbzLYySa6jXAHP35ehx2ihBZNlptRtUwwsK5OayJkU\niSnsGDVtsyXajhhvWzxmd51JQy/ImRT+batxMa3aulWYZGLKJE2/o+toTF5ivsnA9YXTJJL4BX9f\nvGGS+m4QxxzvkKoZilmJxLMcVSZhbmb5ouKfaXgag4+oWMOVZgjojmtz4uH0/CptU1a7yUPnjKmK\niZT42OwKTYbe0KLOpJ6UsDuWMym8mbc89Sr94t8/SYdmgvxHJjlDImXSutj36kooRbuv6XmuTCq4\nYwY6dYZ4jwseS17NjV9qG5xJAy96xr3GlEmhwe169JMffYh+9uOPxgb+ae1MG3MmzYSrIHGV4LiE\nudW5elcVdMVR+zz+d1POUY7JAgSR0yBe5vmVIb3/jhcy952qMWcSV9LwZl5tmwYZSrWqq+cJNUdF\nVTYvmk5JWrQa874yq08TDjitMklxJhmeP2u10DRlEpHemTQ14WjtwaEXV5J5FZ2B1ZVJwaft8bF6\n7YdnllJV1SoJZZIc5ubLyqT4+fgpdXZ8Xoj8mYUeXf/YIXrPrdtStshm6PmJNo9frxs6bINy2LnR\n8mF4Dkz5ursU6la3TWE7Z1ITq7nxM9gyaXSRPiAdPrFj0wbrrDOp7/rh4Cm4GXkJuFXOLvTo6ms2\n0ZZD54mo+ACOpfyfI7/YWU6Y8mFunjTTFP+NGwRBIxMdUzejUQRdtIUqI9YRrd6Tvo3RtRs+Jbnx\nrhrmZnIOm9y8+VX6o9t3FNpnGJvZslMuk0HFsdkVeuuVlxAR0VJG/gcdsjJJZ1TvPjkf+9skZ8hS\naBBfskEJc1PqfdEOZ3ohGNRX7asPTC/S1ddsoq2Hz2vj3KPBSbETLUlhbnzfVjiTwmt0nAaVSeEn\nr1MXVoYi+TZR+r2tW5lUpb3gxl0dS5CPgrbmTNK1nenOpAbC3BRlQpn9iIhWhvmOfl6zZAewLbgt\nxNtwNSQ6y7lYNcxNnnyrgnahERb/zRS+8mm2MonnTEr+lsiZlHO+tEm4TbtOi5X1uP0sO5Mi2y5p\ne01OxIcO/L54ftz55/p+JWXSpooJYsU1+HbbUPnZvXpumX7+U09EK0rzKIiUaIdkmFvcmTpwk87V\noTTW0dWbvMU7WMX34B03bKH/7S/vjx9T+j93iNlq0n3GaMNUUMeOnAuVSVJ432yHVnSrW+hjezW3\nZsLc9O9GVcbDQqofPklh81F30pnEWPAvtppbXpgb8Znn4O8DZwO550fu3hP73lS9Egtz0jwQWS2i\nM55jOZ4AACAASURBVDZEDHfKMUW5UxJwD7xkAm7+20XrgsH06tCLS+UrhrnpryP4NFvNLR2TnD/G\nyiRZNSYnyi4xk5hmKOY5E8vSd73IKDHEld4DW43DMM8Zyhgdn12hf/qPLicii2FuKeU3y5mkL4Nq\nfBc1KM9Yypn01MFzRER03+7TWmly2aMv9uUwt+C7C8t2FiSoApfrX7phqvmcSdL5Vgf5yiSxf02C\nabXqPHPoHP3bz2w2SqYqBkJjYilFkwsjLogBiTC3BpVJeTla0rZXtzQZQKYlrreBumy4OliJ2sL4\nPisDl3y/muOOn7uIM06Hzs5hym+mmORM4uU1yZmU1y9F9SKyf1zPp/fd8Ty9+6YtRCQrk6Z0B0hc\n46QycohCkKIwNz/sj6o8v5uePExnF8sv982LLRzyltpQuR4cOb+caVOqp1RtGTXMU+dsdz0mrkHr\nTMopb16ajzx2Hp9LfCe3jbwfs7ma25WXbqCL10+KVYNlR3eXVnSrW4HLE+H/zwf306m51Zyt82ki\nAXd0BmvaJEvHWVvYFEV00pnEX045V0zRhLN8Bmb/dOBUKnpL8xrlvBW2dM4rI3mhNFhSpdf870s2\nBNe2OvRipawa5paVfyjLVuPnzWqj8mS6wfnzShg/lrpPkWes5qPSbCCdw94L6TMewmh+TM/3M/Mh\nlEFW8+gGGLPLA1oeeMKZVDQB92pKmFsaRXImqfeuqhNVJOCudJSoXk5MONoBddkk6vy6vYo5kwau\nT5995EBhx2DW8YiILt+4rkFlUtLgXok5k/Q3N0rAXVe54mw+cI5ePr1gtHAE7xd0A8suwuvFnc8d\no2vvfmnEpYloW5hbTJlUZNZZuRATR25V5UIWavurDlZ0YW5feeYI/ZtPPkE+Y4UXTODI/UBV20co\nk2Kq5xwbIYUoZ1L6fmIFR400qboyidFKeE95KC2fjI0rk4LPIMwtfhZVmcTvr7x6W7TyYfD3S6fm\nS636WiXZvXwNRNWGrvFl6qP/n11QnF05tqwaIdB3PeKr5hGlOJOkCcOBNgF3tu2gTqbbQD5Wf5hv\n4xc7NqOJiUBlzt//QUyZBGcSR27fP/itnZWP12zOJLvHHRMTqXa4LWnzSXfSmSRmG6TBU1qY29bD\n57UdWNWbmNc+yIPxomFuk5IBoaoo+GCpLyXgVo0abhCsDry4VLZyzqTkd5FDK3+WLeummeQfMG2U\nY2Fumv11DY7vM3p033SiU05rWOtWMBRRcgwNlD1FkY0l3QDjWLiS24+9/lKanHCEA2J6oSeWncwi\nPQG3/gJ43Z1dHtCr55a123CDWK0nqrOqeJibnQTcfHATLKusC3MLPovWLe5MGnqRg7mMM2nXiTn6\n1EP76f49Zwrvq4Nf42Ubp2Ir/NSJLqyFKwGC39Pubb05k9TzHgnrsMn5mpCdF2XPyXIDQ6L4YOrL\nzxyxVKLq6N679ATcTazmFlFlNTeTwXiUW8h+XUtzyHH6mjC3w+eWaHqxL1ZzKvMOyI6fqk4yFu4e\ny5XJPws7kwxWc8tQI3pKXcg7fVTO6LuV0KHFE27zPuQi2ZkUfvos+WZMJkK3AoeTnCNJVoUNPZ9+\n7YvP0Ne2Fl8KvEq/KyYFLddr2aHHw+DVc6YNmNV3OUi67dN/+2bgCBBhbtJddz1fup+6MDd+LP11\nqov02EAuX8+6MonRhOPQxnUTwjkpX/f5DjmTavYlxdp3G5FuzeQyrO7cjR2tfSZSqxFKxTWvTBIx\n+Ey0jrowt9nlAb37pq30x3c8H8Uxh7+p97DoTc3bPk+ZxAsgO4v4LlMZmVZ5YzFwo2S76koKPMxN\nnT2tLvXWzYiEZchoxIaic8w4tkECP6Z8piEbkfFE6fFPmdu2HKHf//J2unvnqdg5Up1JTP//qsg5\nsUzJC6ksg1x/dWU5G85ovvGKjXTJ+klhIH/x8UP0B7flJ3mU62Y8AXfwqb4BPgue5WcfOUDvunGL\n9v3jChRV4Ve13kfOpEqHEfd0asKR1HrJZ1f0ES5plUnFw9x4XX/h+IXC++rg1xgok5oNc+tLDdJq\nLGeSfr9ImVS/M4kxJhyiJmrUrIHlKHA9n371i8/Qnc8eK7V/1dUV60L3KBIDv7DoTSmT+DM3yYeR\nVpVM+pJo2fEalEmqUtQgzG1uZUi+pNYoEyYr993V80VKNmdINIAvdqxlgzC3KGdSvjIpzyISdo/k\n5OCOYO484pOx8kRmLGeSUrmmNMok3s/yiQO5nCt9j3pDv3AEAT9/WdTwxCqrR8nFkN9HNQxPfQ/V\nc+qe++m5Ht0T2p78PqrKpMycSZoyyqjRCzaQj9WzrEzyWWADbpya1DqToEyKkNu2KdXLW4IuKpN4\nicclr2Td1KFU7KQzSVaN8HuhCxfg6od9ZxY1raz+LppW7rxnIBuhOpvB17xM3PERUyYlciZFxpXI\nmcQ7ivC32sPcWPK7rJkf7izITsCtnCPr/Dmk2d1Zux8NlTZc9p0308Ni/7f3RvLzFRl8yyEQtvoB\n2cGjM+TlunrphimRgHtl4NLMYj93JpBf3/qpCWNl1dD3aanv0tnFPp2aT+ZR4IOm6N2q3rEMXJ/O\nhfH5Vb34/NlOTDjapLMmoZ4qvaEnjInT8z2RUPVCCWOLOzZe1ORHKENMmdTAUuoBSYNbDnNLM5RE\nAu6a/Bxy1fF8RkfPB+2NyfsqQl5a4k1aHXo0cH0xKC5K1YF9XegeRdIREpS9qbDNdeGgvUoCbhNH\nDL/MOvJlqAsgqO+gri2cXx0GuX0qhBXKx7MV5hZzCpdUvXBV0FATrsSJciYlf1Pvn2keONl842pN\noWLXOIDkw6rnUHxJNAgXxJGPJSuoVjUOAVOqDG5lhxiRPSWE/D6qyqRIBaQvt679k5OW63KMuh4T\n9Uy3f9ZEKVH9DoLI9kqehzFG9+46ZbQipdiHKFQmRc6kQWedSfUeX7atsoQIpjShTKrL+dMSE6n1\nTAhlksVj2jtUc/AMMa4XyZ91yiTekchGuCN+0x/btHLnPQQvNsDXNLCUNBZ0YW5p5/V8lphtEMqk\nWJhbtG91gypeTiJJwZNxQwaamZbksVnucaLrzL75cRWA/vvEPsqATeSCanjcw+9xEeM5SETP77Gd\n1kE2lnSDEU+qq5dsmBLKJM8PriFvBpKrRTZOTcSMjKzSu140O7fnZDKUToRL5PTeRTqcmaXIUKx6\na6M6FilnYgldSyiT+H3mTcbhmUDxUibMjTdZ+04vWhksc+Pvso1TjSmTfKUtJCKRHyT4PXsSoQll\n0un5nhhcZdVVdbKgLYaSKHvJW1W1H6oL3bOXyxrkNQn+XyWPizlMzDSb5JXjpVf7AJMQL7GSVA3P\nJs0hx+GJdeVyz4dKGe6Q6CnO6O1HZnPbKLltzXLcmKAqXIii9rLoe7BkpExKdyCr99PUmSVvFSmT\npsKyhG1NysSOqtDTKZOiVfsYuZ4fe87ceVWmflVyJoWfNtp1dUKAo+ZMYprto2MwbchqPIdk4DiK\n5Wjyo0gEnWM57/oiu9pe/6ZTJunYfXKe3n/HC/RMuHK22bGDgwdhbjy5e9QPdinMrfacSdK9V3OZ\nlaFsjtFNu07H6nEW1pVJ9fu/xgo+0W6zbnbSmcQbmrwE3LH7pIa5VS1DzhGGMaND560PiyW9TXyz\ndepSGbHzRqix6cKZtC5NmVTVoOIzLsnvMldzU1ar0B+bwuOkb2OqANLNHqrfp51fOPI0jrNYWZj+\n/1Xhx1KN5yyGnp9pwJRBdobqBiOysXvJhimhUuDv5myOM4MPSDeum1RCBzLqkZQT6CWNM0nNQ5Te\nT5n3YGckBVTVhlc44Bwn9k6oOc+KKN0WQyf6ay5eH/t+rkSYmxzKoXPWFYU/j0s3ThWqz1XQ1Z+e\nSZhbDbJfGbntOxiuJGp6PrESUYnz/uX39tD77ni+xJ7p9IdmTts0dAlk20BWmJvns5j6uYn6zFhk\nC5gY+EzTPxMlw9x0z00MVGtczU38nbOaFVHUfg2FEiwq1/HZFXrHDVvow9/bk3le2WclrwRVBtWx\nq/vNlBWeMylTzZ3uTFJDHnNfQ+Fgj+xEro66OLQV+YROXJmUbjupE55DL+4k6bl+7F5xdWgZR3Il\n57PqBLQ0eJXLlMiZpDwP9RHqrkd1xgwkmy7YRwpz07yjeVXQV8YINpDrR5YyiTsui+TYY0RETmAf\n8raWtxOvu2QDzS7303duGXXlYeTIDtp1NsLcSpT3hWMX6H13PC9WR8+D1x17YW52jzfu1LHgTCed\nSbIkm9d7nTIpC/V9KeopzVcmRRvoXk7d7towt8R55Q473oCLMLf1Uc4kqwm4dVJvtbPWEK1Okb6N\nSbicaQhKXO1Bif/rziBWTJqIe2zTyhM3tMzKZQK/D0WUHHXkTIork5KGOD9NFOYWKpO4Myln5qgn\nOZNiYW45ZeLXtzvDmZSXbLJIh8PzJTlO9XsrlEkTTswAUNU0ZZRJr7l4Xez7MjJwuZ164Vj1ULeB\n59P6yQm6aN1kQ0oO/TM3ScBdtzJJbvsOzUTOpEwlZvipqiaL8Oq5Zdp3eqHwfln0hukDBxPaqkzS\nVR6urvjv975MP/HRB8X3jeRMIsmZVKCTUR+LqizVqUN0OYFsoR5TdaKISQCp4HPhZATv2+QZb65a\n2ptTr+MJuKtdF99bpyQtnjOJh7mZ5ExK/qY6FvMGq0xsF23PJ394SgR+XWm5uVT7LulM8mMK497Q\niz13EUZXYkKzSnvhK8+o0mpuUgMR3S8WUy+r2wXnlNNWOFonYjK/qR97rp7v5+RMyr6vXo5NVAad\nMklXfXj7Y6paCQ4ePKsNU5OSMin4fOMVG2h2aTTKpPv3nKaj5/ULwKRRe5hbTJlkIcytxOvG7dBT\nc8n0E9pziPfRcpgbciYZIWXGs3bMTjqT5FkieTU3tVPNchDJ28oOA9OqmPcI5AZfPxOYHCDw77Li\nXuMNeNyo56eRw9yCcyTLVAZ+bvlydEaWiskKMaJzNhhc5cE05Qv+n+Woit97cV0GyqQqA9BTc6u0\n9XAk/41yJhVIwF1HzqSc1dwiZVJgkEZhbsH3as4exphwzBBJYW7rJoySyxLFpd57TiUHEtx4jRJZ\nV78ZvMxXXbqh8qyeTplEFJW3THJS7sT7IY0yqcqiAjbyJg1cn9ZNOmJ2se4ZOiKu5oi3n/EwN/1+\ndczUyKQpk0zaDqFMKmEnDT2f5jX5BKtQNdlqHcvP20DXP/AQ7e88fyL2fRNhm4wxUZdN8o0w5ZOj\nOnJ1fUsUQlOHMil+TPUcA2WyyfMZLYRtociZ5MrvsNn7EAtzs5QzSbYHmPKbKSaruUUhPQZhbjmn\n1ynWlkNlkghzk0LUov2i7VU7SOdMku283tCL3X/e35cJc6vi4IxsVrsNO7/W2eVB0vZlweT2iQur\n+n017Z+afy5YHS/6O8iZFPw/K2dSGlH0gr37IB+Jtym6NpS3P0XVnE64mltfybf1xssvovPLg0bs\nCZUPfHMn3b6l2IqEtYe5Wc6ZVEaZVNQ2EX1Cgf2W+m7qMx9BVeg0vF+xed866UzSxq+zaMZHxXGS\nuZLkezj0/cJJlIsok3R9oc7Rpc2ZpLyl8qH6IqyHHzP4VU6qKEvlK4e5aTokNURHx1BSjCz2hrGZ\neXFsg07ftFH2GdMqDbJ8FtxgmRSxpHyflMZLPl8FY+eWp16l//K1KAylTM4k12O5YXlERIdnlujA\n9KLRMb2cBNxCycXD3Ppxx6aas+e5V2fpX/3tI3Q8THTOZyonJ+IJuHWvYTSgipzHM4v9WK4CP+d9\nK8uZhR6tn5yg1126ofJx+f6qMskLcyQcv7BS+JhRmFtcmTTw/FjiaRN4kd5y5SXWnEnrpyZo47pJ\nYqyZxMs+Y7RhajL2nTwjmteG2EymHztuijIpW60ZfHoZA8s8goF58ZDHLFRFbFHaqkzSXQ534qiD\n53Yrk+LbqspSnSNM5ExqYDU3dTAtFKXh13xlMRn5fvPLy1Pq2VRl6+yBso4KEfKV8UzFZI3GQk8m\n4M5r08JPyakkEnArYW5pk4KqjTPpqM4kpiiT/Nhzj8LcircZVUIv+dmsrObGoolZL7zW6YWkEoMR\n0b/77Gb6N596IjynfAymzX/Gww45cuoCouAd4jaX7h4WGY/YQq53WRMMvP0pokzi/fBFcgJuN1Im\n9d3i9g0R0defO5araMxi6LPUcWYadS3qQRTmDZbuuY2cSWXGM0VNAT5Zbfo2Hjy7SP/8Iw/Qd184\nqT8//88IhEnLfZe+8syRwovM+D6j3/nSc7T5wExNJUtnQjM+rnxMa0dqkLRVOhYVo1keGKi3TPX6\nZ6mY9GQ/hKEXzSqaxtnzzWLe5YTaKt2Y4b9cHM42BTmTGK2f4s6kGhJwh//NMnajMDei37r5Wfr5\nTz6hOXbolMo4jmkYkMeYMHZ0CiLtvZdCkOS/U730sgS5wgs5cP340uXhZ6EwN59JK7akl+XnPvkE\n/cKnnzQ6pmzo6gYYydXc3PD74HfVmTSz1CefBc4ZosiZpBpXusF85AyNS7/3nIpC3WSHV94yuEX6\nm+n5Hr3+8g004VSf1ZPv2UBRJp1Z6MXCEEzhM/iXbJhK/FY01I3ft3/xj19DJ+dWE4lFizL0AmfS\nhrD9aSQJNyNxPs5KLGeS/t5yeXQTyqRDM8uFlFBVciYNfUYD17fq/OgZJA/PLJNBP7Tl0Hn6T7c8\n28gyxRzdmbjRqzqTVoceffz+fXT/njP1lYdFSz0bhQelbKJOBuiVScHOdYS5qc9QHQz3lTC3eY0z\nSVZX8b3z3od4mFtF20djD6irpJkilEkZZcrKmaQ6I3KdSbxfkf6OEnAHziT+TFKVSaozaVJ1JsWd\nRwll0jD/mtMwUbankVjEoPSRgvs3pTh3zy5qnEmM6PhspEpSz6lzBqnKpIHrx8coVcPcaniv5fL1\nJXtOhb/fWatK9oZebPzGWHDfAlVzPAH3Gy/fSETlQvk/eu/L9I1txwvvx/F9VrgvrVOZpN5TGzmT\nqvQBpuPnos7dLWHy9m1HZkuVq04eeOkMfeTul+jau18utF/f9enJ/TP07OHmrynyHdg7ZiedSfK7\nKTesC4qcPxaXqdw0ufGVj2FaufNnAoJ8IUE50mcS4kuDJw1XjYI2gaoY2rguHLyFg6gN1pxJ3OGT\nLE/WwII3ToyIdp3QJ/YVnX7mfTWr+b4f3cP795ymP/n6C7l7R6uT8TLHVV9ZJSkyqFrsxcOPfMbi\nIZHhbyZebn6N8RWHjIuSiTzLqM+xEZWBr+bGGJNyJsUHBHwwtBQ6P+QOWR4o6covK+t8n+hNr72I\nHIdo94lohmlVDmUS9a36zZhe6NMbL99IE45T+Wiy+k1VJvGl4omKPUMeGrdek7S/aBJuXi9/6kde\nS0REL1RUJwVhbhO0IZz9LjpzUwZG2c6k1FdVLJWq32DH0Vmt8frSqXl67tV8Y0Bu52eXB/Sm115E\nRNlGvrqiZJmcSVxhqFN7uJ5P9+46VdhJWjXMzWSp+m1HZmnzgXOxfFd1o13NTeSuUZVJPn3x8UP0\nR1/dUWuZ+Hs9LDC9rV6Ger914bT88KqN8K4bt9D7vlYtgbs6OBkokwJqrru5XGWSWcWT362qqkgx\naabpq0orkzJXc9PXu+C3+PnyTi87kdQy8MNH9k7SWaY7p6pMGnh+PAF3IswtnvemCFWUSbwIJgp6\nE9ZN8NUVg+OoybeJknaH+gh1jk1VZRMk4JbHKNGEt9aZlHNZeXkkTYm9e9J/uVpVVw5+vVlOmL/9\nwV76nS89J50nuG8b102IyVZ+3W8InUmn53taZ14WA9cXzlyVpw6coz/71q7M/X3GiuV+Ijs2+ck5\nfcikWpds5EyqtHqi4a6ugXP3wPQi3bPzFBERHQ9DRt/02otTzlvdWVwW3u49tu9sof2yJk/qxlZ7\nINNJZ5LcIcgdmCrnl2PrxSySWNoi2m7oFR92qi+N5zO6efNh0WC6HhODKK0ySXOcKGfSROK76O9k\nWURyvfC3CccJ5KHh7Ia1MDdNhxw5gdKPrRqLREkHTF5YmbxN3tPyGRON6v98cD9978VTsbLqUJfi\n5X+brOZm6sk/cm6ZfvzaB+lrzx6LlTVIJB93CJokLNZJFW15muW6oqu/ch6XSzdMkesz6ru+eH5z\nijKJGwKLYUfOO2TG4ufi/5OdurKyzmeMLt0wRVdduoFOz0cd7KomL06qMknqcXafmM+sF9MLPXrD\n5RtpwkYCbumexZRJPtGx2SipY5GzcOfcesmBclmoUlLVYXnwZ/rjP3wFTU04lUPd+qEyaWNYtiaS\ncPssanc5qyYJuPnvKS/Qr31xC73zhmcS3//9wwfow9/bbVQumbdeeWlmebL2LQLvH3UGy02bD9P7\n73iB7tl1utAxdQm4nzl4jn71C08bDRZNtlGVjrb4wuMH6frHDmp/093mNGVSv6EwN65MUldAy94v\nvq3qTNLVBf4s1cHJs6/O0qbdxeqHiq7scps/kJTLRMm+gyjevuv6CB1y3anD9omU2vp9vr3jBB1W\nQvrl5NdmOZOSvyVzJpldm7A5iYnBqeokM82ZpOZmGbp+7HqCMLfob+4ULqMy0oWFmRO3T3Mdbykb\nzK8O6fjsilAmecKZpFcmpeE4jvZ6VAeHvHItPx8/p1YpnnNhUSqMzM1ySXM28vqqK4dJAu4j51cS\nOaYciudb5NfNnUm/dfNW+pn/8Yhx2Vloa6+ktN2/fcuz9I3t6aolxhj5rHjup6p2456T8/SvP/Yo\n7TuTDM9T23YbOZPqzvFEJCuT0rf5hU8/SX98ZyAEOBGmgLhk/WT6DlQtjLUsfEwzVVAVJvI9W05D\nYHbu4NNm3rFOOpNiyiSpcVNnX33J+5ml8HH9qOE2rQ7qC/ftHSfouk17haHq+kxSJqXvH8v1ErYL\ncqVMlFvz8NXB84Tj0EXrJ2l1EORMshfmluyQ+blNVnOLOfCUDpV39pkz9Yb1XnYmRfuyTEeLKrvk\nRlVaeeIrbQT/v3nzYfq7B/alnuNAmHj38VciD3Y0I8xif5t0WLysvl9+ljSNeM4vTZ2TVDa8gV/u\nu2JbVcXBj8eNJjnkKRbmpin+ekla7rOgfk9NONoEn0T5hiMPaXrm4Dn6lc8/Rbc+fUS7HWNB+Nkb\nLt9I5DjVcyZJB1DVaEfPr4iOtZgyaUgXr5+M3YsfujRIxl3YmRQe4+L1k/S2/+VyerHiim5DN1Bn\nbgydO43kmWF6ZRI3stRnuPf0Am07Mhu9S5p7z+/LoZnkKi6u59PZRYNlipXjvvWqS0R5U3dR2tYy\n7zZvx3QGy9lwVn3GpPwSqxpn0gfu2knPH5szuhcmA3uunrFt2D6+byY1NE03ScHf04QyKad9/tqz\nR+ktH9pUcZaXiYklk0G4cBYom6qKQJ0zSTgVLOreT86t0oe+s1t7r+T2T51s0pVP7i/ELHReAm6m\nb2/L4Gvtk+z38gN37aRf+szm2Hc8j2VQpvR77Un9a9pvUdkyi54M72dEB6eXYt/x++PFQs4j1HqR\nTMDNssPcDFawS2NQwREYtaHBp2x36yYO0pqbX/z0k7T5wDkRRsTv1/RCP1EP1fogrzDFGIs9d34f\nVWXS0I0n4OYTaUQpCbj1xRbYSsAdC/PUHEp3dN7+ZLWZ86vDmEONt8VyvkV+3VdeuoGIovfH9Jr4\n9ispyiRx7pwJ5KLKpCJ92NnFXsJOOjMfOCx1K9ipbbuNnElV+ixTX46JMkmGOxrrCMOuCp/QXaeJ\nDsiCtz9pyiTGGH34e7tpd0o0TxUi8YK9Y3bSmRRXJsk5k+KNRKSecBIvdKyhdqOcM6a1W30G3Fjn\nM6qu7wsnTlaYm9w468LcTBpKXxlsOE6QuE7kTOLKpKp5AzQOCzUmXQfveNMUZfJxiiTgPr+kH7h4\nftJDP1BmepL7xDsmeelXHfK3vFyBM/FQ6jm40Sw3OmocPK+HJgNvfoUeY0mDsSJ5Dh5h7IZhbkTB\nCjH8saqODD4Y4g2vyJlETGtUy0+Pv0duGB4xMRHktpLfHV0YRN6dOBYmA3/ljD4p+VLfpZWBR2+8\nwk7OJNnJJc8ueozR0dkVenMo4S2ik1zsuXTphqnY8X7oktCZVDCngOyM/sk3v4Z2nZirZFgMNDmT\nlvpu4XIVgRFLOJNWhx5dujGoo+oz/OXPbKZ33rAlUiZpnnFWvhVGQThh3mBVvY1vvSquTLrj2WN0\n9TWbEnn/iJLtu/z90wfPZdZL3j/qDBbez5iupsjpa5xJ0QIX+fWliDKpSj46HYxYuqxccypXaudk\n8vJ/XXfvXmKsemin4wR9mclqbhz1MlRFYJYyyWZy9A/etZPufO4YPX3wXOI3nTOJtzW68Fy5fee7\n5uZMksPcXJ/6rlc654ZOhSR8Mxl1VG07lqVEy1n3Wgy2dMokpb/MG6xGaRCiY796LnCM8z35+dJy\nc6mOF9W5OvT8eAJu14sn4FZW5CpCkbqvwksQ2ZfB35999AD9xy88ndg+7V7yXI+O49CEE9WtmcWe\ncG6IcyqHUJ+hfA+4aldVJg0k5xFRfPGRoWvuBOPYypmkS3ER+13zJW9/eKijjoXVIa0MvFhddRyK\n2Q5D4UyKr1xreml8/7zE3WnH431R8ZxJ5tv+yueeoi89/WrsuxXR3ya3V9sXG8KcJvIUcpvDVEnE\nnUlp4co2cqKVZakf9FdFFZT8NvP++PDMEj25P0rGvdR36atbj9Ej+6btFFRzbpsLznTSmaR67Lmh\nlwhzk4wOsY/mJsoqGcewOqqNN/+bzyS5XpT4OisBt/wT7yzkjlrnBFPzo+jiHzeum0is5lbGq7v7\nxDx94fGDSpklZ1L4me1MSpZPNSr4I8gyjCLDgOiRvdP0U9c9TE8dSBqqjLGEh37g+kaOKk8xqkzC\n3ExsnfNLfeFgkUOS+G2LHG7B3ybJink9kVVXtpqGeOhZ8qiR4zJIwE0UNH7cGEgok9QwN5GwNWO5\n3AAAIABJREFUMehY+FhNn4CbzwYGBtWE49CkqkyKDTbyHZPBubLhEvY38JxJBW7uFx4/SM8citdN\nXk88ZXbS9xkdO79CV1+Zr1ZRWewP6bKNU9SXKuFrL+bKpGLyWdlB+JNvfg0tDzw6cNZs9T8dA0WZ\n1Hc9+tB3dtPvf2Vb6WPm4TPSruZ2SbgoQWqYW4YqLMuZxKvgec2sYXy76MAbpiboh18TSPX5Pb/l\nqcNEFM1CEkltq2YWaX5lSF9+5gj91s3PZiaBFsqk1eRsbORMyix6Al3OpCJLzZokQ14WYW6WnUks\nYyYw/JT716qrueXdj5nFfuqkCGOB7TI16RQy8NVzqgZ4pjNJs5CBjq8/d4zOpZRbLYfuOHKZkmFu\nGmeS69HV12yia+9+SRjtuWFuijLpo/e8TO+8YQsdPJtcTTYPbvu4GuVOkSoqOw0ycyalKOKIdDmT\nsgsgJsrCEp+Z7yXuuW41N536OiiTJg+Q58f6tN7Q1yuTyuRMquBMUu06/veLx+cSYVVE+TaBQ0Ea\nCn6tPKdiEWLOpLBvTCiTlGsOVnzV/xaQUwcMJ9jyiCuT0sc1MjwUK0+ZRJRMRC5sh6FHrsdowiG6\n/KL4yrWmyh9u06/mtN1p7R7/Pm9/lSKTkLPLg4RSmIfp6yZW1DA3G46gJpxJacqkuZVBon6vDFwx\nntA5UomiNngEUW5i4qtoGgdP2GZB3f+5Tz4RyxvGn+2SJsdhVXT5j6vSSWeS3HgMPEaXh7POiTC3\njIZP/pOHgxVBHfSKpdKlBH0bMpxJ0QBB7rCDT1lVkwzPS868q6tXTThhrPHAI0aR86JMEsp7d5+i\nv3vglcBhoYm75v/PatCHiuESfBffXi8jjyPfq21HLhAR0c4TyVAcjzFSFYd9yZmkO4OqRCqjTMri\n9768jf7Hpr1EFFcmRTLzeH0wUiZJK0LZSjDJiYW5aaqNrKITyqSBmzq7zDuPKMyNK5OCTp7fE13x\n5dXcfBYMIiad9DA38XXKrVAdB2kd0Jn5oFN/w+Ubw1BZ83t7/aMH6QdKrhFfqlN9VZl0fpl+5Ie4\nMimJ7zM6PruS+H6x59JlG9fFOrJ1kw5dtmGqcGI/2Zn9k29+DRFRpVA3vpqbbDA/uX+GTmqMeGsw\nog3rkmFu3OGZ1nlmKZP6Xvq7yN+7vIG1fNyrX3eJcHab2G2iLeIDWs+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piRpVNkrbEd5f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\njmbm9JdR9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fXktZ5uZVZBOmJ4dGg+0bshvpsqYxLpEmTE6PZzFErZNZKH6M20m7hk6zR2\nHF3BsChx6+5T3jMBzUMRIJNiYW4MPUCSJIln3KwN83TWJCtSAZ2QCGX2W5K4cHhCuBFnkkfAzcqs\n8+JGUdXG0SWNSS7MjQi43T2S/5Cv0400wcVbp72sjiSOgFsfQmj8v+jCebz8OZtZnZizxlwztA5B\nNxtJylLPfxrDsX0h+M58dMgk16Ydx7QOwOcZv7+fl7VeZ5KRnEnw29hIdZg5hfmca7JSElWD1x4P\nmeQf5luMM0nTRjhjUquRBsgbbgCi9YAMFlqvDNvKkUl5qayjKxbWy0Xq/QBw4ebJKM8nEIlgEGtw\nAkImOeNkI/NpB5R4Xj0BdxKgd9vNTO9zpoimqKvWp9zZRLcvXn6hFBbX+h4ahYd1RdcTUZY02HUG\nOXjSF6mL7Wa0IP3AmCSQSV1nTHL8ZLrfqd/ahoDbRyb5zvQ9kYy0Bxc1F8zuk6tQShMNP2vzFCZb\nmQ5zM2XVhbmVynEa8aEiERweNUbpvw85dfk+zM+GfI7TuuiFuUVecn9Y+mFupbLGwovPmsLi2iDI\nriY5IL0wt4affGLc0Cm59NB77UbQ5/rf/hk94JFi+43mDUoCug7SBfl4q7KnjApz4z8PC4W/uGE3\nrrrnkJ3HhH76xG37zHMUPnnbPpxY6WG23WDh0PHn/Npn78dvfO4BD3lJ7/j/ft3z/PrYJBAKjSzF\njNEfaIzJYwO97zqDGRmx+sPSQ3JJqdMvRskz1pjkEXDDX9AIzmjD3Ni/nb7lDDp8Y69aBMJMK+KA\nbo1UHJmkjUly4aGJk7KDY9UBUo4PBTImscW09MvQpJ36d0ImpWnicXrIhYkGudw8aRJ2zQYy1cpw\n4aZJ7F/oMDRIxJjEDoeAM2YR4blcPHh9YsYkP806NwYmtl/4X27cIAJaEvqJL+rnzk2YTFrMmNSq\nMSZBeYgZKSdX+zXkjiFvizTuAHECbt02eCtKUYYWZi7yXZdK2QUL0Mqf88C69jcNr5dv3NR/V3u5\nF+bG+4DPxcwqXsrz6E+aMLeq9LpcyHikUW7xMDc+FmOZfejQTXW7YH4iuIYfhLuD3DvgjptljSsH\nHixarAFzk36cNJe6x/AyV3pDi/IB4pxJFAZFh0i5Wew9tYb5ySbOmQv7IyZzkw3ryeLzvYozKZM7\nnykD8D0yNupGuWwkW6dbJl586IxJYyAh6InUHzSfaFiSQsmFG4GkQjAoSg8BRvUkcSih1OPzuPqh\nY/j2Ac3b5cJc4P0FQmW2zpNdlApghzlSqnl7jiyt4w+/vsN+Xh8WOgRPaFo03kvl1oh+XqKZpZ6y\nHjuk0u+UGVFCz6XQuCPOLo4M0ZxJ/vvg2bJ6wxInV/tWaaT+ktncspRCsvS4IiXLZo00B975iDET\ncHsfrRWOM6lAwRIOcFtjmgDffcEcHjm2jA/e+BjeeuU23LXX917OTjSw1sux1s/xqTv2QykVrNfc\nmCQPshsOc2PfyflOa3DMqZQmiTX+kme4maWYNmFu1N+9YWnH70YJuAn9NDA6Gt2v0QcFugOdRpw7\nKqqyuenfMpw/PxFNacwJuIeFTgLxggvm8De/8Aqcx9Z/7SjQ/7b6Uen3PV8SCoM8cKGqYTtjiHIg\ndKoBDtXG92RZZCxxgpRRfG30rnnblHL8jmePi0xiYW7kCOKZVmmMPXJ0Be1mFiCTODKG3iXtjxPG\nqCiFcyZp/kXdf9JIS0L6ceBoTYBnb55yyKTgYX5fxepCofRU90bqJ7xQkf6TYtfXJExS0TZILyKG\njulGNG9oL67iWSyN45CH13COlHiYm1+WNNiVSq8B1DcSfbzn5BpD3OvyyTFXlUlsfrLp6fJKwW7k\nE40M/WEpOJPY/Dm6gvdfH2bUJP7Fpe4QC2sD7Dy+gu8+fxZTrQy3PbZgHegyzE3q86v9UN+R67AM\nc/PCYsXc5zoMDyGMGSK8MLfI7x4Bt9GRVw0p9AXzkzh0uhucQwaMs0wpFTUmNTZoTJJ9RmVyXZD3\nH5XrnFByzLn7aL+W82CYax68vSwbcXWYG392GFbGDZX8XdLa2BZOmFt2n8S7rt6Jhw4v6zC3CuM0\nydcfPIov33/ED3MzZT1n6xR++bWXsvpRn5RopglmJwxn0pCSZCVeGXTmqOOrpMijfl7WhgbL9aDO\nsS3lfw1jUuIT8g6Zkqx/j3gcrDHC50DgXedZK4UiLvuYG6vsJpOl0VSDzpiUMAWSle09x3+QUtpi\nz4uVBIscrq6UM7hw4suiYuDJueAOCToLRJokuOSsaew71bHPo4WHL3ZUbeoLWqToXcnFg3dv3JgU\nrW6ATOKhfiRbZ3xjklOq3HPOn5+wKCOy3FO64phoZBKqkUmrvcqFuCgVHjtRnY6dDlbcA8yfkST+\n+y9LFSwQL7xwzvudi06l6j5PtFKPEJgWpVYjNei5cGME4iTw8nuK487LEpNsnhFn0pAZrng5/EBN\nSLxCKRQqDPPkkhk+AylkXKW6vcBwnXDhITqdgQxzG29hJeWO+ChI1gVXzFxF9rQE9aEMnPMtIOAm\n/TRx6Bp6XsPw5kij1rHlHi7cNOmtn3UyzzKx8c2rKptbzJsWO8x7YW5m49s01bQbKQ+HqBKbQZKg\n8GYQTVljkjG8GG4dLrwtcj4P8sJDqVE9SWisNrLE2yd+9arv4D98/C7TPrrWH4e6Xn47rCc7Mg6I\nPF8SkfKxetZMC1+877D9vLw+tDwfXPj6xNfcRpZ6iLLYeiyNP3yfqBM6FPP+T9NwPktj64HFTmAY\n8cLcDGKLZwSiuUj9SQTcNP4kYpiKo7oRL+P6ILfGKsA3kKZJgivOn8Pjp9ctypTChkiIQPMdX9mO\nd359B7594IxdD0i6hmCW10Pv3b4SXes5tPdVH3ok6lWGuZH3l5T5RpqycL/clkkZQmlsnOkMgn0m\nto4RL5Pki2g3MuvQmp8SnEmB4dcVPN1u4IJNkyOQSXof7+clLjlrGufMTuBsE6IGwDqRAKeb0V/O\nJcjLpXDrqn0h0NtY+wHfmETJMnxkkl/eKG4gXecKfUPoh/S5mel2n1rtY9NU0yGRhaceEPoH3BxQ\ngEWalUo72HhCG53NzX9/3ADkwqSZMUnUv5EmWOy4A3+pXMh8jIswJqVSuPTsaXz6F78Xm6dbYyCT\n9F+lfJRWkiRopsSZRMikVGCPRP9FtlYywCQIw9wmmhmWukO846uPAIije2ler9rMsfF207vmPEYH\nFl32ptiYobovd4e498BpdAZFgJ7iCXjkXrrSy3HKGGjo8E16inyeZ0yyurzRAc01k63U8oaRXtNg\nzySHhpTHWbr5h48s4cBCB5efN1drnAZClI1DJrnr1sU6zPfS0uipJLI93FixVoFMIqGQf11u8DP6\nQ/duCDG32htidqKJrTMti87lwh0AnUHhzQHu9AdGh7kRZYdc+wlRxdct3j7JAUdz6Su/+hq0Gqln\nZCNdQM6DYVHi4GLHG1NV86COkzc3WaInWDgpjcXtR/SeLo1J6yw784YIuDsDq1dwOwDf47ljrpEl\nmpi+F0cmcUqP3/3Sw3jFu66vfK6ufxE4snh5gY41BiKW5BlsTPLD3LKIJ9ULHRPZvqwxKfGzs/iD\nwf07QCaJLY9nYnFZHpKoZ57Y8smSzO/ndQvrY2qU+PsTXWIPLCx7G7URcESJQF1K1PjmCugNJEkS\nXHrWtIYjm3FnN+YIgoUMNjQZydAlN5XYZH/PN3biV/7+vuB3H5lE3/kKEz+cbBHIJBIPmTQ/YQ0f\nfphb9FbDmaQ9dLEJd2q1hjNJKZvSNSZTltciJOAGjHGFXd/LC+99vuGKc7F12inLwWYpkUnNzHpZ\nOTKLkEmeoVMcPniZse8bqY7j1oTeHJnUAJA4wxUzfkpJoOeRhlZrZEAlMqkC4dQQh+8XXhAak7jH\nojcsPCU4Ef1w/Y4TUWg1xatrkml3Ay34FDozOxFHJiVJUp/NzZTZGRRQCtEwN4C1l62Lk4bIksvp\nzgBbZ1q1yDAucxNN20auaFRxJsXmhuVMYh5qjso4bcLcthhk0kovt8pKHTKpFGuCI0z2jUlTrSzw\nBtcRaFJmQz6uJaIDQNRI7upmDF2Jf4/8t35+dRspzJX2FRrv3Ljz9td/l6cgvPhZ84bnwy+Xvz++\nFrayJOCUkRIq5KX3t0rImOmhFyOHrTkxPw4udoKx+/jpLn7xb7dhrZ9bz+JkM7UGK+exc3VTcOS9\nAV+K+UtzodVI0cpSq3BbYxKre5IAV1ygDfe0HsgxMNPWBNwPMni5DB1ZHziSeR6KL0P4nyoC7lgi\nDsq0BjiDcKuR2DWL6twblhZdqD2jQ7zmT27C1Q/74ZqxdcwScFNmqry0OgEPifE4k8RY4/NjqpXh\ngk2TONMdBn3DjUm6TYV9tz5Ppjv8y5Bn6noPmWTQHtqYFN//q5BJVF7MGVJFdUB1HyWVnEmlP648\nZBK08fOc2TbTpSL6refMQpSAW8Egk/i+iZBg2g/t8cPc2o00aPvm6ZbHa6P5F+sJuKl2fE5smmzi\ntZedrdvOwvq5xOZgYEgTnEmjkEkxN41zrsaRSZzHphkZK3ToW7FhbpExWLpxfXyFG5O6tv9iOiqV\n9Auf2oaf+vhdWO0NA+dXp5/bMdKK8JPSWkjcbLRfVCGTZieaLLzb7z/K5sY5kzgyqYq0/eDprjWE\n33fwDEoFnDPXHpmcR46JVWFMamWpl82V/0b3L3nE2fovrWl8nq56nEnhu+DlRMPcGGeSpoLQiO/Z\niQY2TVWce9jzJTrXGabGQya99k9vxt/ffTCoG9WbIyr9hBlG3zNVGbK5NN3KPKoE4jgMkEmFwqPH\nfR28yuHL2yF5BIlzlkJnh0VpaR+2H9GoUZvAahhmVPMJuMPncyTmYmdgowAGFcYknn22maWYndA6\nMO2PfK3hlB6Dogz4v0hIp44hky6Yd8lo1oeF955GOQe5PIONSf6GxXkMBhaZpOzvztvnGx0SJH62\nCe/g7P4tFfEAmcTK55uMtCVddu4M3vGmKwD4GaKqYi3l+qKYEkMilQUk/sTj0OpBxNBW2y523Wpv\niDQBLj17Bmv93Hpg6UBz975FfNOmaCTFxTybhRnFQi5K5vmlxfavbt2Ha7bHUz5KZYc+SY4mQCvz\nsXs9zqS5CaSGTJsm7WSrHpmEhFLRb8yYNCpDwlQkzI0PTBnmKD3sv/JDz/WzXUQUJm5g4gTchXLI\nLEIm8ebx/vBIENk13IBKSCEik5NefzrQSu8WlyRxKD7Ny1DNmVSFsKHNMUu1pf+irZPBNRKpwZW8\nVBh5fvPzD+BTd+4PyiAlcN5sgHftXURZulSpdDDiqaW9tiKcg1xoSNEGxcc2PwzEsp60m1ngeTjd\nGWDzVKsS6SVlfrJp2zisUIp4xq7LIlxM1DcxAu6idKSTm6damJto+gTctWFuVmsDECKTaFhOtDK0\nMpGums3VABZeaF4Cf7z7BwyAUkaHfCMAQ00RMol1t+x62sBjwyAvSy/DV1Mg7gDgeefM2Lb/5usv\nw2XnzkYzzfF1w0Mmpak3bmKH1Kl2HJk0SvnsDYsgLILawr3f8wEyqRsYQO7Zfxo3P3oK33jomD3c\nc5LOwJhUKG9uVIWukwE2QYKpdqY5k5gxia+9aZLgBedrYxJ5gQvl706zhvPgoPGUdwZ5kHVvfVhY\nHYPuJe3FMyaNRcDtRCrOEwKZxCVhekOHIZOIs2itn6M3LNAfFtbYN8hLHF/uoTso8Nlth3Dxb19t\n+W2iyKQ0QbPBCLjt3HLXbJpsenuIHGtcwZ1uN3C+CVk7tuwjwuj5pN91+7mdL3xtHzICbhtiYPYl\ni8CJIJNoX4pt56FTzl+bAODm//ZD3jV83ZF9VxXOxaUq5NBxJpnncM4kpXBytY+zZ9teEo4QmeT+\nzY3ZSun01cPC9Qk3JvXyMghzO8rQElQ3Ml5OtkJk0paplp/NrXQJO0aF/1m9H8rTmavCUpxj1ujl\nKtwPKGMkEfQ2Mt+7N8qZnyROZ67lTLLP43qI/kvzV2aK5sIP+Dz0/+BiBxfMT3q0F1yo7Q8bpOXy\n+jBwfmneJFPfCHJq76mOV8/JZqZ1wQgyabatw4S8KAPF9mvjBMuLknEFug6X2cpIDp3u4sXPmsf8\nZNOSNKdJMtKYFIa56bFJY2WqnQUIbz5Gdh5bwav++MagPKqxhwyqQO6QLI0Kc8tdmFuWwnImzU00\nPTT1m158Pn7iey4A4Os6y4Kcm96lzAYeE6UUjixpRG6ITNLvhKNgePtI7+c8YPTcqVbDQyaRLsA5\nk1pmn3/0hJ9pvTLMjX0v91DS2ajtw9xxsm0/qucAjTuLDmL30/iVzyHh+9KZzsCiYkmvysyeaOtK\nulRZGmOSJuAmZwlv4/GVOCpPymmD7OwPy8CRJZ26sfc0jvwvYUySYS/0klysKs+a5hte5OZ31bZD\nOGkWXv7Swuxj4jODqOdsYshD2vPPnbWbQ8oO6h7yBsAHf+al0efoMDdfoZXX6M2KeU8pzI15/0Zl\nHYqVvdrPbZgb4DYMUnY//q19eN91j3rlXLXtoH42C3PjBJwkg7y0i19MKarKpiehsTQRueIgNzua\nH9xAeB4Pc7PpimuMSYYziUjvpJxc7VcerEZ5GWOZt/gz0sSndFwTxqR2I0OLLU7B2C19WKpGIOnr\nS+ViqltZCOPnRfE+9kIc2bOofwjpw1OZJwkzApFBcv9i0J96vOtn60NjeACnO6qMIjRGf/ZVF+GP\nfvIFgbcbYJkOlbKHHFsHdmjIC50xg0NdSWhhnpto4rbHFvAzn7gbV96x375LQhLNtquQSXEjAgnN\nRzooc2QSN6C6bG6uDRPN1EO7AXpz28Jg/6NkjoW58XHFlaL1QYHnnzODx979bwOjAADMtBpIEhHm\nZpWKEkvdASaaOjyCwtyo3XZtj81L85VF/5imu2xu+ofJZuZt3oBvWJAKwSAv0cpSj0iVH2hpLa4K\nndD1pWv9e3Q9RV1KNw7dM9xzPQLuNNzCX3ThvENLZRwC78o7tryOU6t9vMCgarjBqCkMZ7H1WM4f\nKnuxwktM0huWweGJ2r/jD37UfifHzcHFjj2YSEV+YEJs0lTvcRbtYK6jPY94XahPQi45/ZfmTJro\ndnYGhcfX4dcdOHu27Snu8v3THKXvO/3cy1YD6D2BbuM6Cj94AuMRcNchk8iw4jiT/H2FdBPLmdRI\n7bv+5G37cPk7rsVqP7fGpH5R2nd+p+GKIgU/to4RComHubXY/gNoTzT/Q5+rgAAAIABJREFULA9/\nvH81Z5J2DBwTIR30fgmN1R0Wti+5QTMvXUIKh2IzekQkfIFnFU3TClSI/E6sTQBw8dYpb82OcU6S\njCKanmk3ahN+KAaXoX4hZNKp1T7OmZ1gHFBheyRqgs+FZqbDp//qW/tsGCNJf1j4zlrA4zihdZR0\noolGyJm0aarprStkFKa21Qn9XJYCuRtJGqPL8w1vCsq/hhnyCQXQSP3xGwuN49JM/bA4qZ9OMDoK\nwDeyO93QIJPWQwJuqQsDvs55+Mw6zpuf8CIVuCg7VhPzjGEQ5s0PpDFHICX2cNka06jxamV9aMOY\nLM+V+Y/224lmilLpdTwTey1QY0xa7OI5W6dw2bkz2GWNSSGqVkqITKKEI/rzdKsR7EH8nv0LHe+3\nUgEHFjp2TeF9znX3mN6w1B0gSaqTAfVzTsCd6qgHk5iFv7O3v/4y/J4BMfD9vAqZRO80SDwljOqA\nNpbIqscIuD30lpn3koCbnBccmRTjTJqfaqI/LLD7+Cqes3WKPQOB7Dm5iv/3iw/ZzwEyqdRhiS7j\neWHb+cDjS/r3GmfmdLvhzk/muocPL+PXPns/3vzRO7xwy9OdAc6ekcakcD8CTDY34/zuDUvrUODj\nJ5ZsKLYfne4SMqkIkElvedmzvM98Po3LmQU8o41JIptbNMzN/A7mcTDf8S7ii/lqL8fb/u7e4JqA\n40f0MSfNLEo3MaQCyiGracLDO/zyfvwlF+D7Lt0abKwKjkPGPtuW4QwpsbCVdsOFl3HlYLrFId+y\nXezZSm+kzpi0ZtrpNmbL+2DuoU3PGpNMH0j4XD/3ofNSouF+CAm46fm83wNjEnkhWePOm58wXkaX\noaoumxt5TtI0QWy+ne4MAu8z/61OHDKpIswN/vuXaafbTR9ZIA9NhUAmAU7J5Rm6Wo3U9glJVZhb\nFa9SliboD0uc7upFlDapqVbmKVT0/R17FvHea3Z6v9HhmTKIcCOsfGYsrBRwB7KXXrQZb37pswLl\nCPDhpUr54yZhRjXagGPe/dVebtKYunocWOwEYW5VyCSgnuib5nrMmMSFwn5lmBtXgvp5gdV+jq3T\nLe+6OpmbaKCfl+gNCztP5yYannIp+aakpGnicS8BLHa+0GFuWwxEm1AdtP7WhenKNcEajwQB91Qr\nC9AxXjy/NHQX2vvOByVfP2nINSoUMF43qlPm7QOJN69i0GI6EA9yl1ABiCvyZKjlvzeYkQUAvnNQ\nh1xRyLXnCRd7VpQzqeKAf8POE57yJKU3LII601zk3mk5P/YvdO1eIo3xw0Knjc4SjXod5n7/+WOG\nECUxg6T+bJFJSYLJVoZ1Qz4dW1vSJEGSJJ5CK9+/nKMdlpmFZH1QMCOi+z6BP4fHI+B2BQQE3CLM\nzXcOuH2aPMrN1IW5/eO3Hw/aNMxLjxxZl1O9lpBuQjqaPgxl3oF7bqLp7W+SU4YfSKdbDVywSSOT\njgpkkgxzU8qNbR56pdPa+4cankQF8BHiRamzimYGIR7TEcL9ybWfRCd/YYcINu/l/dKbLGV2olGr\n+JfOlmTHfcPQLJxc7ZswN2c4C5BJ/DNbfxR8g3aS+GihgSH050JOSIAjk0yYWzMN+nPLdMsP92Ho\nz6pU3NTN771ml0sdz8aURWJXIpPcZzmfZSh9M5LNzauL+MyvTxJEkUl8feDPc9wtBpnUJ84krn8h\n+I5LUSqcNzdhiNNje5XyKr7UjRiTGDJJ1h9gYW6ME62VpYFjYnl9GDgOpKOY5u9aP2d6jXsmZX/1\n6tfPsdgZ4NlbpvC8c2Ytr1IyBjJJjonesLR7DKCzS64PCjx8eNlx0LJ7pH6vlMIP/fkt9vOw0KFG\nr3z3Dbh+x4nK5wJGl2pmhi4irOuwLO1+6pBJoTFpy3TLcg7x/VwakywyyYa5+Q/9zqEly2lJ8+Lo\n0nowD2ME3Hx9onslAXfTIJM4EvMsY3zh68h3nTuL/QsdPHpiFd99HuOHjYz5bfvPeJ/l3psb5/qE\n1bGc8ejUah937l20c8q+b/acaY5MMkX/9pcewtcfPIr7Dy3haybZUpJoZxs5b2kuEMCCxGZzKwiZ\npN8jGa/5+zu+HCaeiDn/HGdSiTVm4Nvxhz+KX/mh53rXHmL620du3hOUVSXPWGPSpBeX7SOAaIHk\nLPoSmcQjIiRBIDGf83EZEnCLAzozCvH4T6lY8UWQKyKxAzuFXfnP1Wt8jK/GLsIQGRbMtZqA26Ro\nZgvX1hnHrxODZ3t9nQAXbppEq5E6YxJP0ylgiyQeAXfm+Dt6Q20F7g0Li9aIG5P8z7aaif+7QybB\nwgllzD7dy8nBt0y1bGY2zplUjUwiuHd12Bq/lx+gRhmTbDY31g/8IKiRSa48GebGEUAAArJ1jfCR\nxiSnSPIMawn8vuf/5nNOVVzTSBOcXO1BKf0+aNGcMugUEr6YfuUBP9MdD3Mjot3gGGi+qDKKyAPs\nVMSYREoqN6aRaISW/jeRC8bGKXna+PtJkKA70OE9joC7GpkU8+knYpyTt4zDz51njyGTWL8SXJyE\nFHRNSDreVsDJi2n+bJpqoTNwvEbrIhNeTDZNNnHGOyDov3mpsNQd2Hj/WRPmRhssKdFxnhK33gNu\nfE4JziSNTKpub5j9pDSGVfcdV8Lpe+pzUk7q6sbLkiT+pFjxEiyPWl54CSf4e/vbX3gl/vnX/pX3\nHHr/DWHAf9xkCrrifKeIkYTcBJEwt4gx6dy5NpSqX9/Wh5q3hmry8udsxptefIGps1OqYsgkOqRK\n8uq8MMT8aRz1ykmVlYIlMJcKsERJJol2tHQGeSUyieYcNyZJY6QMs17rF8EY01wF+t9eCGXie+El\nstCrv2hH7HrLmTSkkDo+jh1XliNKT6OGd0ITDIrSI0emcnQ9wjmaJi6VPODCifmQIycG/8zFQya1\nG9bYJccFzUGOjIkikwqHTKK5x8lRgRClLsPcbt19yucHiehRQGhY4IYybqCW948i4J5pNyr5QnTZ\nLsMUd7gtrw8xyEucPdt2KG+ENAIBMomFxPn7ZIJXXLwZl5+nw5t7JpMkF569kvRqQjBIwyGg9yev\nLkoxnbfCmMR6mgwffPrSulllTOJZliVnkkTxZmmEM4kVK22rDYbES5KwDtL5ycN7qH/oQBxDJkl0\nXUzOm5/waC888W1JyEsVNYg7ziS/vpeeNc2MSY4LKxZqzY1JnkGOXdZmBnCHuHYXx5BJZOA+Z3YC\nl507Y7/PjIOgTmL9tsbQ0dPtBo4srePfffh2/Mm1uwD471DWR87l9WGBHcdWcGq1j20m26ssg8uk\n0ZVj0TBKMQoHo0cQATfPQDs30bRrHtcDVkYgk6Sx8S0fuxM/9sHbsfPYiq3vkaV1T9/J0sTqlrRu\naeCEG2tVBNyNTCOTyEH+2svOxlu/7zm6Tmycvew5m7HSy7F/oYPnnjNtv48tB6TrkEgncGH6kXMm\nFaXCay87G3MTDXzxvsftekD3ct1/uuX4NAtzliUkHACbuVspPd43T2taCYtMSuKcSXmpLAE3ACyY\ndZPP2Vjm6l4kYoJ0st6w8FDRU61GoO8dYtke/+b2/UFZVfKMNSbxMAWpcNlsbiVt4C5ExdmSSLlP\nAmMS99CQjI1MUjz+0z+AAH49vTC3yEIS83q5LCIJ+47aZOqfyDA3LV42N/Y8TlDNq9E1pKCS5DdN\nE1yydZqFuYXeNdkc2gTJ0zosFVZ6Q7zsj67HLbtPoZ+X9jmjkEkehFh8x8Pcbv3vP4ztf/CjAQpB\n8iN85D+9TGcUEmFuk61qZBKFIFSFuUnhCnkVLJeExuN7r9llF1XP257A00pl3Hy7kXphbkE4R6mC\n8ew8da7/KfzANxSp4B4AeP0V53rl82vIen72bNuOy8lm5il8fEGTqdIBR1avlcIQvUPvvy7LG5fp\nCoViWCiv/bwd1HbaKHcdX8XBRR/SvGLi1blilCb6oDPZymw9qhBFCeIbIgm9y5HIJJHNDdDjgntl\nSOHaOj0+Z9Icy8RG6+zmqSaUgkXidfr1yCRAG6CWujxTCc3JEme6A2yebtr2ccMVvZtYLDc3pgMh\nZxLJZASZxCWWza2Vpf46J4y7gO/Nk0p6yJnk719enHpEweeHiIS1ja/zP3z5OXihyVJIc6vBEGq8\nXau9IbI0sf3MRR7+Yuux7L+iVFYRrTvErIvMQG/7gUu8sdeKGJOamTbGEieRRCYNitIih5pZGowN\njjbRGacMokQeJOl5qXtH581P4NHjqx5nEhf66qItTqElZCPJjJija4xQnqTLkUlUHzJusXFXyxkW\nWTgkkklyJvmHXqfUEmdFI0ssZxIXej+DPCT9tCjXSB0TEwZFY4rCiSUhPbdt16EIp1uZZ8TlQmsl\n1+/oWj4G89JxJtHYobKsgUXsgaUiVJr25L71ym34BiMgD5xfrG1cJpoppkwbarO5jQhzm243akO+\nONqoZHvlqRW3N1tnRASZxKdUAnjGP77HJIneyz/8nzRNgzYAhGst7b/k6LJhbs00ePbmKX+NKsrS\nOh/G0b3IOOaNMVOlKvoK7nOW/J5BtIHI5kZhWlXCr08QZliV5xHfIaT/LbO58aeRoViuMbze55sw\nt2FkPYnVPOBMYtnc5Px84YXzOL7Sw2pvyJBJadTQ7xmTKMpAuQgMwM8OGEP2xmgjiLNn02QTzzvH\nGZPSFAGHl5TYfrfWZ8Yk5ii776BGvdQhk+QQXe3lnv5N7away1NGb5RnkaE1wFCfpChLhZUIMokc\nLbJ9QZibdT7RXh72xZGldbzlY3fahAv9vLTZ+wCDJBTIpNmJpncOkYkOyGjVTDUyifTb77t0q3Us\n8nH2sos22X/zsUnzbqU3xL/5y9uw89hKgJSWeygZ2i1nknE6TTUz/Pj3XIBrth+3/URIX16GRoIn\nFu38yNEV712WSidFIqH13ifgZuc11jc8myplLOXvL0YrINcTwJ05NQG3/7tczw7WIMvr5BlrTJIZ\nVTK2YcWgaLREys0iQYhciaG05SIoJ/aAoaFc/GfImeQdMhnyiK8jDuYZkjsqpSvNy+F8TUAY5kZK\nAoeZ8k30rBlnTCKF9PbHFvCid16HHUdXPGWYmnPJWdPYZ5BJvI1u8fErzheClkEmLXV0WsOjS+vo\nDQvP2ylFIl+4MZA/zXIdGA/ETLthLc7ufrL8ltg01bSGkDT1s7nFOJPu3LOA9133KEOIhYt8DPbL\n0V2jOJM4z83vfWU7AM1NQRJwJvVDZFIs1STJ//zK9sDAxPkSqP+bjTTwiFQRcL/hBefht954eXBN\nI0tw0hiHfGSSVJiqjRl2oVYU5hZH7AE1BNxC4ZmOeNsBPSasManB0Y9uji4bReXQ6S5+8M9u8e5f\nWc8xN+mjrpJEH4anWo7oXKY+d9dGv7ZC83OkMcl6PpkxqekbkyzR9UY4k1gmNuon2uzJC7U+LKLI\nLy6bp5qeUZXWsGGpcKY7xGaGTMpLR2BO9ZdoOyA8sFEoxqRR/iyxfnOEMSlY2/WBlytnMWWQ8wzw\nfqYshLxu/D1Lg3TMUDbZcsakNHFti3ny9XNMnVKnHPI9jKDwkohct8Pvm0Fe/n/kvXfcJUd1JvxU\nd9/45sl5RqORRtJolBMSSKCAQGCQTLAIxmDLGBMMxrs2YBbDLuC8XvDa7Idt+Lxe1nLCa2ywwYCx\nQWCTsSyJoAAoSxPffFP3/lF9qs45VX3vfUfaj9XvO7/f/O6893ao7q6uOvWc5zxHABU6Ek/XTPc0\npvNHttrXAtx6YVa+H8xB3LN+QmyjRXddmltinbKqNLdBXrhFSiwIQM2k6zcAnn7GFjx4fLVaM6m8\nD3zBqwGcIKrf7Ys+liXGptI5FIk+LPDFx0YCsrv93Gk76vbz+93p56Kv1csx3etK8mvxcxdpVlQy\nk5gANxdHBtiYw45N44vVTGIC3GV/0At9/rfujzz1ggP0gf5N+bcopBABk3oDv/TvDwocXuzgzR+2\n4so0juo5sCgK187vlhopHPAJtCepbaoLNbIUezdOBGCv9p9GpblNNrLqlHxqM+QCLksNHmZzs091\nDwGFwF9gwJMUiJbvb4yZBNgiLoD31zyYlCq2HNxcQNYfeCmJKiyJ41dFTlqLvh20WK5ikPFPWcnO\nBL6KTc9l3xXDmUn11G9vDJzmF1lT+Y8yzU0xk6Jl6+PsXRKqB4DN081SOH1IUIa1O2QmDdw5tb97\n1g4b0Lj70SUHbNUdmCTbFGMmOd/eaSaFYHBV1V4yGidn27VAX3fYewLAZW9wm1/tOQCTg+sE7vH1\n1DElaq3PN7/SEyANja+jwCT9uyMtuHtibBpTqWmnx+00sYxmrqN3fKUnGXsEJlUwkwDg5Zfuwamb\np4QOEaURAjY4ebz07ZbL4FE9S0T76f80V9KYnqUJJuqpY0xJxqpvKPcJ+PhOt/Wz3z6EOx6cx3/5\n5Ldx71Gppac1k0gzj4NJg3J8f8H5O9Hp5/irMluCgjCc8UvgIrGd//W+Y+L4p22ZwoV71rm/s8Rm\n59AcnCTAznUsTZ5YuwObwkj9/9EIMynGAtf+x2pvIPxnHZjQwYBhMgXD7IkLJvHIrqp4oplJ9v/2\nc7lr06roF2NCh5yOxMeAIOql/uZlf7kyfagfICNwAzV5cbOpNeH3BnLy8il2flvusNOWXHCPL8Z4\nGfmisPft3R+7o0RsJfJL17N344Rb0PLO+PB8B+/7zF1BFTqiKCamZCYNPGjR6eXleeLlQ/W15TFv\np6B7UVIHh2gmFQXwnk9+B3/xlftE1CxNvGaQ7RdJ8Jw/cfvD+MDn7ikXJSY6yG+fDSuFNSNMDQ7i\nceOO0P/6+gP4n//yfYFGE4hFFrLApFYX1xgAgH/89qOVzKSikMycJJF6Lrw76r5N/YC3J00S9+5s\nUmAS331YmpWBr5qXFwjaxM9Jx9HMIw2WxAS4ATt5cmq2a4Px54zl6JMRM0kzT1Z6A1fRBAgjff5a\nh2smUV9b7IRpbtzIGeCX3chS0Y9oIlo/UQ9EoKuMQDDrENm20EKaQM2lTt+lllXZXLseLXvrmElM\nMwmQkRUgDrjoBRs9A54iRn/HrpfKH2ugikSCOVumrxYYgA9o9Bm7jdrs9ezsd3z+SoxkJbg0N9YM\nuoZBXog0Ny1uq9tE/aAWMJP6mGxkUeBbL5Y6pXA1WTNLQh22vHDgqx4PObC2SuXZHbimz23bw8HW\nU1iaAhCyNEhYm4IoOppKgZ5enrs02dTE0tz8vA3Ycebq0ze7ez2MmcQXPDoyOKWc+sWOZCZNNKSg\nKw946UDZ39/xMDr9AX7xL2/FRe/+lOhnsWGDl46ma5AMRb5w9wtlX5ExiQLfjkU8iGkmhe2h55om\nsuKeTyH19zYtgwdkdSWWz/vThskGi6KrPlkU7nxk9Cy5z9crmW10DF59x2kmsUOTBk9StpMWUvxZ\nyOCgvMfcTtsyhUtP3oCMzZN6f2B0mttEIxSu5jZgbCO/CPX+zaapJkvpq5ZxADwYSxarHEx9plVP\nRXCMjHQ3nVg+62+aLbdOp7nlTJuz4qJ5X6YglATwUZ5fjQHlJ38Hta+UquupJUmgmSSuQSGIGgg5\nf/ccPvGzl7v3VAe31zM/kcZ7Or5jpbPzOTBJsbW3Mb9060zLgTshQ5PmKt/u6Wiam7W6Wj+dtcOy\nRj51x8P44C332GvKUgEi87ZSoISvu/hz5WOr00wa4a8Qk2SmpX2x4f4VEA9mizQ35jtSYIOvT44s\na80keayF1b64DwS8Vul/EfMlLwo8672fxc1f/D4AD/TQ822ytMWpZhYwYgE7XnLm/3EmgM6N3uk+\n04oi2zrTxG+XBaLI7udg0iRnJvUde1Sk8jMGaG+Qi9TbdiNzGRn8XaM21bNEVPjkY7kr1DIYuN/u\nU+CIBlsGOQlwl5pJAy+ncdaOGZyyadLpHtE953MQtYXYzv9aVkEke9UVJws/rZZKMfrEGFyyd70r\nusV94VqauPGAdAx5NyEfno8/X7znCJ78q592v/GgbacXCnDzsXznXFtoJq3FnpBgkoESME3k314z\nCeWnzxe//cF5/NYnv8OYSSZwyHlVATItRMbRTU65K4pCvOTD0kd4WVn+ovHBPGQm+Vx9Mr2NMWEa\nIFCmubnStwxMmuRpbgX+5tYHcfuD8+67KUWXBLwzQMfl9msf/2YwMNJEaRdCNiJCzhfly7sKMSPS\n3HKGJd38xe/j+ErPR90UPR0Iabh5UeCDn78HS91BkHY4KMGUhhOfDttBaQwGJMAtJ+SDZaoJt1ja\nz7YI6GTbLvvMW/7yVtzBngfR66tMT7SUE8ztUBlNPnvnrDsmQJpfPppkoDWTmGOszhPTl+DrUuv4\n0wI/Ey6Wjl4KBp9hmkll/w/TCMp3rjzOjrm2+F3fE11umqw/4MwkGXGlyzq2MhxMmmoqZhIs86DF\nmUlVaW4m1KvgRte9sNp3mi7aioKXW+eTbiLyxWmy0ZoUw2ymFEbmaW4BM6k7qLy/7jjtWrTsbaef\nu7xywC9aHSNDCeTGjG49vfYtlwtv99FpBGQnqWg5GS14uSi01tEAZDldfp95pS7qF5KJoTWTKOrO\nHOqMR1cZ66riWujwGVvE83tmmUm1CjDJfvfxN1yOuXYN3b50KFv1NFjE9XNfVSbPC3zqjofxrPd+\nFv1BLubKld4geK9i5+bA3e71E5KJqx49lSV3QIWaP3iaG+2aJGGaG/2pq8Zctm8DgLi4P43V3IfQ\nYJJ26pc6fdHHJhuZnP/L/xYIU9aPLHXxydsfcSkGRH237S/cfmSrvYFK17UONs2xMjiAQDMpTUIZ\nAIBpJvXH00ziouZ1lu7i09z8/prpoZlJfDGwdcZXIQsj96EeBbVNpLmptETuf5jInNbPCwxK8DIx\nJqgaqK+d6xVpd/D9L7sAb7nu9CAopd+v8dLchswbzAcg34zPiZumG34xH5mBeNseme8IAW6dDg7Y\nMfY/PPsM3PzKJ7k5mdvejRPiuL6EuDx3lJmUe3+rUtOykP6IBWYZYJn4sUruJ9cOBcKxPmAmpRL8\ntLHO6mehBbsB4NTNU+47HdzeOtOMgqCAZULb4Lg/nwM4yu5I1aNIQxQANs80HKirwZMYiU4HrRa5\nALe6H6dtnUKWGLz303c6UfxaGtezyxnjkxMaC/a30DwbAuxzIx9tRlWGpKDkMIuluS0ozSQyYr3w\nuVWnjuk+ttDpiTmRGERV6eHteuokOG5/cN6Vq6d+SeMbz8CYbtaCIAYAbJtt4oFjK/itv/82bnz/\nF3B4qROtuMtTh/Uzy9IkGJP5PLR+ooHlri3QYiUPsiBgNmBM5tWe1xCspQYT9VToDpPxQDSfk3iw\nhB4t3d9BXgSpYLE0t6IoRKVTCkQYY/CCC2S1M30MwUzKC3yDMZOuOm0Trj93u3g2VOVdg4EXn7TO\ntQco09xSU8k+B6w/kCVG+IEf/tp9uO/oCu45tOi24e1eUsxuDvZvn23he4d/gGCSMeYZxphvGWPu\nNMa8KfJ7wxjzJ+Xv/2KM2cN+e3P5/beMMdfqfYec0/+/gpnEWT/8NX3/P93F0qTCHFo6Eg9whtXH\n/AP5pR86gItKGlteyJdcO8t6keyF/vz39P9oOg+ImeK/81EUD0KJChvlZz1L8I17j2Gx0xdiaFyA\nu9vP8Zuf+Ba2MUqsyL0tD0bOABCCIkURRtK481YrhfjoOflKV/Y56MH3nX9zu1pAeKbNl793FP/+\nz74RRN34fdeRnmMrPbc45Y46ARadkolA1RG45UWBXp7b/mPKyYk5i7vWtfHLP3wQ2nQfSxOD9RWL\neP5srzptE648bZP43Zjh6VA6+sKdWbIHjq3g1M2T+KvXXCbOyTWjnGYSe3uqgCK6Jr0N9cPpZibS\n79r1dOiCQV+PTQllmknQz4XaRGCS7JMarNLnI7CPpyjVBTPJX9fxIZpXC6t9TAfRMIyf5obhmkm8\nmttkI5PjILtEGg9n2Xm0ZtKRJVt2draiLTFzzCQWXSNnf3HVOrbLvdGaSXPtOhY7/SAl2TJHPduJ\nAGbSV+JOgjY/bhrxqQU3qwQ4aVzVx+6V1dy408UdLDoPLTIGuUxzW+723XjF0xt8uwsRrOi5qLvf\nRjIbjevnVcCYWzQ7ZpLUElooQc9Yuh8BUPu3TGHnuja6jFkF2AVNALLnQKPcb1AU+On/8VXc9sA8\n5lf7gla/2suRpYaBOvI49M7x96OZpZVjJQAHdvkUKsnEdDpbgxxUhVNr1AB+AehTsuznM8/cAgDR\nRXESeQ468qnF9pc6fdHHdDoCn895mtue9W1sn23h5i993zmP9x7xTrwDodhlrfYGwpElzSLyX/Ki\ncMen3wALiA2L/k9zZlKgERKCWtTPXDW3MhWRWH88MJGoNErtTPN3a8tMk2mVFTi+3HMMybywqQp8\nrHcgO/uul8v+TWnMtr32U4BDRZnmZqQfWsVMojQKoHretv0xvj8wOjV+aow0N/1/nyqbYIrNJXlu\nU8O48WPffWjJA6uFDFzyLvMTTz4J+zZNRvsRBSN9pN+nZvFz/beXnh8EO/LCM5Mq1t8oUAhWGT0v\nMmp/qJnkDmA/irDyLR03TQxecvEuTNSl/mMBCU7qZ87XBTpwCIRM+iwx2FpWLNS+bHeQo9PPxfkI\n3KT+RPfPV+oy2DDRQL1kClXp+/G2aUDcpsHa7WjMoDl/sp5hzwaZmkyFd/Q6ihbtANdMkvdPMJMi\n86dsu/0kH22mVVOVUxEw3TQ4OFIziY3XxAyLBQPI8sKn/s21a1hY7Yv3mYJUsdR9AGjVMgc+FAVc\n4RLyGVL2HpNNNbOolMPW2RYePL6K9/3jXfjnu4/gY7c+FAeTnFh3HvT/WhqSJPgmRE74h289gpVe\n32oEpTJ4088Lt95b7eU+zS1JRPEWPi+4tUMtFesp3j/oGdEcHNNfjQlw54WfZyzT2VdvPXnj5NBj\nTDBm0vxqD3c/uuTeCWobb2MtkWAc9WW6p66qdF4yk4YUizm+Yv23PR9RAAAgAElEQVQ4PsaSjhet\ncSmTIk0MOv2QmcTv8bbZliMarNUeM5hkjEkB/A6AZwI4A8CLjDFnqM1+AsDRoij2AfgtAL9a7nsG\ngBsBHADwDAC/Wx5v1DkBQCDavHM7PQA24fAJarXnB1+DiEPu5kk/AWnqPHdo6lmCP37lJXafwqdg\npIkZmj5SVc2NX6dnIvTwjr++DfMrPRcRI9O6SwYqza3cltKqPvL1ByQziQ2uf/iF7+F7h5fxlmed\n7r6rZb5DuzS3Df4F271OThy2vbLD0kLDACWlOw/AJHKQtYPw+5+7R3RwzkwCbGnbWKUSd27ljHJG\nBH8JSeTORktTxzriRoBGf1A4htyg8M7ij1y4ExONLGCe6D4206pVAigGBje/8hJ88o1X4A9efiE+\n8PIL8f4fPd//bmQ1NyCMeHIbMGeW7IHjq4JC6iKNBavmliWBbhd/DfSEQn8KwKn8kqJiNHHrBf2w\nSBMx8Xx6QcSJLORzD8CkEdXKaMHXK50zQGltGN/nqphJRVHYam5q8Whgxalb9cyxZSoFuE1YqY4b\n9UdKpxtlXMulkaUiInZkqYuZVm2k/gA3p5m00nOOIYk4L5Ylzwd5MbKaG7WLhDL18ySAatKluUlm\nUlTrgQUIAFbNTfW1KqDLggxhCpdnJrE0t4hmEr1PP3Pz14RDutwd4JF5O37FFhFFIZ3RGDOpVZN9\nkX6rBMbKw1O/1zTzhdU+ppvxNDceaa6XTB/BkqqF6b+cmTTIC6bNFzKTWgrc4FZLLaDB370sNSIV\nWxs5f0lSVgrNJZOKVzdy6UlJJEJd0PWlaNY8E+3pB7agXU+xIdIGGrb4HKM1nXR/41FuAIHAtWiV\n8f0qMQYvvGAnPnfnIUddv6+sVCNSkdkRVnu5GsdkmlsBzzrgWosrvcHQMXlKaCZVgEnsQmoOTLLg\ng05z490gTHOTfZT7XltnWj4QUhT4qf/xZbzh5q/bv3O7IOD7e8amZCbx58H9g3HS3Mj4olyn5WsB\nfm2amTSqmluQtj1CgHvA2kD6YnSETdNWfNuw+VsHa/h4N8lKYQM6zS28vpifQws0Cmryash06W99\n1um46vTNAdjaZ6lZ1QVSeMU5L5hORsBwyEyCOG5RSN/fGB/JP3vHDN51w0Gn6Ug27DkApQB3uX2s\nN4QBAuOkE2IshfnVnjgnLaSp2S6gULZ783RTVL7UItyxWxqt5lbQ8e1x3/GcA/jWO5+BJDHYF1mA\nE3jFzQYBJEBEp3ep2sJHp3VE/D2ifnR8pYdWLUUjk3IKxhg877wd2MvALs1860R8iwWumVTn4EV5\n3mGswKLARD3DRXvW4bqDW0swyb/PNJYOYyYZ4+eV4wQm5TL4wfvNdMtrRfEskm0zlpm0kREIhjGT\nPvKNB4L+kSVJAMBxo+v5qT/6imUmNTKkxjNxiCVJYNdqb+AFuFXBB/7sqZ+16mlQWIaM3lti3zyy\nEFY708wkKsBAzCSb9uz7X2wNz30a0uRMDFyK2zllxodj1yl/ho+fqernVA3WCnBLZrAGQgF7v/kY\nS/eSfGZKu9w81cBKLx8amNg+26z8bZQ9HsykiwDcWRTF3UVRdAHcDOC5apvnAvjD8v9/DuAqY5/U\ncwHcXBRFpyiKewDcWR5vLOMis5x50FXOeIFwgHR/mlB3go5UFDx3VB5AUqHlQpoQ5lqSBOwN/qcx\nxlel4M5g+V/OXHrXR+/AB2/5Lpa6A8tMYcehAdQBZMbINLfy823PPgDALuC448LT3A4tdvDU/Rtx\n3ZlbWUqGf/HpBePRIl4WmSyoEkAvk7EgRZdpJtHASi9lbGC+7QGf5hUTTXTMpIFcUAJhpIcbX0gT\nlbRTOuDEOgKAt3/kNvzRF77rnke3n5dOhEGe+zbRefViWjsIs0PApMQAl+xdL6pQPP3AFuxcZx0K\no64PCDWAdCQ1EIzv50Lcjqe5SWaSPBY/jnYcDTsGGb2Xm6bsIOWZSbK9QwW44YE+cuJ1GkABlJFi\n+7lFiVoOOz7go0Nc70an49C1H12Og0lLZTrTdCsLNZO6VkdoFDMJ8O/xYqeP13zoq3h0oRM4uSSg\nHN0fhRPmnGGOEmckAHaSiU1Ow6xZS1HPEquZVL6/5IwsdfvO4RmnmhvgF27aqddpbpRD36kQFwUY\nmO7ewbQ8l7zXrVp1Jb1Yukl3YNkTG5gDxlk+jplUTgL/et9xxUwa4F0fu6PcNjwv5eyT+YqYHEzi\nAIwHSKrGNldhjlV64VHhhU7PFieI7M/HRCofzW93syarXH793mPICwgwiYxrkEWvRZ27llqWhBRg\nHp6aQACmq+bG5hbbBnLOcsfspbGeG/3VyBL83esvx/PPt/T2dRN13PILV+J554d0d89Mqk5z4+Pk\nbLuGFUbrB4BJBQoXzG8BZBlsotzT7vc6MInv7//f6Q8CUFyASYVnRllml9dkGQYmUUWa5e4gTOso\nz88BCfJHEmNQT1O3QOkNLDNKa4gJsWSt4cXu75bpptMQG+Q5Hlno4B+//UgpmGsBxlgaFr8nvbJf\nkHEwyfuCbD7NC+R52E7JTGLvs2Amxe9ploRl07lpAW6tA0UgQ5WGEA8oEYuPmkJzM78WfRgan37/\nZRfgYz/zFJ/KqNoSuzrdjSYbmQsu0ZjUZwtNqHul526umTRsXKBFIAWT+VE0M0kHwoZrJhnxydtq\n95FQXAwwN+43th1CoJO2Ifa/ZiYBZapbJM2NnhmJ++7dOIF6mmDLjPfFev0iqhFk2+MtqObW9ZpJ\n9Px5Ss7WyKI0pmdngU25XVFA3D9+n326bLTJbkw/ttxz875YuJcB/uvP3e6+W6cKkETT3Dr9aF+j\n7wZ5/B7a6/HyDFPNGhZWeyLViHycqmOQADcxfCmQ4IX0JQuGjmmMwZ+88hL82aue5L7fOtOy1dcY\nYyfmi9J9/vBX7w/0KWOFpbjxdQtpJs2v9vDRWx/E575zyL1zkwxM6ue5SyvjawMBWpdzSEtlNTRq\nCb7y1qtxxtZpN8YRk50qwHLrRDSTisIDtcR0pn6pAwC3PXBcpbmlrq3ffGgBgE9Z823kaW5JIK/C\nr7Vfpt318wKZYibFdHY1uE9G9+BoyRzeMtMUFZRjViW9Mo49HmDSdgD3sr/vK7+LblMURR/AcQDr\nx9wXAGCMeaUx5svGmC97Gmb5G6RQl0tzK593URThJEvHQJiT6EukejRUI+q8Q3JB7AI+ksGZSTGn\nnQSfqY3a+AL247c9xNonJygPmvmJMcbEaNZsJ9aaDXpBee2BLUgS43NBjXELs9gYEls4zq/GwSQD\ng1pi0B/kzpFYUgvQ2L0QjlouabBcC8Mxk9j9iUXfycRLXaZS8XLFdG8/9c2Hccudh120p1eKdFMq\nnNdEMNF7ohkE061atMoJMMzhTFw7tWlKK7+DfXW/yAQziTlTpKtVLyNofC7Ji8Jdm9YQcYAU61v0\nHTmPdM2jBLhFe419vwaFfT4xEUU7Wds+u3WmJZgc+lpjNsWYSd3IQp0ztKoGZKpAMdWsqXRW44Qm\nk8RGgCeHMHeoN//5l+/FR299EL/96e/46ywbsRgBk3i0jtoomUkqzW2xi3XttYFJgAWP5le9ZpJL\nc+sMnLZHlcA5GTl6tHDTC3tqNz0Xet6emSS3P7TYwe9/9m4A/j6ct2sOH3zFhThv15zYNgZ+A/YZ\nk7bQVb/5GXzwlnvc+epZgtdduc9N5lqQFpARLD5e8bSnGDNBVwzywRC/DR8/EhblG5ayB3hH36YW\n8zQ3q5kUm5f44nCuXceRpa5iJkkw6frfuQVpYnDG1mkACkzq50EksMk0l2KaSZNN6RxlSRIcg9tK\nd8CAA+O+I+sw4KIo7HOyoKE8jgviJAZ7NkwIv2Buoh512Kj5fNthkb8Nkw0sKQHuSc1Mop+KkslL\nAA+ss3fFqRvdtvT+8LeB//+r3z8m+pFlJnmGYl4UDkjXJYqHLRgIVOZi1WQxoNdVyDOSmTTIbflj\nXc2Ndwuditnp59i5roWXX7rH9f80sWmLnZ71Kz7zrUdRFKWOVsb7UgxMkv4hZ57SpfBL4tp9/BZx\nH5HPT3nuj191R9N0BDNJaSZx38Fq8YXt5MaruREbhMZJGtPoiFoWgvYBgHN2zWLX+rY7f1EU0VRZ\nbjoVe+e6to/Cl8eVkhTyXsUqR8aKznArIH2aopDtcBUA3VrCuG1pf/pbgy1c/4tfFz/3MHpxliY+\n3Zl9T8fQ99MA2FYGx2T6p91uYbUnU1v7MuByzRmb8cFXXIibnrIXjSzBlukSTMoSdAZ5UP3SB6X9\nd7Fqbo6ZpLIWAA9QyuuWlTaJMRZUni1sI2LPP1YNlRtnJlGQS+5vP/nYNtuuVc7dZAurfszm1ZOL\nwl5HBR4HQMozTLcy9AaFW+ADrJpbRZ9p1y2zh7I9jjlmEqWG2bbzgAb5TRfvXS+CYNtKkI/36Rgz\nKcZ0IctSKaOiyRiXnLQOTzllA07aMFFqJqU4VLJXP3/XIfeMPJiUOxYO/x6Qz86nucm+2MhSrJ9s\nYNN0w42zdI9i1ei0L+ECc46ZVIj0S73O+eWPfVP4dG2mmQQAO9e1hlZorCkwjvoe3dMuSy2sJUYA\nyPxZkk01s2ja3Ds/ege++v2jTs5i01QzWv2N2/a5HyyY9P+JFUXx/qIoLiiK4gK6+TzNTWomyYmG\nqkBx6zIGSyUzCYWrQqYpiJqZRJ88x7rGqrl5Zo8/hkxzY9caEeCWbAhFqy2bwqNfIppj/PcTjQyL\nq1KzQYNJqWtz6vZrM6eN7Jd+6Ay88vK9UeBqXkUrvQC3HYw4A2S5HJzpHJqloE1HLrjANKH7fHIY\nJmCmNZPyvPDlihPj8u27/dylUwCWCmtBTJnmRveOnFxHzVR9bLZdq0y9qqLCUx/X1fyAUFBaO7Mx\np0t0kfJ4t9x52FVW0YAaYPsYgQd6sUHjGe9b1Gaf5uapqhz80MAaZ2okxraV+oVlS8lrsZM18Lqr\n9uEDL78wqDBSBdyRbZ627Ztf7TugmPcbereBkHVHRgBqWM3NC3CnxmCykVWmvxqDwBEVz7L8/0Kn\nV1nJDZClcckaNbkoP3oCzCTA6qUc52luTDOJFvFVIAcZ7UNRNs1G1NXcyHiZd25v+otb8aF/+T4A\nPh4bPG3/puBdoRLJWoeH3uf51R7uenQJ7/jr2wVLb6pZwzuecyYAleZW9mOe7sPZOLG5gtsgt6L+\nF+6ZE8euqmhjjHcGtRabPg8XLPUM1sKBkaOquW2fa+H+YytCz6FdTwXAfPrWaXzlrVfjuoNbAUhm\nTm+QBxpCnJmkx7p6akvR89ejphh12lZ6A5fWQsALB3R8mlvuGAo2cKAWUWoRO47FmEn3HQ2joWTr\nJ+oO/CLTwKtn9sCxbrjdeOFOtm24qNZj4z1l6XrA9gvBUCyAqYYvzU0C2YAc399z4zluEUrXO9nI\ncPeji8E15q6f+e/cMY0U4KaItKb9xxYRZJ1+jkv3bsDbn3PAfZclBoOB1yr7xG0P2QWBUcykaJqb\nZN5xTbzY/SWmb2LkPDyMmUR/Vc/tqpqbWpxqgJLfk4l6xlg4cd8pz/3z6JdpbuR+0PtIf9MCmRv1\nV73ALxA+n2HWqqXYta7l3lPHMHKgkmTmAxHfIM8Z+GS/e/8/3YV9b/kYPn/XIVDDuIYjpceTOTAp\nQJQhjlsgZCbx1GEy8VwLycrTTzxLODPJ//qSi3cBCNNejTGOMXCEid3T3D6/2hcug2MmuTSoBE/b\nvwlpYnDl6ZtwxX4LRtdTg14/d5WvfPML/Mwff82xm4GwYMhix+sA0rvE3+FNU+GiV+v2OZ/Zpa75\n89uxj34PweCqNDfqT8cqwSRauPvv5tqjmUmLq33X5y4o9XEvPXm9u45hzKS8KFypefLZHpr3ILwD\nkyqOMdlI8ehih2nhdMW10vvBfYEq1roGOQDrr771WafjVVec7L7j74ouPpWlslq0LnaTJgabppro\n9nOs9AaCafTthxccCObS3PqWqUvPgL87PPDH09y4cQkWF/BlPvpG1Rc1c5iwABoHV3uDUmPNtocv\n05533g587s5D+Nydh3y7FJh61o7ZAFMQul+JEexvupXNWoIdcy189tuHhNQI3zcOJkliwv7NU+7/\nn77jERxdtnIW7UYayM9o+0Ezk+4HsJP9vaP8LrqNMSYDMAPg8Jj7VppDq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8I2+zQ3U/5T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JNE9cBweGG3uTG/k5ds50sPfCuJ9gXQJ8IkoNecdACw6ar2v7DFcIHqoqrNqEw63Lq++qPl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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x113aa8750>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "spy.log_return.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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SWkuUR4kb1wCAeimPAADeoKqq7fKoBY6tJcmDJ4aURwBAbZRHAABvsLiykfWNrdaZPDox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\np+MBSMAYo5WnlGnpjEL9n4c26pbHtuqprQ269cq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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x113c08210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "spy.log_return.plot.density()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11601ca90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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kp6Ra95FJIZYUzIAVKivHl5+f1GrmmNi1mvMqO9BSCFpLwaQOSwpmwAqVd2yN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\n84ElAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1160538d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')\n",
-    "de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')\n",
-    "de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')\n",
-    "mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')\n",
-    "df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)\n",
-    "plt.figure(figsize=(20,10))\n",
-    "df.plot.density()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html b/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html
deleted file mode 100644
index fe2526d..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial07 Random Variables and Distributions.html	
+++ /dev/null
@@ -1,167 +0,0 @@
-<h2>Introduction</h2>
-In the last chapter we learned the definition of mean and variance, which are kind of <strong>point estimation</strong>. Point estimation means using sample data to calculate a single value which is to serve as a 'best estimation' of an unknown population. However, this it not enough because point estimations can be deceiving. We need to use more rigorous methods to test our ideas. That's why we consider distribution and hypothesis testing. Random variable distribution is the basis for almost all quantitative finance topics: linear regression, CAPM, Black-Scholes, binomial tree pricing, etc.
-<h2>Random Variables</h2>
-First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}.
-The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
-<h2>Distributions</h2>
-Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\).
-However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We we cover this later.
-For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It defines as \(P(X&lt;x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
-<h3>Uniform Distribution</h3>
-Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions.
-Here we use python to simulate rolling a dice 10000 times.
-<pre class="prettyprint linenums">import random
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-#define a function to simulate rolling a dice
-def dice():
-    number=  [1,2,3,4,5,6]
-    return random.choice(number)
-
-series = np.array([dice() for x in range(10000)])
-print series
-</pre>
-We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.hist(series,bins = 11,align = 'mid')
-plt.xlabel('Dice Number')
-plt.ylabel('Occurences')
-plt.grid()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot1.png" alt="plot1" />
-Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
-<pre class="prettyprint linenums">print len([x for x in series if x &lt;= 3])/float(len(series))
-[out]: 0.4956
-print np.mean(series)
-[out]: 3.5103
-</pre>
-\(P(X&lt;3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5.
-For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as
-\[\mu = (1+2+3+4+5+6)/6 = 3.5\]
-Or
-\[E(x) = 1*\frac{1}{6} + 2*\frac{1}{6}+...+6*\frac{1}{6} = 3.5\]
-More generally, if we a assume the minimum value in a uniform distribution is a and the maximum value is b, the mean can be given by:
-\[\bar{u} = \frac{a+b}{2}\]
-Usually we use \(\bar{u}\) to represents the <strong>population mean</strong>, or the 'real mean'. Here we create a sample with 1000 observations, the mean we calculated above is the <strong>sample mean</strong>. Sample mean usually doesn't equal to the theoretical population mean unless the number of observation approaches to infinity.
-The variance is given by:
-\[\sigma^2 = \frac{(b-a)^2}{12}\]
-Deducing the formula is out of our lecture scope. It's useful to realize for a given standard distribution, we can formularize its mean and variance.
-<h3>Binomial Distribution</h3>
-A binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent experiments. Let's assume that the market has 50% probability goes up and 50% probability goes down, and we observe it in the next 10 days, what's the distribution of the number of days it goes up? This is a binomial distribution example.
-In general, if we carry out the experiment n times, and each outcome is independent, with the same probability of success p, the probability of getting exactly k successes is given by the function:
-\[P(X = K) = C_n^k p^k (1-p)^{n-k}\]
-Where
-\[C_n^k = \frac{n!}{(n-k)!k!}\]
-Under such circumstance we say X follows the binomial distribution \(X \sim B(n,p)\).
-Let's simulate a binomial experiment with success rate p = 0.7 and experiment times n = 10
-<pre class="prettyprint linenums">def trial():
-    number = [1,2,3,4,5,6,7,8,9,10]
-    a = random.choice(number)
-    if a&lt;= 7:
-        return 1
-    else:
-        return 0
-</pre>
-Each time we execute trial(), we did an experiment. If it succeed, it will return 1, otherwise it will return 0. Now we are going to do the experiment 10 times:
-<pre class="prettyprint linenums">res = [trial() for x in range(10)]
-print sum(res)
-[out]: 7
-</pre>
-Now we did the experiment 10 times, and the number of success is sum(res). However, it just means during these 10 experiments we succeed sum(res) times. If we want to see the binomial distribution, we need experiment N times. When n is large enough, our frequency will approach the theoretical probability. Here we simulate each outcome 10000 times:
-<pre class="prettyprint linenums">def binomial(number):
-    l = []
-    for i in range(10000):
-        res = [trial() for x in range(10)]
-        l.append(sum(res))
-    return len([x for x in l if x == number])/float(len(l))
-
-print binomial(8)
-[out]: 0.2367
-</pre>
-The number printed above is the simulated probability that we succeed 8 times if we experiment 10 times.
-for each possible outcome, we simulate the probability:
-<pre class="prettyprint linenums">prob = []
-for i in range(1,11):
-    prob.append(binomial(i))
-prob_s = pd.Series(prob,index = range(1,11))
-print prob_s
-[out]: 1     0.0002
-       2     0.0013
-       3     0.0087
-       4     0.0373
-       5     0.1041
-       6     0.2000
-       7     0.2674
-       8     0.2342
-       9     0.1153
-       10    0.0283
-</pre>
-Here we got the simulated result of the binomial distribution. Now we are going to check if the simulated frequencies close enough to the theoretical probabilities. Let's take X = 7 and X = 8 as example:
-<pre class="prettyprint linenums">print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)
-[out]: 0.266827932
-print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)
-[out]: 0.2334744405
-</pre>
-As we can see, the simulated results are pretty close to the real probability!
-we can plot the results as follows:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.bar(range(1,11),prob)
-plt.grid()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot2.png" alt="plot2" />
-Another good property of binomial distribution is that its mean and variance are simple enough:
-\[\bar{u} = np\]
-\[\sigma^2 = np(1-P)\]
-We will not introduce the deduction here, but if you are interested in it, we encourage you to do it yourself, based on the probability functions we provided above.
-<h2>Normal Distribution</h2>
-Before looking at normal distribution, let's first talk about continues distribution. As we mentioned above, we use a <strong>probability density function</strong>(PDF) to model the probability that our value is taken our a specific range. We define it as:
-\[P(a&lt;X&lt;b) = \int_{a}^{b}f_x(x)dx\]
-Now we can talk about the normal distribution. The <strong>normal distribution</strong> is most commonly used distribution in natural sciences, of course also in financial research. The PDF of normal distribution is given as follows:
-\[f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\]
-Where \(\mu\) is the mean of the normal distribution, and \(\sigma\) is the standard deviation.
-Generally, if a random variable X follows normal distribution, we represent it by \(X\sim N(\mu, \sigma^2)\).
-Specifically, if a normal distribution has a 0 mean and 1 standard deviation, we called it <strong>standard normal distribution</strong>.
-Now let's simulate a standard normal distribution using Python packages to see what it looks like:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-norm.plot.density()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot3.png" alt="plot3" />
-Financial data is highly disordered and is considered to has lots of noise. Most of the time we believe those noise follows normal distribution. It's also widely believed that the return on an asset over a short period of time follows normal distribution. Let's check it with the daily logarithm rates of return on SPY:
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-spy_table = quandl.get('BCIW/_SPXT')
-spy = spy_table.loc['2009':'2017',['Open','Close']]
-spy['log_return'] = np.log(spy.Close).diff()
-spy = spy.dropna()
-</pre>
-we calculated the logarithm daily return of S&amp;P 500 index from 2009 to present.
-Let's first have a look at the what the time-series return data looks like:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-spy.log_return.plot()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot4.png" alt="plot4" />
-This is a classic daily return chart. Let's now plot the density chart of the returns:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-spy.log_return.plot.density()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot5.png" alt="plot5" />
-If we observe the x-axis and y-axis carefully, we can see the return of asset is not a standard normal distribution. The peak of the standard normal distribution plot is around 0.4, while it's over 0.6 for this chart. This is because the standard deviation \(\sigma\) of the return is obviously not 1.
-We can demonstrate the normal distribution with different mean and variance by simulation:
-<pre class="prettyprint linenums">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
-de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')
-de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')
-mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')
-df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)
-plt.figure(figsize=(20,10))
-df.plot.density()
-plt.show()
-</pre>
-<img src="images/Tutorial07-plot6.png" alt="plot6" />
-<h2>Summary</h2>
-In this chapter we introduced random variable, the difference between discrete random distribution and continuous random distribution, and most importantly, normal distribution. In the next chapter we will introduce how to use these distributions to test our idea or generating trading signals.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial08 Confidence Interval and Hypothesis Testing.html b/Tutorial Series/Introduction to Financial Python/Tutorial08 Confidence Interval and Hypothesis Testing.html
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-<h2>Introduction</h2>
-In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
-
-As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
-<h2>Confidence Interval</h2>
-<h3>Sample Error</h3>
-Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
-#calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
-[out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
-[out]: Population standard deviation: 0.00784267293815
-</pre>
-Now let's check the recent 10 days sample and recent 1000 days sample:
-<pre class="prettyprint linenums">
-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
-[out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
-[out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
-[out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
-[out]: 1000 days sample standard deviation: 0.00766589174299
-</pre>
-As we expected, the two samples has different means and variances.
-<h3>Confidence Interval</h3>
-In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
-\[SE = \frac{\sigma}{\sqrt{n}}\]
-Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
-Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-Where \(\mu\) is the sample mean and SE is the standard error.
-This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later.
-Let's try to calculate the confidence interval using the samples above:
-<pre class="prettyprint linenums">
-#apply the formula above to calculate confidence interval
-bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-#print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
-[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
-[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
-</pre>
-As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
-<h3>Confidence Interval of Normal Distribution</h3>
-Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
-\[\%90 upperabnd = \mu + 1.64*SE\]
-\[\%90 lowerband = \mu + 1.64*SE\]
-The same for other confidence intervals.
-It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
-\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
-\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
-\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
-This can also be remembered by using the chart:
-<img class="alignnone size-medium" src="https://upload.wikimedia.org/wikipedia/commons/a/a9/Empirical_Rule.PNG" width="769" height="558" />
-<h3>Central Limit Theory</h3>
-As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution?
-The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed.
-This is the foundation of population mean confidence interval estimation.
-<h2>Hypothesis testing</h2>
-Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample.
-Let's start from observing our sample:
-<pre class="prettyprint linenums">mean_1000 = np.mean(spy_log_return.tail(1000))
-std_1000 = np.std(spy_log_return.tail(1000))
-mean_10 = np.mean(spy_log_return.tail(10))
-std_10 = np.std(spy_log_return.tail(10))
-s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
-
-[out]: mean_10      0.000845
-       std_10       0.003136
-       mean_1000    0.000463
-       std_1000     0.007666
-</pre>
-We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
-<pre class="prettyprint linenums">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
-upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00039756352254768874, 0.00039756352254768874)
-</pre>
-Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero.
-Can we claim that with 95% confidence level?
-<pre class="prettyprint linenums">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
-upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00047513689280089639, 0.00047513689280089639)
-</pre>
-This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level.
-We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
-\[H_0:\bar{\mu} = 0\]
-\[H_0:\bar{\mu} \neq 0\]
-If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis.
-Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
-\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
-Let's calculate the Z score from our sample:
-<pre class="prettyprint linenums">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
-[out]: 1.90922032428
-</pre>
-We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis.
-one reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
-<pre class="prettyprint linenums">import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
-[out]: 0.025659656888
-</pre>
-It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
-<img class="alignnone " src="http://napitupulu-jon.appspot.com/galleries/coursera-statistics/3w76.jpg" width="663" height="517" />
-If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level.
-Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
-<pre class="prettyprint linenums">mean_1200 = np.mean(spy_log_return.tail(1200))
-std_1200 = np.std(spy_log_return.tail(1200))
-z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
-[out]: z-score =  2.19793023185
-p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
-[out]: p_value =  0.0139770390655
-</pre>
-Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
-<h2>Summary</h2>
-In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial08 Confidence Interval and Hypothesis Testing.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial08 Confidence Interval and Hypothesis Testing.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Population mean: 0.000443353825615\n",
-      "Population standard deviation: 0.00784267293815\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import quandl\n",
-    "spy_table = quandl.get('LSE/SPY5')\n",
-    "spy_total = spy_table[['Last Close']]\n",
-    "spy_log_return = np.log(spy_total['Last Close']).diff().dropna()\n",
-    "print 'Population mean:', np.mean(spy_log_return)\n",
-    "print 'Population standard deviation:',np.std(spy_log_return)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 days sample returns: 0.000845189915474\n",
-      "10 days sample standard deviation: 0.00313558001122\n",
-      "1000 days sample returns: 0.000462827047221\n",
-      "1000 days sample standard deviation: 0.00766589174299\n"
-     ]
-    }
-   ],
-   "source": [
-    "print '10 days sample returns:', np.mean(spy_log_return.tail(10))\n",
-    "print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))\n",
-    "print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))\n",
-    "print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)\n",
-      "1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)\n"
-     ]
-    }
-   ],
-   "source": [
-    "bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))\n",
-    "upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))\n",
-    "bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))\n",
-    "upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))\n",
-    "print '10 days 95% confidence inverval:', (bottom_1,upper_1)\n",
-    "print '1000 days 95% confidence inverval:', (bottom_2,upper_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean_10      0.000845\n",
-      "std_10       0.003136\n",
-      "mean_1000    0.000463\n",
-      "std_1000     0.007666\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean_10 = np.mean(spy_log_return.tail(10))\n",
-    "std_10 = np.std(spy_log_return.tail(10))\n",
-    "mean_1000 = np.mean(spy_log_return.tail(1000))\n",
-    "std_1000 = np.std(spy_log_return.tail(1000))\n",
-    "s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])\n",
-    "print s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(-0.00039756352254768874, 0.00039756352254768874)\n"
-     ]
-    }
-   ],
-   "source": [
-    "bottom = 0 - 1.64*std_1000/np.sqrt(1000)\n",
-    "upper = 0 + 1.64*std_1000/np.sqrt(1000)\n",
-    "print (bottom, upper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(-0.00047513689280089639, 0.00047513689280089639)\n"
-     ]
-    }
-   ],
-   "source": [
-    "bottom = 0 - 1.96*std_1000/np.sqrt(1000)\n",
-    "upper = 0 + 1.96*std_1000/np.sqrt(1000)\n",
-    "print (bottom, upper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.90922032428\n"
-     ]
-    }
-   ],
-   "source": [
-    "print np.sqrt(1000)*(mean_1000 - 0)/std_1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.025659656888\n"
-     ]
-    }
-   ],
-   "source": [
-    "import scipy.stats as st\n",
-    "print (1 - st.norm.cdf(1.9488))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z-score =  2.19793023185\n",
-      "p_value =  0.0139770390655\n"
-     ]
-    }
-   ],
-   "source": [
-    "mean_1200 = np.mean(spy_log_return.tail(1200))\n",
-    "std_1200 = np.std(spy_log_return.tail(1200))\n",
-    "z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200\n",
-    "print 'z-score = ',z_score\n",
-    "p_value = (1 - st.norm.cdf(z_score))\n",
-    "print 'p_value = ',p_value"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html
deleted file mode 100644
index 66d8bba..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.html	
+++ /dev/null
@@ -1,126 +0,0 @@
-<h2>Introduction</h2>
-In finance and economics filed, most of the models are linear ones. We can see linear regression everywhere, from the foundation of the model portfolio theory to the nowadays popular Fama-French asset pricing model. It's very important to understand how linear regression works in order to have a comprehensive understanding of those theories.
-
-If we are holding a stock, we must be curious about the relationship between our stock return and the market return. Let's say we hold Amazon stock on the first day of this year. In order to see the relation directly, we plot the daily return of our stock on the y-axis and plot the S&amp;P 500 index daily return on the x-axis.
-<pre class="prettyprint linenums">import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = '_fgkxjSbt5389zGt4crC'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-amzn_table = quandl.get('WIKI/AMZN')
-#fetch data from Jan 2017 to Jun 2017
-spy = spy_table.loc['2017':'2017-6',['Close']]
-amzn = amzn_table.loc['2017':'2017-6',['Close']]
-#calculate log return
-spy_log = np.log(spy.Close).diff().dropna()
-amzn_log = np.log(amzn.Close).diff().dropna()
-df = pd.concat([spy_log,amzn_log],axis = 1).dropna()
-df.columns = ['spx','amzn']
-print df.tail()
-</pre>
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="center">spy</td>
-        <td align="center">amzn</td>
-    </tr>
-    <tr>
-        <td>2016-12-22</td>
-        <td align="center">-0.004462</td>
-        <td align="center">-0.005543</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.001372</td>
-        <td align="center">-0.007531</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.000928</td>
-        <td align="center">0.000946</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">-0.005671</td>
-        <td align="center">-0.009081</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.002086</td>
-        <td align="center">-0.020172</td>
-    </tr>
-
-</table>
-We successfully create a DataFrame contains the daily logarithm return of Amazon stock and S&amp;P500. Now let's plot it:
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
-plt.figure(figsize = (15,10))
-plt.scatter(df.spy,df.amzn)
-plt.show()
-</pre>
-<img src="images/Tutorial9-plot1.png" alt="plot1" />
-The plot is scattered, but we can see they are approximately correlated: generally the higher SPX's daily return is, the higher Amazon stock's return is. This is called <strong>positively correlated</strong>. We will cover it in the following tutorials.
-<h2>Slope and Intercept</h2>
-It's natural that we want to model the relation between these two rates of return. Intuitively we use a straight line to model it, this is called <strong>Linear Regression</strong>. In order to find the best straight line, it's natural to think that the vertical distances between the points of the data set and the fitted line should be minimized. Those vertical distances are called <strong>residual</strong>. Our objective is to make the sum of squared residuals as small as possible. This method is called <strong>ordinary least square</strong>, or <strong>OLS</strong> method.
-We use x and y to represent the two variable, S&amp;P 500 daily returns and AMZN daily returns. The linear relation is:
-\[Y = Y = \alpha + \beta*X + \epsilon\]
-Where \(\alpha\) is called <strong>intercept</strong>, \(\beta\) is called <strong>slope</strong>.
-Generally, if the scatter points can be represented by\(\left\{\right (x_1,y_1),(x_2, y_2),(x_3,y_3)...(x_n,y_n) \left\}\right\), then the intercept and slope are given by:
-\[\beta = \frac{\sum_{i=1}^{n}(x-\bar{x})(y-\bar{y})}{\sum_{i=1}^{n}(x-\bar{x})^2}\]
-\[\alpha = \bar{y} - \hat{\beta}\bar{x}\]
-Where \(\bar{x}\) is the mean of X, \(\bar{y}\) is the mean of Y.
-In python, we don't need to do the above calculation manually because we have package for it. But it still very important to understand the calculation process of \(\beta\) in order the understand the modern portfolio theory and CAPM, which we will cover in the future.
-<h2>Python Implementation</h2>
-In python, we have a very power package for mathematical models, which is named 'statsmodels'.
-<pre class="prettyprint linenums">import statsmodels.formula.api as sm
-model = sm.ols(formula = 'amzn~spy',data = df).fit()
-print model.summary()
-</pre>
-<img src="images/Tutorial9-plot2.png" alt="plot2" />
-We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
-<pre class="prettyprint linenums">print 'pamameters: ',model.params
-[out]: pamameters:  Intercept    0.000012
-                   spy          0.492112
-print 'residual: ', model.resid.tail()
-[out]: residual:  Date
-2016-12-22   -0.003360
-2016-12-23   -0.008219
-2016-12-28    0.000477
-2016-12-29   -0.006303
-2016-12-30   -0.021211
-print 'fitted values: ',model.predict()
-[out]: fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]
-</pre>
-Now let's have a look at our fitted line:
-<pre class="prettyprint linenums">plt.figure(figsize = (15,10))
-plt.scatter(df.spy,df.amzn)
-plt.xlabel('spx_return')
-plt.ylabel('amzn_return')
-plt.plot(df.spy,model.predict(),color = 'red')
-plt.show()
-</pre>
-<img src="images/Tutorial9-head.png" alt="head" />
-The red line is the fitted linear regression straight line.
-As we can see there are lot of statistical results in the summary table. Now let's talk about some important statistical parameters.
-<h2>Parameter Significance</h2>
-From the summary table we can see 'std err'. This means standard errors of the intercept and slope. The null hypothesis here is \(H_0: \beta = 0\), and the alternative hypothesis is \(H_1: \beta \neq 0\). This hypothesis score is calculated by:
-\[t = \frac{\beta - 0}{SE}\]
-Where SE is given by:
-\[SE = \sqrt\frac{\frac{1}{n-2}\sum_{i = 1}^{n}\hat{\epsilon}^2}{\sum_{i = i}^{n}(x_i - \bar{x})^2}\]
-The distribution used here is 'Student's t-distribution'. It's different from normal distribution but used in the similar way. The column 't' in this table is the test score, and 'p&gt;|t|' is the p-value. By observing the p-value, we can see that the significance level of spy, or the slope, is very high because the p-value is close to zero. In other words, we have 99.999 confidence to claim that the slope is not 0, and there exists linear relation between X and Y. However, regarding the intercept, the p-value is 0.923, which means we have only 7.7% confidence level that the value of intercept is not 0. We can also see from the plot that the line crosses the origin.
-The following 2 columns are the lower band and upper band of the parameters at 95% confidence interval. At 95% confidence level, we can claim that the true value of the parameter is within this range.
-<h2>Model Significance</h2>
-<h3>R-square</h3>
-<strong>Sum of Squared Errors, or SSE</strong>, is used to measure the difference between the fitted value and the actual value. It's given by:
-\[SSE = \sum_{i=1}^{n}(y_i - \hat{y_i})^2 = \sum_{i = 1}^{n}\hat{\epsilon_i}^2\]
-If the linear model perfectly fitted the sample, the SSE would be zero.
-The reason we use squared error here is that the positive and negative errors would offset each other if we simply summed them up.
-Another measurement of the dispersion of the sample is called <strong>total sum of squares, or SS.</strong>. it's given by:
-\[SS = \sum_{i = i}^{n}(y_i - \bar{y}_i)^2\]
-If you are familiar with variance, we can see that SS divided by the number of sample n is the sample variance.
-From SSE and SS, we can calculate the <strong>Coefficient of Determination</strong>, or <strong>r-square</strong> for short. R-square means the proportion of variation that 'explained' by the linear relationship between X and Y, it's calculated by:
-\[r^2 =1 = \frac{SSE}{SS} =1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y_i})^2}{\sum_{i = i}^{n}(y_i - \bar{y}_i)^2}\]
-Let's assume that the model perfectly fitted the sample, which means all of the sample points lie on the straight line. Then the SSE would become zero, and the r-square would become 1. This means perfect fitness. The higher r-square is, the more parts of variation can be explained by the linear relation, the higher significance level the model is.
-Some other parameters, such as F-statistic, AIC and BIC, are related to multiple linear regression, with would be cover in the next chapter.
-<h2>Summary</h2>
-In this chapter we introduced how to implement simple linear in python, and focused on how to read the summary table. In next chapter we will introduced multiple linear regression, which are commonly used to built models in finance and economics.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb
deleted file mode 100644
index c870f22..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial09 Simple Linear Regression.ipynb	
+++ /dev/null
@@ -1,267 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import quandl\n",
-    "spy_table = quandl.get('LSE/SPY5')\n",
-    "amzn_table = quandl.get('WIKI/AMZN')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spy = spy_table.loc['2016',['Last Close']]\n",
-    "amzn = amzn_table.loc['2016',['Close']]\n",
-    "spy_log = np.log(spy['Last Close']).diff().dropna()\n",
-    "amzn_log = np.log(amzn.Close).diff().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>spy</th>\n",
-       "      <th>amzn</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-12-22</th>\n",
-       "      <td>-0.004462</td>\n",
-       "      <td>-0.005543</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-23</th>\n",
-       "      <td>0.001372</td>\n",
-       "      <td>-0.007531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-28</th>\n",
-       "      <td>0.000928</td>\n",
-       "      <td>0.000946</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-29</th>\n",
-       "      <td>-0.005671</td>\n",
-       "      <td>-0.009081</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-30</th>\n",
-       "      <td>0.002086</td>\n",
-       "      <td>-0.020172</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 spy      amzn\n",
-       "Date                          \n",
-       "2016-12-22 -0.004462 -0.005543\n",
-       "2016-12-23  0.001372 -0.007531\n",
-       "2016-12-28  0.000928  0.000946\n",
-       "2016-12-29 -0.005671 -0.009081\n",
-       "2016-12-30  0.002086 -0.020172"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.concat([spy_log,amzn_log],axis = 1).dropna()\n",
-    "df.columns = ['spy','amzn']\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0,0.5,'amzn_return')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b031b70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.figure(figsize = (15,10))\n",
-    "plt.scatter(df.spy,df.amzn)\n",
-    "plt.xlabel('spx_return')\n",
-    "plt.ylabel('amzn_return')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.044\n",
-      "Model:                            OLS   Adj. R-squared:                  0.040\n",
-      "Method:                 Least Squares   F-statistic:                     10.63\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
-      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
-      "No. Observations:                 235   AIC:                            -1214.\n",
-      "Df Residuals:                     233   BIC:                            -1207.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept   1.234e-05      0.001      0.010      0.992      -0.002       0.002\n",
-      "spy            0.4921      0.151      3.261      0.001       0.195       0.789\n",
-      "==============================================================================\n",
-      "Omnibus:                       51.597   Durbin-Watson:                   2.255\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              570.651\n",
-      "Skew:                           0.405   Prob(JB):                    1.22e-124\n",
-      "Kurtosis:                      10.591   Cond. No.                         127.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import statsmodels.formula.api as sm\n",
-    "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
-    "print(model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pamameters:  Intercept    0.000012\n",
-      "spy          0.492112\n",
-      "dtype: float64\n",
-      "residual:  Date\n",
-      "2016-12-22   -0.003360\n",
-      "2016-12-23   -0.008219\n",
-      "2016-12-28    0.000477\n",
-      "2016-12-29   -0.006303\n",
-      "2016-12-30   -0.021211\n",
-      "dtype: float64\n",
-      "fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('pamameters: ',model.params)\n",
-    "print('residual: ', model.resid.tail())\n",
-    "print('fitted values: ',model.predict()[-6:])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1d630588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (15,10))\n",
-    "plt.scatter(df.spy,df.amzn)\n",
-    "plt.xlabel('spx_return')\n",
-    "plt.ylabel('amzn_return')\n",
-    "plt.plot(df.spy,model.predict(),color = 'red')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html
deleted file mode 100644
index 31c5e25..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.html	
+++ /dev/null
@@ -1,320 +0,0 @@
-In the last chapter we introduced simple linear regression, which has only one independent variable. In this chapter we will learn about linear regression with multiple independent variables.
-
-A simple linear regression model is written in the following form:
-\[ Y = \alpha + \beta X + \epsilon \]
-
-A <strong>multiple linear regression</strong> model with <em>p</em> variables is given by:
-\[ Y = \alpha + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_p X_p + \epsilon \]
-
-<h2>Python Implementation</h2>
-
-In the last chapter we used the S&amp;P 500 index to predict Amazon stock returns. Now we will add more variables to improve our model's predictions. In particular, we shall consider Amazon's competitors.
-
-<pre class="prettyprint linenums">import numpy as np
-import pandas as pd
-import quandl
-import matplotlib.pyplot as plt
-import statsmodels.formula.api as sm
-
-# Get stock prices
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-spy_table  = quandl.get('BCIW/_SPXT')
-amzn_table = quandl.get('WIKI/AMZN')
-ebay_table = quandl.get('WIKI/EBAY')
-wal_table  = quandl.get('WIKI/WMT')
-aapl_table = quandl.get('WIKI/AAPL')</pre>
-
-Then we fetch closing prices starting from 2016:
-
-<pre class="prettyprint linenums">
-spy  = spy_table .loc['2016',['Close']]
-amzn = amzn_table.loc['2016',['Close']]
-ebay = ebay_table.loc['2016',['Close']]
-wal  = wal_table .loc['2016',['Close']]
-aapl = aapl_table.loc['2016',['Close']]
-</pre>
-
-After taking log returns of each stock, we concatenate them into a DataFrame, and print out the last 5 rows:
-
-<pre class="prettyprint linenums">
-spy_log  = np.log(spy.Close) .diff().dropna()
-amzn_log = np.log(amzn.Close).diff().dropna()
-ebay_log = np.log(ebay.Close).diff().dropna()
-wal_log  = np.log(wal.Close) .diff().dropna()
-aapl_log = np.log(aapl.Close).diff().dropna()
-df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()
-df.columns = ['SPY', 'AMZN', 'EBAY', 'WAL', 'AAPL']
-df.tail()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>Date</td>
-        <td align="right">SPY</td>
-        <td align="right">AMZN</td>
-        <td align="right">EBAY</td>
-        <td align="right">WAL</td>
-        <td align="right">AAPL</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="right">0.001351</td>
-        <td align="right">-0.007531</td>
-        <td align="right">0.008427</td>
-        <td align="right">-0.000719</td>
-        <td align="right">0.001976</td>
-    </tr>
-    <tr>
-        <td>2016-12-27</td>
-        <td align="right">0.002254</td>
-        <td align="right">0.014113</td>
-        <td align="right">0.014993</td>
-        <td align="right">0.002298</td>
-        <td align="right">0.006331</td>
-    </tr>
-    <tr>
-        <td>2016-12-28</td>
-        <td align="right">-0.008218</td>
-        <td align="right">0.000946</td>
-        <td align="right">-0.007635</td>
-        <td align="right">-0.005611</td>
-        <td align="right">-0.004273</td>
-    </tr>
-    <tr>
-        <td>2016-12-29</td>
-        <td align="right">-0.000247</td>
-        <td align="right">-0.009081</td>
-        <td align="right">-0.001000</td>
-        <td align="right">-0.000722</td>
-        <td align="right">-0.000257</td>
-    </tr>
-    <tr>
-        <td>2016-12-30</td>
-        <td align="right">-0.004601</td>
-        <td align="right">-0.020172</td>
-        <td align="right">-0.009720</td>
-        <td align="right">-0.002023</td>
-        <td align="right">-0.007826</td>
-    </tr>
-</table>
-
-As before, we use the 'statsmodels' package to perform simple linear regression:
-
-<pre class="prettyprint linenums">simple = sm.ols(formula = 'amzn ~ spy', data = df).fit()
-print simple.summary()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="right">coef</td>
-        <td align="right">std err</td>
-        <td align="right">t</td>
-        <td align="right">P>|t|</td>
-        <td align="right">[0.025</td>
-        <td align="right">0.975]</td>
-    </tr>
-    <tr>
-        <td>Intercept</td>
-        <td align="right">9.876e-05</td>
-        <td align="right">0.001</td>
-        <td align="right">0.097</td>
-        <td align="right">0.923</td>
-        <td align="right">-0.002</td>
-        <td align="right">0.002</td>
-    </tr>
-    <tr>
-        <td>spy</td>
-        <td align="right">1.0796</td>
-        <td align="right">0.124</td>
-        <td align="right">8.725</td>
-        <td align="right">0.000</td>
-        <td align="right">0.836</td>
-        <td align="right">1.323</td>
-    </tr>
-</table>
-
-Similarly, we can build a multiple linear regression model:
-
-<pre class="prettyprint linenums">model = sm.ols(formula = 'amzn ~ spy + ebay + wal', data = df).fit()
-print model.summary()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="right">coef</td>
-        <td align="right">std err</td>
-        <td align="right">t</td>
-        <td align="right">P>|t|</td>
-        <td align="right">[0.025</td>
-        <td align="right">0.975]</td>
-    </tr>
-    <tr>
-        <td>Intercept</td>
-        <td align="right">0.0001</td>
-        <td align="right">0.001</td>
-        <td align="right">0.134</td>
-        <td align="right">0.894</td>
-        <td align="right">-0.002</td>
-        <td align="right">0.002</td>
-    </tr>
-    <tr>
-        <td>spy</td>
-        <td align="right">1.0468</td>
-        <td align="right">0.170</td>
-        <td align="right">6.155</td>
-        <td align="right">0.000</td>
-        <td align="right">0.712</td>
-        <td align="right">1.382</td>
-    </tr>
-    <tr>
-        <td>ebay</td>
-        <td align="right">-0.0795</td>
-        <td align="right">0.058</td>
-        <td align="right">-1.364</td>
-        <td align="right">0.174</td>
-        <td align="right">-0.194</td>
-        <td align="right">0.035</td>
-    </tr>
-    <tr>
-        <td>wal</td>
-        <td align="right">-0.0865</td>
-        <td align="right">0.089</td>
-        <td align="right">-0.976</td>
-        <td align="right">0.330</td>
-        <td align="right">-0.261</td>
-        <td align="right">0.088</td>
-    </tr>
-    <tr>
-        <td>aapl</td>
-        <td align="right">0.1529</td>
-        <td align="right">0.084</td>
-        <td align="right">1.831</td>
-        <td align="right">0.068</td>
-        <td align="right">-0.012</td>
-        <td align="right">0.317</td>
-    </tr>
-</table>
-
-As seen from the summary table, the p-values for Ebay, Walmart and Apple are 0.174, 0.330 and 0.068 respectively, so none of them are significant at a 95% confidence level.
-
-The multiple regression model has a higher \( R^2 \) than the simple one: 0.254 vs 0.234. Indeed, \( R^2 \) cannot decrease as the number of variables increases. Why? If an extra variable is added to our regression model, but it cannot account for variations in the response (amzn), then its estimated coefficient will simply be zero. It's as though that variable was never included in the model, so \( R^2 \) will not change.
-
-However, it is not always better to add hundreds of variables or we will overfit our model. We'll talk about this in a later chapter.
-
-Can we improve our model further? Here we try the <strong>Fama-French 5-factor model</strong>, which is an important model in asset pricing theory. We will cover it in the later tutorials.
-
-The data needed are publicly available on French's website.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] We have saved a copy for convenience. The following code fetches the data.
-
-<pre class="prettyprint linenums">
-import urllib2
-from datetime import datetime
-
-url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'
-response   = urllib2.urlopen(url)
-fama_table = pd.read_csv(response)
-
-# Convert time column into index
-fama_table.index = [datetime.strptime(str(x), "%Y%m%d")
-                    for x in fama_table.iloc[:,0]]
-# Remove time column
-fama_table = fama_table.iloc[:,1:]
-</pre>
-
-With the data, we can construct a Fama-French factor model:
-
-<pre class="prettyprint linenums">
-fama = fama_table['2016']
-fama = fama.rename(columns = {'Mkt-RF':'MKT'})
-fama = fama.apply(lambda x: x/100)
-fama_df = pd.concat([fama, amzn_log], axis = 1)
-fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA', data = fama_df).fit()
-print fama_model.summary()
-</pre>
-<img src="images/Tutorial10-fama.png" alt="fama" />
-The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
-
-<pre class="prettyprint linenums">
-result = pd.DataFrame({'simple regression': simple.predict(),
-                       'fama_french': fama_model.predict(),
-                       'sample': df.amzn}, index = df.index)
-
-# Feel free to adjust the chart size
-plt.figure(figsize = (15,7.5))
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])
-plt.legend()
-plt.show()
-</pre>
-<img src="images/Tutorial10-compare.png" alt="compare" />
-Although it's hard to see from the chart above, the predicted return from multiple regression is closer to the actual return. Usually we don't plot the predictions to determine which model is better; we read the summary table.
-
-<h2>Model Significance Test</h2>
-
-Instead of using \( R^2 \) to assess whether our regression model is a good fit to the data, we can perform a hypothesis test: the F test.
-
-The null and alternative hypotheses of an F test are:
-\[ H_0: \beta_1 = \beta_2 = \dots = \beta_p = 0  \]
-\[ H_1: \text{At least one coefficient is not 0} \]
-
-We won't explain F test procedure in detail here. You just need to understand the null and alternative hypotheses. In the summary table of an F test, the 'F-statistic' is the F score, while 'prob (F-statistic)' is the p-value. Performing this test on the Fama-French model, we get a p-value of `2.21e-24` so we are almost certain that at least one of the coefficient is not 0.
-
-If the p-value is larger than 0.05, you should consider rebuilding your model with other independent variables.
-
-In simple linear regression, an F test is equivalent to a t test on the slope, so their p-values will be the same.
-
-<h2>Residual Analysis</h2>
-
-Linear regression requires that the predictors and response have a linear relationship. This assumption holds if the residuals are zero on average, <strong>no matter</strong> what values the predictors \( X_1, \dots, X_p \) take.
-
-Often it's also assumed that the residuals are independent and normally distributed with the same variance (homoskedasticity), so that we can contruct prediction intervals, for example.
-
-To check whether these assumptions hold, we need to analyse the residuals. In statistical arbitrage, residual analysis can also be used to generate signals.
-
-<h3>Normality</h3>
-
-The residuals of a linear model usually has a normal distribution. We can plot the residual's density to check for normality:
-
-<pre class="prettyprint linenums">plt.figure()
-#ols.fit().model is a method to access to the residual.
-fama_model.resid.plot.density()
-plt.show()
-</pre>
-<img src="images/Tutorial10-residual.png" alt="residual" />
-
-As seen from the plot, the residual is normally distributed. By the way, the residual mean is always zero, up to machine precision:
-
-<pre class="prettyprint linenums">print 'Residual mean:', np.mean(fama_model.resid)
-[out]: Residual mean: -2.31112163493e-16
-print 'Residual variance:', np.var(fama_model.resid)
-[out]: Residual variance: 0.000205113416293
-</pre>
-
-<h3>Homoskedasticity</h3>
-
-This word is difficult to pronounce but not difficult to understand. It means that the residuals have the same variance for all values of X. Otherwise we say that 'heteroskedasticity' is detected.
-
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.scatter(df.spy,simple.resid)
-plt.axhline(0.05)
-plt.axhline(-0.05)
-plt.xlabel('x value')
-plt.ylabel('residual')
-plt.show()
-</pre>
-<img src="images/Tutorial10-variance.png" alt="variance" />
-As seen from the chart, the residuals' variance doesn't increase with X. The three outliers do not change our conclusion. Although we can plot the residuals for simple regression, we can't do this for multiple regression, so we use statsmodels to test for heteroskedasticity:
-
-<pre class="prettyprint linenums">from statsmodels.stats import diagnostic as dia
-het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])
-print 'p-value: ', het[-1]
-[out]:p-value of Heteroskedasticity:  0.144075842844
-</pre>
-
-No heteroskedasticity is detected at the 95% significance level.
-
-<h2>Summary</h2>
-
-In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb
deleted file mode 100644
index 3ae3a01..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial10 Multiple Linear Regression.ipynb	
+++ /dev/null
@@ -1,487 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import quandl\n",
-    "import matplotlib.pyplot as plt\n",
-    "import statsmodels.formula.api as sm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
-    "goog_table = quandl.get('WIKI/GOOG')\n",
-    "amzn_table = quandl.get('WIKI/AMZN')\n",
-    "ebay_table = quandl.get('WIKI/EBAY')\n",
-    "wal_table = quandl.get('WIKI/WMT')\n",
-    "aapl_table = quandl.get('WIKI/AAPL')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>goog</th>\n",
-       "      <th>amzn</th>\n",
-       "      <th>ebay</th>\n",
-       "      <th>wal</th>\n",
-       "      <th>aapl</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2016-12-23</th>\n",
-       "      <td>-0.001708</td>\n",
-       "      <td>-0.007531</td>\n",
-       "      <td>0.008427</td>\n",
-       "      <td>-0.000719</td>\n",
-       "      <td>0.001976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-27</th>\n",
-       "      <td>0.002074</td>\n",
-       "      <td>0.014113</td>\n",
-       "      <td>0.014993</td>\n",
-       "      <td>0.002298</td>\n",
-       "      <td>0.006331</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-28</th>\n",
-       "      <td>-0.008246</td>\n",
-       "      <td>0.000946</td>\n",
-       "      <td>-0.007635</td>\n",
-       "      <td>-0.005611</td>\n",
-       "      <td>-0.004273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-29</th>\n",
-       "      <td>-0.002883</td>\n",
-       "      <td>-0.009081</td>\n",
-       "      <td>-0.001000</td>\n",
-       "      <td>-0.000722</td>\n",
-       "      <td>-0.000257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016-12-30</th>\n",
-       "      <td>-0.014113</td>\n",
-       "      <td>-0.020172</td>\n",
-       "      <td>-0.009720</td>\n",
-       "      <td>-0.002023</td>\n",
-       "      <td>-0.007826</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                goog      amzn      ebay       wal      aapl\n",
-       "Date                                                        \n",
-       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
-       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
-       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
-       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
-       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goog = goog_table.loc['2016',['Close']]\n",
-    "amzn = amzn_table.loc['2016',['Close']]\n",
-    "ebay = ebay_table.loc['2016',['Close']]\n",
-    "wal = wal_table.loc['2016',['Close']]\n",
-    "aapl = aapl_table.loc['2016',['Close']]\n",
-    "goog_log = np.log(goog.Close).diff().dropna()\n",
-    "amzn_log = np.log(amzn.Close).diff().dropna()\n",
-    "ebay_log = np.log(ebay.Close).diff().dropna()\n",
-    "wal_log = np.log(wal.Close).diff().dropna()\n",
-    "aapl_log = np.log(aapl.Close).diff().dropna()\n",
-    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
-    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  Close   R-squared:                       0.346\n",
-      "Model:                            OLS   Adj. R-squared:                  0.319\n",
-      "Method:                 Least Squares   F-statistic:                     12.51\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
-      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
-      "No. Observations:                 124   AIC:                            -827.3\n",
-      "Df Residuals:                     118   BIC:                            -810.4\n",
-      "Df Model:                           5                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
-      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
-      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
-      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
-      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
-      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
-      "==============================================================================\n",
-      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
-      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
-      "Kurtosis:                       7.142   Cond. No.                         410.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
-    "print(model2.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
-      "Model:                            OLS   Adj. R-squared:                  0.348\n",
-      "Method:                 Least Squares   F-statistic:                     134.7\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
-      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
-      "No. Observations:                 251   AIC:                            -1401.\n",
-      "Df Residuals:                     249   BIC:                            -1394.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
-      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
-      "==============================================================================\n",
-      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
-      "Skew:                          -0.374   Prob(JB):                         0.00\n",
-      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
-    "print(simple.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
-    "fama_table = pd.read_csv(url)\n",
-    "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
-    "fama_table.index = index\n",
-    "fama_table = fama_table.iloc[:,1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fama = fama_table['2016']\n",
-    "fama = fama.rename(columns = {'Mkt-RF':'MKT'})\n",
-    "fama = fama.apply(lambda x: x/100)\n",
-    "fama_df = pd.concat([fama,amzn_log],axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  Close   R-squared:                       0.387\n",
-      "Model:                            OLS   Adj. R-squared:                  0.375\n",
-      "Method:                 Least Squares   F-statistic:                     30.97\n",
-      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
-      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
-      "No. Observations:                 251   AIC:                            -1407.\n",
-      "Df Residuals:                     245   BIC:                            -1386.\n",
-      "Df Model:                           5                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept      0.0010      0.001      1.028      0.305      -0.001       0.003\n",
-      "MKT            0.9612      0.125      7.691      0.000       0.715       1.207\n",
-      "SMB           -0.5890      0.182     -3.235      0.001      -0.948      -0.230\n",
-      "HML           -0.1335      0.211     -0.632      0.528      -0.549       0.282\n",
-      "RMW           -0.4851      0.264     -1.840      0.067      -1.005       0.034\n",
-      "CMA           -1.5555      0.324     -4.801      0.000      -2.194      -0.917\n",
-      "==============================================================================\n",
-      "Omnibus:                       69.457   Durbin-Watson:                   1.937\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2012.675\n",
-      "Skew:                           0.241   Prob(JB):                         0.00\n",
-      "Kurtosis:                      16.864   Cond. No.                         399.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
-    "print(fama_model.summary())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a160d0128>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "result = pd.DataFrame({'simple regression':simple.predict(),'fama_french':fama_model.predict(),'sample':df.amzn},index = df.index)\n",
-    "plt.figure(figsize = (15,7.5))\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
-    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x103c271d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "simple.resid.plot.density()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "print 'residual mean: ', np.mean(fama_model.resid)\n",
-    "print 'residual variance: ', np.var(fama_model.resid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df.goog,simple.resid)\n",
-    "plt.axhline(0.05,color = 'r')\n",
-    "plt.axhline(-0.05,color = 'r')\n",
-    "plt.axhline(0,color = 'black')\n",
-    "plt.xlabel('x value')\n",
-    "plt.ylabel('residual')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pandas.core import datetools\n",
-    "from statsmodels.stats import diagnostic as dia"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "p-value of Heteroskedasticity:  0.14407584284381386\n"
-     ]
-    }
-   ],
-   "source": [
-    "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
-    "print('p-value of Heteroskedasticity: ', het[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2668279319999998\n"
-     ]
-    }
-   ],
-   "source": [
-    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.html b/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.html
deleted file mode 100644
index e5124ed..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.html	
+++ /dev/null
@@ -1,225 +0,0 @@
-Many papers in statistics and quantitative finance make heavy use of linear algebra, so you need to have a working knowledge of it in order to read and apply them to your trading.
-
-<h2>Vectors</h2>
-
-A vector can be thought of as an arrow pointing from the origin to a specific point. Any vector or point can be represented by its coordinates i.e. an array of numbers, such as \((x,y)\) for a 2-dimensional vector, or \((x,y,z)\) for a 3-dimensional one. We usually write a vector as a column:
-\[ \mathbf{v} = \begin{pmatrix}
-x \\ y \\ z
-\end{pmatrix} \]
-<a name="scalar"></a>
-The <strong>scalar product</strong> of two vectors \( \mathbf{x} \) and \( \mathbf{y} \) in 2-dimensional space is defined as:
-\[ \mathbf{x}^T \mathbf{y} = \begin{pmatrix} x_1 &amp; x_2 \end{pmatrix} \begin{pmatrix} y_1 \\ y_2 \end{pmatrix} = x_1 y_1 + x_2 y_2 \]
-
-This definition can be easily generalized to <em>n</em> dimensional space. Clearly, we cannot take the scalar product of two vectors with different dimensions.
-
-<h2>Matrices</h2>
-
-If we have a few vectors \( \mathbf{v}_1, \mathbf{v}_2, \dots, \mathbf{v}_n \) with the same dimension, then we can put them side-by-side to form a <strong>matrix</strong>. For example, the vectors
-\[ v_1 = \begin{pmatrix} 1 \\ 2 \\ 3 \end{pmatrix} \qquad
-   v_2 = \begin{pmatrix} 2 \\ 2 \\ 2 \end{pmatrix} \qquad
-   v_3 = \begin{pmatrix} 3 \\ 1 \\ 1 \end{pmatrix} \]
-
-can be combined to produce a matrix:
-\[ m = \begin{pmatrix}
-1 &amp; 2 &amp; 3 \\
-2 &amp; 2 &amp; 2 \\
-3 &amp; 1 &amp; 1
-\end{pmatrix} \]
-
-m is a 3 &times; 3 matrix. We typically describe the dimensions of a matrix as \(m \times n\) where m = number of rows and n = number of columns.
-
-A <strong>square</strong> matrix is one with as many rows as columns.
-
-Notation: \(x_{ij}\) refers to a specific value in row \(i\) and column \(j\) of a matrix \(X\). For example, \(x_{23}\) is the number in the second row and third column of \(X\).
-
-<h3>Python Implementation</h3>
-
-In Python, the NumPy package deals with linear algebra. The array we learned in the NumPy chapter can be deemed as a vector:
-
-<pre class="prettyprint linenums">import numpy as np
-a = np.array([1,2,3])
-b = np.array([2,2,2])
-c = np.array([3,1,1])
-matrix = np.column_stack((a,b,c))
-print matrix
-print type(matrix)
-[out]:
-[[1 2 3]
- [2 2 1]
- [3 2 1]]
-</pre>
-
-It is worth noticing that we used column_stack() here to ensure that the vectors are vertical and placed side-by-side to form a matrix. Without the column_stack() function, the vectors will be made horizontal and stacked on top of one another:
-
-<pre class="prettyprint linenums">matrix2 = np.array([a,b,c])
-print matrix2
-[out]:
-[[1 2 3]
- [2 2 2]
- [3 1 1]]
-</pre>
-
-<h2>Matrix Multiplication</h2>
-
-How are two matrices multiplied? Suppose \(X = AB\). Each entry \(x_{ij}\) of matrix \(X\) is the <a href="#scalar">scalar product</a> of row \(i\) from matrix \(A\) with column \(j\) from matrix \(B\). This is best illustrated with an example:
-
-\[ AB = \begin{pmatrix}
-a_{11} &amp; a_{12} \\
-a_{21} &amp; a_{22} \\
-a_{31} &amp; a_{32}
-\end{pmatrix}
-\begin{pmatrix}
-b_{11} &amp; b_{12} \\
-b_{21} &amp; b_{22}
-\end{pmatrix} = \begin{pmatrix}
-x_{11} &amp; x_{12} \\
-x_{21} &amp; x_{22} \\
-x_{31} &amp; x_{32}
-\end{pmatrix} \]
-
-Then
-\[ x_{\color{red}11} = a_{{\color{red}1} 1} b_{1{\color{red} 1}} + a_{{\color{red}1} 2} b_{2 {\color{red}1}} \]
-\[ x_{\color{red}12} = a_{{\color{red}1} 1} b_{1 {\color{red}2}} + a_{{\color{red}1} 2} b_{2 {\color{red}2}} \]
-\[ x_{\color{red}21} = a_{{\color{red}2} 1} b_{1 {\color{red}1}} + a_{{\color{red}2} 2} b_{2 {\color{red}1}} \]
-\[ x_{\color{red}22} = a_{{\color{red}2} 1} b_{1 {\color{red}2}} + a_{{\color{red}2} 2} b_{2 {\color{red}2}} \]
-\[ \vdots \]
-
-In NumPy, we can multiply matrices with the dot() function:
-
-<pre class="prettyprint linenums">A = np.array([[2,3],[4,2],[2,2]])
-B = np.array([[4,2],[4,6]])
-x = np.dot(A,B)
-print x
-[out]:
-[[20 22]
- [24 20]
- [16 16]]
-</pre>
-
-Since matrix multiplication is defined in terms of scalar products, the matrix product \(AB\) exists only if \(A\) has as many columns as \(B\) has rows. It's useful to remember this shorthand: (m &times; n) &times; (n &times; p) = (m &times; p) which means that an (m &times; n) matrix multiplied by an (n &times; p) matrix yields an (m &times; p) matrix.
-
-Reversing the order of multiplication results in an error since B does not have as many columns as A has rows:
-
-<pre class="prettyprint linenums">x = np.dot(B,A)
-</pre>
-
-A natrual consequence of this fact is that matrix multiplication is <strong>not commutative</strong>. In other words, \(AB \neq BA\) in general.
-
-<h2>Inverse</h2>
-
-An <strong>identity</strong> matrix is a square matrix with ones on the main diagonal and zeros elsewhere. Here is an \(n \times n\) identity matrix:
-
-\[ I_n = \begin{pmatrix}
-   1 &amp; 0 &amp; 0 &amp; ... &amp; 0 \\
-   0 &amp; 1 &amp; 0 &amp; ... &amp; 0 \\
-   0 &amp; 0 &amp; 1 &amp; ... &amp; 0 \\
-   \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
-   0 &amp; 0 &amp; 0 &amp; ... &amp; 1
-\end{pmatrix} \]
-
-Multiplying any matrix by an identity matrix (of the correct shape) is like multiplying a number by 1. Concretely, if \(A\) is an \(m \times n\) matrix, then:
-\[ I_mA = AI_n = A \]
-
-\( A^{-1} \) is the <strong>inverse</strong> matrix of a square matrix \(A\) if:
-\[ AA^{-1} = I = A^{-1}A \]
-
-Some caveats:
-<ul>
-    <li>A rectangular matrix will not have an inverse, but it may have a pseudoinverse (not covered in this tutorial).</li>
-    <li>A square matrix may not have an inverse i.e. it may be "singular".</li>
-    <li>If a square matrix has an inverse, then its inverse is unique.</li>
-</ul>
-
-Inverse matrices are computed using the <a href="https://www.mathsisfun.com/algebra/matrix-inverse-row-operations-gauss-jordan.html" target="_blank">Gauss-Jordan method</a>. In NumPy, we use the linalg.inv() function to do it:
-
-<pre class="prettyprint linenums">print matrix
-print '\n-------------------------\n'
-print np.linalg.inv(matrix)
-[out]:
-[[1 2 3]
- [2 2 1]
- [3 2 1]]
-
--------------------------
-
-[[ 0.   -1.    1.  ]
- [-0.25  2.   -1.25]
- [ 0.5  -1.    0.5 ]]
-</pre>
-
-Now let's check if the multiplication is \(I\):
-
-<pre class="prettyprint linenums">inverse = np.linalg.inv(matrix)
-print np.dot(matrix, inverse)
-print '\n-------------------------\n'
-print np.dot(inverse,matrix)
-[out]:
-[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]
- [  0.00000000e+00   1.00000000e+00   1.11022302e-16]
- [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]
-
--------------------------
-
-[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]
- [  6.66133815e-16   1.00000000e+00   0.00000000e+00]
- [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]
-</pre>
-
-Not surprisingly, we ended up with an identity matrix. We can form a non-invertible matrix by making one of its rows a multiple of another:
-
-<pre class="prettyprint linenums">singular = np.array([[1,2,3],[1,2,3],[3,3,3]])
-inv = np.linalg.inv(singular)
-[out]: numpy.linalg.linalg.LinAlgError: Singular matrix
-</pre>
-
-<h2>Linear Equations</h2>
-
-A common problem in linear algebra is solving linear equations. Consider the following linear equations:
-
-\[  2x + y - z = 8    \]
-\[ -3x - y + 2z = -11 \]
-\[ -2x + y + 2z = -3  \]
-
-If we let:
-\[ A = \begin{pmatrix}
-   2  &amp; 1  &amp; -1 \\
-   -3 &amp; -1 &amp; 2  \\
-   -2 &amp; 1  &amp; 2
-\end{pmatrix} \qquad
-\mathbf{x} = \begin{pmatrix} x \\  y  \\ z  \end{pmatrix} \qquad
-\mathbf{b} = \begin{pmatrix} 8 \\ -11 \\ -3 \end{pmatrix} \]
-
-Then the linear equations above can be written as \( A\mathbf{x} = \mathbf{b} \)
-
-If A is invertible, then we can multiply \(A^{-1}\) on both sides of the equation to obtain the solution:
-\[ A^{-1}A \mathbf{x} = A^{-1}\mathbf{b} \]
-Hence
-\[ \mathbf{x} = A^{-1}\mathbf{b} \]
-
-As long as \(A^{-1}\) exists, we can compute it to solve the linear equations:
-
-<pre class="prettyprint linenums">A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])
-b = np.array([[8],[-11],[-3]])
-inv_A = np.linalg.inv(A)
-print np.dot(inv_A, b)
-[out]:
-[[ 2.]
- [ 3.]
- [-1.]]
-</pre>
-
-The solution is <em>x</em> = 2, <em>y</em> = 3, <em>z</em> = &minus;1. However, computing the inverse matrix is not recommended, since it is numerically unstable i.e. small rounding errors can dramatically affect the result.
-
-Instead, NumPy solves linear equations by <a href="https://www.youtube.com/watch?v=m3EojSAgIao" target="_blank">LU decomposition</a>:
-
-<pre class="prettyprint linenums">print np.linalg.solve(A, b)
-[out]:
-[[ 2.]
- [ 3.]
- [-1.]]
-</pre>
-
-Of course, we get the same solution. We can check the correctness of the solution by substituting <em>x</em>, <em>y</em> and <em>z</em> into the linear equations.
-
-<h2>Summary</h2>
-In this chapter we have introduced vectors, matrices, inverse matrices and linear equations. Some applications in finance include: finding arbitrage opportunities by solving linear equations, computing portfolio variance, etc. In the next chapter, we will introduce modern portfolio theory and CAPM.
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb
deleted file mode 100644
index 06e6da5..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial11 Linear Algebra.ipynb	
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([1,2,3])\n",
-    "b = np.array([2,2,2])\n",
-    "c = np.array([3,1,1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 1]\n",
-      " [3 2 1]]\n",
-      "<class 'numpy.ndarray'>\n"
-     ]
-    }
-   ],
-   "source": [
-    "matrix = np.column_stack((a,b,c))\n",
-    "print(matrix)\n",
-    "print(type(matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 2]\n",
-      " [3 1 1]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "matrix2 = np.array([a,b,c])\n",
-    "print(matrix2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = np.array([[2,3],[4,2],[2,2]])\n",
-    "B = np.array([[4,2],[4,6]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[20 22]\n",
-      " [24 20]\n",
-      " [16 16]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = np.dot(A,B)\n",
-    "print(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-ac89428c1137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)"
-     ]
-    }
-   ],
-   "source": [
-    "x = np.dot(B,A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [2 2 1]\n",
-      " [3 2 1]]\n",
-      "\n",
-      "-------------seperation line------------\n",
-      "\n",
-      "[[ 3.70074342e-17 -1.00000000e+00  1.00000000e+00]\n",
-      " [-2.50000000e-01  2.00000000e+00 -1.25000000e+00]\n",
-      " [ 5.00000000e-01 -1.00000000e+00  5.00000000e-01]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(matrix)\n",
-    "print('\\n-------------seperation line------------\\n')\n",
-    "print(np.linalg.inv(matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.00000000e+00 -6.66133815e-16  3.33066907e-16]\n",
-      " [ 0.00000000e+00  1.00000000e+00  1.11022302e-16]\n",
-      " [ 0.00000000e+00 -2.22044605e-16  1.00000000e+00]]\n",
-      "\n",
-      "-------------seperation line------------\n",
-      "\n",
-      "[[ 1.00000000e+00  0.00000000e+00  1.11022302e-16]\n",
-      " [ 0.00000000e+00  1.00000000e+00 -4.44089210e-16]\n",
-      " [-1.11022302e-16  0.00000000e+00  1.00000000e+00]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "inverse = np.linalg.inv(matrix)\n",
-    "print(np.dot(matrix,inverse))\n",
-    "print('\\n-------------seperation line------------\\n')\n",
-    "print(np.dot(inverse,matrix))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 2.]\n",
-      " [ 3.]\n",
-      " [-1.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])\n",
-    "B = np.array([[8],[-11],[-3]])\n",
-    "inv_A = np.linalg.inv(A)\n",
-    "print(np.dot(inv_A,B))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 2.]\n",
-      " [ 3.]\n",
-      " [-1.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.linalg.solve(A,B))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.html b/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.html
deleted file mode 100644
index 4b8c325..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.html	
+++ /dev/null
@@ -1,155 +0,0 @@
-The Modern Portfolio Theory (MPT) suggests how investors should spread their wealth across various assets to minimize risk and maximize return.
-
-This chapter is mathematically intense, so don't feel demoralized if you don't understand it on your first reading.
-
-<h2>Risk Aversion</h2>
-
-In portfolio theory, the riskiness of an asset is often measured by the variance (or standard deviation) of its returns. Risk-averse investors do not want their wealth to fluctuate wildly.
-
-Risk aversion can be illustrated with a simple example. Which of the following assets do you prefer?
-<ul>
-    <li>Asset A pays $200 or $0 with 50% probability each.</li>
-    <li>Asset B pays $400 or &minus;$200 (i.e. you lose $200) with 50% probability each.</li>
-</ul>
-
-The expected payouts of A and B are:
-\[ \mathbb{E}(A) = 0.5 \times 200 + 0.5 \times 0 = 100 \]
-\[ \mathbb{E}(B) = 0.5 \times 400 + 0.5 \times (-200) = 100 \]
-
-The standard deviation of their payouts are:
-\[ \sigma_A = \sqrt{0.5(200-100)^2 + 0.5(0-100)^2} = 100 \]
-\[ \sigma_A = \sqrt{0.5(400-100)^2 + 0.5(-200-100)^2} = 300 \]
-
-If you are an risk seeker, you may choose asset B, because you can potentially get a higher payout. MPT assumes that investors prefer asset A since both assets have the same expected payout, but asset A has less risk.
-
-<h2>Portfolio</h2>
-
-Suppose we invest some fraction \(w_1, w_2, \dots, w_n\) of our wealth in <em>n</em> risky assets (labelled 1 to <em>n</em>), and the remainder \(w_0\) in a riskless asset such as cash in a bank account.
-
-Clearly \(w_0 + w_1 + \dots + w_n = 1\) since our wealth comprises all those assets.
-
-Let \(R_0, R_1, \dots, R_n\) be the respective asset returns, then our portfolio return is
-\[ R_P = w_0 R_0 + w_1 R_1 + \dots + w_n R_n \]
-
-Alternatively, we can eliminate \(w_0\) to get
-\[ R_P - R_0 = w_1 (R_1 - R_0) + \dots + w_n (R_n - R_0) \]
-
-Our <strong>expected</strong> portfolio return is
-\[ \mathbb{E}(R_P) = w_0 R_0 + w_1 \mathbb{E}(R_1) + \dots + w_n \mathbb{E}(R_n) \]
-
-Note that \( \mathbb{E}(R_0) = R_0 \) since the riskless return is known with certainty, by definition.
-<a name="correlation"></a>
-<h3>Correlation</h3>
-
-Before computing portfolio risk, we need to first understand covariance and correlation. They measure the linear relationship between two random variables.
-
-The covariance of two random variables X and Y is defined as
-\[ \text{Cov}(X, Y) = \mathbb{E} \left[ (X-\mathbb{E}(X)) (Y-\mathbb{E}(Y)) \right] \]
-
-The correlation of X and Y, which is always between &minus;1 and 1, is their covariance after being standardized:
-\[ \text{Corr}(X, Y) = \text{Cov} \left( \frac{X-\mathbb{E}(X)}{\sigma_X}, \frac{Y-\mathbb{E}(Y)}{\sigma_Y} \right) = \frac{\text{Cov}(X, Y)}{\sigma_X \sigma_Y} \]
-
-<h3>Risk</h3>
-
-Now we are ready to compute portfolio risk, as measured by the variance of portfolio returns:
-\[ \text{Var}(R_P) = \text{Var}(w_0 R_0 + w_1 R_1 + \dots + w_n R_n) \]
-
-Recall that \( \text{Var}(X + c) = \text{Var}(X) \) if <em>c</em> is a known constant, so the term \( w_0 R_0 \) involving the riskless return can be omitted. It will be convenient to use <a href="https://www.mathsisfun.com/algebra/sigma-notation.html" target="_blank">sigma notation</a>:
-
-\[ \text{Var}(R_P) = \text{Var} \left( \sum_{k=1}^n w_k R_k \right) \]
-\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k R_k - \mathbb{E} \left( \sum_{k=1}^n w_k R_k \right) \right)^2\right] \]
-\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k \, \left( R_k - \mathbb{E}(R_k) \right) \right)^2\right] \]
-
-So we have a squared sum of <em>n</em> terms. How do we expand it?
-\[ \left( \sum_{k=1}^n u_k \right)^2 = (u_1 + \dots + u_n) \, (u_1 + \dots + u_n) \]
-
-If we expand the brackets on the right hand side, every term has the form \( u_i u_j \) where <em>i</em> and <em>j</em> can be 1, 2, ... , or <em>n</em>.
-\[ \left( \sum_{k=1}^n u_k \right)^2 = \sum_{i=1}^n \sum_{j=1}^n u_i u_j \]
-
-Therefore
-\[ \text{Var}(R_P) = \mathbb{E} \left[ \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, (R_i - \mathbb{E}(R_i)) (R_j - \mathbb{E}(R_j)) \right] \]
-\[ = \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, \text{Cov}(R_i, R_j) \]
-
-The last step arises from the definition of <a href="#correlation">covariance</a>. The only thing left is to express portfolio risk in matrix notation:
-
-\[ \text{Var}(R_P) = \mathbf{w}^T \Sigma \mathbf{w} \]
-where
-\[ \mathbf{w} = \begin{pmatrix} w_1 \\ \vdots \\ w_n \end{pmatrix} \qquad
-\Sigma = \begin{bmatrix}
-\text{Cov}(R_1, R_1) & \text{Cov}(R_1, R_2) & ... & \text{Cov}(R_1, R_n) \\
-\text{Cov}(R_2, R_1) & \text{Cov}(R_2, R_2) & ... & \text{Cov}(R_2, R_n) \\
-\vdots & \vdots & \ddots & \vdots \\
-\text{Cov}(R_n, R_1) & \text{Cov}(R_n, R_2) & ... & \text{Cov}(R_n, R_n)
-\end{bmatrix} \]
-
-<h3>Intuition</h3>
-
-How can we make sense of portfolio risk? Consider a simple case with a riskless asset and only <em>n</em> = 2 risky assets.
-
-\[ \text{Var}(R_P) = w_A^2 \text{Var}(R_A) + w_B^2 \text{Var}(R_B) + 2w_A w_B \text{Cov}(R_A, R_B) \]
-
-Portfolio risk can be reduced by choosing two assets that are negatively correlated. This is the benefit of diversification.
-
-<h2>Mean-Variance Analysis</h2>
-
-We now try to find a portfolio \( \mathbf{w} = (w_1, ..., w_n) \) that minimizes risk and maximizes return.
-
-The chart below has risk (standard deviation of returns) on the horizontal axis and expected return on the vertical axis. The 10 black points represent individual stocks, while each green / blue point is a portfolio of stocks:
-
-<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
-
-Notice that all points (i.e. stocks and portfolios) are enclosed by a hyperbola, known as the <strong>efficient frontier</strong>.
-
-All portfolios on the efficient frontier have the maximum expected return for a given level of risk, if we only consider portfolios of risky stocks. Can we achieve higher returns by including a riskless asset? Yes.
-
-<h3>Capital Market Line</h3>
-
-The black line on the chart is the Capital Market Line (CML). It is <strong>tangent</strong> to the efficient frontier and cuts the vertical axis at the riskfree return. The point of tangency represents the so-called <strong>market portfolio</strong>.
-
-Every point on the CML represents a portfolio comprising the market portfolio and riskless asset in some proportion. Why?
-
-Suppose some fraction <em>w</em> of a CML portfolio is the market portfolio, and the remainder (1 &minus; <em>w</em>) is the riskless asset. Then its expected return is
-
-\[ \mathbb{E} (R_P) = w \mathbb{E} (R_{\text{market}}) + (1-w) R_0 \]
-
-Since there is only <em>n</em> = 1 risky asset, the variance of the CML portfolio return is
-
-\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \]
-
-Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight:
-
-\[ \sigma_P = w \sigma_{\text{market}} \]
-
-This equation can be used to eliminate <em>w</em> in the calculation of expected return:
-
-\[ \mathbb{E} (R_P) = R_0 + \frac{\mathbb{E} (R_{\text{market}}) - R_0}{\sigma_{\text{market}}} \sigma_P \]
-
-This proves that when \( \mathbb{E} (R_P) \) is plotted against \( \sigma_P \), we will obtain a straight line: the CML.
-
-<h3>Portfolio Selection</h3>
-
-Why is the CML significant? For any given level of risk, CML portfolios have a higher return than those on the efficient frontier, so investors should select any of them according to their risk tolerance.
-
-Risk-averse investors may give the riskless asset a larger weight in their portfolio. Risk-seeking investors may borrow money (i.e. sell the riskless asset) to invest >100% of their wealth in the market portfolio.
-
-Regardless of their risk tolerances, all investors should hold the same stocks in the same proportion in the market portfolio. In other words, they should not pick stocks according to their risk tolerance.
-
-<h3>Diversification</h3>
-
-What happens to the efficient frontier and hence the CML if we have only 3 stocks (IBM, GE, and PFE) instead of 10?
-
-<img class="aligncenter size-full wp-image-2155" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown2.png" alt="" width="1184" height="592" />
-
-Since we have fewer stocks to choose from, it's not too surprising that our maximum expected return is lower for any level of risk.
-
-This demonstrates why diversification is often said to be a "free lunch" in investing.
-
-<h2>Summary</h2>
-
-In this chapter we have learnt about the modern portfolio theory. It recommends investors to spread their wealth across many asset classes to maximize returns while minimizing risk. In the next chapter, we will introduce the Capital Asset Pricing Model.
-
-<h2>Algorithm:</h2>
-
-Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=71761bc8ce01e7c81b5ea91f54a82640' ></script>
\ No newline at end of file
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.ipynb
deleted file mode 100644
index 70bfe67..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial12 Modern Portfolio Theory.ipynb	
+++ /dev/null
@@ -1,431 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import quandl\n",
-    "from cvxopt import solvers\n",
-    "from cvxopt import matrix\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.optimize import minimize\n",
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class stock(object):\n",
-    "    def __init__(self,ticker):\n",
-    "        self.ticker = ticker"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "tickers = ['KO','JNJ','PFE','NKE','PG','WMT','MMM','IBM']\n",
-    "stocks = []\n",
-    "leng = len(tickers)\n",
-    "for i in tickers:\n",
-    "    vars()[i] = stock(i)\n",
-    "    stocks.append(vars()[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.027400000000000001"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
-    "rf = (rf.ix[-1][0]/100)\n",
-    "rf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    table = quandl.get('WIKI/%s'%i.ticker)\n",
-    "    i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
-    "    i.mean = np.mean(i.rate)*252\n",
-    "    i.std = np.std(i.rate)*np.sqrt(252)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         mean       std\n",
-      "KO   0.072506  0.188207\n",
-      "JNJ  0.102055  0.165611\n",
-      "PFE  0.080836  0.227700\n",
-      "NKE  0.149412  0.286772\n",
-      "PG   0.052920  0.177927\n",
-      "WMT  0.078416  0.194245\n",
-      "MMM  0.116281  0.221956\n",
-      "IBM  0.053190  0.222997\n"
-     ]
-    }
-   ],
-   "source": [
-    "stock_list = [x.ticker for x in stocks]\n",
-    "rate_list = [x.rate for x in stocks]\n",
-    "mean_list = [x.mean for x in stocks]\n",
-    "std_list = [x.std for x in stocks]\n",
-    "cov_matrix = np.cov(rate_list)\n",
-    "df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
-    "print df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def min_var_generator(rate):\n",
-    "    def target(x, sigma, mean,r):\n",
-    "        sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
-    "        return sr_inv\n",
-    "\n",
-    "    x = np.ones(leng)/leng\n",
-    "    mean = mean_list\n",
-    "    sigma = cov_matrix\n",
-    "    r = rf\n",
-    "    c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
-    "        {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
-    "    bounds = [(-1,1) for i in range(leng)]\n",
-    "    res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
-    "#     return res['x']\n",
-    "    return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 0.20992676,  0.27623188, -0.00295919, -0.10248824,  0.31125431,\n",
-       "         0.23690646, -0.10606759,  0.1771956 ]), 0.1455789711354554)"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min_var_generator(0.06)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
-    "simu_var = []\n",
-    "for i in simu_rate:\n",
-    "    try:\n",
-    "        res = min_var_generator(i)\n",
-    "        simu_var.append(res[1])\n",
-    "    except:\n",
-    "        print i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "rate      0.179200\n",
-       "std       0.240626\n",
-       "sharpe    0.630854\n",
-       "Name: 1518, dtype: float64"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
-    "port_df.head()\n",
-    "port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
-    "opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
-    "opt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Simulation#\n",
-    "monte_rate, monte_std = [],[]\n",
-    "for i in range(100000):\n",
-    "    w = np.random.dirichlet(np.ones(leng),size=1)\n",
-    "    monte_rate.append(np.dot(w,mean_list))\n",
-    "    monte_std.append(np.sqrt(np.dot(np.dot(w,cov_matrix),w.reshape(8,1)))*np.sqrt(252))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simu_df = pd.DataFrame({'x':monte_std,'y':monte_rate})\n",
-    "simu_df['sharpe'] = (simu_df['y'] - rf)/simu_df['x']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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Sy+XSwoULFRUVqod7NVC/nvVVp35bKaKnHM6oQMcEUM5RQAEAAABAJeTxeDRv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y7oq2cekW6qrqSUiJN13RDAaDwfCDYwQog8Fg+BGpq6vj1VdfZeLEiWzatIkOHTrw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\nERERERGRZKUClIiIiIiIiIiIJKv/A907XhlB7lZHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x118f8a990>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
-    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
-    "plt.scatter(0,rf,color = 'r',s = 30)\n",
-    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
-    "plt.annotate('risk-free',(0,rf),size = 15)\n",
-    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
-    "for i in stocks:\n",
-    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
-    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
-    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
-    "plt.xlim(0)\n",
-    "plt.xlabel('Standard Deviation')\n",
-    "plt.ylabel('Expected Return')\n",
-    "plt.axhline(rf,ls = '--')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "###three stocks version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def efficient_frontier(tickers):\n",
-    "    stocks = []\n",
-    "    leng = len(tickers)\n",
-    "    for i in tickers:\n",
-    "        vars()[i] = stock(i)\n",
-    "        stocks.append(vars()[i])\n",
-    "    \n",
-    "    rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
-    "    rf = (rf.ix[-1][0]/100)\n",
-    "    for i in stocks:\n",
-    "        table = quandl.get('WIKI/%s'%i.ticker)\n",
-    "        i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
-    "        i.mean = np.mean(i.rate)*252\n",
-    "        i.std = np.std(i.rate)*np.sqrt(252)\n",
-    "        \n",
-    "    stock_list = [x.ticker for x in stocks]\n",
-    "    rate_list = [x.rate for x in stocks]\n",
-    "    mean_list = [x.mean for x in stocks]\n",
-    "    std_list = [x.std for x in stocks]\n",
-    "    cov_matrix = np.cov(rate_list)\n",
-    "    df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
-    "    print df\n",
-    "    \n",
-    "    def min_var_generator(rate):\n",
-    "        def target(x, sigma, mean,r):\n",
-    "            sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
-    "            return sr_inv\n",
-    "\n",
-    "        x = np.ones(leng)/leng\n",
-    "        mean = mean_list\n",
-    "        sigma = cov_matrix\n",
-    "        r = rf\n",
-    "        c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
-    "            {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
-    "        bounds = [(-1,1) for i in range(leng)]\n",
-    "        res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
-    "        return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))\n",
-    "    \n",
-    "    simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
-    "    simu_var = []\n",
-    "    for i in simu_rate:\n",
-    "        try:\n",
-    "            res = min_var_generator(i)\n",
-    "            simu_var.append(res[1])\n",
-    "        except:\n",
-    "            print i\n",
-    "    \n",
-    "    port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
-    "    port_df.head()\n",
-    "    port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
-    "    opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
-    "    return port_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         mean       std\n",
-      "PG   0.052920  0.177927\n",
-      "IBM  0.053190  0.222997\n",
-      "KO   0.072506  0.188207\n"
-     ]
-    }
-   ],
-   "source": [
-    "new = efficient_frontier(['PG','IBM','KO'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
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Wac+fr/u7XaCISnfIRHSWMcx4AnB6KKAAAAAAoBw6cOCAJkyYoNTUVP3222+6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RAAAg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LQyZ3Tnafw2a4ehIwkhhBAvTn9/CZ5KluC9MSaGDmBIjRs3JiIiAgsLi5c67vPPP2fz5s0o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\nUwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x115569390>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
-    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
-    "plt.scatter(new['std'],new['rate'],c = new['sharpe'],s = 0.5)\n",
-    "plt.scatter(0,rf,color = 'r',s = 30)\n",
-    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
-    "plt.annotate('risk-free',(0,rf),size = 15)\n",
-    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
-    "for i in stocks:\n",
-    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
-    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
-    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
-    "plt.xlim(0)\n",
-    "plt.xlabel('Standard Deviation')\n",
-    "plt.ylabel('Expected Return')\n",
-    "plt.axhline(rf,ls = '--')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.html b/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.html
deleted file mode 100644
index 392b745..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.html	
+++ /dev/null
@@ -1,92 +0,0 @@
-In the financial literature, you may hear terms like the "beta" or "market risk" of an asset. This chapter will explain where these terms come from and how they can be useful.
-
-<h2>Capital Asset Pricing Model (CAPM)</h2>
-
-As we shall see later, the name "Asset Pricing" is a bit misleading because the CAPM tells us the expected return, rather than the price, of an asset.
-
-In the last chapter, we introduced the Capital Market Line (CML) shown in black:
-
-<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
-
-All investors should hold a portfolio on the CML, which is constructed by investing some fraction <em>w</em> of our wealth in the market portfolio and the remainder (1 &minus; <em>w</em>) in the riskless asset. So the return on a CML portfolio is
-\[ R = w R_{\text{market}} + (1-w) R_0 \]
-
-If we let &beta; = w, then the equation above becomes
-\[ R - R_0 = \beta (R_{\text{market}} - R_0) \]
-
-Notice that &beta; is a measure of how sensitive our CML portfolio return is to the market return. Taking expectation on both sides results in the CAPM:
-\[ \mathbb{E}(R) - R_0 = \beta (\mathbb{E} (R_{\text{market}}) - R_0) \]
-
-Taking covariance on both sides instead yields
-\[ \text{Cov} (R - R_0, R_{\text{market}}) = \beta \text{Cov} (R_{\text{market}} - R_0, R_{\text{market}}) \]
-
-Now apply two basic facts about covariance:
-<ul>
-    <li>\( \text{Cov} (X + c, Y) = \text{Cov} (X, Y) \) where \( c \) is constant</li>
-    <li>\( \text{Cov} (X, X) = \text{Var} (X) \)</li>
-</ul>
-
-Hence we obtain
-\[ \beta = \frac {\text{Cov} (R, R_{\text{market}})} {\text{Var} (R_{\text{market}})} \]
-
-<h2>Computing &beta; in Practice</h2>
-
-While the Capital Asset Pricing Model is straightforward, applying it may not be. We first need to choose the timeframe for computing returns: should we use daily, weekly or monthly returns? Then we need to consider the number of data points available for linear regression. For example, if we compute beta using monthly returns in the past 1 year, then we only have 12 data points which is too few.
-
-Furthermore, the &beta; of an asset can change over time. The following plot is the daily rolling beta of GE stock with a 6-month rolling windows:
-
-<img class="aligncenter size-full wp-image-2161" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-3.png" alt="" width="1156" height="558" />
-
-The &beta; of GE ranged from 0.1 to 0.5 approximately. This is why you need to be careful when using &beta;. It makes no sense to talk about &beta; without a timeframe in mind.
-
-The following graph is the rolling p-value of beta. The p-value stays close to zero most of the time. However, during some period it suddenly increased close to 0.1, which corresponds to a 90% confidence interval. This might be caused by some mispricing or market turmoil.
-
-<img class="aligncenter size-full wp-image-2164" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-4.png" alt="" width="1163" height="558" />
-
-<h2>Market-Neutral</h2>
-
-A portfolio is market-neutral if its <strong>&beta; is zero</strong>. In other words, the portfolio's returns are uncorrelated with market returns. We say that it has no "market risk". Some classical market-neutral strategies are pairs trading, beta-hedged equity portfolio and other derivatives strategies.
-
-We have daily returns of Dow 30 stocks from March 2012 to Jan 2015. For each stock, its &beta; on any given day is computed using the past 6 months' returns. As this 6-month window moves forward in time, &beta; will of course change.
-
-Once we know how each stock's &beta; has changed over time, we can ask: which stocks' betas are correlated with each other? The table below shows the correlation between each stock's beta.
-
-<img src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-5.png" alt="" width="1056" height="578" class="aligncenter size-full wp-image-2174" />
-
-How can we construct a market-neutral portfolio? Consider two stocks A and B:
-\[ R_A = R_0 + \beta_A (R_{\text{market}} - R_0) \]
-\[ R_B = R_0 + \beta_B (R_{\text{market}} - R_0) \]
-
-Let's allocate <em>w</em> on stock A and (1 &minus; <em>w</em>) on stock B, then market neutrality means
-\[ w\beta_A + (1-w) \beta_B = 0 \qquad \Rightarrow \qquad
-   w = \frac{\beta_B}{\beta_B - \beta_A} \]
-
-As mentioned earlier, \(\beta_A\) and \(\beta_B\) will change with time, so will \(w\) in a market-neutral portfolio. However we can achieve market neutrality with constant \(w\) so long as \(\beta_A\) and \(\beta_B\) have a linear relationship:
-\[ \beta_A = m\beta_B + c \]
-
-Then eliminate \(\beta_A\) to get
-\[ w = \frac{\beta_B}{(1-m) \beta_B - c} \]
-
-If <em>c</em> &approx; 0 then <em>w</em> is roughly constant:
-\[ w = \frac{1}{1-m} \]
-
-Note: If <em>c</em> is signficant, then we need 3 stocks to get zero net beta. Usually 2 stocks are sufficient to cancel out most of the market risk.
-
-<h3>Example</h3>
-
-The 6-month rolling betas of PG and KO stocks have a correlation of 93%. The linear relationship between their betas is
-\[ \beta_{KO} = -0.0097 + 0.969 \beta_{PG} \]
-
-Therefore the market neutral weights are
-\[ w_{KO} = \frac{1}{1-0.969} = 32.3 \qquad w_{PG} = 1 - w_{KO} = -31.3 \]
-
-We tested this portfolio both in-sample (March 2012 to Jan 2015) and out-of-sample. It achieved a beta of 0.01 and -0.004 respectively. See the backtest below.
-
-<h2>Summary</h2>
-
-This chapter has explained what market risk means in the context of CAPM, and how market risk can be reduced. In the next chapter we will generalize CAPM to multi-factor models, such as the Fama-French models.
-
-<h2>Algorihtm</h2>
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8bac244b346cbd9bc3b39dca76192919'></script>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5c75197259238b36a6cea72832c9e709'></script>
\ No newline at end of file
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb
deleted file mode 100644
index 9990b04..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial13 Capital Asset Pricing Model.ipynb	
+++ /dev/null
@@ -1,325 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from googlefinance import getQuotes\n",
-    "import json\n",
-    "import statsmodels.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats.mstats import normaltest\n",
-    "import time\n",
-    "from cvxopt import matrix\n",
-    "import seaborn as sns\n",
-    "import statsmodels.tsa.stattools as ts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class stock(object):\n",
-    "    def __init__(self,ticker):\n",
-    "        self.ticker = ticker\n",
-    "tickers = [\"MMM\", \"AXP\", \"AAPL\", \"BA\", \"CAT\", \"CVX\", \"CSCO\",\"KO\",\n",
-    "                \"DIS\",\"DD\",\"XOM\",\"GE\",\"GS\",\"HD\",\"IBM\",\"INTC\",\"JPM\",\"MCD\",\n",
-    "                \"MRK\",\"MSFT\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"WMT\"]         \n",
-    "stocks = []\n",
-    "for i in tickers:\n",
-    "    vars()[i] = stock(i)\n",
-    "    stocks.append(vars()[i])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    table = quandl.get('WIKI/%s'%i.ticker,start_date = '2012-03-21',end_date = '2015-01-01')\n",
-    "    i.rate = np.log(table['Adj. Close']).diff().dropna()\n",
-    "    i.mean = np.mean(i.rate)*252\n",
-    "    i.std = np.std(i.rate)*np.sqrt(252)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "spy = quandl.get('LSE/SPY5')\n",
-    "spy = np.log(spy['Last Close']).diff().dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class rolling(object):\n",
-    "    def __init__(self,ticker,series,spy_series):\n",
-    "        self.ticker = ticker\n",
-    "        self.prices = series\n",
-    "        self.spy = spy_series\n",
-    "        self.df = pd.concat([self.prices,self.spy],axis = 1).dropna()\n",
-    "        self.df.columns = ['SPY','%s'%self.ticker]\n",
-    "        self.prices = self.df['%s'%self.ticker]\n",
-    "        self.spy = self.df['SPY']\n",
-    "        \n",
-    "    def roll(self, length):\n",
-    "        df_leng = self.df.shape[0]\n",
-    "        beta, beta_p, inter, inter_p,resid = [],[],[],[],[]\n",
-    "        loop = df_leng - length\n",
-    "        for i in range(loop):\n",
-    "            x = sm.add_constant(self.spy[i:i+length])\n",
-    "            model = sm.OLS(self.prices[i:i+length],x).fit()\n",
-    "            beta.append(model.params[1])\n",
-    "            beta_p.append(model.pvalues[1])\n",
-    "            inter.append(model.params[0])\n",
-    "            inter_p.append(model.pvalues[0])\n",
-    "        beta_df = pd.DataFrame({'beta':beta,'beta_p':beta_p,'inter':inter,'inter_p':inter_p},index = self.df.index[length:])\n",
-    "        self.beta_df = beta_df\n",
-    "        self.mean_beta = np.mean(beta)\n",
-    "        self.std_beta = np.std(beta)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in stocks:\n",
-    "    i.r = rolling(i.ticker, i.rate,spy)\n",
-    "    i.r.roll(21*6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      mean_beta   sd_beta  sd_beta_p\n",
-      "MMM    0.391424  0.106368   0.000174\n",
-      "AXP    0.260624  0.055665   0.000784\n",
-      "AAPL   0.095790  0.041038   0.095875\n",
-      "BA     0.190906  0.070859   0.023826\n",
-      "CAT    0.191012  0.063119   0.019710\n",
-      "CVX    0.280610  0.089215   0.011553\n",
-      "CSCO   0.132116  0.067679   0.115558\n",
-      "KO     0.181293  0.166132   0.309038\n",
-      "DIS    0.254268  0.063585   0.000340\n",
-      "DD     0.247352  0.057332   0.002610\n",
-      "XOM    0.292914  0.150067   0.026876\n",
-      "GE     0.256557  0.075137   0.010253\n",
-      "GS     0.237748  0.029856   0.000318\n",
-      "HD     0.219472  0.061830   0.012097\n",
-      "IBM    0.212634  0.112679   0.055888\n",
-      "INTC   0.152051  0.073508   0.057509\n",
-      "JPM    0.222741  0.057927   0.008795\n",
-      "MCD    0.243713  0.113179   0.189099\n",
-      "MRK    0.170320  0.062014   0.049013\n",
-      "MSFT   0.211149  0.104175   0.008382\n",
-      "NKE    0.115547  0.066427   0.252076\n",
-      "PFE    0.247528  0.092036   0.012432\n",
-      "PG     0.197096  0.159905   0.280305\n",
-      "UTX    0.318099  0.081374   0.000436\n",
-      "UNH    0.135254  0.032307   0.036826\n",
-      "VZ     0.170965  0.087304   0.082649\n",
-      "WMT    0.193440  0.062965   0.062210\n"
-     ]
-    }
-   ],
-   "source": [
-    "tickers = [x.ticker for x in stocks]\n",
-    "mean_betas = [x.r.mean_beta for x in stocks]\n",
-    "sd_betas = [x.r.std_beta for x in stocks]\n",
-    "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n",
-    "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n",
-    "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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K2fj3Ofp0b6SfSQYba13nzc7WHG1K1cZgSP8ANv1dNYLkQWju5UBcWi4J6fk6e4hS07uR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Lc/ZtfJfI9dMYZGLHhMLRGpVeg0R94yYKB0N/CGzkgNLJmn2XDRPiJVUB4a09kcsEvJ2taevt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10Pej0eA8PwGTXC4L6+spK473+kOCkZM1sbOr70AlaurgAUp6Ry5aefuV5WhiAIBM9+B4W5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\nr6xEn5dLfWUFzhHmb4+cz/+DIJPhMWIE/rOv7XI2hAZtJQqP1nu18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yUilc8fEUy4uxOP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f9ifI14EzsZlM/v6wXuVDjV28W2MXWy6zcHkDu3i7CbsYqLGL914PoX9vDwDmLT7Ljn3x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\n1RqB+SuDWf+VeFzS9mNxxN5VMfP5zkTGZ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bLt0A1Wbw3XzvvNnvTq7AaA3NQIB1s5XZ/6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\nGW1fmMiVxb8gaDR49O+DpYc7sbv2YuPrjXNAJzwG9CVi9XrOfjwHYwtzO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qOt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2cAX3+9HkdHWwBefdWfF18corMMR9bu4PZ5TVmMmDoWp5aVy+L4xn2EHw2lMDef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\nwWAwGAwGt/N/S3w2JE4ATzkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11663fbd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cov_matrix = np.corrcoef(beta_list)\n",
-    "cov_df = pd.DataFrame(cov_matrix)\n",
-    "plt.figure(figsize = (20,10))\n",
-    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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v448vrefjt73Eaf/5MN97YAlrtjYYJkmSJEmSBpxh0gAaWpjgv99+DL95/8nc\ncO3xDCnI7ZfnnjdzBEWJGAW5MS6e3YPqmt2cOLGMdAg/e2IFY4cV8C8Xz2BVdT2f/cMrpNIhiXgO\nxTTSlFMIp348qkx65Fv7PmjbcuLpFpakx7GjvhVO+wQUDAMgJ3//YVJ3plQW81jqaIqqX4P6fQea\n90nd5p5XJrVJ5XT93/HUqRW8fc443nzUKB54vYrnVmxl2eZaquuaSfag7e3ulzcwe0wpP732BGaO\nKuGGR5dx5ncf4f1/2U6YE4dNCzLfuyRJkiRJfRDP9gYOR6dNrejX5xUkYnzivGmEQHFe7/6THjt+\nKLGcgB0NrVx+zGg+cvZUrj9jMg8u2gQEzH18OcUbGmmJFZE/6Uw4/j3wzI/hyCthzPEdD1r/IgCL\nw/Fsa2hhfPlQOOvzNP/1S6QLuq46ysTooQU8FzuOgDtgxSNw1NV9et4urY3QvLNnlUlDJ0AsD1LN\nhPsJk9q99fgx3Pb8Gt4x99ld9xKxHI4YXcot7z2RYUWJLt+7Yksdr63fyRcvPYKLZ4/k4tkj2biz\nkd+/sJYfPLiUzRXTGLFuXuZ7lyRJkiSpD6xMOkR88KwpfOisKb1+f3FenFmjopPMzp5RCUA8lsPF\ns0dx8eyRzB4zhOKggWR7q9qFX4fiEXD3xyDZ0vGgxffSUjiCReF4tje03T/5Q1ycM5dY0bBe7w+i\n+VCnnn4e28JiNr3wxz49aw+1VdG1J5VJOTGomA5AOqfrIKjdiRPL+Punz+RX7zuJH77zWL56+ZFc\nPWcsr6zdwbMr9l9lFQV6cOnRo3bdGzWkgE+dP50LZ43gkbpxhBvms3zTTp5efgCGk0uSJEmStBvD\nJO1y6pRyChMx3jR53wqiI0eXUkIjqfYwKX8IXPZ92Pw6PPWD6F5LAyx7iKYpFxOSw472MCkIWN9S\nQFEvq6Z29+Fzp/NI3jmMWHMvPHNDr55x+7y13N42BwqIhm9DzyqTACrbwqQgs3bFaSNKOHN6JVcc\nO4b3nDqR/3jLLHJjAa+2nfDXlZXV9ZQXJRg1pGCf1z5x3jSeb5lM0FLP92/7M9f/6sUDc2qcJEmS\nJEltDJO0yyfPn8b9nzyDwsS+oc+x44ZRHDQS5JV03JxxCcy+Ch77DmxeBMsfhmQjidmXk5+bw5NL\no4qb1lSalmSa4k6e21N58Rj1Z36Fv6dOIPzbF6G5tvs37aa6rpkv372AGx5d1nFzV2VST8OkmQCk\nY91XJnW/CG7zAAAgAElEQVQmLx5j5shSXl23Y7/r1mxrYFxZYaevzR4zhLxJbwKgaMvL1DUneWnN\n/p8nSZIkSVJfGCZpl8JEnAnlRZ2+NmNkCUeUBZSV7TXv6ZLvQKIQHvo6zP8VFJSRP/UsrjlpPH96\neT1rtzVQ35wE6JfKJICLjx7LbelzCcIUVO1/8PTPHl/BxT94nE///mVuemIF375vMU2taVZva6Cp\nNRUtaq9M6kmbG8DwWQC0xDoPejJx1NghvLpuJ2EYdrlmzbYGxncRJgG8/cKz2BEW8Z7cB5mSU8UT\nS7f0ej+SJEmSJHXHMEkZK0g3kJNfuufNogo4+cOw5F5Y+gCc8lGI5XL9mZOJBQE/fWw5dW1hUnF+\n/4RJw0vzSYw5Nvqm6tUu17Uk09z42HJ2Nrby9PJqvnHvIu6cv46K4gRhCMs210ULa6sgJw6FPRwQ\nPuMS/nfEN1idO7WXPwkcPWYItU1JVm9t6PT11lSaDTuamFDedZh03IQyHp36BabGt3Jrwfd4/I0u\nwqSaDbBjDewnuJIkSZIkqTuGScpccy3s3ubW7uQPQqIY8ofCSdcD0YDoq+eM5Q/z1rF8Sz3Q+5Pm\nOnPSUbOoDkupWz2/yzWPvbGFbfUtfPPK2Tz3b+fzwr+fz2/efzL/d90cAJZubmuRq9sERcMhp4f/\nHHJizM8/mdx47/8ZHTV2CAAvr+28NW3jjiZS6bDLNrd2/3DdJ0ic+3lGpTawYf0a1m3fK5xaPx/+\n5wj4wVHw/Nxe71eSJEmSJMMkZSad7jpMKiyDt/4Mrv457Fa59OGzppAKQ77/9zeA/mtzAzh1WgUL\n0xNoXfdyl2v+2FaFdMa06HS6ypI8TptawdFjh5AbC3hjUx0km6PKpJ4O326TTIXEc4JevRdgxogS\nRg/J59fPru601W3NtigU2l+b2y6jo2qt4+Kr+NZ9i/Z8bfXT0bWgDFY92ev9SpIkSZJkmKTMtNYD\nYedhEsDMN8PU8/e4Na6skH84dsyuqpvivFi/bWf68BKWxyZTUrMMki37vF7XnOShxZu57OjR5Mb2\n/GueG8thUkURU5bcBN8eB+te6Pm8pDYtqfQ+z++JeCyHD589hRdXb+eZ5Vv3eb1HYdLIo4GA903e\nyX2vVfHk0uqO1za8BKVjYPLZsKHrAE6SJEmSpO4YJikz7aemdRUmdeEj50whaCvcKc7L7bft5OQE\npEYcTZwk4aZ9h3A/tmQLLck0l8zuPCS6Lv9Jrt7+M0i3QnNNHyqT+hYmAbxtzjgqS/L49bOr93lt\n9bZ6ErEcRpTmd/+g/FIon8qJeWuYUF7If9yzgJZkOnpt48sw+rioemnnGqjfN7iSJEmSJCkThknK\nzNbl0bWHQ6qnVBbz5qNGAVDUj5VJACUzz6E1jFH7wu/2ee2vr1dRXpRgzsSyfd/Y2sSV23/BvPR0\nWi75fnSvl5VJyXRIPNb7NjeA/NwYp04p56U1e85NqmtO8uTSasaWFRDLtJVu9LHEql7hy5fNYvmW\nen759Cr+9Owi2LqM1MhjokAJYONLfdqzJEmSJOnwZZikzLx4C+QNgann9fit//bmI/jMBdMZM7Sg\nX7d0/KzpPJg+nsTC2/dodWtOpnhk8WbOP2JE5yHMi7+guGUL/518G0tGXg5nfBZmv7VXe2hJpon3\ndHB3J44dN5SqmiaqdjYBsLWumWvmPsviqlo+ff70zB80+nioWc95uQs4d+ZwfvjQUv7ywP0ArMqb\nDqOOidZtMEySJEmSJPWOYZK6V7cFFt4Nx7wTEkU9fvuYoQV8/LxpBEHfKnj2NnV4MX9NXEh+y3ZY\nct+u+6uqG6hrTnLq1E6qqOb/Gv72RRrGns4z6Vm8saUBzvsSVM7o1R6S6ZBEvO8/1zHjhgLRqW7r\ntjfwtp8+wxubapl73Qm85ZjRmT/o2HfBiNnw+2v55vG1tCTTzGp9HYBnGsZC/hAom+LcJEmSJElS\nrxkmqXuL7o5mC53w3mzvZA9BEBCfdh5VlBPO//Wu++t3REOrxw7bbWh1Og0PfhXu+RhMOpPEu35D\nIhbjjc21fdpDMtU/lUmzRpWSGwu4++X1XH3jM1TXNXPr+0/mvCN6OMupYChcdxeUjmHUve9h7rlp\nPlj8JPNzjuKJDW1rRsyCLYv7vGdJkiRJ0uHJMEndWzcPiiph+BHZ3sk+Tp02gt8nz4TlD8GOtQCs\n394IwNhhbW11rU1w5z/Bk/8Dx78H3nU78cKhTK4sYummuj59fmuq7zOTIJqbdMSoUu5fUEU6DPn9\nB0/hxM7mPWWieDi8+24oGMbZT72HoqYqXhrzTuat2s7STbUky6bDtpWQbO7zviVJkiRJhx/DJHVv\n3TwYMwf6uU2tP5w+rYI/pM6Ovnn5twCs29FIIpZDZXFedGrZr66A1/8I538F3vJDiEWnyk0bUcIb\nm/pWmdSaSpPo42lu7S49ahRHji7ljg+dyhGjSvv2sCFj4D33RCFg2WSKZl/K1voWLvj+4zyxsxzC\nVMdQdUmSJEmSesAwSfvXuAO2LoWxJ2R7J50aUZpPQeUkXs87Fl66FdJp1m9vZPTQfHJqN8DNF0XD\npq/+BZz+6T0CsenDi1m3vZH65uSuew0tSZ5eVk0Yhhl9fn+c5tbug2dN4d5PnMH48sLuF2di2ET4\nyNPwvge44MjRXHTkCOI5AcvDsdHr1Uv653MkSZIkSYcVwyTt34b50XXM4AyTAE6bWsHPG8+AnWtg\n5aOs39HIxKE5UUVSbVU0Q6iT09qmjywB4LX1OwFYu62Bt97wNO+66Tl++NDSXesWV9XwlXtepzWV\n3ucZrf00M+mAKRgGxcMpL87j/66bw9DCBKuDMUAAWwyTJEmSJEk9N4h/C9agsO7F6Dr6+OzuYz9O\nn1rBvS0n0JoYCvN/xfrtjVzbckdUUfWOX8HE07p8X2l+nF8+vYqnllXzlh8/yYYdjZw9o5IfPLiU\n21+IZjD99NHl3PL0Ku58cd0+z2hNpUnED55/RiX5cXa0xmDYBMMkSZIkSVKvxLO9AQ1yW5fBkHHR\nKWGD1MmTy0jlJHil/GJOWHQHU5pmcU7yt3D0O2HKuV2+rygvznWnTOCGR5fzt4WbmFxRxM/ePYcx\nwwp43y0v8K93vUZpQZy/LdwEwP8+tJQrjx9DXjy26xnJVEg8Z/DNkupKcV6cuqZWqJhhmCRJkiRJ\n6pWDp6RC2dG0I2qVGsRK8nM5btxQftp8EYQhN+d+lzAnFy74Wrfvfc+pEynNz+X8I4Zz10dPY2JF\nEbmxHG689gRmjCjhQ7fOp6ElxcfOmcqGnU387vm1u94bhmHbzKSD559RcV6cuuYkVE6PgsJ0Kttb\nkiRJkiQdZA6e34KVHY07BnVVUrvTplbw8MY8Noy7jIKghY1Hvh9KRnT7vuEl+Tz/7+fxf9fNoTiv\no1CvOC/OLf94ImOGFjBmaAGfvmA6J08q48ePLKOxJQpgkuloSHfuwVSZlB+ntikJlTMh1QzbV2V7\nS5IkSZKkg4xhkvavaSfkD8n2Lrp1+rQK0iF8fttl3JY6j4KzPp3xe3dvW9vd8NJ87v3E6fzhQ6cQ\nywn4zIUz2FLbzK+eWQWwayB37sE0M6m9MqliRnTDVjdJkiRJUg8dPL8FKzuadkD+4K9MOnbcUIoS\nMZ6sLuK5I79ERXl5vzx3aGGC0UMLADhpUhlnTq/kp48tp7apldZUVJl0UM1Myt+tzQ2g2jBJkiRJ\nktQzhknav4OkMik3lsPJk6MA6fozpxywz/nshdPZ3tDKzU+uItlemXSwzUxqShLmlULJKCuTJEmS\nJEk9dvD8FqyBl2yB1oaDYmYSwCfOm8ZXLz+SWaNLD9hnHD12KGdOr+TO+et2VSYdVGFSfpxkOqQ5\nmYZKT3STJEmSJPXcwfNbsAZe087oehC0uUHU6vaeUyce8M85d0Yla7Y1sLK6HoB47OBpcyvJzwWI\nhnBXzIDqNyAMs7wrSZIkSdLBxDDpcFK/tWfBQdOO6HqQhEkD5fRpFQA8+sZmAHIPpjCp7cS6aG7S\nDGipg5r1Wd6VJEmSJOlgYph0uNj4Cnx3Mrz6+8zf09gWJh0kbW4DZUplMSNK83h08RbgIGtzawuT\naptaozAJbHWTJEmSJPXIwfNbsPrm9bui65bFmb9nV5vb4B/APZCCIOC0qRUs2VQLQDzn4PlnVJzf\nVpnUlITKmdFNwyRJkiRJUg8cPL8Fq2/WPh9dS8dk/h7b3Lp0+tSKXV8fTG1uuyqTmpNQVAEFZVBt\nmCRJkiRJypxh0uEgnYZ1L0RfJ5szf9+uMMnKpL2dtluYFD+I2txKdq9Mgqg6ycokSZIkSVIPHDy/\nBav3Ni+EVEv0dbIp8/c1GiZ1ZURpPtOGFwMHZ2VSXXN7mDQ9an30RDdJkiRJUoYMkw4Ha57p+LpH\nlUk7IZ4Pufn9v6dDQHt10kE1gDt/7zBpJjRuh/rqLO5KkiRJknQwOXh+C1bvbVoQzT2K5fWsMqlp\nh/OS9uOCWSMIAqgozsv2VjKWF4+RiOVQ297mVjE9ujo3SZIkSZKUIcOkw0HVAhh5VFRh1N7ulonG\nHVBgmNSV06ZW8Py/nc+kiqJsb6VHivPj1DW3Rt+MOBKCGNz/Bdj4auYPWXgP3PE+WPGYLXKSJEmS\ndJiJZ3sDOsDSqWhm0vHviQYt96gyaafzkrpRWXLwVCW1K86LdwzgLhkJ77gV/vIp+Nk5MPp42LYc\n8kqhcgaMmB0FkSOPgpwYPPkDmHAa3P+5qD1uwZ3Rmjd9GI56O8QT2f3hJEmSJEkHnGHSoW7bSmht\ngJGzYfG9PT/NrXjkgdubsqI4L05Ne5gEMPPNMOEU+Pt/RC2RMy+F5lrYvBiW/h3C1J4PePEXUTXT\n9Y9GVW/P3gh3fxS2rYDzvjyQP4okSZIkKQsMkw51m16LriNmQzyv5wO4K2YcmH0payZXFvHi6u17\n3iwYBpf/776LW5tgy6IoNKrZALPfCs/PhbLJMPq46M9x18INp8DmRQPzA0iSJEmSsqpfwqQgCC4G\nfgjEgJvCMPzPLtZdBdwBnBiG4bz++Gx1Y/XTURVJ5cyeh0nOTDokzZkwjL+8upH1OxoZM7Rg/4tz\n8ztCo3Zv/u6ea4IAiiuhYVv/b1aSJEmSNOj0eQB3EAQx4CfAJcAs4JogCGZ1sq4E+CTwXF8/Uxl6\n6GtRFckRb4lCgXgPTnNLp6G5xplJh6A5E8sAmLeqH8OfwnJo2Np/z5MkSZIkDVr9cZrbScCyMAxX\nhGHYAvwOuKKTdV8H/gvowQRo9dq8X8AT/x0N3r7q59G9eA9Oc2uphTAN+VYmHWpmjiyhKBFj3qrt\n3S/OlGGSJEmSJB02+iNMGgOs3e37dW33dgmC4HhgXBiG9+7vQUEQXB8EwbwgCOZt2bKlH7Z2mFr1\nFNz3WZh6Plz2fYi1dTP2pDKpaWd0tc3tkBOP5XDc+GHM23tuUl8Ulkenu6VT3a+VJEmSJB3U+iNM\n2q8gCHKA/wE+093aMAznhmE4JwzDOZWVlQd6a4emHWvg9utg2KSoIikn1vFarAdhUuOO6Gqb2yFp\nzsRhLK6qoaaptX8eWFgOhB1/byRJkiRJh6z+CJPWA+N2+35s2712JcBs4NEgCFYBbwLuCYJgTj98\ntnbXUg+3vQtSSbjmtn2riuJ5kMywza2pPUyyMulQNGdCGWEIL63pp/CnsDy62uomSZIkSYe8/giT\nXgCmBUEwKQiCBPBO4J72F8Mw3BmGYUUYhhPDMJwIPAtc7mluB8CzN8KmBXD1zVAxbd/X4/k9b3Oz\nMumQdOz4oeQE8GJ/DeEuGBZdDZMkSZIk6ZDX5zApDMMk8DHgAWARcHsYhq8HQfC1IAgu7+vz1QPV\nb8CQcTDt/M5fjycg2ZzZs9rblZyZdEgqzosza3QpL/TXEO69K5PS6f55riRJkiRp0OmXmUlhGN4X\nhuH0MAynhGH4zbZ7Xw7D8J5O1p5tVdIBsmMtDB3X9etWJmk3cyaU8fLaHbSm+iH42T1M2r4avjUK\n1vnPXJIkSZIORQd8ALcG0M51MGRs16/H8yDVg5lJQQ4kSvpnbxp0TpgwjMbWFIs21vT9Ye1hUuM2\nWPdCFFquebbvz5UkSZIkDTqGSQeD5jp4+bdR5VFXUkmoWR+1uXWl/TS3MOz+M5t2RlVJOf4VOVTN\nmRjNOeqXVrdEIcQLosqkzQuje9Vv9P25kiRJkqRBx6TgYPDwN+BPH4YfHAVL/tr5mtqNEKa6b3ML\n05BOdv+ZjTtscTvEjRpSwJihBby4up+GcBeWQ8M22Lwo+n7rss7XrZ8Pr98FdVv653MlSZIkSQPK\nMGmw274KXrgJjnwrlE2Cx7/TeWXRzraqpe7a3CCzIdxNOyDf4duHujkThzFv1XbCTKrVulNY1n1l\nUhjC76+DP7wXbjy1YzbX7rathFuvhh1r+r4nSZIkSVK/M0wa7J78AeTE4KJvwps+AutfhLXPd7we\nhnD7u+HZG6Lvh4zv+lnx/OiaUZi008qkw8CcCcPYXNvMnfPXs3FnY98eVlgetWJuXx0FkfVbOk4F\nbFf9BtSsg2OvhfrN8MwNe77e2hT9fV7292j2kiRJkiRp0DFMGsySzVE70KwroHQ0HHNNFPA8+5OO\nNQ1bYeHdsOjP0ff7rUxKtD03gxPdGndAgZVJh7o5E8sA+OwfXuF7D/RxxtHQcbD5dSCEGW+O7u3d\n6rb8keh61ufgiLfAMz+GR77d0Rr3189D1avR1w391H4nSZIkSepXhkmD2bKHonazo94WfZ9XDCe8\nNwqOtq+O7lUv7VhfWBENQu5Ke2VSysokRWaOLOE7Vx3NhPJCNtdmEDLuzzn/3jEAftbl0XXvVrcV\nj8CwSTBsIlz4TRh9HDz2X3DDm+CHx8CLt8ApH4vWGiZJkiRJ0qBkmJRtzbXwp4/A4vv2fe3V30Wt\nQ5PP7rh30vVAAM/Pjb7f2hYmxfP3X5UEzkzSPoIg4O0njmNqZTHb6lv69rCSkXDdXXDul2Dq+dHp\ngX/9V7jjfdFphEsfhJVPwJRzovXDJsB7/wKfWQJv/l4URM26As7/KuSVQqNhkiRJkiQNRvFsb+Cw\n1toEv74ymg2z8B748JNRxQbAuheje6d9AmK5He8ZMhaO/AeY/ys4+wtRZVIsAW//9Z7rOhNrD5O6\nqUBpbYrW2OZ22BhWlGDhxpq+P6hiGpz52ejra26D1+6AZQ/Cgjuje0PGw0kf3PM9JSPgpA9Ef9oV\nDLMySZIkSZIGKcOkbFr+cBQknfsleOqH8KM5UDIKSkdBzQYoHgFnfHbf973pI9Ev5y/dGs2kKZsM\n0y/s/vMyrUxqP2HLNrfDRnlRgq31LYRhSBAE/fPQqedFf9Jp2PQabF4MMy+N2jW7U1gezQOTJEmS\nJA06hknZtOqJqD3t1I/D5HNg0T1QuzH6k1cShUz5pfu+b+wcGHsSPHtjdNLb8FmZfV6mp7ntCpOs\nTDpclBUlaEmmqW9JUZzXz/+3kJMDo46J/mSqsMwwSZIkSZIGKcOkbFr1BIw7KaoYGntC9CdTZ3wG\nbntH9PWsKzJ7T8ZhUttx7oZJh41hRdFJf9vrW/o/TOqNgrI9h8tLkiRJkgYNB3BnS8M2qFoAE8/o\n3ftnXAxHtJ2YVT41s/fEo8Cg25lJjW1hkjOTDhvlbWHS1r4O4e4vheXOTJIkSZKkQWoQlCAchsIQ\nXvkdEPY+TILoBKx4Pkw5L7P17ZVJqW4CA2cmHXbK2sKkbfUZnPQ3EArLoKUWki0dIagkSZIkaVAw\nTBoIW5fDfZ+Fxu3QXAfNtVBXFc09Gjun988tGQFX/Szz9fEMT3Ozze2w0xEmtWZ5J20KhkXXxu3R\n33NJkiRJ0qBhmDQQXrgJVj4Bk8+GYRMhUQQjj4E574PYAP4niPU0TLIy6XAxKCuTIBrCbZgkSZIk\nSYOKYdKBlk7Bgj/C9Ivgnb/J7l52VSZ10+bWuANyC20vOowU58VJxHIG18wkgEbnJkmSJEnSYOMA\n7gNt9VNRS9vsq7K9k91Oc+uuMmmnLW6HmSAIKCtKsH2whEkF7ZVJhkmSJEmSNNgYJh1or90BiWKY\nfnG2dwKx9tPcumllatphi9thaFhRgm2DJUzavc1NkiRJkjSoGCYdaGd+Dq76OSQKs70TyMmJAqXu\nKpMad0CBlUmHm/KixOBpc2uvTHryf+C2d0WhbEsDtNTDzy+CB7/af5+VTvXfsyRJkiTpMODMpANt\n6Ljoz2ARz4dUN4FB004oHT0w+9GgUVmSx8I3amhsSVGQiGV3M4lCGH08tDbAhpdgyb1Rhd+QcbBl\nEQRB/3zOznXw4xPhmt/B5LP655mSJEmSdIizMulwUzwcFtwJq5/pek3TDmcmHYauOWk82+pbuPGx\n5dneSuT6R+Cjz8GnX4f3/AVmvxUaqqF4ZBQC9YflD0eB1drn+ud5kiRJknQYMEw63Lztl5Aoglsu\nhWd+AmG475qmnba5HYZOmlTGW44Zzf89tpx12xuyvZ0OOTkw6Qy4/EfwuWVw3P+Dmg3905628ono\nWv1G358lSZIkSYcJw6TDzcjZcP2jMOMSeODf4PZ3Q1NNx+vpdPS9A7gPS/96yUyCAL5136Jsb6Vr\npWMgTEHdpr49JwxhlWGSJEmSJPWUYdLhKH8IvONWuPAbsPhemHs2bHo9eq25BghtcztMjR5awEfO\nnsp9r1Xx0KI+hjUHypC2GWSL/gxzz4GGbb17ztblULsx+rtevTQKUiE67fDR/4LvTIGXf9s/e5Yk\nSZKkQ4hh0uEqCODUj8N7/gwtdfCz82Dh3dG8JLAy6TB2/ZmTmTWqlE/9/mVWbKnL9nb2NWRMdH3m\nJ7BhPiy5r+u1YRi1w6WSkGyB1qboVLimnfDQV6I1x10bzU2q3QCN2+HXb4VHvxV9vfLxA/7jSJIk\nSdLBxjDpcDfxNPjgE1H7210fhqoF0X1nJh228nNjzH33CeTGcvjAr+ZR29Sa7S3tqbQtTNqxOrou\n+nPn6za8DHPPgq+VwdfL4RuV8M0R8K1R8J/jo/dd9C2YfnG0funf4ecXwrrn4a03wcTTbX+TJEmS\npE7Es70BDQIlI+Dqm+Enb4J7PxPdszLpsDZ2WCE/edfxXPvz5/j0719m7nVzyMkJsr2tSP4QSJRA\nSy0EMVj+CNRWQU4upFshnh8FTTdfFLWwnfkvEEtE1XhBTsd1xJEw9XyobWvn+8unomdfd1cUJK19\nDl69PapuCgbJzy5JkiRJg4BhkiJDx8Ol/w0PfiU6er18WrZ3pCw7ZUo5X75sFv9xz+t8/8E3+MyF\nM7K9pUgQRK1uWxbD8dfBi7fAf++2tyAnCpsKK6Jh88WV+39e8fDo73w8Af/vDqhse1bFdGjeCXWb\no9lKC+6E0z8NhWUH6AeTJEmSpIODYZI6HHtN9Edq8+5TJrBg/U5+9PAyxg0rZFNNE2fPGM5RY7Nc\nuVbaFiad+gkYc0I0AymWgJw47FwLKx6DS7/XfZAEUTj1/gejqqT80o77FW2B6sNfiwZxh2lY9hC8\n+09RACVJkiRJhynDJEldCoKAb1w5m6Wb6/iXO18F4EePLOOoMUMoTMS4+b0nkhvLwui1yplRmFQ2\nGcqn9P15Q8fte69ienR96dYosDrtU/DH6+GWS+Hd90DpqL5/riRJkiQdhBzALWm/8uIx5l53Au89\ndSK/ff/JnD29krqmJE8srea3z63JzqbO/Xf4wMMHdpZR6WjILYq+PvNzMOtyuPZOqNkAv7gEdqw9\ncJ8tSZIkSYNYEIZhtvfQqTlz5oTz5s3L9jYkdSIMQ679+XO8vqGGRz97NkMLE9ne0oHxs/OiFrqP\nPg85bdn72hfg1qugcBi8/2EoKs/uHiVJkiSpB4IgeDEMwzl9eYaVSZJ6LAgCvnTZLGoaW/nBg0uz\nvZ0D56qfwXV/7AiSAMad2FahtBH+8B5Ip7O3P0mSJEnKAsMkSb0yc2Qp15w0nl8/u5plm2uzvZ0D\no2xydNLh3sadCOf/B6x6ArYewmGaJEmSJHXCMElSr/3zBdMpTMT4xr2Lsr2VfpNKh9z/2kZaU91U\nHI07ObpuXX7gNyVJkiRJg4hhkqReKy/O45PnTePRJVt4ZMnmbG+nX9z2/Bo+/Jv53PLUqv0vLJsc\nXbetOOB7kiRJkqTBxDBJUp+8+5SJlBcl+PPLG7K9lT5rTqa44ZFlANz42HLqmpNdLy4sg/yhsM3K\nJEmSJEmHF8MkSf+fvfsOk6o83zj+PdO29w4svSOgoKIiltglaqKJLRpjS7MlJiYmMc10TfKLBZNY\nEhONxo69S2wIgtKrdNhdFnaX7WXa+f3xzmyBrbOz7M5yf66La3ZnzpzzrsDI3PM8z9srHpeDIekJ\nVNR7+3spvfb0J7sormrk+6ePp6LOy1NLd3b+hKwxqkwSEREREZFDjsIkEem19EQ3++p9/b2MXvH6\ng9y3YDNHDE/nupPHkp0cx/qSLgaLZ46GcoVJIiIiIiJyaFGYJCK9lpHooTLGK5Oe/mQXRZUNfOfU\n8ViWRWFmAjv31R9wnG3brC6qYl1JNWSOgaqd4GvshxWLiIiIiIj0D1d/L0BEYl9Gopt9dbEbJnn9\nQeYt2MQRw9M5YVw2AMMzE/l0x77mY8prm5i/vJinlu5k/e4ahqYn8OHZYwAbKrdDzoR+Wr2IiIiI\niMjBpTBJRHotPdFDdaMffyCIyxlbBY9vrytlVVEVRZUN/OaLh2FZFgCFGYm8tLIEfyDI4q0VXPnw\nErz+INOHpXHM6Ew+3lpBIH0UToDyTQqTRERERETkkKEwSUR6LSPRDUBVg4+s5Lh+Xk33bSyt4ep/\nLQXg8MJ0Thyf0/xYYWYCgaDNlrI6bn12JcPSE7jvshlMzE/lkUXbWbSlgvKk0eRaDihZARPn9teP\nISIiIiIiclApTBKRXktP9ACwrz62wqQte+sAuP7ksZw/Y2hzVRJAYWYiAL94YQ07Kxp47NpZTMxP\nBfz2H70AACAASURBVCAvxfyMpQ0ucnMmQdEnB3nlIiIiIiIi/Se2+lFEZEBKD1UmxdoQ7h0VJky6\n9oTRjM5JbvNYYYYJkxZuLue4MVkcNya7+bG81HgASqsbYegMKPoUbPsgrVpERERERKR/KUwSkV7L\naFWZFEu2l9eTnugmLcF9wGMFafE4HaZS6fwZw9o8Fg6T9tQ0wdCZ0FAB+7b1+XpFREREREQGAoVJ\nItJrLWFSrFUm1TM81M62P5fTwdD0BOLdDs48LL/NY9nJHiwrXJk009ypVjcRERERETlEaGaSiPRa\nelKstrnVM3VoWoePn3f4EByWRXJc25dKl9NBVlIce2oaIXcSuOKheBlM/VJfL1lERERERKTfKUwS\nkV5LiXPhclhUxlCbmz8QpGhfA5+fVtDhMd87fUKHj+WlxlFa3QRON2SOgYotfbHMFv4mePQCqCmB\nw78Cc25u+/hbv4BgAE7/Vd+uQ0REREREDnlqcxORXrMsi/REd0zNTCqpasQftDtsc+tKXmq8aXMD\nyBzV92FSdRFse9+ESm//Ej6a1+qxElh4Dyz9JwT8fbsOERERERE55ClMEpGoSE/0xFSb2/byegCG\nZyZF9PzmyiSAzNFQsRWCwWgt70Bes15Oux0mnwev/xiWP27uW/oQBP3grYGSFX23BhERERERERQm\niUiUZCS6Y2oA9/zlRTgsGJMbWZiUmxJPeV0T/kDQVCYFmqCmuGcnsW0TQtl218f6GsxtXAqc/wCM\nOgGevw4W/RU+fgAKZ5nHt77bszWIiIiIiIj0kMIkEYkKU5kUG21uL6wo5ulPdvHtk8aSmxIf0Tly\nUuJMFlTnNZVJ0PNWt4V3w92Hw7oXuj7WV2du3QngioOLH4P8qfDarWYA+Bf+CjmTTCuciIiIiIhI\nH1KYJCJRkZXkobyu88qkV1eVUF7bdJBW1L73Nu7l+0+uYOaIDG46dVzE5/G4zMunNxDsWZhUvhkW\n3w+PXQRv/szc99mbXT8v3ObmDs14ikuBrzwNR38DvvYyZI2BUXNgxyIzV0lERERERKSPKEwSkajI\nSvZQUeclGGy/Zeud9aV86z+f8pe3PjvIK2vx8dYKvv7IUsbkJvOPK47C7Yz8JdDlsAAIBG1IHQpO\nT+dhUl0Z/HU23DMDXr0F9q6HY6+Hsad1r5rIFwqTPK3a8pJz4Ow7IHus+X7MKea47R9G+FOJiIiI\niIh0TWGSiERFVlIcgaBNVcOBrW5N/gC3v7gWgBdXFuP19+Gg6g4s31nJVQ8vYWh6Ao9cfTRpie5e\nnc8ZCpP8QRscTsgYaeYfdeSV70PZRjjzD3DDp3DTCjjjNzD2FNi3DSp3dH7BcJjkTuj4mFEnmJa3\njW/06GcRERERERHpCYVJIhIVWckeAMrrDmyxmvfOJraV13PV7FFU1vv40bOruOO19aaqp498umMf\nNY0m2FpXUs0V//iYzCQP/7nmGLKT43p9fpfDvHw2/wz502DDq2Yg9v4Dtdc+D2uegxN/CMd807Sk\nhY2cY263dlGdFB7A7e5kYLgn0QRKG1/r3lBvERERERGRCChMEpGoCAc0ZbVt5yatLqpi3v82c/6M\nofz47IlkJ3t45tNd3Pe/zfz42VXUe/1RX8uji7Zz/n0L+e4TK9i8t5bLH1pMosfJf66ZRX5aZAO3\n9+dyhiqTAqHQ5uw7YeypZiD2E5dBwz5zf105vPw9KDgcZn/nwBPlToa4NCha2vkFva0GcHdm3Omw\nb6uZzSQiIiIiItIHXP29ABEZHMKVSWW1Tby4opjTJufhsCxueXolmUkefvb5ybicDh6+8mi8gSBv\nrytl3oLNvLK6hAtmDOOyY0YwNje51+t4+pNd3DZ/Nbkpcby1rpRPtlfgdFg8es0sCjMTe33+sDYz\nkwASM+GSx+GjefDWz+HvJ5gd1z74CzRUwlefB2c7L7kOh6lU6qxFDkKVSVbXYdLwY8zt7pUts5RE\nRERERESiSGGSiERFVpKpTHpzbSnPLy/mljMm4A/YrCup5v7LZ5KeaMKmw4amATBjeAYnTcjl0UXb\n+c/i7Ty8cBufn1bAXy46HFcEg7EbfQEe+mArf3pjA8ePzWbepTM4++73qW3y89jVsxiT0/ugqrWW\nmUmt5j9ZFhx3PRTOgie/Cv84E7y1cPJPIG9KxyfLHA27lnR+QV+92cnNsjo/LnMMYEFZ/w06FxER\nERGRwU1hkohERUaiG8uCRVvKAbj/vS3UNfk5d/oQTp+S3+5zjhqZyVEjM/np5yfzjw+2ct//NpOZ\n5OH28w7r9nVt2+b1Nbv5zSvr2FnRwJlT8vnzRdNJ9Lh46pvHYllQkNZFNU8EDpiZ1FrhUXDly/DP\nuabq6Pjvdn6yzFGw5lnwe8Hlaf8YX33XVUlg5ialF0LZhq6PFRERERERiYDCJBGJCpfTQUaih9Jq\nM4C7qsFHdrKHX5zbSUVOSHZyHD84cyL+oM39721hTE4yVxw3ssvn+QJBrvnXUt7duJcJeSn855pZ\nzB6b3fz4kPToh0hhbXZza0/maLhhKWCBs4ud4zJHgx2Eqp1th3O35q03QVF3ZI83O8eJiIiIiIj0\ngaiESZZlnQncBTiBB23b/v1+j38TuA4IALXA123bXhuNa4vIwJGV5KGizsvE/BTOnlrAMaOzyEzq\noNKmHT88cyJb9tbxyxfXMGt0JhPzUzs9fm1xNe9u3Mv1J4/lO6eOi6g9LlLhAdyd7kjn6WTntdYy\nRpnbii0dh0m+OtPm1h3ZE2DbhxAMmplMIiIiIiIiUdTrdxmWZTmBecBZwGTgEsuyJu932GO2bU+1\nbftw4A7gz729rogMPOEh3GNyk7nxlHEcPSqzR893Oix+dPZEgjasK6nu8vht5WaHs3MPH3JQgyTo\nRmVST2SONredDeH2NfQgTBoH/gao3tX7tYmIiIiIiOwnGu++jgY22ba9xbZtL/Bf4LzWB9i23fpd\nYRIQhXdfIjLQZCWbIdxjsrtZkdOOnBRzjrIab5fHbi+vB2B4FHdp666W3dyCXRzZDcm54E4ylUkd\n8dZ3v9Ipe7y5VaubiIiIiIj0gWiESUOBna2+3xW6rw3Lsq6zLGszpjLpxihcV0QGmOyklsqkSKXE\nufA4HZTVNnV57LbyOgrS4ol3OyO+XqTClUm+QBSyccsyQ7g7C5O6O4AbWoVJ2tFNRERERESi76D1\nhdi2Pc+27THAD4Hb2jvGsqyvW5a11LKspXv37j1YSxORKMkOVyblRB4mWZZFdrKHvd0Ik7aX1zMi\n6+BXJUEXu7lFIncSlKwAu4Pz+eq73+aWlA3x6apMEhERERGRPhGNMKkIKGz1/bDQfR35L/CF9h6w\nbft+27aPtG37yJycnCgsTUQOppkjM5g2LI2xvahMAshOiaO8tjttbnWMzIq8pa43ojozCWDEbKjd\nDeWb23+8JzOTLCu0o5sqk0REREREJPqiESYtAcZZljXKsiwPcDHwQusDLMsa1+rbuYDe4YgMQseN\nyeaF64/vddtZdnJcl21uNY0+ymq9jOinMCmqM5MARs4xt9veb/9xbx14elCFlT1elUkiIiIiItIn\neh0m2bbtB64HXgfWAU/atr3GsqzbLcs6N3TY9ZZlrbEsazlwM3BFb68rIoNXVpKnyzApPHx7ZD+1\nuTVXJkVjZhJA1hhIzoPtH7b/eE8qk8Ds6FZbCg2V0VmfiIiIiIhIiCsaJ7Ft+xXglf3u+1mrr2+K\nxnVE5NAQbnMLBm0codAGwBcIMn9ZERPyU9hZ0QDA8P6ameQMVyZFKUyyLBh5PGx9H4IBcLSq7goG\nwd/TMCk0hLt8Eww7MjprFBERERER4SAO4BYR6a7s5Dj8QZvqRh8Atm3z5tpSzvi/97jl6ZX8+c2N\nFFWayqTCzH6uTIpWmAQw6VwzN2nZI23v95mftUdtbjkTzK1a3UREREREJMoUJonIgJOd7AGgrLaJ\nNcVVfOXBxVz776VgwcT8FIr2NVBc2UhynIvUeHe/rDHqu7kBTD4Phh8Hb98ODfta7veZKqweVSal\njwCHW2GSiIiIiIhEncIkERlwspPjAPj1y+v4/D0fsK6kml+eO4XXv3MCx43JpqiygeLKBgrS4vtt\njX1SmWRZcNYfTJC04Hct9/vqzG1PwiSny8xh0o5uIjIANPoCrC6qYv6yInZW1EflnHVNfvyBKG2C\nICIiIj0SlZlJIiLRFA6T/rdhL6dNzuOPX5pOWqKpQBqSHk+9N8C63dWMzk7utzVGfTe3sIJpMPNK\nWPIgzLwC8qa0VCb1pM0NzBDuvRuiuz4ROST5A0E+3lrB5CGppCd6Oj3W6w9yy9MrsABfwGb97mq2\nldc3V3JOG5bG/G/PbjMTr6eeW7aL7z6xgjiXgxdvOJ7xeSk9PkdxZQNn3fU+j107iylD0iJei4iI\nyKFIlUkiMuCE29wcFtw2d1JzkAQwND0BgJ0VDQxJH2SVSWGfuw3iU+HVH4Jtgzf0KX5PKpPADOGu\n2AIBX/TXKCKHjA8+K2Pu3R9w6YOLOeGOBTzw3haa/IEOj39u2S6eX17Mh5vLWVVUxeicZL590hju\nvfQIfnL2JFbuquLFlcW9WtOqXdUANPmDbCuri+gcH2+toKrBx5/eUDuwiIhIT6kySUQGnIxED3Eu\nB6dOzmNEVlKbx4ZmJDR/XZCWsP9TDxq3MzQzKdAHYVJipgmUXv4erJ0Pidmhi0YQJgX9ULEVcsZH\nf50iMmjVe/1s2F3DvAWbeWtdKYWZCfzu/Km8vmY3v3llHf9etI0fnjmRuVMLsKyWCiN/IMi8BZuZ\nNiyN56+b3eYxgGDQZv7yIu54bQNnTMkn3u3c/9LdUlzZgNtp4QvY1Hs7DrY6kxxn/hn86Y59XRwp\nIiIi+1OYJCIDjsNh8fjXj2FMO21sQ9IT2v36YAt3Z/RJZRKYVrelD8Prt8EZvzb39ThMGmduyzYq\nTBKRTn2yvYJXV+3msz21bNpTS1Glaa9N8jj5wZkTuGr2KOLdTi45ejjvf7aX37y8jusfW8abh5fy\nmy9ObQ5m3lpXyo6Ken4yd+YBQRKY1/efnD2JSx9czMMLt/HNE8dEtN6iygbG5qawrqSa2iZ/ROdo\nDFVXVdb78PqDeFwq2BcREekuhUkiMiDNGJ7R7v1ZSaZqqckfZEg/DuC2LAuXw4rubm6tOZxw9h3w\nz7NMoASQkN6zc2S1CpNa++Rh2LcdTv15r5cpIrHv+eVFfO/JFTgdFmNykpk5IoOLjipkbG4yR4/K\nbJ5jFzZnXA4v35jNfQs28X9vbWTlriruvfQIpgxJ44UVxWQnezhlYm6H1ztubDanTMxl3jubuPDI\nQjKTOp/B1J7iygaOH5fNupJq6r0Rhkm+lpl3K3ZVctTIzIjOIyIicijSRzAiElMsy2qem1TQj5VJ\nYOYm9VllEsCI42DaxVC3Fz73U7M7W0/Ep0JKwYE7ui17FD79d/TWKSIDgm3bPQ64//nhVm7673KO\nHJnBkttO5ZWb5nD3JUdw4ynjOHtqwQFBUpjTYXHDKeN4/NpjqPf6+eJ9C3ngvS28vW4Pc6cW4HJ2\n/k/MH509kXpfgLvf7vmOk42+AOV1XsbkmOrVuqbI2twafS3PW7KtIqJziIiIHKpUmSQiMWdIegJb\nyuoo6MfKJDA7uvX5ttTn3Qtn/R4S2q/U6lL2+LaVSbZtdnhrqjaDvXu6Q5yIDCglVQ18uKmcDzeV\n8eGmMgA+vPVzzXPdOmLbNn96YyP3LtjEGVPyuOviIyKaXzRrdBav3DiH7z21gt+8sg6Ac6YP6fJ5\nY3NTuPioQh5dtJ1xeclcMGNYt69fHGrBG5aRQKLH2YvKpJYwaU91U0TnEBEROVQpTBKRmDMmJ4mt\nZXURD26Nlj6vTAJwuiMPkgDyp8Liv8GWd2H0iVBdZIIkgKqdkDMhOusUkYNm055a/rVwGx9uKmNL\naCezrCQPBenxrC6qpqSykeFZnQfFv39tPX9/dwuXHF3Ir78wtXmHykhkJcfxjyuO4h8fbmVtSXWH\nbcr7u/m08awuruYnz63mL299xpWzR/KVWSNIS3B3+rziykbA7O6Z6HFRF+EA7ia/+TAgNyWOynpv\nROcQERE5VClMEpGYc/PpE7j6+NH9vQxcTkffzUyKlhNugU1vwxOXw9WvmzAprFJhkkgs+tu7m5m/\nrIg547K5dNZwZo/NZkJeCou3VnDJA4vYua++0zDpXwu38fd3t3DZMcP51XmHtTsou6ccDotr5vTs\ndTkrOY753z6OhZvL+du7m7njtQ3ct2Azl84azlWzR5HfQfVpUWU9YKpUk+Kc1EU4gLvJF8CyIC81\nnn31vojOISIicqhSmCQiMSctwd3lJ9cHw0GpTOqthHT4ypPw4Knw6JfgsPNbHqva0X/rEpGIef1B\nhmcm8s8rj25zf2GmmSO3o6Ke2R0897XVu/nFi2s4bXIevzw3OkFSb1iWxeyx2cwem83qoiruf28L\nD76/hX9+uJU/fnk65x0+9IDnFFU2YlmQnxZvKpMinZnkDxLncpCR5FFlkoiISA9pALeISITMbm59\nPDMpGtKHw6VPQn0ZLLwHErPA4TKVSSIScwJBG0c7bWkFaQm4HBY7K+rbfd4n2/dx03+XMX1YOndf\nfESvWtv6wmFD07j7kiN495aTmT4snVufWcWmPbVtjgkGbdYWV5GXEo/b6SA5rnczk+LdTjIS3VQo\nTBIREekRVSaJiEQoJiqTwoYcDid8H975NeROhsod5peIxBx/MIirnSDI6bAYmpHAzn0NvLW2lG3l\ndXgDQbz+IKXVTby0opiCtHgeuuJIEjz9O3OuM4WZicz7ygzOuut9rn/sU+ZfN5t4t5OdFfV8/6kV\nLN5awdeOGwlAosdFZUNkLWqNvgDxLicZiR4q69TmJiIi0hMKk0REImQqk2IkTAI47iYzP2n8GbDx\ndTOAW0RiTiBod1hVNDwzkSVbK3hxRXGb+5M8Tk6akMutZ00kKznuYCyzV/JS4/nThdO58p9LuPnJ\n5Rw1MpM/vr4By7K480vT+NLMYQAkxTmbd3frqUZfkHi3g4xEDzVNfnyBYJe74ImIiIihMElEJEIx\nVZkE4PLAVa+Zr0vXwpb/9etyRCQygaDdbmUSwLCMRN7/rAyA174zh5FZSbidjgHX0tYdJ0/I5ebT\nxvOXtzbyyqrdHDs6izu/PI1hGS3DxRM9Luoj3M2tuc0tyczgq6z3kZMy8IM2ERGRgUBhkohIhFwO\nB4FADIVJraUXQk0J+L0mZBKRmOHvpDIpPIT7sKGpTMxPPZjL6hM3njKOi44qZMveOmaNyjxgVlSS\nx0ltpLu5hQZwpyea18DKeq/CJBERkW5SmCQiEqGYq0xqLX04YEN1EWSO6u/ViEgPdNXmBnDWYQUH\nc0l9Ki81nrzU+HYfS4pz9WoAd1xoADdARZ2GcIuIiHSXGsNFRCLkcsbIbm7tSSs0txrCLRJzOqtM\nmjUqi5Mm5HDBjGEHeVX9IynOhS9g4/X3/LW40R8M7eZmKpP21WsIt4iISHepMklEJEKxXZkUCpM0\nhFsk5gSDNh53+7ux5aTE8fCVRx/kFfWfxNCudPVeP54etuw2+QLEp8SRkdTS5iYiIiLdo8okEZEI\nxdxubq2lDgMsqFSYJBJrOqtMOtQkecznonURDOFuHsAdanNTZZKIiEj3KUwSEYlQTFcmuTyQUqDK\nJJEY1NnMpENNYpypTKqLYAh3oy9IvNtBgtuJx+VgnyqTREREuk1tbiIiEXI5HDT4ItuSekBIL9TM\nJJEYpMqkFs2VSZGESX5TmWRZFhmJbvbVeamo87KxtIbPSmvYUFrDxtJaDhuSxs/OmRztpYuIiMQ0\nhUkiIhFyOS38jTE6gBvMEO5dS/p7FSLSQ8GgjUthEmAGcAPUR9Dm1uQzA7gBMhI9PPPpLp76ZFfz\n4ylxLiwLdlXUK0wSERHZj8IkEZEIuWK5zQ1MZdLa+RAMgKP9Yb4iMvD4g0FVJoWEB3D3tDLJtm0a\n/QHiXGbiw7VzRrN4aznj81IYl5fC+Lxk8lPj+dGzq3hn/Z6or1tERCTWKUwSEYmQM5YHcAOkD4eg\nH2p2Q9rQ/l6NiHSTZia1iLQyyRsIYts0VyZdMHMYF8wcdsBxLmeMf2ggIiLSRzSAW0QkQi6HI7bf\nZKQNN7eamyQSUzQzqUVSqDKptoeVSY0+06Ic53LA9o+gvqLd41wOB/5ADLczi4iI9BGFSSIiEYr9\nyqRCc6sd3URiSkAzk5olhiqT9tY0Udfkx7bNa3KTP8Bji3ewYmdlu89rCm2ekOXbDf88E179YbvH\nxXw7s4iISB9Rm5uISITMm4wY/sQ6LdTSocokkZhi2tz0eSBAotuJx+ngrrc/4663P8OyWnZ4q23y\nM2dcNo9cPeuA54Urk8aVvmLu8De0e36XM8YrUEVERPqIwiQRkQg5HRaBQAy/yfAkQWKWKpNEYowJ\nk/p7FQODw2Hx6DWz+GxPDXVNfmob/dQ2BWj0B9iwu4Z1JTXtPq/JHwBsRha9YO6IT2/3OJfDUpub\niIhIOxQmiYhEaFAMZk0fDpUKk0RiiT9o41JlUrOjR2Vy9KjMA+5/8P0t/PrldeytaSInJa7NY42+\nIJOsHSTXbjd3NLUfOrmcFkEbgkEbh1oLRUREmulfIiIiEYr5mUkAaYVqcxOJMdrNrXsmFaQCsGH3\ngUFRoz9AgVXecoe3tt1zhGdTxfwHByIiIlGmMElEJEIxv5sbmMqkql1gx/jPIXII0QDu7pmYnwLA\n+t3VBzzW6AuQRp35Jm14J5VJ5p/KMT0fT0REpA8oTBIRidCgqUzyN0BdWX+vRES6KaCWq27JSo4j\nJyWu3blJjb4gaVYoTEov7DhMUmWSiIhIuxQmiYhEKOZ3cwNTmQRQpVY3kVjhDwZVmdRNE/NTOqxM\nSrdCrW2pQ7sOk2J5swUREZE+oDBJRCRCg6IyKb3Q3GpukkhMCAZtgjaamdRNkwtS+ay09oAd2cJt\nbkFPKiSkq81NRESkhxQmiYhEyFQmxXiYlBYOk7Sjm0gsCITmm6kyqXsmFqTgDQTZWlbX5v4mf5BU\nqw47Pg3iUkyY1M7sOFUmiYiItE9hkohIhJwOB3Zoy+iYlZAOcalQpTBJJBaEqyE1M6l7JuabHd3W\nlrRtdWv0BUinLvQamAJ2AHwNBzy/uTJJYZKIiEgbCpNERCLkcpo3c75Yb39IH67KJJEYEQ6TVJnU\nPWNyknE5LNbvbmlja/AGeG5ZEVnOeqzETBMmQbutbi0DuGP8dV5ERCTKFCaJiEQoPLMk5ucmpRWq\nMkkkRoRba50O/ROuOzwuB2Nzk1kfqkyybZsfPbuStSXVjEv140hIB08oTPLWHvD88IcGMd/SLCIi\nEmX6l4iISIQGzZbR6YUawC0SI1SZ1HOTClKbK5Me+mAr85cXc/Op40kK1kJ8eqvKpAN3fXM51OYm\nIiLSHoVJIiIRaq5MivU3GWmF5k1UQ2V/r0REuqCZST03MT+FkqpG7n9vM797dT1nTMnjupPGQMO+\nlplJoDY3ERGRHlCYJCISoZYto2M8TEofbm7V6iYy4Kkyqee+cMRQRmQl8ttX1jM6O4k/XXg4jkAj\nBLyQkNF5mKQ2NxERkXYpTBIRiZBrsMxMSi80txrCLTLghStknAqTui0vNZ5nvnUc1xw/igevOJLk\nOBc0hiox27S5tTMzSW1uIiIi7XL19wJERGKVc7C0P6SFKpM0N0lkwFNlUmSyk+O47fOTW+5o2Gdu\nE7qYmRSuTArE+Ou8iIhIlKkySUQkQoOmMikpG1wJanMTiQEtu7kpTOqV8Iy4rtrcBstGCyIiIlGm\nMElEJELOwfImw7IgbZgqk0RiQFBhUnS0bnNzxYPD1cHMpPBsPFUmiYiItKYwSUQkQuFZGjFfmQSQ\nWgC1e/p7FSLSBb/a3KKjuTIp3QTqcSngbW9mUrjNbRC8zouIiESRwiQRkQg5B9ObDHcS+Or6exUi\n0oVAc2WS/gnXK+GZSfHp5taTot3cREREekADuEVEIjRoZiYBuBPA19DfqxCRLrTMTOrnhcS6sg1m\nXlJ8mvk+LgW2L4T514Gv3rwe+uoZUV/LD1xD8QeP6N/1ioiIDDD6p4iISISczt7v5mbbNr6BsEuQ\nJ1FhkkgMUGVSlBQvh4LDTYsbwMjZZje3LQtg90qo3gX+Rtw1uzjf+b52cxMREdmPKpNERCLU28qk\ntcXVXP7QYsrrvNw2dxLXzBkdzeX1jDsRvGpzExnoApqZ1H3bP4LELMgZ3/Z+XyPsWQvH3dBy39l3\nml/7aXjuZhKWPz442plFRESiSB9riYhEqDe7uTV4A3z3ieU4HBb5qfF8tLk82svrGbcqk0RiQbgS\nUru5dcML18MbPznw/j1rIOg3lUldsDyJJNCkmUkiIiL7UWWSiEiEwru5dfSJ9aY9NWzZW0dGkoeM\nRDfxbieLt1Tw5tpS3vtsL/XeAP/82lE8u6yIZTv2HcylH8idCIEmCAbA4ezftYhIh1ra3BQmdamh\nEkpWHHh/8XJzO6TrOUiWJxGPFSDgb4ry4kRERGKbwiQRkQi1VCYF8QeC7KioZ3d1I3uqm1hTXMU/\nP9zW7qfZealxfPGIoZwzfQjHjM5iTXEVL64oprbJT3JcP70sexLNra/eDKIdJBp9AXyBICnx7v5e\nikhUKEzqgaYaE5LXlEJKXsv9JcvN8O304V2ewuFJMl/4GvtokSIiIrFJYZKISIRaz0y66b/LeXlV\nSZvHzzt8CFfOHkV1g4999V5qGv1MHZrG1KFpOFq9EZyQnwrAxtIaZgzPOHg/QGvuBHPrHVxh0pl/\neY9t5fVs+/3c/l6KSFRoZlI3+ZtMkARmoHbKaS2P7T98uxNWc9CumXIiIiKtKUwSEYlQuDJgb00T\nr6/ZzeenFXDp0cPJS4snPzWepG5WGU3IM+HNxt39GSa1qkyKAVvL6nhz7W6uPn50hxUaTf4A5pjc\nPwAAIABJREFU28pj4+cR6S6/KpO6p6m25euS5TAuFCa1N3y7E45QmGT5NVNORESkNYVJIiIRcjnN\nm7mnPtmFP2jzrZPGMGVIWo/PMywjgQS3kw2lNdFeYvfFWJj0o2dXsmhLBV5/kOs/N67dYxZtqTjI\nqxLpey2VSdpDpVNN1S1fl6xs+boHw7cBnHGmzc3qzQYF5ZthyUNw+q9Bv28iIjJI6P9oIiIRGpmV\nxMisRD7Zvo9xuclMLkiN6DwOh8X4vGRWF1VFeYU90BwmDfxP3z/aXM6iLRUMSYvnz29uZMm29kOj\nN9bsBiDOpf/VyeDRUpnUzwsZ6JpC4bwrwbS5hfVg+Da0VCY5ehO0b3gFFs2D2tLIzyEiIjLA6J8i\nIiIRinc7efpbx3H65DxuPGUcVjfmb3TktMl5LNm2jzXF/RQoeWKnMum+/20iNyWOF244nsLMRG58\nfBmvrS7hhRXFPLV0Jy+sKGZnRT0vrigGwNGL3xeRgSbYHCbpn3CdCodJhUfDvm3QGHpt7cHwbQAr\nNIC7V21uDaHdOtUqJyIig4j+JSIi0gvZyXHc/9UjOWf6kF6d5/JjR5Ic5+K+/22O0sp6qPUA7mAA\n3rgN9qzv++sG/D06fGdFPe9/VsZXZo0gOzmOey+ZQXmdl28++ik3Pr6MW55eyY2PL+OUP7+LP2gz\nd2oBvkCwjxYvcvD5NYC7e8Jh0sjjze3uVea2eFm3h28Dza+NjmiESdoRTkREBhGFSSIiA0Bagpsr\njhvByytLeHtdP7RCuMPbX9fDlgWw8B5YO79vr1mxBX6T33aeSReeXLoThwUXHjUMgKnD0nj/Byfz\n0g3H89bNJ/L+D07mnkuOICc5jl9/4TDG5SXjD9rN1RwisS4QNOGoBnB3Yf8wqWRlaPj2OhjSvXlJ\nAIQqkxz+XgRB9aFWXFUmiYjIIBKVMMmyrDMty9pgWdYmy7Jubefxmy3LWmtZ1krLst62LGtENK4r\nIjKY3PC5cUwZksp3nljOtrKDvA11uDLJVw/LHzNfVxf37TUrd0LQBzsXd+vwrWV1/GfxDk4cn0NB\nWkLz/Xmp8Rw2NI2xuckUZiZyzvQhfHjr5zh/xjDcocEyvqCqk2Rw0G5u3RQewJ05BpLzzdykHg7f\nBppfG52BXrQAqzJJREQGoV6HSZZlOYF5wFnAZOASy7Im73fYMuBI27anAU8Dd/T2uiIig02828nf\nLpuJ02HxzUc/od7bsxawXgl9+k51Cax7yXxdU9KnlwyE3lgF9mzo8titZXVcfP9HAPz47EndvoYn\nHCYFVJkkg0NQYVL3hCuT4lKgYJqpTOrh8G2geXMCZ6AXQVDrmUnPfavlNVZERCSGRaMy6Whgk23b\nW2zb9gL/Bc5rfYBt2wts2w5/pLMIGBaF64qIDDqFmYncffERbCit4UfPrsK2D1IIEq5M2vw2BJog\nMdsES31oW6lp/dizpfM2t3CQ5AvYPH7tMYzLS+n2NdxO84bb51dlkgwOmpnUTU01YDnNa1v+NNi7\nHnZ81KPh20BzmOTy96YyqdLc+hph5ROw7sXIzyUiIjJARCNMGgrsbPX9rtB9HbkaeDUK1xURGZRO\nGJ/D904bz/PLi3l44baDc1FXKEyq2GJuhx0J1UV9esmmRvNJv7tyU7uP7yg3O7K1DpIm5Hc/SAJw\nu8KVSQqTZHAIqDKpe5pqIC7ZDNoeczLYAVj1dM+GbwO44oEoVSY1VZt1VG6P/FwiIiIDxEEdwG1Z\n1mXAkcCdHTz+dcuyllqWtXTv3r0Hc2kiIgPKt08ay2mT8/jNy+tYtKW87y/ocJhAqTY0/Dt/GjRU\n9OmMj4DPDKPNDpazduuuto8FbS78+0fc8Pgy/BEGSUDzzCSvwqTBZfHf4Zlr+nsV/UIzk7qpqQbi\nUs3XI4+HMZ8D7J4N3wZwOGggDlcgwuHZAR94Qy134UHc+xQmiYhI7HNF4RxFQGGr74eF7mvDsqxT\ngZ8AJ9q23dTeiWzbvh+4H+DII4/UgAsROWQ5HBZ/unA6X7j3Qy6+fxGZSR6umTOKa+eMbg5Ios6d\nYGZ6JGa1tIHUlEDmqD65XMDbElS9/9FCJo+6sPn7RVvK2V3dyC/PncIFM4eRHBfZ/66GVHzMhc6P\n8AVO6u1ypa8F/FCyHErXgOUAhyv0yxn6Ffq+pgRe/SFgwzl3tcz7OkSoMqmbmqrNvKSw026H7R/B\nqBN7fiorDncwwmA9XJUEUB/6YKCmBPxN4IqL7JwiIiIDQDTCpCXAOMuyRmFCpIuBS1sfYFnWEcDf\ngTNt294ThWuKiAx6qfFu/nPtLF5YXsyiLeXc8doGXA6Lr58wpm8u6Eky1UjJeZA6xNzXl2GSr+Vz\nhe0bl9Pou4B4txOA+cuKSI5zcdFRhc33RWL85oe5ybWGusDPe71eiYJgEN75FSTnwqxvQskK2Pou\nbH3fzLPx1nbvPK5Q8LlnHax5DmZ9o2dzcGJYoHlm0kEtLo89TTVtw6T8qfCjneB09/xUvalMah0m\nNYQqk7DNbpbZYyM7p4iIyADQ6zDJtm2/ZVnXA68DTuAftm2vsSzrdmCpbdsvYNrakoGnLNOnvsO2\n7XN7e20RkcGuIC2Bb5w4hm+cOIYv/20hj3+8k2vnjCb0WkqjL9CrsKWN8BDu5NyWMKm6ODrnbkcw\n1EJnYzHMv5NXV5dw3vShPL5kB6+sKuHMwwp6/bMl126jHh/7NIC7/wX88ObPYNE88/1nb5qB7wDZ\n42HaRTBqjtlpy3KaLdyDATNjJuhv9StgKpQeOBmWPQKfPGzamU76Yb/9aAdTuM1NhUld8NaaYdut\nRRAkATQ54nEH2y2q71p7lUkAldsUJomISEyLRmUStm2/Aryy330/a/X1qdG4jojIoezio4bzvadW\ncM87myitbmTRlnK2lNXxl4sO5421pTgti7sv6cGW1/sL7VrkTcjBk1Jg7qvpux3d7FBlkp05mqlV\nu5m3ZCdef5CfPLeaifkpfOuk0b27gL+JhPpdBIjTAO7+tvV9eO1WKF0NM6+EnR+bIOn4m01VUUp+\nz87n95pAadXT5vuiT6K/5gEqEAzidFjNgbJ0oKkmatVq3qi1uVW0fK25SSIiEuOiEiaJiEjfO3tq\nAb98cQ1/fnMjyXEujhqZgdvp4LtPLCdUrMAVx41g5ojMyC4QCpP+uaKelMIqLnUnQnXfhUlBfxMB\n28KRO4nDGlaxaEsFpdVNjM1N5tWb5nT+ZrnsM9j4Ohx7Xcc7M1VsxbKDxOPFF9AYvoPGtk3IM+kc\n09bz2o9g7XxIGw4X/hsmnQu1e6BiM4w4LrJruDyQOQbKNpjvi5aa6x4CAYs/aGteUnfs3+bWm1NZ\n8XiiHSZpRzcREYlxCpNERGJEgsfJM986jgZfgMkFqbicDnaU1zP3nvc5dnQWS7fv4663N/Hvq46O\n7AIeEybttdP53fzVfDk/H3dN37W54W+iCQ+JORNI3/AqcZafrWV1/ODMCZ0HSf4meOJy2LsOxp4C\nuZPaP658EwBuK4DP5+2DH0DatWsJPHsNnPkHWPeiqRo66ccw+8aWVsqUPPOrN3LGmzDJnWTah/Zt\n67P5XgNJMGjjUpjUtda7ufWSzxFPcrA6sieHwySnp22bmyqTREQkxml6o4hIDBmXl8K0Yem4Qju6\nDc9K5IMffo6/XTaTb544mvc27uX55QdsqNk9oTf6e+00AHbbGX1amYS/EZ/lguzxWHaAC0f7sCz4\nwuFDO3/eu3eYIAlgwysdH1f+WfOXAW+Ew3Ol50pWmNtP/wXbP4Q5N5t5RuEgKVpyJprbI75ibg+R\nVjdVJnVDMGBmJkWpMsnr6GZl0nt3wn3HmkHzYfUVZnfCpNyWAdy5U2D9S/DWL8BbH5U1ioiIHGwK\nk0REYlxaghuHw+Kq2aOYOSKD255bzZ6aCFoyQm1ue0knM8lDSTAD+rIyKeDFh9sMXwZumGZz36Uz\nGJLeSehQsRUW3g3TLjaDmtd3FiZtanWpGA2TbBs+/TfM/7aZExQLSleb2z1rARsmn9c31xl2NDjc\nZlc4VwIs+isUfdo31xpAAgqTutYUqiKKUpjkc8QTZ3cxgNvXAB/NM3/ui5a23N9QAfHpJkwN+s19\n599vBs5/8H9w3zFmGL2IiEiMUZgkIjJIuJwOfvb5ydQ0+Vm8paLrJ+zP3dLmNm1YGtu8aVCz2wQa\nfcAKePFZnuYwKbdpG2dNLWh7UNln8MFfYMHvzE5gz1xtBi+f+nOYMNe8aaspbf8CZS1hUtAb4U5M\n/alyJzzyRXjhBlj+H1jzXH+vqHt2r4bEbPN1zkTImdA31xl3GtzyGWSNgTN/ZyrRHjgZHr3ADPge\npPxqc+va3o3mNiM6bY8mTOoioF/1VEtL27oXTJvdG7eZMDhnArjjW45NGwZfuA++9jK44uE/X4IV\nT0RlrSIiIgeLwiQRkUFkYkEKLofF+t0RzPcIhUl17kwm5KWwoT4ZAt62cz6iyBFowm95IC4ZUoea\nN4CN1bDwHvMp/5718NBp8NbP4d3fw6K/QflmOP1XkDoEJpxlTrTx1QNPXroGdi3Bn2zCqYAvhlpJ\nbBuW/sNULOz8GM7+owllFt7TZ8Fe1AQDpjJj6peg8Bg46pq+u5ZltWz9fuSV8J3VcMrPoXiZ+XOz\n6S1++9JKfvrssr5bQz8IBFSZ1KVwq2XB9KiczudI6Loyadmjpn1tzCkmGLr3KPN3dvolcNGjpnou\nLPRay8jj4ZsfQMZIWPNs1wvZvaptC52IiEg/0gBuEZFBJM7lZExOMutLanr+5IwR1LgyCVppjMlN\n5p1ABjiB6mJIyo76Wq1AEwGH23yTPR7KNpo5Im/cBsXLzbwdpweuX2p27nLs9/lH3hSz9feGV2Hm\n11ru9zXAK7dAfCo1R3+XjHd+gO2NcCemg62hEp78Kmx9F0adCOfeAxkjTPXCC9fD1vdg9In9vcqO\nVWwFXz3kT4Wz/nBwrx2fauYzzfoG3DMTPprH2VtK8AcCFJ38P4ZmJB3c9fSRgG3j2v/vQnu2LzSV\nhe5EUxWTnNfxsPrBpmQ5JOWY0DkK/M544mjqeMdAX6NpsTz2OsgaC5vfhoLDTYg07EhzTLgyyeEy\nuxGGuTww5nOw8kkI+MDpbn8RRZ/AA5+DI6+GuX86JHYuFBGRgU2VSSIig8zEghTWlURQmXTUNfx8\n5CMkxXsYl5tMqR2q+qjpmyHczqDPVCaBaQMp+wz2rjffr37ahEKXz4fscQcGSWDeTE04G7b8D7x1\nsHcDvPdHmDfLBFGn/Qor9GYy6IuRmUkrHjdB0tw/w1efN0ESwNQvm7krnzzcr8vr0u6V5jbvsD6/\nlG3bNPoC/Pi5VWwrq2t5wJMEM74Km9/hcHsdRzo2suql+/p8PQdLIGi3+9ehjbpyeHguPH0lPH4R\n/Ps8U+nWqvVzUCtZYaqSohS4+J3xOLDB30EovXslBH0w7Cg4/Ctw1etw7TstQRK0VCa52wk1R59s\nBobvWnrgY2Fb3ze3Sx8yba8iIiL9TGGSiMggM6kgleKqRqrqfT17osNJuddDcrybsbnJ7LYzzf3V\nfTOE2xlsIugIhUnZ48BXB5sXQOZoOPZ6+Op8yJvc+UkmnGXe4N09A+YdDe/8ylRgXPEizLgcp8e0\nk9gdvQkcaIqXQcoQOOrqtm+E3fEw/WJTuVXXN22HUbHxdYhPg9wuft966dcvrWXu3R/wwvJiHlu8\ng/n772A44wpsy8mi4CSWWxM5ftOfePPeG3hj8Qoq61sGmb+xZjdz7niHP72xAV+gbftQoy9AVYOP\nmkYftU1+6r1+Gn2BPv25usPMTOrin287F4MdhPMfMKHG+Q+a+w+FHe98DbBnnakMipKAMxQEVReb\n6sC9G9oeEA6Bhh1pgu/hx4DD2fYYV5y5bW9Xw1FzAMsE4x3Z8ZGp0EwbDpvejuTHEBERiSq1uYmI\nDDIT880ORut2V3PM6KwePbe2yU9KnIuUeDeOlDyCPgeOPguTvAQ9yeab7NCQ5t0rYdK5cMZvuneS\nEbNNNYA7EU74Pkyc26a1xRln3rjZvhgJk4o+NbvUtWfGFbD4b/DEZTDr66YqK/wGdSDwNcD6l2HK\nF9q28fTArn31rCupwbZtLMvCwrw3t7DAAodlsb28jgc/2ArAL15cA8DynZVtT5Q2lBen/5XbFwV4\n5PLD2PzKrZxS9gi+Vx7j+ZeOZ2HOhez1DKNm+woKk5K55516FmzYw2WzRrC6uIplOypZv7uGQPDA\nGVXZyXFMyE9mfF4K4/NSmFSQyvRhaVgHqe0oEAx2PTNpx0emRXTSuSaIzJ8Oz19n/n5Nv+igrLPf\nlK4FOwBDohcmFSVPwYcL971HmpAOzG6CR1wGh51vNgJIK4SU/I5PEg6RQgF3GwkZMHQmbHwNTv7R\ngY8Hg7BjEUw6x8yw27O29z+UiIhILylMEhEZZCYVpAKwviSCMKnRT06yCShG56ezryidrJo+CpNs\nH0FHKAxpveNXT3b/crrhmrc6fNjlMXNKrFioTGqsNjuSTevgzX7eZDjjt2b78ae+Ztrepn7ZvKGN\n4hvniH32Jnhr4LALuv2UynovH20u54NNZXy4qYxt5d0blD6pIBWP02LFririXA5W7KxsDqDC3moY\nT1z6PiZNmQZTXsG/ZyNV79zFFzc+xYUV/6OeBBLjGsAPW6Zdw5c2n8mtz64iOc7F4YXpfOvEMaQn\nmvk1QdvGtk1V0PbyOjaU1vLEkp3Ue02l0l0XH855hw/twX+syAW6s5vbjkUwZEbLnB6ny/z5KV3d\n9wvsbyWhgetRGr4NUJI8hYsdd/LMzLVQOAtqd8Onj8CLN8JrtwKW2V2wM67Q70V7bW4Ak8+DN39q\nNhnIGtP2sbIN0FgJI46D8k2mAtDfNLDCZBEROeQoTBIRGWRyU+LITPKwLoIh3LVNfpLjzf8axuQk\nU7wjnczqEvqi5sJl+2hyhipYknJMe1RjVUuVUhS440JtbrFQmRTegaqjyiQwA35nfdO0wyz/j9l2\nfMkDcMFDZge1/rT6GfP7OHJOtw5ftKWcyx9ajC9gkxzn4pjRmXz12JEcMTwdt9OBbYONTdA285Fs\nQrc2TBmSxvKdlfxk/irOmz6U/3trI9vL6xmRlcj/NuzlhRXFfLCpjJkjMpqv58odT+7F86Dudvjk\nHyRW7jSDjz97g9HLH+SDc4+haOiZjM5J7tZuacGgTVFlA+f/dSFvrC09qGGSo7MqKF+DaZc89rq2\n9+dPNZVjHQ2RHiyKl0NCpqkUihKXw8FnwaEw96qWO4+93rS3Lfs3rHvRVAp2JlyZ1F6bG8CUL5ow\nac1zpsoybN82eC1UrTT8GFNxZgfMhgX5UyP+mURERHpLYZKIyCBjWRYT81NYv7vnQ7hrGn0kx5n/\nNYzLS6YkmMHEyiI62F+oV9y2l8ZwO5RlmRBp18eQMz5q17DcMVCZtGe9GXK+6mnzfWdhEphZLGNP\nMb8a9sGd40zFSX+GSU01plriiMtMFUw3PPTBVtIS3Pz98plMG2YCpJ44dkwW73zvJNaVVPN/b21k\n3oJNrCqqYv3uGjIS3TT6gpw4PufAJyZlwQm3tHw/4Wwo30ziqzcy7pq3wDGlW9d3OCwKMxM5eUIO\nr67ejS8Q7PHPEAl/0Mbl7CQM2rXUDIMefkzb+/OmmvCxpiRqu5wNSFEevg3gclj49295tCwoPMr8\nOveebpwkVJnUXpsbQHqhqXpa+QQcd4O5b+E9ZlMBywFn3WnmyfmbzGN71ilMEhGRfqUB3CIig9DE\n/FQ2lLY/86Ujtm2bmUmhyqRxuSlmCHcf7OZm2zZufOBs1aaRMx6wIGtc9C4U2kHJCgzQMMlbB387\nHh75Aix/1LQmJfWgNTEhA1ILoKrVAOq6crj/ZHj3DvB7O35uNG14DfwNcNgF2LbNU0t30uDteFj1\nnppG3lm/hwtmDGPmiMxehTDj81JI9Dh56pNdBG2bP315Oh//5FTW3n4Glx0zousTuDxw4b8gLhUe\nPBWe/YYZshwMdv1c4HMTc6lp9PPIR9v5dMe+yH4Ib72pGOqGQNDuvHJq8ztm+/kRx7W9Pxw8FC+L\nbI2xwN9kQpYot326nA78ge6/lrbL3clubmGzbzIVR4980exK+c6vYPzpcP0SMycNIGssONxQuqZ3\n6xEREeklVSaJiAxCkwpSaPQF2VZex5ic5G49p8EXIGjTXJk0NjeZ/9kZuL1VpnWmo/aMCDT5g3jw\nt535cfQ3zKDgjj65j0To/Fb40/yBpr7CVJHM/g4ccTlkjur5OVKHtt1xb/2LUPyp+bX6GTjnbhg+\nK3prbs/qZ8w6CmexpriaW55eSVWDj2vmjG738Oc+LSIQtPnykb1vRXI6LOZ9ZQbYcNKEnMgGYafk\nw9deho/ugdXPwsr/QvpwyJlo/gw548ytK97MHhp+nHnM4eD4cTm4nRa3v2SGIs8Zl80PzpjI1GFp\n3bv2rqXwny+ZirSL/tPln39/oIuZSZveMsOh4/e7fsF0SMqFN39mWhHjU7u3vliyZ635+xTFeUkQ\nrkzqXrjY8UnCM5M6eR2dOBdOux3e/DkUHg1n33ngLCanG7LHh6rMdpuB3Rkje7c2ERGRCKgySURk\nEGoZwt39uUm1jX6A5plJmUke6jy55sEo7+jW6AsQhw+rdZhUMK3l0/docYcrkwZomNQUakUccjhk\njz1wO/HuSB0C1a0qk9a9BOkj4NInTeXTP86Al24286j6QsM+E2BM+SI4HOyuMlVgr6/Z3eFTnltW\nxBHD0xmb272gsysnT8jl5Im5vdtRLXssnHMXfG8DnP8g5EyC2j1QtskEc1veNaHZy9+Dvx4Ld46G\nxy8hedkD/PXiqdx76RHcNncSq4uqOOfeD7jusU/Zsre282tufR/+fZ4JqzYvgMcvMlVKnQjYncxM\nqt1jdmwbe8qBj3kS4cv/hIqtMP9b3a6EiinFy81tQbQrkyyCtpmTFbHOdnNrbfZN8ONiuPqNjod6\nn3iLCZvWvWgqmN69A2JhLpyIiAwqqkwSERmExuaaIcLrSqqZO62gW8+paQqFSXEt/2twZQyBCkyr\n2/47DHVmz3oz36ODLeIbfUEy8GGFP63vK04PQSwcAzVMCgc8+1eR9ETqEBMg2baZXbT1XTj66zD+\nDBgxGxb8Bhb/DTa8YiodJp0TnbWHrXvJVIOEdnErrTFvapdu38femiZyUtruOLWxtIb1u2v4xTmT\no7uOaPEkwrQvm1/7s20zEHn7QtixELZ/BBte4dTP1TUPTb7wqEIefG8LD36wlddW7+bCIwu56ZRx\n5Kft92d94+sEn/gq5Z4CrvL+hHOyNnLttj9iPXYhXPoEeNpvhwoEbeLdDmiohKpdJkgM34bDlPbC\nJICRx5vKlzd+Ah/eBcd/J8L/SANUyXLzdynKlTrhNkx/0MbTjeHs7QoH5521uYV1FThN+aL5VVVk\nfi8X/AaWPwZf+gcMnRHZ+kRERHpIlUkiIoNQvNvJ6OykHg3hDlcmhWcmAaTkDAfA7kllUsUWU7mx\naF6HhzR4/cRZfhzuPt7a2rLw4sYxUGcmNYZ+f+J6EyYNg0AT1JebwCjghYmfD503Gc78HVzzFiRm\nwxOXwWs/jm5VyupnIGNU8+DwPdUmuLNteHNt6QGHv7C8GIcFc6fF4BBoyzKtiEd8Bc6bBzd+CqNO\nhE8ehqCZEZUa7+bm0yfw7i0nc/kxI3j6k52ceOcCfvXSWt5aW8q2sjqWvPwQ/scuYY0vn7OrbiWr\nYAR3lx3J9/3fJrjtQ4KPfgma2q9q8gdtLqz+F/xhBPxtNjx2Ibx8M3zwF9i7wWwxn99Jm9ex15kg\n4u1fmmqrwaQPhm8DzTOqetXq5upiN7dIpA2FLz8Ml8+Hxkp4/0/RO7eIiEgXVJkkIjJITSxI5dPt\n3R8IXNtcmdSyd1vOkJGwAerKdtLthqTlj4MdhLXPw/HfbfeQxgbTyuNw93FlEuDFg3OgViaF29x6\nM78mvDNX1U5TbZIz0ewK1drQmfD1BfD6j03Il5zbcVWKrwFW/Nd8nZRtQqikbEjMgvh0cDhg1yem\nva1guqmEOv7m5jfwe2qayE72kBLvZv7yIi6dNbz51NWNPv67ZCezx2YfULEUs466Gp78qgnyWlV9\n5aTE8Yu54/n6NBfPLFjEho8W8tZH9Wyw9vBN54uscU5k8ey/8vKsSeSmxlNS1cAvX8jmpnXwlx33\nUfvQeSRfPR/iUtpcLsVfztnVT8G402H6JZA2zMyrSsnvXpukZcG590LpWrPuU34KR3y1wyrCdu3b\nBgt+a9obs8aZ4d75U02rau7ktrPQ+srW9+C9O+GiR001kt9rhlLP+mbUL+VqDpMiC2Ft26YRNwkQ\n3ZlwYWNOhswx4Ou8RVJERCSaFCaJiAxSkwpSeHFFMVUNPtIS3F0eX9N4YJvbiCEF1NrxVO/Z0b0w\nKRiEFY+D5TS7RlXtMm9299PkbQAOTpjkszw4ggM0TIpKm9tQc7vkQTOA+PwHTOCzP6cbzvwDVO40\nFQxHXWMql1orWQnPXgt717d/LcsJCemmCspymPkudrC5xQ1gT3UjOSnxfH5aAXe+voFtZXWMzE6i\n0RfgjtfWU17XxC1nHBn5zzvQTDjbtFU9+3WYdhHUl5n2o+piqC1lCDY3ALT6K1gx5EQO++pjTItv\n+e9fkJbA3y6fyZtrh/GzZ+P5Zemf2X7XmaR//UXS0jObj5vb8BJO/HDm73vWetpaXDJc8jjM/7aZ\nAfXhXXDirWb9zk7+aVi7F97/Iyx5yARXk84xf8dX/BeWPGCOScox87r6ut1q+WMmUFrwWzjrD+bP\nbMAb9eHb0BImzfnDAh69elb3h6uH/N9bn7FywVoe9tC9NrdIuOIP3u6NIiIiKEwSERm0JuWHh3BX\nM2t019vNhyuTWre5jctLptTOwLmvqKOntbX0IajaSeCEW3G+93vm//dBzrzqp8S721acyar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8rx/ApMeh2v3DyKi/qHdehaAyJVCHbdmDgGRbu3ukqInqQ+THJLZZLMTBJCCOEeEiYJIYRoV6Up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\n7f8DDi7hGT7PuDIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1164b3110>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n",
-    "plt.plot(KO.r.beta_df['beta'])\n",
-    "plt.plot(PG.r.beta_df['beta'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                   beta   R-squared:                       0.870\n",
-      "Model:                            OLS   Adj. R-squared:                  0.870\n",
-      "Method:                 Least Squares   F-statistic:                     3363.\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          6.92e-225\n",
-      "Time:                        12:43:08   Log-Likelihood:                 704.82\n",
-      "No. Observations:                 505   AIC:                            -1406.\n",
-      "Df Residuals:                     503   BIC:                            -1397.\n",
-      "Df Model:                           1                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "const         -0.0097      0.004     -2.285      0.023      -0.018      -0.001\n",
-      "beta           0.9690      0.017     57.988      0.000       0.936       1.002\n",
-      "==============================================================================\n",
-      "Omnibus:                       11.655   Durbin-Watson:                   0.068\n",
-      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               11.842\n",
-      "Skew:                           0.369   Prob(JB):                      0.00268\n",
-      "Kurtosis:                       3.133   Cond. No.                         6.50\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "-3.19434626171\n"
-     ]
-    }
-   ],
-   "source": [
-    "x = sm.add_constant(PG.r.beta_df['beta'])\n",
-    "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n",
-    "print(coint.summary())\n",
-    "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n",
-    "print(adf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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nFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116b40890>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "plt.scatter(df['sd_beta'],df['sd_beta_p'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GWZdSzCwCAABgLtq2CxUWQQYzi8ZtaG17TAEAADjM2rYPFRax0CYfx3Fl0XzWAgAAwOL4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\nvRfl+z5tPf3cvb6Zy86YOOTfofBk3LiaMi5fPDq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PgE8UJrCZJcIfHwAywPMsmDjuMWAy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\nRERERCRPxSIREREREREREcn7/0SUm9TtupAjAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10e3db810>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize = (20,10))\n",
-    "GE.r.beta_df['beta_p'].plot()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.html b/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.html
deleted file mode 100644
index 9d818b1..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.html	
+++ /dev/null
@@ -1,96 +0,0 @@
-In previous chapters, we learnt that the Capital Asset Pricing Model (CAPM) treats the market return as the only factor affecting the return of any asset. This chapter will generalize CAPM to multi-factor models of the following form:
-\[ R = \alpha + \beta_1 f_1 + \beta_2 f_2 + \dots + \beta_n f_n \]
-where each \(f_i\) is a factor.
-
-<h2>Fama-French Three-Factor Model</h2>
-
-This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns.[ref] <strong>Fama, E F; French, K R</strong> (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref]
-
-The 3-factor model is
-\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \]
-
-where
-<ul>
-    <li>MKT is the excess return of the market.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
-    <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
-    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
-</ul>
-
-Data on these factors can be downloaded from <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">French's website</a>.
-
-<h2>Model test</h2>
-
-To test the 3-factor model, we use it to predict returns on NASDAQ US Small Cap Index and NASDAQ US Large Cap Index. We estimate the model with daily returns in the past 6 years.
-
-Results for US Small Cap returns:
-<img class="aligncenter size-full wp-image-2206" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.20.02-PM.png" alt="" width="1342" height="830" />
-
-The coefficient of SMB is positive, so when small caps outperform large caps, the Small Cap Index will have higher returns, which is not surprising. By comparing the t statistics of those factors, we know that MKT and SMB are more important factors driving the Small Cap Index.
-
-Results for US Large Cap returns:
-<img class="aligncenter size-full wp-image-2205" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.18.48-PM.png" alt="" width="1346" height="822" />
-
-As expected, the coefficient of SMB is negative for the Large Cap Index. The coefficient of HML is quite low, which suggests that value and growth stocks take approximately the same weight in the Large Cap Index.
-
-<h2>Factor Returns</h2>
-
-How do we find out the returns on a single factor? We can do so by constructing a <strong>tracking portfolio</strong> and computing its returns. For example, if we want a tracking portfolio of the HML factor, we only need to find 4 stocks to construct a portfolio that has MKT and SMB coefficients of 0 and HML coefficient of 1.
-
-Consider stocks A, B, C, and D whose returns can be explained by the 3-factor model:
-
-\[ R_A = \alpha_1 + \beta_{11} MKT + \beta_{12} SMB + \beta_{13} HML \]
-\[ R_B = \alpha_2 + \beta_{21} MKT + \beta_{22} SMB + \beta_{23} HML \]
-\[ \vdots \]
-
-Let their weights in the tracking portfolio be \(w_A,w_B,w_C,w_D\). We write the coefficients into a matrix:
-\[ \Sigma = \begin{pmatrix}
-   \alpha_1 &amp; \beta_{11} &amp; \beta_{12} &amp; \beta_{13} \\
-   \alpha_2 &amp; \beta_{21} &amp; \beta_{22} &amp; \beta_{23} \\
-   \alpha_3 &amp; \beta_{31} &amp; \beta_{32} &amp; \beta_{33} \\
-   \alpha_4 &amp; \beta_{41} &amp; \beta_{42} &amp; \beta_{43}
-\end{pmatrix} \]
-
-The tracking portfolio is determined by the solution to these linear equations:
-\[ w^T \Sigma = \begin{pmatrix} 0 \\ 0 \\ 0 \\ 1 \end{pmatrix} \]
-
-Economic interpretation: The 3 Fama-French factors represent "systematic risk" which cannot be reduced by diversification. Investors earn those factor returns for taking such risks. Any idiosyncratic risk, or firm-specific risk, can be diversified away and so investors are not paid to take such risks.
-
-<h2>Other Factors</h2>
-
-The Fama-French 5-Factor model comprises two more factors:
-<ul>
-    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
-    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
-</ul>
-
-RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
-
-<blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote>
-
-CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that:
-
-<blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote>
-
-Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
-
-<h2>Summary</h2>
-
-In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
-
-<h2>Algorithm</h2>
-
-Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.[ref]<strong>AQR (2013)</strong>. A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>[/ref]
-
-The paper recommends picking stocks by their value, quality (profitability) and momentum.
-
-The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
-
-The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets.
-
-There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
-
-In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015.
-
-You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=480128b21eebc545b1321e0b8caa26d1'></script>
diff --git a/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.ipynb b/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.ipynb
deleted file mode 100644
index ab5d1bf..0000000
--- a/Tutorial Series/Introduction to Financial Python/Tutorial14 Fama-French Multi-Factor Models.ipynb	
+++ /dev/null
@@ -1,338 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import statsmodels.formula.api as sm\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats.mstats import normaltest\n",
-    "import time\n",
-    "from cvxopt import matrix\n",
-    "import seaborn as sns\n",
-    "import statsmodels.tsa.stattools as ts\n",
-    "from scipy import stats\n",
-    "from scipy.optimize import minimize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "fama_5 = quandl.get('KFRENCH/FACTORS5_D')\n",
-    "fama_5 = fama_5/100\n",
-    "fama_5 = fama_5.rename(columns = {'Mkt-RF':'mkt'})\n",
-    "spy = quandl.get('LSE/SPY5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "spy = np.log(spy['Last Close']).diff().dropna()\n",
-    "spy.name = 'spy'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>mkt</th>\n",
-       "      <th>SMB</th>\n",
-       "      <th>HML</th>\n",
-       "      <th>RMW</th>\n",
-       "      <th>CMA</th>\n",
-       "      <th>RF</th>\n",
-       "      <th>spy</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2017-06-23</th>\n",
-       "      <td>0.0024</td>\n",
-       "      <td>0.0077</td>\n",
-       "      <td>-0.0050</td>\n",
-       "      <td>-0.0035</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-26</th>\n",
-       "      <td>0.0004</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>0.0069</td>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>0.0017</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>0.000738</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-27</th>\n",
-       "      <td>-0.0084</td>\n",
-       "      <td>-0.0025</td>\n",
-       "      <td>0.0130</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>0.0032</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-28</th>\n",
-       "      <td>0.0102</td>\n",
-       "      <td>0.0082</td>\n",
-       "      <td>0.0021</td>\n",
-       "      <td>-0.0018</td>\n",
-       "      <td>-0.0007</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.000205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017-06-29</th>\n",
-       "      <td>-0.0083</td>\n",
-       "      <td>0.0007</td>\n",
-       "      <td>0.0135</td>\n",
-       "      <td>-0.0025</td>\n",
-       "      <td>0.0036</td>\n",
-       "      <td>0.00003</td>\n",
-       "      <td>-0.005262</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               mkt     SMB     HML     RMW     CMA       RF       spy\n",
-       "Date                                                                 \n",
-       "2017-06-23  0.0024  0.0077 -0.0050 -0.0035  0.0005  0.00003 -0.000615\n",
-       "2017-06-26  0.0004  0.0005  0.0069  0.0025  0.0017  0.00003  0.000738\n",
-       "2017-06-27 -0.0084 -0.0025  0.0130  0.0031  0.0032  0.00003 -0.000779\n",
-       "2017-06-28  0.0102  0.0082  0.0021 -0.0018 -0.0007  0.00003 -0.000205\n",
-       "2017-06-29 -0.0083  0.0007  0.0135 -0.0025  0.0036  0.00003 -0.005262"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.concat([fama_5,spy],axis = 1).dropna()\n",
-    "df.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                    spy   R-squared:                       0.340\n",
-      "Model:                            OLS   Adj. R-squared:                  0.339\n",
-      "Method:                 Least Squares   F-statistic:                     210.6\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          3.73e-110\n",
-      "Time:                        16:03:03   Log-Likelihood:                 4457.8\n",
-      "No. Observations:                1229   AIC:                            -8908.\n",
-      "Df Residuals:                    1225   BIC:                            -8887.\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept   9.267e-05      0.000      0.503      0.615      -0.000       0.000\n",
-      "mkt            0.5700      0.023     24.636      0.000       0.525       0.615\n",
-      "SMB           -0.0753      0.039     -1.942      0.052      -0.151       0.001\n",
-      "HML            0.0658      0.039      1.670      0.095      -0.011       0.143\n",
-      "==============================================================================\n",
-      "Omnibus:                      142.776   Durbin-Watson:                   2.657\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              609.967\n",
-      "Skew:                          -0.472   Prob(JB):                    3.53e-133\n",
-      "Kurtosis:                       6.319   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = sm.ols(formula = 'spy ~ mkt+SMB+HML',data = df).fit()\n",
-    "print model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  small   R-squared:                       0.995\n",
-      "Model:                            OLS   Adj. R-squared:                  0.995\n",
-      "Method:                 Least Squares   F-statistic:                 9.912e+04\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
-      "Time:                        17:13:18   Log-Likelihood:                 8506.2\n",
-      "No. Observations:                1517   AIC:                        -1.700e+04\n",
-      "Df Residuals:                    1513   BIC:                        -1.698e+04\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept     -0.0001   2.29e-05     -6.254      0.000      -0.000   -9.82e-05\n",
-      "mkt            1.0407      0.003    403.477      0.000       1.036       1.046\n",
-      "SMB            0.8707      0.005    181.700      0.000       0.861       0.880\n",
-      "HML            0.1050      0.005     21.744      0.000       0.095       0.114\n",
-      "==============================================================================\n",
-      "Omnibus:                       84.277   Durbin-Watson:                   1.984\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              260.380\n",
-      "Skew:                          -0.213   Prob(JB):                     2.88e-57\n",
-      "Kurtosis:                       4.985   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "small_table = quandl.get('NASDAQOMX/NQUSS')\n",
-    "small = np.log(small_table['Index Value']).diff().dropna()\n",
-    "small.name = 'small'\n",
-    "small_df = pd.concat([fama_5,small],axis = 1).dropna()\n",
-    "small_model = sm.ols(formula = 'small~mkt+SMB+HML',data = small_df).fit()\n",
-    "print small_model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  large   R-squared:                       0.998\n",
-      "Model:                            OLS   Adj. R-squared:                  0.998\n",
-      "Method:                 Least Squares   F-statistic:                 2.425e+05\n",
-      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
-      "Time:                        17:12:41   Log-Likelihood:                 9634.6\n",
-      "No. Observations:                1517   AIC:                        -1.926e+04\n",
-      "Df Residuals:                    1513   BIC:                        -1.924e+04\n",
-      "Df Model:                           3                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "Intercept     -0.0001   1.09e-05    -11.144      0.000      -0.000   -9.98e-05\n",
-      "mkt            0.9891      0.001    806.811      0.000       0.987       0.992\n",
-      "SMB           -0.1468      0.002    -64.463      0.000      -0.151      -0.142\n",
-      "HML           -0.0076      0.002     -3.305      0.001      -0.012      -0.003\n",
-      "==============================================================================\n",
-      "Omnibus:                       97.595   Durbin-Watson:                   1.959\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              365.063\n",
-      "Skew:                          -0.190   Prob(JB):                     5.34e-80\n",
-      "Kurtosis:                       5.373   Cond. No.                         221.\n",
-      "==============================================================================\n",
-      "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
-     ]
-    }
-   ],
-   "source": [
-    "large_table = quandl.get('NASDAQOMX/NQUSL')\n",
-    "large = np.log(large_table['Index Value']).diff().dropna()\n",
-    "large.name = 'large'\n",
-    "large_df = pd.concat([fama_5,large],axis = 1).dropna()\n",
-    "large_model = sm.ols(formula = 'large~mkt+SMB+HML',data = large_df).fit()\n",
-    "print large_model.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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@@ -1,115 +0,0 @@
-<h2>About</h2>
-<p class="p1"><span class="s1">The goal of this series is to introduce options to those who are option novices and have basic knowledge of applied mathematics, statistics and financial markets. We will primarily talk about the fundamentals of options and cover topics such as what are options, key terms and concepts option traders need to be familiar with(exercise and assignment, The moneyness, Intrinsic and time value of options etc.) After knowing the basics of options, we will teach how to use QuantConnect API to conduct your options research with over 4000 underlying stock symbols.</span></p>
-<p class="p1"><span class="s1">The following few options tutorials were created to help you understand exactly how options are used as the investment and risk hedging tools. We will further discuss the pricing method of options like BSM model and Monte Carlo method. And then several metrics to gauge the options risks like the Greek letters, different kinds of volatilities used in options pricing and trading. At the end of some tutorials, we will apply the knowledge in that tutorial to demonstrate some simple algorithms developed with Python on Quantconnect attempting to help you gain an insight into options trading and learn more efficient API tools to better customize your own trading algorithms. </span></p>
-
-<div class="row tutorials-page-about">
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-<h3>8 Tutorials</h3>
-</div>
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-<h3>3 Backtests</h3>
-</div>
-<div class="col-xs-12 col-md-4">
-
-<p><i class="fa fa-code" aria-hidden="true"></i></p>
-<h3>65 Code Snippets</h3>
-</div>
-</div>
-<h2>What Will I Learn?</h2>
-<div class="row tutorials-learn">
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>General Features of Options</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>QuantConnect Options API</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Options Pricing: Black-Sholes-Merton Model</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Stochastic Process</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Monte Carlo Method</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>The Greek Letters</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Historical Volatility and Implied Volatility</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Local Volatility and Stochastic Volatility</div>
-</div>
-<table class="table qc-table tutorials-table">
-<tbody>
-<tr onclick="window.document.location='/tutorials/introduction-options-general-features-options';"  class="clickable" >
-<td>1</td>
-<td>
-<h2>General Features of Options</h2>
-Options Contracts
-The Value of Options
-Option Moneyness
-Option Exercise and Assignment
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect';"  class="clickable" >
-<td>2</td>
-<td>
-<h2>QuantConnect Option API</a></h2>
-Option Data Access
-Option Contracts filtration
-Properties of Contracts
-Order Placement
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-put-call-parity-and-arbitrage-strategies';"  class="clickable" >
-<td>3</td>
-<td>
-<h2>Put-Call Parity and Arbitrage Strategies</h2>
-Options Payoff
-Put-Call Parity
-Synthetic Positions
-Arbitrage Strategy
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/stochastic-processes-monte-carlo-method';"  class="clickable" >
-<td>4</td>
-<td>
-<h2>Stochastic Processes and Monte Carlo Method</h2>
-Brownian Motion
-Wiener Process
-Monte Carlo Simulation of Stock Price
-Monte Carlo Simulation of European Options Price
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-black-scholes-merton-model';"  class="clickable" >
-<td>5</td>
-<td>
-<h2>Options Pricing: Black Scholes Merton Model</h2>
-Determinants of Options Price
-Factors of BSM model
-Model Assumptions
-BSM pricing Formulas
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-the-greek-letters';"  class="clickable" >
-<td>6</td>
-<td>
-<h2>The Greek Letters</h2>
-Delta (definition, impact factors, charts)
-Gamma (definition, impact factors, charts)
-Vega (definition, charts)
-Theta (definition, charts)
-Rho (definition, charts)
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-historical-volatility-implied-volatility';"  class="clickable" >
-<td>7</td>
-<td>
-<h2>Historical Volatility and Implied Volatility</h2>
-Historical Volatility (Definition, Calculation)
-Implied Volatility(Definition, Calculation, affect factors)
-Volatility Smile
-Volatility Skew
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-stochastic-volatility';"  class="clickable" >
-<td>8</td>
-<td>
-<h2>Local Volatility and Stochastic Volatility</h2>
-Local Volatility (Definition, Calculation)
-Stochastic Volatility(Definition, Calculation)
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-</tr>
-</tbody>
-</table>
diff --git a/Tutorial Series/Introduction to Options/Tutorial01 General Features of Options.html b/Tutorial Series/Introduction to Options/Tutorial01 General Features of Options.html
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-<h2>Introduction</h2>
-In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options markets are organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
-<h2>1. Definition</h2>
-<div class="page" title="Page 1">
-<div class="layoutArea">
-<div class="column">
-
-An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
-
-Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
-
-</div>
-</div>
-</div>
-First, we give the building blocks of an option contract and will discuss them further.
-<table class="table qc-table">
-<tbody>
-<tr>
-<td style="text-align: left;"><strong>Style</strong></td>
-<td style="text-align: left;"><strong>American Option:</strong> the holder of an option has the right to exercise his option at any time before the expiration date
-<strong>European Option:</strong> an option which can only be exercised on its expiration date</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Type</b></td>
-<td style="text-align: left;">Call option, Put option</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Underlying Asset</strong></td>
-<td>The security on which the option is bound. The underlying could be stocks(stock option), stock indices(index option), exchange rate(Foreign exchange option) or even futures(Futures Options).
-Note: The options in the tutorial refer to the stock options.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Premium</strong></td>
-<td style="text-align: left;">The price of the option. (Premiums are quoted on a per share basis).
-The option premium depends on the strike price, volatility of the underlying, as well as the time remaining to expiration.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Strike Price</strong></td>
-<td>The specified price at which the stock can be bought or sold when the option is exercised.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Expiration Date</strong></td>
-<td>The last day that an options contract is valid. All options expire after a certain period of time. The right to exercise the option will no longer exist once the stock option expires.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Participants</b></td>
-<td style="text-align: left;"><strong>Holder:</strong> People who buy the options(have long positions)
-<strong>Writer:</strong> People who sell the options(have short positions)</td>
-</tr>
-</tbody>
-</table>
-<h2>2. Options Contracts</h2>
-Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
-
-Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
-
-In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
-<h2 class="p1">3. The Value of Options</h2>
-The option's premium consists of two parts: the intrinsic value and the time value.
-
-\[Intrinsic Value_{call} = max(Current Underlying Price-Strike Price,0)\]
-\[Intrinsic Value_{put} = max(Strike Price-Current Underlying Price,0)\]
-
-From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
-
-\[Time Value= Premium-Intrinsic Value\]
-
-For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contact is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
-<div class="page" title="Page 243">
-<div class="layoutArea">
-<div class="column">
-<h2>4. Option Moneyness</h2>
-Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
-
-</div>
-<div class="page" title="Page 54">
-<div class="layoutArea">
-<div class="column">
-<table class="table qc-table">
-<tbody>
-<tr>
-<td style="text-align: left;"><b>At the Money(ATM)</b></td>
-<td style="text-align: left;">The strike price is the same as the current price of the underlying asset</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>In the Money(ITM)</b></td>
-<td style="text-align: left;"><strong>Call Option</strong>: the strike price is below the current trading price of the underlying
-<strong>Put option</strong>:  the strike price is above the spot price of the underlying</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Out of the Money(OTM)</b></td>
-<td style="text-align: left;"><strong>Call Option: </strong>the strike price is above the current trading price of the underlying
-<strong>Put Option:</strong> the strike price is below the current trading price of the underlying</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p class="p1"> For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.</p>
-<p class="p1">But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.</p>
-Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
-<h2>5. Option Exercise and Assignment</h2>
-When you are the buyer of an option you have three ways to deal with your options.
-<ol>
- 	<li>You can close out the position at any given point prior to expiration (For buyers, write options and for sellers, buy options);</li>
- 	<li>Wait until expiration date and out-of-the-money options will become worthless;</li>
- 	<li>Exercise the options which are in-the-money, resulting in a trade of the underlying stock (The seller will be assigned the obligation to sell or buy the underlying stocks)</li>
-</ol>
-For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
-<ol>
- 	<li><strong>Exercise:</strong> Exercising option means that the option holder executes the right to buy or sell the underlying assets at the strike price.</li>
- 	<li><strong> Assignment: </strong>When an option is exercised by the option holder, the option writer will be assigned the obligation to deliver the terms of the options contract. This is called the option assignment.</li>
-</ol>
-<div class="page" title="Page 10">
-<div class="layoutArea">
-<div class="column">
-
-To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
-
-</div>
-</div>
-<h2 class="p1">Summary</h2>
-<p class="p1"><span class="s1">An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.</span></p>
-
-</div>
-</div>
-</div>
-</div>
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html b/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html
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--- a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.html	
+++ /dev/null
@@ -1,158 +0,0 @@
-<h2>Introduction</h2>
-In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
-<h3>Option Payoff</h3>
-Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
-
-From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
-
-\[Call_{payoff}=max[0,S_T-K]\]
-<div class="page" title="Page 55">
-<div class="layoutArea">
-<div class="column">
-
-On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
-
-\[Put_{payoff}=max[0,K-S_T]\]
-
-</div>
-</div>
-</div>
-Where \(S_T\) is the price of underlying assets at maturity. K is the strike price.
-
-We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Contract Name</th>
-<th>Type</th>
-<th>Expire Date</th>
-<th>Strike</th>
-<th> Premium</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> GOOG170714C00940000</td>
-<td> Call</td>
-<td> 07/14/2017</td>
-<td>$ 940</td>
-<td> $7.5</td>
-</tr>
-<tr>
-<td>GOOG170714P00960000</td>
-<td> Put</td>
-<td> 07/14/2017</td>
-<td> $960</td>
-<td> $19.5</td>
-</tr>
-</tbody>
-</table>
-If you long these two options, the payoff at expire date would be as follows
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
-%pylab inline
-price = np.arange(900,1000,1)
-strike = 940
-premium = 7.5
-payoff = [max(-premium, i - strike-premium) for i in price]
-plt.plot(price, payoff)
-plt.xlabel('Price at T S_T ($)')
-plt.ylabel('payoff')
-plt.title('Call option Payoff at Expiry')
-plt.grid(True)
-price = np.arange(900,1000,1)
-strike = 960
-premium = 19.5
-payoff = [max(-premium, strike - i -premium) for i in price]
-plt.plot(price, payoff)
-plt.xlabel('Price at T S_T ($)')
-plt.ylabel('payoff')
-plt.title('Put option Payoff at Expiry')
-plt.grid(True)
-</pre>
-<img src="images/call-payoff.png" alt="call options payoff" />     <img src="images/put-payoff.png" alt="put options payoff" />
-
-The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay premuim for purchasing the contracts.
-<h3>2. Put-Call Parity</h3>
-Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next we will demonstrate how to derive the put-call parity according to John Hull's book.
-
-We consider two portfolios as follows,
-
-Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
-
-Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
-<table class="table qc-table">
-<tbody>
-<tr>
-<td> Payoff</td>
-<td> \(S_t &gt; K\)</td>
-<td>\(S_t &lt; K\)</td>
-</tr>
-<tr>
-<td width="33%">Portfolio A</td>
-<td>\(S_t - K+K=S_t\)</td>
-<td>\(0+K=K\)</td>
-</tr>
-<tr>
-<td>Portfolio B</td>
-<td>\(0+S_t =S_t\)</td>
-<td>\(K-S_t+S_t=K\)</td>
-</tr>
-</tbody>
-</table>
-From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolio must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
-
-\[Price_{call}+Ke^{-rT}=Price_{put}+S_0\]
-
-If the dividend is paid during the option holding period, the share holder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put–call parity relationship is(D is the present value of dividends):
-
-\[Price_{call}+D+Ke^{-rT}=Price_{put}+S_0\]
-<h3>3. Synthetic Positions</h3>
-<div class="page" title="Page 57">
-<div class="layoutArea">
-<div class="column">
-
-Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in an underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
-
-[ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017 <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
-
-There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
-<table class="table qc-table">
-<tbody>
-<tr>
-<td><strong>Strategy</strong></td>
-<td><strong>Content</strong></td>
-</tr>
-<tr>
-<td width="33%">Conversion</td>
-<td>Synthetic Short Position: short call + long put
-The actual stock position: long the underlying stocks</td>
-</tr>
-<tr>
-<td>Reversal</td>
-<td>Synthetic Long Position: long call + short put
-The actual stock position: short the underlying stocks</td>
-</tr>
-</tbody>
-</table>
-Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
-<h3>Algorithm</h3>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
-<h2>Summary</h2>
-<div class="page" title="Page 264">
-<div class="layoutArea">
-<div class="column">
-
-In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
-
-Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
-
-</div>
-</div>
-</div>
-&nbsp;
-
-&nbsp;
-
-</div>
-</div>
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.ipynb b/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.ipynb
deleted file mode 100644
index f8720db..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial03 Put-Call Parity and Arbitrage Strategies.ipynb	
+++ /dev/null
@@ -1,99 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%pylab inline "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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d8q6oOOYM1Zd31NcoSrPWzNoChH/XRRyPSErZtbeIH02YwaIvt/HIFVnlFgmR\nqpCKheJlYGj4fCjwUoSxiKSUomLn+kkfM3XpRu67+FgGdT8k6pAkBqK+PfZp4CPgKDNbZWYjgbuB\nM8xsMfDtcFgk9oL+TXP51/y13HFuDy44rn3UIUlMRHqNwt0vK2PS6TUaiEgtcO+/FvF03kquG9SN\nESd3jTociZFUPPUkIiU89v5SHsn9jMsHdOLG7xwZdTgSMyoUIinuufxV3PXqQs7pfSi/OV/9m6Tm\nqVCIpLC3F67lf5+bw0ndWvHAD/tQt46KhNQ8FQqRFDV92UaufXImPdsdzF8H9+OgeurfJNFQoRBJ\nQQvXbGXE+Om0b9GIx4f1J+OglP5urKQ5FQqRFLNiww6GjMujSYN6TBw5gFbq3yQRU6EQSSHrtu1i\n8Lhp7C0qZuLIbNo3bxR1SCIqFCKpYuuuvQwdN511W3czblh/jshsGnVIIoAKhUhK2LW3iKtyZrB4\n7TYeubIvfTvp97okdegKmUjECouKue6pj5m+bCN//GEfBh6l/k2SWnREIRIhd+eW5+fy1sK1/Oq8\nnpzfR/2bJPWoUIhE6O5/fsLk/FVcf/oRDDmhS9ThiJRKhUIkImPe+4y/vreUwcd35oZvHxF1OCJl\nUqEQicDkGSv53WufcO4xbbnzvJ7q3yQpTYVCpIa9uWAto5+fy7eOaM39l6h/k6Q+FQqRGjRt6Qau\ne2omvdo349Ers2hQT/8FJfXpX6lIDZm/egtX5cygQ9i/qYn6N0ktoUIhUgOWb9jO0HHTadow6N/U\nskmDqEMSqTAVCpFqtm7rLq4cO42i4mImjBxAO/VvklpGx74i1WjLzr0MGZfHhoI9PPWj4+l2SEbU\nIYlUmo4oRKrJzj1FXJUznc++KuCvg7Po07F51CGJJEVHFCLVYG9RMdc9NZMZyzfx58uO41tHtIk6\nJJGk6YhCpIoVFzu/eG4Ob3+yjl+f34tzj2kXdUgiB0SFQqQKuTu/e20hz8/8gp99+0gGH9856pBE\nDpgKhUgVevTdpTz2wecMPaEzPz29W9ThiFSJlC0UZnaWmS0ysyVmNjrqeET255npK7jn9U8479h2\n/PJ76t8k6SMlC4WZ1QUeAs4GegCXmVmPaKMSKdvr877klufncsqRbbjv4mOpo/5NkkZSslAA2cAS\nd1/q7nuAScD5EcckUqqPPtvATyd9zLEdm/PolX3Vv0nSjrl71DH8FzO7CDjL3a8KhwcDA9z9uoR5\nRgGjADJJPH6/AAAMAUlEQVQzM7MmTZqU1LoKCgrIyIjfl6DimHd15LxsSxF35+2iZSPj1uxGZDRI\nvSMJbev4qGzegwYNynf3fvubr9Z+j8LdxwBjAPr16+cDBw5Majm5ubkk+9raLI55V3XOn6/fzo2P\n/JtWTRvx92tOoG2z1GzNoW0dH9WVd6oeI38BdEwY7hCOE0kJa7fuYvDYaTgwYWR2yhYJkaqQqoVi\nOnCEmXU1swbApcDLEcckAsCWHXsZMjaPTdv3kDM8m8PbxO8Uh8RLSp56cvdCM7sO+BdQFxjn7vMj\nDkuEnXuKGJEznc/Xb2f88P707tAs6pBEql1KFgoAd38NeC3qOET22VtUzLVP5vPxik08dHlfTuzW\nOuqQRGpEyhYKkVRSXOzcPHk2UxZ9xf9d2Juze7eNOiSRGpOq1yhEUoa785tXF/DirNXcfOZRXJbd\nKeqQRGqUCoXIfjw0ZQmPf7iMESd15dqBh0cdjkiNU6EQKceT05Zz3xuf8v3j2nP7d49W/yaJJRUK\nkTK8NncNt784j0FHteHei45R/yaJLRUKkVJ8uGQ9N0yaRd9OLXj4iizq19V/FYkv/esXKWHOqs2M\nmjCDrq2bMG5ofxo1qBt1SCKRUqEQSfDZVwUMe3w6LZo0YMLIbJo1rh91SCKRU6EQCa3ZspMhY/Oo\nYzBx5AAyD24YdUgiKUFfuBMBNm3fw5CxeWzZuZdJo46na+smUYckkjJUKCT2duwpZETOdJZv3EHO\n8Gx6tVf/JpFEOvUksbansJhrnpjJ7JWbefDS4zjh8FZRhySScnREIbFVXOzcNHk27376FXdf2Juz\neh0adUgiKUlHFBJL7s6v/jGfl2ev5n/POopL1b9JpEwqFBJLf35nCTkfLeeqk7tyzanq3yRSHhUK\niZ2JU5dz/5ufcmHf9tx6jvo3ieyPCoXEyitzVnPHS/M4vfsh3PMD9W8SqQhdzJbYmLe+iD+9OYt+\nnVvw0BV91b9JpIJiXSimLt3APXk7GbN4atSh1LhNm+KXd/6yXRzepimPDe1Pw/rq3yRSUbEuFMXu\nFHnwW8hxE8e8e7WuyyMjsmnWSP2bRCoj1oXixMNbc+uARgwceGLUodS43Nzc2OWdm5vLIerfJFJp\nOkkrIiLlUqEQEZFyqVCIiEi5IikUZnaxmc03s2Iz61di2i1mtsTMFpnZmVHEJyIiX4vqYvY84ELg\nr4kjzawHcCnQE2gHvGVmR7p7Uc2HKCIiENERhbsvdPdFpUw6H5jk7rvd/XNgCZBds9GJiEiiVLs9\ntj2Q+C2wVeG4/2Jmo4BRAJmZmeTm5ia1woKCgqRfW5vFMe845gzxzDuOOUP15V1thcLM3gJKa/B/\nm7u/dKDLd/cxwBiAfv36+cCBA5NaTvB9guReW5vFMe845gzxzDuOOUP15V1thcLdv53Ey74AOiYM\ndwjHlSs/P3+9mS1PYn0ArYH1Sb62Notj3nHMGeKZdxxzhsrn3bkiM6XaqaeXgafM7H6Ci9lHAHn7\ne5G7t0l2hWY2w9377X/O9BLHvOOYM8Qz7zjmDNWXd1S3x37fzFYBJwCvmtm/ANx9PvAssAB4Hfgf\n3fEkIhKtSI4o3P0F4IUypv0W+G3NRiQiImXRN7PDC+IxFMe845gzxDPvOOYM1ZS3uXt1LFdERNKE\njihERKRcKhQiIlKuWBQKM7vezOaFjQhvCMe1NLM3zWxx+LdFON7M7MGwMeEcM+sbbfTJKSPn35vZ\nJ2FeL5hZ84T506IZY2l5J0y70czczFqHw2m7rcPxPwm393wzuzdhfNpuazPrY2ZTzWyWmc0ws+xw\nfK3c1mY2zszWmdm8hHGV3neZ2dBw/sVmNrTSgbh7Wj+AXgRNCBsT3OX1FtANuBcYHc4zGrgnfH4O\n8E/AgOOBaVHnUIU5fweoF85zT0LOPYDZwEFAV+AzoG7UeVRV3uG0jsC/gOVA6xhs60Hh84PC+Q6J\nw7YG3gDOTti+ubV5WwOnAH2BeQnjKrXvAloCS8O/LcLnLSoTRxyOKI4meMN2uHsh8C5B59rzgZxw\nnhzggvD5+cAED0wFmptZ25oO+gCVmrO7vxEOQ9BTq0P4PF2aMZa1rQEeAP4XSLx7I223NXANcLe7\n7wZw93Xh/Om+rR04OJynGbA6fF4rt7W7vwdsLDG6svuuM4E33X2ju28C3gTOqkwccSgU84BvmVkr\nM2tMUHU7Apnuviac50sgM3zeHliZ8PoyGxOmsLJyTjSC4NMHpEfOUEbeZnY+8IW7zy4xfzrkXda2\nPjIcP83M3jWz/uH86ZAzlJ33DcDvzWwlcB9wSzh/uuQNld93HXDuqdbCo8q5+0Izu4fgkHQ7MAso\nKjGPm1na3Ce8v5zN7DagEHgymgirRxl5HwTcSnDaLe2Us63rEZxqOB7oDzxrZodFFmgVKyfva4Cf\nuftzZnYJMBZIpu9crVBT+644HFHg7mPdPcvdTwE2AZ8Ca/cdeoZ/9x2aJ9WYMNWUkTNmNgw4F7jC\nwxOYpEnOUGre8wnOxc82s2UEuc00s0NJk7zL2NargOfD0xB5QDFBw7i0yBnKzHso8Hw4y2S+Pq2W\nNnlT+X3Xgece9cWamnjw9YW8TsAnQHPg93zzgtC94fPv8s0LQnlRx1+FOZ9F0EerTYl5e/LNC5xL\nqYUXOMvKu8T0ZXx9MTudt/XVwK/D8UcSnHqwdN/WwEJgYDj+dCC/tm9roAvfvJhdqX0XwZHl5wQX\nsluEz1tWKoao34QaeqPfD3eQs4HTw3GtgLeBxQR3TLQMxxvwEMHdIHOBflHHX4U5Lwl3GLPCx6MJ\n898W5ryI8K6R2vgoLe8S0xMLRTpv6wbAEwTn8mcCp8VhWwMnA/nhuGlAVm3e1sDTwBpgL8FR4shk\n9l0E1ySXhI/hlY1DLTxERKRcsbhGISIiyVOhEBGRcqlQiIhIuVQoRESkXCoUIiJSLhUKEREplwqF\n1FpmVhS2k55nZpPDnj+lzfdaYkv1Ko6hi5ldXsr43mFss8xso5l9Hj5/q5R5bwtbZc8J5xlQxrpe\nCKcvMbMtCcs/sZR5/2hmp5QYd2cpMY6vbM4SP/oehdRaZlbg7hnh8ycJvoV7f8J0I/g3XlyNMQwE\nbnL3c8uZZzzwirv/vZRpJwD3E3ybeLcFv5XRwN1Xl5y3ous0s1bAq+5+fDh8MvAHoDPBFy5Hu/vb\n4bS3gBHuvqIC6UpM6YhC0sX7QLfwE/4iM5tA8K3kjma2zL7+saIh4Sf32WY2MRzXxsyeM7Pp4eOk\nkgsPl/u+mc0MH/s+xd9N0MV0lpn9LIm42wLr/et24OvLKxIV9APg9YTh+wlarD9K0CBvccK0fwCX\nHuD6JM2pUEitZ2b1gLMJ2hYAHAE87O493X15wnw9gdsJ2lkcC1wfTvoT8IC79yfYyT5WymrWAWe4\ne1/gh8CD4fjRwPvu3sfdH0gi/DcIitmnZvawmZ2axDJKOomgjcU+e4A2AO6+pcTRwwzgW1WwTklj\nad9mXNJaIzObFT5/n6CldDtguQc/3FLSacBkd18P4O77fhDm20CP4EwVAAebWYa7FyS8tj7wFzPr\nQ9DO+siqSMDdC8wsi2BnPQh4xsxGu/v4A1hsW+CrhOFRBL9oeJKZHQXc4u7LwmnrCN4zkTKpUEht\nttPd+ySOCHf22yu5nDrA8e6+q5x5fgasBY4N5y9v3kpx9yIgF8g1s7kErbLHH8AidwINE5a/APie\nmf2WoL30WILOqoTz7TyAdUkM6NSTxMk7wMXhxV7MrGU4/g3gJ/tmCo8aSmoGrAkvjA8G6objtwFN\nkw3IzI4ysyMSRvUh+F3vA7GQ4Pej962jV/h0L0En2cR4jyS4liNSJhUKiQ13nw/8FnjXzGYTXOQF\n+CnQL7zIvYDgtxxKehgYGr6uO18ftcwBisKL48lczM4AcsxsgZnNAXoAdyaxnESvAgMThq81s38T\ntKh+lODC9j6DwvlFyqTbY0XSkJl9AJzr7psTxt3p7ncmDB8EvAuc7O6FNR+l1BY6ohBJTzcS/PJb\notwSw50IvlOhIiHl0hGFSAoysxcIfqo00S/c/V9RxCPxpkIhIiLl0qknEREplwqFiIiUS4VCRETK\npUIhIiLl+v+FudKxQidIjgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106d33c90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "price = np.arange(900,1000,1)\n",
-    "strike = 940\n",
-    "premium = 7.5\n",
-    "payoff = [max(-premium, i - strike-premium) for i in price] \n",
-    "plt.plot(price, payoff)\n",
-    "plt.xlabel('Price at T S_T ($)')\n",
-    "plt.ylabel('payoff')\n",
-    "plt.title('Call option Payoff at Expiry')\n",
-    "plt.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Lsft2jrosEclwOuspzbTObcUdp/WndNft+fX9b1L+3vKoSxKRDKdGkYYKW+cwfuQAenUq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\nAriyzQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106d06690>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "price = np.arange(900,1000,1)\n",
-    "strike = 960\n",
-    "premium = 19.5\n",
-    "payoff = [max(-premium, strike - i -premium) for i in price] \n",
-    "plt.plot(price, payoff)\n",
-    "plt.xlabel('Price at T S_T ($)')\n",
-    "plt.ylabel('payoff')\n",
-    "plt.title('Put option Payoff at Expiry')\n",
-    "plt.grid(True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.html b/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.html
deleted file mode 100644
index e247c0d..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.html	
+++ /dev/null
@@ -1,180 +0,0 @@
-<h2>Introduction</h2>
-Last few chapters we introduced about the basic principles and mechanism of options trading. We already knew what an option contract is and the basic relationship between call and put options' price. But how do these contracts traded in the exchange are being priced and where does the option premium come from? In the next few chapters, we will discuss the pricing of options.
-<h2>Stochastic Process</h2>
-In order to value the derivatives like options, the most significant part is to find a model to represent the underlying stock price so that we can price the options based on the underlying price.  We usually use the stochastic process to model the security price.
-
-First, you need to know what the stochastic process is. We say any variable that changes over time in an uncertain way follows a stochastic process. The price of a certain stock at a future time t is unknown at the present so it is a random variable \(S_t\). Then we can think of the movement path of the stock price is a stochastic process since\(S_t\) is a random variable at each time t in the future.
-<h3>1. Brownian motion</h3>
-Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
-<div class="page" title="Page 327">
-<div class="layoutArea">
-<div class="column">
-
-Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables {\(W_t\mid t\geq0} \)} indexed by time t. The one-dimensional Brownian motion is called Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
-<ol>
- 	<li>\({W_0=0\)</li>
- 	<li>For \(t\geq0\) and \(\Delta t\geq0\), the increment \(W_{t+\Delta t}-W_t\) is normally distributed with mean 0 and standard deviation \(\sqrt{\Delta t}\) .</li>
- 	<li>For any partitions \(0\leq t_1&lt;t_2&lt;\cdot\cdot\cdot &lt;t_n\), the increments \(W_{t_1}- W_{t_0},W_{t_2}-W_{t_1},\cdot\cdot\cdot, W_{t_n}-W_{t_{n-1}}\) are independent random variables.</li>
- 	<li>With probability 1, the function W(t) is continuous at t.</li>
-</ol>
-Intuitively understanding of the definition, Wiener process has independent and normally distributed increments and has continuous sample path.
-
-Next, we simulate the Wiener process and plot the paths attempting to gain an intuitive understanding of a stochastic process. Each path is an independent Wiener process.
-<pre class="prettyprint linenums">import numpy as np
-import matplotlib.pyplot as plt
-%pylab inline
-def wiener_process(T, N):
-    """
-    T: total time
-    N: The total number of steps
-    """
-    W0 = 0
-    dt = T/float(N)
-    # simulate the increments by normal random variable generator
-    increments = np.random.normal(0, 1*np.sqrt(dt), steps)
-    W = [0] + list(np.cumsum(increments))
-    return W
-t = np.linspace(0.0, N*dt, N+1)
-plt.figure(figsize=(15,10))
-for i in range(5):
-    W = wiener_process(10, 1000)
-    plt.plot(t, W)
-    plt.xlabel('time')
-    plt.ylabel('W')
-    plt.grid(True)
-</pre>
-</div>
-</div>
-</div>
-<img src="images/wiener-process.png" alt="wiener process"/>
-<div class="page" title="Page 328">
-<div class="layoutArea">
-<div class="column">
-
-[ref]Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.[/ref]In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
-
-\[\Delta x=x_{t+\Delta t}-x_t=a\Delta t+b\epsilon\sqrt{\Delta t}\]
-
-Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
-<h3>2. Stochastic process followed by stock price</h3>
-Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
-
-On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
-
-In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
-
-</div>
-</div>
-</div>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
-
-Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
-<div class="page" title="Page 338">
-<div class="layoutArea">
-<div class="column">
-
-Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
-
-</div>
-</div>
-</div>
-The discrete approximation form of (1) is
-
-\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
-
-We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
-
-\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
-
-Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln S during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
-
-\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-
-\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-
-Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
-
-\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
-
-Equivalently
-
-\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
-
-If we change t to 0 and change \(\Delta t\) to T, we get
-
-\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
-
-according to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
-<div class="page" title="Page 333">
-<div class="layoutArea">
-<div class="column">
-<h2>Monte Carlo Method</h2>
-Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
-<h3>1. Monte Carlo Simulation of Stock Price</h3>
-We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
-data = quandl.get('WIKI/GOOG')
-close = data['2015-01':'2017-07']['Adj. Close']
-annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
-annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
-mu = annual_return # 0.39644
-sigma = annual_vol # 0.2344
-s0 = close[-1] # 903.5
-T = 3.0/12
-delta_t = 0.001
-num_reps = 500
-steps = T/delta_t
-plt.figure(figsize=(15,10))
-for j in range(num_reps):
-    price_path = [s0]
-    st = s0
-    for i in range(int(steps)):
-        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
-        price_path.append(st)
-    plt.plot(price_path)
-plt.ylabel('stock price',fontsize=15)
-plt.xlabel('steps',fontsize=15)</pre>
-</div>
-</div>
-</div>
-<img src="images/stock-price-path.png" alt="stock price path"/>
-<h3>1. Monte Carlo Simulation of European Options</h3>
-Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
-
-The essence of using Monte Carlo method to price the options is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
-<ol>
- 	<li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
- 	<li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
- 	<li>Calculate the payoff of options according to the \(S_T\)</li>
- 	<li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
- 	<li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
-</ol>
-There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
-<pre class="prettyprint linenums">def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
-    payoff_sum = 0
-    for j in range(num_reps):
-        st = s0
-        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
-        if option_type == 'c':
-            payoff = max(0,st-strike)
-        elif option_type == 'p':
-            payoff = max(0,strike-st)
-        payoff_sum += payoff
-    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
- return premium
-mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
-</pre>
-Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
-<h2>Summary</h2>
-In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
-
-In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
-
-</div>
-</div>
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.ipynb b/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.ipynb
deleted file mode 100644
index ac941c0..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial04 Stochastic Processes and Monte Carlo Method.ipynb	
+++ /dev/null
@@ -1,214 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%pylab inline "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def wiener_process(T, N):\n",
-    "    \"\"\"\n",
-    "    T: total time\n",
-    "    N: The total number of steps\n",
-    "    \"\"\"\n",
-    "    W0 = 0\n",
-    "    dt = T/float(N)\n",
-    "    # simulate the increments by normal random variable generator\n",
-    "    increments = np.random.normal(0, 1*np.sqrt(dt), steps)\n",
-    "    W = [0] + list(np.cumsum(increments))\n",
-    "    return W"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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UcH24dtztd4ecV2YtA2CMZQxTk6ZS016D1+/tcr1gpWJi4sQuz30r41skRCRoP/9qzq/4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pDGYwn2XjfPr6vhYEtgIKS4bFZ32OiwOcnwVc3DiZ69182mGqnKvPTtBWh9fTn/78Ip/OCcoQXH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G3OHErn3wGAf8POR4SGqrq776biu98jUFlJsG4b0L3q6L7GMWIEobo6Gv/xOLW33U7bv/4F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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a205c90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "t = np.linspace(0.0, N*dt, N+1)\n",
-    "plt.figure(figsize=(15,10))\n",
-    "for i in range(5):\n",
-    "    W = wiener_process(10, 1000)\n",
-    "    plt.plot(t, W)\n",
-    "    plt.xlabel('time')\n",
-    "    plt.ylabel('W')\n",
-    "    plt.grid(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import quandl\n",
-    "quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'\n",
-    "data = quandl.get('WIKI/GOOG')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "930.5"
-      ]
-     },
-     "execution_count": 236,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "close = data['2015-01':'2017-07']['Adj. Close']\n",
-    "annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1\n",
-    "annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)\n",
-    "s0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x119954cd0>"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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zadQoHhuQ12XFVNHrO0IitP89yH7kYUz9+1Pzm98Qbt13hfl4JkFRCCGEEEIc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7AAAg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2zKMsyEXVKTm2TgejQhvI1iWgVPkxNvtWRIUWBBnM/4GAKx7E3OvAoHeRqjoAHidC5BAmiGos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hUIgoB15YWgKHg//AhrUjKm9AwAZly+ilFdBf3SVTy1z8XYYICZK/PxBKMM4qQ5Ywcqk8Kc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1HIemj0hMwlc0EgVM0TRz3H8FDYOPYAib8Ew7g88okCKoOFgSQS1GaJJzyBLb8Gg7KXaN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\nuVztpbbzcdriDZynkXDBMQ747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guSlERM4jEcyx6ndXouze+cQgESGTbCJgtLwgd4tOdJ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\nJwK8VfM0qZp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g0aXoqrPxdWEVbZkwTelSNmoq58QcBlGmsTiDvSjJdLuJy8vn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efZt30T37r9MQKVlZjOz2KaDuJAxz0YeFaV+fFikrTbhD8eZcu5k6w/d5CU3sa6kSOsnTuB\nKEgsLL+fRvsKPrw0SWfXYaZstaycOEPn5d+RMer51jWdDElXSdu6EZUCZ2eeIHnqJ8SVGTqMjSx3\nb2Nltgt3wcMnFu9BLk2yqnSCkxVV7E8JSMYFVtBI9aZ50AQqxsvptoepRc+g3s5nSylKV3s4qb5F\nuroVg5KjMrQXRVNIe3ehAZNeG6um57iYi9OS1LjtrVM0vdPPXW8+iWdGx2RjB9WLBlIWC/F0LU6j\nh6G2lcxU1ZCw/J+dKEq7d+/+Xx7as2fPD4D/tnv37vF/xWYAPrt79+7H/+3d+7fj5z//+e6Pf/zj\n/+uDf+L/fkb3wcUXIBMCmx8SARh6Da7/W7BXkj65QH4shuyTKEWLOG9vRhAFBgcHOXbxh5yyx0iL\nIl27vkf9rh/w2xEjFwYu0tnZidnfQOzwk4hKEwJtiGUNBH/5JKJsQOfvJTVyhr6TFzEsSPglL0Ky\nFaVpC0vrb6Ja38Cl+B8YfOkJjh59j9DEGCGTmaKcRTSsIhVfpJJh7lqpYjb3YU+VKBo1aspGWHE1\nzKShnOeSW/DWW8mHjHQubMarrsUvXWGjMs3zBoXlNpX684+R1MfYuvkanpcruSGk0Ewl4dKdZJJr\nacRPZ1kTarLA9CWBVC5GxYanyMgav1nUcW/QxmFLnC53lHx9gPRSB8m5DlpmFjGOmjAJ11K48iwF\nKcXSJ+2cKzqp8ORYdlHAGY2zvPEMxv40ptEMoTYLq3um/sfThCQzCzYDrdctENVk5pUqLPEoPc4F\nzistCMEgvnSBymiEw1/cQL0QY71xlL1aI0VDmnsKUSrFEPeVlrholHjeYWNer8OhKKzOlpjVi4zb\nQvQzxNWyEk9sXKLfO0xtGax05em72sFFIc5We57FjJOyizqWzAHKsmZM6U6KWgsrxP1sSyr45QzL\nLe+wTp3mGYcdpDxbM3l+ay3jjapxbq49yZr6GVAERLNKbp8N+6UctNhwFyxcNaYxlO3HOdNLUm3E\nqqRoULrxqwZO6SdpTF7g9aq7WKr0oT8XYWixHI8+wxfqQpimphGVSt4xHaDGl2VTNsfCKQdOyzL2\nF0dpMtbjcURJtu1FKBo4H2vmlcXVVLoDbMuP0FiaJSk5iGUlLEtODKqJvytWM2AKMybGMAhJFMWE\nOTiNlClAvYW8doEf1BzHqJWYT2wiUNyFWmvl4dyvsZ/fTloHaccQDsMMf7V0GzmW2CtHSGdrKRVd\nFBUjbUKYGdVDAWiWnmK9/BPOFs3EIirnLE3sxYpXSiBlHsJn+QespRS7tYeoCAcYWLaWFxruZYd2\nkOrJ23AK7/HfWzbgDF1FyWscWO8gKB2lJDdiiv2AKlEhVSxytCdMx4IRR8LAhDtB56QfnWmU1dYp\nwj4JsSVDulwiWCXTUz5IMOrj3ovnaAmN4zg5jmV2iguda/l57QfYMHkIs7mJ0ZZuhmsb+f7VX/Cs\nfxsjPgdfO2+kvqgng8Y3qr7MHxyjdM51IMgrkI0i/3izE4oqb9ieICcUqUreR527ign5EGhlpC31\nXKg18V6HifEKmapogVx/hK8G8ng0gZPpvVxjeoPRTBmPWx6lVVfPikUHWVniF9d7UaWTdFnmELPT\nnNXGeXNlH9/gIGO5zaQXl1G1/xju6V6CnjU4ddM829PIY08+RypoAEWgfOICusrVvJM5h1nJUBBk\nzGqOtGAiYvdRbY4yXCrwB4vKWb2IcWYF8tTnycVrycVbiJvneK3lN3QtbsaT3kK4KLFgH+dQlx1V\nNLOtz8WWjhMEs16IpSlKImWRGB55FDkyQTzTSyKXIWM089KuD1GYdnMi2AGSii6+itHw9XSb3yBk\nsPGe6xpixn56xvPIeZlnjVs5PLWe5vEJ1KRIx85R5k1WqjOrsS+sJ7nxRfy9QXLrVVKbNNJbVaQB\nCWcwybjXRWV+EV1OY93pMazXDpOY7iQ5J3P59pX0+TdhyWZ5+KXXWNykw7I1TjJRRmmml3A+StN8\nhJULo7x24y7OejbxztWtZEIy+RMWDAcWqZ6YxpoNYI4tsfrMJQwjGYJI4KnnyR1xbhR20CsOoWZi\nOOZL2Hp2EKucZVGdJXUFVnt34DLbkBQjoqZnYiGNsWIK2afhOltADoTRZxRmzDeyzAx6vRHm+ykW\nkszXrsNknSHd/Qo6UaHVPoIkigRTswSHLMjWHK71cd5qXEe90E1waZGWSBYlO0kpO4HOcpqVuhHK\nFyeJyiaybSUMiEQiddydW0fKWORoxQimhSSh7AwJu0La7UEOraLBINKadRGXx2jIzzBe7ma77U3m\nhm8hP7idOtvrpIxzLEku6s0VKNYIK381jiucwbGUItKlIxGyIuUyXDE28VrgesqmrTxdkHh1ahOZ\noJ0tb4+zZDOjCmCKaXia70In6BBLKeaLMoFjIq2jM1yoqqWgE/CmshRkDZehgRrWs764hi1ZD5H+\nX/PctTdxasUm7i7VY0kGOWe+SP7KcSrr78egs7IsZoL+3yIYSvhTq6g2r+AHFU9zQU7zu7H7yIhm\nrlgaqMvFqJ+/yNLcZRz113BBUHAJEl/DxAOihS0qnBfmWLN4BH9+ge7rruArD5ObW41Va8TdNkq+\nUI/LHuKmxLUMSgGSopnqXBUGQY/fP4LelCFHAatvgPzQIDp5C5URHfWLY0y03wGFEppUTUo5SmXF\nZjrkWtLpKFx6jqvrbsUneRCrVlNMzJMyV6C030lITXPONM6cLYAlW8KabqfO2kFTww5mTJXkVRMe\n2cCU7GdaynHYvMT98ruMlep4t2c9+kKWNbovsyW9iW8WPWxCT6BwhdPzr6KaHVQmi1hSCq85eqFM\nz5rRE9jTSa6tepivX9PGzkgQS8bC92uXgVyiajqAoZDkxU163KkIjaFBckIITyKDMZNDF59hr2OJ\ns6Xj9NlkFiZfYa8m0Wb2sS3VxqL7Z4zN+Yk6DtMYWsMNFROYHHnCI23kkwEare9S2/seB+e3olOh\nGZSxAAAgAElEQVRFXkfk1sh67qcDWbKSKKb4pX8Xg5KZ9twoeVmkdT6EY6nEieQkT5WvIi5YmdLV\nkaoYo8e9RM+RSa60tRIs85PVmVnwlPNO5Rp6epqpr6r/Twt5/zX27Nkzv3v37p//MWf/WEVRAiz/\nE5sFkP/Ie/7En/jPZ/hNMNihZj0c+Ae48gZYyqBiJfB+2amuykR+cBKDehbhva9TSoW5+sZ3OOea\noEXTUW7y8cLIC+hlmXvvuw+AF154gQt947yxuBOT9DM0DCSvrsK66zvYbvscAFqgnNWLtVjlh4gr\nH0fXeAPp6gYUEhysPM9X1h/gqbVBRK3ItqEpHrQm+dDdX6WxCXbW9HN30xmU0OuU9ALzV25Bu9DA\nos/IrxpcfLdpPTW313J11SVe6H4cs0vHMkHmovGLRHUSS6KAFGnAUO7GXCxHsDRyORjBqvsxSUM/\nZxQj0aKMPm8heXKB7x/6Rw6UP8HJrgOYPHkOpyXuWChwLwe5x/r+igAdIpvu3EQxbSV0wotjbjOK\nEiMXmEdoz5MeNfJnZzL8zeIcV5a14w2FmelzErliw9GcpnfFNCrvq7URo4ytIkPjjQH0ZWmqNy+y\nesd7LGzWE7HJWBcm0CkqyyLTLP2NRlt1H741o8zZmviZQeYTZdWczl5Diz1MOGvCP/d+tbw91sZr\n00t8bSmKWdOoSFm47J3iDfdBfGolLUvX0VuWJVgU6BOXOBl3kgxXcF/La+TMYwiaQLt0GUmvkQm3\nMlC8gbQqM1Lo5UTyAYwFB+viNl6y2fiLygoe9xl52JNnpzvD0qSVxiMFKEGyTcCSybBx5AQRUwbZ\nMgKAkKrHoqX5IfvoLOo4Zc5w3OoHh4bilhESRXSRLD5TkL/b8nXyy58h+F9DzNzyC853CSyP6Hhx\ntpuoaMUVT7PavpVF41GsqSZ0o+t4aWoHP+p/jFvtQ3zWNodN7X3/7QodbE30oJbK+K6oI4bC3+f1\nHGprRFN1oKrYZ0OMNnRyxrmW57wZ5vQaO+c/yncXH6K9wY4vp3LHietxqCL3VBynulhO1BDlqiFA\nteFvKZS9DaqJ6VwNKyrPErGqSIJCLUF+qt7IlGpjcUGjJzvM7tYf4BEi/EX+z/iLUhhfdo6nym9l\nUPe+qtwYusq1CwdYOdWKmttGTjKwKdfHW9vu5K0tIiO2w6wLRfjMU/9E22KBt+sDvLsujDVrYNNE\nFytSrWTlIjGPD2c+gl4sMlVmxZhTaD6Sp/+kj4rxPNFDDfQtNjJ/roAzFuKgfx3723eSbi8n6XBS\naXQw1lqHJxIkuxTlVI2F2nSKhoyGBny+7rucs6hEiuUoulYEQcKRn6duqchA3TRx8wRZ7ToG1tfw\n++ZuFF0lUUMf9oyKXgGTAjf1B/j22QRissglBLJSDDVzDrOuyC/k2yn3LtGTFFElPfAaRTHCK+bP\nMhX24l0ZQ99bYn8ggN65QMWaJynGa3hl4y2MNW4mQYHf2zScqQXsmTSLDgtnGiuRiwWmz/2Atvgk\nRsGIIhcQVA13KcbMVT1js1ZGSyJ6QcOnibzivMyZlh/wpCFKKVXOOc9Zeu2zyAUQEIl5szy7pYar\nFZX0jKYx5pPMH3+MTfWXkAy9THsdDJfGyY1cRLdPJj47gQacu/Z20mYb88WNmFT4Ue5uHpeXsWgN\nMU8D7Y7zSFoBf3oZosmBOzXOVxz/yIOBF8jN6YmWd+Ixmqj1h/GM38HFhldxVkUwvybh+b6O5Es+\nFEkke0cRc77EZcNKRE1jc/QELbqLCKJKMmznrR0f5xXzbWzRDvDt0f+KqGk8sexDFCWJgej1XOjY\nxfnaLk42VFOfXODrv/opn+p/HWm2xKHZLVgyN9K6EGbBYWZ2j8aJ7f+FxfKVNEwVMQlN+LrzrMrc\nwnUFWO47Q3koQ9YvMBdpJzbtxV45T7N3Fa3WDHLBQUHLA7BKfzfRSzdSMNpJ36wQujvIwq19dKx+\nEbc5gwmRrLcNMTTGbl+RpdbnEYtm5LMfRrKGmfe+ycTbNYBGy+3TVLcv8C3t89zsfYJ16yV6Hh6l\nYl2SJYOO9KKNxDDIeVjsHWdASjMSaqZFsiIDw+VvoxQaOLBqiZyscLw9REyyoSlGRjMiebWXvslr\niQ/H+FjpZ2Qj9YxdvY2U+zhzlhxDuha84Sn0rhnaf6HDOKvw6iObUBwa3v73l6nrJIXbk2+QVWR+\nUthOcH4rvYqHTQcnkEsl4rWtOBp34NSs6AU9OSWDSWdjasJA79ExYiYDcZMbzJsoWA3sLH+QdaVH\ncU1dT9KmoDN7sDXfwkT9+1Ohf1iX41N1v+Kbjc/w3E1l2A3l7HUeRVJEuvy7eNd7D6u82xg2XuWo\naYCTrqPYy9/mKU2mITvNydoNSJ5WqhLzxPNxtsoKX8JAGSUWslfp1+VYr7MhmFu4sbGaZVe/ROfI\no2w3W9gh+7h9/gF6NQ+OrB+rZsAYj9CoC7PdJnG93UBvtotr4lu5qdDLkwmVH984xqzlIJrpGiKV\n69kq+5FLQwiSHclyI13SWgAux94vtS+fnuecrPBbg45D7R/jdOdjHMkKnM+Y2TL2UdoiPRxsPoDV\nuMhEQccVLYUieFGVBaYLOSwi1FpkpkQf0WI370g1qE4ZSTnE2MRH+WC6jFlUfqZliMReQ9arvHjX\n52DlPXjTSzxUCnDPO69RGZwlsPx6TP4w9028QNHQzwsV9ZwxruV4507MBZmrXgcVQR8NIQXBWo6m\nJbjQUI4rk+REcxlSSWOs2oUn5QAtz6MGB3vRcJbsrL7yICO6QWTFwu2ikyrnPL8pPsYax3ZSpSjT\n4QQ3jY+w03eaWb3K4yUTn9MqmdHgGaeO3pbr+GT/qyRkG+OGelRBoK+5m3Gjke8uu5mqzAJ/ffaf\nMRcLDBQfIjPvRAFSlc3cGCzDLGQ4pSvjTKmK8WT63yWU/Y/ij1UUdwIrdu/e/cy/YvsesLR79+5f\n/5t792/InxTFPwGAqsLrn4fGa+Dav4ETP4bIBHTcBh23UAxmSOybwlYzRjZYicV7Hnn8n1BO/oLL\n8gSv2azs3votfLZKXhp9iVsbb8Xv9OP1ejnXN8ziaR1OSSZVX8Zk+iUu6AyUmW0U0iqUVERNeH/K\nmVDiF9Vf4GtVb3DYOsVHHvkM12y9juRCkD5hgKuVSdYU0lRt+jrR2A9Zmfg+HnGMmP9ehgtpCmUq\nUyc/QerSPPLqAAWLwN5UngPzfQxFhrij4zY+qK2hFM4xotrY33yU86pCWixQFWzGstjJUn+U1TGB\nrFKiP7uSVFbHOVVhulhkn/ksr9e8REIfx+YapNeicCRlYVuFhuughn+kiGoQSJaLWJQyogM6wpYm\nmvwNFMb+gLY0SV1TjFXWAK1WkUVhBfvcPdRPjKOLaUjVJarXxhBEDfQGlJLKpWQ1sQ0lSqqA8RkT\nF0dqKWZ0lLVJzPkUCgjUBRLkP5lDNJeon84Q9BmI+u28Qg5/qh5j4NMsFGROzkscapygoDdw/8AX\nOSxVcavuMKNaE+edce46dzubph+iKbSBFYJEZcthLo7VMWBIYg5tpnP8Frwt76JzzWKbdzIfuQdV\nFQGVXOURDsmbmZ7/KKeEOr6nbqARK1POYcZ1In9tTdPsUHk9pid2RWaLFieRMJDpeX+fXNlYmN92\ntKLW/AFHrJJA7no+KBwllboTS8Ul1mx7nC01J7DYM7xc9QCrxs6w3NXP5Ug7O+sOEp9voH5mG6mG\nywQNMq2XTVyJ+Yg6XbTU3ILb4CImG9GXDCyGu/hevInHPOf5VPQayuPtjOhL1AqHGDUvx5/YxXXo\nkTWRX7n205+toS0qcblR4HyVD2c8w8X2laSNYQKG/fTEWvl0cS2T9jxPNvr48ESBzVGZS8UiG0pJ\nInkjY44xzES4IpUYcM9QCG8FJKaTNaTzZq6vPMCD+RFeUNZiCCcopgu0rk+xmUt8JHOYhHIHZ9FR\nLkRpjX6AD6s+nKYaLOEpamoG6R59gNnsVYqOoxSUGV6wLJKSA3QuOFl1QkanCFyf78JvUpiQC9w7\nvAVlaZpe0w7eKT+NOSeyTVvA70ow1mLGOaVxYKaW3JiL8Qk/pVyJK3QjFxIYJJWk5mKmUIZYaSBQ\nUU9ZsIk3Vlay/vIR7LEQe1fcRHP8FYqX9rPXcYQLzhzy1P00LVxPvWJFEPRk1DRmfZQh08tIapp4\n7edomw7w9Dm4qgwTNF7hqwMdVI8ZGGiUES1mPjqr8TJ5xsmzkJrhdssbWOQil7L30R7uQBP0rM1c\noKPjANsWjvNSzZ0Majpq0gmu8U0T8bgJuW1YJYlLQQ8ViXUUCxNEShcY7/KzfugSy0evkO3YwRIJ\nUnqJ1vlFmpai+KIRvJkcOoORTHkIIW1AQeBMa5D6NNxwzM2iVcdQ2SIroq2UpWvoWv8EOw4qaJke\nDrWW89wGN4hWtp+fY+VkjoKwhFisIqtzIWqVKMosS0YdMx47V8uclESJqa0dPCTNk9GpzJv93D5c\nQNJEgqY814sWZi3zGJoDXAkvJyVVsPrceXBmSU1ZsNmzhCqu52n9Kqrmr6FlZgeSbZpM16+QxwXc\nT0nIpQb+uf0juNUwlmVBxKsi1VMJamNhpsscJHxmHF0x+mIPc6C9kg+GLnC/+bs492uI8xLfvP/T\nTOZaCDll+q7aSGcE6ooTvLF1FzvPHsczGKDeFmS39SnKj82jKHlON/iRy3PY2kaYDX6AmsARJEMD\ncWcnq/WXMVW9xJxBxPC2zGhlBUPFWQqZIt7OGDXKKuTkJsbVU5SJdSjJeSSjE3u2naGLy2ifvwd7\ncCVBRUGpPUSs9l3mbXOECnnKRmbxOmYxr+6n2L+K0JCeRDGMoyFJ6LILa0WW8nIPX9b9DaZUmmrH\nWRzeIdLzKxic2clLPX1YqyyUJx6h9vp9LCuTqDj5l1SnGrlOqmS041fYmw8xZp1iQF/kUmOCvAF2\njjyMqFwlq+Xx621U2MoJd53B7IhzYvALHK5Pc2/iTYaEVhBE1h87z8X8OJ2X4gTv0OHcMoPoLOE5\nrDDhc1HtSBCLmbl7vI9Pnn2DlYsTCIkgK6dCZE0mhtdvotu0DiVyFX3NOvY6k7TljTijS1inzvLL\nrpsouG+nrnCZbRV3YpKdvBc7RmHHd5iUB0ks+ukwddHvktHyIeZ1B0E4ww3HLXw0ehd2vZ+/aXgB\ntDzXFNawEQdmZL5Z+Ssm5x5EFkqU3CfYX2NidtlWvrSoB38vQnwKu1rCYWtCA0RVwa730ISRXtXK\nVoMft9JCSY2RvbyfUSHLUXscb95DrVpOfaGKw9Yz+KfCVFlXUCc6OZ2f57JhhH7nACvTPRgEHYsh\nBU/QTkXeypK9GWfgPdqqryNQyNFiqaRalhhIZtD0FtzTB5gXzEwaO5HyGhV6jQ6DjlLuIt22Cha1\nDP5IDyPlx5i299MV2sZSUaSgShSYRNEqMNskNqDwe1HhjFZPtMyF6jcjFhW+M+unSjXyRTLsKAVY\nCA8zsmwNpzt6yDSfordvlsaUQsDnRslF+PVN99OajZJcSDFdquKJ7hqW56dYEJ3kDCaq58bISEbe\nWztD3OPDrTUgpBYJuK1kZSMDLTlWJe/A53iOXEiPv2YXmxSZA/p5tueakXRZurJGOlbuI15wcTj7\naT66YCViO8tY0ESFlKaQuosHJDcrJD37i2H+Wixgcp1CqBlg/dkC3ugkRytW0Jka5u2KDbzcsIOK\n/ALXChfJ11fyyMnfMWWpYK/1Gg7WruKDWhXlPjupsIMXqOLWygt85YHH/nPj3n+F/y+K4h+bKE4C\nu/fs2XPLnj175D179tTs2bPn2j179vwIWA98dPfu3dP/W17/O/OnRPFPABA4Byd/DJu/AC3XweQR\niE5SqP8IRa2BzJlFinNJ5NwZCoUGfqUscEmox6pF+Fu/hcayTh7o/BA6UcfbV99GL+mpS7dz4fUl\nigEHeoPKVpPMC24Dm59+nv5GP5d0S6TEU9SZe9DlRX65ycFA8Q1edFzmM5EYH6u/i866FQgGO4Fn\n38Y3A/NlEV5u01NtO0OxboSS4OBI4O8ZE65D7H6dzEIbro6DxG1FjgoCa11p1uudfHbr42yvv5Nb\nYlvJHlug2OXl/JUIL1e+jaZpJMQSnSE3cr6VFzsNrI4PkC414Kn30NcQ4CWnnQFliYXGX1KT9/PT\nia9TVhFCNk9TLxcxH61Gf6BIes6I25gj1iqSyZ3EWrpAxusim3ehDh1Dzsd4q/JTnCwVqLA8jpDf\nzLBuHoqzxEQDwxu66dVdYdBdQ0U6zPlEBeFeBRwqmT4X/aFqSnkdmQUzzn05Em4dZT0xxNV5DGqJ\n5f1J/OEC5xUDgivHBjTEiW7M6W52On6NZF/khVqFulkXdfHlyIUWfiN0sGxpFxd9p0G+wgprgJLO\nhXPZ68hyhgt993M5dRPeuB1Nl6bfeY7V7gRu5xTGeBUNhgNIq47ibB/A6w4Qn9yGVLTQkTditozS\n23KZraKZSleBxbMeXimK2C0ptsS7SI4UKK3NMq08wHm3npPLD2HOG3CM7CSm93FLzoamg5XLvoWn\nkEJzFDg4t5WL/jVsPreP9pkRPLEQ+bCelp7L+EcD5Gx1NEoRAhfdFDQjWVQyWp6U6KGZcgZQWSma\nuFfTWJ1tQkBAQ+PzLRUYjUu45ndgFjyMWRSe8fye/lQ5tpKfQaVEpdfHaGUtc+U1IBfJlJ7GpeTY\nNfUF9Jt38/uqaqa0Rv7uYh6zIlDjCBBUrZhUCyU5yyHLKCcNRoSSjRsCEVrDY2yMnGB95BTu6Shy\ncZFiScIfm8JalaF69VU6RzK8pW7ldrWJS8I4L6vbWZ9Nc1KJ0yOX02ZeRn2sHVEx8Gb0eZ6oT/CM\ny4Inl2HtRS/d4+8XvQgCVMmX0FkkWNjGTKaF5dpJnJ4QZ/QZksY4D6VjhOskUm4dF49+lTrnAL9p\nidAUF7nUpFE/l0WQW1HEStzZYcpdWWqmxjjXs4HJcpmMUeT+c3s5WNHLdFUTt728F10yhiWq4pzs\n5sH+45TZl6PoTEgIKBgY9f+IkP4qSdeHMU9Y+cmlML+XTNiSS4x7J0CXo3WuG5shQF9VGa2pNMdi\nYRYlM/9F/e/0OmY5nvwIqcxaKpeOsV76Jzqb9lFeXMCSK3I1b+FC1S2cnVe5enUVa7wTKMYE/9T/\nIWomD2PQtSNIDsLyGH2bb+Czzz+BqKpULvsY79mXEAtJLtc1ICh53FqJ8kgKu2cZy+sWmMzfx8nW\nI8z4CpRf6eCjR8e5Q58iY/bSNncXbvcI9bqjOJ/VkVvbxbe2tFIbUniwL4k1HcCdL+PVVbOsEcZJ\nBTYguqcQ1K003/I7mhUjQrlA2fpZljf0o3MM0q07wgljNzujbhrG9rO4wokpM4yrahGrY57CEQ/7\ne7fQYh+gs2sIU7SJ+pvHaa67jH6pxB9EJ6vUftTlP0FKani+pwNBh2Xb1xmzhzjsXM5y7TRaa4Ha\nfSlEO1z2LkNJpzG4dTztuxl7Ksyf2XejJtuQXtVI26xMeL1cqO0lPaxRCqqUml20L1ymaBFY2lSk\n+UqKxtF5UgsOdLEw52vLUASBoiziWz6D9+goJlGHfWmcZ7Z8gGBoFaIiYl0Ywzgk8Lc9j9JdnMdY\nKMfZEUXT5ygL6qgQTlFkLfnBV8iWt2CXZDw2O05VYj45zcX5LnpDO4kLo4hVlxDd8zj6BCy1QdI1\nEkPvWsiVMvjS61B1OZbGdTTXVnGev+Atfxktbw2Qn21DDm1i/tKd5ExzWCWVY5ZxJMVC+eI1eFv3\nM6lTKdcMuN0zpNteRFNFnJLKoYzI6ukytl/YgFG3jBXen1JrOs1YbiPe6mGK7fuJDm7kVW89n1t8\nmpmojYJcgd2xQFeon44LAolWHaVtOSR7iajfgvmERFwnEymacaRzhOwmYtV6Zs0OaiMpaiMJil1G\nBsqaMIsWPCUzOmcdn9zgoXtugVa5gbHoML/tvpPVepFbbMsoKhkOLjxPJDOCtSqO2ZZi5EQUh281\n25c0zsePkDK8hC7Zw/n8J3nYXM+gU8+01cBB84tsj62hXDXztuMYp1CIJJezsqKTaVsBsu+wYqma\nD0b8/LDZwLfWrOKu8UlMrhZEQUQQBALKEm+EX2Qh3o/UdY64/nl0v30XcWmMZDbAhHcDmbyFRoOE\nKsA/Vj1F3ZzERst25osq3v1/hyXQzzNtl9DpTNyY3MZo7VrciwP0DvYRqNpOKR1Dnt5HZ80qfLKO\nmaKGlJojVxcjtzCBMzbFM9dvI28dwigEUMMXWciepN69nFalxDhhHJlmzlYew6DPUxbpRECgqAuh\n18p5uSPNWKKPBWOaaKkMd5MNSQ+fGUxyXdrNBcdZPMZ3qJeLhIMhVjTdT6yxyN3qt8kEfNhGJwj4\nnBisJa60bOeEvYzWmXle72qhKJv56tkI1WP7eXHTLhbFCk7JK5GabEz6e2ktVVCZqiVZGmPek8Ok\nuwOdOIlhtoBT9vPkdVUU7ZPcutDEWdMYNyTW06x3kLNPUjLHcU9spTMpkd/2FKmrBiKp1ax1rsaD\njm+Q41ciOAtR1jbNMBipZzTl5eHBt9nsqmZWv4Qln0KnKWyPHUWrrEXV6Qh7yvho3yvUxedJVnaz\nvX2AwKrvU2mwYhmOcvOKl3HVbsZg8P3nxr7/L/7NE8Xdu3dP7dmz5zCwA/gYcB9wIzADfFjTtMP/\n/939j+FPieKfAN7fhTh9Am79AehNaAMvQmwSZXqW8Jk2CoEcturL5MJ+4kIWrbeCjdffydFGP/uD\nx/nGpr/na8e+xpODT+IyuhiYu4T0u1a0Iqy/o5HNqytRrkSIjr1N+eIUj9//MXrCUxgVjfZ0E6Bj\n39wPeaPyIFsTbXw5EqRibj8c/xFL+3/B4WGN65whPl0cI+ZdSatvkpwCOWcJQ3ANS7l5XC0HsFqW\nIVouYPXFeS6lYBNaqdAHSEZexl0qwl4vpqYyfPe28vrZN+j3nOXPgvcwaL5MuX6cSfuN9AtRfsRf\nscL5Mvr6t9E0Gxc8TYjCk2iGGF96Oo2vYIbll9CMORwX7qH82VOoooioaaRnTMyZa7GSRWkpYK07\nh1Z7BMNQgVyhgpnKu9hWtRk5IyEARjVPt/M1flp1H4LJSZthiIZUEIAj7gpsrSk6hzO4g0ZKYypR\nqwmAJZOZ+LQNU1Kl3hhl2XCK0WIFXyg385pgpfqMF19bgpqqq+TjDuaiW3m2skBv1Sz3NUTJcYV0\nZAu+vB9BNdLuvsy13dM4a+dxNR5Fti6RuXITTxZW0bE4wZfOv8Dx1pOcscF8TmCFo4DkGSLfuIjR\nNUNkfDMW3ziaIUVpbgU6wFc1h1ccx+dLsdjvxnEFRHeSg3I1Y8EtvGC/j3X1fcwbC/zCqSCUDNx0\n0s4R5062k8eb8eNtfR19ooWGqa04FpbzVnMtk5Kfe8Z/hy1Vg1pMYAhnqbKYiSxfIh3sxlMY5vKV\nGrqcvZQZmxhNnsUin6bcuJEqdJTQMAgS4v/oMhDQW+PUtP4GR3YZr1jH+OLalUxZFGyTDawopBix\nm5nu8uCJLhF1lWHIHIB8PzeY3VyXXEak7gg/4c/ZEcpyw1ICUTFiKDkZMR6np9iBrKn0ufoRdRnu\nNqYpPycgGHxcb+vlrVW9rPT1EV804U0soQgSL5fdziOLKVxM8Iyyhdrsb4kt5jlj7WGoGKQx+AqZ\njoNEpjUqDe2kSkM8WdHHtKfEn0eSfG4qx1KwAlGE5WunCcfKmEnayBmb+bT+DzxgfZFu5yL10jgR\nWeGYS+SxTJirLVayaSNj09eSid1AoOYdpluTPLi4jmg4jOa/kTGzgic5gSeSwBMJsFS+gplyCxWR\nEg3RQxzsvBFPLMgH0i9TKj6CIPhoCfVhE6pI+tYgaYCQ4r2W3zHkGaWm5GZp8k5Wjw9RsrhIZgwc\nVi3UiMP0e8fpVctxjdcx2JHlig/C0xKPaq/xYVsfA+ldXBauZWspQ77sVd5bu4bWfIJU+qsE8h+h\nNHySS/WVZMraiI44iU2sYXTAgmNmHncxzJTPiKfUwnDLSkK2HB9/5TkWKzaSdvdwRKzDkb6EsZhi\nutzHA6tPEQmUIS+FSVX9JSOtJ1jIj7LkFAiHHmRVcojspAmhtJK8vApXzytUHAohzcNP732USbOR\nr5z6Ce3FObKZVYyV50jVavzDwteYsFWTXlwLqp4KZz9y6zj65hjKJSfqWzL6lIW0B9YZjpNNrack\nljDnA/g0K1VthzGMF6k7GOelzbtocV7BG47Q89wEF2sfxVV+kqaGy9xQfwBTYz+aBPY/rCQbyPHu\n6h5cjtX8cpmJ/poWzhXXsMF7iCVDOakVJqp5mLhyiqVpJwd7d/Ch+eepLhsjf7SG6vNTRP1GljQL\ngw2dqLKeRneUHYIZMT+Df2qCdcYIa5vHuKh4cc5mmXM7OVtVx/GOGqqDcco6I5QKWVLCWpxTk5xp\nquatDe0sO+6i8dBl0gYLs7t0dDWrtNbpETM1ZPwnkKaiSKZG5lr3M9mRZnxxOQ2ijEkTMAoXiGR+\nSd61kZZcJfv6LyPOqrhbkshjAsZpyA+3sKyYwG3KIJbfQ3IiR6IU5ZLpg/xmpY3uWIlVZ94mrwlk\n09soCQpZ61U+Fr+VQecpBpyDqCUBtyjSXHOOltA6Aj0/IZTTExm6larqy+jPddFwRUSw3EjOmOVA\n3T7eMd1MU90+aNiPIV2Jfm4jnx3+BuO5Ohz5zewU19FyVUG9cB7B5kWXyGHtg/B6iXdNN5Ce8rB2\neJgZjx2dqrFuLIAvmKU+laQsnkOnKJxatpqcTSYo5GjUdyAXizSfeoWOE69iaNyBWLuaD2GlS5CZ\ny4xyePFF4tYS2co1WIQpXA1xZgfLeGLLDu6fVZANi/QbAizNPkKnZuB+1cCQtcQ9CzaebKpkTPgd\nzpKN33nfYWz+A+A1MNJZzdZQC4vqu9RmBDbFm/lZZYiQv5JPRKrQshEK4+8R05JU2FppN/S47ywA\nACAASURBVC1jwv0uJ9IDrHk2y5zbxJGeZsoyDWTta5myTvL/sPeeQXZcZ5rmkzev9/fWNeV9FcoX\nvPeOAL0VKYqUKGlkKb/yrW5RVMuy1VJLTZGiHEmRFCl6gAQNSACEd1UAqlDe+6rrvct7M/cHZzd2\nNmYipqfV0bsxev6ciJMZb3w/Tka83/nO+dKec+BRq3nHdpq1mg9Rmjdx1TfBQE2WstkJrrtQIBMK\nEq/fwrawSO1YD7ryVmqcFVS4qrGXrUIU1WSlDOcTea56LzGrTCLm1NTNTjFvjmNPR8nmgiSDIySK\nq7FpZilVtVGlyrJQUGNOlnGi4hAVkovStAqfoRhDVkbKn+Jy7SVidhVGuYl4nY1dPpnPTutZVAUQ\nVv+QpFvP3MkEWk8TuzPLsBh/hRx1EBi9De/iaYaKDFSb2lhbqOT5KiOLdguzTg8fGkhxjR9qX/8N\nM55qLq1qJ1trJ2puIastYtrpZI9f5sn2Qwi6ZQwlN7BKcZEOX8CzTGJ30WmqJpvJFg3QEFqJr/It\nTOEmnDN7UGmitPiKSRbCXDg7jZwxs634HvJShqMLz/KysZy8Sk29rOdipJrZZAlqk8DukXOYjB4k\nnY1Cbo7VC6OEl7WiaDSYlTBxnZ2JolIGzWXcrS8jt/xJ0sgUigZpKspR0ft3yHoVlpry/wTD+z/m\nr54oAjzwwAOTDzzwwO+/973v/RPwCPBdRVF+8//1SuL/xd8Sxb8BwJvfBmcdrP0EpEJw6KtkCivQ\nib2Yq3zobrkP3diviMX206NdYt9Hb8Hj8fDguQepsFSwrmQdf+z7I3uq9jAVnSIpJzBpjdx5/2Za\nWmvIXg2QmYpiPPUwg40aFjet4PNTfySbv4USxcWUdoHHal7Cmtaz9aIXq3gXDu04oipCsLAdX9rO\nllUtqJ2b0duGyAtRfrRkpMMoY/IOYNKnwTxPJBFFQUCjyWIUZbZ0/ISzb2spLTGTyh1FESTsmtVE\n3hjmBftrRNUJbgh9jIQ8yRmbn3vT77E3PsRyZQK1DPZIlnjDFErsEhOaeeqnmrnj7CwZ5zyxLfPY\nhtuw//EseasVshm6W1soW/LhnjHisv0U5/R1WAIy6aJhsmviRPwFPKKTClUVF/1vIslZqtUNvDaz\niDU6wwV1PTGbhTXKCL6MnugKEEMCtRE3RS49yrEoWklCJcsk9TpEi0KVIcvqyDx/nNnET2pzpPNa\n7n3bxckyibeMEi06mZK6y2R1Klobz9NqLJBMmPBWBZgsZLEE2hDcQzSsfJ1oQeCArwjrpY+jxBxc\nGLqeczqFe+IhMvoBXtiQZlOfzMUiEW+3jbKqCEJSZOL014lPbEPUpnDVH2M20MAWSsgsewxzSZTI\nZTs/m7+fexyneSl9F+Oh25gRizArOYzmIMs8g7ze+yUy0fX0mzvJqzTcHpxGpbGzwqpQFtiLrDhR\n5Up51ubm2jefQOPPs8u7lWWWTYxGu3DEtlHMSiJlx2FAZCFgY3nRNRSVqfDLXSwEdATlcTSKCavG\ngUpQIZEnLajodYhs9qnwlx7jlewcv234MIqSJ6OvZaelF6/7F5yp2gcqLfe98DC9DU2kjCVI2lps\n43vYYRzloMdJl7CO7/QWyFhPYE2VI6sylMiVmGQb85KH9xzHMamgdbgOIQwvFd/AJ/U1ZM0uOkve\nwLZqBp0nQ8GhJZSu5l7xKUKYuV48zzF/JUpOg2gqcEXfjlMOYY/H8EfCVJmaSSkCT9UeoSlcz57p\nNMsti/TGPGib0/w69Gk0WoUvF73BTn0X0Xwlr+V2MLhk5GrYg0uT4kiRhga1hKZMTT6nYzpWji5j\nIa5SM6KbpPJyHkFsoMvQzhlbkhWRDMhBBMFAiT/JWG0164ZyHK1bhd9dxDfnHmVbOEK/dBsqTQkp\nczV5zxryqEEQGLYeo6v6OOunrmFrKMHp9CpWacIUx2qxy2pcWKhJ+Zh2hxlzddM4sxunaYn3nNVs\n8Z/nn3mEyUw7R1OfwLP6T6id8IUV93DYtYmEtI/VQSsmtYy3UuFcJMliWRWKVmDXe6/ysYuHWTM/\nibEgMlpVxWRJJWvGC2w++yzVvikyy+5gmdPNHlGLXl9FLrvEpZoGrkuew28yIUxmsFiqca7v5Vg8\nijdspyF2E1e8m3muYQeSvgVnARyHj+CaiDK8poJ/2XAjwmyKc74SbvS+zENrdjPryfNS7+fRC3ny\nxlFCmgakVBHT1CJrYS61jIuTy9n93mXebAP5sgdrSwKN5QzjIS9NUgVrrQKpyqPYXlVRNJTk0NYt\nJPRWPjBwBHNflpLBPobLv0x5wkz6gsRjwZs5MH4LT2vW8NyynVwwtTJvzNPdUoo1FsVv8XChsI71\nVUeIKkUYEkNUTE6wKJsp9y+wc/nbmJ7VUvKuH5WgoGpMU1O8jStqkWCpi6f7D7B9vpPKrI7x+CB1\nWj9n7FZeK7kZjaJhwqHlpcpNjBfWsDIziMUZRVUKcVcZmYU87cMj9NXUcO/BhzAlw8w33EHH9gPo\nxQRVffejy+mIVpxAdC4yWzdG1jaHxrrEwsh2Rq1mqvOLoDQwFJeocKxlLD1MJHKVhKmJm8IKGdMi\nhXEDuriEkgLrRJCZyALTGh91luU80dlCyGXgn67kUMUXkCQLarGFgmmegjZBk1zGVf0sQmKOq555\nxtMJNlsFkuUnkYUC40uNjCa11LvmcMoWJiK1OIXVqIpe52DxNLcUT5JWq3Cal7hw6bPsjHeSjnVS\nLm6k1FzHWPwk+tNPoLJ4MW35DkulZTy/TMsfdDHGc0X0rruJmyJvkUq6CRpEClbwqHOEdG4MQgZ1\nJk/Q7oKcQlaECkMr+vAMrrNPEdQqXOnYiEcwMxK/RNfSASZil1HyBRZKzcx4/VQuZnA0ROhLruLt\nhmV4/e9yXXwb3sUKjqisfFBvoDEv8Gg6xg15IzPmOrqs45w0vkyr/zaG08VkW1x8d0zCOhwirelj\nyjbFxjkNS6k5+uvbUcbeZm7qVdzjl2DpKvm6ZegLJubnX+GawxKjpRr+8a48PmsWT+G/QGGRImuW\n+bSWKo2anb4mavAwmpXpEy/w+JY53m0P0DRvp2kmQDwzj8fZgdmzHL25nHQ+SECSmJM0pCeOcVFd\nhkb2oa04RVvbFYJz9dROTpIVCkhSEimfJ2a2IzjcbDx3GHPdDIK8DYdahxQqx6GkOFT9OmlrhDLf\nelTSJJr4SS4sg0TpF4hX2pFEgZvHxylKSei1D2M2LRBc0JCY0PGXrfvxyH04Kt5h+thXyKm8aJKX\n8VnUtNm243H30GsoMGKvwpBXWD0r0TZwDmGui4BzOfPmPMZEkm8deIFu7xuE7TuYUt4krB/ipjMe\nrtU7KcsGWUxP4O8UaFrcTm+oiSerIqwOmNDHqukteQ27WsQ2uw19zsFI8AoqeZS24r3YVRUcW3qO\nsCaLwyKyLFVGo5LnjEZHARVVhgQd88MY5/oRYovMOa00L/gpDwQJl9sxWaL0pGt5S9uBaKrkAyWT\nRCuP8njfXRjCy7DXnCDhuoLaZcFVtv4/2/3+N/yHJIr/D3ErYAXyDzzwQPrfHt5/Dn9LFP8GkRl4\n94H3k8TK9SiX/4wwfIi460EMe/aguvgI6sBxUvOlZOUVxFfrWdbejC/l4xfdv+Du5rt5c/JN4rk4\nT+x/gh3idbwx8SYzpiGeGXuaucQcRf0asvEY5p7DPL+jgRH1IbalYlRl7mVS8PHL8idJq7P8rvjz\nXJiJ87umYhzjLqoMBVzq45Qa7iMV3MS8cZRY2Snah+L4Ihs4og2x2hZDtM9gFdYj60ZZXGpkWoyw\n2pynzruGXDZG1bgbQRFJeLsw9HQyrJrgKc/rtCxtxudrwJZ2cdV7nr1JH7dmx1CAh+s7WRdYYCln\n4tfaPPU6mW91z6H2C4x/XoPWmMQ5fhOaQBBFynKi1kvWIlJt3wyzXUjJRTQlqzCourAsagmZQmjW\nZykRW5jP+unTzuAXJFrEZYwaqwlkJmmIDtFHHbdpz3LeUIxYm6NiLI8czWDLzDAWtOMKZLFkckxV\nVeBv8rJBexmfoqJ3oYTqRT1V0SqSYprjKwIIGTvHJYm6pIbSqnF8BYGLPdewPAe+mERjUz+iNkpp\nx0ukMnZ+3HczM8YejPFivpx8nBeFdiZxcp1YzEvresnlY3zjRehqKeayS2Lz752Y34Ppov2oFA1J\nfw3WygvYS3tJm7swlM8QHHBypP9zXDF5eEHeRkRxIBad4G7rYzyUe42hqiDlphxKKIh7Lkm9dZbN\nvhgW9XJqtVqqpPd3HBUF+tM9aHpfRS9JCHIeWWWizFxGXzZEXArSIezHO7+d/qU4hTy0eFYws/bH\nmJcFEAoiYZ/MZHSQ8XgPZcYGzgYPcLHWw+FlMfZNWzkX1/FozQbyumo+MnScTnmQp727mVLXE7PU\n0HTxCi2zPVytnSZl20VBW0lTXIfbfoHHLNdRnk3xmSE1Z4zdlKu15BURueAkJea5KMPZTDEf9fYi\nnjYSa2+m39jKypzA8hTY1S/gL5bROyRUPeV8XfcGCBnUFOivsNEzUkZOBm9siSljFf3mVhIpDU2K\nhmR2gbkymeP2y3RMViAuyawsmkdVInF5qZWPGY/ySd1b5GQtby0upyv7dWJRgXBylri4jQW/wkhV\nmIROS7NJ5sDMNbwY2EiDwYdXEZGjk5T4jGhM++mx5lksOFALBkrTI5h0Xr7k/iPGvp2caDIzUWZl\n/aVu7j3+Aj7rNpZyjWScXWiyzeQFDSAAcLzuAOV+AxvmPsasYmdcMLI16wBivFP3Mu3BDtKqVta4\nJzgqLiKoUjQMr+JinYpHl36MJq3wWvTHCKYY0bEdvLbBT7duBSujo7xV7EbvOoyp8ae8pcrx7tRm\nKuxLxFxFfOvF3zLgqGaiwg1qPfvOHqfVP8JwSR1aVT3O+BzOpn2kFIGpnEyN1sQy2wry1mJGU0Vs\ndp5mbqoKITqKYriZt+wRdo/dj0fWU55X05FT4y2oGNemebhmFWpzjp/t+BRZmxHTlTA5GV7ccA31\nvkn+0vcVitQJIjk93kKS+NphAktr0QaLSc92oky3Ys1VgKBmQZykIifwnnUP7UUXqSgfwlRzikTZ\naeSUCtXzVnSSxPh1RZwVN7Ht7fNYazKoR7OY5gcommrAdPIiotCMR6mnOTRNS2iEPkcZ4w02ZKsG\n07kwhVCWRImbi8JGVptP09B7laKzCrIgIMspSg/ncA9ICAqgCFjUOaTKDB/OPMfvKu7Amq5ndfwq\nOaOO+cgs8/kGqsUPoJnoJiokMdQluM48zJu5jRQtxilRz2FrjuN3wgvGD3DDyXfZf+Y9whYLo+vX\ns2TYiXq2gQWzn2y8mNJ0PYmSt0iaFGxz26kbMeMv8aFSRMbDldygvp9ofjOlplU48yrO+Q8gIZEo\nq6EitB2daYqSpiW6rm1hoPAN8hoTQq6HkEmHqXoTL3VWsnsswrKJAj6hBkGzDA0yu/QOihU1oraP\nZxwnefDJHB1LMiGzgx2pG8hHexhUaXBFc2gu5dBYirDXjKMO7iOfcuJyHqD2Qgzrgo2WqnFemb+W\nFwJNJDRx1mnLSShxYhd+if3qcWQVTBn0TNbsxKwt5ceeWbAPQmEWn30/y89M4UknyBTp8ctaRqrX\noJfyeGf9oCjo5BzN/YOUzs3hrN3PvJAgGxrhYoWDxeQwX7pxG/O5OarGezFIeZ6ruJH+lhMsmafw\nyxLLPAVe0NyCnPoDffoLrJn2sNLYhlsrslVRc7mQ509IHEPhiyk1zyxbzYp4KydClaAT+Wq4wI2+\nAs+XzrDTb+Ckp49iawJTT5Su9o3k8mlc6ST+mloaRsdILPbByDuUTCU5scHGdz75MMuUAjeMtSJJ\n1VSNPENz+TYmxTBSxky1wYYkZbmQUhgt72XScoGUqY1k+X14Znqpnh4n5b+KPHWGV1wLjC8eYybR\nw7y+nbCxjoIgkkkfRqObJye5WVKqaBgaZrGilsllbbyzfi8n1+yifmEGxajQaT7M1xru4gafmowi\noQ51UqqLcczZRXneiC0jkpb8dK/4IpqCjEqWKYhqPtcf4x3tRVS5FOdyG/hh+9dxZyJskEdZTIgU\nprcgxysYqY1iDM6TU6XotLayWH2ONvcrTFPNDarnabW+hOPALGmVjllriI6h83QOdeGILpIqDxPR\nmEnJb2DPNXJXZA1tpnbOhA8TENT8vvARwhPTyJlhTIikSNGar2RRiDFlfJfq2BYEQUBteJyNzjRa\n+cMM60dJuA6TnRepM1Wj16gxp90sqH2oBYm9oy+SUkNBa0VVs4GAEiXmcNIyNkFFcJLEljgvBnYg\nF1T8Nhwgsvx1ovognpSNf57cxx2xZcjuXjS6D+KtrPlPMr7/ff4jup4iCMKdgiAMAD5gEPAJgjAg\nCMId/4tx/o2/8d8nMgNnH4VM7K+rO/zm+2PjfgDks88gyeUYdu1EWPlh2P8QLFwhKW8hIERZtf39\nX4eenj8NQJW1iuOzx7mr6S50oo6F3iQb524gq05TYirh0Pghcv4k2UyEvChyeO1XsBl/RCL/EFpF\nxZ88xxkyTvGxwM1YTg9yd/cZrt29D03lNQSkb5KTrdg0jyNp/PjrX8QR1OHwObk39Qk+v/oXvJf4\nr82FRxoQgeJqkReiIhl0DA/9HR6OY5/fgmN6L7ImyeDaf+BI2y9QBAVXtIX6nIg3Xo0rW8Q5j5eA\nupwhs5XfFMIc1ZbwpqyQlwVut+VQdxvocdTh85UAAsZEO8qqDzGoFUiJIumUhM9Tir+0CmX+EkSm\nORf1cLEvydChenTxcnwtT5Lf9mtWXPMuldvf4YIqzE6tG21jG1G9nW+pngUgUysTj5r4qf+jXJWr\nERSFS1VtqGWFtNVBorKBGe0ELZkQQ0IjhzYsoJcMmENhulqyKKi47dInqfB38q8SPDps4RfzZjyj\n1zIV3M+RwQ8QGbPgaDhOPmXEd/Sr7F5spiRWx8XSt5mx6TgjFtOpROgyDTJknOS2Uwpqu52ypR0k\ntAn6q/XYYwkU/VEUWUIpGLly6UOYzH601cMsXSpCPVHLOev79xB06gyfunKEGu0U5ywCJnWCgyEN\nqbyKneVLrCuS+V5+E5tM768xc/uzfL3pm4xV/ZpTvtfoXTrBvLeC2to7KTctYyLWR067wF3eO7i5\n5B4KgFJQ44/7cbo1TG76O2RdAu3ARlq9a2j50CDlapFsIcnwwttEdAX+UmlhrfFZzjnOIOj2kDWt\nY+fEcdZYnqJk6mX2+N5m0dROSSTFjtFDBG1ZmuUeSuR5RDnPkTo9Xyy5kxRGPjmWRBQmWEiZSdpG\nMWSLmHP/iIvlj3JRlcSl2Ci/Wg0y7NAGuFU1xRlFwlRQcIcSKAosZd0scyZRqwKohTznS2qZtepR\nCipsxpUYnJ/mlkw5dZKGLtsKHvJs4zm1jretp3DnTJTNBInkDcynbTSn0nzL9garVKMcka7lqcWv\nMBrTk8vNks+cJqAtJZ85h1bTQk22hgG1QGTcTP9AIzoyXM3HqTIfpHXcRtSqR6V28/mOf+Yl/XeZ\nM2tJWBupLxnjN6ov88y6cvorzOy5lGRfn4uLur+jN7kfs+hDF2pHEAooSg65EKRAAb9llpnoHuRC\nAEu6iv35BMacmXWp3/Djd4/S0fOvuDNZ6P4vNAZbuVR8hpQmyPV9V2nLjHNMvIMFBPLxUvxmHW+K\n+9kqH+GL1m/Tplzh17Z9XFq6mz/330WZGOYj7xxn39kj2DJJ3mhZxY8//iW+86mvcLy2HuvcCKuG\nXkEQtFxc/kWuZgWOShK9qRgHIqc4X+hnd0hPq/Za3ov9lKnmVpSoj9OhRa7vv5+EXstv9tn5p5sd\nvLZeZLY2QtvaP7BWHOQP1bcQL3eyYT6HPR8ARURMFvj+z39K8ICB3gk7vx9fyYPpOzlg3E3j3m9T\ne/03ELc8gGvkSdS5Caaq9uEuPEBev5XSKTOuMx/CMtKCY3QH6cvbcD+iIi3qmV3vYIv2PRRBxbC+\nlvdCpbBKj202TG7gVcTSdhLOVtyygWJTM+W29dynRFHKjGjnUqSyCo3+PPe+FyKVs/GA+H1SXaUM\n1jbws499BaM6S6/Ly/EN6/HeGKWoOU52UkvV+W6qM/OsW5jjInO8Y3mJzxZSBM21pFNzdM8cJCpn\nWDs2zeYLs+wI9rAy2sObNVuYCm8C4KB8OxfbVpM3e1BVyHzra1/jX/fuwC3H8CdrCU6uRivLCORp\nuNhC4/E7KB74CIVYDGmhCnvtca6TnkOjSmIw/ZiMSiYo+cilAzTZEyAI+MK9vFKAz1XruTq3DIUC\np9anGPcU4Y0mEbsO8vE3RlnXrTAtFXCqQUocxMsfiQsBjGkRV3Y73+v/COaEQuk+ia8YdmE+KeL6\nuYYt/yDgeRKUsJ4DgU5U6hxacwBrSTfV8jTXXVDYdHgJ7z9oueWPZ1geHuHZnJ7vi+f4ROOvCZhy\nKEC+JM+vW2/jiipFkSqDwXkUTawUGRFb8HlevnYPZTNzbDo1hDEn45jqpa2vF8ldhCKIOJeCCIrC\nbE0HBllg3CRztsKM1lFDUTJJx+BleptXsyoexqlSk6s6B5oouoUVTLqz/GDWgT9xEFGa5e6iAt+3\n2nmCLDfm1LhzcBgJULAKUJ9SWB/Icspch5KU+YCs4baohvTiFeIVXrYJezEoZnr0adavGqI9PsLV\nuk6OuzYQKSriV8tvxR4OETCm+fGHzJg3VLA6Oc2A6QNk4pvQZfuo9A2wuHQOSyLAXE7FRHKc3miU\nnBCm3z35/qaFdicXa6oY8Troad2MKjJHSlRTiPtRVCpUSBQMA+gFEEWZaU0B/7iVR2bvQJVLM11R\nQt3UJIJaw5S3Akmt4bnSLfy96W7W5X+DNnCZM+oh2rQmQKZpYTfrYh28UfEKNV4L11R9Cqes54bD\nzwDvd3q2qlysaTxPrC7Jc9XXIGm0HNh8G+7U7dyZ3sr2TCfXWEU+GtWQsmSwpzJMJB7n/EQLjxa+\nwE7lMLXhKJphAXV0iukiPYKUArsXyWpn0ahnfZ+IKvY0KjmGaNpDvWUFhxLHSWWWMLtFWsUF3vau\n5HHXFl5JtvLLrI15JUN9bDmL3QppOYqCgr5+iQXpKyTJYM+nUbf70FpyZILnWJNzAAL7pBjX6QfJ\nldXw55t28catTr5TsRqnsRJJq+fMpk0sZZ38tPsLiEl49NJBDBVW0u4BopKafMrBnboEPwk04Dz9\nABrpf4Oup4IgfBB4BrgA/AB4DHgHKAO++b3vfW/4gQceuPofGOe/m79VFP9/QHwJ3n0QXvkMjLwF\n48eg6QbQGv86+kd/+P6449sokWlUR/+elP52Hja28HbfIg0rt6GxtZAcrCRQJtG4rQOAP/T+gXAm\njAqRscgoP9n6E3QqPUefGqSzupXNWzs5MH6AVd5V7JtcDwsjDNrU7LIt57NDalSCmQdbZHq0v6ck\nbuZLvvtAqUFbtZy2CQFXTOa37igtF9/FUTLNbGmYpGuJ5Vf9jErrsGY3UV/ThLp2JU+NHuHNTJjO\nbC1TmgFOJVRstFVgLMSp6P4qomRmQoCk6yKyUOAvYSNetcL40vW0Z3VovL20LDvOGleKoLmU5kkb\nJ/UK04VG3nZEuS1ppSOaxHoKbOkkltULKAYHmvFrMEoauhil0tRCXs4xkx2jr3M5TZMzKPOXuaJO\nkssUqHZuozF8L2OpBKO+GqS4F1fxCG+nvGxP1JLRqdlnfZZl+kWO5SsR2zIcG9zEwcQujsnL+ZR4\nkAvGFqoGF0lYLUw0OtialGljhB8WK2iz7ZjzW0maU5xvGGb5/E5qomuoitax5DnChB7cS8Vc3xVh\nUdxCoVCMNGYmF13i4Mg+yhPF2ApFlKWNXCzpYszZQf/CWmpKuun1vIkpq+cLryY41VnKkrEOn32Q\npEHFrksJlowdpJlCzo0QTuiYEbSIIxJyXqBbeytdBQu2AlSWvEJX0xKKKsuCIcExaQMzVj+tgTV4\nq/pw1/UTKD1NhDTaZAx9+0tcWbKiOashlgkTbWrnuV0f5JujasxqG6OxSxjlUmbMCyz6BxiwzTMg\n9JELLdJu3o26eAm9v5304g4etB+i1hKnPP9h8rEcS6kJtnVf5eyeFlzSa/zeXMPRiuXUZ/38JPUN\npAoFl7dAdK6YSEpP2qKmuf8Ks64owfiHuN59hTO6TvIqgXqG+Wbm52zp3YdZfIugUIm5vBv87Tzg\neZm39D4CriMUbOeZzidpy3tZn/8EdRknT5LFbR5iwrxAukhNx1SI1sgI71lWUZubZ6Epz0xXOamQ\nFp1xL+W6PEHLOHtzXppTSyzIQfqc5YSKX6Fh0kRp4P07rKJuOfWGK/QlN/NG+B9YTG3C1vEuqdk8\nhdwQIIOlDV1mBrlVweKd4IosYZ0opWNhjFXRK1SHJ0jM68lroWuZRF1kI4nFNSzlVlGXKme6sp2n\nWq7hYEcLQYuK3VfCbBjKo6i0FPQqCpjIKRbs6jlK5d/Ra4hiFryEDVF6bHM0L5XiTsxj0TTiyFtp\nM7zFFu9BHMVZXpP1VGwdQm8LIExtZsx9GZ9lkr9fGMekW+DLjd/GsZjGXNDw3BaRnEbL348/wkHN\nXgLhZwno1nLGtBytL8eO+acRkz62dw8QN1t55GMfJW0ws2XxTQ5tuoGq2Qmax4conztO2lNLVO1E\nyQuIgha1UEE86+SUScKjEmkSHYzWDzOruhVJu5w5y1UGdgWQfbNEXW5KrAO8WdrIiJjhtzOPMFLf\nxISjin/tHecW5SGeK+zksb4f4RxLoDEVYFyHLa/h6qb78CRdVDqOIKoKVF75LMscG6myeLAuniEh\nq4lrO1g514ftyGuoz/k5PWPnD2zm92U3cb64leZ9V9DJKc5md5DW67jzvWOMmhvxuALopBylm8fo\nT99FZehd6sdfwVCh5vW6lcy6NXy1d4pmSeLWrBYlpsUxMkCskOXe4wd4dtd1lDVNMhsGpwAAIABJ\nREFU0lbRQ3DUQVIpsNE5Rthhxa5KMjLv5o18C6XDXVRP9JBcUOOKBThRO0+Dr4BPY8cZhhb/DGQF\nokMWVi0OUKG2cK1rPyGdjl/br+GOGZnK2VG6K0WuMfbxVMUt1E2exixVYElbWKG1489PciTYQTLX\nQIm2wHnzQSYWP0Jx/XGqhAEssTyfrvo2idgYc9F+CukQ64s+xFVNGFNwimc7xljUGlgx9GEminqY\nFg5THtBjF5poHb1A+/ARSmOjNFqMVBWVEEq9TCidZDZ6hWW2LQioKNKWMeLSkU1aKE3eQOr8r4lr\nIWLRUxJP0eAPkNLbKamZR2NdYv6SiZJTS+RsKkL/R5ZenFTO5Nk01Yu+LspLbOPGRS2bus6QbZI5\nl13BS/U72F5zmG7NOH2mEe47ZOG8twON5gxjpbfQuqafYLASUyxMwGwkWnw3mlIZoxxDjKUJWqBr\n800sz3sZXniLrBwhUlGNw93JJuFdDlbuZLGilK3mN3mjeJ5NMYH9b7tYPipxtklG1PnIzN/Bon8b\nC5KTPlWKcq0fXQF+I0NWUNGJyDoKPOb8Gcw0ImQESqtMbA3KRAoRbo2UIqhFTpUOMsI0W+ozqIwF\nLmg2Ui33k8qVcsZYw9lGFS/umeDW7C20ja2hcP7P1IXKMeYMhMTLLBkzLBSWyKQnUTQOEmkLIZ2L\nM9WHmXB040pUkjBkCNvXUepfRLCUcXx1EzZnK9HwAFlPBYaczPX2fZj0av5Rkyaq0tCcGGJCV0Yk\nZ2addoTiMR+D5dVcbOpEE8tQcBvYNHyKxqV+BK2RfcLzWPPrCagkQmkrztAypop6eKvoLDuja9gT\n0POzzStIGYwgCLizCjUjKS773RxdsY5PjGbxGUQOV2jpnCsgZhWSMhgKMkPhU3hSIiUTWfrrK0hK\nNi4Ka9jcFUVzPo41HKa3wo3ZegMnrDY8RRpMQopsQMWMJ0peY2Ky/GPMZgcxjxzBWR/FtTlBvW4G\nY0KPTiNTHPaR1ibo02e5OV9MKh2j3NyICpGZdAseqYIhcZ6QpcDP1F3sKk8QGrIyl+xG0NWiVrnJ\nmWdQ9FraBnsomk5Q6R6juT1CdWsek1HNn8p34MsX8Wt1P5VlN+Krf4GcdRqLIFKp1rIyUc5fMmae\nRWJZq57Wirq/jo/9K/FvqSiq/yc1/w54TFGUT/+/5p8UBOFR4DvAs/+GGP/G3/hvWbwKv98L+Qys\nuAcq1sHrX4HHr4V7XwZr6b9Pf+EKjB2Fde8v4fzRP6EBYmvu5JF3x1AUePrcNA/aDWwFKrc3v/9e\nIc97c6fJ6jt4PFbDfXU34NQ7mRsOk47lqFvpoaGhHQWF757+Lj8sTfLVbiOWzlspnZIxeXvpv+tu\n/Ff+gXwgy4peBy97zrBrTovduxo5mkO3xsOmR3/AUKYUq0FNrGoQw7wKU6bAJVszu7NaMsNhVkbq\nCC3s458sx3hISlAeA6Naj60wSUnPp9GmigE4Tw+RsIm95SFKtArmsY9zVVCjX/EMleUXURmy+CUB\nt2OaQefHWBvM8ob7JHpZxeeDg4SCbmRzHkO2QKhWxnQujK7nB9D5d2xwX49TX8ZscoTzgdfpSxuJ\nr/oInzjxMLaYmlhRMY32jfQTpTdcRkl6ilTgA6Rd0+ypPExstoTbpAcZ0MAW+3puLp+mLadiIn0j\n1xaP8GHDIwjzsFo1TI+rlvbAONbSCaqWxkhkRCZxUxpdTt4W4UTJBKJkYuXsHgC2GV6gUYrzy4ST\ndaMFGisPMy3sp7igJWJtojA2TH1hgKg6ScGygzr1CKtScF6ewFzzC3r1PoySkQ+ebUOlBPnx7V9E\nkxnDOtrKiPsKcT0U8jPIhEAwUJTrh9MQMjhQ7VZ4obcEBKgwn8AYHqF9wU7AmmNiy32cL2li2ejz\nDPbt56XZnXxEN4ip/gpFba+RKSolctLKigkrOo2NVY1e7l+zjo7xMbSyGZeuFJeujK7kcZ5ePc5d\nExVMJdOkFSetgoJoLmFh4muIwhm+V/kLMmqJQzO1/DTegmTpZyktMFbsQW8M8FvhYWSLnZZIggeH\nsvhbDYgZmblBL9aBCW6LjXChcyMHd3+Q2vHfMpyo4uOXjaxdMQ76RT4v/JzUwgcRUIFmlNjsWuZc\n8/xr2e/wqWFXzMmhzCraDecYccd4ztvHsPAcSwuVxAxH+Z5tDpUCD18O0pROcta0hY7weSI2NUnR\nTGCiBojSYtFTo1GxghVggFX6avSLf+AX5R4QYGt4BTWOcvrTk5xwVDO/tpPh14qQtccJC3o6FiRU\nmnYK2S5MhkYslOJXaXnNu4MvuU5zcEnN4coS7hr1oRILvO3dSZ2ugt7Wi0SVw1xEQJvIkLKInKsx\nE3fpsaTT7OiRkHKvogudoSB/EVF0IClmRGTuKrofbCnmqxR2O0cZfPkaFqynsUQ7+VzLnzl7pA5F\nvwEtMqrRQzzuaeRDZaO4Ni5hrvFhHm7iG7YMYnAHGs8BGvULnGQt/aUOlrlVHNbAgsvG3u4k327+\nJvMGF1pNAxsvBOle4SW72oFm3kbQUUNN9+s8u/duUiYrG7r6uVCxgbC9hMdv/yjSsZO4RoZZ4ahg\nSfJxPnqWorRI1LkNMTtBUbiZASXPjCiTiNxIwZTmVPWfKE3M8Zm4iavWNJbcixRLPkw/cHCupZN/\nLP0s71ZsYudilrpML5IhjD6XI7hkoYJFyneHeLzwYbYeOsbnXnoGw7rP4Jevp5DwUkjaeE+dIqaK\nYCo10H7iGS6s/hapkERX+Woebr+BBbUWeybOutAkNWW9lNrnebt3Dw2qHi42r0VARVtfDzmDmbnK\nYhaX1iDr1LimLmPxTvOSrYXueg2t0xm8cSMbVQIXsiIGImzQe1Cdev/UiFBpZrfuz2QilUTLG7HO\nDPP8dAfNbT7mS3Sc15Rgyko0aIt56Pr9qAMp9l98lU0TGaarSjib3slvux7iRHU70a0u1r18mRK1\ni1vKNxMmzfeVGxBlgbvH4lw2KMQiGmoC3Rgzk7y6qoWfvTtPr7aUt6NQoA5FkJjLq5F0I5yvE1mb\nPoYYMfF8xQb+5PkcabXEsQoj7uASnz+iwayuxpYbo6uqwKJZxcfjK9EUDKzIRygfr0IQTTy75y4O\nbr6WjZd/yYbBOeTzj6FZbaXEIbPg87LMasOkttEffI4a0y6ai3bhz8yQnz+FmE3gdxXhFHM03jzP\nzLserus9SdJTIHjtAp0tPhwnRMIfzvOa6EIpa2H5uW4MuQx3HjrF+IdKWf3eOVIaHcoY/HbnjdRE\n5tArXfzJkWLzYiPbw/28lN1HRDmHPvoKp72rubPsLXJzaqaLrJTNvMpYVTXuQIjBcvjRB9Rcm5yH\ndAep7BKS0Y5ZsLNNamEkeQxXMsT50haWCkWo5Chfn1kkNXWZT+/5KskpI+bOELdrL/BKsIWMoudb\na/6Z/skVfM+/lbwKNMicE7I84OxjZbKW90IClqIM1skLKKkaPI5WIp7TBFte4bpCgF/59fgGtdyh\nf4/Haj+LpzbODs8LLA1ej999mlKdjBSrpNRYh61qE7qog2zmJfSZGXSKSPNSml6XDjn2BknLbQjI\nzNrGubb/s2QdSfzWZ1EXPs4L++/+v+3Nn2WJ9iGFtdPz7NVsxYiGB9VzhPJ2ikwmZFHNjvAprphb\nebFoM/fY32LnqZM8uflGCv1J2OQmUW5j7cxpFgM5RqqWE0zraRNVvEcBfcGEN30/AeNPuL/+t4RK\nHgRBRJAVPMF5XvM6uHNiGyMrbVgkmVsO/ZlWl8K3b72H76wxsL97nCp5CnNABKDEWY9p6jD1CwbS\nukVm7GV0621sTE3yiS+J1KRU3Dhv50tyD1pVNwfde3Fas4RsU6wesKDXzvBG23JONtSwOXmRkUEH\nX6v6C6tWHeew8FEe/fHv+O49cYZM8Ol0BTdaN/FH/WtcG95CXbqWc6Ye/KoohxWJlErAHpQJ2RvJ\nJJOk7SG0GQ12fYh0XqRsZ4Ai9yyCAEJeTzZngQ4/93ECJeVGF+ggGj1DvPwUxmArspgja5miLNLM\nF8jxfbIIcc2/z7/+J/M/myjWA1/+Hzx7EbjvrxLN3/jfl+4nQCnA/efBVf/+nL0S/nwX/OEa+Phh\nsBT/r2nnc/DSf60mn30Y5fxvUMsKOaGVo1oPn1AS3F7iIBnLYg5LzOoKHDfqKJ8P8vTEeVJSlLS1\nmYy+g43V7x8VHO3yodaoqG53AXBrw63MTob53dy/8MAOA/tnb2Mip6CV2lDePsmS+hTLU5VYUDOn\nC5J2r8UiCIQVGXvPHFUzU/zonjs4XJ9ht+5FGmJJUhEtjvVryAbyFAYWGbd/HWdbiG/OruKXuQX6\n0mo6LV4McTtm/0pyKBx2B/mL8wCKIrFF0vJxsYJPJRvYphJYK5Xh0x9jLFgHM2PQKuBreQLfhdX4\nDX72BVuxF+ZxOBbheoiZReZ1DkSNgVkxTDx8lJVFe1EUBbehCLXaTuPSIC+U3IynrR2dKsEuwwZU\ngsAVzSAFbYG6TccJ/bKI5OWNuLa/QN77KD/UGnneqsci+Gm35DgfNLJq9hzXnn6FgqwhulLPyqoR\nHlp2BytOjtJ/eCV3NlzivFFLWWAjJQWFaWcfCcMSW2avw6jLsNb5SzryZ0lcqOM7PQpn6/XETAKy\nkAFFy0TlEbzZ/Tgjz2EyD/ETyw5gP8LcRky1P0cr51m+tIp2v5NVI7301q8mr3GR17io8xRYKnTz\nixvN1M4v4pE3IGq3kVBi6KQxavf8gX8evx9RlpHNk0Sdb7HtVBlqdR1FShuvmzpRVFpGqz9J1Bfh\nvnPPUGbx86L+Gxg9h9jufoexyXqs+hLWld3F56p+yYLpbrZOPIMj/wYJw0eQakAcLFC/4GTWnabc\nr0cQRY6u38C/tDgpaMzAPlT5DRiTs2wfCrOQnaA/MIoKgadv3c9p9W506V5+Mfogqfl9VLOBgYVl\nTJ5PE4sbSTsMZIoMLB84x7H1+yh3bEU3VOA71VXEjQ6uly+jQkaKbCWrixJ0KyRHjnM5laPXO8Kn\nQ9dy2Qhb5438kAkumdT8fZmDI/Ip1I6T5DQyHw5mOGER+brLyWeCbqRaifW9Wa46rYy9+18QlB40\nmmIGDZMsCWe5RpGRFBvpwmbWeDegt/+ZXKqCoGMTr3fUMmyXCat1wF74wPufuFgo4Dl4Ho/Ri7H2\nOHbAv1jJ8a23c6PtZayiQtmCh+bpaRTBgFJQ0aaP8rKqmZpUFbKpQCI9z0XJASEgFESnEtiYTtGZ\n0/FqSY6WmTKyVgNGGUQEcsjsqTPjlD180Rok1NeGIGtZMs3yMbGcdH0G1xWJYOQVHAaZr27ezTcT\ng/gJsEPIMZwz0bvQgpzXI3mtXB9/Ea1QYH6+GW8wwturrKQEBUM8hGSSmDd4MU7FSFd00NehcN2F\nOIfWmHjups/w+V8/Ql6lwZZfzvXnonRMFXOpzY5KLjBQVsOhVTHCe2/iV1dFpmaPk5NHeGS3n5WB\n/TSPG0gknuDyms+yZSJKk2zg553jzIgXuXngDqwzdlpW/Qu5goZLx/Zyc+Qw150+xttrN3NhYTVf\nG9GT0UzzkOmTmHKLEMjic2vprtzLsyV3s/tKgszcOS5EH2XT1D1kcgbeFUbwGecACOmgSZumKNTP\nTPU+HtcFkQX4gaJnKDXDNRPHkPb1oYoLbJ/pIyeJXGzbyDdv/AyfOPUkZck8xSML9De70FojWKNT\nfL/5XsbKNpHVqNk8GCSU0JOQVQjAanMRmUIAYeEqYZ2Z20r/BU1M5tuaAFvNepqqlhifcnL8Ui2i\nItNkC1Ez2YAwdJ7O3S281rmd+cwxynsVzAtJ7gu/iUqRebzuelJ+DYYaP1VVnyVGgrd0lxkvv4Eb\nZ9M4ZvtJ75lFflfP0+lKVOnzzJR+AHH6H6louZ+CItHHCeyhILLldgIxhTczOU6rr3KPVuJx7b3E\n1VAcjFIeTtBfUsyfd93Ipuffwm6a4e3N05RkBTSXXMjKIVzB4wQKHi43J7nQKNI27WC6yYuwZp72\ndxQK3TGe2Hotq7ZOoszsRSaHqfkYc8OnqNQ+hFdbSnTkV4zbykiJBqqCk3zVXcJXOyWyxzRo3lSh\ncxXgiJmEUeb7yseZSz/Brb4pjJkMBUFAzMO3n3gBAfjpqjvp2nievPgUzdMhHhfSyAh8LHgTJutl\nrjEN8WRoM3rVEU6mvsRtu59nIrsewlEyqhjrL5+irwp+ersajayjOmMnIiSQ5Cx5SwmeoAmDVUtS\n3spnJ57lJ9VthDQhSuZX0XN6FqFIYdpUTL7ERNlVH+ML1eRsGlzaECXaMNUdB4iP5ah3j6GX8/xp\n6Ha6o3Wkgh2oVRK3NL7KDU9Xo6pZwyFNN/biAxSpfWya7OQv6gEO5szc4o+xzt7LKct2rlWeQ1P+\nFIqkYqdg5DHJzkaVjEFaSTo/i5CZQTCtYYcsIy3+BWtIw5nGaqTEy2Tdu7it53NImfd4oWMfusxz\ntI6/QHW0kv+TvfcOkuO67v0/3ZPzzE7Ymc05Z8RFJEEABAHmrEQFKlCBCpYlmdaTBdiWw0+UZFnR\nEiVaJMUg5gAQJIicgd3F7mJzznFmdnKe7vcHXbbf7/dePcdXfr/Sp6qrq/rUPfdUV3XV+d6+51xp\nbRqXs5TnS9Ncr9nGWIWEZzhDfvcbzBhLqDXGKZJ9SKYG9KFhtqxdhjW4UpTH/t5+PnT0NV41r8cx\ntkx3/Ubyx6eoWhliIf0ACzaBGoVI2AKmYJbB4kKyfBRV+Gc0zr7KR737kTIyZ53XeLP0Vs67ZE7m\nKnmgdxFDxe1sFpWcOh0noRB4viRNecEzLJwsRPCryHPfjH/PBk6UaznaWIEkCsTFRi7aXwRZZNw6\ny3etf0Hj4nbuQkQiydvObkDJmjHJA28/zVx1Jecq7+Rt903kSH76U/VsML2BKablzY02woYwpZKL\nRTHJa45T/PHcJ/GknUjILAkpfuV+DU1gPe6khcnZrzCtMuArv0qh7EelC2LLm6TOM0JWUjI3W0/Y\nW8qdvrvwZzM8r+znXs8CadcogfyzUHQcgJyJW/GVvcXs9Y9SjkA7Kl5AiV+t+bflrv9F+JfWKC4D\n6/8XtvX/YP89/z9m8loHXUde/7cNXuqDVx+Bten/uT2bgf5XoermfxKJAKXb4aE3ILQAZx7/V0+b\nnAyy+sR1ks98C1aHQJaRt3+NuO1DRDL7Sa07RNnxBR5Cg0UpErIoOelW890aHd+fWeEPhmeZ9HUA\n4IgeRpQznIgYkCSZ8WurFDfYeca7xvbLg7Rd6Gd5upbH5h9mzJng6ZofoS8/wum23/Ij5bfQpPQ0\n962nsWkb1TjxiG6eVgb4hRRHmVThr6ynO/9VrOZhkMDjTxCe0nC47/ssCT9CyCjRr23i0uRuLHk9\n/PXcl9BJWmZi8xh7PssqMk+T4DfOZ1GIKR4wl+Na2ozoGOYLLX/H3Tu/xkrdM0TidnYOdVM/ZmD+\nSh4udZZA7TWMMSV3Te9hbum/MXsmh/nLVo7o2wCoZ54bN45TaXr5H9/tr5VG1K793G7ZyrOJEG05\nLdSr1+FwttGfGSUmxHEsbCa0UssPqupIdwyiWBEYqQ3ysllNbWgDn9FYEIH3UhLL6teJimpWbBbe\nUGxFLWbYkjuA12jiI4FXKSLARZWdK+FtHLXO0Z/TTzrYTHppB9ccU1Rnurgaqcd8OYWca0aXztCz\nkodzaRyvKNPn28mzNhshi4NoUKYsNs0fdD3LutApPJMfYufiPip8HqJqHdbAHJea2gGwR2boy9uM\nM7KN6+UJXt++yC92vMKVwsMo1AIRznP5Yj1T4SLMLKAveJot/Xa02t2ojXcwWVSDLKppmb2CqNQQ\nW28kWr+Rr37s/+H5dUX8WniEn0lfIS5o2Jizmxdtb7NqfL+pTUIcJmLQ4dX9kGcNaUL6NDVTWtIK\niYgxjyfu/xLXmm9FzE5RN3qRPxiM0+LNEjXV0ZW3kTPho6QkBVkR+j1NKJPj/OnAH7N9YYBRhY+B\n0HmunxBJJ5XsKhvh5fseYrXBgyqV5YZLr3NZu5dgcxERjRlbxkuXvAmbN8tGL8RVw6xavfS3pDif\nm+buYAV3+Ur4yspbPKH5Ph2RT6P03ccTEz4sEQU2WeanMR9fDa3wEZWKuCDyK60SjkeRBDh9tZlE\nIIac9ZGrzWVB4eNUspIfyDZm1e8xqfLh1enJatdIB9fxsyIHYyYFbdE5tkz28sGrp3j47BPsOf06\ngixztaGZTE6AV3Z8mL9ouQeA6piXjfYu3hvfiWWmmTyflkSeD71BRenCFJIs4R5/f+GnxxBDFkCV\nL3B35eso5TRXVToGjEtMR3ZzznELekn7j99DTOMnrUixrArwnbFq3gxVvv9cMrDHv4G0LLO8zouc\nXWA5ssjnAtPcLHZiFmLkecO0X/JRFPoeH5x7C6WjgHuiWQbUKsb1bdzakSWqEZDVIsJkmlNVTlom\nEjxwYYyHToaJqeD1zVoePBPGEI1z41Qn8+5W1BmJoqG/50LpIgmNyL6uBKIkc76mhQGXlSNOJYvx\nQUI5eoyyi4Gcn4O6HGM8itpxippbv4h264/Ij1xAzKiZZyMOfxOx+UpOdH0B95iPgNHEkwfuYe+V\nc7zw5Lexx1P8KFTP3pdO0xjNUOtdpKM4j69Wf42bJ1ZROGtQZNK0praQzAogZtgnVpCbcSLKAhpN\nglBdGxb9DGlRjUdhp92sZwdKHoz40FQPkXXIjI81cSVcRsHyFOpkkuGmBn63+w7+9KFP8odf/zbL\nziY8QprFzW5w1pIqNNC2NsgB6SkESSQNCGQ5rjnPydALNPgm6XRVoekX6etqpXXISf21K9QJQfYV\nDxNUmSl3eTiQ309O63awl7PzjYskFFq6qu/G32RHSKWwJRfwVRpZMuTQErtMVfFulIIKnWBk3FUK\nCoHbl0CKrjAUL2QmN071jIUfT7wHwMViFxuSb1JhfpV4ogONRYVBAcmYh8Z0MWLCyd+Eq1kVcrnh\nzGFue+vvGbdbAOhzGvn6bfvwOVaYU4bZfbmIRKSPZGyY4aidgDVOd5WFrFJD7YIXOdJKhSNB+jNp\nkk6Zz186grBkolzhJOzoIdESJ3NXiD7VU6QmT6NIhMmIIn/U/givlO5ANV/H79ZqSKnViBmBq9fu\nxz4T53LBRnLjWuwJB0ULPtIqFQpZZsVuRwCmTLnY6q8iG2fRyhJDpTFiWomGtVw8ilKyyyXc+dMz\n2GbrIKMl6/87nkq5UG2dQxZFuqsKebNdy1/er8Cc1KNTPkpRqpQp7SxpMYsyKrPmP0ZYHaQ0U4Yt\n2oXZ/2vS6mpsinWoVPDYzZ9HVgioonFG4mVcsW5gvWKcm8VxRq82MxMu4K6qw5RZZ6hMWvkL4wJV\n7qfYLo6wv/wc24wXsXpK8Ce8xOz92HOXYLSdsrk1doVSjOYkeUlTxz3edwkqdfx8JZe5lIKHrGks\ng/uoSen4hZQgmBBIpztRK7I0u8qRPW1IgkjA6uKJ+z6LrNChWxskp/gSkdIx7r+Qxh2uJag6yibp\nu9SM93HTSIyfnFDy60t+6kPw5w1aXi5U0xQfZqNyjnwhjjI4hiD9U+/JjuY23t24jQfPHeEzk0/R\n3nGSrELJqR13I4gm4vF3+bk6TJdG5niLjhPNGmIcJmDdhDpjQQ71MiP52LUmcZfShyhn+eMmHYIM\n9y2omNEtsrQ2TmL0HXzaCB+ZKCITbCfqBZ1FQ0pnJtdQwjrZxQ09c+zr8BLP/ga/SUXpyi52p/+E\nG0MbuJZ/ij/JD/Jq/lvMGecojBYymR/H1lpDrb+V+y/M0z5+Hb8jh1fsO3mNezBrg/RUxrGk1Xy+\ncBrj0Af524nH8KRy+QNivKeIcyCyDlfaxofKr/ClwiizRpnfmRKcSjajFaG87TU8eaOsTW9i4PhX\nuDK8H2m5GUFW8D2XktezFRxbrqT/LQvTb91PfucfYh74NLpANYVdf8DOtWZkJM4UP81h3TkyoeC/\nOn/9r8S/qEbx0KFDVuBbhw4d4tChQyuHDh3KHjp0qPjQoUOfBf4E+NHBgwdP/yfH+u/i9zWK/z7e\nevwQI+eO46lpwuLK/dcNfueb0PsC9DwLjqr3r3/O1Bno+BXc+E1w1vyPNnMeBOeh+xlo+RBozf/b\n6TL+BGuvjBI8MokiOog58B0EQUbe9AUC8Q8TGihC0XaAcE8GRSLLhUYLv1hv4q/MWTzXDvOlv/8e\nMysGHt21iVjkJSQ5SyC+SHvxvbzrT3AgrmLozAK2XR6+5F2hRJDZ2XuB2wejWGLFZANlDHjOcd44\nQkwV4l7P7Wzu2oMy3oQpmGa73Ey/cpouxQg3G9LkBhTE3KWoyaXRdZZ5XxEFSwnCVzV0OO9guKaA\nzTOFHF+xsl39PFhlLq/lcFNwE++YOrCncng17uGYqYu0+SS3W1NsM/lJWibISiJqZZolXzHu+Sz2\njhCnp2qZjtqYDpdTmB+jwRxHfbGEpeUBhNAaukkvR3M2490kolGk+Nb1z1EfH6bBukg8u46knMMW\nQUu10oJCVKMS1RToq8g1l+OTJZ5XzWPLaLFkzARnN3BBY2aixEaFsQOHQ6Iq6GCnexKzxcfcmUKY\nMXC8JUHAlEusrJXHdfewRezlgOIKxzLr8DhGKTYnOB15AHvu8yzZR8iurSe+eB/TKplviU/w2/I7\neJMdDJZVcrrhw7hDGeKs0jx6lnRkAhkNfoOeeclNZWyc5uAw0cJSZm31WASJPDGCpFTSuNCPc8bL\nT+69DQELD58KMeQx4rPV8sGj82wZnKe7TMWidQxrzI5jaYaThl0E1XoKS39HlddK+WweKsNuAE7W\npUhrFHzstZdZtxziXEUp52vrcCRlvtyfolDVy2HzJlYLq/FExnk691Uitg8hCBqy6Rfo1Nj5kSvF\n1yNTdGotFC2qcYQ0XG7Zwby7iCcu/5DrwisoIj1UJxM8NllHQJnhotZLc889DkaSAAAgAElEQVRF\nptxRbGENGaWadu9Fvh7tZvy8nYA5zJx3iXxDOfqaNZpVyzxe9ClyQt0UTaySH/QhqjfQMpHkxo4w\ni0KMmdxCPtt7HVXyBlw8jSfUy+PuGG2JJO3iLjbEfogopTnhr2Kz+RpmxRozXi26mUIeVw5SGYtz\nMfpBJuY+zarNw4KxG5+spC5iodN0I7pwPlJqEKu9mguFDoZShVzI1ONYvYt8JE65OhnWTZMO34u0\npuTjEwlumZYomj9GShZo6hvD4J0lYczjWmUecsjLVU8Ttyy+jTUZY13jc6SyAkcu7mK7t5MRN1yq\n9vJg6AFmQr34VVbqMkVM5l4i6t2BXhDI+iUKbIvUOcfp81cwKRgwp0MUy06KMwpWNAH0WQ0CIj3u\nE0iSnoxumYal7RiyetzKDBnJxnC2j5ayFd6MmkhokjwgDePWRHjSsgOpYo3spJb15kXWWWYxK5Lc\n4r/CT2wOztol7Mn1XC95f0VasqghCx84s4ZDthNPZlGuDjFVWUQ8kORLb79D0VI/L+75GOPa1xhu\nbCdkt1Ga0LE/kOFEnhZ3QsaUlllSJKkYOsuF2gUUag9zWh8KdTPatJLN1YcRdAKSZpEXMj6atDAX\nruXmtJOkv55rLg97jr2At7SZF27/CEmtgprOTlJk+JPcTWyMqchdnqXcP8Zvbv0EKUWCL/eHMRlE\n0mOXUBpzeTc3wnXlHMXpfJokD7XZAvRo0J97CsPyKEPuTRTKBnaKYRYkiQWNC/nAGVK+UmLdj0JK\nQ7G6k2CRnamcUubySggbLcw53BT4JNpFLdoSNxZTGW/lKfhI38tsyHubmfh6MkkrgpgmQQrP2jiF\niyv8tPlOburoperSMutH13B7Q+iSCdylEdbc97JVasekfJUEuazm3kph/znMIT/vtm2hP7eZtv5e\n4mqJK3kbmMydYsfyEnuM+8mQJaZI8E5pEWGlzFeHEsRCE+SaWrmgyFDoC+Az5tOVX01Ip6Pt2Gt8\ncbOT5rE4P9rzSTpcKjYsKDkdamMltAFcLtL6fO6bepqj7TtYCXhQzEVRSBKz+UaW8hIUTs5SMqNF\nMm3ntzdeoKsywHReBKvxdpKKHG4YHyAnUEOq6SQZUYfYHMd0UcQ1tYrDczO+grfQ/2KFpEeJpnkK\n7cujSC4bf7XjNrySg25XJZvso9RsGUWRk0AzIFK4Oo6ATNyhQR2bQmGxsfvCHFqNCzEZ4fhNN9Fn\nKORnNXcQLjtJVKGhYeJ2Vi2TmGIJbukWqTHuxqtWsZAnI4tZejO3IDhirCSn6SCGy6clIyd5eUcI\nSVXCbMmfEtU5eHRcTYepnyFlLxWBAySzg2j1aSSdhYP557BnRTzhe7hav5FXN+wmO5FEZ5X4WvgZ\nlmQ7j4m/RVBaKVeOY8fHn85+DuOKmcDoZnYu3ckp1RTGVTPprIrRvEbaY13k+h9gxSRgb/wtQtJC\nZd/D5Ch/yVqshNO2DBctYSZZQk52ExaWaVjegGJoD9aaIC3GQbIBLdq4lmz0JM02H5XaO5lkmiu2\nOr79qYco9uupnouRTfVj2zSMNPR5IlqJE406pPRVLhtVVM+bUKQENubciJyMMa7/MRM6C6PlO8hf\nGyF/bhKNP4KQDSLnbEOj2EzAbOFo+xZmPW4OXDiFKZFi3pyP6LIz4XYhKUq52FaNrz6fixaRaaca\nQ/RNVuxbsSQSbA52M6SfoWq8FadZR6nfwzljHDkZZdu0gpD2Lb5f8BvO2zswTQ1jG+/GV7mTvAk3\nE95uqowb0RiLmdMKtC/HWP/m4zy3eZKkYpCI7RGWPNtYMDn54UAdbb71WJdaMCrsWFLF3Oht5BOB\n/UxbNjHpNOMVV8gLTjPmysercTAoNJJERTr5JjtUGkrFBO3DbRRo8vgKca7ka7jepOHB+QSF0Vps\nVcfRK6OkyuY5VX4bWb2Gz1jfROscZ6D/BtKz25ED5TiTVhr0EmqFiYNbzWzLyHxlzYk/ucSiv5uX\nKrfyl7XraIiuUhR5v6fGFce7DK1ZCSqi5JeWU1X+f2+N4r9UKJ4BtMAfAl8Cvg58HtgMfB84dPDg\nQfnfGvD/CX4vFP/teGensVw4xE2eMc5dnaPixjsQFYp/2eB0HN54FMp3gaiCSz9haeAaupZ/5uPs\n98A3TviW76NRqf+/Plx1cPnn79cvVu39X04lJbOEjs/gf2GIrC+B+cY8bMk/hpiXrOxiZeGLJCdj\nGLbmkej3EVEIPJwKMbnOyJlokkd/9xtumh4m1+Vke+c7nB+Z53T+VconYvzlsyJFs8u8XttGzrvT\nqKIyf9ugomZumL2v/IKS+SRl5s1cikrkp13oG1sZ0RXxUduDqJ56HaXYg1suY52hBH86wqtiJ+6k\nk9LOXpJygnxrCwPGaUoKOlnpfYDBpUcIocW50scPbvwkW2fi7FV8j4L4IlIixMGcFW4NtxEQkhy1\nXqQuUs943rOYJCMfs0eoufYT3ombsJlHScWMmPqMcDxCf9SNz6aifcP9PC21ImXLqck/TYXLiGLe\nR1nXDDMFOax+REWLpxchC2O9ebxcNc+bNi3OdC9/lN7GaTL8ijh3SiDLMl4xyOvCAD9FzYSg53PC\nm2zXvkh37FZKswL71/+C30QFGswJcm0RpIzM6ttVjK99lEp/OUP2FYYLlrgSuYkNqgXWt56hcClO\nyGJAkbuMQZY5rajlpGeEjWEzIwufxpwVSYsSh0tu4lzJZry2HAZLKhkrMHIdM21znUyaPWxamGT7\nbBe3jp3nzokLmBMJ5nKMVE5PonUbUOqDSJIGDVn2jb5HMKLgiXs/SNlCiKLhTtzzZ+irW8/16iY2\njoR42DdJwJbknGsEkjn0aPfQFujhsdg+bo5tZlpykxHVpHSLvL2umJrRLhoWFpCC05SP91AxN8iD\nE9ME7VWM6qbYZ3iFY4YbOZlv5s7zi/TWHaB18ByL5mssqaOos3ouGjPM2SMselIMlvhZyb8Hd3CZ\n76w+TjzloOyqB6NhiIS7ghc9eWy+9Dq6RIT4zhBzbKJ5oIOmwCwjqlI6cJOJKChdn2WD8Cmedg3y\njj2PUet2rN63qJzMQEZkc94c1a7DJGZayY8b6ajUYc+aqPPZeWBjC2HO06lS8I1omlW1i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s5OSSnVoid9XLTEUp6aJ8BvNPs37QTOXyCl11Vbxr28DO8adJCnNMaALsLjqFfTXFBbLc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PPO5mcIrC6SW/URWW5kzXICeyZJ083L1M6NcbvmPJdU/exVXeA3tu/T65oiICzhyS0RlAQq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\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RUt5GNiss3HvnHZy5FPayyJ2wGX94Ek1cIO5uZDqqp9E+izUiYo+7MRTsdNz9GaaWKKf1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4tVlZq8REKZAEOT56hZ8rP5J08i69zI+3fvoMiCp/YopXY9lwoFZIZe7nfYcAi1zBYmGHel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j9jlfYj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\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8mzfdSbV0OwlriomZn2Bu/jrJ+UlaXKvYNVXPrN3HBVMRy5JinFYtNbtKuZnL8l1rlm3L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\n/zDdcMSI9R44AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x119944910>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mu = annual_return\n",
-    "sigma = annual_vol\n",
-    "s0 = close[-1]\n",
-    "T = 3.0/12 \n",
-    "delta_t = 0.001\n",
-    "num_reps = 500\n",
-    "steps = T/delta_t\n",
-    "plt.figure(figsize=(15,10))\n",
-    "for j in range(num_reps):\n",
-    "    price_path = [s0]\n",
-    "    st = s0\n",
-    "    for i in range(int(steps)):\n",
-    "        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))\n",
-    "        price_path.append(st)\n",
-    "    plt.plot(price_path)\n",
-    "plt.ylabel('stock price',fontsize=15)\n",
-    "plt.xlabel('steps',fontsize=15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):\n",
-    "    payoff_sum = 0\n",
-    "    for j in range(num_reps):\n",
-    "        st = s0\n",
-    "        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))\n",
-    "        if option_type == 'c':\n",
-    "            payoff = max(0,st-strike)\n",
-    "        elif option_type == 'p':\n",
-    "            payoff = max(0,strike-st)\n",
-    "        payoff_sum += payoff\n",
-    "    premium = (payoff_sum/float(num_reps))*(e**(-r*maturity))\n",
-    "    return premium     "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "151.57654346653473"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,10000)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Options/Tutorial05 Option Pricing Black Scholes Merton Model.ipynb b/Tutorial Series/Introduction to Options/Tutorial05 Option Pricing Black Scholes Merton Model.ipynb
deleted file mode 100644
index a8507d4..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial05 Option Pricing Black Scholes Merton Model.ipynb	
+++ /dev/null
@@ -1,106 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from math import log, sqrt, exp, pi\n",
-    "from scipy.stats import norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class BsmModel:\n",
-    "\n",
-    "    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):\n",
-    "        self.s = price  # Underlying asset price\n",
-    "        self.k = strike  # Option strike K\n",
-    "        self.r = interest_rate  # Continuous risk fee rate\n",
-    "        self.q = dividend_yield  # Dividend continuous rate\n",
-    "        self.T = expiry  # time to expiry (year)\n",
-    "        self.sigma = volatility  # Underlying volatility\n",
-    "        self.type = option_type # option type \"p\" put option \"c\" call option\n",
-    "\n",
-    "    def n(self, d):\n",
-    "        # cumulative probability distribution function of standard normal distribution\n",
-    "        return norm.cdf(d)\n",
-    "\n",
-    "    def dn(self, d):\n",
-    "        # the first order derivative of n(d)\n",
-    "        return norm.pdf(d)\n",
-    "\n",
-    "    def d1(self):\n",
-    "        d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
-    "        return d1\n",
-    "\n",
-    "    def d2(self):\n",
-    "        d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
-    "        return d2\n",
-    "\n",
-    "    def bsm_price(self):\n",
-    "        d1 = self.d1()\n",
-    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
-    "        if self.type == 'c':\n",
-    "            price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))\n",
-    "            return price\n",
-    "        elif self.type == 'p':\n",
-    "            price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))\n",
-    "            return price\n",
-    "        else:\n",
-    "            print \"option type can only be c or p\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.1573056407749949"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)\n",
-    "a.bsm_price()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Options/Tutorial05 Options Pricing Black Scholes Merton Model.html b/Tutorial Series/Introduction to Options/Tutorial05 Options Pricing Black Scholes Merton Model.html
deleted file mode 100644
index 7cb5708..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial05 Options Pricing Black Scholes Merton Model.html	
+++ /dev/null
@@ -1,140 +0,0 @@
-<h2>Introduction</h2>
-In the last chapter, we modeled the stock price with the Geometric Brownian motion. Then the logarithm of return \(\text{ln}(S_T/S_0)\) follows the normal distribution \(N\left[(\mu-\sigma^2/2)T,\sigma^2T\right]\). It means the logarithm of stock price\(\text{ln}(S_T)\)follows the normal distribution \(N\left[\text lnS_0+(\mu-\sigma^2/2)T,\sigma^2T\right]\). Based on this basic assumption, in this chapter, we will talk about a famous option pricing model: Black Scholes Merton Model.
-<h2>Determinants of  Option Price</h2>
-In different kinds of asset pricing model like bond pricing, <span class="s1">enterprise valuation, </span>the most commonly used valuation method is to calculate the present value of the expected cash flows of that asset. But options have some characteristics that are different from the common asset. For example, the options value depends on its underlying assets. In addition, the cash flows on options are not constant in each time period but depend on the occurrence of specific events which are not predictable. In fact, the value of the option is determined by lots of variables.
-<ol>
- 	<li><strong>the underlying price:</strong> Change in the value of the underlying asset is the most important factor which affects the options price. For the call option, holders can earn profit from the price rising and put option holders earn profits from the price decline. Therefore, call options become more valuable as the underlying prices increase while the put options will become less valuable.</li>
- 	<li><strong>The volatility of the underlying asset:</strong> In tuition, volatility is a measure of the degree of fluctuation of the underlying stock price. It is a forward volatility, which means it is a prediction of how much the stock price of the underlying will move in the future over a certain period of time. The higher the predicted volatility is, the higher the probability that the underlying price will move a lot. Thus the greater will the value of the option is both for calls and puts. We will further discuss the different type of volatilities in the subsequent tutorials.</li>
- 	<li><strong>The strike price of the option:</strong> Call options gain profit when stock prices greater than the strike, it is easy to understand that the higher the strike price is for the call option, the fewer profits the holder can get, the less valuable of the call option. Thus for the put options, options with higher strike price are more valuable.</li>
- 	<li><strong>Time to expiration: </strong>If the current date is t, the expiration date of the option contract is T. Then the time to expiration is T-t. For v=both call and put options, the longer time to expiration means there are more changes for the function of stocks prices, and higher probability for the option holders to gain profits from price movement.</li>
- 	<li><strong><strong>The risk-free interest rate: </strong></strong>As interest rates increase, the expected return required by investors from the stock tends to increase. On the other hand, the present value of discounted cash flow will decrease. Thus the increase in interest rate will increase the call option price and will decrease the put option price.</li>
- 	<li><strong>The dividend yield: </strong>After the dividends paid, the share price of the stock will decrease. During the options holding period, the decline in underlying price is unfavorable for call options holder. Therefore, the increase in dividend yield will decrease the price of call options and increase the price of put options.</li>
-</ol>
-<h2>Factors in the BSM model</h2>
-After we get an intuition about affecting factors of options price. Now we will introduce the BSM option pricing model. The Black-Scholes model for pricing stock options was developed by Fischer Black, Myron Scholes and Robert Merton in the early 1970’s.
-
-First, we introduce the factors in the model. For all the factors listed below, only volatility is not known.  There are many types of volatilities. Then which volatility should be used is a critical question in option pricing model. We will further discuss this part in the next few chapters.
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Factors in the model</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> Stock Price</td>
-<td>S</td>
-</tr>
-<tr>
-<td> Strike Price</td>
-<td> K</td>
-</tr>
-<tr>
-<td> Time to Expiration</td>
-<td> T-t</td>
-</tr>
-<tr>
-<td> Interest Rates</td>
-<td> r</td>
-</tr>
-<tr>
-<td> Future Volatility of the underlying Stock</td>
-<td> σ</td>
-</tr>
-</tbody>
-</table>
-<div class="page" title="Page 58">
-<div class="layoutArea">
-<div class="column">
-<pre class="prettyprint linenums">class BsmModel:
-    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):
-        self.s = price # Underlying asset price
-        self.k = strike # Option strike K
-        self.r = interest_rate # Continuous risk fee rate
-        self.q = dividend_yield # Dividend continuous rate
-        self.T = expiry # time to expiry (year)
-        self.sigma = volatility # Underlying volatility
-        self.type = option_type # option type "p" put option "c" call option
-</pre>
-There are some details we need to pay attention to about the input of BSM model. Firstly, the model works in continuous time, rather than discrete time. Therefore the risk-free rate r has to be modified to the continuous form. Secondly, the time to expiration should be converted to year. The volatility is the annual volatlity.
-<h2>Assumptions</h2>
-The market assumptions behind the Black–Scholes formula for pricing European options are as follows:
-<ol>
- 	<li>The volatility of the underlying assets is constant over time</li>
- 	<li>The underlying asset price follows lognormal distribution, this means that the log-returns of stock prices are normally distributed</li>
- 	<li>The underlying asset can be traded continuously</li>
- 	<li>The underlying stock does not pay dividends during the option's life. But the basic Black-Scholes model was later adjusted for dividends, here we demonstrate the later version with dividend yields.</li>
- 	<li>There are no transaction costs or taxes</li>
- 	<li>All securities are perfectly divisible, meaning that it is possible to buy any fraction of a share</li>
- 	<li>The risk-free rate of interest, r, is constant and the same for all maturities</li>
-</ol>
-</div>
-<h2>Equations</h2>
-The derivation of the Black-Scholes model is far too complicated to present here, we only show the formula here. The basic principle is based on the idea of creating a portfolio of the underlying asset and the riskless asset with the same cash flows and hence the same cost as the option being valued. Then we get the Black–Scholes–Merton differential equation. The solutions to the differential equation are the Black-Scholes-Merton formulas for the price of European call and put options
-
-\[c = S_0N(d_1)-Ke^{-rT}N(d_2)\]
-
-\[p =Ke^{-rT}N(-d_2)- S_0N(-d_1)\]
-
-\[d_1=\frac{ln(S_0/K)+(r+\sigma^2/2)T}{\sigma\sqrt{T}}\]
-<div class="column">
-
-\[d_2=\frac{ln(S_0/K)+(r-\sigma^2/2)T}{\sigma\sqrt{T}}=d_1-\sigma\sqrt{T}\]
-<div class="page" title="Page 359">
-<div class="layoutArea">
-<div class="column">
-
-N(x) is the cumulative probability distribution function for a variable with a standard normal distribution. It can be calculated by the integral of the probability density function of standard normal distribution from 0 to x. In Python, you can use the <code>norm.pdf(x)</code> in spicy.stats library. For the following chart, we plot the probability density curve of the standard normal distribution. For example, N(-1) is the area of the left hand of the red line under the curve.
-<pre class="prettyprint linenums">import scipy.stats as sp
-mu = 0
-variance = 1
-x = np.linspace(mu-3*variance,mu+3*variance, 100)
-y = [sp.norm.pdf(i) for i in x]
-plt.plot(x,y)
-d = [-1]
-plt.plot(d*100,np.linspace(0,sp.norm.pdf(d), 100))
-</pre>
-<img class="alignnone wp-image-2055" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/n-1-1-300x198.png" alt="" width="356" height="235" />
-Then in our BSM model class, we will calculate the European call and put option prices by using BSM formula.
-<pre class="prettyprint linenums">def n(self, d):
-    # cumulative probability distribution function of standard normal distribution
-    return norm.cdf(d)
-
-def dn(self, d):
-    # the first order derivative of n(d)
-    return norm.pdf(d)
-
-def d1(self):
-    d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
-    return d1
-
-def d2(self):
-    d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
-    return d2
-
-def bsm_price(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    if self.type == 'c':
-        price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))
-        return price
-    elif self.type == 'p':
-        price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))
-        return price
-    else:
-        print "option type can only be c or p"
-
-a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)
-a.bsm_price()
-</pre>
-</div>
-For a call option which expires in 90 days and no dividends paid, the underlying price is $42, the strike is $35, the risk-free rate is 0.1, the volatility is 0.2. The price of this option is $6.157.
-
-</div>
-</div>
-</div>
-</div>
-<h2 class="page" title="Page 354"> Summary</h2>
-This tutorial discussed the factors affecting the options price and introduced a famous option pricing model including the input parameters, the assumptions and the formula.
-
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.html b/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.html
deleted file mode 100644
index a8e4c9c..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.html	
+++ /dev/null
@@ -1,229 +0,0 @@
-<div class="page" title="Page 87">
-<div class="layoutArea">
-<div class="column">
-<h2>Introduction</h2>
-Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<div class="page" title="Page 425">
-<div class="layoutArea">
-<div class="column">
-
-Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<pre class="prettyprint linenums">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)</pre>
-<h4>Impact Factors</h4>
-Stock price, days remaining to expiration and implied volatility will impact the Delta.
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-
-In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-<pre class="prettyprint linenums">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-<img src="images/delta.png" alt="Greeks letter: options gamma" />
-
-The color of the graph above represents delta value.
-<h3>2. Gamma</h3>
-Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-
-Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<pre class="prettyprint linenums">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-<pre class="prettyprint linenums">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-<img src="images/gamma.png" alt="The Greeks letters: gamma" />
-
-The color of the graph above represents delta value.
-<h3>3. Vega</h3>
-<div class="page" title="Page 438">
-<div class="layoutArea">
-<div class="column">
-
-The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-
-Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<pre class="prettyprint linenums">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-<pre class="prettyprint linenums">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-</div>
-    
-<img src="images/vega.png" alt="The Greeks letters: vega" />
-The color of the graph above represents Vega.
-
-<h3>4. Theta</h3>
-<div class="page" title="Page 432">
-<div class="layoutArea">
-<div class="column">
-
-Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-
-The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<pre class="prettyprint linenums">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-<pre class="prettyprint linenums">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-    
-<img src="images/theta.png" alt="The Greeks letters: theta" />
-<h3>5. Rho</h3>
-Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<pre class="prettyprint linenums">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-<pre class="prettyprint linenums">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-<div class="page" title="Page 438">
-<div class="layoutArea">
-<div class="column">
-
-<img src="images/rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-
-As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-
-<h2> Summary</h2>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.ipynb b/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.ipynb
deleted file mode 100644
index b99ba7d..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial06 The Greek Letters.ipynb	
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "from math import log\n",
-    "import scipy.stats as stats\n",
-    "import numpy as np\n",
-    "from numpy import shape\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "from matplotlib import cm\n",
-    "from matplotlib import animation\n",
-    "%pylab inline "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "class BsmModel:\n",
-    "\n",
-    "    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):\n",
-    "        self.s = price  # Underlying asset price\n",
-    "        self.k = strike  # Option strike K\n",
-    "        self.r = interest_rate  # Continuous risk fee rate\n",
-    "        self.q = dividend_yield  # Dividend continuous rate\n",
-    "        self.T = expiry  # time to expiry (year)\n",
-    "        self.sigma = volatility  # Underlying volatility\n",
-    "        self.type = option_type # option type \"p\" put option \"c\" call option\n",
-    "\n",
-    "    def n(self, d):\n",
-    "        # cumulative probability distribution function of standard normal distribution\n",
-    "        return stats.norm.cdf(d)\n",
-    "\n",
-    "    def dn(self, d):\n",
-    "        # the first order derivative of n(d)\n",
-    "        return stats.norm.pdf(d)\n",
-    "\n",
-    "    def d1(self):\n",
-    "        d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
-    "        return d1\n",
-    "\n",
-    "    def d2(self):\n",
-    "        d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
-    "        return d2\n",
-    "\n",
-    "    def bsm_price(self):\n",
-    "        d1 = self.d1()\n",
-    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
-    "        if self.type == 'c':\n",
-    "            price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))\n",
-    "            return price\n",
-    "        elif self.type == 'p':\n",
-    "            price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))\n",
-    "            return price\n",
-    "        else:\n",
-    "            print \"option type can only be c or p\"\n",
-    "\n",
-    "    ''' Greek letters for European options on an asset that provides a yield at rate q '''\n",
-    "\n",
-    "    def delta(self):\n",
-    "        d1 = self.d1()\n",
-    "        if self.type == \"c\":\n",
-    "            return exp(-self.q * self.T) * self.n(d1)\n",
-    "        elif self.type == \"p\":\n",
-    "            return exp(-self.q * self.T) * (self.n(d1)-1)\n",
-    "\n",
-    "    def gamma(self, ):\n",
-    "        d1 = self.d1()\n",
-    "        dn1 = self.dn(d1)\n",
-    "        return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))\n",
-    "\n",
-    "    def theta(self):\n",
-    "        d1 = self.d1()\n",
-    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
-    "        dn1 = self.dn(d1)\n",
-    "\n",
-    "        if self.type == \"c\":\n",
-    "            theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \\\n",
-    "                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \\\n",
-    "                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)\n",
-    "            return theta\n",
-    "        elif self.type == \"p\":\n",
-    "            theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \\\n",
-    "                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \\\n",
-    "                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)\n",
-    "            return theta\n",
-    "\n",
-    "    def vega(self):\n",
-    "        d1 = self.d1()\n",
-    "        dn1 = self.dn(d1)\n",
-    "        return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)\n",
-    "\n",
-    "    def rho(self):\n",
-    "        d2 = self.d2()\n",
-    "        if self.type == \"c\":\n",
-    "            rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)\n",
-    "        elif self.type == \"p\":\n",
-    "            rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)\n",
-    "\n",
-    "        return rho"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "s = np.array([range(10,70,1) for i in range(23)])\n",
-    "I = np.ones((shape(s)))\n",
-    "time = np.arange(1,12.5,0.5)/12\n",
-    "T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "contracts = []\n",
-    "for i in range(shape(s)[0]):\n",
-    "    for j in range(shape(s)[1]):\n",
-    "        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))\n",
-    "theta = [x.theta() for x in contracts]  \n",
-    "gamma = [x.gamma() for x in contracts]  \n",
-    "delta = [x.delta() for x in contracts]  \n",
-    "vega = [x.vega() for x in contracts]  \n",
-    "rho = [x.rho() for x in contracts]  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gamma = np.array(gamma).reshape(shape(s))\n",
-    "delta = np.array(delta).reshape(shape(s))\n",
-    "theta = np.array(theta).reshape(shape(s))\n",
-    "vega = np.array(vega).reshape(shape(s))\n",
-    "rho = np.array(rho).reshape(shape(s))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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BoinHpfeIDsZh97wrioJWq8VwN6CD7tnvVbSuV4DvVbQuHuCTVrSOiA6eCGFzP+IrdGSz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Bu823iWgxSPsu/vZBW4WCxI05SzszOyLFtbBZ4PAjwpq8D74Vk99is8y/PnPC5K0OJJ4lkq4//M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\nFwTZNl6SY1E+jNA7blsobHiY5JiiYDXeZ9/XmRd9VlQYn5dbR+5BGShyxb6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qg/OU8Id/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vazgrj2mN45GqPIufRQF/FeL8MZeHqtdSoQzolw82iMUSVJYCgrXXtn5++CW4cTRmHJtSig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\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RzzLatY83PABACE63A76f1rgzGLIx86J6z6SovKwVuU/oBgneexaYsCxyTmpLM9LEgUKI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\n5OWkRcofRxECVTi02GkSIZ1YQD9qH73rC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YubJRHUic2OxTvy+JmCyVTJZcaIg+siFI1o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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10bcdff90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = gamma\n",
-    "fig = plt.figure(figsize=(20,11))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.view_init(12,320)\n",
-    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
-    "ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)\n",
-    "ax.set_zlim3d(0, z.max())\n",
-    "ax.set_xlabel('stock price')\n",
-    "ax.set_ylabel('Time to Expiration')\n",
-    "ax.set_zlabel('gamma')\n",
-    "m = cm.ScalarMappable(cmap=cm.jet)\n",
-    "m.set_array(delta)\n",
-    "cbar = plt.colorbar(m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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GcTr//vTnv8V/81d/tqPz1MvifACFQkFhXwaXRStKDyBjzD+m1U5v5dv+qxX/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bIgK+SFy8/8LBIVSwTkwj6RZnFZNqhkLVKGCqRYaIxY1Up8Yg03hP6Y1Z7PnaTMBbXL9Cvl5qMw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v4M7MZ/lbv/5Ffv1zhz7kEbkbRVGwbRvbtqlUKjueS9N0hwlqq9XaDnRumaBms1mSJCEIgj0d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\nAvKagi5naEr3NSwHMUamcmLMRr9jvrfSCqmpBrsklT1DObIswwsTl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PLy8gMlAb2/ZyUJ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zKTk7GCCEQpMluuZxyLKZnrSRSnF+I0QXHgdv9eV3wpyhLZjXRudYWB/SFhl64XK4UaPR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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a0b9510>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = delta\n",
-    "fig = plt.figure(figsize=(20,11))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.view_init(40,290)\n",
-    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
-    "ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)\n",
-    "ax.set_zlim3d(0, z.max())\n",
-    "ax.set_xlabel('stock price')\n",
-    "ax.set_ylabel('Time to Expiration')\n",
-    "ax.set_zlabel('delta')\n",
-    "m = cm.ScalarMappable(cmap=cm.jet)\n",
-    "m.set_array(z)\n",
-    "cbar = plt.colorbar(m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Aa02/35dQQAghhLgNJAAQQpwKJ6HP/yjFao//9KFt1v/0LTzw6AXSQRvX3V8ubRkGoQnd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ntpPp8nn8+P/y0Mw3Ec4ebmJr1eD03TxsVWLpfDsqz3lRSYGL892iMA7wZCCNLpNOl0mrm5uXEa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Yk1uVfN8Xa68FRdOkyd+IcKd/sZruZLS9/rTufgXjnHW+kddFXjw2qOzXyFRHBRq34SEre4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\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TaJpEeA0Mn+R+cZFrS3uEvMZ8trxSNLqCWi/KWnZwQfEveVhcmJv8n7W2Yj1MLDJhITzG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/zlL/n1r3/NsmXL8ro9CwuLAkKd6B04MSRJ+kfgB8Bs4PeSJL0jhLgI2AosB26TJOm2999+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\nhBBCCCFEB/D/Ac5v+W5NxDg+AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1041f4790>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = theta\n",
-    "# facecolors aren't normalizing as might be expected\n",
-    "# we need to normalize it to avoid all dark color for value under 0\n",
-    "norm = matplotlib.colors.Normalize() \n",
-    "fig = plt.figure(figsize=(20,11))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.view_init(35,300)\n",
-    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
-    "ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)\n",
-    "ax.set_zlim3d(z.min(), z.max())\n",
-    "ax.set_xlabel('stock price')\n",
-    "ax.set_ylabel('Time to Expiration')\n",
-    "ax.set_zlabel('rho')\n",
-    "m = cm.ScalarMappable(cmap=cm.jet)\n",
-    "m.set_array(z)\n",
-    "cbar = plt.colorbar(m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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18o1Go63FPpfLwbbtgbfYE/WbDs/baIv/3NxceLtq8XccB/fu3Wtr8Y+u62eLfzt1IdA0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\nf8brs6/zzvw0PjPYXBCjyGNL6DxeQIuACDrVJNKHQGFyN6in12QYNDm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EexOPmdaYSKNz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AgrQa1cSoMoHCoCjIY7Mh8FoplOGh4/tiSejUHM8hUaRx3pEkycbW4pxHxA7HqlEb0h+e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\ngW0DjFJEdwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108c51d50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = vega\n",
-    "norm = matplotlib.colors.Normalize() \n",
-    "fig = plt.figure(figsize=(20,11))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.view_init(20,45)\n",
-    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
-    "ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)\n",
-    "ax.set_zlim3d(z.min(), z.max())\n",
-    "ax.set_xlabel('stock price')\n",
-    "ax.set_ylabel('Time to Expiration')\n",
-    "ax.set_zlabel('vega')\n",
-    "m = cm.ScalarMappable(cmap=cm.jet)\n",
-    "m.set_array(z)\n",
-    "cbar = plt.colorbar(m)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.html b/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.html
deleted file mode 100644
index 7488f06..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.html	
+++ /dev/null
@@ -1,192 +0,0 @@
-<h2>Introduction</h2>
-The change of volatility can have a significant impact on any options trading strategies performance. In addition to the Vega we explained in Greeks letter chapter, this part of the volatility tutorial will discuss the concept of volatility, specifically, we discuss realized and implied volatility, their meanings, measurements, uses, and limitations.
-
-<h2>Historical Volatility</h2>
-
-<h3>1. Definition</h3>
-It is a measurement of how much the price of the asset has changed on average during the certain period of time. In common, volatility is said to be the standard deviation of the return of assets price.
-<h3>2. Calculation</h3>
-Here we discuss how to estimate the historical volatility of the option written on specific underlying asset empirically.
-
-\[r_i=ln(\frac{S_i}{S_{i-1}})\quad for\ i=0,1,2,3,...,n\]
-
-Where (n+1) is the number of observations, \(S_i\) is the stock price at end of it time interval
-
-The  standard deviation of the \(r_i\) is given by
-\[std=\sqrt{\frac{1}{n-1}\sum_{i=1}^n(r_i-\overline{r})^2}\]
-where \(\overline{r}\) is the mean of \(r_i\)
-
-If we assume there are n trading days per year. Then the estimate of historical volatility per annum is
-
-\[std\times\sqrt{n}\]
-<pre class="prettyprint linenums">import pandas as pd
-from numpy import sqrt,mean,log,diff
-import quandl
-quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
-goog_table = quandl.get('WIKI/GOOG')
-# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016
-close = goog_table['2016-01':'2016-08']['Adj. Close']
-r = diff(log(close))
-r_mean = mean(r)
-diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]
-std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))
-vol = std*sqrt(252)</pre>
-An asset has a historical volatility based on its past performance as described above, investors can gain insight on the fluctuations of the underlying price during the past period of time. But it does not tell us anything about the volatility in the market now and in the future. So here we introduce the implied volatility.
-<h2><strong>Implied Volatility</strong></h2>
-In contrast to historical volatility, the implied volatility looks ahead. It is often interpreted as the market’s expectation for the future volatility of a stock and is implied by the price of the stock’s options. Here implied volatility means it is not observable in the market but can be derived from the price of an option.
-<h3>1. Definition</h3>
-
-We use volatility as an input parameter in option pricing model. If we take a look at the BSM pricing, the theoretical price or the fair value of an option is P, where P is a function of historical volatility σ, stock  price S, strike price K, risk-free rate r and the time to expiration T.  That is \(P=f(\sigma,S,K,r,T)\). But the market price of options is not always the same with the theoretical price. Now in contrast, if we are given the market’s prices of calls and puts written on some asset and also the value of S, K, r, T. For each asset we can solve a new volatility that corresponds to the price of each option – the implied volatility. Then the implied volatility is \(\IV=f^{-1}(P,S,K,r,T)\).
-<h3>2. Calculation</h3>
-Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
-<pre class="prettyprint linenums">def bsm_price(option_type, sigma, s, k, r, T, q):
-    # calculate the bsm price of European call and put options
-    sigma = float(sigma)
-    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))
-    d2 = d1 - sigma * np.sqrt(T)
-    if option_type == 'c':
-        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))
-        return price
-    elif option_type == 'p':
-        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))
-        return price
-    else:
-        print('No such option type %s') %option_type
-def implied_vol(option_type, option_price, s, k, r, T, q):
-    # apply bisection method to get the implied volatility by solving the BSM function
-    precision = 0.00001
-    upper_vol = 500.0
-    max_vol = 500.0
-    min_vol = 0.0001
-    lower_vol = 0.0001
-    iteration = 0
-
-    while 1:
-        iteration +=1
-        mid_vol = (upper_vol + lower_vol)/2.0
-        price = bsm_price(option_type, mid_vol, s, k, r, T, q)
-        if option_type == 'c':
-
-            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)
-            if (lower_price - option_price) * (price - option_price) &gt; 0:
-                lower_vol = mid_vol
-            else:
-                upper_vol = mid_vol
-            if abs(price - option_price) &lt; precision: break if mid_vol &gt; max_vol - 5 :
-                mid_vol = 0.000001
-                break
-
-        elif option_type == 'p':
-            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)
-
-            if (upper_price - option_price) * (price - option_price) &gt; 0:
-                upper_vol = mid_vol
-            else:
-                lower_vol = mid_vol
-            if abs(price - option_price) &lt; precision: break if iteration &gt; 50: break
-
-    return mid_vol
-implied_vol('c', 0.3, 3, 3, 0.032, 30.0/365, 0.01)
-</pre>
-From the result above, the implied volatility of European call option (with premium c=0.3, S=3, K=3, r=0.032, T =30 days, d=0.01) is 0.87.
-<h3>3. Factors Affecting Implied Volatility</h3>
-According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
-
-<h2>Volatility Skew</h2>
-For European options of the same maturity and the same underlying assets, the implied volatilities vary with the strikes. For a series of put options or call options, if we plot these implied volatilities for a series of options which have the same expiration date and the same underlying with the x axis being the different strikes, we would get a convex curve. The shape of this curve is like people's smiling, it is being called the volatility. The shape of volatility smile depends on the assets and the market conditions.
-
-Here we give an example how to plot the volatility smile by using the real time options data of SPDR S&amp;P 500 ETF(NYSEARCA: SPY).
-<pre class="prettyprint linenums"># download option data for all expiry months from Yahoo Finance
-# provide a formatted DataFrame with a hierarchical index
-opt = Options('spy', 'yahoo')
-opt.expiry_dates  # list all the available expiration dates
-def IV_plot(opt,option_type,expiry_index):
-    expiry = opt.expiry_dates[expiry_index]
-    if option_type == 'c':
-        data = opt.get_call_data(expiry=expiry)
-    elif option_type == 'p':
-        data = opt.get_put_data(expiry=expiry)
-    r = 0.01 # risk free rate
-    d = 0.01 # continuous devidend yield
-    s = opt.underlying_price # data_call['Underlying_Price']  undelying price
-    expiry = data.index.get_level_values('Expiry')[0] # get the expiry
-    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date
-    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
-    premium = (data['Ask'] + data['Bid'])/2 # option premium
-    strike = list(data.index.get_level_values('Strike')) # get the strike price
-    IV = []
-    for i in range(len(data)):
-        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))
-
-    plt.figure(figsize=(16, 7))
-    a = plt.scatter(strike,IV, c='r', label="IV by solving BSM")
-    b = plt.scatter(strike,data['IV'],c = 'b', label="IV from Yahoo Finance")
-    plt.grid()
-    plt.xlabel('strike')
-    if option_type == 'c':
-        plt.ylabel('Implied Volatility for call option')
-        plt.legend((a,b), ("IV(call) by solving BSM", "IV(call) from Yahoo Finance"))
-    elif option_type == 'p':
-        plt.ylabel('Implied Volatility for put options')
-        plt.legend((a,b), ("IV(put) by solving BSM", "IV(put) from Yahoo Finance"))
-
-    return strike,IV
-k_call, IV_call = IV_plot(opt,'c',23)
-k_put, IV_put = IV_plot(opt,'p',23)
-plt.figure(figsize=(16, 7))
-e = plt.scatter(k_call,IV_call, c ='red', label="IV(call options)")
-f = plt.scatter(k_put,IV_put, c = 'black', label="IV(put options)")
-plt.xlabel('strike')
-plt.ylabel('Implied Volatility')
-plt.legend((e,f), ("IV (call options)", "IV (put options)"))
-</pre>
-The current date is 08/14/2017. We plot the implied volatilities for SPY options which expire on 12/21/2018.
-
-<h2><img src="images/ivcall.png" alt="implied volatility of call options"/></h2>
-<img src="images/ivput.png" alt="implied volatility of put options"/>
-
-Plotting these implied volatilities across strikes gives us the implied volatility skew. For the shape of volatility smile, it should be a symmetry convex curve. But from the above chart, the implied volatility curve slope downward to the right. This is referred to the skew, which means that options with low strikes have higher implied volatilities than those with higher strikes. The smile is not symmetry. The skew of a distribution is a measure of its assymetry. Although the volatility skew is dynamic, in equity markets it is almost always a decreasing function of the strike. Other asset classes such as FX and commodities have differently shaped skews.
-If we plot the call and put options implied volatility smile in the same chart for the same expiration date:
-<img src="images/ivputcall.png" alt="implied volatility of call and put options"/>
-From the above chart, we can see the implied volatility for put options is higher than call options. Usually, put options trade for a higher price than call options, because traders place more risk in the short put positions, which raises the amount of reward they require to sell the position. Higher option prices signify an increase in risk and are represented by higher implied volatility levels derived from the option pricing model.
-Then we scattered all the implied volatilities of contracts across all the strikes.
-<pre class="prettyprint linenums">opt = Options('spy', 'yahoo')
-r = 0.01 # risk free rate
-d = 0.01 # continuous devidend yield
-expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]
-current_date = opt.quote_time.date()  ## get the current date
-s = opt.underlying_price # undelying price
-num_expiry = len(expiry_dates)
-IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]
-
-for expiry_index in range(num_expiry):
-    data = opt.get_put_data(expiry=expiry_dates[expiry_index])
-    expiry = expiry_dates[expiry_index] # get the expiry
-    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
-    premium = (data['Ask'] + data['Bid'])/2.0 # option premium
-    strike = data.index.get_level_values('Strike') # get the strike price
-    num_strike = len(data)
-    for j in range(num_strike):
-        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])
-x= [IV[i][0] for i in range(len(IV))]
-y= [IV[i][1] for i in range(len(IV))]
-z= [IV[i][2] for i in range(len(IV))]
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,10)
-ax.scatter(x,y,z)
-</pre>
-<img src="images/volsurface.png" alt="implied volatility surface"/>
-<h2>Volatility Surface</h2>
-By fixing a maturity and looking at the implied volatilities of European options on the same underlying but different strikes, we obtain the implied volatility skew or smile. The volatility surface is the three-dimensional surface obtained when we plots the market implied volatilities of European options with different strikes and different maturities.
-By using the interpolation method, we can generate the implied volatility surface of SPY options for both put and call options as follows:
-<img src="images/vol-surface-call.png" alt="implied volatility surface for call options"/>
-<img src="images/vol-surface-put.png" alt="implied volatility surface for put options"/>
-<h2>The Reason for Volatility Skew</h2>
-The volatility skew shows that for Put options, implied volatility is higher for deep OTM options and then is decreasing as it moves towards ITM options. For call options, the implied volatility is higher for deep ITM options and then is decreasing as it moves towards OTM options. From the demand and supply degree, the skew reflects that investors are more willing to buy deep OTM puts and ITM calls. Why there is volatility skew in the market? 
-First, the majority of the equity positions are long. Investors usually have two ways to hedge those long positions risks: Buying downside puts or selling upside calls. The increase in demand create increases in price of downside puts and decreases in price of upside calls. The volatility is a reflection of options price. Therefore the volatility of in-the-money put is higher and the volatility of in-the-money call is lower.
-The second reason for volatility skew is that the market moves down faster than it moves up. The downside market move is riskier than the upside move. Thus the price of OTM puts is higher than OTM calls.
-<h2>Summary</h2>
-In this chapter, we discussed the historical volatility and the implied volatility. The historical volatility of an asset is the statistical measure we know as the standard deviation of the stock return series. The implied volatility of the same asset, on the other hand, is the volatility parameter that we can infer from the prices of traded options written on this asset. In contrast to historical volatility, which looks at fluctuations of asset prices in the past, implied volatility looks ahead. The two volatilities do not necessarily coincide, and although they may be close, they are typically not equal.
-Now we know the constant volatility assumption in Black-Sholes-Merton model is not applicable in the real market because there is volatility skew for most of the options. Then in next chapter we will introduce some volatility models to capture the volatility skew in options pricing.
-
diff --git a/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.ipynb b/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.ipynb
deleted file mode 100644
index a877f12..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial07 Historical Volatility and Implied Volatility.ipynb	
+++ /dev/null
@@ -1,438 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "from numpy import sqrt,mean,log,diff\n",
-    "import quandl\n",
-    "import scipy.stats as stats\n",
-    "from scipy import interpolate \n",
-    "from scipy.interpolate import Rbf\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "from matplotlib import cm\n",
-    "from matplotlib import animation\n",
-    "from pandas_datareader.data import Options\n",
-    "import pandas_datareader.data as web\n",
-    "import datetime\n",
-    "%pylab inline "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1010,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1011,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "data = quandl.get('WIKI/GOOG')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1012,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.21326449960467742"
-      ]
-     },
-     "execution_count": 1012,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016 \n",
-    "close = data['2016-01':'2016-08']['Adj. Close']\n",
-    "r = diff(log(close))\n",
-    "r_mean = mean(r)\n",
-    "diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]\n",
-    "std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))\n",
-    "vol = std*sqrt(252)\n",
-    "vol"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def bsm_price(option_type, sigma, s, k, r, T, q):  \n",
-    "    # calculate the bsm price of European call and put options\n",
-    "    sigma = float(sigma)\n",
-    "    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))\n",
-    "    d2 = d1 - sigma * np.sqrt(T)\n",
-    "    if option_type == 'c':\n",
-    "        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))\n",
-    "        return price\n",
-    "    elif option_type == 'p':\n",
-    "        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))\n",
-    "        return price\n",
-    "    else:\n",
-    "        print('No such option type %s') %option_type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def implied_vol(option_type, option_price, s, k, r, T, q):\n",
-    "    # apply bisection method to get the implied volatility by solving the BSM function\n",
-    "    precision = 0.00001\n",
-    "    upper_vol = 500.0\n",
-    "    max_vol = 500.0\n",
-    "    min_vol = 0.0001\n",
-    "    lower_vol = 0.0001\n",
-    "    iteration = 0    \n",
-    "\n",
-    "    while 1:\n",
-    "        iteration +=1 \n",
-    "        mid_vol = (upper_vol + lower_vol)/2.0\n",
-    "        price = bsm_price(option_type, mid_vol, s, k, r, T, q)\n",
-    "        if option_type == 'c':\n",
-    "\n",
-    "            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)\n",
-    "            if (lower_price - option_price) * (price - option_price) > 0:\n",
-    "                lower_vol = mid_vol \n",
-    "            else:\n",
-    "                upper_vol = mid_vol\n",
-    "            if abs(price - option_price) < precision: break\n",
-    "            if mid_vol > max_vol - 5 : \n",
-    "                mid_vol = 0.000001\n",
-    "                break\n",
-    "#             print(\"mid_vol=%f\" %mid_vol)\n",
-    "#             print(\"upper_price=%f\" %lower_price)\n",
-    "\n",
-    "        elif option_type == 'p':\n",
-    "            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)\n",
-    "\n",
-    "            if (upper_price - option_price) * (price - option_price) > 0:\n",
-    "                upper_vol = mid_vol \n",
-    "            else:\n",
-    "                lower_vol = mid_vol            \n",
-    "#             print(\"mid_vol=%f\" %mid_vol)\n",
-    "#             print(\"upper_price=%f\" %upper_price)            \n",
-    "            if abs(price - option_price) < precision: break \n",
-    "            if iteration > 50: break\n",
-    "                \n",
-    "    return mid_vol "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[datetime.date(2017, 8, 16),\n",
-       " datetime.date(2017, 8, 18),\n",
-       " datetime.date(2017, 8, 23),\n",
-       " datetime.date(2017, 8, 25),\n",
-       " datetime.date(2017, 8, 30),\n",
-       " datetime.date(2017, 9, 1),\n",
-       " datetime.date(2017, 9, 6),\n",
-       " datetime.date(2017, 9, 8),\n",
-       " datetime.date(2017, 9, 13),\n",
-       " datetime.date(2017, 9, 15),\n",
-       " datetime.date(2017, 9, 22),\n",
-       " datetime.date(2017, 9, 29),\n",
-       " datetime.date(2017, 10, 20),\n",
-       " datetime.date(2017, 11, 17),\n",
-       " datetime.date(2017, 12, 15),\n",
-       " datetime.date(2017, 12, 29),\n",
-       " datetime.date(2018, 1, 19),\n",
-       " datetime.date(2018, 3, 16),\n",
-       " datetime.date(2018, 3, 29),\n",
-       " datetime.date(2018, 6, 15),\n",
-       " datetime.date(2018, 6, 29),\n",
-       " datetime.date(2018, 7, 20),\n",
-       " datetime.date(2018, 9, 21),\n",
-       " datetime.date(2018, 12, 21),\n",
-       " datetime.date(2019, 1, 18),\n",
-       " datetime.date(2019, 3, 15),\n",
-       " datetime.date(2019, 6, 21),\n",
-       " datetime.date(2019, 12, 20),\n",
-       " datetime.date(2020, 7, 17)]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# download option data for all expiry months from Yahoo Finance \n",
-    "# provide a formatted DataFrame with a hierarchical index\n",
-    "opt = Options('spy', 'yahoo')\n",
-    "opt.expiry_dates  # list all the available expiration dates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def IV_plot(opt,option_type,expiry_index):\n",
-    "    expiry = opt.expiry_dates[expiry_index]\n",
-    "    if option_type == 'c':\n",
-    "        data = opt.get_call_data(expiry=expiry)\n",
-    "    elif option_type == 'p':\n",
-    "        data = opt.get_put_data(expiry=expiry)\n",
-    "    r = 0.01 # risk free rate\n",
-    "    d = 0.01 # continuous devidend yield\n",
-    "    s = opt.underlying_price # data_call['Underlying_Price']  undelying price\n",
-    "    expiry = data.index.get_level_values('Expiry')[0] # get the expiry \n",
-    "    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date\n",
-    "    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration\n",
-    "    premium = (data['Ask'] + data['Bid'])/2 # option premium\n",
-    "    strike = list(data.index.get_level_values('Strike')) # get the strike price\n",
-    "    IV = []\n",
-    "    for i in range(len(data)):  \n",
-    "        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))\n",
-    "   \n",
-    "    plt.figure(figsize=(16, 7))\n",
-    "    a = plt.scatter(strike,IV, c='r', label=\"IV by solving BSM\")\n",
-    "    b = plt.scatter(strike,data['IV'],c = 'b', label=\"IV from Yahoo Finance\")\n",
-    "    plt.grid()\n",
-    "    plt.xlabel('strike')\n",
-    "    if option_type == 'c':\n",
-    "        plt.ylabel('Implied Volatility for call option')\n",
-    "        plt.legend((a,b), (\"IV(call) by solving BSM\", \"IV(call) from Yahoo Finance\"))\n",
-    "    elif option_type == 'p':\n",
-    "        plt.ylabel('Implied Volatility for put options')\n",
-    "        plt.legend((a,b), (\"IV(put) by solving BSM\", \"IV(put) from Yahoo Finance\"))\n",
-    "    \n",
-    "    return strike,IV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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MXiGEE7PdjzHemuXZKuC/Y4wTurn338AlMcbH2q4fAv4txrigm7p1pEdvGT16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\nZ90nousYkB6hPf7/UPvOtTTyz1SyjMB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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x134190e90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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WEGOM+xc9OkmSJEmSssiZzMYYF3S9Tx6+cCRJkiRJGlg+58x+O582SZIkSZKG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\nn9CZ2clVoo4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1339dc610>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "k_call, IV_call = IV_plot(opt,'c',23)\n",
-    "k_put, IV_put = IV_plot(opt,'p',23)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1325bd5d0>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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5MSJgdHQ0UDsAAAAAREEjuxn/VhLzUSMqkUioWCxWbQcAAACAqKoZZs3sw+7+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\nSZIk9Y7JrCRJkiSpd/4GbolVqR9oXvIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x131883d10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(16, 7))\n",
-    "e = plt.scatter(k_call,IV_call, c ='red', label=\"IV(call options)\")\n",
-    "f = plt.scatter(k_put,IV_put, c = 'black', label=\"IV(put options)\")\n",
-    "plt.xlabel('strike')\n",
-    "plt.ylabel('Implied Volatility')\n",
-    "plt.legend((e,f), (\"IV (call options)\", \"IV (put options)\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "r = 0.01 # risk free rate\n",
-    "d = 0.01 # continuous devidend yield\n",
-    "expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]\n",
-    "current_date = opt.quote_time.date()  ## get the current date\n",
-    "s = opt.underlying_price # undelying price\n",
-    "num_expiry = len(expiry_dates)\n",
-    "IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]\n",
-    "\n",
-    "for expiry_index in range(num_expiry):\n",
-    "    data = opt.get_put_data(expiry=expiry_dates[expiry_index])\n",
-    "    expiry = expiry_dates[expiry_index] # get the expiry  \n",
-    "    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration    \n",
-    "    premium = (data['Ask'] + data['Bid'])/2.0 # option premium\n",
-    "    strike = data.index.get_level_values('Strike') # get the strike price\n",
-    "    num_strike = len(data)\n",
-    "    for j in range(num_strike):  \n",
-    "        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#  IV = [i for i in IV if i[2]>0.01 and i[2]<3] # delete the implied volatility with 0 value or much higher value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "x= [IV[i][0] for i in range(len(IV))]\n",
-    "y= [IV[i][1] for i in range(len(IV))]\n",
-    "z= [IV[i][2] for i in range(len(IV))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x11a551f50>"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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XwYOmaVheXsbKygpmZmZw9uxZhEKhnly2DAFJr3enMk0Tz/x3Ck9fTcPlAt5+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GWzddGJadoOECUZXoqfn1lQr/zUcGScf292u+6oUsl6PxqbaETtmNqtZDIQmlJGHquL4gCAUx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ee/47XWOJfaYT1BFEWOHTtGOp1et+RN7WV3zTXX8MTIAnOlKZoDHrJn45guBQtwLVxPpDWi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LW15/Kmsq/WiaRldX10lvF74U/H7/BF97pAefS8YWjpuu1OfKlZJNJHRes9lpnS7htP0+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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1356245d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(20,12))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.view_init()\n",
-    "ax.scatter(x,y,z)\n",
-    "plt.xlabel(\"Time to Expiration\")\n",
-    "plt.ylabel(\"Strikes\")\n",
-    "plt.title(\"Implied Volatility\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorial Series/Introduction to Options/Tutorial08 Local Volatility and Stochastic Volatility.html b/Tutorial Series/Introduction to Options/Tutorial08 Local Volatility and Stochastic Volatility.html
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-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-<h2>Introduction</h2>
-In the last chapter, we discussed two of the volatilities: historical volatility and the implied volatility. This chapter, we will further extend the concept of volatility and introduce the local volatility and the stochastic volatility.
-<h2>Motivation</h2>
-We already knew that volatility is a measure of the fluctuation degree of the underlying assets price series. There are many types of volatilities. As we discussed in the last chapter, historical volatility is the standard deviation of the price series during a certain period. It is a constant and represents the price movement in the past. However, the implied volatility is not based on the historical pricing data of stocks. It is the value of volatility parameter derived from the market quote of options in BSM pricing model. In contrast to historical volatility, implied volatility is forward looking and varies with different options contracts.
-<div class="page" title="Page 82">
-<div class="layoutArea">
-<div class="column">
-
-In the Black–Scholes model, the asset’s price is modeled as a log-normal random variable, which means that the asset’s log-returns are normally distributed. One of the most significant assumptions in BSM model is that the volatility is a constant term over time.
-
-\[\sigma=\sigma_{implied}\]
-
-But in the real world, it could be constant in a small time period but never constant in long term. As we discussed in the last chapter, there is volatility skew for most of the options, which means that the volatility is not constant across strikes.
-
-[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]One way to capture the volatility skew is to assume that that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. But on the other hand, the introduction of additional sources of randomness will increase the complexity of the model. The other way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
-<h2>Local Volatility</h2>
-<h3>1. Definition</h3>
-The constant volatility assumption in BSM model seems not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. That means there exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
-
-[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]The local volatility of the underlying assets is a deterministic function of assets price and the time t.
-
-\[\sigma=\sigma(S_t,t)\]
-
-Therefore under local volatility model, the stochastic process followed by the stock price is
-
-\[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
-
-If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness fro m the stock price: \(W_t\).
-<h3>2. Model Calibration</h3>
-How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.
-
-\[\sigma_{Local}(K,T)=\sqrt{\frac{\frac{\partial C}{\partial T}}{\frac{1}{2}K^2\frac{\partial^2 C}{\partial K^2}}}\]
-
-If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with the different combination of strike prices and expires. For a given date, time(t) and the underlying stock prices(St), a local volatility is derived from the equation that options prices calculated using local volatility equal the market options prices.
-
-But most of the time, we can only get a limited number of contracts with a few strikes and maturities, we can follow the steps below to get the local volatility estimation:
-<ol>
- 	<li>First, use the available quoted price to calculate the implied volatilities.</li>
- 	<li>Appy interpolation method to produce a smooth implied volatility surface.</li>
- 	<li>Plug implied the implied volatilities into BSM model to get all the market prices of European calls.</li>
- 	<li>Calculate the local volatility according to Dupire formula. In order to avoid taking derivatives, we could use finite differences to approximate the derivatives.</li>
-</ol>
-\[\frac{\partial C}{\partial T}\approx\frac{C(K,T+\Delta T)-C(K,T-\Delta T)}{2\Delta t}\]
-
-\[\frac{\partial^2 C}{\partial K^2}\approx\frac{C(K-\Delta K,T)-2C(K,T)+C(K+\Delta K,T)}{(\Delta K)^2}\]
-
-</div>
-<div class="column">
-<h2>Stochastic Volatility</h2>
-<h3>1. Definition</h3>
-</div>
-</div>
-</div>
-In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock prices are log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is[ref]Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011[/ref]
-
-</div>
-</div>
-</div>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-\[dS_t = \mu_tS_tdt+\sqrt{v_t}S_tdW_{1,t}\]
-
-\[dv_t=-\lambda(v_t-\overline{v})\ dt+\eta\sqrt{v_t}\ dW_{2,t}\]
-
-\[dW_{1,t},\ dW_{2,t}=\rho \ dt\]
-
-Where the instantaneous variance of the stock price \(v_t\) itself is a stochastic process.
-
-\(\lambda\) is the speed of reversion of \(v_t\) to its long-term mean \(\overline{v})\). We can think of\(\lambda\) as the rate at which the stock price variance reverts back to its long term average value.
-
-\(\eta\) is the volatility of the variance process \(v_t\) (often called the volatility of volatility)
-
-\(W_{1,t}\) and \(W_{2,t}\) are two dependent Wiener processes with correlation coefficient \(\rho\).
-<h3>2. Simulation of the Heston Process</h3>
-We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization[ref]Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>[/ref].
-
-\[v_{t+\Delta t}=\left(\sqrt{v_t}+\frac{1}{2}\eta\sqrt{\Delta t}W_1\right)^2-\lambda(v_t-\overline{v})\Delta t-\frac{\eta^2}{4}\Delta t\]
-
-Then, we take the following steps to simulate Heston process: Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above (Here note that in options pricing, Monte Carlo method use risk neutral result, so here the expected return \(\mu\) should equal the risk free rate r):
-<ul>
- 	<li>Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above</li>
- 	<li>We obtain \(S_{t+\Delta t}\) using\[S_{t+\Delta t}=S_t\ \text {exp}\left[(r-\frac{1}{2}v_t)\Delta t+\sqrt{v_t\Delta t}W_2\right]\]</li>
- 	<li>To generate \(W_1\) and \(W_2\) with correlation \(\rho\), we first generate two independent standard normal variables \(Z_1\) and \(Z_2\), set \(W_1=Z_1\), then \[W_2=\rho Z_1+\sqrt{1-\rho^2}Z_2\]</li>
-</ul>
-<pre class="prettyprint linenums">from numpy import sqrt, exp
-import numpy as np
-
-def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):
-    """
-    option_type:    'p' put option 'c' call option
-    S0:              the spot price of underlying stock
-    K:              the strike price
-    T:              the maturity of options
-    initial_var:    the initial value of variance
-    long_term_var:  the long term average of price variance
-    rate_reversion: the mean reversion rate for the variance
-    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)
-    corr:           the correlation between the standard normal random variables W1 and W2
-    r:              the risk free rate
-    reps:           the number of repeat for monte carlo simulation
-    steps:          the number of steps in each simulation
-    """
-    delta_t = T/float(steps)
-    payoff = 0
-    for i in range(num_reps):
-        vt = initial_var
-        st = S0
-        for j in range(steps):
-            w1 = np.random.normal(0, 1)
-            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)
-            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \
-                 - rate_reversion * (vt - long_term_var) * delta_t \
-                 - 0.25 * vol_of_vol**2 * delta_t
-            st = st * exp((r - 0.5*vt)*delta_t + sqrt(vt*delta_t) * w2)
-        if option_type == 'c':
-                payoff += max(st - K, 0)
-        elif option_type == 'p':
-                payoff += max(K - st, 0)
-
-    return (payoff/float(num_reps)) * (exp(-r*T))
-</pre>
-<h3>3. Calibration of Model Parameters</h3>
-The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from the prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to the paper <em>Parameters recovery via calibration in the Heston model</em>[ref]Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.[/ref] for details of different error measure.
-
-There are five parameters need to be estimated in Heston model:
-<ol>
- 	<li>\(v_t\) : the initial value of the variance (Bounds of 0 and 1)</li>
- 	<li>\(\overline{v}\) : the long term average variance of stock price (Bounds of 0 and 1)</li>
- 	<li>\(\lambda\) : the speed of reversion (non-negativity)</li>
- 	<li>\(\eta\) : the volatility of the volatility (non-negativity)</li>
- 	<li>\(\rho\) : the correlation coefficient between two Wiener process (Bounds of -1 and 1)</li>
-</ol>
-Here we use QuantLib[ref]Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>[/ref] Python library to calibrate the parameters.
-
-Let us look at how we can calibrate the Heston model to some market quotes. For example, let's say we are interested in trading SPDR S&amp;P 500 ETF (SPY) options with 4-months maturity. Here we choose all the options contracts written on SPY expire in 4 months. We need the strikes and the market prices of those contracts and the underlying price as the input of our objective function to minimize.
-<pre class="prettyprint linenums">import pandas as pd
-from numpy import sqrt,mean,log,diff
-import QuantLib as ql
-from pandas_datareader.data import Options
-import pandas_datareader.data as web
-import datetime
-opt = Options('spy', 'yahoo')
-expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]
-expiry_index = 14 # choose the contracts expire on 11/17/2017
-data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])
-strikes = list(data.index.get_level_values('Strike'))
-premium = list(data['Last'])
-day_count = ql.Actual365Fixed()
-calendar = ql.UnitedStates()
-calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year) # 08/10/2017
-spot = opt.underlying_price  # spot price is 244.82
-ql.Settings.instance().evaluationDate = calculation_date
-dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))
-risk_free_rate = 0.01
-dividend_rate = 0.0
-flat_ts = ql.YieldTermStructureHandle(
-    ql.FlatForward(calculation_date, risk_free_rate, day_count))
-dividend_ts = ql.YieldTermStructureHandle(
-    ql.FlatForward(calculation_date, dividend_rate, day_count))
-# dummy parameters
-initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;
-# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;
-process = ql.HestonProcess(flat_ts, dividend_ts,
-                           ql.QuoteHandle(ql.SimpleQuote(spot)),
-                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)
-model = ql.HestonModel(process)
-engine = ql.AnalyticHestonEngine(model)
-heston_helpers = []
-date = expiration_dates[expiry_index]
-for j, s in enumerate(strikes):
-    t = (date - calculation_date)
-    p = ql.Period(t, ql.Days)
-    sigma = premium[j]
-    helper = ql.HestonModelHelper(p, calendar, spot, s,
-                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),
-                                  flat_ts,
-                                  dividend_ts)
-    helper.setPricingEngine(engine)
-    heston_helpers.append(helper)
-lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)
-model.calibrate(heston_helpers, lm,
-                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))
-long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()
-print "long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)
-</pre>
-We get the market data at 08/10/2017 and choose the contracts which expire on 11/17/2017. Then we get the following parameters estimation
-<pre class="prettyprint linenums">long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778
-</pre>
-When you get the parameter estimation, you can plug the parameter values into the Heston Monte Carlo options pricing model and get the price estimation with stochastic volatility. But as we already discussed for Heston model, the introduction of randomness of volatility increases the complexity of the estimation. No matter which error measure is chosen, the objective function is highly non-linear and far from being convex and we have 5 parameters in the model. All these drawbacks of Heston models will make the estimated parameter values quite sensitive the initial guess of parameters. Therefore options prices generated by the Heston model are also parameter sensitive. From the above, we can get a sense of how computationally expensive it can be to get accurate values of options in a stochastic volatility model.
-<h2> Summary</h2>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-In Black–Scholes, that volatility is assumed to be constant, it is not reasonable especially for some exotic options in which the option's payoff is based on the changing volatility. Therefore we introduced the two volatility models to capture the volatility skew.The first approach, local volatility, assumes that the volatility is a deterministic function of time and the underlying asset price. This function must be chosen as to match the observed market option prices. In another stochastic volatility models, the asset price and its volatility are both assumed to be random processes.
-
-The calibration of these models needs the market price of the Vanilla options. When we get the model estimation we can use these models to pricing the corresponding exotic options.
-
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
diff --git a/Tutorial Series/Introduction to Options/Tutorial08 Local Volatility and Stochastic Volatility.ipynb b/Tutorial Series/Introduction to Options/Tutorial08 Local Volatility and Stochastic Volatility.ipynb
deleted file mode 100644
index 3eaa816..0000000
--- a/Tutorial Series/Introduction to Options/Tutorial08 Local Volatility and Stochastic Volatility.ipynb	
+++ /dev/null
@@ -1,409 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from numpy import sqrt,mean,log,diff\n",
-    "import QuantLib as ql\n",
-    "from pandas_datareader.data import Options\n",
-    "import pandas_datareader.data as web\n",
-    "import datetime "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 368,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "opt = Options('spy', 'yahoo')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 369,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]\n",
-    "expiry_index = 14 # choose the contracts expire in 4 months\n",
-    "data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])\n",
-    "strikes = list(data.index.get_level_values('Strike'))\n",
-    "premium = list(data['Last'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 534,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.date(2017, 11, 17)"
-      ]
-     },
-     "execution_count": 534,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "opt.expiry_dates[expiry_index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 370,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "day_count = ql.Actual365Fixed()\n",
-    "calendar = ql.UnitedStates()\n",
-    "calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year)\n",
-    "spot = opt.underlying_price\n",
-    "ql.Settings.instance().evaluationDate = calculation_date\n",
-    "dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))\n",
-    "risk_free_rate = 0.01\n",
-    "dividend_rate = 0.0\n",
-    "flat_ts = ql.YieldTermStructureHandle(\n",
-    "    ql.FlatForward(calculation_date, risk_free_rate, day_count))\n",
-    "dividend_ts = ql.YieldTermStructureHandle(\n",
-    "    ql.FlatForward(calculation_date, dividend_rate, day_count))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 518,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# dummy parameters\n",
-    "initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;\n",
-    "# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;\n",
-    "process = ql.HestonProcess(flat_ts, dividend_ts, \n",
-    "                           ql.QuoteHandle(ql.SimpleQuote(spot)), \n",
-    "                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)\n",
-    "model = ql.HestonModel(process)\n",
-    "engine = ql.AnalyticHestonEngine(model) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 519,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "heston_helpers = []\n",
-    "date = expiration_dates[expiry_index]\n",
-    "for j, s in enumerate(strikes):\n",
-    "    t = (date - calculation_date)\n",
-    "    p = ql.Period(t, ql.Days)\n",
-    "    sigma = premium[j]\n",
-    "    helper = ql.HestonModelHelper(p, calendar, spot, s, \n",
-    "                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),\n",
-    "                                  flat_ts, \n",
-    "                                  dividend_ts)\n",
-    "    helper.setPricingEngine(engine)\n",
-    "    heston_helpers.append(helper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 520,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)\n",
-    "model.calibrate(heston_helpers, lm, \n",
-    "                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))\n",
-    "long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 521,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778\n"
-     ]
-    }
-   ],
-   "source": [
-    "print \"long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f\" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 522,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):\n",
-    "    \"\"\"\n",
-    "    option_type:    'p' put option 'c' call option\n",
-    "    S0:              the spot price of underlying stock\n",
-    "    K:              the strike price\n",
-    "    T:              the maturity of options\n",
-    "    initial_var:    the initial value of variance\n",
-    "    long_term_var:  the long term average of price variance\n",
-    "    rate_reversion: the mean reversion rate for the variance\n",
-    "    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)\n",
-    "    corr:           the correlation between the standard normal random variables W1 and W2\n",
-    "    r:              the risk free rate\n",
-    "    reps:           the number of repeat for monte carlo simulation\n",
-    "    steps:          the number of steps in each simulation\n",
-    "    \"\"\"\n",
-    "    delta_t = T/float(steps)\n",
-    "    payoff = 0\n",
-    "    for i in range(num_reps):\n",
-    "        vt = initial_var\n",
-    "        log_st = log(S0)\n",
-    "        for j in range(steps):\n",
-    "            w1 = np.random.normal(0, 1)\n",
-    "            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)\n",
-    "            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \\\n",
-    "                 - rate_reversion * (vt - long_term_var) * delta_t \\\n",
-    "                 - 0.25 * vol_of_vol**2 * delta_t\n",
-    "            if vt < 0: vt = 0.00\n",
-    "            log_st = log_st + (r - 0.5*vt)*delta_t + sqrt(vt)*sqrt(delta_t)*w2\n",
-    "        st = e**(log_st)\n",
-    "        if option_type == 'c':\n",
-    "                payoff += max(st - K, 0)\n",
-    "        elif option_type == 'p':\n",
-    "                payoff += max(K - st, 0)\n",
-    "        \n",
-    "    return (payoff/float(num_reps)) * (exp(-r*T))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 523,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "44.383434958491186"
-      ]
-     },
-     "execution_count": 523,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mc_heston('c',spot,strikes[20],t/365.0,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,0.01,100,1000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 524,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "36.119999999999997"
-      ]
-     },
-     "execution_count": 524,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "premium[20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 525,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "heston = []\n",
-    "\n",
-    "for i in range(len(strikes)):\n",
-    "    heston.append(mc_heston('c',spot,strikes[i],t/365.0,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,0.01,100,100))\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 529,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "diff = [(heston[i]-premium[i])**2 for i in range(len(premium))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 530,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8885.1013305184661"
-      ]
-     },
-     "execution_count": 530,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(diff)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 531,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(110.46562812345368, 127.83),\n",
-       " (110.6988943943971, 112.12),\n",
-       " (55.926776121382609, 55.710000000000001),\n",
-       " (52.981820291517408, 54.030000000000001),\n",
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-       " (16.549370172554656, 0.32000000000000001),\n",
-       " (15.127520335730379, 0.25),\n",
-       " (10.679068848118462, 0.19),\n",
-       " (17.091513222601169, 0.16),\n",
-       " (15.164841725875474, 0.13),\n",
-       " (12.162076252506601, 0.12),\n",
-       " (11.179740065544316, 0.10000000000000001),\n",
-       " (15.126628346299601, 0.089999999999999997),\n",
-       " (10.827121868063745, 0.080000000000000002),\n",
-       " (7.955281416676554, 0.080000000000000002),\n",
-       " (11.584285727318449, 0.070000000000000007),\n",
-       " (6.8753197768030745, 0.070000000000000007),\n",
-       " (10.195241428465291, 0.070000000000000007),\n",
-       " (11.098792968645823, 0.080000000000000002),\n",
-       " (10.97397900549743, 0.059999999999999998),\n",
-       " (8.8726648972088906, 0.059999999999999998),\n",
-       " (11.791012392977612, 0.040000000000000001)]"
-      ]
-     },
-     "execution_count": 531,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zip(heston, premium)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
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-  "language_info": {
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-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
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diff --git a/Tutorial Series/Introduction to Options/images/call-payoff.png b/Tutorial Series/Introduction to Options/images/call-payoff.png
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diff --git a/Tutorial Series/README.md b/Tutorial Series/README.md
deleted file mode 100644
index c8e32ec..0000000
--- a/Tutorial Series/README.md	
+++ /dev/null
@@ -1,124 +0,0 @@
-![QuantConnect Logo](https://cdn.quantconnect.com/web/i/logo-small.png)
-# QuantConnect Tutorial Series
-----------
-
-This repository is a collection of WordPress and Jupyter notebook tutorials for LEAN demonstrating basic financial concepts, Python usage and introduction to Options and Machine Learning. Tutorials are categorized into 3 folders with each Category. The Jupyter notebook and the associated HTML webpage must use matching file names. The HTML is generated and displayed with wordpress.
-
-## Introduction to Financial Python ##
-This tutorial series starts from basic Python and finance concepts. If you have great investment ideas but don’t know how to build, or you think you need to learn some basic skills in quantitative finance, here is a good start point. These series include four parts: Python, basic math and statistics, basic finance regarding investment, and financial time series analysis, which is an advanced curriculum in quantitative finance.
-
-We not only introduce the concepts but also show you how to apply the introduced technique step by step using Python code snippets. We use real financial datasets as examples, and show you what those charts in text book look like with real-world financial data. After each chapter, we also design a QC algorithm applying what we learned. We do this in order to let you have a more comprehensive understanding and see the differences between the theories and the practice — sometimes it can be hard to apply a simple concept.
-
-
-## Tutorial Series ##
-
-<table width="100%">
-    <thead>
-        <tr>
-            <th colspan="2" align="left">Introduction to Financial Python</th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-data-types-data-structures/"> 1. Data Types and Data Structures </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-confidence-interval-hypothesis-testing/"> 8. Confidence Interval and Hypothesis Testing </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-logical-operation-loop/"> 2. Logical Opertions and Loops </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-simple-linear-regression/">9. Simple Linear Regression </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-functions-object-oriented-programming/"> 3. Functions and Object-Oriented Programming </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-multiple-linear-regression-residual-analysis/"> 10.Multiple Linear Regression </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-numpy-basic-pandas/"> 4. NumPy and Basic Pandas </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-linear-algebra/"> 11. Linear Algebra </a> </td>
-        </tr>
- <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-pandas-resampling-dataframe/"> 5. Pandas-Resampling and DataFrame </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-modern-portfolio-theory/"> 12. Modern Portfolio Theory </a> </td>
-        </tr>
-        
-  <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-rate-return-mean-variance/"> 6.Rate of Return, Mean and Variance </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/capital-asset-pricing-model/">13. Capital Asset Pricing Model </a> </td>
-        </tr>
-        
- <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-python-random-variable-distributions/
-"> 7. Random Variable and Distributions </a> </td>
-            <td> <a href="None"> 14. Fama-French Multi-Factor Models</a> </td>
-        </tr>
-    </tbody>
-</table>
-
-## Introduction to Options ##
-The goal of this series is to introduce options to those who are option novices and have basic knowledge of applied mathematics, statistics and financial markets. We will primarily talk about the fundamentals of options and cover topics such as what are options, key terms and concepts option traders need to be familiar with(exercise and assignment,The moneyness, Intrinsic and time value of options etc.) After knowing the basics of options, we will teach how to use QuantConnect API to conduct your options research with over 4000 underlying stock symbols.
-
-The following few options tutorials was created to help you understand exactly how options are used as investment and risk hedging tools. We will further discuss the pricing method of options like BSM model and monte carlo method. And then several metrics to gauge the options risks like the Greek lettters, different kinds of volatilities used in options pricing and trading. At the end of some tutorials, we will apply the knowledge in that tutorial to demonstrate some simple algorithms developed with Python on Quantconnect attempting to help you gain an insight into options trading and learn more efficient API tools to better customize your own trading algorithms. 
-
-<table width="100%">
-    <thead>
-        <tr>
-            <th colspan="2" align="left">Introduction to Options</th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-general-features-options/"> 1. General Features of Options </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-black-scholes-merton-model/"> 5. Options Pricing: Black-Scholes-Merton Model </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-using-options-in-quantconnect/"> 2. Using Options in QuantConnect </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-the-greek-letters/"> 6. The Greek Letters </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-put-call-parity-and-arbitrage-strategies/"> 3. Put-Call Parity and Arbitrage Strategies </a> </td>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-historical-volatility-implied-volatility/"> 7. Historical Volatility and Implied Volatility </a> </td>
-        </tr>
-        <tr>
-            <td> <a href="https://www.quantconnect.com/tutorials/introduction-options-stochastic-volatility/"> 4. Stochastic Processes and Monte Carlo Method </a> </td>
-            <td> <a href="8"> 8. Local Volatility and Stochastic Volatility </a> </td>
-        </tr>
-    </tbody>
-</table>
-
-
-## Introduction to Machine Learning ##
-This tutotial series aims to give you a general idea of what are machine learning algorithms and how could they be applied to your trading strategies. We start from supervised learning algorithms like the well-known neural network, support vehicle machine, etc. and then go to cover tree-based classifiers such as decision trees, random forest, etc. Next, we reach out to reinforcement learning and association rules. And in the end of this tutotial series, we introduce unsupervised learning, as well as heuristic algorithm. All of them play important roles in financial decisions.
-
-Unlike most teachings in textbooks, our tutorials keep from going deep into math which dampens readers' enthusiasm. Instead, we use simple and easy-to-understand examples to illustrate the complex logic behind each machine learning algorithm. We the display the APIs in Python to make these tools handy for you. Finally, we end each tutorial with a trading algorithm combining and applying what you've learned with real market data.
-
-<table width="100%">
-    <thead>
-        <tr>
-            <th colspan="2" align="left">Introduction to Machine Learning</th>
-        </tr>
-    </thead>
-    <tbody>
-        <tr>
-            <td> <a href=""> 1. Structure and Construction of Neural Networks</a> </td>
-            <td> <a href=""> 2. Kernel Function and Construction of Support Vector Machines</a> </td>
-        </tr>
-        <tr>
-            <td> <a href=""> 3. Bayes' Theorem and Classification of Market Signals by Naive Bayes</a> </td>
-            <td> <a href=""> 4. Using Entropy and Information Gain to Construct ID3 Decision Tree</a> </td>
-        </tr>
-        <tr>
-            <td> <a href=""> 5. Random Subtrees and Class Prediction with Random Forest</a> </td>
-            <td> <a href=""> 6. Boosting Ensemble and Making Predictions with AdaBoost</a> </td>
-        </tr>
-        <tr>
-            <td> <a href=""> 7. Unsupervised Reinforcement Mechanism and Implementation of Q-Learning Algorithms</a> </td>
-            <td> <a href=""> 8. Association Rule and Market Trend Analysis with Apriori Algorithms</a> </td>
-        </tr>
- <tr>
-            <td> <a href=""> 9. Centroid Rebalance and Stock Classification with K-Nearest Neighbors</a> </td>
-            <td> <a href=""> 10. Optimizing Technical Indicators by Simulating Evolution Processes to Construct Genetic Algorithms</a> </td>
-        </tr>
-    </tbody>
-</table>
-
-[1]: https://www.quantconnect.com/tutorials/introduction-options-general-features-options/
-

From 627f798c070e7cb53a79dc9ce1dfef9b739cf5c3 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 11:05:37 -0400
Subject: [PATCH 012/753] Add home file

---
 00 Home/01 Home/01 Home.html | 96 ++++++++++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100644 00 Home/01 Home/01 Home.html

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
new file mode 100644
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--- /dev/null
+++ b/00 Home/01 Home/01 Home.html	
@@ -0,0 +1,96 @@
+<div style="border-bottom: solid 1px  #e4e6ea;">
+    <div style="float: left; padding-right: 30px">
+        <img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
+    </div>
+    <div>
+        <h1>Documentation</h1>
+        <h2 style="margin-top: 8px;">
+            <span style="font-size: 22px;">Learn to use QuantConnect and explore our features</span>
+        </h2>
+    </div>
+</div>
+<div>
+    <div class="home-buttons-backdrop"></div>
+    <div class="row home-buttons" style="padding-top: 18px;">
+        <a class="docs-internal-link" href="/docs/key-concepts/developing-in-the-ide">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_using_qc.rev0.png">
+            Using QuantConnect
+        </a>
+        <a class="docs-internal-link" href="/docs/algorithm-reference/overview">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_ref_rev0.png">
+            Algorithm Reference
+        </a>
+        <a class="docs-internal-link" href="/docs/algorithm-framework/overview">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_framework_rev0.png">
+            Algorithm Framework
+        </a>
+    </div>
+    <div class="row home-buttons" style="padding-bottom: 18px;">
+        <a class="docs-internal-link" href="/docs/research/overview">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_research_rev0.png">
+            Research
+        </a>
+        <a class="docs-internal-link" href="/docs/tutorials/tutorial-series">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_tutorials_rev0.png">
+            Tutorials
+        </a>
+        <a class="docs-internal-link" href="/docs/live-trading/overview">
+            <img src="https://cdn.quantconnect.com/web/i/docs/docs_live_trading_rev0.png">
+            Live Trading
+        </a>
+    </div>
+</div>
+<style>
+    .page-sub-heading,
+    .article-body h1.automated {
+        display: none;
+    }
+
+    .home-buttons-backdrop {
+        height: 570px;
+        background: url('https://cdn.quantconnect.com/web/i/docs/docs_background_rev0.png');
+        position: absolute;
+        left: -55px;
+        min-width: 910px;
+        width: calc(100vw - 317px);
+        z-index: -1;
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+        width: 233px;
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+        background-color: #fff;
+        border-radius: 5px;
+        height: 233px;
+        margin: 17px;
+        font-family: Norpeth-DemiBold, Helvetica, Arial, sans-serif;
+        color: #26262a;
+        font-size: 20px;
+        float: left;
+    }
+
+    .home-buttons > a:focus,
+    .home-buttons > a:active,
+    .home-buttons > a:hover {
+        text-decoration: none;
+        color: #93a5b6;
+    }
+
+    .home-buttons > a:last-child {
+        margin-right: 0;
+    }
+
+    .home-buttons > a > img {
+        display: table-row;
+        height: 80px;
+        margin: 50px auto 36px auto;
+    }
+    
+    .documentation #doc-content .pre-title-area > span {
+        display: none;
+    }
+    .documentation #doc-content .pre-title-area > span:first-child{
+        display: inline;
+    }
+</style>

From 119e84a7a57a7e30f204f6f576a9d15d2f1c51cd Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 11:20:26 -0400
Subject: [PATCH 013/753] add consolidator tutorial

---
 .../01 Consolidating Data to Build Bars.html  | 102 ++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
new file mode 100644
index 0000000..712e79d
--- /dev/null
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -0,0 +1,102 @@
+<p>
+    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
+    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
+    term signals.
+    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
+    once. There are three key steps to create and register a consolidator:
+</p>
+<ol>
+    <li>Create the consolidator object.</li>
+    <li>Bind an event handler to handle the new bars.</li>
+    <li>Register it with the subscription manager to start receiving data.</li>
+</ol>
+In <b>C#</b> this looks like:
+
+<div class="section-example-container">
+	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+{
+	public override void Initialize()
+	{
+		// backtest parameters
+		SetStartDate(2016, 1, 1);
+		SetEndDate(DateTime.Now);
+
+		// cash allocation
+		SetCash(25000);
+
+		//assets or universe selection
+		AddEquity("SPY", Resolution.Minute);
+
+		//create a consolidator object; for tradebars; for a timespan of 30 minutes
+		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
+
+		//bind event handler to data consolidated event.
+		thirtyMinutes.DataConsolidated += OnHalfHour;
+
+		//register the consolidator for data.
+		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
+	}
+
+	//event handler for data!
+	public void OnHalfHour(object sender, TradeBar bar) {
+		Debug(Time.ToString("u") + " " + bar);
+	}
+
+	public override void OnData(Slice data)
+	{ }
+}</pre>
+    <pre class="python">from datetime import datetime, timedelta
+class DataConsolidationAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.SetStartDate(2016,1,1)  #Set Start Date
+        self.SetEndDate(datetime.now())   #Set End Date
+        # Find more symbols here: http://quantconnect.com/data
+        self.AddEquity("SPY", Resolution.Minute)
+
+        # define our 30 minute trade bar consolidator. we can
+        # access the 30 minute bar from the DataConsolidated events
+        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
+
+        # attach our event handler. The event handler is a function that will
+        # be called each time we produce a new consolidated piece of data.
+        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
+
+        # this call adds our 30-minute consolidator to
+        # the manager to receive updates from the engine
+        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
+
+    def ThirtyMinuteBarHandler(self, sender, bar):
+        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
+        self.Debug(str(self.Time) + " " + str(bar))
+    def OnData(self, data):
+        pass
+</pre>
+</div>
+
+The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
+
+<div class="section-example-container">
+	<pre class="csharp">// From tick data sources
+var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
+
+//from renko bars
+var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
+</pre>
+    <pre class="python"></pre>
+</div>
+
+There are two key points to remember:
+<ol>
+    <li>Request a smaller resolution than what you want to produce.</li>
+    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
+        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
+    </li>
+</ol>
+<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
+    Using these building blocks you can combine data together to get any other resolution of data required.</p>
+
+https://www.youtube.com/watch?v=bbZy__qd1aA
+

From 09c5a8fd954a74c6a95b319f750f67255813915a Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 12:39:23 -0400
Subject: [PATCH 014/753] Update the view

---
 .../01 Consolidating Data to Build Bars.html    | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 712e79d..97490e9 100644
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -5,13 +5,11 @@
     Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
     once. There are three key steps to create and register a consolidator:
 </p>
-<ol>
+<ul>
     <li>Create the consolidator object.</li>
     <li>Bind an event handler to handle the new bars.</li>
     <li>Register it with the subscription manager to start receiving data.</li>
-</ol>
-In <b>C#</b> this looks like:
-
+</ul>
 <div class="section-example-container">
 	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
 {
@@ -75,9 +73,7 @@
         pass
 </pre>
 </div>
-
-The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
-
+<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
 <div class="section-example-container">
 	<pre class="csharp">// From tick data sources
 var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
@@ -87,14 +83,13 @@
 </pre>
     <pre class="python"></pre>
 </div>
-
-There are two key points to remember:
-<ol>
+<p>There are two key points to remember:</p>
+<ul>
     <li>Request a smaller resolution than what you want to produce.</li>
     <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
         triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
     </li>
-</ol>
+</ul>
 <p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
     Using these building blocks you can combine data together to get any other resolution of data required.</p>
 

From c63768e9afbb96e0b30356e4074aab1ca9d96c27 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 12:40:13 -0400
Subject: [PATCH 015/753] Update the view

---
 .../01 Consolidating Data to Build Bars.html                   | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 97490e9..70c2089 100644
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -93,5 +93,4 @@
 <p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
     Using these building blocks you can combine data together to get any other resolution of data required.</p>
 
-https://www.youtube.com/watch?v=bbZy__qd1aA
-
+<iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From f75203d80f20437358820ef14276bc054390a72f Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 12:44:07 -0400
Subject: [PATCH 016/753] Update video iframe holder

---
 .../01 Consolidating Data to Build Bars.html                 | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 70c2089..2b88805 100644
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -92,5 +92,6 @@
 </ul>
 <p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
     Using these building blocks you can combine data together to get any other resolution of data required.</p>
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<div class="video-holder">
+    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+</div>

From af7943c35ffc2741474d72fdc5a37765e58870cf Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:07:16 -0400
Subject: [PATCH 017/753] Update about view

---
 01 API Tutorials/00 Overview/00 About.html | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/01 API Tutorials/00 Overview/00 About.html b/01 API Tutorials/00 Overview/00 About.html
index 8166e1e..a866e19 100644
--- a/01 API Tutorials/00 Overview/00 About.html	
+++ b/01 API Tutorials/00 Overview/00 About.html	
@@ -5,16 +5,16 @@
     these videos are in C# -- however the API is virtually the same in Python and F#. If you're a Python user just click
     the "Python" code snippet button on the documentation page to view the python examples.</p>
 <div class="row tutorials-page-about">
-    <div class="col-xs-12 col-md-4">
+    <div class="col-xs-12">
         <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-        <h3>6 Tutorials</h3>
+        <p>6 Tutorials</p>
     </div>
     <div class="col-xs-12 col-md-4">
         <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-        <h3>6 Backtests</h3>
+        <p>6 Backtests</p>
     </div>
     <div class="col-xs-12 col-md-4">
         <p><i class="fa fa-code" aria-hidden="true"></i></p>
-        <h3>25 Code Snippets</h3>
+        <p>25 Code Snippets</p>
     </div>
 </div>
\ No newline at end of file

From 8b55e87400171d46ce4b37eb08a28c9f6d91b0b2 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:08:38 -0400
Subject: [PATCH 018/753] Add missing class

---
 01 API Tutorials/00 Overview/00 About.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Overview/00 About.html b/01 API Tutorials/00 Overview/00 About.html
index a866e19..874484d 100644
--- a/01 API Tutorials/00 Overview/00 About.html	
+++ b/01 API Tutorials/00 Overview/00 About.html	
@@ -5,7 +5,7 @@
     these videos are in C# -- however the API is virtually the same in Python and F#. If you're a Python user just click
     the "Python" code snippet button on the documentation page to view the python examples.</p>
 <div class="row tutorials-page-about">
-    <div class="col-xs-12">
+    <div class="col-xs-12 col-md-4">
         <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
         <p>6 Tutorials</p>
     </div>

From 6c905e4f3d102f974f716518963ae0992b34be1c Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:17:55 -0400
Subject: [PATCH 019/753] Add missing break lines

---
 01 API Tutorials/00 Overview/02 Tutorials.html | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/01 API Tutorials/00 Overview/02 Tutorials.html b/01 API Tutorials/00 Overview/02 Tutorials.html
index 016d41b..42777ed 100644
--- a/01 API Tutorials/00 Overview/02 Tutorials.html	
+++ b/01 API Tutorials/00 Overview/02 Tutorials.html	
@@ -5,42 +5,42 @@
         <td>
             <h2>Consolidating Data to Build Bars</h2>
             Create any bar size by combining data together with Consolidators.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/desktop-charting-with-lean/';" class="clickable">
         <td>2</td>
         <td>
             <h2>Desktop Charting with LEAN</h2>
             Setting up desktop charting with the open source algorithmic trading engine LEAN.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/use-api-file-provider/';" class="clickable">
         <td>3</td>
         <td>
             <h2>Downloading Data with the API File Provider</h2>
             Guide to downloading and interacting with your QuantConnect data repository.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/scheduled-events-lean/';" class="clickable">
         <td>4</td>
         <td>
             <h2>Using Scheduled Events</h2>
             Scheduling events to trigger code at specific times of day.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/tracking-and-managing-orders/';" class="clickable">
         <td>5</td>
         <td>
             <h2>Tracking and Managing Orders</h2>
             Simple guide to the basics of tracking and managing orders in QuantConnect.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect/';" class="clickable">
         <td>6</td>
         <td>
             <h2>Using Options in QuantConnect</h2>
             The key API methods for requesting option data financial and focusing in on specific contracts.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     </tbody>
 </table>
\ No newline at end of file

From 7d2fe82a02e863d91437ba9cc2a0a109d2acbb50 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:26:28 -0400
Subject: [PATCH 020/753] testing internal link

---
 01 API Tutorials/00 Overview/02 Tutorials.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Overview/02 Tutorials.html b/01 API Tutorials/00 Overview/02 Tutorials.html
index 42777ed..590d203 100644
--- a/01 API Tutorials/00 Overview/02 Tutorials.html	
+++ b/01 API Tutorials/00 Overview/02 Tutorials.html	
@@ -1,6 +1,6 @@
 <table class="table qc-table tutorials-table">
     <tbody>
-    <tr onclick="window.document.location='/tutorials/consolidating-data-build-bars/';" class="clickable">
+    <tr class="docs-internal-link clickable" href="/tutorials2/api-tutorials/consolidating-data-to-build-bars">
         <td>1</td>
         <td>
             <h2>Consolidating Data to Build Bars</h2>

From 6aba909491a91a0dc5e10f7daed7515753e7c31f Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:47:30 -0400
Subject: [PATCH 021/753] Add applied options view

---
 .../01 Applied Options/00 About.html          | 25 ++++++++
 .../01 What Will I learn %3F.html             |  6 ++
 .../01 Applied Options/02 Tutorials.html      | 60 +++++++++++++++++++
 .../01 Strategy Library.html                  |  0
 4 files changed, 91 insertions(+)
 create mode 100644 03 Tutorial Series/01 Applied Options/00 About.html
 create mode 100644 03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html
 create mode 100644 03 Tutorial Series/01 Applied Options/02 Tutorials.html
 create mode 100644 04 Strategy Library/01 Strategy Library/01 Strategy Library.html

diff --git a/03 Tutorial Series/01 Applied Options/00 About.html b/03 Tutorial Series/01 Applied Options/00 About.html
new file mode 100644
index 0000000..f389a52
--- /dev/null
+++ b/03 Tutorial Series/01 Applied Options/00 About.html	
@@ -0,0 +1,25 @@
+<p>
+    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
+    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
+    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
+    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
+    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
+    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
+    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
+    demonstrate the QuantConnect algorithm implemented in Python.
+</p>
+<div class="row tutorials-page-about">
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+        <p>8 Tutorials</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+        <p>10 Backtests</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-code" aria-hidden="true"></i></p>
+        <p>45 Code Snippets</p>
+    </div>
+</div>
+
diff --git a/03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html b/03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html
new file mode 100644
index 0000000..661425d
--- /dev/null
+++ b/03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html	
@@ -0,0 +1,6 @@
+<div class="row tutorials-learn">
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
+</div>
\ No newline at end of file
diff --git a/03 Tutorial Series/01 Applied Options/02 Tutorials.html b/03 Tutorial Series/01 Applied Options/02 Tutorials.html
new file mode 100644
index 0000000..83b28bc
--- /dev/null
+++ b/03 Tutorial Series/01 Applied Options/02 Tutorials.html	
@@ -0,0 +1,60 @@
+<table class="table qc-table tutorials-table">
+    <tbody>
+    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
+        <td>1</td>
+        <td>
+            <h2>Covered Call</h2>
+            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
+        <td>2</td>
+        <td>
+            <h2>Bull Call Spread</a></h2>
+            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
+        <td>3</td>
+        <td>
+            <h2>Long Straddle</h2>
+            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
+        <td>4</td>
+        <td>
+            <h2>Long Strangle</h2>
+            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
+        <td>5</td>
+        <td>
+            <h2>Butterfly Spread</h2>
+            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
+        <td>6</td>
+        <td>
+            <h2>Iron Condor</h2>
+            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
+        <td>7</td>
+        <td>
+            <h2>Iron Butterfly</h2>
+            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
+        <td>8</td>
+        <td>
+            <h2>Protective Collar</h2>
+            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
+            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    </tbody>
+</table>
\ No newline at end of file
diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.html b/04 Strategy Library/01 Strategy Library/01 Strategy Library.html
new file mode 100644
index 0000000..e69de29

From 82d2de37bb1e89293c4a710aabc0f42b428cf0dc Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 15:49:29 -0400
Subject: [PATCH 022/753] Add missing break lines

---
 .../01 Applied Options/02 Tutorials.html       | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/03 Tutorial Series/01 Applied Options/02 Tutorials.html b/03 Tutorial Series/01 Applied Options/02 Tutorials.html
index 83b28bc..6281670 100644
--- a/03 Tutorial Series/01 Applied Options/02 Tutorials.html	
+++ b/03 Tutorial Series/01 Applied Options/02 Tutorials.html	
@@ -5,56 +5,56 @@
         <td>
             <h2>Covered Call</h2>
             A brief introduction to Covered Call Strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
         <td>2</td>
         <td>
-            <h2>Bull Call Spread</a></h2>
+            <h2>Bull Call Spread</h2>
             A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
         <td>3</td>
         <td>
             <h2>Long Straddle</h2>
             A brief introduction to Long Straddle strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
         <td>4</td>
         <td>
             <h2>Long Strangle</h2>
             A brief introduction to Long Strangle strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
         <td>5</td>
         <td>
             <h2>Butterfly Spread</h2>
             A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
         <td>6</td>
         <td>
             <h2>Iron Condor</h2>
             A brief introduction to Iron Condor strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
         <td>7</td>
         <td>
             <h2>Iron Butterfly</h2>
             A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
         <td>8</td>
         <td>
             <h2>Protective Collar</h2>
             A brief introduction to Protective Collar strategy and the algorithm implemented in python.
-            <a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     </tbody>
 </table>
\ No newline at end of file

From aca0ec18830c56c5bc376a3c9eec9bf6275ea079 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 16:25:03 -0400
Subject: [PATCH 023/753] Add testing strategy library examples

---
 .../02 Example 1/01 Example 1.html            | 98 +++++++++++++++++++
 .../03 Example 2/01 Example 2.html            | 98 +++++++++++++++++++
 .../04 Example 3/01 Example 3.html            | 98 +++++++++++++++++++
 3 files changed, 294 insertions(+)
 create mode 100644 04 Strategy Library/02 Example 1/01 Example 1.html
 create mode 100644 04 Strategy Library/03 Example 2/01 Example 2.html
 create mode 100644 04 Strategy Library/04 Example 3/01 Example 3.html

diff --git a/04 Strategy Library/02 Example 1/01 Example 1.html b/04 Strategy Library/02 Example 1/01 Example 1.html
new file mode 100644
index 0000000..c5b2b93
--- /dev/null
+++ b/04 Strategy Library/02 Example 1/01 Example 1.html	
@@ -0,0 +1,98 @@
+<p>Example 1</p>
+<p>
+    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
+    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
+    term signals.
+    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
+    once. There are three key steps to create and register a consolidator:
+</p>
+<ul>
+    <li>Create the consolidator object.</li>
+    <li>Bind an event handler to handle the new bars.</li>
+    <li>Register it with the subscription manager to start receiving data.</li>
+</ul>
+<div class="section-example-container">
+	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+{
+	public override void Initialize()
+	{
+		// backtest parameters
+		SetStartDate(2016, 1, 1);
+		SetEndDate(DateTime.Now);
+
+		// cash allocation
+		SetCash(25000);
+
+		//assets or universe selection
+		AddEquity("SPY", Resolution.Minute);
+
+		//create a consolidator object; for tradebars; for a timespan of 30 minutes
+		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
+
+		//bind event handler to data consolidated event.
+		thirtyMinutes.DataConsolidated += OnHalfHour;
+
+		//register the consolidator for data.
+		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
+	}
+
+	//event handler for data!
+	public void OnHalfHour(object sender, TradeBar bar) {
+		Debug(Time.ToString("u") + " " + bar);
+	}
+
+	public override void OnData(Slice data)
+	{ }
+}</pre>
+    <pre class="python">from datetime import datetime, timedelta
+class DataConsolidationAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.SetStartDate(2016,1,1)  #Set Start Date
+        self.SetEndDate(datetime.now())   #Set End Date
+        # Find more symbols here: http://quantconnect.com/data
+        self.AddEquity("SPY", Resolution.Minute)
+
+        # define our 30 minute trade bar consolidator. we can
+        # access the 30 minute bar from the DataConsolidated events
+        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
+
+        # attach our event handler. The event handler is a function that will
+        # be called each time we produce a new consolidated piece of data.
+        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
+
+        # this call adds our 30-minute consolidator to
+        # the manager to receive updates from the engine
+        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
+
+    def ThirtyMinuteBarHandler(self, sender, bar):
+        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
+        self.Debug(str(self.Time) + " " + str(bar))
+    def OnData(self, data):
+        pass
+</pre>
+</div>
+<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
+<div class="section-example-container">
+	<pre class="csharp">// From tick data sources
+var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
+
+//from renko bars
+var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
+</pre>
+    <pre class="python"></pre>
+</div>
+<p>There are two key points to remember:</p>
+<ul>
+    <li>Request a smaller resolution than what you want to produce.</li>
+    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
+        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
+    </li>
+</ul>
+<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
+    Using these building blocks you can combine data together to get any other resolution of data required.</p>
+<div class="video-holder">
+    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+</div>
diff --git a/04 Strategy Library/03 Example 2/01 Example 2.html b/04 Strategy Library/03 Example 2/01 Example 2.html
new file mode 100644
index 0000000..54ce442
--- /dev/null
+++ b/04 Strategy Library/03 Example 2/01 Example 2.html	
@@ -0,0 +1,98 @@
+<p>Example 2</p>
+<p>
+    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
+    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
+    term signals.
+    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
+    once. There are three key steps to create and register a consolidator:
+</p>
+<ul>
+    <li>Create the consolidator object.</li>
+    <li>Bind an event handler to handle the new bars.</li>
+    <li>Register it with the subscription manager to start receiving data.</li>
+</ul>
+<div class="section-example-container">
+	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+{
+	public override void Initialize()
+	{
+		// backtest parameters
+		SetStartDate(2016, 1, 1);
+		SetEndDate(DateTime.Now);
+
+		// cash allocation
+		SetCash(25000);
+
+		//assets or universe selection
+		AddEquity("SPY", Resolution.Minute);
+
+		//create a consolidator object; for tradebars; for a timespan of 30 minutes
+		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
+
+		//bind event handler to data consolidated event.
+		thirtyMinutes.DataConsolidated += OnHalfHour;
+
+		//register the consolidator for data.
+		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
+	}
+
+	//event handler for data!
+	public void OnHalfHour(object sender, TradeBar bar) {
+		Debug(Time.ToString("u") + " " + bar);
+	}
+
+	public override void OnData(Slice data)
+	{ }
+}</pre>
+    <pre class="python">from datetime import datetime, timedelta
+class DataConsolidationAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.SetStartDate(2016,1,1)  #Set Start Date
+        self.SetEndDate(datetime.now())   #Set End Date
+        # Find more symbols here: http://quantconnect.com/data
+        self.AddEquity("SPY", Resolution.Minute)
+
+        # define our 30 minute trade bar consolidator. we can
+        # access the 30 minute bar from the DataConsolidated events
+        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
+
+        # attach our event handler. The event handler is a function that will
+        # be called each time we produce a new consolidated piece of data.
+        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
+
+        # this call adds our 30-minute consolidator to
+        # the manager to receive updates from the engine
+        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
+
+    def ThirtyMinuteBarHandler(self, sender, bar):
+        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
+        self.Debug(str(self.Time) + " " + str(bar))
+    def OnData(self, data):
+        pass
+</pre>
+</div>
+<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
+<div class="section-example-container">
+	<pre class="csharp">// From tick data sources
+var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
+
+//from renko bars
+var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
+</pre>
+    <pre class="python"></pre>
+</div>
+<p>There are two key points to remember:</p>
+<ul>
+    <li>Request a smaller resolution than what you want to produce.</li>
+    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
+        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
+    </li>
+</ul>
+<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
+    Using these building blocks you can combine data together to get any other resolution of data required.</p>
+<div class="video-holder">
+    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+</div>
diff --git a/04 Strategy Library/04 Example 3/01 Example 3.html b/04 Strategy Library/04 Example 3/01 Example 3.html
new file mode 100644
index 0000000..8507a7a
--- /dev/null
+++ b/04 Strategy Library/04 Example 3/01 Example 3.html	
@@ -0,0 +1,98 @@
+<p>Example 3</p>
+<p>
+    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
+    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
+    term signals.
+    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
+    once. There are three key steps to create and register a consolidator:
+</p>
+<ul>
+    <li>Create the consolidator object.</li>
+    <li>Bind an event handler to handle the new bars.</li>
+    <li>Register it with the subscription manager to start receiving data.</li>
+</ul>
+<div class="section-example-container">
+	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+{
+	public override void Initialize()
+	{
+		// backtest parameters
+		SetStartDate(2016, 1, 1);
+		SetEndDate(DateTime.Now);
+
+		// cash allocation
+		SetCash(25000);
+
+		//assets or universe selection
+		AddEquity("SPY", Resolution.Minute);
+
+		//create a consolidator object; for tradebars; for a timespan of 30 minutes
+		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
+
+		//bind event handler to data consolidated event.
+		thirtyMinutes.DataConsolidated += OnHalfHour;
+
+		//register the consolidator for data.
+		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
+	}
+
+	//event handler for data!
+	public void OnHalfHour(object sender, TradeBar bar) {
+		Debug(Time.ToString("u") + " " + bar);
+	}
+
+	public override void OnData(Slice data)
+	{ }
+}</pre>
+    <pre class="python">from datetime import datetime, timedelta
+class DataConsolidationAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.SetStartDate(2016,1,1)  #Set Start Date
+        self.SetEndDate(datetime.now())   #Set End Date
+        # Find more symbols here: http://quantconnect.com/data
+        self.AddEquity("SPY", Resolution.Minute)
+
+        # define our 30 minute trade bar consolidator. we can
+        # access the 30 minute bar from the DataConsolidated events
+        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
+
+        # attach our event handler. The event handler is a function that will
+        # be called each time we produce a new consolidated piece of data.
+        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
+
+        # this call adds our 30-minute consolidator to
+        # the manager to receive updates from the engine
+        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
+
+    def ThirtyMinuteBarHandler(self, sender, bar):
+        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
+        self.Debug(str(self.Time) + " " + str(bar))
+    def OnData(self, data):
+        pass
+</pre>
+</div>
+<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
+<div class="section-example-container">
+	<pre class="csharp">// From tick data sources
+var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
+
+//from renko bars
+var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
+</pre>
+    <pre class="python"></pre>
+</div>
+<p>There are two key points to remember:</p>
+<ul>
+    <li>Request a smaller resolution than what you want to produce.</li>
+    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
+        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
+    </li>
+</ul>
+<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
+    Using these building blocks you can combine data together to get any other resolution of data required.</p>
+<div class="video-holder">
+    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+</div>

From 0281d913d905ba2ec2e5f24710801ab4a3ad8429 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 17:52:49 -0400
Subject: [PATCH 024/753] Create a strategy library php driven.

---
 .../01 Strategy Library.html                  |  0
 .../01 Strategy Library.php                   | 62 +++++++++++++++++++
 2 files changed, 62 insertions(+)
 delete mode 100644 04 Strategy Library/01 Strategy Library/01 Strategy Library.html
 create mode 100644 04 Strategy Library/01 Strategy Library/01 Strategy Library.php

diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.html b/04 Strategy Library/01 Strategy Library/01 Strategy Library.html
deleted file mode 100644
index e69de29..0000000
diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php
new file mode 100644
index 0000000..9b612b4
--- /dev/null
+++ b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
@@ -0,0 +1,62 @@
+<?php
+/**
+ * Created by IntelliJ IDEA.
+ * User: Gustavo
+ * Date: 5/24/2018
+ * Time: 4:42 PM
+ */
+
+$strategyMap = [
+    [
+        'name' => 'Example 1',
+        'link' => 'strategy-library/example-1',
+        'description' => 'Testing description of testing tutorial 1'
+    ],
+    [
+        'name' => 'Example 2',
+        'link' => 'strategy-library/example-2',
+        'description' => 'Testing description of testing tutorial 2'
+    ],
+    [
+        'name' => 'Example 3',
+        'link' => 'strategy-library/example-3',
+        'description' => 'Testing description of testing tutorial 3'
+    ],
+];
+
+?>
+<div class="row">
+    <div class="search col-xs-12">
+        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="Tutorials.OnSearchChange()" onkeyup="Tutorials.OnSearchChange()">
+    </div>
+</div>
+<br>
+<table class="table table-striped qc-table">
+    <thead>
+    <tr>
+        <th>
+            Strategy Name
+        </th>
+    </tr>
+    </thead>
+    <tbody>
+    <?php foreach ($strategyMap as $strategy) { ?>
+        <tr>
+            <td>
+                <a class="docs-internal-link" href="/tutorials2/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
+                <p><?= $strategy['description'] ?></p>
+            </td>
+        </tr>
+    <?php } ?>
+    </tbody>
+</table>
+
+<style>
+    .documentation #doc-content .pre-title-area > span {
+        display: none;
+    }
+
+    .documentation #doc-content .pre-title-area > span:first-child {
+        display: inline;
+    }
+</style>

From 61180281944a20b53c22cd1bd47af16d2023263d Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 18:02:00 -0400
Subject: [PATCH 025/753] Update style of the table and page components

---
 .../01 Strategy Library.php                    | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php
index 9b612b4..047f5e3 100644
--- a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
@@ -31,7 +31,7 @@
     </div>
 </div>
 <br>
-<table class="table table-striped qc-table">
+<table class="table table-striped qc-table strategy-table">
     <thead>
     <tr>
         <th>
@@ -50,8 +50,16 @@
     <?php } ?>
     </tbody>
 </table>
-
 <style>
+
+    .documentation .page-sub-heading {
+        display: none;
+    }
+
+    .documentation #strategy-library-search-box {
+        margin: 30px 0;
+    }
+
     .documentation #doc-content .pre-title-area > span {
         display: none;
     }
@@ -59,4 +67,10 @@
     .documentation #doc-content .pre-title-area > span:first-child {
         display: inline;
     }
+
+    .documentation #doc-content .strategy-table tbody > tr > td > a {
+        font-size: 24px;
+        margin-top: 16px;
+        display: block;
+    }
 </style>

From 1f6363184b8f3a6656e8a6d8b72b52fa7b866219 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 18:14:42 -0400
Subject: [PATCH 026/753] Add id to strategy table and searchable keywords to
 test search engine

---
 .../01 Strategy Library/01 Strategy Library.php       | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php
index 047f5e3..83a141b 100644
--- a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
@@ -10,17 +10,17 @@
     [
         'name' => 'Example 1',
         'link' => 'strategy-library/example-1',
-        'description' => 'Testing description of testing tutorial 1'
+        'description' => 'Testing description of testing tutorial 1 eggs'
     ],
     [
         'name' => 'Example 2',
         'link' => 'strategy-library/example-2',
-        'description' => 'Testing description of testing tutorial 2'
+        'description' => 'Testing description of testing tutorial 2 milk'
     ],
     [
         'name' => 'Example 3',
         'link' => 'strategy-library/example-3',
-        'description' => 'Testing description of testing tutorial 3'
+        'description' => 'Testing description of testing tutorial 3 cake'
     ],
 ];
 
@@ -31,7 +31,7 @@
     </div>
 </div>
 <br>
-<table class="table table-striped qc-table strategy-table">
+<table id="strategy-library-table" class="table table-striped qc-table">
     <thead>
     <tr>
         <th>
@@ -51,7 +51,6 @@
     </tbody>
 </table>
 <style>
-
     .documentation .page-sub-heading {
         display: none;
     }
@@ -68,7 +67,7 @@
         display: inline;
     }
 
-    .documentation #doc-content .strategy-table tbody > tr > td > a {
+    .documentation #doc-content #strategy-library-table tbody > tr > td > a {
         font-size: 24px;
         margin-top: 16px;
         display: block;

From 58c87a2868a9c2ca1406c7eb0ccd35f02d63f9a1 Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Thu, 24 May 2018 18:49:45 -0400
Subject: [PATCH 027/753] Self contain strategy library file

---
 .../01 Strategy Library.php                   | 78 ++++++++++++++++++-
 1 file changed, 77 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php
index 83a141b..1640eb4 100644
--- a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/01 Strategy Library/01 Strategy Library.php	
@@ -27,7 +27,7 @@
 ?>
 <div class="row">
     <div class="search col-xs-12">
-        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="Tutorials.OnSearchChange()" onkeyup="Tutorials.OnSearchChange()">
+        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="SLSearch.OnSearchChange()" onkeyup="SLSearch.OnSearchChange()">
     </div>
 </div>
 <br>
@@ -73,3 +73,79 @@
         display: block;
     }
 </style>
+<script type="application/javascript">
+    /**
+     * SLSearch static controller
+     */
+    var SLSearch = /** @class */ (function () {
+        function SLSearch() {
+        }
+
+        /**
+         * Handler for the vent of change in the strategy library search box
+         * @constructor
+         */
+        SLSearch.OnSearchChange = function () {
+            if (!SLSearch._BuildSearchMap())
+                return;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            var search = $('#strategy-library-search-box').val();
+            var options = {
+                shouldSort: true,
+                threshold: 0.8,
+                location: 0,
+                distance: 100,
+                maxPatternLength: 32,
+                minMatchCharLength: 3,
+                keys: [
+                    "text"
+                ]
+            };
+            console.log('starting search');
+            var result = SLSearch._StrategyLibraryMap;
+            if (search != '') {
+                var fuse = new Fuse(SLSearch._StrategyLibraryMap, options);
+                result = fuse.search(search);
+                if (result.length == 0) {
+                    var cols = table.find('thead th').length;
+                    result = [{
+                        html: "<td colspan='" + cols + "' class='text-center' > No matching result </td>",
+                        text: ''
+                    }];
+                }
+            }
+            var tableHtml = '';
+            for (var i in result) {
+                tableHtml += '<tr>' + result[i].html + '</tr>';
+            }
+            table.find('tbody').html(tableHtml);
+            console.log(result);
+        };
+        /**
+         * Builds the strategy library search map
+         * @private
+         */
+        SLSearch._BuildSearchMap = function () {
+            if (SLSearch._StrategyLibraryMap !== null)
+                return true;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            SLSearch._StrategyLibraryMap = [];
+            table.find('tbody > tr').each(function (i, el) {
+                var text = $(el).text();
+                text = text.replace(/(\s+|\n)/g, ' ');
+                text = text.replace(/(\s+|\n)/g, ' ');
+                SLSearch._StrategyLibraryMap.push({
+                    'html': $(el).html(),
+                    'text': text
+                });
+            });
+            return true;
+        };
+        SLSearch._StrategyLibraryMap = null;
+        return SLSearch;
+    }());
+</script>
\ No newline at end of file

From 2f58c1cab927c076eea7d6d89aeb4649f9576b7c Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Fri, 25 May 2018 09:39:24 -0400
Subject: [PATCH 028/753] Add template of tutorials series and strategies of
 the strategy library.

---
 .../00 Overview}/00 About.html                |  0
 .../01 What Will I learn %3F.html             |  0
 .../00 Overview}/02 Tutorials.html            |  0
 .../00 Overview/00 About.html                 | 25 ++++++++
 .../00 Overview/01 What Will I learn %3F.html |  6 ++
 .../00 Overview/02 Tutorials.html             | 60 +++++++++++++++++++
 .../00 Overview/00 About.html                 | 25 ++++++++
 .../00 Overview/01 What Will I learn %3F.html |  6 ++
 .../00 Overview/02 Tutorials.html             | 60 +++++++++++++++++++
 .../00 Overview/00 About.html                 | 25 ++++++++
 .../00 Overview/01 What Will I learn %3F.html |  6 ++
 .../00 Overview/02 Tutorials.html             | 60 +++++++++++++++++++
 .../01 Strategy Library.php                   |  0
 .../02 Example 1/01 Example 1.html            |  0
 .../03 Example 2/01 Example 2.html            |  0
 .../04 Example 3/01 Example 3.html            |  0
 16 files changed, 273 insertions(+)
 rename {03 Tutorial Series/01 Applied Options => 02 Applied Options Tutorials/00 Overview}/00 About.html (100%)
 rename {03 Tutorial Series/01 Applied Options => 02 Applied Options Tutorials/00 Overview}/01 What Will I learn %3F.html (100%)
 rename {03 Tutorial Series/01 Applied Options => 02 Applied Options Tutorials/00 Overview}/02 Tutorials.html (100%)
 create mode 100644 03 Introduction to Financial Python/00 Overview/00 About.html
 create mode 100644 03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html
 create mode 100644 03 Introduction to Financial Python/00 Overview/02 Tutorials.html
 create mode 100644 04 Introduction to Machine Learning/00 Overview/00 About.html
 create mode 100644 04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html
 create mode 100644 04 Introduction to Machine Learning/00 Overview/02 Tutorials.html
 create mode 100644 05 Introduction to Options/00 Overview/00 About.html
 create mode 100644 05 Introduction to Options/00 Overview/01 What Will I learn %3F.html
 create mode 100644 05 Introduction to Options/00 Overview/02 Tutorials.html
 rename {04 Strategy Library => 06 Strategy Library}/01 Strategy Library/01 Strategy Library.php (100%)
 rename {04 Strategy Library => 06 Strategy Library}/02 Example 1/01 Example 1.html (100%)
 rename {04 Strategy Library => 06 Strategy Library}/03 Example 2/01 Example 2.html (100%)
 rename {04 Strategy Library => 06 Strategy Library}/04 Example 3/01 Example 3.html (100%)

diff --git a/03 Tutorial Series/01 Applied Options/00 About.html b/02 Applied Options Tutorials/00 Overview/00 About.html
similarity index 100%
rename from 03 Tutorial Series/01 Applied Options/00 About.html
rename to 02 Applied Options Tutorials/00 Overview/00 About.html
diff --git a/03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html b/02 Applied Options Tutorials/00 Overview/01 What Will I learn %3F.html
similarity index 100%
rename from 03 Tutorial Series/01 Applied Options/01 What Will I learn %3F.html
rename to 02 Applied Options Tutorials/00 Overview/01 What Will I learn %3F.html
diff --git a/03 Tutorial Series/01 Applied Options/02 Tutorials.html b/02 Applied Options Tutorials/00 Overview/02 Tutorials.html
similarity index 100%
rename from 03 Tutorial Series/01 Applied Options/02 Tutorials.html
rename to 02 Applied Options Tutorials/00 Overview/02 Tutorials.html
diff --git a/03 Introduction to Financial Python/00 Overview/00 About.html b/03 Introduction to Financial Python/00 Overview/00 About.html
new file mode 100644
index 0000000..f389a52
--- /dev/null
+++ b/03 Introduction to Financial Python/00 Overview/00 About.html	
@@ -0,0 +1,25 @@
+<p>
+    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
+    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
+    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
+    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
+    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
+    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
+    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
+    demonstrate the QuantConnect algorithm implemented in Python.
+</p>
+<div class="row tutorials-page-about">
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+        <p>8 Tutorials</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+        <p>10 Backtests</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-code" aria-hidden="true"></i></p>
+        <p>45 Code Snippets</p>
+    </div>
+</div>
+
diff --git a/03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html b/03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html
new file mode 100644
index 0000000..661425d
--- /dev/null
+++ b/03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html	
@@ -0,0 +1,6 @@
+<div class="row tutorials-learn">
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
+</div>
\ No newline at end of file
diff --git a/03 Introduction to Financial Python/00 Overview/02 Tutorials.html b/03 Introduction to Financial Python/00 Overview/02 Tutorials.html
new file mode 100644
index 0000000..6281670
--- /dev/null
+++ b/03 Introduction to Financial Python/00 Overview/02 Tutorials.html	
@@ -0,0 +1,60 @@
+<table class="table qc-table tutorials-table">
+    <tbody>
+    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
+        <td>1</td>
+        <td>
+            <h2>Covered Call</h2>
+            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
+        <td>2</td>
+        <td>
+            <h2>Bull Call Spread</h2>
+            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
+        <td>3</td>
+        <td>
+            <h2>Long Straddle</h2>
+            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
+        <td>4</td>
+        <td>
+            <h2>Long Strangle</h2>
+            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
+        <td>5</td>
+        <td>
+            <h2>Butterfly Spread</h2>
+            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
+        <td>6</td>
+        <td>
+            <h2>Iron Condor</h2>
+            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
+        <td>7</td>
+        <td>
+            <h2>Iron Butterfly</h2>
+            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
+        <td>8</td>
+        <td>
+            <h2>Protective Collar</h2>
+            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    </tbody>
+</table>
\ No newline at end of file
diff --git a/04 Introduction to Machine Learning/00 Overview/00 About.html b/04 Introduction to Machine Learning/00 Overview/00 About.html
new file mode 100644
index 0000000..f389a52
--- /dev/null
+++ b/04 Introduction to Machine Learning/00 Overview/00 About.html	
@@ -0,0 +1,25 @@
+<p>
+    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
+    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
+    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
+    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
+    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
+    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
+    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
+    demonstrate the QuantConnect algorithm implemented in Python.
+</p>
+<div class="row tutorials-page-about">
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+        <p>8 Tutorials</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+        <p>10 Backtests</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-code" aria-hidden="true"></i></p>
+        <p>45 Code Snippets</p>
+    </div>
+</div>
+
diff --git a/04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html b/04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html
new file mode 100644
index 0000000..661425d
--- /dev/null
+++ b/04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html	
@@ -0,0 +1,6 @@
+<div class="row tutorials-learn">
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
+</div>
\ No newline at end of file
diff --git a/04 Introduction to Machine Learning/00 Overview/02 Tutorials.html b/04 Introduction to Machine Learning/00 Overview/02 Tutorials.html
new file mode 100644
index 0000000..6281670
--- /dev/null
+++ b/04 Introduction to Machine Learning/00 Overview/02 Tutorials.html	
@@ -0,0 +1,60 @@
+<table class="table qc-table tutorials-table">
+    <tbody>
+    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
+        <td>1</td>
+        <td>
+            <h2>Covered Call</h2>
+            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
+        <td>2</td>
+        <td>
+            <h2>Bull Call Spread</h2>
+            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
+        <td>3</td>
+        <td>
+            <h2>Long Straddle</h2>
+            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
+        <td>4</td>
+        <td>
+            <h2>Long Strangle</h2>
+            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
+        <td>5</td>
+        <td>
+            <h2>Butterfly Spread</h2>
+            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
+        <td>6</td>
+        <td>
+            <h2>Iron Condor</h2>
+            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
+        <td>7</td>
+        <td>
+            <h2>Iron Butterfly</h2>
+            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
+        <td>8</td>
+        <td>
+            <h2>Protective Collar</h2>
+            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    </tbody>
+</table>
\ No newline at end of file
diff --git a/05 Introduction to Options/00 Overview/00 About.html b/05 Introduction to Options/00 Overview/00 About.html
new file mode 100644
index 0000000..f389a52
--- /dev/null
+++ b/05 Introduction to Options/00 Overview/00 About.html	
@@ -0,0 +1,25 @@
+<p>
+    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
+    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
+    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
+    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
+    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
+    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
+    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
+    demonstrate the QuantConnect algorithm implemented in Python.
+</p>
+<div class="row tutorials-page-about">
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+        <p>8 Tutorials</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+        <p>10 Backtests</p>
+    </div>
+    <div class="col-xs-12 col-md-4">
+        <p><i class="fa fa-code" aria-hidden="true"></i></p>
+        <p>45 Code Snippets</p>
+    </div>
+</div>
+
diff --git a/05 Introduction to Options/00 Overview/01 What Will I learn %3F.html b/05 Introduction to Options/00 Overview/01 What Will I learn %3F.html
new file mode 100644
index 0000000..661425d
--- /dev/null
+++ b/05 Introduction to Options/00 Overview/01 What Will I learn %3F.html	
@@ -0,0 +1,6 @@
+<div class="row tutorials-learn">
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
+    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
+</div>
\ No newline at end of file
diff --git a/05 Introduction to Options/00 Overview/02 Tutorials.html b/05 Introduction to Options/00 Overview/02 Tutorials.html
new file mode 100644
index 0000000..6281670
--- /dev/null
+++ b/05 Introduction to Options/00 Overview/02 Tutorials.html	
@@ -0,0 +1,60 @@
+<table class="table qc-table tutorials-table">
+    <tbody>
+    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
+        <td>1</td>
+        <td>
+            <h2>Covered Call</h2>
+            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
+        <td>2</td>
+        <td>
+            <h2>Bull Call Spread</h2>
+            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
+        <td>3</td>
+        <td>
+            <h2>Long Straddle</h2>
+            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
+        <td>4</td>
+        <td>
+            <h2>Long Strangle</h2>
+            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
+        <td>5</td>
+        <td>
+            <h2>Butterfly Spread</h2>
+            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
+        <td>6</td>
+        <td>
+            <h2>Iron Condor</h2>
+            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
+        <td>7</td>
+        <td>
+            <h2>Iron Butterfly</h2>
+            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
+        <td>8</td>
+        <td>
+            <h2>Protective Collar</h2>
+            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
+            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+    </tr>
+    </tbody>
+</table>
\ No newline at end of file
diff --git a/04 Strategy Library/01 Strategy Library/01 Strategy Library.php b/06 Strategy Library/01 Strategy Library/01 Strategy Library.php
similarity index 100%
rename from 04 Strategy Library/01 Strategy Library/01 Strategy Library.php
rename to 06 Strategy Library/01 Strategy Library/01 Strategy Library.php
diff --git a/04 Strategy Library/02 Example 1/01 Example 1.html b/06 Strategy Library/02 Example 1/01 Example 1.html
similarity index 100%
rename from 04 Strategy Library/02 Example 1/01 Example 1.html
rename to 06 Strategy Library/02 Example 1/01 Example 1.html
diff --git a/04 Strategy Library/03 Example 2/01 Example 2.html b/06 Strategy Library/03 Example 2/01 Example 2.html
similarity index 100%
rename from 04 Strategy Library/03 Example 2/01 Example 2.html
rename to 06 Strategy Library/03 Example 2/01 Example 2.html
diff --git a/04 Strategy Library/04 Example 3/01 Example 3.html b/06 Strategy Library/04 Example 3/01 Example 3.html
similarity index 100%
rename from 04 Strategy Library/04 Example 3/01 Example 3.html
rename to 06 Strategy Library/04 Example 3/01 Example 3.html

From 9d31d90f03a52fcdc8915280b7f40f84798c5471 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Fri, 25 May 2018 14:59:36 +0000
Subject: [PATCH 029/753] Automated push from server made by Gustavo Avils

---
 .../00 Overview/00 About.html                 | 31 +++++++++++--------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/04 Introduction to Machine Learning/00 Overview/00 About.html b/04 Introduction to Machine Learning/00 Overview/00 About.html
index f389a52..d6d97e5 100644
--- a/04 Introduction to Machine Learning/00 Overview/00 About.html	
+++ b/04 Introduction to Machine Learning/00 Overview/00 About.html	
@@ -1,5 +1,7 @@
 <p>
-    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
+Testing saving function from server</p>
+<p>
+The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
     options markets and most importantly, we will teach users how to start your simple options trading algorithm on
     QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
     interesting relationship between profit and stock price. We will primarily talk about the strategies like the
@@ -9,17 +11,20 @@
     demonstrate the QuantConnect algorithm implemented in Python.
 </p>
 <div class="row tutorials-page-about">
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-        <p>8 Tutorials</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-        <p>10 Backtests</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-code" aria-hidden="true"></i></p>
-        <p>45 Code Snippets</p>
-    </div>
+	<div class="col-xs-12 col-md-4">
+		<p><i class="fa fa-file-o" aria-hidden="true"></i>
+		</p>
+		<p>8 Tutorials</p>
+	</div>
+	<div class="col-xs-12 col-md-4">
+		<p><i class="fa fa-line-chart" aria-hidden="true"></i>
+		</p>
+		<p>10 Backtests</p>
+	</div>
+	<div class="col-xs-12 col-md-4">
+		<p><i class="fa fa-code" aria-hidden="true"></i>
+		</p>
+		<p>45 Code Snippets</p>
+	</div>
 </div>
 

From ee3663c85a1c4e6e521e2b00e05c3b3824f4d75e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 May 2018 15:17:34 +0000
Subject: [PATCH 030/753] Automated push from server made by Gustavo Avils

---
 04 Introduction to Machine Learning/00 Overview/00 About.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Introduction to Machine Learning/00 Overview/00 About.html b/04 Introduction to Machine Learning/00 Overview/00 About.html
index d6d97e5..35f113f 100644
--- a/04 Introduction to Machine Learning/00 Overview/00 About.html	
+++ b/04 Introduction to Machine Learning/00 Overview/00 About.html	
@@ -1,5 +1,5 @@
 <p>
-Testing saving function from server</p>
+Testing saving function from server, full test from production.</p>
 <p>
 The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
     options markets and most importantly, we will teach users how to start your simple options trading algorithm on

From acc1b25c85ab4f5ac3860f0dde95fde070bb5518 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 May 2018 15:40:35 +0000
Subject: [PATCH 031/753] Automated push from server made by Gustavo Avils

---
 04 Introduction to Machine Learning/00 Overview/00 About.html | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/04 Introduction to Machine Learning/00 Overview/00 About.html b/04 Introduction to Machine Learning/00 Overview/00 About.html
index 35f113f..032955e 100644
--- a/04 Introduction to Machine Learning/00 Overview/00 About.html	
+++ b/04 Introduction to Machine Learning/00 Overview/00 About.html	
@@ -1,5 +1,6 @@
 <p>
-Testing saving function from server, full test from production.</p>
+Testing saving function from server, full test from production <b style="color:red">v2</b>
+</p>
 <p>
 The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
     options markets and most importantly, we will teach users how to start your simple options trading algorithm on

From c20318aea67072d13145cfa1e87dd7afe6a73c3d Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Fri, 25 May 2018 12:07:20 -0400
Subject: [PATCH 032/753] Testing bracket name

---
 .../00 Overview/00 About.html                                     | 0
 .../00 Overview/01 What Will I learn %3F.html                     | 0
 .../00 Overview/02 Tutorials.html                                 | 0
 .../00 Overview/00 About.html                                     | 0
 .../00 Overview/01 What Will I learn %3F.html                     | 0
 .../00 Overview/02 Tutorials.html                                 | 0
 6 files changed, 0 insertions(+), 0 deletions(-)
 rename {03 Introduction to Financial Python => 03 Introduction to Financial Python [Financial Python 101]}/00 Overview/00 About.html (100%)
 rename {03 Introduction to Financial Python => 03 Introduction to Financial Python [Financial Python 101]}/00 Overview/01 What Will I learn %3F.html (100%)
 rename {03 Introduction to Financial Python => 03 Introduction to Financial Python [Financial Python 101]}/00 Overview/02 Tutorials.html (100%)
 rename {04 Introduction to Machine Learning => 04 Introduction to Machine Learning [Machine Learning 101]}/00 Overview/00 About.html (100%)
 rename {04 Introduction to Machine Learning => 04 Introduction to Machine Learning [Machine Learning 101]}/00 Overview/01 What Will I learn %3F.html (100%)
 rename {04 Introduction to Machine Learning => 04 Introduction to Machine Learning [Machine Learning 101]}/00 Overview/02 Tutorials.html (100%)

diff --git a/03 Introduction to Financial Python/00 Overview/00 About.html b/03 Introduction to Financial Python [Financial Python 101]/00 Overview/00 About.html
similarity index 100%
rename from 03 Introduction to Financial Python/00 Overview/00 About.html
rename to 03 Introduction to Financial Python [Financial Python 101]/00 Overview/00 About.html
diff --git a/03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html b/03 Introduction to Financial Python [Financial Python 101]/00 Overview/01 What Will I learn %3F.html
similarity index 100%
rename from 03 Introduction to Financial Python/00 Overview/01 What Will I learn %3F.html
rename to 03 Introduction to Financial Python [Financial Python 101]/00 Overview/01 What Will I learn %3F.html
diff --git a/03 Introduction to Financial Python/00 Overview/02 Tutorials.html b/03 Introduction to Financial Python [Financial Python 101]/00 Overview/02 Tutorials.html
similarity index 100%
rename from 03 Introduction to Financial Python/00 Overview/02 Tutorials.html
rename to 03 Introduction to Financial Python [Financial Python 101]/00 Overview/02 Tutorials.html
diff --git a/04 Introduction to Machine Learning/00 Overview/00 About.html b/04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/00 About.html
similarity index 100%
rename from 04 Introduction to Machine Learning/00 Overview/00 About.html
rename to 04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/00 About.html
diff --git a/04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html b/04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/01 What Will I learn %3F.html
similarity index 100%
rename from 04 Introduction to Machine Learning/00 Overview/01 What Will I learn %3F.html
rename to 04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/01 What Will I learn %3F.html
diff --git a/04 Introduction to Machine Learning/00 Overview/02 Tutorials.html b/04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/02 Tutorials.html
similarity index 100%
rename from 04 Introduction to Machine Learning/00 Overview/02 Tutorials.html
rename to 04 Introduction to Machine Learning [Machine Learning 101]/00 Overview/02 Tutorials.html

From adaaf475a967ff6dea015cdf80b5c74b2e64f832 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 May 2018 17:01:24 +0000
Subject: [PATCH 033/753] Automated push from server made by Jared Broad

---
 .../01 Consolidating Data to Build Bars.html  | 20 +++++++++----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 2b88805..1eb7757 100644
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -1,14 +1,13 @@
-<p>
-    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
+<p><code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
     useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
     term signals.
     Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
     once. There are three key steps to create and register a consolidator:
 </p>
 <ul>
-    <li>Create the consolidator object.</li>
-    <li>Bind an event handler to handle the new bars.</li>
-    <li>Register it with the subscription manager to start receiving data.</li>
+	<li>Create the consolidator object.</li>
+	<li>Bind an event handler to handle the new bars.</li>
+	<li>Register it with the subscription manager to start receiving data.</li>
 </ul>
 <div class="section-example-container">
 	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
@@ -43,7 +42,7 @@
 	public override void OnData(Slice data)
 	{ }
 }</pre>
-    <pre class="python">from datetime import datetime, timedelta
+	<pre class="python">from datetime import datetime, timedelta
 class DataConsolidationAlgorithm(QCAlgorithm):
 
     def Initialize(self):
@@ -81,17 +80,16 @@
 //from renko bars
 var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
 </pre>
-    <pre class="python"></pre>
+	<pre class="python"></pre>
 </div>
 <p>There are two key points to remember:</p>
 <ul>
-    <li>Request a smaller resolution than what you want to produce.</li>
-    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
+	<li>Request a smaller resolution than what you want to produce.</li>
+	<li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
         triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
     </li>
 </ul>
 <p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
     Using these building blocks you can combine data together to get any other resolution of data required.</p>
-<div class="video-holder">
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<div><iframe width="770" height="433" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
 </div>

From 73be82685035019c7c0be40b6911def9e39fce19 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 May 2018 21:01:58 +0000
Subject: [PATCH 034/753] Automated push from server made by Jared Broad

---
 00 Home/01 Home/01 Home.html | 46 +++++++++++++-----------------------
 1 file changed, 16 insertions(+), 30 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index 041739b..845cb8b 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -1,44 +1,30 @@
 <div style="border-bottom: solid 1px  #e4e6ea;">
-    <div style="float: left; padding-right: 30px">
-        <img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
-    </div>
-    <div>
-        <h1>Documentation</h1>
-        <h2 style="margin-top: 8px;">
-            <span style="font-size: 22px;">Learn to use QuantConnect and explore our features</span>
-        </h2>
-    </div>
+	<div style="float: left; padding-right: 30px"><img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
+	</div>
+	<div>
+		<h1>Tutorials</h1>
+		<h2 style="margin-top: 8px;"><span style="font-size: 22px;">Learn to use QuantConnect with guided tutorials</span>
+		</h2>
+	</div>
 </div>
 <div>
-    <div class="home-buttons-backdrop"></div>
-    <div class="row home-buttons" style="padding-top: 18px;">
-        <a class="docs-internal-link" href="/docs/key-concepts/developing-in-the-ide">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_using_qc.rev0.png">
+	<div class="home-buttons-backdrop"></div>
+	<div class="row home-buttons" style="padding-top: 18px;"><a class="docs-internal-link" href="/docs/key-concepts/developing-in-the-ide"><img src="https://cdn.quantconnect.com/web/i/docs/docs_using_qc.rev0.png">
             Using QuantConnect
-        </a>
-        <a class="docs-internal-link" href="/docs/algorithm-reference/overview">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_ref_rev0.png">
+        </a><a class="docs-internal-link" href="/docs/algorithm-reference/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_ref_rev0.png">
             Algorithm Reference
-        </a>
-        <a class="docs-internal-link" href="/docs/algorithm-framework/overview">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_framework_rev0.png">
+        </a><a class="docs-internal-link" href="/docs/algorithm-framework/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_framework_rev0.png">
             Algorithm Framework
         </a>
-    </div>
-    <div class="row home-buttons" style="padding-bottom: 18px;">
-        <a class="docs-internal-link" href="/docs/research/overview">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_research_rev0.png">
+	</div>
+	<div class="row home-buttons" style="padding-bottom: 18px;"><a class="docs-internal-link" href="/docs/research/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_research_rev0.png">
             Research
-        </a>
-        <a class="docs-internal-link" href="/docs/tutorials/tutorial-series">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_tutorials_rev0.png">
+        </a><a class="docs-internal-link" href="/docs/tutorials/tutorial-series"><img src="https://cdn.quantconnect.com/web/i/docs/docs_tutorials_rev0.png">
             Tutorials
-        </a>
-        <a class="docs-internal-link" href="/docs/live-trading/overview">
-            <img src="https://cdn.quantconnect.com/web/i/docs/docs_live_trading_rev0.png">
+        </a><a class="docs-internal-link" href="/docs/live-trading/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_live_trading_rev0.png">
             Live Trading
         </a>
-    </div>
+	</div>
 </div>
 <style>
     .page-sub-heading,

From 7e557118904843b13dbac4e1df8e2859795c368e Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 4 Jun 2018 18:03:56 -0400
Subject: [PATCH 035/753] update tutorial structure

---
 01 API Tutorials/00 Overview/00 About.html    |   20 -
 .../00 Overview/01 What Will I learn %3F.html |    8 -
 .../00 Overview/02 Tutorials.html             |   46 -
 .../01 Consolidating Data to Build Bars.html  |   57 +-
 .../03 Scheduled Events.html                  |   70 +
 .../04 Tracking and Managing Orders.html      |  138 ++
 .../06 Using Options in QuantConnect.html     |  246 +++
 .../00 About.html                             |    0
 .../01 What Will I learn %3F.html             |    0
 .../02 Tutorials.html                         |    0
 .../02 Introduction to Options}/00 About.html |    0
 .../01 What Will I learn %3F.html             |    0
 .../02 Tutorials.html                         |    0
 .../03 Applied Options}/00 About.html         |    0
 .../01 What Will I learn %3F.html             |    0
 .../03 Applied Options}/02 Tutorials.html     |    0
 .../00 About.html                             |    0
 .../01 What Will I learn %3F.html             |    0
 .../02 Tutorials.html                         |    0
 .../01 Desktop Charting with LEAN.html        |   22 +
 ...02 How do I use the API File Provider.html |   16 +
 .../01 Strategy Library.php                   |   60 +-
 ... Ranking Strategy on Dow 30 Companies.html |  126 ++
 ...eversion and Momentum in Forex Market.html |  195 +++
 ...Pairs Trading-Copula vs Cointegration.html |  425 +++++
 .../04 The Dynamic Breakout II Strategy.html  |   64 +
 .../05 Dual Thrust Trading Algorithm.html     |   50 +
 ... Can Crude Oil Predict Equity Returns.html |   93 +
 ...orrelation and Cointegration Approach.html |  319 ++++
 ...w Frequency Component of Forex Market.html |  182 ++
 ...Strategy Based on Fundamental Factors.html |  212 +++
 ...hort-Term Reversal Strategy in Stocks.html |   81 +
 ...undamental Factor Long Short Strategy.html |  109 ++
 .../01 Data Types and Data Structures.html    |  220 +++
 .../01 Data Types and Data Structures.ipynb   |  512 ++++++
 .../02 Logical Operations and Loops.html      |  194 +++
 .../02 Logical Operations and Loops.ipynb     |  372 ++++
 ...ns and Objective-Oriented Programming.html |  194 +++
 ...s and Objective-Oriented Programming.ipynb |  418 +++++
 .../04 NumPy and Basic Pandas.html            |  229 +++
 .../04 NumPy and Basic Pandas.ipynb           |  478 +++++
 .../05 Pandas-Resampling and DataFrame.html   |  678 ++++++++
 .../05 Pandas-Resampling and DataFrame.ipynb  | 1540 +++++++++++++++++
 .../06 Rate of Return, Mean and Variance.html |  122 ++
 ...06 Rate of Return, Mean and Variance.ipynb |  146 ++
 ...07 Random Variable and Distributions.ipynb |  364 ++++
 ...07 Random Variables and Distributions.html |  167 ++
 ...dence Interval and Hypothesis Testing.html |  134 ++
 ...ence Interval and Hypothesis Testing.ipynb |  219 +++
 .../09 Simple Linear Regression.html          |  127 ++
 .../09 Simple Linear Regression.ipynb         |  267 +++
 .../10 Multiple Linear Regression.html        |  320 ++++
 .../10 Multiple Linear Regression.ipynb       |  487 ++++++
 .../11 Linear Algebra/11 Linear Algebra.html  |  225 +++
 .../11 Linear Algebra/11 Linear Algebra.ipynb |  234 +++
 .../12 Modern Portfolio Theory.html           |  155 ++
 .../12 Modern Portfolio Theory.ipynb          |  431 +++++
 .../13 Capital Asset Pricing Model.html       |   92 +
 .../13 Capital Asset Pricing Model.ipynb      |  325 ++++
 .../14 Fama-French Multi-Factor Models.html   |   96 +
 .../14 Fama-French Multi-Factor Models.ipynb  |  338 ++++
 .../01 General Features of Options.html       |  128 ++
 ...-Call Parity and Arbitrage Strategies.html |  158 ++
 ...Call Parity and Arbitrage Strategies.ipynb |   99 ++
 ...stic Processes and Monte Carlo Method.html |  180 ++
 ...tic Processes and Monte Carlo Method.ipynb |  214 +++
 ...n Pricing Black Scholes Merton Model.ipynb |  106 ++
 ...ns Pricing Black Scholes Merton Model.html |  140 ++
 .../05 The Greek Letters.html                 |  229 +++
 .../05 The Greek Letters.ipynb                |  318 ++++
 ...cal Volatility and Implied Volatility.html |  191 ++
 ...al Volatility and Implied Volatility.ipynb |  438 +++++
 ... Volatility and Stochastic Volatility.html |  209 +++
 ...Volatility and Stochastic Volatility.ipynb |  409 +++++
 .../02 Example 1/01 Example 1.html            |   98 --
 .../03 Example 2/01 Example 2.html            |   98 --
 .../04 Example 3/01 Example 3.html            |   98 --
 .../01 Covered Call/01 Covered Call.html      |   86 +
 .../02 Bull Call Spread.html                  |   74 +
 .../03 Long Straddle/03 Long Straddle.html    |   64 +
 .../04 Long Strangle/04 Long Strangle.html    |   77 +
 .../05 Butterfly Spread.html                  |  105 ++
 .../06 Iron Condor/06 Iron Condor.html        |  110 ++
 .../07 Iron Butterfly/07 Iron Butterfly.html  |  112 ++
 .../08 Protective Collar.html                 |   85 +
 85 files changed, 15040 insertions(+), 409 deletions(-)
 delete mode 100644 01 API Tutorials/00 Overview/00 About.html
 delete mode 100644 01 API Tutorials/00 Overview/01 What Will I learn %3F.html
 delete mode 100644 01 API Tutorials/00 Overview/02 Tutorials.html
 mode change 100644 => 100755 01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
 create mode 100755 01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html
 create mode 100755 01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html
 rename {02 Applied Options Tutorials/00 Overview => 02 Tutorial Series/01 Introduction to Financial Python}/00 About.html (100%)
 rename {02 Applied Options Tutorials/00 Overview => 02 Tutorial Series/01 Introduction to Financial Python}/01 What Will I learn %3F.html (100%)
 rename {02 Applied Options Tutorials/00 Overview => 02 Tutorial Series/01 Introduction to Financial Python}/02 Tutorials.html (100%)
 rename {03 Introduction to Financial Python [Financial Python 101]/00 Overview => 02 Tutorial Series/02 Introduction to Options}/00 About.html (100%)
 rename {03 Introduction to Financial Python [Financial Python 101]/00 Overview => 02 Tutorial Series/02 Introduction to Options}/01 What Will I learn %3F.html (100%)
 rename {03 Introduction to Financial Python [Financial Python 101]/00 Overview => 02 Tutorial Series/02 Introduction to Options}/02 Tutorials.html (100%)
 rename {05 Introduction to Options/00 Overview => 02 Tutorial Series/03 Applied Options}/00 About.html (100%)
 rename {04 Introduction to Machine Learning [Machine Learning 101]/00 Overview => 02 Tutorial Series/03 Applied Options}/01 What Will I learn %3F.html (100%)
 rename {04 Introduction to Machine Learning [Machine Learning 101]/00 Overview => 02 Tutorial Series/03 Applied Options}/02 Tutorials.html (100%)
 rename {04 Introduction to Machine Learning [Machine Learning 101]/00 Overview => 02 Tutorial Series/04 Introduction to Machine Learning}/00 About.html (100%)
 rename {05 Introduction to Options/00 Overview => 02 Tutorial Series/04 Introduction to Machine Learning}/01 What Will I learn %3F.html (100%)
 rename {05 Introduction to Options/00 Overview => 02 Tutorial Series/04 Introduction to Machine Learning}/02 Tutorials.html (100%)
 create mode 100755 03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
 create mode 100755 03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
 rename {06 Strategy Library/01 Strategy Library => 04 Strategy Library/00 Strategy Library}/01 Strategy Library.php (58%)
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Combining Mean Reversion and Momentum in Forex Market.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Pairs Trading-Copula vs Cointegration.html
 create mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html
 create mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html
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diff --git a/01 API Tutorials/00 Overview/00 About.html b/01 API Tutorials/00 Overview/00 About.html
deleted file mode 100644
index 874484d..0000000
--- a/01 API Tutorials/00 Overview/00 About.html	
+++ /dev/null
@@ -1,20 +0,0 @@
-<p>In this tutorial series we introduce the core concepts of the QuantConnect/LEAN API with the aim of answering the
-    commonly asked questions about using QuantConnect. </p>
-<p>The core of the documentation can be found on the <a href="/docs">documentation page</a>, but these videos should
-    give you an even deeper understanding. The QuantConnect underlying technology (LEAN) is written in C#, and most of
-    these videos are in C# -- however the API is virtually the same in Python and F#. If you're a Python user just click
-    the "Python" code snippet button on the documentation page to view the python examples.</p>
-<div class="row tutorials-page-about">
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-        <p>6 Tutorials</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-        <p>6 Backtests</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-code" aria-hidden="true"></i></p>
-        <p>25 Code Snippets</p>
-    </div>
-</div>
\ No newline at end of file
diff --git a/01 API Tutorials/00 Overview/01 What Will I learn %3F.html b/01 API Tutorials/00 Overview/01 What Will I learn %3F.html
deleted file mode 100644
index 5fd71d4..0000000
--- a/01 API Tutorials/00 Overview/01 What Will I learn %3F.html	
+++ /dev/null
@@ -1,8 +0,0 @@
-<div class="row tutorials-learn">
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Consolidating Data</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Desktop Charting</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using API File Provider</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Scheduling Events</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Order Management</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check" aria-hidden="true"></i> Using Options</div>
-</div>
\ No newline at end of file
diff --git a/01 API Tutorials/00 Overview/02 Tutorials.html b/01 API Tutorials/00 Overview/02 Tutorials.html
deleted file mode 100644
index 590d203..0000000
--- a/01 API Tutorials/00 Overview/02 Tutorials.html	
+++ /dev/null
@@ -1,46 +0,0 @@
-<table class="table qc-table tutorials-table">
-    <tbody>
-    <tr class="docs-internal-link clickable" href="/tutorials2/api-tutorials/consolidating-data-to-build-bars">
-        <td>1</td>
-        <td>
-            <h2>Consolidating Data to Build Bars</h2>
-            Create any bar size by combining data together with Consolidators.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/desktop-charting-with-lean/';" class="clickable">
-        <td>2</td>
-        <td>
-            <h2>Desktop Charting with LEAN</h2>
-            Setting up desktop charting with the open source algorithmic trading engine LEAN.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/use-api-file-provider/';" class="clickable">
-        <td>3</td>
-        <td>
-            <h2>Downloading Data with the API File Provider</h2>
-            Guide to downloading and interacting with your QuantConnect data repository.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/scheduled-events-lean/';" class="clickable">
-        <td>4</td>
-        <td>
-            <h2>Using Scheduled Events</h2>
-            Scheduling events to trigger code at specific times of day.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/tracking-and-managing-orders/';" class="clickable">
-        <td>5</td>
-        <td>
-            <h2>Tracking and Managing Orders</h2>
-            Simple guide to the basics of tracking and managing orders in QuantConnect.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect/';" class="clickable">
-        <td>6</td>
-        <td>
-            <h2>Using Options in QuantConnect</h2>
-            The key API methods for requesting option data financial and focusing in on specific contracts.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    </tbody>
-</table>
\ No newline at end of file
diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
old mode 100644
new mode 100755
index 1eb7757..5d1de5b
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -1,16 +1,13 @@
-<p><code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
-    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
-    term signals.
-    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
-    once. There are three key steps to create and register a consolidator:
-</p>
-<ul>
-	<li>Create the consolidator object.</li>
-	<li>Bind an event handler to handle the new bars.</li>
-	<li>Register it with the subscription manager to start receiving data.</li>
-</ul>
-<div class="section-example-container">
-	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+<code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
+
+Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized once. There are three key steps to create and register a consolidator:
+<ol>
+ 	<li>Create the consolidator object.</li>
+ 	<li>Bind an event handler to handle the new bars.</li>
+ 	<li>Register it with the subscription manager to start receiving data.</li>
+</ol>
+In <b>C#</b> this looks like:
+<pre class="prettyprint linenums">public class ConsolidatorDemoAlgorithm : QCAlgorithm
 {
 	public override void Initialize()
 	{
@@ -42,7 +39,8 @@
 	public override void OnData(Slice data)
 	{ }
 }</pre>
-	<pre class="python">from datetime import datetime, timedelta
+In <b>Python</b>:
+<pre class="prettyprint linenums">from datetime import datetime, timedelta
 class DataConsolidationAlgorithm(QCAlgorithm):
 
     def Initialize(self):
@@ -71,25 +69,22 @@
     def OnData(self, data):
         pass
 </pre>
-</div>
-<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
-<div class="section-example-container">
-	<pre class="csharp">// From tick data sources
+The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
+
+In <b>C#</b> this looks like:
+<pre class="prettyprint linenums">// From tick data sources
 var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
 
 //from renko bars
 var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
 </pre>
-	<pre class="python"></pre>
-</div>
-<p>There are two key points to remember:</p>
-<ul>
-	<li>Request a smaller resolution than what you want to produce.</li>
-	<li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
-        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
-    </li>
-</ul>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
-    Using these building blocks you can combine data together to get any other resolution of data required.</p>
-<div><iframe width="770" height="433" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-</div>
+
+There are two key points to remember:
+<ol>
+ 	<li>Request a smaller resolution than what you want to produce.</li>
+ 	<li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.</li>
+</ol>
+<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.</p>
+https://www.youtube.com/watch?v=bbZy__qd1aA
+
+&nbsp;
diff --git a/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html b/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html
new file mode 100755
index 0000000..9237c12
--- /dev/null
+++ b/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html	
@@ -0,0 +1,70 @@
+Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
+
+The scheduling is set with two rules: the <code>DateRules</code> and <code>TimeRules</code> classes. The schedule manager takes one of each and executes an action code block.
+In C# this looks like:
+<pre class="prettyprint linenums">Schedule.On(DateRules.On(2013, 10, 7), TimeRules.At(13, 0), () =&gt;
+{
+         Debug("Specific Time: Fired at : " + Time);
+});</pre>
+Or in Python:
+<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.On(2013, 10, 7), self.TimeRules.At(13, 0), Action(self.SpecificTime))
+def SpecificTime(self):
+    self.Log("SpecificTime: Fired at : {0}".format(self.Time))</pre>
+The date rules can be specified as below:
+<table class="table table-striped qc-table">
+<thead>
+<tr>
+<th colspan="2">Scheduled Events Date Rules</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="50%"><code>DateRules.On(2013, 10, 7)</code></td>
+<td>On a specific date</td>
+</tr>
+<tr>
+<td><code>DateRules.EveryDay("SPY")</code></td>
+<td>Every trading day for SPY</td>
+</tr>
+<tr>
+<td><code>DateRules.Every(DayOfWeek.Monday, ...)</code></td>
+<td>On specific days of week</td>
+</tr>
+<tr>
+<td><code>DateRules.EveryDay()</code></td>
+<td>Every day</td>
+</tr>
+<tr>
+<td><code>DateRules.MonthStart("SPY")</code></td>
+<td>SPY trading start of each month</td>
+</tr>
+</tbody>
+</table>
+The time rules trigger specify when on the day the event should be triggered. They can be specified as below:
+<table class="table table-striped qc-table">
+<thead>
+<tr>
+<th colspan="2">Scheduled Events Time Rules</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="50%"><code>TimeRules.At(13, 0)</code></td>
+<td>At a specific time, 1:00pm</td>
+</tr>
+<tr>
+<td><code>TimeRules.AfterMarketOpen("SPY", 10)</code></td>
+<td>Run 10 minutes after market open for SPY</td>
+</tr>
+<tr>
+<td><code>TimeRules.BeforeMarketClose("SPY", 10)</code></td>
+<td>Run 10 minutes before market close for SPY</td>
+</tr>
+<tr>
+<td><code>TimeRules.Every(TimeSpan.FromMinutes(10))</code></td>
+<td>Every 10 minutes during day specified</td>
+</tr>
+</tbody>
+</table>
+For a demonstration see the tutorial video below:
+https://youtu.be/SUu4MKlm93I
diff --git a/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html
new file mode 100755
index 0000000..bd19104
--- /dev/null
+++ b/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html	
@@ -0,0 +1,138 @@
+<h2>Overview</h2>
+Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
+
+When you place a trade you receive an <code>OrderTicket</code> for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
+
+You can place several types of orders including:
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Supported Order Types</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="25%">Market Order</td>
+<td><code>MarketOrder("SPY", 100);</code></td>
+</tr>
+<tr>
+<td>Limit Order</td>
+<td><code>var ticket = LimitOrder("SPY", 100, 100.10m);</code></td>
+</tr>
+<tr>
+<td>Stop Market Order</td>
+<td><code>var ticket = StopMarketOrder("SPY", 100, 100.10m);</code></td>
+</tr>
+<tr>
+<td>Stop Limit Order</td>
+<td><code>var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code></td>
+</tr>
+<tr>
+<td>Market On Open Order</td>
+<td><code>var ticket = MarketOnOpen("SPY", 100);</code></td>
+</tr>
+<tr>
+<td>Market On Close Order</td>
+<td><code>var ticket = MarketOnClose("SPY", 100);</code></td>
+</tr>
+</tbody>
+</table>
+<h2>Updating Orders</h2>
+Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
+<pre class="prettyprint linenums">var currentStopPrice = _ticket.Get(OrderField.StopPrice);</pre>
+Or update the order fields (LimitPrice, StopPrice, Tag or Quantity):
+<pre class="prettyprint linenums">_ticket.Update(new UpdateOrderFields
+{
+    LimitPrice = newLongLimit,
+    Tag = "Update #" + (longOrder.UpdateRequests.Count + 1)
+});</pre>
+You can also cancel your order if required:
+<pre class="prettyprint linenums">_ticket.Cancel();</pre>
+In <b>Python</b>, you can update order like this:
+<pre class="prettyprint linenums"># Retrive the 'StopPrice' for an order from the ticket
+currentStopPrice = _ticket.Get(OrderField.StopPrice)
+# update the order fields (LimitPrice, StopPrice, Tag or Quantity)
+updateOrderFields = UpdateOrderFields()
+updateOrderFields.LimitPrice = newLongLimit
+updateOrderFields.Tag = "Update #{0}".format(len(_ticket.UpdateRequests) + 1)
+_ticket.Update(updateOrderFields)
+# cancel your order if required
+_ticket.Cancel()
+</pre>
+In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
+https://www.youtube.com/watch?v=HykXfstdNW0
+
+<h2>Full Python Example</h2>
+This is a complete port from C# to Python of this Quick Start Lesson.
+<pre class="prettyprint linenums">
+```Lesson 6 - Tracking and Managing Orders: 
+Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
+```
+import numpy as np
+from decimal import Decimal
+
+class BasicTemplateAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        self._limitTicket = None
+        self._stopMarketTicket = None
+        self._stopLimitTicket = None
+        self.SetCash(25000)
+        self.SetStartDate(2009,1,1)
+        self.SetEndDate(2009,6,1)
+        self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
+        
+    def OnData(self, slice):
+        if self._limitTicket is None:
+        
+            self.MarketOrder(self.spy, 100)
+            
+            self._limitTicket = self.LimitOrder(
+                self.spy, 
+                100, 
+                slice["SPY"].Close * Decimal(0.9), 
+                "limit order")
+                
+            self._stopMarketTicket = self.StopMarketOrder(
+                self.spy,
+                -100,
+                slice["SPY"].Close * Decimal(0.95),
+                "stop market")
+                
+            self._stopLimitTicket = self.StopLimitOrder(
+                self.spy,
+                -100,
+                slice["SPY"].Close * Decimal(0.9),
+                slice["SPY"].Close * Decimal(0.8),
+                "stop market")
+
+
+    def OnEndOfDay(self):
+        ```End of each day, plot our asset & order prices```
+        if self._limitTicket is None:
+            return
+        
+        self.Plot("Order Tickets", "SPY", self.Portfolio['SPY'].Price)
+        
+        if self._stopMarketTicket.Status != OrderStatus.Filled:
+            self.Plot("Order Tickets", "Stop Price", 
+                      self._stopMarketTicket.Get(OrderField.StopPrice))
+            
+        if self._stopLimitTicket.Status != OrderStatus.Filled:
+            self.Plot("Order Tickets", "Limit Price", 
+                      self._stopLimitTicket.Get(OrderField.LimitPrice))
+
+    def OnOrderEvent(self, OrderEvent):
+        ```Event when the order is filled. Debug log the order fill. :OrderEvent:``` 
+        
+        if OrderEvent.FillQuantity == 0:
+            return
+        
+        fetched = self.Transactions.GetOrderById(OrderEvent.OrderId)
+        
+        self.Debug("{} was filled. Symbol: {}. Quantity: {}. Direction: {}"
+                   .format(str(fetched.Type), 
+                           str(OrderEvent.Symbol), 
+                           str(OrderEvent.FillQuantity), 
+                           str(OrderEvent.Direction)))
+</pre>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html b/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html
new file mode 100755
index 0000000..93086b9
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html	
@@ -0,0 +1,246 @@
+QuantConnect provides US options trade and quotes price data for approximately 4000 symbols, each of which has roughly 10 strikes on average. Data is available starting January 1st, 2010. In this tutorial, we will discuss how to use QuantConnect to start your options trading algorithm.
+<h2> Step 1: Add Options</h2>
+Before trading options, you need to add options for a given underlying equity and set the resolution in step <code>Initialize</code> with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
+resolution,
+fillDataForward)</td>
+<td><strong>underlying(string):</strong> The underlying equity symbol
+<strong>resolution:</strong> Tick, Second, Minute, Hour, or Daily. Default is minute
+<strong>fillDataForward(bool):</strong> If true, returns the last available data even if none in that time slice. The default value is true.</td>
+</tr>
+</tbody>
+</table>
+<pre class="prettyprint linenums">def Initialize(self):
+    self.SetStartDate(2017, 01, 01)  #Set Start Date
+    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetCash(50000)  #Set Strategy Cash
+    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
+    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
+    self.symbol = option.Symbol
+</pre>
+The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
+<h2>Step 2: Filter Contracts - Coarse Selection</h2>
+After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a>(
+min strike,
+max strike,
+minexpiry,
+maxExpiry)</td>
+<td><strong>min Strike, max Strike:</strong> The min and max strike rank relative to market price
+<b><b>min Expiry, max Expiry: </b></b>The range of time to expiration to include, for example, TimeSpan.FromDays(10) would exclude contracts expiring in less than 10 days</td>
+</tr>
+</tbody>
+</table>
+Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
+
+For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
+
+For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.
+<pre class="prettyprint linenums"># filter the contracts with strikes between (market price - 10, market price + 10)
+option.SetFilter(-10,10)
+# filter the contracts which expires more than 30 days but no longer than 60 days
+option.SetFilter(TimeSpan.FromDays(30),TimeSpan.FromDays(60))
+# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
+option.SetFilter(-10, +10, timedelta(0), timedelta(180))</pre>
+<div class="page" title="Page 242">
+<div class="layoutArea">
+<div class="column">
+<h2>Step 3: Choose Contracts - Fine Selection</h2>
+For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
+
+<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Properties of Option Contract x</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><code>x.Symbol.Value</code></td>
+<td> Get the string of option contract's symbol</td>
+</tr>
+<tr>
+<td><code>x.AskPrice, x.BidPrice</code></td>
+<td> Get the ask price,  Get the bid price</td>
+</tr>
+<tr>
+<td><code>x.Expiry</code></td>
+<td> Get the expiration date</td>
+</tr>
+<tr>
+<td><code>x.Strike</code></td>
+<td> Get the strike price</td>
+</tr>
+<tr>
+<td><code>x.ImpliedVolatility</code></td>
+<td> Get the implied volatility</td>
+</tr>
+<tr>
+<td><code>x.Greeks</code></td>
+<td>Get the Greeks letter</td>
+</tr>
+<tr>
+<td><code>x.Right</code></td>
+<td>Get the right being purchased
+x.Right ＝ OptionRight.Call  call option[right to buy]
+x.Right ＝ OptionRight.Put  put option[right to sell]</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingLastPrice</code></td>
+<td>Get the last price the underlying security traded at</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingSymbol</code></td>
+<td>Gets the underlying security's symbol</td>
+</tr>
+</tbody>
+</table>
+We can print out the details of the contract after filtering with Python data frame to show these properties. Assume today is 01/03/2017.  The stock price at 01/03/2017 09:31:00 is $776.01 per share. Here we use  <code>SetFilter(-1, +1, timedelta(0), timedelta(60)) </code>to filter the contracts.
+<pre class="prettyprint linenums">def OnData(self,slice):
+    for i in slice.OptionChains:
+        if i.Key != self.symbol: continue
+	optionchain = i.Value
+	self.Log("underlying price:" + str(optionchain.Underlying.Price))
+	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+			   index=[x.Symbol.Value for x in optionchain],
+			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
+</pre>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Symbol</th>
+<th>type(call 0, put 1)</th>
+<th>Strike</th>
+<th>Expiry</th>
+<th>Ask Price</th>
+<th>Bid Price</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>GOOG 170217C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>26.4</td>
+<td>27.9</td>
+</tr>
+<tr>
+<td>GOOG 170120P00782500</td>
+<td>1</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>14.7</td>
+<td>16.3</td>
+</tr>
+<tr>
+<td>GOOG 170120C00782500</td>
+<td>0</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>9.4</td>
+<td>10.2</td>
+</tr>
+<tr>
+<td>GOOG 170120P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>13.4</td>
+<td>15.0</td>
+</tr>
+<tr>
+<td>GOOG 170120C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>10.6</td>
+<td>11.5</td>
+</tr>
+<tr>
+<td>GOOG 170217P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>28.9</td>
+<td>30.8</td>
+</tr>
+<tr>
+<td>GOOG 170120P00777500</td>
+<td>1</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>12.2</td>
+<td>13.7</td>
+</tr>
+<tr>
+<td>GOOG 170120C00777500</td>
+<td>0</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>11.8</td>
+<td>12.9</td>
+</tr>
+</tbody>
+</table>
+&nbsp;
+
+Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
+<pre class="prettyprint linenums">for i in slice.OptionChains:
+    if i.Key != self.symbol: continue
+    chain = i.Value
+# differentiate the call and put options
+call = [x for x in optionchain if chain.Right == 0]
+put = [x for x in optionchain if chain.Right == 1]
+# choose ITM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
+# or choose ATM contracts
+contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+# or choose OTM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
+# sort the contracts by their expiration dates
+contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+</pre>
+Finally, we trade the options by using the contract's symbol.
+<pre class="prettyprint linenums">if len(contracts) == 0: continue
+# trade the contracts with the farthest expiration
+symbol = contracts[0].Symbol
+self.MarketOrder(symbol, 1)
+self.MarketOnCloseOrder(symbol, -1)
+
+</pre>
+<h2>Algorithm</h2>
+This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c4467dd2b39f215cd685caaac9069ef3'></script>
+<h2>Summary</h2>
+After mastering the basic knowledge of options market, this tutorial we take a close at how to use Quantconnect to customize your own options trading. For example, <span lang="EN-GB">how you can access an option chain, </span><span lang="EN-GB">how to view the details of the contract as a Python data frame, and the most important how to trade the specific option contract.</span>
+
+Next chapter we will examine some important topics of options like the payoff, Put-Call parity, and the synthetic positions. By learning all those concepts, we will start some brief hedging strategies involving options.
+
+</div>
+</div>
+</div>
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diff --git a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
new file mode 100755
index 0000000..ff69fd4
--- /dev/null
+++ b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
@@ -0,0 +1,22 @@
+With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
+
+Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
+
+Two configuration changes are required for desktop charting to work:
+<ol>
+ 	<li>Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file.
+<pre class="prettyprint">  "environment": "backtesting-desktop",</pre>
+</li>
+ 	<li>Insert your API token and user id into configuration file. This is required for chart streaming.
+<pre class="prettyprint">  // To get your api access token go to quantconnect.com/account
+  "job-user-id": "....",
+  "api-access-token": "...........",</pre>
+</li>
+</ol>
+With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
+
+If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
+
+In the tutorial video below we demonstrate this feature on LEAN:
+
+https://www.youtube.com/watch?v=m6llfznP4d4
\ No newline at end of file
diff --git a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
new file mode 100755
index 0000000..01a444d
--- /dev/null
+++ b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
@@ -0,0 +1,16 @@
+Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually; but using the API File Provider we provide a way for you to automatically install this into your LEAN project.
+
+Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-data-library-screen-shot.png" alt="Data library collection for FXCM EURUSD data" />
+
+Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.
+
+This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:
+<ol>
+ 	<li>Insert your <code>job-user-id</code> and <code>api-access-token</code> into the relevant config fields. You can find these on your <a href="/account">Account</a> page.</li>
+ 	<li>Update the <code>data-provider</code> configuration to refer to the APIDataProvider class. This is called <code>QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider</code>.</li>
+</ol>
+Check out the video below where we guide you step by step through how to use the API Data Provider.
+
+https://www.youtube.com/watch?v=uyHp_jyeSpA
diff --git a/06 Strategy Library/01 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
similarity index 58%
rename from 06 Strategy Library/01 Strategy Library/01 Strategy Library.php
rename to 04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1640eb4..6c36a5b 100644
--- a/06 Strategy Library/01 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -8,19 +8,59 @@
 
 $strategyMap = [
     [
-        'name' => 'Example 1',
-        'link' => 'strategy-library/example-1',
-        'description' => 'Testing description of testing tutorial 1 eggs'
+        'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
+        'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
+        'description' => 'Applying CAPM model to rank Dow Jones 30 companies'
     ],
     [
-        'name' => 'Example 2',
-        'link' => 'strategy-library/example-2',
-        'description' => 'Testing description of testing tutorial 2 milk'
+        'name' => 'Combining Mean Reversion and Momentum in Forex Market',
+        'link' => 'strategy-library/combining-mean-reversion-and-momentum-in-forex-market',
+        'description' => 'Combining momentum and mean reversion techniques in the forex markets'
     ],
     [
-        'name' => 'Example 3',
-        'link' => 'strategy-library/example-3',
-        'description' => 'Testing description of testing tutorial 3 cake'
+        'name' => 'Pairs Trading-Copula vs Cointegration',
+        'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
+        'description' => 'Applying Copula and Cointergration method to pairs trading'
+    ],
+    [
+        'name' => 'The Dynamic Breakout II Strategy',
+        'link' => 'strategy-library/the-dynamic-breakout-ii-strategy',
+        'description' => 'A demonstration of dynamic breakout II strategy'
+    ],
+    [
+        'name' => 'Dual Thrust Trading Algorithm',
+        'link' => 'strategy-library/dual-thrust-trading-algorithm',
+        'description' => 'A demontration of Dual Thrust Intraday strategy'
+    ],
+    [
+        'name' => 'Can Crude Oil Predict Equity Returns',
+        'link' => 'strategy-library/can-crude-oil-predict-equity-returns',
+        'description' => 'Applying regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate'
+    ],
+    [
+        'name' => 'Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach',
+        'link' => 'strategy-library/intraday-dynamic-pairs-trading-using-correlation-and-cointegration-approach',
+        'description' => 'A high frequency pairs trading algorithm based on cointegration'
+    ],
+    [
+        'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
+        'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
+        'description' => 'Applying high frequency filter to the momentum strategy'
+    ],
+    [
+        'name' => 'Stock Selection Strategy Based on Fundamental Factors',
+        'link' => 'strategy-library/stock-selection-strategy-based-on-fundamental-factors',
+        'description' => 'MorningStar Fundamental factors universe selection algorithm'
+    ],
+    [
+        'name' => 'Short-Term Reversal Strategy in Stocks',
+        'link' => 'strategy-library/short-term-reversal-strategy-in-stocks',
+        'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action'
+    ],
+    [
+        'name' => 'Fundamental Factor Long/Short Strategy',
+        'link' => 'strategy-library/fundamental-factor-long-short-strategy',
+        'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors'
     ],
 ];
 
@@ -148,4 +188,4 @@ function SLSearch() {
         SLSearch._StrategyLibraryMap = null;
         return SLSearch;
     }());
-</script>
\ No newline at end of file
+</script>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html
new file mode 100755
index 0000000..26f5877
--- /dev/null
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html	
@@ -0,0 +1,126 @@
+<h2>Abstract</h2>
+This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM) [ref]<a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>[/ref], a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
+<ol>
+	<li>How to use historical data</li>
+	<li>Set an event handler</li>
+	<li>Conduct linear regression</li>
+	<li>Build your own functions in the QuantConnect Algorithm Lab</li>
+</ol>
+ The implementation of the strategy demonstrates that stocks beat the market last month are likely to beat the market again in the subsequent month. This algorithm performs well when the market is smooth. However when the market volatility increases the model fails to capture alpha and it performs poorly. What we learn from this is that market fluctuations decrease the significance level of the linear regression coefficients, especially when we are using daily returns to fit the model.
+<h2>CAPM Theory</h2>
+The capital asset pricing model (CAPM) describes the relationship between systematic risk and expected return for assets, typically stocks. The formula for calculating the expected return of an asset given its risk is as follows:
+
+\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \]
+
+where:
+
+\[r_f = Risk Free Rate\]
+\[\beta = Beta of the security\]
+
+\[r_m = Expected market return\]
+
+\[\epsilon = Tracking error\]
+
+This formula can be better understood if we refactor the formula as seen below:
+
+\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \]
+
+The left side of the equation gives us the difference between the asset return and risk free rate, the <strong>"excess return"</strong>. If we regress the <strong>market excess return</strong> against the <strong>asset excess return </strong>the slope represents the <strong>"beta"</strong> of the asset. Therefore, beta can also be calculated by the equation:
+
+\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\]
+
+So beta can be described as:
+
+\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\]
+
+The formula above indicates that beta can be explained as<strong> "correlated relative volatility"</strong>. To make this simpler, beta can be calculated by doing a simple linear regression which can be viewed as a factor to explain the return, and the tracking error can represent alpha.
+
+To make this theory more convenient for our algorithm, we change the above formula into the following form:
+
+\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\]
+
+<strong>r*(1-β) </strong>on the right hand side of the equation is a very small item, making it negligible in the context of the Dow 30 companies. If we regress the stocks return with the return of the benchmark, the slope and intercept will be beta and alpha.
+
+<h2>Method</h2>
+Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:
+<ol>
+ 	<li>Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.</li>
+ 	<li>Conduct simple linear regression on the return of each stock against a benchmark (S&amp;P 500 index, SPY).</li>
+ 	<li>Rank the stocks by their intercepts.</li>
+ 	<li>Liquidate all our positions and purchase the first 2 stocks in our sorted list.</li>
+</ol>
+Dow Jones components change very infrequently, with the last change being on March 19th, 2015 [ref]<strong> <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a></strong>[/ref]. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
+<h3>Step 1: Setup Event Handler</h3>
+In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
+<pre class="prettyprint linenums">def Initialize(self):
+    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))</pre>
+<h3>Step 2: Linear Regression Function</h3>
+In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope.
+<pre class="prettyprint linenums">def regression(self,x,y):
+    x = np.array(x)
+    x = np.diff(x)/x[:-1]
+    y = np.array(y)
+    y = np.diff(y)/y[:-1]
+    A = np.vstack([x, np.ones(len(x))]).T
+    result = np.linalg.lstsq(A, y)[0]
+    beta = result[0]
+    alpha = result[1]
+    return(alpha,beta)</pre>
+<h3>Step 3: History Function</h3>
+Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
+<pre class="prettyprint linenums">def get_regression_data(self,symbol,history):
+    symbol_price = []
+    benchmark_price = []
+    for i in history:
+        bar = i[symbol]
+        benchmark = i[self.benchmark]
+        symbol_price.append(bar.Close)
+        benchmark_price.append(benchmark.Close)
+
+    result = self.regression(symbol_price,benchmark_price)
+    return result</pre>
+<h3>Step 4: Rebalance Function:</h3>
+This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: <a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>.
+<pre class="prettyprint linenums">def rebalance(self):
+    # get historical stock symbols and prices, then put them in tuples
+    history = self.History(self.regression_dates, Resolution.Daily)
+    filter = []
+    for i in self.symbols:
+        filter.append((i,self.get_regression_data(i, history)[0]))
+    # sort the filter by alpha
+    filter.sort(key = lambda x : x[1],reverse = True)
+    sorted_symbols = []
+    for i in range(2):
+    	sorted_symbols.append(filter[i][0])
+    # get the symbols of our holding stocks
+    holding_list = []
+    for i in self.Portfolio:
+    	if i.Value.Invested:
+    		holding_list.append(i.Value.Symbol)
+    # if we have holdings and we are not going to hold them anymore, sell them
+    if holding_list:
+    	for i in holding_list:
+    		if i not in sorted_symbols:
+    			self.Liquidate(i)
+    # Long the 2 stock in our list.
+    for i in sorted_symbols:
+    	self.SetHoldings(i,1)</pre>
+<h2>Conclusion</h2>
+We have demonstrated that during a smooth market, the stocks that beat the market last month are likely to beat the market again in the subsequent month. When there is market fluctuation, the significance level of linear regression will reduce and the model performance will decrease.
+
+We can understand this by looking at the covariance of the asset(x) and the benchmark (y). As the covariance reduces to zero, the beta will decrease.
+
+\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \b{x})^2}\]
+
+As an experiment, we tested the algorithm on market data from 2015. This was a much more volatile period for the market with a fluctuation that returned a mean close to zero and dropped neaerly 10% from Aug 18th to Aug 25th of that year. The algorithm performed quite poorly in this year with a return rate of -11.58%.
+
+The risks associated with this strategy include a high drawdown, lack of hedging and not stop-loss. Since we are using leverage, the risk is increased and it has a margin call in January as a result.
+
+We can improve the performance by applying the following techniques:
+
+<ol>
+	<li>Conduct optimizations: we can implement mean-variance analysis to determine the asset allocation each month and select more stocks to trade. This will lower our risk and manage the portfolio more scientifically.</li>
+	<li>Take beta into consideration: If we want to be more aggressive, we can select targets by a combination of alpha and beta. This means we choose stocks with a high alpha that are more volatile than the market. If we are conservative investors however, we can make the strategy market-neutral, which means the portfolio would not be affected by the market performance. For example, if we long two stocks with beta 1 and -1 respectively at the same position size, our portfolio becomes market-neutral.</li>
+</ol>
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
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+<h2>Abstract</h2>
+In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research [ref]<strong>Alina F. Serban,</strong> Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>[/ref] which was based on research in the equity market by Ronald J. Balvers and Yangru Wu [ref]<strong>Ronald J. Balvers, Yangru Wu,</strong> Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
+
+In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity, UIP [ref]<strong>Investopedia</strong>, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>[/ref]). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang [ref]<strong>Chiang, T., Jiang, C., 1995. </strong>Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref].
+
+We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
+<h2>Introduction</h2>
+The strategy is centered on <strong>uncovered interest parity (UIP)</strong> theory. UIP states that the change in the exchange rate should incorporate any interest rate differentials between the two currencies. By looking for patterns in the deviation from UIP we can potential generate abnormal returns.
+<h3>Interest Parity Conditions</h3>
+UIP states that an investor who borrows money in their home country and lends it in another country with a higher interest rate should expect a zero return due to the changes in exchange rate. In other words:
+
+\[1+r_t = (1+r^{i}_t)(\frac{F^{i}_t}{S^{i}_t})\]
+Where \(r_t\) is the domestic interest rate, \(r^{i}\) is the foreign interest rate, \(S^{i}\) is the spot exchange rate and \(F^{i}\) is the forward rate. We can also replace F forward rate with expected spot rate:
+
+\[1+r_t = (1+r^{i}_t)(\frac{E(S^{i}_{t+1})}{S^{i}_t})\]
+
+Taking logs of the above two equations, we obtain:
+
+\[r_t - r^{i}_t = \ln F^{i}_t - \ln S^{i}_t\]
+
+\[r_t - r^{i}_t =\ln S^{i}_{t+1} - \ln S^{i}_t\]
+
+The deviation from UIP is denoted by y and defined as follows:
+
+\[y^{i}_{t+1} =\ln S^{i}_{t+1} -\ln F^{i}_t\]
+<h3>Model and Parameter Estimation</h3>
+Fama and French and Summers [ref]<strong>Fama, E., 1984. </strong>Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.[/ref] constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
+
+\[x^{i}_t = (1 - \delta ^{i})\mu ^{i} + \delta ^{i}x^{i}_{t-1} + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
+
+Using the equation above Serban adapts it to find the abnormal return in the forex market. The \(\delta \) represents the speed of mean reversion and can differ by country, while the \(\rho \) represents the momentum strength and can vary by country and by lag. The parameter \(\mu \) also varies by country. Accounting for these changes:
+
+\[y^{i}_t = -(1 - \delta ^{i})(x^{i}_{t-1} - \mu ^{i}) + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
+<h3>Trading Strategy</h3>
+The trading strategy from the paper allows \(\mu \) to change by country, while let \(\rho \) and \(\delta \) stay fixed. By applying Ordinary Least Squared(OLS) regression, the model estimates the return y for each currency. We construct the portfolio by taking a long position on the currency with the highest expected return and taking a short position on the currency with the lowest expected return. We hold these positions for one month, and repeat the process each month. There are two exceptions to this strategy: if all expected returns are positive, we take a long position only, and vice versa.
+
+To limit the number of parameters we need to estimate and find a solution easily we only allow \(\mu \) to change by country. According to the paper if we let ρ stay fixed and J =3, we can obtain the highest return for this strategy. If so the equation can be simplified as:
+
+\[y^{i}_t = -(1-\delta )(x^{i}_{t-1} - \mu ^{i}) + \rho (x^{i}_{t-1} - x^{i}_{t-4}) + \epsilon ^{i}_t\]
+
+When applying the above equation, we found that the scale of the mean reversion for each currency are very different, and this difference in scale is large enough to affect the accuracy of our rank. We made an adjustment to standardize the mean-reversion. While calculating \(\mu \) (the mean of the log prices) we also calculated standard deviation σ. In this tutorial we replace \(x-\mu \) with \(\frac{x - \mu}{\sigma } \). This captures the mean reversion factor better than the author's technique.
+<h3>Data Description</h3>
+The paper used monthly exchange rate data for the Canadian Dollar/USD, German Mark/Euro, UK Pound/USD and Japanese Yen/USD, from 1978 to 2008. Due to data availability, we used Euro/USD instead of German Mark/Euro, and the earliest data available starts from 2004. Each time we launch the strategy we use all of the available historical data prior to the start date to build the OLS model and uses that model for the entire backtest. The paper used 1/3 of their data as the training dataset and the rest of the test set. We directly test our model on backtesting, because QuantConnect makes this easier.
+<h2>Method</h2>
+In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution.
+<h3>Step 1: Request Historical Data</h3>
+The first function takes two arguments: symbol and number of daily data points requested. This function requests historical <a href="https://www.quantconnect.com/docs#Consolidating-Data-TradeBars-vs-QuoteBars">QuoteBars</a> and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">DataFrame</a>. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
+<pre class="prettyprint linenums">def get_history(self,symbol, num):
+    data = {}
+    dates = []
+    history = self.History([symbol], num, Resolution.Daily).loc[symbol]['close'] #request the historical data for a single symbol
+    for time in history.index:
+        t = time.to_pydatetime().date()
+        dates.append(t)
+    dates = pd.to_datetime(dates)
+    df = pd.DataFrame(history)
+    df.reset_index(drop=True)
+    df.index = dates
+    df.columns = ['price']
+    return df
+
+def calculate_return(self,df):
+    #calculate the mean for further use
+    mean = np.mean(df.price)
+    # cauculate the standard deviation
+    sd = np.std(df.price)
+    # pandas method to take the last datapoint of each month.
+    df = df.resample('BM',how = lambda x: x[-1])
+    # the following three lines are for further experiment purpose
+    # df['j1'] = df.price.shift(1) - df.price.shift(2)
+    # df['j2'] = df.price.shift(2) - df.price.shift(3)
+    # df['j3'] = df.price.shift(3) - df.price.shift(4)
+    # take the return as depend variable
+    df['log_return'] = df.price - df.price.shift(1)
+    # calculate the reversal factor
+    df['reversal'] = (df.price.shift(1) - mean)/sd
+    # calculate the momentum factor
+    df['mom'] = df.price.shift(1) - df.price.shift(4)
+    df = df.dropna() #remove nan value
+    return (df,mean,sd)</pre>
+<h3>Step 2: Build Predictive Model</h3>
+The concat function requests history and joins the results into a single DataFrame. As \(\mu \) varies by country so we assign the mean and standard deviation to the symbol for each currency for future use. The OLS function takes the resulting DataFrame to conduct an OLS regression. We write it into a function because it's easier to change the formula here if we need.
+<pre class="prettyprint linenums">def concat(self):
+    # we requested as many daily tradebars as we can
+    his = self.get_history(self.quoted[0].Value,20*365)
+    # get the clean DataFrame for linear regression
+    his = self.calculate_return(his)
+    # add property to the symbol object for further use.
+    self.quoted[0].mean = his[1]
+    self.quoted[0].sd = his[2]
+    df = his[0]
+    # repeat the above procedure for each symbols, and concat the dataframes
+    for i in range(1,len(self.quoted)):
+        his = self.get_history(self.quoted[i].Value,20*365)
+        his = self.calculate_return(his)
+        self.quoted[i].mean = his[1]
+        self.quoted[i].sd = his[2]
+        df = pd.concat([df,his[0]])
+    df = df.sort_index()
+    # remove outliers that outside the 99.9% confidence interval
+    df = df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() &lt; 3).all(axis=1)]
+    return df
+
+def OLS(self,df):
+    res = sm.ols(formula = 'return ~ reversal + mom',data = df).fit()
+    return res</pre>
+<h3>Step 3: Apply Predictive Model</h3>
+The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
+<pre class="prettyprint linenums">def predict(self,symbol):
+    # get current month in string
+    month = str(self.Time).split(' ')[0][5:7]
+    # request the data in the last three months
+    res = self.get_history(symbol.Value,33*3)
+    # pandas method to take the last datapoint of each month
+    res = res.resample('BM',how = lambda x: x[-1])
+    # remove the data points in the current month
+    res = res[res.index.month != int(month)]
+    # calculate the variables
+    res = self.calculate_input(res,symbol.mean,symbol.sd)
+    res = res.ix[0]
+    # take the coefficient. The first one will not be used for sum-product because it's the intercept
+    params = self.formula.params[1:]
+    # calculate the expected return
+    re = sum([a*b for a,b in zip(res[1:],params)]) + self.formula.params[0]
+    return re
+
+def calculate_input(self, df, mean, sd):
+    df['reversal'] = (df.price - mean)/sd
+    df['mom'] = df.price - df.price.shift(3)
+    df = df.dropna()
+    return df
+</pre>
+There are a few points of note:
+<ol>
+ 	<li>We need historical TradeBars for the last three months. To do this we requested 99 bars and use a pandas DataFrame to extract a data point for the end of each month.</li>
+ 	<li>We use event schedule to execute the strategy at the first trading day, however, sometimes the first day of the month could be on the 2nd if the 1st falls on a weekend. To fix this we remove the data from the current month, leaving only the last 3 months of data.</li>
+ 	<li>We start from the second element of res (res[1:]) because res and params are different lengths. This was hard to detect because Python would not throw error when running [a*b for a,b in zip(res,params)] even if the length of the two lists are different.</li>
+ 	<li>This function also used pandas DataFrame methods extensively. For more information please refer to <a href="http://pandas.pydata.org">pandas</a>.</li>
+</ol>
+<h3>Step 4: Initializing the Model</h3>
+In the Initialize function we prepare the data and conduct a linear regression. The class property 'self.formula' is the result of the OLS regression. We will use this object each time we rebalance the portfolio.
+<pre class="prettyprint linenums">def Initialize(self):
+    self.SetStartDate(2013,6,1)
+    self.SetEndDate(2016,6,1)
+    self.SetCash(10000)
+    self.syls = ['EURUSD','GBPUSD','USDCAD','USDJPY']
+    self.quoted = []
+    for i in range(len(self.syls)):
+        self.quoted.append(self.AddForex(self.syls[i],Resolution.Daily,Market.Oanda).Symbol)
+    df = self.concat()
+    self.Log(str(df))
+    self.formula = self.OLS(df)
+    self.Log(str(self.formula.summary()))
+    self.Log(str(df))
+    self.Log(str(df.describe()))
+    for i in self.quoted:
+        self.Log(str(i.mean) + '   ' + str(i.sd))
+    self.Schedule.On(self.DateRules.MonthStart(), self.TimeRules.At(9,31), Action(self.action))</pre>
+<h3>Step 5: Performing Monthly Rebalancing</h3>
+Every month we rebalance the portfolio using the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Schedule Event</a> helper method. The predicted returns are added to the rank array and then sorted by return. The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
+<pre class="prettyprint linenums">def action(self):
+    rank = []
+    long_short = []
+    for i in self.quoted:
+        rank.append((i,self.predict(i)))
+# rank the symbols by their expected return
+    rank.sort(key = lambda x: x[1],reverse = True)
+# the first element in long_short is the one with the highest expected return, which we are going to long, and the second one is going to be shorted.
+    long_short.append(rank[0])
+    long_short.append(rank[-1])
+    self.Liquidate()
+
+# the product &lt; 0 means the expected return of the first one is positive and that of the second one is negative--we are going to long and short.
+    if long_short[0][1]*long_short[1][1] &lt; 0:
+        self.SetHoldings(long_short[0][0],1)
+        self.SetHoldings(long_short[1][0],-1)
+        # this means we long only because all of the expected return is positive
+    elif long_short[0][1] &gt; 0 and long_short[1][1] &gt; 0:
+        self.SetHoldings(long_short[0][0],1)
+# short only
+    else:
+        self.SetHoldings(long_short[1][0],-1)</pre>
+<h2>Results</h2>
+The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1” />
+
+From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are -4.074 and 1.417 for the reversal factor and momentum factor respectively. The p-value of the reversal factor is very small which means this factor has a very high significance level.
+<h3>Backtest Sensitivity Results</h3>
+We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2” />
+The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
+<h2>Conclusion</h2>
+The paper demonstrates there are inefficiencies in the UIP which can be exploited with a hybrid momentum and mean reversion strategy. Although the sample size of the paper is much larger than ours the parameter and significance level of the two models are very close. The strategies we discussed above keep \(\rho \) fixed and only allow \(\mu \) to change by country. If we let \(\rho \) change by lag the significance level of the model might increase, but this could potentially make modeling more difficult by introducing multicollinearity. To test this we wrote this implementation in the algorithm and commented out the lines. If you are interested in exploring this extension to the model you can change these lines to test your strategy.
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c1eea4df64cf986eba481f5e5e376e8a'></script>
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+<h2>Abstract</h2>
+We investigate two pairs trading methods and compare the results. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
+
+In this tutorial, we will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a> [ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref] </em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012)[ref]Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012.[/ref]. The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
+<h2>Framework of Copula</h2>
+<h3>1. Definition</h3>
+Given a random vector \(X_1,X_2,...,X_p\), its marginal cumulative distribution functions (CDFs) are \(F_i(x) = P[X_i \leq x]\). By applying the probability integral transform to each component, the marginal distributions of \((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\) are uniform (from <a href="https://en.wikipedia.org/wiki/Copula_(probability_theory)">Wikipedia</a>).
+
+Then the copula of \(X_1,X_2,...,X_p\) is defined as the joint cumulative distribution function of \(U_1,U_2,...,U_p\), for which the marginal distribution of each variable U is uniform as  \(U(0,1)\).
+
+\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
+
+Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.[ref]Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>[/ref]
+<h3>2. Bivariate Copulas</h3>
+Since this research focuses on bivariate copulas (for pairs trading we have 2 random variables) some probabilistic properties are specified.
+
+Let X and Y be two random variables with cumulative probability function \(F_1(X)\) and \(F_2(Y)\). \(U=F_1(X), V=F_2(Y)\) which are uniformly distributed.  Then the copula function is \(C(u,v)=P(U\leq u,V\leq v)\). Taking the partial derivative of the copula function over U and V would give the conditional distribution function as follows:
+
+\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\]
+
+\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\]
+<h3>3. Archimedean Copulas</h3>
+There are many copula functions that enable us to describe dependence structures between variables, other than the Gaussian assumption. Here we will focus three of these; the <em>Clayton</em>, <em>Gumbel</em> and <em>Frank</em> copula formulas from the Archimedean class.
+
+Archimedean copulas[ref]Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>[/ref] are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\) [ref]LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.[/ref].
+
+\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
+
+The probability density function is:
+
+\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\]
+
+Where \(\phi_{(2)}^{-1}\) is the inverse of the second derivative of the generator function.
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: left;">Copula</th>
+<th style="text-align: center;">Copula function C(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton Copula</td>
+<td style="text-align: left;">\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Gumbel Copula</td>
+<td style="text-align: left;">\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank Copula</td>
+<td>\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]</td>
+</tr>
+</tbody>
+</table>
+&nbsp;
+
+Genest and MacKay (1986) [ref]Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283[/ref] proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
+
+\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
+
+So we can easily estimate the parameter in Archimedean copulas if we know Kendall’s tau rank measure and the generator function. Please refer to step 3 to see the formulas.
+<h2>Part I: Copula Method</h2>
+ETFs have many different stock sectors and asset classes which provide us a wide range of pairs trading candidates. Our data set consists of daily data of the ETFs traded on the NASDAQ or the NYSE.
+
+We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
+<h3>Step 1: Selecting the Paired Stocks</h3>
+The general method of pair selection is based on both fundamental and statistical analysis.[ref]<strong>Jean Folger</strong>. Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>[/ref]
+<h4><strong>1) Assemble a list of potentially related pairs</strong></h4>
+Any random pairs could be correlated. It is possible that those variables are not causally related to each other, but because of a spurious relationship due to either coincidence or the presence of a certain third, unseen factor. Thus, it is important for us to start with a list of securities that have something in common. For this demonstration, we choose some of the most liquid ETFs traded on the Nasdaq or the NYSE.  The relationship for those potentially related pairs could be due to an index, sector or asset class overlap. e.g. QQQ and XLK are two ETFs which track the market leading indices.
+<h4><strong>2) Filter the trading pair with statistical correlation</strong></h4>
+<div class="page" title="Page 12">
+<div class="section">
+<div class="layoutArea">
+<div class="column">
+
+To determine which stock pairs to include in the analysis, correlations between the pre-selected ETF pairs are analyzed. Below are three types of correlation measures we usually use in statistics:
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="3">Correlation Measurement Techniques</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="33%">Pearson correlation</td>
+<td>\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]</td>
+</tr>
+<tr>
+<td>Kendall rank correlation</td>
+<td>\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]</td>
+</tr>
+<tr>
+<td>Spearman rank correlation</td>
+<td>\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]</td>
+</tr>
+<tr>
+<td colspan="2"> \(n\) = number of value in each data set
+\(n_c\) = number of concordant
+\(n_d\) = number of discordant
+\(d_i\) = the difference between the ranks of corresponding values \(x_i\) and \(y_i\)</td>
+</tr>
+</tbody>
+</table>
+</div>
+We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading.
+
+We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
+
+\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n where n is the number of price data
+<pre class="prettyprint linenums">def _pair_selection(self):
+
+    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
+                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
+    logreturn={}
+    for i in range(2):
+        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
+        history = self.History(syl, self.lookbackdays,Resolution.Daily)
+        # generate the log return series of paired stocks
+        close = history['close'].unstack(level=0)
+        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+        for j in tick_syl[i]:
+            logreturn[j] = df_logreturn[j]
+    # estimate coefficients of different correlation measures
+    tau_coef,pr_coef,sr_coef= [],[],[]
+    for i in range(len(tick_syl[i])):
+        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
+        tau_coef.append(kendalltau(tik_x, tik_y)[0])
+        pr_coef.append(pearsonr(tik_x, tik_y)[0])
+        sr_coef.append(spearmanr(tik_x, tik_y)[0])
+    index_max = tau_coef.index(max(tau_coef))
+    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+</pre>
+<h3>Step 2: Estimating Marginal Distributions of log-return</h3>
+In order to construct the copula, we need to transform the log-return series \(R_x\) and \(R_y\) to two uniformly distributed values u and v. This can be done by estimating the marginal distribution functions of \(R_x\) and \(R_y\) and plugging the return values into a distribution function. As we make no assumptions about the distribution of the two log-return series, here we use the empirical distribution function to approach the marginal distribution \(F_1(R_x)\) and \(F_2(R_y)\). The Python ECDF function from the statsmodel library gives us the Empirical CDF as a step function.
+<h3>Step 3: Estimating Copula Parameters</h3>
+As discussed above, we estimate the copula parameter theta by the relationship between the copula and the dependence measure Kendall’s tau, for each of the Archimedean copulas.
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Copula</th>
+<th style="text-align: center;">Kendall's tau</th>
+<th style="text-align: center;">parameter θ</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Clayton Copula</td>
+<td>\[\frac{\theta}{\theta +2}\]</td>
+<td>\[\theta=2\tau(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel Copula</td>
+<td>\[1-\theta^{-1}\]</td>
+<td>\[\theta=(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Frank Copula</td>
+<td>\[1+4[D_1(\theta)-1]/\theta\]</td>
+<td>\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]</td>
+</tr>
+<tr>
+<td colspan="3">\[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]</td>
+</tr>
+</tbody>
+</table>
+</div>
+<pre class="prettyprint linenums">def _parameter(self, family, tau):
+    if  family == 'clayton':
+        return 2*tau/(1-tau)
+    elif family == 'frank':
+        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
+    	# frank_fun is the squared difference
+    	# Minimize the frank_fun would give the parameter theta for the frank copula
+      integrand = lambda t: t/(np.exp(t)-1)
+    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
+      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
+    elif family == 'gumbel':
+        return 1/(1-tau)</pre>
+</div>
+</div>
+<h3>Step 4: Selecting the Best Fitting Copula</h3>
+Once we get the parameter estimation for the copula functions, we use the AIC criteria to select the copula that provides the best fit in algorithm initialization.
+
+\[AIC=-2L(\theta)+2k\]
+
+where \(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\) is the log-likelihood function and k is the number of parameters, here k=1.
+
+The density functions of each copula function are as follows:
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">Copula</th>
+<th style="text-align: center;">Density function c(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton Copula</td>
+<td style="text-align: left;">\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel Copula</td>
+<td>\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank Copula</td>
+<td style="text-align: left;">\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]</td>
+</tr>
+<tr>
+<td colspan="2">\[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]</td>
+</tr>
+</tbody>
+</table>
+<div class="page" title="Page 23">
+<div class="layoutArea">
+<div class="column">
+<pre class="prettyprint linenums">def _lpdf_copula(self, family, theta, u, v):
+    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
+    if  family == 'clayton':
+        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
+    elif family == 'frank':
+        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
+        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
+        pdf = num/denom
+    elif family == 'gumbel':
+        A = (-np.log(u))**theta + (-np.log(v))**theta
+        c = np.exp(-A**(1/theta))
+        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
+    return np.log(pdf)
+</pre>
+The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" &amp; "XLK".
+<pre class="prettyprint linenums">self.family = ['clayton', 'frank', 'gumbel']
+tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
+AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
+for i in self.family:
+    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
+    # Replace nan with zero and inf with finite numbers in lpdf list
+    lpdf = np.nan_to_num(lpdf)
+    loglikelihood = sum(lpdf)
+    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
+    # choose the copula with the minimum AIC
+    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+</pre>
+&nbsp;
+<h3>Step 5: Generating the Trading Signals</h3>
+The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I(2013)[ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref], the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
+
+Given current returns \(R_x, R_y\) of stock X and stock Y, we define the "mis-pricing indexes" are:
+
+\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\]
+
+\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
+
+For further <span class="s1">mathematical proof</span>, please refer to Xie W, Wu Y(2013)[ref]Xie W, Wu Y. Copula-based pairs trading strategy[C]//Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.[/ref]
+
+The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
+
+Gumbel Copula
+
+\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\]
+
+\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]
+
+</div>
+</div>
+</div>
+Clayton Copula
+
+\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+Frank Copula
+
+\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \]
+
+\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]
+
+After selection of trading pairs and the best-fitted copulas, we take the following steps for trading. Please note we implement the Steps 1, 2, 3 and 4 on the first day of each month using the daily data for the last 12 months, which means our empirical distribution functions and copula parameters theta estimation are updated once a month. In summary each month:
+<ol>
+ 	<li>During the 12 months' rolling formation period, daily close prices are used to calculate the daily log returns for the pair of ETFs and then compute Kendall's rank correlation.</li>
+ 	<li>Estimate the marginal distribution functions of log returns of X and Y, which are ecdf_x and ecdf_y separately.</li>
+ 	<li>Plug Kendall's tau into copula parameter estimation functions to get the value of theta.</li>
+ 	<li>Run linear regression over the two price series. The coefficient is used to determine how many shares of stock X and Y to buy and sell. For example, if the coefficient is 2, for every X share that is bought or sold, 2 units of Y are sold or bought.</li>
+</ol>
+<pre class="prettyprint linenums">def _set_signal(self):
+    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
+    # generate the log return series of paired stocks
+    close = history['close'].unstack(level=0)
+    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
+    # estimate Kendall'rank correlation each trading day
+    tau = kendalltau(x, y)[0]
+    # etstimate the copula parameter: theta
+    self.theta = self._parameter(self.copula, tau)
+    # simulate the empirical distribution function for returns of two paired stocks
+    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
+    # run linear regression over the two history return series
+    self.coef = stats.linregress(x,y).slope
+</pre>
+Finally during the trading period, each day we convert today's returns to u and v by using empirical distribution functions ecdf_x and ecdf_y. After that, two mispricing indexes are calculated every trading day by using the estimated copula C.  The algorithm constructs short positions in X and long positions in Y on the days that \(MI_{Y|X}&lt;0.05\) and \(MI_{X|Y}&gt;0.95\). It constructs short position in Y and long positions in X on the days that \(MI_{Y|X}&gt;0.95\) and \(MI_{X|Y}&lt;0.05\).
+<pre class="prettyprint linenums">def OnData(self,data):
+    for i in self.syl:
+        self.price_list[i].append(self.Portfolio[i].Price)
+    # compute today's log return of 2 stocks
+    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
+    else:
+        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
+        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
+    # Convert the two returns to uniform values u and v using the empirical distribution functions
+    u_value = self.ecdf_x(return_x)
+    v_value = self.ecdf_y(return_y)
+    # Compute the mispricing indices for u and v by using estimated copula
+    self._misprice_index(self.copula, self.theta, u_value, v_value)
+    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
+        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+        else:
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
+        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+        else:
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)</pre>
+<h2 class="page" title="Page 5">Part II: Cointegration Method</h2>
+For the cointegration pairs trading method, we choose the same ETF pair "GLD" &amp; "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
+<h3 class="page" title="Page 5">Step 1: Generate the Spread Series</h3>
+At the start of each month, we generate the log price series of two ETFs with the daily close. Then the spread series is estimated using regression analysis based on log price series data.
+
+For equities X and Y, we run linear regression over the log price series and get the coefficient β.
+
+\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
+<h3>Step 2: Compute the Threshold</h3>
+<div class="page" title="Page 5">
+<div class="layoutArea">
+<div class="column">
+
+Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
+
+</div>
+<pre class="prettyprint linenums">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
+                     index = [i.Time for i in self.symbols[0].hist_window])
+
+price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
+                     index = [i.Time for i in self.symbols[1].hist_window])
+if len(price_x) < 250: return
+spread = self.regr(np.log(price_x), np.log(price_y))
+mean = np.mean(spread)
+std = np.std(spread)
+ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
+if spread[-1] > mean + self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
+        self.Sell(self.symbols[1], 100)
+        self.Buy(self.symbols[0],  ratio * 100)
+
+elif spread[-1] < mean - self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
+        self.Sell(self.symbols[0], 100)
+        self.Buy(self.symbols[1], ratio * 100)
+else:
+    self.Liquidate()
+</pre>
+</div>
+</div>
+<div class="page" title="Page 5">
+<div class="layoutArea">
+<div class="column">
+<h3>Step 3: Set up the Trading Signals</h3>
+On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread&gt;mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its <span class="s1">equilibrium (defined as less than half a standard deviation from zero spread).</span>
+
+The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
+
+</div>
+</div>
+</div>
+<h2>Conclusion</h2>
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">method</th>
+<th style="text-align: center;">Transactions</th>
+<th style="text-align: center;">Profit</th>
+<th style="text-align: center;">Sharpe Ratio</th>
+<th style="text-align: center;">Drawdown</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: center;">Copula</td>
+<td style="text-align: center;">346</td>
+<td style="text-align: center;">274.293%</td>
+<td style="text-align: center;">1.022</td>
+<td style="text-align: center;">19.4%</td>
+</tr>
+<tr>
+<td style="text-align: center;">Cointegration</td>
+<td style="text-align: center;">91</td>
+<td style="text-align: center;">26.358%</td>
+<td style="text-align: center;">0.298</td>
+<td style="text-align: center;">23.7%</td>
+</tr>
+</tbody>
+</table>
+Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
+
+Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method.
+
+It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
+<h2>Backtest</h2>
+Backtest for copula method
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=343dc596a484a20a579ae77c79a12495'></script>
+Backtest for cointegration method
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d4443ef5bd0d45da3d003fe8e49a966e'></script>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html
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+<h2>Abstract</h2>
+In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
+
+First we decide the look-back period based on the change rate of volatility, then we make trading decisions based on the highest high and lowest low from the look back period as well as a Bollinger Bands indicator. It is an auto adaptive trading system that can adjust its buy and sell rules depending on the performance of these rules in the past. In addition to Forex markets it is widely used in future and equity markets. You can refer to <a href="https://www.youtube.com/watch?v=MxGVqSvLqDQ">this video</a> to learn more about dynamic break out II.
+
+The original Dynamic Break Out system was developed by George Pruitt for Futures Magazine in 1996. The logic behind the dynamic breakout system is that the volatility component changes the lookback period, then the enter and exit points are decided by the highest high and lowest low price over the lookback period.  The newer version of the Dynamic Break Out is just like the original, except we introduce the Bollinger Band and adjust the number of look back days using the market volatility, so different market conditions perform better with different parameters. In addition, the stop loss signal is fixed in version one, but in version two the liquidate point is based on the moving average indicator and the length of moving average is dynamically changed with the look-back period.
+
+We backtested the strategy on EURUSD and GBPUSD over 6 years period.  The result suggests a drawdown of 20% and the strategy caught the market turning points. It is especially profitable in a trending market.
+<h2>Method</h2>
+<h3>Step 1: Determine the look back periods</h3>
+
+The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.[ref]<strong>Robert C. Miner(October 20, 2008)</strong>. High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>[/ref]   <b> </b>
+
+To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
+<pre class="prettyprint linenums">close = self.History(self.syl, 31, Resolution.Daily)['close']
+   todayvol = np.std(close[1:self.numdays+1])
+   yesterdayvol = np.std(close[0:self.numdays])
+   deltavol = (todayvol - yesterdayvol) / todayvol
+   self.numdays = round(self.numdays * (1 + deltavol)) # the number of days must be integer
+</pre>
+
+Though the look back days are dynamic, it needs to be restricted within an acceptable range of 20 to 60.
+
+<h3>Step 2: Choose the algorithm buy/sell point</h3>
+
+For a buy setup, the close price of the previous day must be above the upper Bollinger Band. In addition the ask price must be above the highest high of the most recent N days. Where N is the look back days from Step-1.
+
+For a sell setup, the close price of previous day must be below the lower Bollinger Band and the ask price must be below the lowest low of the most recent N days. The length of the Bollinger Band calculation is the same number of look back days that is generated by Step-1.
+
+Bollinger Band is a popular technical indicator. k is a constant. Here we choose k=2.
+\[ Upper Band = moving\ average + k\times standard\  deviation \]
+
+\[ Lower Band = moving\ average - k\times standard\  deviation \]
+
+&nbsp;
+
+QuantConnect provides more than 100 technical indicators for you to use in your algorithm. These are provided as class objects in Python. A full list of the indicators and their properties can be found in the reference table of <a href="https://www.quantconnect.com/docs#Indicators">the documentation page</a>.
+<pre class="prettyprint linenums">self.bolband = self.BB(self.syl,self.numdays,decimal.Decimal(2),MovingAverageType.Exponential,Resolution.Daily)
+     self.upband = self.bolband.UpperBand
+     self.lowband = self.bolband.LowerBand
+</pre>
+<h3>Step 3: Choose the algorithm liquidation point</h3>
+The exit signal for an existing holding is determined by calculating a simple moving average of closing prices for the past look back days. That is to say, we liquidate a long position if the current price is lower than the moving average of the close price over the look back period, and vice versa for selling a short position.
+<pre class="prettyprint linenums">self.buypoint = max(self.high)
+self.sellpoint = min(self.low)
+historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
+self.longLiqPoint = np.mean(historyclose)
+self.shortLiqPoint = np.mean(historyclose)
+self.yesterdayclose = historyclose.iloc[-1]
+</pre>
+<h2>Conclusion</h2>
+For six years backtesting of EURUSD, the overall statistics show an annual rate of return of 2.3% and with a Sharpe Ratio of 0.31. EURUSD has a significant uptrend from 2010 to 2012. This momentum strategy outperforms the market and seems to be profitable from 2010 to 2014.  The maximum drawdown occurs in May 2015 to December 2015 and is roughly 14%. From our results we find the strategy works best in an trending forex market.
+
+In contrast, GBPUSD is pretty volatile during the tested period from 2010 to 2016. Our testing demonstrated a negative annual rate of return with a drawdown of approximately 19%.
+
+When the volatility decreases, the price tends to continue following the current trend. Volatility causes the the look back days to decrease when computing the bollinger bands, making it easier to enter a trade.  If the market volatility increases we increase the look back days in order to filter the fake signals, making it harder to enter a trade.
+
+Here we use the standard deviation of price as a measure of market volatility. To improve the model we could choose other measures of volatility like standard deviation of logarithm return series or other stochastic volatility measures.
+
+<h2>Algorithm</h2>
+Backtest for EURUSD
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=87d3047e8c358ea9b4779e0b5d238a74'></script>
+Backtest for GBPUSD
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4bacf930111e3502993b7e7c9b315be0'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html
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+<h2>Abstract</h2>
+The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+
+In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
+<h2>Method</h2>
+<h3>Step 1 : Initialization of algorithm</h3>
+Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
+<pre class="prettyprint linenums">def Initialize(self):
+  self.SetStartDate(2004, 1, 1)
+  self.SetEndDate(2017, 8, 30)
+  self.SetCash(100000)
+  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
+  self.syl = equity.Symbol
+</pre>
+Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
+<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
+</pre>
+Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
+<h3>Step 2:  Implementation of algorithm</h3>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+
+In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
+<pre class="prettyprint linenums">history = self.History([self.syl.Value], 4, Resolution.Daily)
+  self.high = history.loc[self.syl.Value]['high']
+  self.low = history.loc[self.syl.Value]['low']
+  self.close = history.loc[self.syl.Value]['close']
+</pre>
+<h3>Step 3: Trading Implementation</h3>
+The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+
+This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
+<pre class="prettyprint linenums"> holdings = self.Portfolio[self.syl].Quantity
+        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
+            if holdings &gt;= 0:
+                self.SetHoldings(self.syl, 1)
+            else:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, 1)
+        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, -1)
+            else:
+                self.SetHoldings(self.syl, -1)</pre>
+<h2>Conclusion</h2>
+We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
+This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+<h2>Algorithm</h2>
+Backtest result for ETF: SPY from 2004 to 2017
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html
new file mode 100755
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+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html	
@@ -0,0 +1,93 @@
+<h2>Abstract</h2>
+In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". [ref]<strong>Gerben, Driesprong (2007)</strong>. Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>[/ref] by?by Gerben, Ben and Benjamin (2007).
+
+If the predicted return is larger than the risk-free rate, the portfolio is fully invested in stock; if the predicted return is lower than the risk-free rate, the portfolio is invested in short-term U.S T-bills. The backtesting period starts in 1980 and is divided into an in-sample period where regression analysis is made and an out?of sample period where the regression result is embedded "statically" into the strategy.
+
+In our implementation of the strategy we adapt the method of the original paper to make it more applicable to the current market. We have set our backtesting period to be from 2010 to 2017 and we refresh our regression analysis each month to form a rolling dynamic projection. This is because?empirical evidence shows?us the correlation between oil and stocks is not as strong as in the 1980's. We use?the price of S&amp;P GSCI? Crude Oil Total Return Index ETNs to represent spot oil price, and import T-bill data from Quandl by defining a custom class.?We use the "Schedule" API to trigger an event every month automatically?and the "History" function to retrieve data for regression analysis.
+
+Our analysis shows this strategy under performs the market in recent years. In the 9 year analysis period the algorithm was mostly long the S&amp;P500 index and only 9 trades were performed as the markets were strongly bullish. The trades could potentially simply be due to the weakening of the relationship between stocks and oil.
+<h2>Background</h2>
+We assume the predicted return of the stock is proportional to the return of oil. This can be represented by the regression equation:
+
+\[r^{stock}_t=a_0+a_1r^{oil}_{t-1}+e_t\]
+
+with
+
+\[e_t=r^{stock}_t-E_{t-1}[r^{stock}_t]\].
+
+The independent variable is the return of the oil and the dependent variable is the return of the stock. We use the monthly returns over a regression period of 2 years, giving us 22 observations to regress.?Every month regression analysis is conducted, and we use the estimated coefficient from the regression to compute the expected stock return with the given return of oil.
+<h2>Method</h2>
+The algorithm implementation consists of mainly three parts: Defining the custom imported data, initialization of the strategy parameters, and monthly re-balancing of the portfolio.
+<h3>Step 1: Defining Custom Imported Data</h3>
+We import T-Bill data from Quandl - a marketplace for financial, economic and alternative data. This requires defining a small class that tells QuantConnect how to interpret?the Quandl data.
+<pre class="prettyprint linenums">class TBill(PythonData):
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/USTREASURY/BILLRATES.csv?api_key=XXXXXXXXX&amp;order=asc", SubscriptionTransportMedium.RemoteFile)
+    def Reader(self, config, line, date, isLiveMode):
+        tbill = TBill()
+        tbill.Symbol = config.Symbol
+        # Example Line Format:
+        # Date      4 Wk Bank Discount Rate
+        # 2017-06-01 		0.8
+        if not (line.strip() and line[0].isdigit()): return None
+        try:
+            data = line.split(',')
+            value = float(data[1])*0.01
+            value = decimal.Decimal(value)
+            if value == 0: return None
+            tbill.Time = datetime.strptime(data[0], "%Y-%m-%d")
+            tbill.Value = value
+            tbill["Close"] = float(value)
+            return tbill;
+        except ValueError:
+            return None
+</pre>
+We first provide the source of the data as a URL to Quandl's API in the GetSource method. We need to make sure the data is?organized in ascending order which is done with the?"order=asc" parameter.?You need to substitute the API key in the URL for your personal Quandl API token.
+
+The Reader method parses a line of the data file. When using custom data you need to minimally set?the Time property?and Value property. In this example we find the value property from the headings in the spreadsheet is the second column and we reference it with data[1]. We set the Close property to the same value.
+
+In our "Initialize" function we use the following commands to add the custom data into our portfolio.
+<pre class="prettyprint linenums">self.AddData(TBill, "tbill")
+self.tbill = self.Securities["tbill"].Symbol
+</pre>
+<h3>Step 2: Initialization of the Strategy Parameters</h3>
+In our?"Initialize" function we set the cash amount, start-end date as well as other parameters that are specific to this strategy.?We set two parameters for the regression analysis period:
+<pre class="prettyprint linenums">self.regPeriod = 24
+self.daysInMonth = 21
+</pre>
+The variable "regPeriod" indicates how many months we are going to take into consideration in our regression analysis. We assume 21 days per month and request a historical period of approximately 2 years. We jump back in steps of 21 days and assume it is roughly 1 month of return.
+
+We need to set up "Schedule" function in "Initialize" so as to trigger the monthly re-balancing function every month.
+<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy),Action(self.MonthlyReg))
+</pre>
+<h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
+Every month we reconstruct the regression analysis to determine whether to be 100% long stocks or T-Bill contracts. We perform this re-balancing in the MonthlyReg function at the start of each month.?We use the History function to retrieve historical data for oil and stocks?and then divide the T-Bill rate by 12 to make it comparable to the monthly expected return of stocks.
+<pre class="prettyprint linenums">hist = self.History([self.oil, self.spy], self.regPeriod*self.daysInMonth, Resolution.Daily)
+oilSeries = hist.loc[str(self.oil)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
+spySeries = hist.loc[str(self.spy)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
+rf = float(self.Securities[self.tbill].Price)/12.0
+</pre>
+Then we make an OLS regression by using "numpy" to make the prediction on next month's stock return.
+<pre class="prettyprint linenums">x = np.array(oilSeries)
+x = (np.diff(x)/x[:-1])
+y = np.array(spySeries)
+y = (np.diff(y)/y[:-1])
+A = np.vstack([x[:-1],np.ones(len(x[:-1]))]).T
+beta, alpha = np.linalg.lstsq(A,y[1:])[0]
+yPred = alpha + x[-1]*beta
+</pre>
+Finally, we compare the expected return of stocks with risk-free rate. If the former is larger than the latter, we invest fully in stocks; otherwise we liquidate our holdings. Because we cannot purchase T-Bill contracts the performance is likely slightly underestimated.
+<pre class="prettyprint linenums">if yPred &gt; rf:
+	self.SetHoldings(self.spy, 1)
+else:
+	self.Liquidate(self.spy)</pre>
+<h2>Conclusion</h2>
+We backtested this strategy over the period beginning in 2010 and ending in 2017. It has a sharpe ratio of 0.72 beating the benchmark's 0.6 over a similar period.
+
+Although the annual return closely matches that of the paper,?it is largely a coincidence of the strong bull market in recent years.?If we look at the monthly regression results, we could find that in most cases, the p-value is not small enough to reject the null hypothesis that there is no correlation between oil and stocks. So the investment decisions based on the insignificant statistical results are almost meaningless. The performance of this strategy cannot effectively beat the benchmark, mostly?due to?the weakened correlation between oil and stocks.
+
+Further research and backtesting could be conducted on assets other than oil that have a stronger relationship with stocks.
+<h2>Algorithm</h2>
+Strategy code, as well as backtesting result, is attached below. We also put other choices of implementation in the comments.
+
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8242236c21a102fbff14d8c3eb30f841'></script>
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+<h2>Abstract</h2>
+In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work.[ref]<strong>George J. Miao </strong>High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>[/ref]We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
+
+This strategy is especially profitable when the market is performing poorly. The profit is resulted from mispricing, and mispricings are likely to happen when the market goes down or volatility increases.
+
+To explore this strategy further, we design this strategy to be flexible. We can change the data resolution into 5 minutes, 10 minutes or even 30 minutes by simply changing a parameter. It's also essential to choose optimized entering, closing and stop loss threshold. Everyone can has his/her own version of this strategy.
+<h2>Introduction</h2>
+High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012[ref]<strong>Cartea &amp; Penalva, 2012 </strong>Where is the value in high frequency trading?<strong><a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a></strong>[/ref]).
+<div class="page" title="Page 14">
+<div class="layoutArea">
+<div class="column">
+
+Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006[ref]<strong>Gatev, Goetzmann, &amp; Rouwenhorst, 2006 </strong>Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>[/ref]) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
+
+</div>
+<h3>Data Description</h3>
+In order to have more pairs with high correlation, we select stocks in a specific industry. Economically, we prefer traditional sectors because the companies in these sector are more likely to be close substitutes. If we selected N stocks, the number of pairs can be calculated by \(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\). In the demonstrated strategy we used 80 stocks, so we have 3160 pairs in total. We used minute data and aggregate them into lower resolution, thus 1 minute is the highest resolution for this strategy.
+<h3>Correlation Approach</h3>
+Correlations measure the relationship between two stocks that have price trends. They tend to move together, and thus are correlated. Correlation filter is the first step to screen the candidate pairs.
+
+Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
+
+\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}}\]
+
+Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
+
+However, the pairs trading based on a correlation approach alone would have a disadvantage of instabilities over time. Correlation coefficients do not necessarily imply  mean-reversion between the prices of the two stock pairs. In order to overcome the above issue, a cointegration approach was further used as the second-step of the selection process for the pairs.
+<h3>Cointegration Approach</h3>
+The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger[ref]<strong>Engle and Granger</strong> Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236">Online Copy</a></strong>[/ref]Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
+
+The definition of cointegration is the following: assume that \(x_t\) and \(y_t\) are two time series that were non-stationary. If there exists a parameter \(\gamma\) such that the following equation:
+
+\[z_t = y_t - \gamma x_t\]
+
+was a stationary process, then \(x_t\) and \(y_t\) would be cointegrated. This process is a powerful tool for investigating common asset trends in multivariate time series.
+
+In our case, Let \(p_t^A\) and \(p_t^B\) be the prices of two stocks A and B respectively. If it is assumed that {\({p_t^A, p_t^B}\)} is individually non-stationary, there exists the parameter \(\gamma\) such that the following equation was a stationary process
+
+\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]
+
+where \(\mu\) is a mean of the cointegration model. \(\epsilon_t\) is a stationary, mean-revering process and was referred to as a <em>cointegration residual</em>. The parameter \(\gamma\) is known as a <em>cointegration coefficient. </em>The equation above represents a model of cointegrated pair for stocks A and B.
+
+It's essential to understand how the conitegration residual together with the cointegration coefficient determines our trading direction. If \(\epsilon\) is positive, in a given confidence interval, this is a signal that stock A is relatively overpriced and stock B is relatively underpriced, and we are going to long B and short A; If If \(\epsilon\) is negative, we are going to long A and short B.
+<h3>Cointegration Verification(optional reading part)</h3>
+<div class="page" title="Page 5">
+<div class="layoutArea">
+<div class="column">
+
+In the Engle-Granger method(Engle &amp; Granger, 1987), we first set up a cointegration regression between stock A and stock B as stated in the equation above, and then estimate the regression parameters \(\mu\) and \(\gamma\) using an ordinary least squares(OLS). Subsequently, we tested the regression residual \(epsilon_t\) to determine whether or not it was stationary.
+
+The most popular stationary test in the area of cointegration, the Augmented Dickey Fuller (ADF) test, was used on the regression residual \(\epsilon\) to determine whether it had a unit root.
+<div class="page" title="Page 5">
+<div class="layoutArea">
+<div class="column">
+
+Testing for the presence of the unit root in the regression residual using the ADF test was given by
+
+\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \DeltaZ_{t-i} + \mu_t\]
+
+where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
+
+The number of lag order p in the equation is usually unknown and therefore had to be estimated. To determine the number of lag p, the information criteria for lag order selection was used. Here we choose Bayesian Information Criterion(BIC)
+
+\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]
+
+Where T is the sample size.
+
+The unit root test for the regression residual \(\epsilon\) using the ADF test was then carried out under the null hypothesis \(H_0 : \gamma = 0\) versus the alternative hypothesis \(H_1 : \gamma &lt; 0\). A statistical value of the ADF test was obtained by
+
+\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]
+
+The test result in the equation above is compared with the critical value of the ADF test. If the test result is less than the critical value, then the null hypothesis is rejected. This means the regression residual \(\epsilon\) is stationary. Thus, the two stock prices {\({p_t^A, p_t^B}\)} are cointegrated.
+<h3>Pairs Trading Strategy</h3>
+The pairs trading strategy uses trading signals based on the regression residual \(\epsilon\) and were modeled as a mean-reverting process.
+
+In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
+
+The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
+
+In the training period, each of the training data contained a 3-month period, which is a dynamic rolling window size. Immediately after the training period, we begin our one-month trading period, and the dynamic rolling window automatically shift ahead to record the new prices of the stocks in each pair. After the first trading period, we use the updated stock prices to select our pairs for trading again, and begin another trading period.
+<h3>Parameter Adjustment</h3>
+The performance of the strategy is sensitive to the parameters. There are  mainly four parameter to adjust: Opening Threshold, Closing Threshold, Stop-loss Threshold and data resolution.
+
+Opening threshold represents by how many times the residual \(\epsilon\) exceed the standard deviation, which is calculated by \(\frac{\epsilon - \bar{\epsilon}}{\sigma}\). By default we set it to 2.32 and -2.32, which is the critical value for 99% confidence interval if we assume the residual follows normal distribution.
+
+Closing threshold is calculated in the same way as opening threshold, we set it to 0.5 by default to close early to prevent further divergence.
+
+Stop-loss Threshold is set to 4.5. This depends on the level of mispricing we can bear. The higher degree our tolerance to risk is, the higher we can set this parameter. However, if we set this number too low, we may have too many pairs closed before reversion to stop loss.
+
+</div>
+&nbsp;
+<h2>Method</h2>
+In this trading strategy we would define a class named 'pairs'. We manage pairs instead of stocks directly to make it's more convenient for us to calculate correlation and cointegration, update stock prices in the pair and trade on the selected pairs.
+<h3>Step 1: Pairs Class Definition</h3>
+The pairs is made up of two stocks, stock A and stock B. This class has several properties. The basic properties include symbols of stock A and stock B, the pandas DataFrame that contains time and prices of the two stocks, the current error, the error of the last datapoint, and the lists to record stock prices for update purpose. Instead of updating the DataFrame every 5 minutes, we record the prices in lists to update the DataFrame monthly. This would speed up the algorithm at least 10 times because manipulating DataFrame is very time consuming.
+The cor_update method is used every month to update the correlation between the two stocks in this pair. The cointegration_test method is also used monthly to do OLS regression, conduct ADF test, and calculate the mean and standard deviation of the residual. The method also assign these calculated values as properties to the pair object.
+<pre class="prettyprint linenums">class pairs(object):
+    def __init__(self, a, b):
+        self.a = a
+        self.b = b
+        self.name = str(a) + ':' + str(b)
+        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
+    # The number of bars in the rolling window would be determined by the resolution, so we get this
+      information from the shape of the DataFrame here.
+        self.num_bar = self.df.shape[0]
+        self.cor = self.df.corr().ix[0][1]
+    # Set the initial signals to be 0
+        self.error = 0
+        self.last_error = 0
+        self.a_price = []
+        self.a_date = []
+        self.b_price = []
+        self.b_date = []
+
+    def cor_update(self):
+        self.cor = self.df.corr().ix[0][1]
+
+    def cointegration_test(self):
+        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
+    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
+      the tuple is the test value.
+        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
+        self.mean_error = np.mean(self.model.resid)
+        self.sd = np.std(self.model.resid)
+
+    def price_record(self,data_a,data_b):
+        self.a_price.append(float(data_a.Close))
+        self.a_date.append(data_a.EndTime)
+        self.b_price.append(float(data_b.Close))
+        self.b_date.append(data_b.EndTime)
+
+    def df_update(self):
+        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
+                 [self.a_date]).dropna()
+        self.df = pd.concat([self.df,new_df])
+        self.df = self.df.tail(self.num_bar)
+    # after updating the DataFrame, we empty the lists for the incoming data
+        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
+            list = []
+</pre>
+<h3>Step 2: Generate and Clean Pairs</h3>
+The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
+The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
+<pre class="prettyprint linenums">def generate_pairs(self):
+    for i in range(len(self.symbols)):
+        for j in range(i+1,len(self.symbols)):
+            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
+
+    self.pair_list = [x for x in self.pair_list if x.cor &gt; self.pair_threshold]
+
+    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
+
+    if len(self.pair_list) &gt; self.pair_num:
+        	self.pair_list = self.pair_list[:self.pair_num]
+
+def pair_clean(self,list):
+    l = []
+    l.append(list[0])
+    for i in list:
+        symbols = [x.a for x in l] + [x.b for x in l]
+        if i.a not in symbols and i.b not in symbols:
+            l.append(i)
+        else:
+            pass
+    return l
+</pre>
+<h3>Step 3: Warming up Period</h3>
+This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
+<pre class="prettyprint linenums">if len(self.symbols[0].prices) &lt; self.num_bar:
+    for symbol in self.symbols:
+        if data.ContainsKey(i) is True:
+    	    symbol.prices.append(float(data[symbol].Close))
+            symbol.dates.append(data[symbol].EndTime)
+        else:
+            self.Log('%s is missing'%str(symbol))
+            self.symbols.remove(symbol)
+    self.data_count = 0
+    return</pre>
+<h3>Step 4: Pairs Selection</h3>
+This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
+<pre class="prettyprint linenums">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
+    if self.generate_count == 0:
+        for symbol in self.symbols:
+        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
+
+        self.generate_pairs()
+        self.generate_count +=1
+        self.Log('pair list length:'+str(len(self.pair_list)))
+
+        for pair in self.pair_list:
+            pair.cor_update()
+    # Update the DataFrame and correlation selection
+    if len(self.pair_list[0].a_price) != 0:
+        for pair in self.pair_list:
+    	    pair.df_update()
+            pair.cor_update()
+
+    self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
+    # Cointegration test
+    for pair in self.selected_pair:
+        pair.cointegration_test()
+
+    self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
+    self.selected_pair.sort(key = lambda x: x.adf)
+    # If no pair passed the two-stage test, return.
+    if len(self.selected_pair) == 0:
+        self.Log('no selected pair')
+        self.count += 1
+        return
+    # clean the pair to avoid overlapping stocks.
+    self.selected_pair = self.pair_clean(self.selected_pair)
+    # assign a property to the selected pair, this is a signal that would be used for trading.
+    for pair in self.selected_pair:
+        pair.touch = 0
+        self.Log(str(pair.adf) + pair.name)
+    # limit the number of selected pairs.
+    if len(self.selected_pair) &gt; self.selected_num:
+        self.selected_pair = self.selected_pair[:self.selected_num]
+
+    self.count +=1
+    self.data_count = 0
+    return
+</pre>
+<h3>Step 5: Trade Period</h3>
+It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
+<h4>Updating Pairs</h4>
+This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
+<pre class="prettyprint linenums">num_select = len(self.selected_pair)
+for pair in self.pair_list:
+    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
+        i.price_record(data[i.a],data[i.b])
+    else:
+        self.Log('%s has no data'%str(pair.name))
+        self.pair_list.remove(pair)
+
+for pair in self.selected_pair:
+    pair.last_error = pair.error
+
+for pair in self.trading_pairs:
+    pair.last_error = pair.error</pre>
+<h4>Opening Pairs Trading</h4>
+This is most complex part. For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
+When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
+<pre class="prettyprint linenums">for i in self.selected_pair:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
+    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
+    self.trading_pairs:
+        if i.touch == 0:
+            if i.error &lt; i.mean_error - self.open_size*i.sd and i.last_error &gt; i.mean_error -
+            self.open_size*i.sd:
+                i.touch += -1
+            elif i.error &gt; i.mean_error + self.open_size*i.sd and i.last_error &lt; i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error &gt; i.mean_error - self.open_size*i.sd and i.last_error &lt; i.mean_error -
+            self.open_size*i.sd:
+                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
+                i.record_model = i.model
+                i.record_mean_error = i.mean_error
+                i.record_sd = i.sd
+                self.trading_pairs.append(i)
+                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
+                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
+                i.touch = 0
+         elif i.touch == 1:
+             if i.error &lt; i.mean_error + self.open_size*i.sd and i.last_error &gt; i.mean_error +
+             self.open_size*i.sd:
+             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
+             i.record_model = i.model
+             i.record_mean_error = i.mean_error
+             i.record_sd = i.sd
+             self.trading_pairs.append(i)
+             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
+             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
+             i.touch = 0
+         else:
+             pass
+    else:
+        pass
+</pre>
+<h4>Closing Pairs Trading</h4>
+This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
+<pre class="prettyprint linenums">for i in self.trading_pairs:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
+    if ((i.error &lt; i.record_mean_error + self.close_size*i.record_sd and i.last_error &gt;i.record_mean_error + self.close_size*i.record_sd) or (i.error &gt; i.record_mean_error -
+    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
+    self.stop_loss*i.record_sd:
+        self.Log('close %s to stop loss'%str(i.name))
+        self.Liquidate(i.a)
+        self.Liquidate(i.b)
+        self.trading_pairs.remove(i)
+    else:
+        pass</pre>
+<h2>Result</h2>
+We used 10-minute resolution data to backtest the strategy from Jan 2013 to Dec 2016. To demonstrate the in sample training results, we randomly selected a training period that from 2016-09-07 to 2013-11-30.
+<h3>Training Result</h3>
+The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1” />
+
+The upper part of the following chart plots the stock prices of pair ING vs TCB. The lower part plots by how many times standard deviation the residual deviate from its mean. There are 5 trading opportunities if we set the opening threshold to be 2.32.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2” />
+
+The following chart is the density plot of the residual error. From the shape we can see the error is approximately normal distributed.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3” />
+
+<strong>Backtesting Result</strong>
+
+The strategy is considered to be market neutral strategy because it a long/short strategy betting on price convergence. Out backtested beta is -0.112, which is within our expectation.
+
+Theoretically, the higher resolution we use, the higher win rate is because on one hand the higher resolution would increase the number of datapoint in our training period, which would make it's harder to past the two-stage test; on the other hand the higher resolution data would let us capture minor profit more accurately. However, there is a trade off between performance and backtesting time. The higher resolution will lead backtesting time to increase drastically.
+
+The number of stocks in the initialize step would also affect our performance. Theoretically, the more stock we have, we better pairs we are likely to pick. But too many stocks would also be time consuming.
+
+what's worth mentioning is that the optimized parameters are different for each sector. It depends on the features of the price patterns in the specific industry. Plotting the pairs prices and the residual to observe is good option to adjust the thresholds.
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5d0576da03c50667c3af8aefa86f4f44'></script>
+</div>
+</div>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html
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+<h2>Abstract</h2>
+Trend estimation is a family of methods to detect and predict tendencies and trends in price series just using the history information. Moving average is a commonly used trend following trading tool. Lots of momentum trading strategies in the Forex market are based on the moving average rule, in which signals are triggered if the close is above or below the moving average. But MA has the time lag, therefore can't be used to predict the turning points of market price changes.
+
+In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009) [ref]Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>[/ref].
+
+This strategy exploits short-term momentum in the non-linear trend component of the exchange rate which is generated by Hodrick-Prescott Filter (rather than the exchange rate itself) and uses the MA(1, 2) rule to measure this momentum. The strategy was tested on seven kinds of exchange rates and the results shows less robustness and the performance is sensitive to the change of model parameters.
+
+<h2>Introduction</h2>
+Hodrick-Prescott Filter decomposes a time series\(y_t\) into two components: the cyclical part(which is short-term) and the trend part(which is long term).
+
+\[y_t=\x _t +c_t\]
+
+The filter is the solution to the following optimization problem for \(x_t\)
+
+\[\min _{x_t}\left[\sum_{t=1}^n(y_t-x_t)^2+\lambda\sum_{t=2}^{n-1}[(x_{t+1}-x_t)-(x_{t}-x_{t-1})^2] \right]\]
+
+[ref]Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>[/ref]The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
+
+\[\min_{\bf x}{\parallel {\bf{y}}-{\bf{x}}\parallel}_2^2+\lambda {\parallel D\bf x\parallel}_2^2\]
+
+where \({\bf y}=(y_1,y_2,...,y_n),{\bf x}=(x_1,x_2,...,x_n)\in {\rm I\!R}^n\),\(\parallel\cdot\parallel_2\) is the Euclidean norm. D is the (n-2)*n matrix:
+
+\[
+
+\left[
+\begin{matrix}
+1 &amp; -2 &amp; 1 &amp; \\
+&amp; 1 &amp; -2 &amp; 1 \\
+&amp; &amp; &amp; \ddots &amp;\\
+&amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
+&amp; &amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
+\end{matrix}
+\right]
+
+\]
+
+The solution of this optimization problem is given by solving the following linear system:
+
+\[y=(I+2\lambda D^TD)^{-1}x\]
+<pre class="prettyprint linenums">def hpfilter(self,X, lamb=1600):
+    X = np.asarray(X, float)
+    if X.ndim &gt; 1:
+    X = X.squeeze()
+    nobs = len(X)
+    I = sparse.eye(nobs,nobs)
+    offsets = np.array([0,1,2])
+    data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1)
+    K = sparse.dia_matrix((data, offsets), shape=(nobs-2,nobs))
+    use_umfpack = True
+    self.trend = spsolve(I+lamb*K.T.dot(K), X,use_umfpack=use_umfpack)
+    cycle = X - self.trend
+</pre>
+
+<h2>Method</h2>
+
+This low-frequency momentum trading strategies are applied to daily data on seven kinds of exchange rates. We use five years history data before January 2011 for initial estimation of the trend model. Daily exchange rates for the period January 2011 to May 2017 is used for out of sample trading.
+<pre class="prettyprint linenums">def Initialize(self):
+    self.SetStartDate(2011,1,1)
+    self.SetEndDate(2017,5,30)
+    self.SetCash(100000)
+    self.numdays = 360*5  # set the length of training period
+    self.syl = self.AddSecurity(SecurityType.Forex, "EURUSD", Resolution.Daily).Symbol
+    self.n,self.m = 2, 1
+    self.trend = None
+    self.SetBenchmark(self.syl)
+    self.MA_rules = None
+    history = self.History(self.numdays,Resolution.Daily)
+    self.close = [slice[self.syl].Close for slice in history]</pre>
+
+<h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
+The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(\1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more” />
+
+If we just plot the curve for the first 100 days, we find that the smaller the λ, the more apparent the trend. The curve does not change too much as the λ  smaller than 100. Thus here we choose  λ=100 to extract the trend of daily price data. This trend is our low-frequency component.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-less.jpg" alt="low-frequency-component-less”/>
+
+<p style="text-align: center;">Out-of-sample EUR/USD Trend Estimation</p>
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation” />
+<h3>Step 2: Setting up the Moving Average Rule</h3>
+
+Moving average (MA) rules are very commonly used to generate buy and sell signals from data on the spot exchange rate. The MA rule compares a short-run moving average of the current and lagged exchange rate with a long-run moving average.
+
+\[MA(m,n)=\frac{1}{m}\sum_{i=0}^{m-1}S_{t-i}-\frac{1}{n}\sum_{i=0}^{n-1}S_{t-i}\]
+
+
+For HP filter, the non-linear trend is estimated recursively as the paper did. The initial estimation was undertaken using 3 years history data before 2011. The estimation period is then rolled forward each day through the trading period from January 2011 to May 2017.
+
+<h3>Step 3: Generating the Trading Signals</h3>
+We generate buy and sell signals by applying an MA(1, 2) rule to the estimated low-frequency component. For MA(m,n), m must be 1 which denotes the current value of low-frequency component. n should be small since large n would generate large time lag, the judgment of turning points is not accurate.
+
+A buy signal is generated when the current day’s low-frequency trend is higher than the last day’s low-frequency trend and a sell signal is generated when it is lower.
+<pre class="prettyprint linenums">
+def OnData(self,data):
+    self.close.append(self.Portfolio[self.syl].Price)
+    self.hpfilter(self.close[-self.numdays:len(self.close)+1], 100)
+    self.MA_rules_today = (np.mean(self.trend[-self.m : len(self.trend)]) - np.mean(self.trend[-self.n : len(self.trend)]))
+    self.MA_rules_yesterday = (np.mean(self.trend[-self.m-1: len(self.trend)-1]) - np.mean(self.trend[-self.n-1 : len(self.trend)-1]))
+    holdings = self.Portfolio[self.syl].Quantity
+
+    if self.MA_rules_today &gt; 0 and self.MA_rules_yesterday &lt; 0:
+       self.SetHoldings(self.syl, 1)
+    elif self.MA_rules_today &lt; 0 and self.MA_rules_yesterday &gt; 0:
+       self.SetHoldings(self.syl, -1)
+</pre>
+<p style="text-align: center;">Trading Signals when λ=1600</p>
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-1600.jpg" alt="trading-signal 1600.jpg” />
+
+<p style="text-align: center;">Trading Signals when λ=100</p>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-100.jpg" alt="trading-signal-100.jpg" />
+
+The above charts are the in-sample trading signals after applying MA rules on the low-frequency component. The trend curve is more smooth with larger λ. Thus when we applied MA rules, a less smooth trend will trigger more trading opportunities.
+<h2>Conclusion</h2>
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;" colspan="8">Strategy Performance for different currencies in Forex</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Currency</td>
+<td>USDCAD</td>
+<td>EURUSD</td>
+<td> USDCHF</td>
+<td> EURGBP</td>
+<td>CADUSD</td>
+<td>USDNOK</td>
+<td>USDZAR</td>
+</tr>
+<tr>
+<td>Sharp Ratio</td>
+<td>0.361</td>
+<td>0.375</td>
+<td> 0.131</td>
+<td> 0.337</td>
+<td> 0.052</td>
+<td> 0.054</td>
+<td> 0.195</td>
+</tr>
+<tr>
+<td> Total Trades</td>
+<td> 14</td>
+<td> 11</td>
+<td> 12</td>
+<td> 11</td>
+<td> 6</td>
+<td> 16</td>
+<td> 9</td>
+</tr>
+<tr>
+<td> Annual Return</td>
+<td> 3.128%</td>
+<td> 3.272%</td>
+<td> 1.162%</td>
+<td> 2.689%</td>
+<td> 0.043%</td>
+<td> 0.093%</td>
+<td> 2.201%</td>
+</tr>
+<tr>
+<td> Max Drawdown</td>
+<td> 10.3%</td>
+<td> 8.3%</td>
+<td> 24.8%</td>
+<td> 17.6%</td>
+<td> 22.5%</td>
+<td> 30.3%</td>
+<td> 23%</td>
+</tr>
+</tbody>
+</table>
+
+The table reports the strategy performance statistics during six and a half years backtesting period. From the table we can see, most of them have the higher maximum drawdown. The number of total trades is small because we applied MA rules on the smoothed trend component.  As the author indicated in the paper, we still find that the performance of this strategy is very sensitive to the choice of lag parameters in MA rules and in a non-monotonic way.
+
+The strategy does not generate more stable profits in Forex market generally. That might because that the HP filter technique was designed to be viewed as a trend curve through the entire set of data. When we applied it in trading strategy, the entry of new data into the filter model can cause the trend line to change the trend through past data, makes it harder to identify the trend accurately.
+
+We also tried the kernel regression method in the paper. However, because the distribution parameter in the Python kernel regression package can not be adjusted. The appropriate trend component is not being extracted well from kernel regression. The smoothing effect of the regression is not good as expected. Thus here we choose not to move on to the trading part of kernel regression method.
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5188517de3244425b56af3e717c90cb9'></script>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/09 Stock Selection Strategy Based on Fundamental Factors.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/09 Stock Selection Strategy Based on Fundamental Factors.html
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+<h2>Abstract</h2>
+In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em>Ayhan Yüksel[ref]<em>Factor </em>Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>[/ref]
+<h2>Part 1. Factor Selection</h2>
+QuantConnect provides Morningstar fundamentals data for US Equities. Valuation Ratios is daily data. For others like operation ratios and financial statements data are available for multiple periods depending on the property. Please refer to <a href="https://www.quantconnect.com/data#fundamentals/usa/morningstar">Data Library</a> for detailed factors available.
+
+The algorithm is designed to test the significance of one factor each time.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2005,01,01)  #Set Start Date
+	self.SetEndDate(2012,03,01)    #Set End Date
+	self.SetCash(50000)            #Set Strategy Cash
+	self.UniverseSettings.Resolution = Resolution.Daily
+	self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
+	self.AddEquity("SPY") # add benchmark
+	self.numOfCourseSymbols = 200
+	self.numOfPortfolio = 5
+	self._changes = None
+	self.flag1 = 1  # variable to control the monthly rebalance of coarse and fine selection function
+	self.flag2 = 0  # variable to control the monthly rebalance of OnData function
+	self.flag3 = 0  # variable to record the number of rebalancing times
+        # store the monthly returns of different portfolios in a dataframe
+	self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1))
+        # schedule an event to fire at the first trading day of SPY
+	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))</pre>
+<h3>Step 1: Ranking the stocks by factor values</h3>
+1. First, we sort the stocks by daily dollar volume and take the top stocks with the highest dollar volumes as our candidates. There is a convenient way using our universe selection API. Universes are refreshed every day by default, but can be refreshed as often as required. This is controlled by the variable <code>UniverseSettings.Resolution</code>. You can refer to the <a href="https://www.quantconnect.com/docs#Universes">documentation</a> for more details.  Here we use <a href="https://www.quantconnect.com/docs#Scheduled-Events">Scheduled events API</a> to trigger code to run at the first trading day each month and use three flag variables to control the rebalancing of <code>CoarseSelection</code>, <code>FineSelection</code> and <code>Ondata</code> functions.
+
+Coarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property <code>x.HasFundamentalData</code> to exclude them from our candidate stocks pool.
+<pre class="prettyprint linenums"># sort the data by daily dollar volume and take the top entries
+def CoarseSelectionFunction(self, coarse):
+    if self.flag1:
+        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
+        sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
+        top = sortedByVolume[:self.numOfCourseSymbols]
+				return [i.Symbol for i in top]
+    else:
+        return []
+</pre>
+2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
+<pre class="prettyprint linenums">def FineSelectionFunction(self, fine):
+	if self.flag1:
+		self.flag1 = 0
+		self.flag2 = 1
+		# filter the fine by deleting equities wit zero factor value
+		filtered_fine = [x for x in fine if x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths != 0 ]
+		# sort the fine by reverse order of factor value
+		sorted_fine = sorted(filtered_fine, key=lambda x: x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths, reverse=True)
+		self.symbol = [str(x.Symbol.Value) for x in sorted_fine]
+		# factor_value = [x.ValuationRatios.PERatio for x in sorted_fine]
+		self.flag3 = self.flag3 + 1
+ 	 	return []
+	else:
+		return []
+</pre>
+<h3>Step 2: Compute the monthly return of portfolios</h3>
+1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
+<pre class="prettyprint linenums">sorted_symbol = self.symbol
+self.AddEquity("SPY") # add benchmark
+for x in sorted_symbol:
+	self.AddEquity(x)
+history = self.History(20,Resolution.Daily)
+monthly_return =[]
+new_symbol_list =[]
+for j in range(len(sorted_symbol)):
+	try:
+		daily_price = []
+		for slice in history:
+			bar = slice[sorted_symbol[j]]
+			daily_price.append(float(bar.Close))
+		new_symbol_list.append(sorted_symbol[j])
+ 		monthly_return.append(daily_price[-1] / daily_price[0] - 1)
+	except:
+		self.Log("No history data for " + str(sorted_symbol[j]))
+		del daily_price
+# the length of monthly_return list should be divisible by the number of portfolios
+monthly_return = monthly_return[:int(math.floor(len(monthly_return) / self.numOfPortfolio) * self.numOfPortfolio)]
+</pre>
+<div class="page" title="Page 15">
+<div class="layoutArea">
+<div class="column">
+
+2. We divide the stocks into 5 portfolios and compute the average monthly returns of each portfolio. Then we add the monthly return of benchmark "SPY" at the last line of the data frame <code>df_return</code>.
+<pre class="prettyprint linenums">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
+# calculate the average return of different portfolios
+port_avg_return = np.mean(reshape_return,axis=1).tolist()
+# add return of "SPY" as the benchmark  to the end of the return list
+benchmark_syl = self.AddEquity("SPY").Symbol
+history_benchmark = self.History(20,Resolution.Daily)
+benchmark_daily_price = [float(slice[benchmark_syl].Close) for slice in history_benchmark]
+benchmark_monthly_return = (benchmark_daily_price[-1]/benchmark_daily_price[0]) - 1
+port_avg_return.append(benchmark_monthly_return)
+self.df_return[str(self.flag3)] = port_avg_return
+</pre>
+<h3>Step 3: Generate the metrics to test the factor significance</h3>
+After getting the monthly returns of portfolios and the benchmark, we compute the average annual return and excess return over benchmark of each portfolio across the whole backtesting period. Then We generate three metrics to judge the significance of each factor.
+<ol>
+ 	<li>The first metrics is the correlation between the portfolio' returns and their rank. The absolute value of the correlation coefficient should larger than 0.8.</li>
+ 	<li>If the return of the rank first portfolio larger than the portfolio at the bottom of the return rankings, we define it the win portfolio and the loss portfolio and vice versa. The win probability is the probability that the win portfolio return outperform the benchmark return. The loss probability is the probability that the loss portfolio return underperform the benchmark.  If the factor is significant, both loss and win probability should greater than 0.4.</li>
+ 	<li>The excess return of win portfolio should be greater than 0.25, while the excess return of loss portfolio should be lower than 0.05.</li>
+</ol>
+<pre class="prettyprint linenums">def calculate_criteria(self,df_port_return):
+	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
+	annual_return = (total_return+1)**(1./6)-1
+	excess_return = annual_return - np.array(annual_return)[-1]
+	correlation = annual_return[0:5].corr(pd.Series([5,4,3,2,1],index = annual_return[0:5].index))
+	# higher factor with higher return
+	if np.array(total_return)[0] &gt; np.array(total_return)[-2]:
+		loss_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[0]
+		loss_port_excess_return = np.array(excess_return)[-2]
+	# higher factor with lower return
+	else:
+		loss_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[-2]
+		loss_port_excess_return = np.array(excess_return)[0]
+	test_result = {}
+	test_result["correelation"]=correlation
+	test_result["win probality"]=win_prob
+	test_result["loss probality"]=loss_prob
+	test_result["win portfolio excess return"]=win_port_excess_return
+	test_result["loss portfolio excess return"]=loss_port_excess_return
+
+	return test_result</pre>
+</div>
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;" colspan="8">Factor Significance Testing Result</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> Factor</td>
+<td> FCFYield</td>
+<td> BuyBackYield</td>
+<td> PriceChange1M</td>
+<td>TrailingDividendYield</td>
+<td> EVToEBITDA</td>
+<td> RevenueGrowth</td>
+<td>BookValuePerShare</td>
+</tr>
+<tr>
+<td> The correlation</td>
+<td> -0.936</td>
+<td> -0.987</td>
+<td> 0.918</td>
+<td> -0.981</td>
+<td> 0.939</td>
+<td>0.89</td>
+<td>-0.92</td>
+</tr>
+<tr>
+<td>Win Probability</td>
+<td>0.630</td>
+<td>0.639</td>
+<td> 1</td>
+<td> 0.667</td>
+<td> 0.722</td>
+<td> 0.69</td>
+<td>0.69</td>
+</tr>
+<tr>
+<td>Loss probability</td>
+<td> 0.426</td>
+<td>0.472</td>
+<td> 1</td>
+<td> 0.518</td>
+<td> 0.472</td>
+<td> 0.42</td>
+<td>0.40</td>
+</tr>
+<tr>
+<td> Excess Return(Win)</td>
+<td> 0.324</td>
+<td> 0.212</td>
+<td>0.303</td>
+<td> 0.225</td>
+<td> 0.414</td>
+<td> 0.23</td>
+<td> 0.27</td>
+</tr>
+<tr>
+<td> Excess Return(Loss)</td>
+<td> 0.060</td>
+<td> 0.037</td>
+<td> -1.67</td>
+<td> 0.043</td>
+<td> 0.042</td>
+<td> 0.07</td>
+<td> 0.06</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.
+<h2>Part 2. Stock Selection</h2>
+<h3>Step 1: Rank the stocks by factor values</h3>
+First, we remove the  stocks without fundamental data or have zero factor value. For each pre-selected factor, we rank the stocks by those factor values. The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive.
+<h3>Step 2: Calculate equally weighted composite factor scores</h3>
+The second step is using different selected factor variables to calculate an equally weighted composite factor score for each stock.
+<ul>
+ 	<li>First, according to the factor order, we place our universe stocks into 5 distinct quintile portfolios, named P1, P2, P3, P4 and P5. The ranking of portfolios sets out the preference of the factor model, i.e. the first portfolio (P1) corresponds to the “most preferred” stocks, while the fifth (P5) corresponds to the “least preferred” stocks.Suppose there are n stocks in total. Then the stocks fall into the first rank portfolio will have score p, the stocks fall into the second rank portfolio will get score p-1 and so on. Then we can get a score for every stock. We did the same calculation for each factor.</li>
+ 	<li>Second, we calculate a “Composite Factor Score” by combining the six-factor scores and using an equal weighting scheme. Then we get composite factor score for each stock.</li>
+ 	<li> Third, we then rank the stocks in our universe according to their Composite Factor Scores and choose the highest ranked 20 stocks to construct our portfolios at the beginning of each month.</li>
+ 	<li>At the end of each month, we repeat the above steps to construct the new portfolio and adjust the holding stocks.</li>
+</ul>
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4f8946295bb7358518b35ecbd3d46f01'></script>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html
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+<h2>Abstract</h2>
+This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". [ref]<strong>Groot, Wilma (2011)</strong>. Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>[/ref] The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
+
+One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
+
+In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
+
+In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
+<h2>Method</h2>
+The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
+<h3>Step 1:?Initialization</h3>
+In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use <em>self._numOfWeeks</em> to count?the number of weeks that have passed since the start date, and <em>self.</em>_LastDay to indicate whether it is a new week. <em>self._ifWarmUp</em> is true when the <em>self._numOfWeeks</em> is 3, which means as long as next week's data come, we can make our investment decisions.??<em>self._stocks</em> is a list containing all the symbols of the 100 stocks that are taken into consideration. <em>self._values</em> is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
+<pre class="prettyprint linenums">def Initialize(self):
+        self.SetStartDate(2002, 1, 3)
+        self.SetEndDate(2016, 12, 1)
+        self.SetCash(1000000)
+
+        self.UniverseSettings.Resolution = Resolution.Daily
+        self.AddUniverse(self.CoarseSelectionFunction)
+        self._numberOfSymbols = 100
+        self._numberOfTradings = 0.1 * self._numberOfSymbols
+
+        self._numOfWeeks = 0
+        self._LastDay = -1
+        self._ifWarmUp = False
+
+        self._stocks = []
+        self._values = {}</pre>
+Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
+<pre class="prettyprint linenums">def CoarseSelectionFunction(self, coarse):
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+        top100 = sortedByDollarVolume[:self._numberOfSymbols]
+        list = List[Symbol]()
+        for x in top100:
+                list.Add(x.Symbol)
+        return list
+</pre>
+<h3>Step 2:?Warm Up</h3>
+Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
+<pre class="prettyprint linenums">self._stocks = []
+self.uni_symbol = None
+symbols = self.UniverseManager.Keys
+for i in symbols:
+        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
+                self.uni_symbol = i
+        for i in self.UniverseManager[self.uni_symbol].Members:
+                self._stocks.append(i.Value.Symbol)
+                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
+
+</pre>
+We get all the symbols of qualified stocks from UniverseManager and keep them in <em>self._stocks</em> which is a list. Then we create for each key in the dictionary <em>self._values</em> a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
+<pre class="prettyprint linenums">for stock in self._stocks:
+        self._values[stock].append(self.Securities[stock].Price)</pre>
+<h3>Step 3:?Weekly Rebalancing</h3>
+After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
+<pre class="prettyprint linenums">returns = {}
+for stock in self._stocks:
+        newPrice = self.Securities[stock].Price
+        oldPrice = self._values[stock].pop(0)
+        self._values[stock].append(newPrice)
+        returns[stock] = newPrice/oldPrice
+</pre>
+Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
+<pre class="prettyprint linenums">newArr = [(v,k) for k,v in returns.items()]
+newArr.sort()
+for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
+        self.SetHoldings(stock, 0)
+for ret, stock in newArr[0:self._numberOfTradings]:
+        self.SetHoldings(stock, 0.5/self._numberOfTradings)
+for ret, stock in newArr[-self._numberOfTradings:]:
+        self.SetHoldings(stock, -0.5/self._numberOfTradings)
+</pre>
+Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
+<h2>Conclusion</h2>
+In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead.
+
+If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&amp;P 500 benchmark.
+
+Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc.
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75595e3ad41ca49ebcc6ab95aeaa92b8'></script>
diff --git a/04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html b/04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html
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+<h2>Introduction</h2>
+In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm[ref]A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>[/ref]. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
+
+<h3>Factors</h3>
+The paper strategy used three factors together to rank stocks: value, quality and momentum.
+
+Value: The most commonly used measure for value is P/B ratio(price-to-book value). Intuitively, the stocks with high P/B ratio are likely to be overpriced, and those stocks are labeled as growth stock. On the other hand, the stocks with low P/B value are considered to be value stocks. Following this logic, we use book value per share as a measure for value in our algorithm: the stocks with high book value per share rank high.
+
+Quality: Quality is a comprehensive factor. The paper used total profits over asset, gross margins, and free cash flow over assets. For simplicity, we used only operation margin as our quality factor. Here we assume that the companies with high operation margin are profitable, and their stocks are the quality ones.
+
+Momentum: The paper strategy is quarterly rebalanced, so it used recent one-year return, three-month and returns around earning events as measures for momentum. While our algorithm is monthly rebalanced, we simply use recent monthly return as our momentum factor.
+
+<h3>Ranking</h3>
+Ranking is the core process for stock selection. We first rank all the stocks according to each factor, then assign weights to each factor to get the final rank.
+
+Specifically, in our algorithm we have 250 stocks in total. We rank them according to their book values per share, operation margins and one-month returns by descending order. For each stock, its index in each sorted list is its score on each factor. e.g. If stock A ranks 1st by value, 10th by quality and 30th by momentum, its scores on the value, quality and momentum are 1, 10 and 30 respectively.
+
+The last step is to calculate the final score of each stocks. We use the same weight as the paper does: 40% on value, 40% on quality and 20% on momentum. In this way, stock A's final score is 1*0.4 + 10*0.4 + 30*0.2 = 10.4. Finally, we can rank all the stocks by their scores by <strong>ascending</strong> order. It worth attention that the stock with the <strong>lowest</strong> score is the best and it ranks 1st, and the one with the highest score is the worst.
+
+<h2>Implementation</h2>
+In this implementation, the FineSelectionFunction would be the core part because we have to rank the stocks in this process. We also need a ScheduledEvent handler to rebalance the portfolio every month. We would introduce the process step by step.
+
+<h3>CoarseSelectionFunction</h3>
+This function is a filter for the whole asset universe(around 8000 stocks). We select the top 250 with highest dollar volume to ensure liquidity. We also filter out the stocks without fundamental information or with a too low price(less than $5).
+<pre class="prettyprint linenums">
+def CoarseSelectionFunction(self, coarse):
+    # if the rebalance flag is not 1, return null list to save time.
+    if self.reb != 1:
+        return return self.long + self.short
+
+    # make universe selection once a month
+    # drop stocks which have no fundamental data or have too low prices
+    selected = [x for x in coarse if (x.HasFundamentalData)
+                and (float(x.Price) > 5)]
+
+    sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
+    top = sortedByDollarVolume[:self.num_coarse]
+    return [i.Symbol for i in top]
+</pre>
+
+<h3>FineSelectionFunction</h3>
+Here is the core function. The process is that we make three sorted list to store the stocks, and then use a dictionary to store the score information. For the dictionary, the keys are Symbols and the values are their scores. Finally we sort the dictionary to get the final rank. we store the top 20 stocks to long in the list self.long and the bottom 20 stocks to short in the list self.short.
+<pre class="prettyprint linenums">
+  def FineSelectionFunction(self, fine):
+      # return the same symbol list if it's not time to rebalance
+      if self.reb != 1:
+          return self.long+self.short
+      self.reb = 0
+
+  # drop stocks which don't have the information we need.
+  # you can try replacing those factor with your own factors here
+
+      filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
+                                      and x.ValuationRatios.PriceChange1M
+                                      and x.ValuationRatios.BookValuePerShare]
+
+      self.Log('remained to select %d'%(len(filtered_fine)))
+
+      # rank stocks by three factor.
+      sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+      sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
+      sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+
+      stock_dict = {}
+
+      # assign a score to each stock, you can also change the rule of scoring here.
+      for i,ele in enumerate(sortedByfactor1):
+          rank1 = i
+          rank2 = sortedByfactor2.index(ele)
+          rank3 = sortedByfactor3.index(ele)
+          score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
+          stock_dict[ele] = score
+
+      # sort the stocks by their scores
+      self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
+      sorted_symbol = [x[0] for x in self.sorted_stock]
+
+      # sotre the top stocks into the long_list and the bottom ones into the short_list
+      self.long = [x.Symbol for x in sorted_symbol[:self.num_fine]]
+      self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]]
+
+      return self.long+self.short</pre>
+<h3>Rebalance</h3>
+Our portfolio is rebalanced monthly, so first of all we should write a ScheduledEvent handler in the Initialize function:
+<pre class="prettyprint linenums">
+def Initialize(self):
+# Use SPY as a benchmark for market open
+    self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))
+Our rebalanced method is straightforward: We first liquidate the stocks that are no longer in the long/short list, and then assign equal weight to the stocks we are going to long or short.
+def rebalance(self):
+# if this month the stock are not going to be long/short, liquidate it.
+    long_short_list = self.long + self.short
+    for i in self.Portfolio.Values:
+        if (i.Invested) and (i.Symbol not in long_short_list):
+            self.Liquidate(i.Symbol)
+
+    # Assign each stock equally. Always hold 10% cash to avoid margin call
+    for i in self.long:
+        self.SetHoldings(i,0.9/self.num_fine)
+
+    for i in self.short:
+        self.SetHoldings(i,-0.9/self.num_fine)
+</pre>
+<h2>Summary</h2>
+The strategy was backtest at a 60-year timespan. It's persistent, systematic and intuitive. Although our version is very different from the paper one, the logic and intuition behind are the same. It has a better performance than the paper strategy because we added short positions.
+For further development, we can try to rank a large number of stocks, and do some portfolio optimization instead of holding stocks equally.
+
+<h2>Algorithm</h2>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.html
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+This tutorial provides a basic introduction to the Python programming language. If you are new to Python, you should run the code snippets while reading this tutorial. If you are an advanced Python user, please feel free to skip this chapter.
+
+<h2>Basic Variable Types</h2>
+
+The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
+
+Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
+<pre class="prettyprint linenums">my_string1 = 'Welcome to'
+my_string2 = "QuantConnect"
+print(my_string1 + ' ' + my_string2)
+[out]: Welcome to QuantConnect
+</pre>
+
+An integer is a round number with no values after the decimal point.
+<pre class="prettyprint linenums">my_int = 10
+print(my_int)
+[out]: 10
+print(type(my_int))
+[out]: type 'int'
+</pre>
+
+The built-in function int() can convert a string into an integer.
+<pre class="prettyprint linenums">my_string = "100"
+print(type(my_string))
+[out]: type 'str'
+my_int = int(my_string)
+print(type(my_int))
+[out]: type 'int'
+</pre>
+
+A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
+<pre class="prettyprint linenums">my_float = 1.0
+print(type(my_float))
+[out]: type 'float'
+my_int = 1
+print(type(my_int))
+[out]: type 'int'
+</pre>
+
+As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
+<pre class="prettyprint linenums">my_string = "100"
+my_float = float(my_string)
+print(type(my_float))
+[out]: type 'float'
+</pre>
+
+A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
+<pre class="prettyprint linenums">my_bool = False
+print(my_bool)
+[out]: False
+print(type(my_bool))
+[out]: type 'bool'
+</pre>
+
+<h2>Basic Math Operations</h2>
+
+The basic math operators in python are demonstrated below:
+<pre class="prettyprint linenums">
+print("Addition ", 1+1)
+print("Subtraction ", 5-2)
+print("Multiplication ", 2*3)
+print("Division", 10/2)
+print("exponent", 2**3)
+[out]:
+Addition  2
+Subtraction  3
+Multiplication  6
+Division  5
+exponent 8
+</pre>
+
+<h2>Data Collections</h2>
+
+<h3>List</h3>
+
+A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
+<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
+print(my_list)
+[out]: ['Quant', 'Connect', 1, 2, 3]
+</pre>
+
+The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
+<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
+print(len(my_list))
+[out]: 5
+print(my_list[0])
+[out]: Quant
+print(my_list[len(my_list) - 1])
+[out]: 3
+</pre>
+
+You can also change the elements in the list by accessing an index and assigning a new value.
+<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
+my_list[2] = 'go'
+print(my_list)
+[out]: ['Quant', 'Connect', 'go', 2, 3]
+</pre>
+
+A list can also be sliced with a colon:
+<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
+print(my_list[1:3])
+[out]: ['Connect', 1]
+</pre>
+
+The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
+<pre class="prettyprint linenums">print(my_list[1:])
+[out]: ['Connect', 1, 2, 3]
+</pre>
+
+And all elements up to but excluding index 3:
+<pre class="prettyprint linenums">print(my_list[:3])
+[out]: ['Quant', 'Connect', 1]
+</pre>
+
+If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
+<pre class="prettyprint linenums">my_list = ['Hello', 'Quant']
+my_list.append('Hello')
+print(my_list)
+[out]: ['Hello', 'Quant', 'Hello']
+my_list.remove('Hello')
+print(my_list)
+[out]: ['Quant', 'Hello']
+</pre>
+
+When there are repeated instances of "Hello", the first one is removed.
+
+<h3>Tuple</h3>
+
+A tuple is a data structure type similar to a list. The difference is that a tuple is immutable, which means you can't change the elements in it once it's defined. We create a tuple by putting comma-separated values between parentheses.
+<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
+</pre>
+
+Just like a list, a tuple can be sliced by using index.
+<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
+print(my_tuple[1:])
+[out]: ('to', 'QuantConnect')
+</pre>
+
+<h3>Set</h3>
+
+A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
+<pre class="prettyprint linenums">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
+stock_set = set(stock_list)
+print(stock_set)
+[out]: set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])
+</pre>
+Set is an easy way to remove duplicate elements from a list.
+
+<h3>Dictionary</h3>
+
+A dictionary is one of the most important data structures in Python. Unlike sequences which are indexed by integers, dictionaries are indexed by keys which can be either strings or floats.
+
+A dictionary is an <strong>unordered</strong> collection of key : value pairs, with the requirement that the keys are unique. We create a dictionary by placing a comma-separated list of key : value pairs within the braces.
+<pre class="prettyprint linenums">my_dic = {'AAPL': 'Apple', 'FB': 'FaceBook', 'GOOG': 'Alphabet'}
+</pre>
+
+After defining a dictionary, we can access any value by indicating its key in brackets.
+<pre class="prettyprint linenums">print(my_dic['GOOG'])
+[out]: Alphabet
+</pre>
+
+We can also change the value associated with a specified key:
+<pre class="prettyprint linenums">my_dic['GOOG'] = 'Alphabet Company'
+print(my_dic['GOOG'])
+[out]: Alphabet Company
+</pre>
+
+The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
+<pre class="prettyprint linenums">print(my_dic.keys())
+[out]: ['GOOG', 'AAPL', 'FB']
+</pre>
+
+<h2>Common String Operations</h2>
+
+A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
+<pre class="prettyprint linenums">my_str = 'Welcome to QuantConnect'
+print(my_str[8:])
+[out]: to QuantConnect
+</pre>
+
+There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
+<pre class="prettyprint linenums">
+print("Counting the number of e's in this sentence".count('e'))
+[out]: 6
+print('The first time e appears in this sentence'.find('e'))
+[out]: 2
+print('all the a in this sentence now becomes e'.replace('a','e'))
+[out]: ell the e in this sentence now becomes e
+</pre>
+
+The most commonly used method for strings is string.split(). This method will split the string by the indicated character and return a list:
+<pre class="prettyprint linenums">Time = '2016-04-01 09:43:00'
+splited_list = Time.split(' ')
+date = splited_list[0]
+time = splited_list[1]
+print(date, time)
+[out]: 2016-04-01 09:43:00
+hour = time.split(':')[0]
+print(hour)
+[out]: 09
+</pre>
+
+We can replace parts of a string by our variable. This is called string formatting.
+<pre class="prettyprint linenums">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
+print(my_time)
+[out]: Hour: 9, Minute: 43
+</pre>
+
+Another way to format a string is to use the % symbol.
+<pre class="prettyprint linenums">print 'pi is %f' % 3.14
+[out]: pi is 3.140000
+print('%s to %s' % ('Welcome', 'QuantConnect'))
+[out]: Welcome to QuantConnect
+</pre>
+
+%s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
+
+<h2>Summary</h2>
+
+We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.ipynb b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.ipynb
new file mode 100755
index 0000000..e4566f2
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.ipynb	
@@ -0,0 +1,512 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to QuantConnect\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_string1 = 'Welcome to'\n",
+    "my_string2 = \"QuantConnect\"\n",
+    "print(my_string1 + ' ' + my_string2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "<class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_int = 10\n",
+    "print(my_int)\n",
+    "print(type(my_int))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'str'>\n",
+      "<class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_string = \"100\"\n",
+    "print(type(my_string))\n",
+    "my_int = int(my_string)\n",
+    "print(type(my_int))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_string = \"100\"\n",
+    "my_float = float(my_string)\n",
+    "print(type(my_float))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "<class 'bool'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_bool = False\n",
+    "print(my_bool)\n",
+    "print(type(my_bool))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Addition  2\n",
+      "Subtraction  3\n",
+      "Multiplication  6\n",
+      "Division  5.0\n",
+      "exponent 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Addition \", 1+1)\n",
+    "print(\"Subtraction \", 5-2)\n",
+    "print(\"Multiplication \", 2*3)\n",
+    "print(\"Division \", 10/2)\n",
+    "print('exponent', 2**3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3333333333333333\n",
+      "0.3333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(1/3)\n",
+    "print(1.0/3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Quant', 'Connect', 1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = ['Quant', 'Connect', 1,2,3]\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "Quant\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = ['Quant', 'Connect', 1,2,3]\n",
+    "print(len(my_list))\n",
+    "print(my_list[0])\n",
+    "print(my_list[len(my_list) -1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Quant', 'Connect', 'go', 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = ['Quant','Connect',1,2,3]\n",
+    "my_list[2] = 'go'\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Quant', 'Connect']\n",
+      "['Connect']\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = ['Quant']\n",
+    "my_list.append('Connect')\n",
+    "print(my_list)\n",
+    "my_list.remove('Quant')\n",
+    "print(my_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Connect', 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_list = ['Quant','Connect',1,2,3]\n",
+    "print(my_list[1:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Connect', 1, 2, 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Quant', 'Connect', 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_list[:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "my_tuple = ('Welcome','to','QuantConnect')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('to', 'QuantConnect')\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_tuple = ('Welcome','to','QuantConnect')\n",
+    "print(my_tuple[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']\n",
+    "stock_set = set(stock_list)\n",
+    "print(stock_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "my_dic = {'AAPL':'AAPLE', 'FB':'FaceBook', 'GOOG':'Alphabet'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alphabet\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_dic['GOOG'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alphabet Company\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_dic['GOOG'] = 'Alphabet Company'\n",
+    "print(my_dic['GOOG'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['GOOG', 'AAPL', 'FB']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(my_dic.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "to QuantConnect\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_str = 'Welcome to QuantConnect'\n",
+    "print(my_str[8:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n",
+      "2\n",
+      "ell the e in this sentence now becomes e\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Counting the number of e appears in this sentence'.count('e'))\n",
+    "print('The first time e appears in this sentence'.find('e'))\n",
+    "print('all the a in this sentence now becomes e'.replace('a','e'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2016-04-01 09:43:00\n",
+      "09\n"
+     ]
+    }
+   ],
+   "source": [
+    "Time = '2016-04-01 09:43:00'\n",
+    "splited_list = Time.split(' ')\n",
+    "date = splited_list[0]\n",
+    "time = splited_list[1]\n",
+    "print(date, time)\n",
+    "hour = time.split(':')[0]\n",
+    "print(hour)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hour: 09, Minute:43\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_time = 'Hour: {}, Minute:{}'.format('09','43')\n",
+    "print(my_time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the pi number is 3.140000\n",
+      "Welcome to Quantconnect\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('the pi number is %f'%3.14)\n",
+    "print('%s to %s'%('Welcome','Quantconnect'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html
new file mode 100755
index 0000000..abd7ba1
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html	
@@ -0,0 +1,194 @@
+We discussed the basic data types and data structures in Python in the last tutorial. This chapter covers logical operations and loops in Python, which are very common in programming.
+
+<h2>Logical Operations</h2>
+
+Like most programming languages, Python has comparison operators:
+<pre class="prettyprint linenums">print 1 == 0    # 1 equals 0
+print 1 == 1    # 1 equals 1
+print 1 != 0    # 1 is not equal to 0
+print 5 &gt;= 5    # 5 is greater than or equal to 5
+print 5 &gt;= 6    # 5 is greater than or equal to 6
+[out]:
+False
+True
+True
+True
+False
+</pre>
+
+Each statement above has a boolean value, which must be either True or False, but not both.
+
+We can combine simple statements P and Q to form complex statements using logical operators:
+<ul>
+    <li>The statement "P and Q" is true if both P and Q are true, otherwise it is false.</li>
+    <li>The statement "P or Q" is false if both P and Q are false, otherwise it is true.</li>
+    <li>The statement "not P" is true if P is false, and vice versa.</li>
+</ul>
+
+<pre class="prettyprint linenums">print 2 &gt; 1 and 3 &gt; 2
+print 2 &gt; 1 and 3 &lt; 2
+print 2 &gt; 1 or  3 &lt; 2
+print 2 &lt; 1 and 3 &lt; 2
+[out]:
+True
+False
+True
+False
+</pre>
+
+When dealing with a very complex logical statement that involves in several statements, we can use brackets to separate and combine them.
+<pre class="prettyprint linenums">print (3 &gt; 2 or 1 &lt; 3) and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
+print 3 &gt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
+[out]:
+False
+True
+</pre>
+
+Comparing the above two statements, we can see that it's wise to use brackets when we make a complex logical statement.
+
+<h2>If Statement</h2>
+
+An if statement executes a segment of code only if its condition is true. A standard if statement consists of 3 segments: if, elif and else.
+<pre class="prettyprint linenums">if condition1:
+    # if condition1 is true, execute the code here
+    # and ignore the rest of this if statement
+elif condition2:
+    # if condition1 is false, and condition2 is true, execute the code here
+    # and ignore the rest of this if statement
+else:
+    # if none of the above conditions is True, execute the code here
+</pre>
+
+An if statement doesn't necessarily has elif and else part. If it's not specified, the indented block of code will be executed when the condition is true, otherwise the whole if statement will be skipped.
+<pre class="prettyprint linenums">i = 0
+if i == 0: print 'i == 0 is True'
+[out]: i==0 is True
+</pre>
+
+As we mentioned above, we can write some complex statements here:
+<pre class="prettyprint linenums">p = 1 &gt; 0
+q = 2 &gt; 3
+if p and q:
+    print 'p and q is true'
+elif p and not q:
+    print 'q is false'
+elif q and not p:
+    print 'p is false'
+else:
+    print 'None of p and q is true'
+[out]: q is false
+</pre>
+
+<h2>Loop Structures</h2>
+
+Loops are an essential part of programming. The "for" and "while" loops run a block of code repeatedly.
+
+<h3>While Loop</h3>
+
+A "while" loop will run repeatedly until a certain condition has been met.
+<pre class="prettyprint linenums">i = 0
+while i &lt; 5:
+    print i
+    i += 1
+[out]:
+0
+1
+2
+3
+4
+</pre>
+
+When making a while loop, we need to ensure that something changes from iteration to iteration so that the while loop will terminate, otherwise it will run forever. Here we used i += 1 (short for i = i + 1) to make i larger after each iteration. This is the most commonly used method to control a while loop.
+
+<h3>For Loop</h3>
+
+A "for" loop will iterate over a sequence of value and terminate when the sequence has ended.
+<pre class="prettyprint linenums">for x in [1,2,3,4,5]: print x
+[out]:
+1
+2
+3
+4
+5
+</pre>
+
+We can also add if statements in a for loop. Here is a real example from our pairs trading algorithm:
+<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+selected = ['AAPL','IBM']
+new_list = []
+for stock in stocks:
+    if stock not in selected:
+        new_list.append(stock)
+print new_list
+[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
+</pre>
+
+Here we iterated all the elements in the list 'stocks'. Later in this chapter we will introduce a smarter way to do this, which is just an one-line code.
+
+<h3>Break and continue</h3>
+
+These are two commonly used commands in a for loop. If "break" is triggered while a loop is executing, the loop will terminate immediately:
+<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+for stock in stocks:
+    print stock
+    if stock == 'FB': break
+[out]:
+AAPL
+GOOG
+IBM
+FB
+</pre>
+
+The "continue" command tells the loop to end this iteration and skip to the next iteration:
+<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+for stock in stocks:
+    if stock == 'FB': continue
+    print stock
+[out]:
+AAPL
+GOOG
+IBM
+F
+V
+G
+GE
+</pre>
+
+<h2>List Comprehension</h2>
+
+List comprehension is a Pythonic way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence. For example, if we want to create a list of squares using for loop:
+<pre class="prettyprint linenums">squares = []
+for i in [1,2,3,4,5]:
+    squares.append(i**2)
+print squares
+[out]: [1, 4, 9, 16, 25]
+</pre>
+
+Using list comprehension:
+<pre class="prettyprint linenums">foo = [1,2,3,4,5]
+squares = [x**2 for x in foo]
+print squares
+[out]: [1, 4, 9, 16, 25]
+</pre>
+
+Recall the example above where we used a for loop to select stocks. Here we use list comprehension:
+<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+selected = ['AAPL','IBM']
+new_list = [x for x in stocks if x not in selected]
+print new_list
+[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
+</pre>
+
+A list comprehension consists of square brackets containing an expression followed by a "for" clause, and possibly "for" or "if" clauses. For example:
+<pre class="prettyprint linenums">print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
+print [str(x) + ' vs ' + str(y) for x in ['AAPL','GOOG','IBM','FB']
+                                for y in ['F','V','G','GE'] if x != y]
+[out]:
+[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
+['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']
+</pre>
+
+List comprehension is an elegant way to organize one or more for loops when creating a list.
+
+<h2>Summary</h2>
+This chapter has introduced logical operations, loops and list comprehension. In the next chapter we will introduce functions and object-oriented programming, which will enable us to make our codes clean and versatile.
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.ipynb b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.ipynb
new file mode 100755
index 0000000..4270da9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.ipynb	
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n",
+      "True\n",
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 1 == 0\n",
+    "print 1 == 1\n",
+    "print 1 != 0\n",
+    "print 5 >= 5\n",
+    "print 5 >= 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 2 > 1 and 3 > 2\n",
+    "print 2 > 1 and 3 < 2 \n",
+    "print 2 > 1 or 3 < 2\n",
+    "print 2 < 1 and 3 < 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (3 > 2 or 1 < 3) and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))\n",
+    "print 3 > 2 or 1 < 3 and (1!=3 and 4>3) and not ( 3 < 2 or 1 < 3 and (1!=3 and 4>3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "expected an indented block (<ipython-input-5-91506eaa57cd>, line 5)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-5-91506eaa57cd>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    elif statement2:\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+     ]
+    }
+   ],
+   "source": [
+    "if statement1:\n",
+    "    # if the statement1 is true, execute the code here.\n",
+    "    # code.....\n",
+    "    # code.....\n",
+    "elif statement2:\n",
+    "    # if the statement 1 is false, skip the codes above to this part.\n",
+    "    # code......\n",
+    "    # code......\n",
+    "else:\n",
+    "    # if none of the above statements is True, skip to this part\n",
+    "    # code......"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "i==0 is True\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "if i == 0:\n",
+    "    print 'i==0 is True'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "q is false\n"
+     ]
+    }
+   ],
+   "source": [
+    "p = 1 > 0\n",
+    "q = 2 > 3\n",
+    "if p and q:\n",
+    "    print 'p and q is true'\n",
+    "elif p and not q:\n",
+    "    print 'q is false'\n",
+    "elif q and not p:\n",
+    "    print 'p is false'\n",
+    "else:\n",
+    "    print 'None of p and q is true'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "while i < 5:\n",
+    "    print i\n",
+    "    i += 1 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in [1,2,3,4,5]:\n",
+    "    print i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['AAPL', 'GOOG', 'IBM', 'FB', 'F', 'V', 'G', 'GE']\n"
+     ]
+    }
+   ],
+   "source": [
+    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "selected = ['AAPL','IBM']\n",
+    "new_list = []\n",
+    "for i in stocks:\n",
+    "    if i not in selected:\n",
+    "        new_list.append(i)\n",
+    "print stocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AAPL\n",
+      "GOOG\n",
+      "IBM\n",
+      "FB\n"
+     ]
+    }
+   ],
+   "source": [
+    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "for i in stocks:\n",
+    "    print i\n",
+    "    if i == 'FB':\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AAPL\n",
+      "GOOG\n",
+      "IBM\n",
+      "F\n",
+      "V\n",
+      "G\n",
+      "GE\n"
+     ]
+    }
+   ],
+   "source": [
+    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "for i in stocks:\n",
+    "    if i == 'FB':\n",
+    "        continue\n",
+    "    print i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 4, 9, 16, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "squares = []\n",
+    "for i in [1,2,3,4,5]:\n",
+    "    squares.append(i**2)\n",
+    "print squares"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 4, 9, 16, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "list = [1,2,3,4,5]\n",
+    "squares = [x**2 for x in list]\n",
+    "print squares"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['AAPL', 'IBM']\n"
+     ]
+    }
+   ],
+   "source": [
+    "stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "selected = ['AAPL','IBM']\n",
+    "new_list = [x for x in stocks if x in selected]\n",
+    "print new_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]\n",
+      "['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]\n",
+    "print [str(x)+' vs '+str(y) for x in ['AAPL','GOOG','IBM','FB'] for y in ['F','V','G','GE'] if x!=y]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html
new file mode 100755
index 0000000..560e7f9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html	
@@ -0,0 +1,194 @@
+In the last tutorial we introduced logical operations, loops and list comprehension. We will introduce functions and object-oriented programming in this chapter, which will enable us to build complex algorithms in more flexible ways.
+
+<h2>Functions</h2>
+
+A function is a reusable block of code. We can use a function to output a value, or do anything else we want. We can easily define our own function by using the keyword "def".
+
+<pre class="prettyprint linenums">def product(x,y):
+    return x*y
+print product(2,3)
+[out]: 6
+print product(5,10)
+[out]: 50
+</pre>
+
+The keyword "def" is followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The product() function above has "x" and "y" as its parameters. A function doesn't necessarily have parameters:
+
+<pre class="prettyprint linenums">def say_hi():
+    print "Welcome to QuantConnect'
+say_hi()
+[out]: Welcome to QuantConnect
+</pre>
+
+<h2>Built-in Function</h2>
+
+<strong>range()</strong> is a function that creates a list containing an arithmetic sequence. It's often used in for loops. The arguments must be integers. If the "step" argument is omitted, it defaults to 1.
+<pre class="prettyprint linenums">
+print range(10)
+[out]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+print range(1, 11)
+[out]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+print range(1, 11, 2)
+[out]: [1, 3, 5, 7, 9]
+</pre>
+
+<strong>len()</strong> is another function used together with range() to create a for loop. This function returns the length of an object. The argument must be a sequence or a collection.
+<pre class="prettyprint linenums">
+tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
+print "The number of tickers is {}".format(len(tickers))
+for k in range(len(tickers)):
+    print k + 1, tickers[k]
+[out]:
+The number of tickers is 8
+1 AAPL
+2 GOOGL
+3 IBM
+4 FB
+5 F
+6 V
+7 G
+8 GE
+</pre>
+
+Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
+
+<strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
+<pre class="prettyprint linenums">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+print(list(map(len,tickers)))
+[out]: [4, 5, 3, 2, 1, 1, 1, 2]
+</pre>
+
+The <strong>lambda operator</strong> is a way to create small anonymous functions. These functions are just needed where they have been created. For example:
+<pre class="prettyprint linenums">map(lambda x: x**2, range(10))
+[out]: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+</pre>
+
+map() can be applied to more than one list. The lists have to have the same length.
+<pre class="prettyprint linenums">map(lambda x, y: x+y, [1,2,3,4,5], [5,4,3,2,1])
+[out]: [6, 6, 6, 6, 6]
+</pre>
+
+<strong>sorted()</strong> takes a list or set and returns a new sorted list:
+<pre class="prettyprint linenums">sorted([5,2,3,4,1])
+[out]: [1, 2, 3, 4, 5]
+</pre>
+
+We can add a "key" parameter to specify a function to be called on each list element prior to making comparisons. For example:
+<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+sorted(price_list, key = lambda x: x[1])
+[out]:
+[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
+</pre>
+
+By default the values are sorted by ascending order. We can change it to descending by adding an optional parameter "reverse'.
+<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+sorted(price_list, key = lambda x: x[1], reverse = True)
+[out]:
+[('GOOGL', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]
+</pre>
+
+Lists also have a function list.sort(). This function takes the same "key" and "reverse" arguments as sorted(), but it doesn't return a new list.
+<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+price_list.sort(key = lambda x: x[1])
+print price_list
+[out]:
+[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
+</pre>
+
+<h2>Object-Oriented Programming</h2>
+
+Python is an object-oriented programming language. It's important to understand the concept of "objects" because almost every kind of data from QuantConnect API is an object.
+
+<h3>Class</h3>
+A class is a type of data, just like a string, float, or list. When we create an object of that data type, we call it an <strong>instance</strong> of a class.
+
+In Python, everything is an object - everything is an instance of some class. The data stored inside an object are called <strong>attributes</strong>, and the functions which are associated with the object are called <strong>methods</strong>.
+
+For example, as mentioned above, a list is an object of the "list" class, and it has a method list.sort().
+
+We can create our own objects by defining a class. We would do this when it's helpful to group certain functions together. For example, we define a class named "Stock" here:
+<pre class="prettyprint linenums">class Stock:
+    def __init__(self, ticker, open, close, volume):
+        self.ticker = ticker
+        self.open = open
+        self.close = close
+        self.volume = volume
+        self.rate_return = float(close)/open - 1
+
+    def update(self, open, close):
+        self.open = open
+        self.close = close
+        self.rate_return = float(self.close)/self.open - 1
+
+    def print_return(self):
+        print self.rate_return
+</pre>
+
+The "Stock" class has attributes "ticker", "open", "close", "volume" and "rate_return". Inside the class body, the first method is called __init__, which is a special method. When we create a new instance of the class, the __init__ method is immediately executed with all the parameters that we pass to the "Stock" object. The purpose of this method is to set up a new "Stock" object using data we have provided.
+
+Here we create two Stock objects named "apple" and "google".
+<pre class="prettyprint linenums">apple  = Stock('AAPL', 143.69, 144.09, 20109375)
+google = Stock('GOOGL', 898.7, 911.7, 1561616)
+</pre>
+
+Stock objects also have two other methods: update() and print_return(). We can access the attribues of a Stock object and call its methods:
+<pre class="prettyprint linenums">apple.ticker
+[out]: 'AAPL'
+google.print_return()
+[out]: 0.0144653388227
+google.update(912.8,913.4)
+google.print_return()
+[out]: 0.000657318141981
+</pre>
+
+By calling the update() function, we updated the open and close prices of a stock. Please note that when we use the attributes or call the methods <strong>inside a class</strong>, we need to specify them as self.attribute or self.method(), otherwise Python will deem them as global variables and thus raise an error.
+
+We can add an attribute to an object anywhere:
+<pre class="prettyprint linenums">
+apple.ceo = 'Tim Cook'
+apple.ceo
+[out]: 'Tim Cook'
+</pre>
+
+We can check what names (i.e. attributes and methods) are defined on an object using the dir() function:
+<pre class="prettyprint linenums">dir(apple)
+[out]:
+['__doc__',
+ '__init__',
+ '__module__',
+ 'ceo',
+ 'close',
+ 'open',
+ 'print_return',
+ 'rate_return',
+ 'ticker',
+ 'update',
+ 'volume']
+</pre>
+
+<h3>Inheritance</h3>
+
+Inheritance is a way of arranging classes in a hierarchy from the most general to the most specific. A "child" class is a more specific type of a "parent" class because a child class will inherit all the attribues and methods of its parent. For example, we define a class named "Child" which inherits "Stock":
+<pre class="prettyprint linenums">class Child(Stock):
+    def __init__(self, name):
+        self.name = name
+</pre>
+
+Then we create an object:
+<pre class="prettyprint linenums">aa = Child('AA')
+print aa.name
+[out]: 'AA'
+aa.update(100, 102)
+print aa.open
+[out]: 100
+print aa.close
+[out]: 102
+print aa.print_return()
+[out]: 0.02</pre>
+As seen above, the new class Child has inherited the methods from Stock.
+
+<h2>Summary</h2>
+
+In this chapter we have introduced functions and classes. When we write a QuantConnect algorithm, we would define our algorithm as a class (QCAlgorithm). This means our algorithm inherited the QC API methods from QCAlgorithm class.
+
+In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.ipynb b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.ipynb
new file mode 100755
index 0000000..261e947
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.ipynb	
@@ -0,0 +1,418 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "def product(x,y):\n",
+    "    return x*y\n",
+    "print(product(2,3))\n",
+    "print(product(5,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Welcome to QuantConnect\n"
+     ]
+    }
+   ],
+   "source": [
+    "def say_hi():\n",
+    "    print('Welcome to QuantConnect')\n",
+    "say_hi()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "range(0, 10)\n",
+      "range(1, 11)\n",
+      "range(1, 11, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(range(10))\n",
+    "print(range(1,11))\n",
+    "print(range(1,11,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The length of tickers is 8\n",
+      "AAPL\n",
+      "GOOG\n",
+      "IBM\n",
+      "FB\n",
+      "F\n",
+      "V\n",
+      "G\n",
+      "GE\n"
+     ]
+    }
+   ],
+   "source": [
+    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "print('The length of tickers is {}'.format(len(tickers)))\n",
+    "for i in range(len(tickers)):\n",
+    "    print(tickers[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 4, 3, 2, 1, 1, 1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']\n",
+    "print(list(map(len,tickers)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(lambda x: x**2, range(10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[6, 6, 6, 6, 6]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(lambda x, y: x+y, [1,2,3,4,5],[5,4,3,2,1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted([5,2,3,4,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
+    "sorted(price_list, key = lambda x: x[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('GOOG', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
+    "sorted(price_list, key = lambda x: x[1],reverse = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOG', 911.71)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "price_list = [('AAPL',144.09),('GOOG',911.71),('MSFT',69),('FB',150),('WMT',75.32)]\n",
+    "price_list.sort(key = lambda x: x[1])\n",
+    "print(price_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class stock:\n",
+    "    def __init__(self, ticker, open, close, volume):\n",
+    "        self.ticker = ticker\n",
+    "        self.open = open\n",
+    "        self.close = close\n",
+    "        self.volume = volume\n",
+    "        self.rate_return = float(close)/open - 1\n",
+    " \n",
+    "    def update(self, open, close):\n",
+    "        self.open = open\n",
+    "        self.close = close\n",
+    "        self.rate_return = float(self.close)/self.open - 1\n",
+    " \n",
+    "    def print_return(self):\n",
+    "        print(self.rate_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "apple = stock('AAPL', 143.69, 144.09, 20109375)\n",
+    "google = stock('GOOG', 898.7, 911.7, 1561616)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.014465338822744034\n",
+      "0.0006573181419806673\n"
+     ]
+    }
+   ],
+   "source": [
+    "apple.ticker\n",
+    "google.print_return()\n",
+    "google.update(912.8,913.4)\n",
+    "google.print_return()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tim Cook'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apple.ceo = 'Tim Cook'\n",
+    "apple.ceo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'ceo',\n",
+       " 'close',\n",
+       " 'open',\n",
+       " 'print_return',\n",
+       " 'rate_return',\n",
+       " 'ticker',\n",
+       " 'update',\n",
+       " 'volume']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(apple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class child(stock):\n",
+    "    def __init__(self,name):\n",
+    "        self.name = name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "aa\n",
+      "100\n",
+      "102\n",
+      "0.020000000000000018\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "aa = child('aa')\n",
+    "print(aa.name)\n",
+    "aa.update(100,102)\n",
+    "print(aa.open)\n",
+    "print(aa.close)\n",
+    "print(aa.print_return())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html
new file mode 100755
index 0000000..31ef442
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html	
@@ -0,0 +1,229 @@
+Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas.
+
+NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data.
+
+Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package:
+<pre class="prettyprint linenums">import numpy as np</pre>
+
+<h2>NumPy</h2>
+
+<h3>Basic NumPy Arrays</h3>
+
+A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. Here we make an array by passing a list of Apple stock prices:
+<pre class="prettyprint linenums">price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
+price_array = np.array(price_list)
+print price_array, type(price_array)
+[out]: [ 143.73  145.83  143.68  144.02  143.5   142.62]
+&lt;class 'numpy.ndarray'&gt;</pre>
+
+Notice that the type of array is "ndarray" which is a multi-dimensional array. If we pass np.array() a list of lists, it will create a 2-dimensional array.
+<pre class="prettyprint linenums">Ar = np.array([[1,3], [2,4]])
+print Ar, type(Ar)
+[out]: [[1 3]
+        [2 4]]
+&lt;class 'numpy.ndarray'&gt;</pre>
+
+We get the dimensions of an ndarray using the .shape attribute:
+<pre class="prettyprint linenums">print Ar.shape
+[out]: (2, 2)</pre>
+
+If we create an 2-dimensional array (i.e. matrix), each row can be accessed by index:
+<pre class="prettyprint linenums">print Ar[0]
+[out]: [1 3]
+print Ar[1]
+[out]: [2 4]</pre>
+
+If we want to access the matrix by column instead:
+<pre class="prettyprint linenums">print 'First column:', Ar[:,0]
+[out]: First column: [1 2]
+print 'Second column:', Ar[:,1]
+[out]: Second column: [3 4]</pre>
+
+<h3>Array Functions</h3>
+
+Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array:
+<pre class="prettyprint linenums">np.log(price_array)
+[out]: [4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]</pre>
+
+Other functions return a single value:
+<pre class="prettyprint linenums">np.mean(price_array)
+[out]: 143.896666667
+print np.std(price_array)
+[out]: 0.967379047852
+print np.sum(price_array)
+[out]: 863.38
+print np.max(price_array)
+[out]: 145.83
+</pre>
+The functions above return the mean, standard deviation, total and maximum value of an array.
+
+<h2>Pandas</h2>
+
+Pandas is one of the most powerful tools for dealing with financial data. First we need to import Pandas:
+<pre class="prettyprint linenums">import pandas as pd</pre>
+
+<h3>Series</h3>
+
+Series is a one-dimensional labeled array capable of holding any data type (integers, strings, float, Python object, etc.)
+
+We create a Series by calling pd.Series(data), where data can be a dictionary, an array or just a scalar value.
+<pre class="prettyprint linenums">price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
+s = pd.Series(price)
+print s
+
+0    143.73
+1    145.83
+2    143.68
+3    144.02
+4    143.50
+5    142.62</pre>
+
+We can customize the indices of a new Series:
+<pre class="prettyprint linenums">s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f'])
+print s
+
+a    143.73
+b    145.83
+c    143.68
+d    144.02
+e    143.50
+f    142.62</pre>
+
+Or we can change the indices of an existing Series:
+<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
+print s
+
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62</pre>
+
+Series is like a list since it can be sliced by index:
+<pre class="prettyprint linenums">print s[1:]
+print s[:-2]
+
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62
+dtype: float64
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+dtype: float64</pre>
+
+Series is also like a dictionary whose values can be set or fetched by index label:
+<pre class="prettyprint linenums">print s[4]
+s[4] = 0
+print s
+
+143.68
+6    143.73
+5    145.83
+4      0.00
+3    144.02
+2    143.50
+1    142.62
+dtype: float64</pre>
+
+Series can also have a name attribute, which will be used when we make up a Pandas DataFrame using several series.
+<pre class="prettyprint linenums">s = pd.Series(price, name = 'Apple Prices')
+print s
+print s.name
+
+0    143.73
+1    145.83
+2    143.68
+3    144.02
+4    143.50
+5    142.62
+Name: Apple Prices, dtype: float64
+Apple Prices</pre>
+
+We can get the statistical summaries of a Series:
+<pre class="prettyprint linenums">print s.describe()
+
+count      6.000000
+mean     143.896667
+std        1.059711
+min      142.620000
+25%      143.545000
+50%      143.705000
+75%      143.947500
+max      145.830000</pre>
+
+<h3>Time Index</h3>
+
+Pandas has a built-in function specifically for creating date indices: pd.date_range(). We use it to create a new index for our Series:
+<pre class="prettyprint linenums">time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D')
+print time_index
+s.index = time_index
+print s
+
+DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
+               '2017-01-05', '2017-01-06'],
+              dtype='datetime64[ns]', freq='D')
+2017-01-01    143.73
+2017-01-02    145.83
+2017-01-03    143.68
+2017-01-04    144.02
+2017-01-05    143.50
+2017-01-06    142.62
+Freq: D, Name: Apple Prices, dtype: float64</pre>
+
+Series are usually accessed using the iloc[] and loc[] methods. iloc[] is used to access elements by integer index, and loc[] is used to access the index of the series.
+
+iloc[] is necessary when the index of a series are integers, take our previous defined series as example:
+<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
+print s
+print s[1]
+
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62
+Name: Apple Prices, dtype: float64
+142.62</pre>
+
+If we intended to take the second element of the series, we would make a mistake here, because the index are integers. In order to access to the element we want, we use iloc[] here:
+<pre class="prettyprint linenums">print s.iloc[1]
+[out]: 145.83
+</pre>
+
+While working with time series data, we often use time as the index. Pandas provides us with various methods to access the data by time index.
+<pre class="prettyprint linenums">s.index = time_index
+print s['2017-01-03']
+[out]: 143.68</pre>
+
+We can even access to a range of dates:
+<pre class="prettyprint linenums">print s['2017-01-02':'2017-01-05']
+
+2017-01-02    145.83
+2017-01-03    143.68
+2017-01-04    144.02
+2017-01-05    143.50
+Freq: D, Name: Apple Prices, dtype: float64</pre>
+
+Series[] provides us a very flexible way to index data. We can add any condition in the square brackets:
+<pre class="prettyprint linenums">print s[s &lt; np.mean(s)]
+print s[(s &gt; np.mean(s)) &amp; (s &lt; np.mean(s) + 1.64*np.std(s))]
+
+2017-01-01    143.73
+2017-01-03    143.68
+2017-01-05    143.50
+2017-01-06    142.62
+Name: Apple Prices, dtype: float64
+2017-01-04    144.02
+Freq: D, Name: Apple Price List, dtype: float64</pre>
+
+As demonstrated, we can use logical operators like &amp; (and), | (or) and ~ (not) to group multiple conditions.
+
+<h2>Summary</h2>
+
+Here we have introduced NumPy and Pandas for scientific computing in Python. In the next chapter, we will dive into Pandas to learn resampling and manipulating Pandas DataFrame, which are commonly used in financial data analysis.
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.ipynb b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.ipynb
new file mode 100755
index 0000000..32fd4f9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.ipynb	
@@ -0,0 +1,478 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[143.73 145.83 143.68 144.02 143.5  142.62] <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
+    "price_array = np.array(price_list)\n",
+    "print(price_array, type(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 3]\n",
+      " [2 4]] <class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "Ar = np.array([[1,3],[2,4]])\n",
+    "print(Ar, type(Ar))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Ar.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 3]\n",
+      "[2 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Ar[0])\n",
+    "print(Ar[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first column:  [1 2]\n",
+      "the second column:  [3 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('the first column: ', Ar[:,0])\n",
+    "print('the second column: ', Ar[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.log(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.89666666666668\n",
+      "0.9673790478515796\n",
+      "863.38\n",
+      "145.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.mean(price_array))\n",
+    "print(np.std(price_array))\n",
+    "print(np.sum(price_array))\n",
+    "print(np.max(price_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    143.73\n",
+       "1    145.83\n",
+       "2    143.68\n",
+       "3    144.02\n",
+       "4    143.50\n",
+       "5    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]\n",
+    "s = pd.Series(price)\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    143.73\n",
+       "b    145.83\n",
+       "c    143.68\n",
+       "d    144.02\n",
+       "e    143.50\n",
+       "f    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s = pd.Series(price,index = ['a','b','c','d','e','f'])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    143.73\n",
+       "5    145.83\n",
+       "4    143.68\n",
+       "3    144.02\n",
+       "2    143.50\n",
+       "1    142.62\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s.index = [6,5,4,3,2,1]\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "dtype: float64\n",
+      "6    143.73\n",
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[1:])\n",
+    "print(s[:-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.68\n",
+      "6    143.73\n",
+      "5    145.83\n",
+      "4      0.00\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[4])\n",
+    "s[4] = 0\n",
+    "print(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    143.73\n",
+      "1    145.83\n",
+      "2    143.68\n",
+      "3    144.02\n",
+      "4    143.50\n",
+      "5    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "Apple Price List\n"
+     ]
+    }
+   ],
+   "source": [
+    "s = pd.Series(price, name = 'Apple Price List')\n",
+    "print(s)\n",
+    "print(s.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
+      "               '2017-01-05', '2017-01-06'],\n",
+      "              dtype='datetime64[ns]', freq='D')\n",
+      "2017-01-01    143.73\n",
+      "2017-01-02    145.83\n",
+      "2017-01-03    143.68\n",
+      "2017-01-04    144.02\n",
+      "2017-01-05    143.50\n",
+      "2017-01-06    142.62\n",
+      "Freq: D, Name: Apple Price List, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "time_index = pd.date_range('2017-01-01',periods = len(s),freq = 'D')\n",
+    "print(time_index)\n",
+    "s.index = time_index\n",
+    "print(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6    143.73\n",
+      "5    145.83\n",
+      "4    143.68\n",
+      "3    144.02\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "142.62\n"
+     ]
+    }
+   ],
+   "source": [
+    "s.index = [6,5,4,3,2,1]\n",
+    "print(s)\n",
+    "print(s[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "145.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s.iloc[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "143.68\n"
+     ]
+    }
+   ],
+   "source": [
+    "s.index = time_index\n",
+    "print(s['2017-01-03'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2017-01-02    145.83\n",
+      "2017-01-03    143.68\n",
+      "2017-01-04    144.02\n",
+      "2017-01-05    143.50\n",
+      "Freq: D, Name: Apple Price List, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s['2017-01-02':'2017-01-05'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6    143.73\n",
+      "4    143.68\n",
+      "2    143.50\n",
+      "1    142.62\n",
+      "Name: Apple Price List, dtype: float64\n",
+      "[6    False\n",
+      "5    False\n",
+      "4    False\n",
+      "3     True\n",
+      "2    False\n",
+      "1    False\n",
+      "Name: Apple Price List, dtype: bool]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[s < np.mean(s)] )\n",
+    "print([(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html
new file mode 100755
index 0000000..7805251
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html	
@@ -0,0 +1,678 @@
+In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
+
+Here we use data from the yahoo_finance API.
+<pre class="prettyprint linenums">import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'</pre>
+
+We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
+<pre class="prettyprint linenums">aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table['Adj. Close']['2017']
+print aapl</pre>
+
+Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
+<pre class="prettyprint linenums">print aapl['2017-3']
+Date
+2017-03-01    138.657681
+2017-03-02    137.834404
+2017-03-03    138.647762
+2017-03-06    138.211326
+2017-03-07    138.389868
+2017-03-08    137.874080
+2017-03-09    137.556672
+2017-03-10    138.012946
+2017-03-13    138.072460
+2017-03-14    137.864161
+2017-03-15    139.322254
+2017-03-16    139.550391
+2017-03-17    138.856061
+2017-03-20    140.314154
+2017-03-21    138.707276
+2017-03-22    140.274478
+2017-03-23    139.778528
+2017-03-24    139.500796
+2017-03-27    139.738852
+2017-03-28    142.635200
+2017-03-29    142.952608
+2017-03-30    142.764147
+2017-03-31    142.496334</pre>
+
+Or in several consecutive months:
+<pre class="prettyprint linenums">aapl['2017-2':'2017-4']</pre>
+
+.head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
+<pre class="prettyprint linenums">print aapl.head()
+print aapl.tail(10)</pre>
+
+The output:
+<pre class="prettyprint linenums">
+Date
+2017-01-03    114.715378
+2017-01-04    114.586983
+2017-01-05    115.169696
+2017-01-06    116.453639
+2017-01-09    117.520300
+Name: Adj. Close, dtype: float64
+Date
+2017-08-08    159.433108
+2017-08-09    160.409148
+2017-08-10    155.270000
+2017-08-11    157.480000
+2017-08-14    159.850000
+2017-08-15    161.600000
+2017-08-16    160.950000
+2017-08-17    157.870000
+2017-08-18    157.500000
+2017-08-21    157.210000
+Name: Adj. Close, dtype: float64</pre>
+
+<h2>Resampling</h2>
+
+series.resample(freq) is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
+
+series.resample.mean() is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
+<pre class="prettyprint linenums">by_month = aapl.resample('M').mean()
+print by_month
+
+Date
+2017-01-31    118.093136
+2017-02-28    132.456268
+2017-03-31    139.478802
+2017-04-30    141.728436
+2017-05-31    151.386305
+2017-06-30    147.233064
+2017-07-31    147.706190
+2017-08-31    157.444303</pre>
+
+We can also aggregate the data by week:
+<pre class="prettyprint linenums">by_week = aapl.resample('W').mean()
+print by_week.head()
+
+Date
+2017-01-31    120.932434
+2017-02-28    136.551200
+2017-03-31    143.532630
+2017-04-30    144.179981
+2017-05-31    156.100000
+2017-06-30    155.450000
+2017-07-31    153.460000</pre>
+
+We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
+<pre class="prettyprint linenums">three_day = aapl.resample('3D').mean()
+two_week  = aapl.resample('2W').mean()
+two_month = aapl.resample('2M').mean()</pre>
+
+Besides the mean() method, other methods can also be used with the resampler:
+<pre class="prettyprint linenums">std = aapl.resample('W').std()    # standard deviation
+max = aapl.resample('W').max()    # maximum value
+min = aapl.resample('W').min()    # minimum value</pre>
+
+OFten we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
+<pre class="prettyprint linenums">last_day = aapl.resample('M').agg(lambda x: x[-1])
+print last_day
+
+Date
+2017-01-31    119.851150
+2017-02-28    135.880362
+2017-03-31    142.496334
+2017-04-30    142.486415
+2017-05-31    152.142689
+2017-06-30    143.438008
+2017-07-31    148.248489
+2017-08-31    157.210000</pre>
+
+Or directly calculate the monthly rates of return using the data for the first day and the last day:
+<pre class="prettyprint linenums">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
+print monthly_return
+
+Date
+2017-01-31    0.045940
+2017-02-28    0.070409
+2017-03-31    0.033823
+2017-04-30   -0.007736
+2017-05-31    0.039829
+2017-06-30   -0.073528
+2017-07-31    0.033035
+2017-08-31    0.004505</pre>
+
+Series object also provides us some convenient methods to do some quick calculation.
+<pre class="prettyprint linenums">print monthly_return.mean()
+print monthly_return.std()
+print monthly_return.max()
+[out]: 0.0208974076157
+       0.0476398315185
+       0.0704090212384</pre>
+
+Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
+<pre class="prettyprint linenums">print last_day.diff()
+print last_day.pct_change()
+
+Date
+2017-01-31          NaN
+2017-02-28    16.029211
+2017-03-31     6.615972
+2017-04-30    -0.009919
+2017-05-31     9.656274
+2017-06-30    -8.704681
+2017-07-31     4.810482
+2017-08-31     8.961511
+Freq: M, Name: Adj. Close, dtype: float64
+Date
+2017-01-31         NaN
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449</pre>
+
+Notice that we induced a NaN value while calculating percentage changes i.e. returns.
+
+When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
+<pre class="prettyprint linenums">daily_return = last_day.pct_change()
+print daily_return.fillna(0)
+
+Date
+2017-01-31    0.000000
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449</pre>
+
+Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
+<pre class="prettyprint linenums">daily_return = last_day.pct_change()
+print daily_return.fillna(method = 'bfill')
+
+Date
+2017-01-31    0.133743
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449</pre>
+
+As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
+
+We can also simply remove NaN values by <strong>.dropna()</strong>
+<pre class="prettyprint linenums">daily_return = last_day.pct_change().dropna()
+print daily_return
+
+Date
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.038050</pre>
+
+<h2>DataFrame</h2>
+
+The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
+
+More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
+
+<h3>Create DataFrames</h3>
+
+The most common method of creating a DataFrame is passing a dictionary:
+<pre class="prettyprint linenums">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
+        'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
+        'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
+dates = pd.date_range('2017-07-03', periods = 5, freq = 'D')
+df = pd.DataFrame(dict, index = dates)
+print df
+
+              AAPL    GOOG     IBM
+2017-07-03  143.50  898.70  155.58
+2017-07-04  144.09  911.71  153.67
+2017-07-05  142.73  906.69  152.36
+2017-07-06  144.18  918.59  152.94
+2017-07-07  143.77  926.99  153.49</pre>
+
+<h3>Manipulating DataFrames</h3>
+
+We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
+
+If a column name contains no spaces, then we can also use df.column_name to fetch a column:
+<pre class="prettyprint linenums">df = aapl_table
+print df.Close.tail(5)
+print df['Adj. Volume'].tail(5)
+
+Date
+2017-07-24    152.09
+2017-07-25    152.74
+2017-07-26    153.46
+2017-07-27    150.56
+2017-07-28    149.50
+Name: Close, dtype: float64
+Date
+2017-07-24    21122730.0
+2017-07-25    18612649.0
+2017-07-26    15172136.0
+2017-07-27    32175875.0
+2017-07-28    16832947.0
+Name: Adj. Volume, dtype: float64</pre>
+
+All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
+<pre class="prettyprint linenums">aapl_2016 = df['2016']
+aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
+print aapl_month</pre>
+
+<pre class="prettyprint linenums">
+              Open      High     Low   Close      Volume  Ex-Dividend  \
+Date
+2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0
+2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0
+2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0
+2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0
+2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0
+2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0
+2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0
+2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0
+2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0
+2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0
+2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0
+2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0
+
+            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \
+Date
+2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495
+2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856
+2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931
+2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748
+2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913
+2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775
+2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363
+2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460
+2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885
+2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287
+2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807
+2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583
+
+            Adj. Volume
+Date
+2016-01-31   64416504.0
+2016-02-29   35216277.0
+2016-03-31   25888449.0
+2016-04-30   68531478.0
+2016-05-31   42307212.0
+2016-06-30   35836356.0
+2016-07-31   27733688.0
+2016-08-31   29662406.0
+2016-09-30   36379106.0
+2016-10-31   26419398.0
+2016-11-30   36162258.0
+2016-12-31   30586265.0</pre>
+
+We may select certain columns of a DataFrame using their names:
+<pre class="prettyprint linenums">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
+print aapl_bar
+
+              Open      High     Low   Close
+Date
+2016-01-31   94.79   97.3400   94.35   97.34
+2016-02-29   96.86   98.2300   96.65   96.69
+2016-03-31  109.72  109.9000  108.88  108.99
+2016-04-30   93.99   94.7200   92.51   93.74
+2016-05-31   99.60  100.4000   98.82   99.86
+2016-06-30   94.44   95.7700   94.30   95.60
+2016-07-31  104.19  104.5500  103.68  104.21
+2016-08-31  105.66  106.5699  105.64  106.10
+2016-09-30  112.46  113.3700  111.80  113.05
+2016-10-31  113.65  114.2300  113.20  113.54
+2016-11-30  111.56  112.2000  110.27  110.52
+2016-12-31  116.65  117.2000  115.43  115.82</pre>
+
+We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
+<pre class="prettyprint linenums">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
+
+              Open    High     Low   Close
+Date
+2016-03-31  109.72  109.90  108.88  108.99
+2016-04-30   93.99   94.72   92.51   93.74
+2016-05-31   99.60  100.40   98.82   99.86
+2016-06-30   94.44   95.77   94.30   95.60</pre>
+
+The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
+<pre class="prettyprint linenums">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
+print above
+
+              Open      High     Low   Close
+Date
+2016-03-31  109.72  109.9000  108.88  108.99
+2016-08-31  105.66  106.5699  105.64  106.10
+2016-09-30  112.46  113.3700  111.80  113.05
+2016-10-31  113.65  114.2300  113.20  113.54
+2016-11-30  111.56  112.2000  110.27  110.52
+2016-12-31  116.65  117.2000  115.43  115.82</pre>
+
+<h3>Data Validation</h3>
+
+As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
+<pre class="prettyprint linenums">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
+print aapl_bar
+
+              Open      High     Low   Close  rate_return
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
+
+Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
+<pre class="prettyprint linenums">missing = aapl_bar.isnull()
+print missing
+print '---------------------------------------------'
+print missing.describe()
+
+             Open   High    Low  Close rate_return
+Date
+2016-01-31  False  False  False  False        True
+2016-02-29  False  False  False  False       False
+2016-03-31  False  False  False  False       False
+2016-04-30  False  False  False  False       False
+2016-05-31  False  False  False  False       False
+2016-06-30  False  False  False  False       False
+2016-07-31  False  False  False  False       False
+2016-08-31  False  False  False  False       False
+2016-09-30  False  False  False  False       False
+2016-10-31  False  False  False  False       False
+2016-11-30  False  False  False  False       False
+2016-12-31  False  False  False  False       False
+
+---------------------------------------------
+
+         Open   High    Low  Close rate_return
+count      12     12     12     12          12
+unique      1      1      1      1           2
+top     False  False  False  False       False
+freq       12     12     12     12          11</pre>
+
+The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
+
+We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
+
+We can also find the rows with missing values easily:
+<pre class="prettyprint linenums">print missing[missing.rate_return == True]
+
+             Open   High    Low  Close rate_return
+Date
+2016-01-31  False  False  False  False        True</pre>
+
+Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
+<pre class="prettyprint linenums">drop = aapl_bar.dropna()
+print drop
+print '\n--------------------------------------------------\n'
+fill = aapl_bar.fillna(0)
+print fill
+
+              Open      High     Low   Close  rate_return
+Date
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+
+--------------------------------------------------
+
+              Open      High     Low   Close  rate_return
+Date
+2016-01-31   94.79   97.3400   94.35   97.34     0.000000
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
+
+<h3>DataFrame Concat</h3>
+
+We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
+
+In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
+<pre class="prettyprint linenums">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
+s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
+data_frame = pd.concat([s1, s2], axis = 1)
+print data_frame
+
+     AAPL    GOOG
+0  143.50  898.70
+1  144.09  911.71
+2  142.73  906.69
+3  144.18  918.59
+4  143.77  926.99</pre>
+
+The "axis = 1" parameter will join two DataFrames by columns:
+<pre class="prettyprint linenums">log_price = np.log(aapl_bar.Close)
+log_price.name = 'log_price'
+print log_price
+print '\n--------------------------------------------\n'
+concat = pd.concat([aapl_bar, log_price], axis = 1)
+print concat
+
+Date
+2016-01-31    4.578210
+2016-02-29    4.571510
+2016-03-31    4.691256
+2016-04-30    4.540525
+2016-05-31    4.603769
+2016-06-30    4.560173
+2016-07-31    4.646408
+2016-08-31    4.664382
+2016-09-30    4.727830
+2016-10-31    4.732155
+2016-11-30    4.705197
+2016-12-31    4.752037
+Freq: M, Name: log_price, dtype: float64
+
+--------------------------------------------
+
+              Open      High     Low   Close  rate_return  log_price
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037</pre>
+
+We can also join two DataFrames by rows. Consider these two DataFrames:
+<pre class="prettyprint linenums">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
+print df_volume
+print '\n-------------------------------------------\n'
+df_2017 = aapl_table.loc['2016-10':'2017-04', ['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
+print df_2017
+
+                Volume  Split Ratio
+Date
+2016-10-31  26419398.0          1.0
+2016-11-30  36162258.0          1.0
+2016-12-31  30586265.0          1.0
+2017-01-31  49200993.0          1.0
+2017-02-28  23482860.0          1.0
+2017-03-31  19661651.0          1.0
+2017-04-30  20247187.0          1.0
+
+-------------------------------------------
+
+              Open     High     Low   Close
+Date
+2016-10-31  113.65  114.230  113.20  113.54
+2016-11-30  111.56  112.200  110.27  110.52
+2016-12-31  116.65  117.200  115.43  115.82
+2017-01-31  121.15  121.390  120.62  121.35
+2017-02-28  137.08  137.435  136.70  136.99
+2017-03-31  143.72  144.270  143.01  143.66
+2017-04-30  144.09  144.300  143.27  143.65</pre>
+
+Now we merge the DataFrames with our DataFrame 'aapl_bar'
+<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1)
+print concat
+
+              Open      High     Low   Close  rate_return      Volume  \
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0
+2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0
+2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0
+2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0
+2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0
+
+            Split Ratio
+Date
+2016-01-31          NaN
+2016-02-29          NaN
+2016-03-31          NaN
+2016-04-30          NaN
+2016-05-31          NaN
+2016-06-30          NaN
+2016-07-31          NaN
+2016-08-31          NaN
+2016-09-30          NaN
+2016-10-31          1.0
+2016-11-30          1.0
+2016-12-31          1.0
+2017-01-31          1.0
+2017-02-28          1.0
+2017-03-31          1.0
+2017-04-30          1.0</pre>
+
+By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
+<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
+print concat
+
+              Open    High     Low   Close  rate_return      Volume  \
+Date
+2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0
+2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0
+2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0
+
+            Split Ratio
+Date
+2016-10-31          1.0
+2016-11-30          1.0
+2016-12-31          1.0
+</pre>
+Only the intersection part was left if use 'inner join' method.
+Now let's try to append a DataFrame to another one:
+<pre class="prettyprint linenums">append = aapl_bar.append(df_2017)
+print append
+             Close      High     Low    Open  rate_return
+Date
+2016-01-31   97.34   97.3400   94.35   94.79          NaN
+2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.54  114.2300  113.20  113.65          NaN
+2016-11-30  110.52  112.2000  110.27  111.56          NaN
+2016-12-31  115.82  117.2000  115.43  116.65          NaN
+2017-01-31  121.35  121.3900  120.62  121.15          NaN
+2017-02-28  136.99  137.4350  136.70  137.08          NaN
+2017-03-31  143.66  144.2700  143.01  143.72          NaN
+2017-04-30  143.65  144.3000  143.27  144.09          NaN
+</pre>
+'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
+<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_2017], axis = 0)
+print concat
+             Close      High     Low    Open  rate_return
+Date
+2016-01-31   97.34   97.3400   94.35   94.79          NaN
+2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.54  114.2300  113.20  113.65          NaN
+2016-11-30  110.52  112.2000  110.27  111.56          NaN
+2016-12-31  115.82  117.2000  115.43  116.65          NaN
+2017-01-31  121.35  121.3900  120.62  121.15          NaN
+2017-02-28  136.99  137.4350  136.70  137.08          NaN
+2017-03-31  143.66  144.2700  143.01  143.72          NaN
+2017-04-30  143.65  144.3000  143.27  144.09          NaN
+</pre>
+Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
+<pre class="prettyprint linenums">df_2017.columns = ['Change', 'High', 'Low', 'Close']
+concat = pd.concat([aapl_bar, df_2017], axis = 0)
+print concat
+
+            Change   Close      High     Low    Open  rate_return
+Date
+2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN
+2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN
+2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN
+2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN
+2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN
+2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN
+2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN
+2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN</pre>
+
+Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
+
+<h2>Summary</h2>
+
+Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.ipynb b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.ipynb
new file mode 100755
index 0000000..a67e05c
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.ipynb	
@@ -0,0 +1,1540 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
+    "aapl_table = quandl.get('WIKI/AAPL')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aapl = aapl_table['Adj. Close']['2017']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-03    114.715378\n",
+      "2017-01-04    114.586983\n",
+      "2017-01-05    115.169696\n",
+      "2017-01-06    116.453639\n",
+      "2017-01-09    117.520300\n",
+      "2017-01-10    117.638817\n",
+      "2017-01-11    118.270913\n",
+      "2017-01-12    117.777088\n",
+      "2017-01-13    117.569682\n",
+      "2017-01-17    118.517825\n",
+      "2017-01-18    118.507948\n",
+      "2017-01-19    118.300542\n",
+      "2017-01-20    118.517825\n",
+      "2017-01-23    118.596837\n",
+      "2017-01-24    118.488195\n",
+      "2017-01-25    120.374604\n",
+      "2017-01-26    120.433863\n",
+      "2017-01-27    120.443739\n",
+      "2017-01-30    120.127692\n",
+      "2017-01-31    119.851150\n",
+      "2017-02-01    127.159749\n",
+      "2017-02-02    126.942467\n",
+      "2017-02-03    127.485673\n",
+      "2017-02-06    128.680728\n",
+      "2017-02-07    129.905412\n",
+      "2017-02-08    130.409113\n",
+      "2017-02-09    131.347379\n",
+      "2017-02-10    131.049809\n",
+      "2017-02-13    132.210332\n",
+      "2017-02-14    133.926319\n",
+      "                 ...    \n",
+      "2017-11-16    171.100000\n",
+      "2017-11-17    170.150000\n",
+      "2017-11-20    169.980000\n",
+      "2017-11-21    173.140000\n",
+      "2017-11-22    174.960000\n",
+      "2017-11-24    174.970000\n",
+      "2017-11-27    174.090000\n",
+      "2017-11-28    173.070000\n",
+      "2017-11-29    169.480000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-01    171.050000\n",
+      "2017-12-04    169.800000\n",
+      "2017-12-05    169.640000\n",
+      "2017-12-06    169.010000\n",
+      "2017-12-07    169.452000\n",
+      "2017-12-08    169.370000\n",
+      "2017-12-11    172.670000\n",
+      "2017-12-12    171.700000\n",
+      "2017-12-13    172.270000\n",
+      "2017-12-14    172.220000\n",
+      "2017-12-15    173.870000\n",
+      "2017-12-18    176.420000\n",
+      "2017-12-19    174.540000\n",
+      "2017-12-20    174.350000\n",
+      "2017-12-21    175.010000\n",
+      "2017-12-22    175.010000\n",
+      "2017-12-26    170.570000\n",
+      "2017-12-27    170.600000\n",
+      "2017-12-28    171.080000\n",
+      "2017-12-29    169.230000\n",
+      "Name: Adj. Close, Length: 249, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-03-01    138.657681\n",
+      "2017-03-02    137.834404\n",
+      "2017-03-03    138.647762\n",
+      "2017-03-06    138.211326\n",
+      "2017-03-07    138.389868\n",
+      "2017-03-08    137.874080\n",
+      "2017-03-09    137.556672\n",
+      "2017-03-10    138.012946\n",
+      "2017-03-13    138.072460\n",
+      "2017-03-14    137.864161\n",
+      "2017-03-15    139.322254\n",
+      "2017-03-16    139.550391\n",
+      "2017-03-17    138.856061\n",
+      "2017-03-20    140.314154\n",
+      "2017-03-21    138.707276\n",
+      "2017-03-22    140.274478\n",
+      "2017-03-23    139.778528\n",
+      "2017-03-24    139.500796\n",
+      "2017-03-27    139.738852\n",
+      "2017-03-28    142.635200\n",
+      "2017-03-29    142.952608\n",
+      "2017-03-30    142.764147\n",
+      "2017-03-31    142.496334\n",
+      "Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl['2017-3'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-02-01    127.159749\n",
+       "2017-02-02    126.942467\n",
+       "2017-02-03    127.485673\n",
+       "2017-02-06    128.680728\n",
+       "2017-02-07    129.905412\n",
+       "2017-02-08    130.409113\n",
+       "2017-02-09    131.347379\n",
+       "2017-02-10    131.049809\n",
+       "2017-02-13    132.210332\n",
+       "2017-02-14    133.926319\n",
+       "2017-02-15    134.412350\n",
+       "2017-02-16    134.248686\n",
+       "2017-02-17    134.620649\n",
+       "2017-02-21    135.592711\n",
+       "2017-02-22    135.999390\n",
+       "2017-02-23    135.424088\n",
+       "2017-02-24    135.553035\n",
+       "2017-02-27    135.820848\n",
+       "2017-02-28    135.880362\n",
+       "2017-03-01    138.657681\n",
+       "2017-03-02    137.834404\n",
+       "2017-03-03    138.647762\n",
+       "2017-03-06    138.211326\n",
+       "2017-03-07    138.389868\n",
+       "2017-03-08    137.874080\n",
+       "2017-03-09    137.556672\n",
+       "2017-03-10    138.012946\n",
+       "2017-03-13    138.072460\n",
+       "2017-03-14    137.864161\n",
+       "2017-03-15    139.322254\n",
+       "                 ...    \n",
+       "2017-03-17    138.856061\n",
+       "2017-03-20    140.314154\n",
+       "2017-03-21    138.707276\n",
+       "2017-03-22    140.274478\n",
+       "2017-03-23    139.778528\n",
+       "2017-03-24    139.500796\n",
+       "2017-03-27    139.738852\n",
+       "2017-03-28    142.635200\n",
+       "2017-03-29    142.952608\n",
+       "2017-03-30    142.764147\n",
+       "2017-03-31    142.496334\n",
+       "2017-04-03    142.536010\n",
+       "2017-04-04    143.597342\n",
+       "2017-04-05    142.853418\n",
+       "2017-04-06    142.496334\n",
+       "2017-04-07    142.178926\n",
+       "2017-04-10    142.010303\n",
+       "2017-04-11    140.482777\n",
+       "2017-04-12    140.651400\n",
+       "2017-04-13    139.907475\n",
+       "2017-04-17    140.681157\n",
+       "2017-04-18    140.056260\n",
+       "2017-04-19    139.540472\n",
+       "2017-04-20    141.286216\n",
+       "2017-04-21    141.117593\n",
+       "2017-04-24    142.476496\n",
+       "2017-04-25    143.369205\n",
+       "2017-04-26    142.487208\n",
+       "2017-04-27    142.625281\n",
+       "2017-04-28    142.486415\n",
+       "Name: Adj. Close, Length: 61, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aapl['2017-2':'2017-4']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-03    114.715378\n",
+      "2017-01-04    114.586983\n",
+      "2017-01-05    115.169696\n",
+      "2017-01-06    116.453639\n",
+      "2017-01-09    117.520300\n",
+      "Name: Adj. Close, dtype: float64\n",
+      "Date\n",
+      "2017-12-15    173.87\n",
+      "2017-12-18    176.42\n",
+      "2017-12-19    174.54\n",
+      "2017-12-20    174.35\n",
+      "2017-12-21    175.01\n",
+      "2017-12-22    175.01\n",
+      "2017-12-26    170.57\n",
+      "2017-12-27    170.60\n",
+      "2017-12-28    171.08\n",
+      "2017-12-29    169.23\n",
+      "Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl.head(5))\n",
+    "print(aapl.tail(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    118.093136\n",
+      "2017-02-28    132.456268\n",
+      "2017-03-31    139.478802\n",
+      "2017-04-30    141.728436\n",
+      "2017-05-31    151.386305\n",
+      "2017-06-30    147.233064\n",
+      "2017-07-31    147.706190\n",
+      "2017-08-31    158.856375\n",
+      "2017-09-30    157.606500\n",
+      "2017-10-31    157.811627\n",
+      "2017-11-30    172.214500\n",
+      "2017-12-31    171.893100\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_month = aapl.resample('M').mean()\n",
+    "print(by_month)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-08    115.231424\n",
+      "2017-01-15    117.755360\n",
+      "2017-01-22    118.461035\n",
+      "2017-01-29    119.667448\n",
+      "2017-02-05    124.313346\n",
+      "Freq: W-SUN, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "by_week = aapl.resample('W').mean()\n",
+    "print(by_week.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-01-31    120.443739\n",
+       "2017-02-28    135.999390\n",
+       "2017-03-31    142.952608\n",
+       "2017-04-30    143.597342\n",
+       "2017-05-31    155.469192\n",
+       "2017-06-30    154.821818\n",
+       "2017-07-31    152.839860\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    164.050000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    175.880000\n",
+       "2017-12-31    176.420000\n",
+       "Freq: M, Name: Adj. Close, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aapl.resample('M').max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "three_day = aapl.resample('3D').mean()\n",
+    "two_week = aapl.resample('2W').mean()\n",
+    "two_month = aapl.resample('2M').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std = aapl.resample('W').std()\n",
+    "max = aapl.resample('W').max()\n",
+    "min = aapl.resample('W').min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.045940\n",
+      "2017-02-28    0.070409\n",
+      "2017-03-31    0.033823\n",
+      "2017-04-30   -0.007736\n",
+      "2017-05-31    0.039829\n",
+      "2017-06-30   -0.073528\n",
+      "2017-07-31    0.033035\n",
+      "2017-08-31    0.047890\n",
+      "2017-09-30   -0.049112\n",
+      "2017-10-31    0.094252\n",
+      "2017-11-30    0.022247\n",
+      "2017-12-31   -0.003357\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
+    "print(monthly_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    119.851150\n",
+      "2017-02-28    135.880362\n",
+      "2017-03-31    142.496334\n",
+      "2017-04-30    142.486415\n",
+      "2017-05-31    152.142689\n",
+      "2017-06-30    143.438008\n",
+      "2017-07-31    148.248489\n",
+      "2017-08-31    164.000000\n",
+      "2017-09-30    154.120000\n",
+      "2017-10-31    169.040000\n",
+      "2017-11-30    171.850000\n",
+      "2017-12-31    169.230000\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
+    "print(last_day)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "by_week = aapl.resample('W').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-01-31    1.781512\n",
+       "2017-03-31    4.323834\n",
+       "2017-05-31    5.454495\n",
+       "2017-07-31    3.865333\n",
+       "2017-09-30    3.634223\n",
+       "2017-11-30    8.011704\n",
+       "2018-01-31    2.291229\n",
+       "Freq: 2M, Name: Adj. Close, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "twomon = aapl.resample('2M').std()\n",
+    "twomon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-01-31    119.851150\n",
+       "2017-02-28    135.880362\n",
+       "2017-03-31    142.496334\n",
+       "2017-04-30    142.486415\n",
+       "2017-05-31    152.142689\n",
+       "2017-06-30    143.438008\n",
+       "2017-07-31    148.248489\n",
+       "2017-08-31    164.000000\n",
+       "2017-09-30    154.120000\n",
+       "2017-10-31    169.040000\n",
+       "2017-11-30    171.850000\n",
+       "2017-12-31    169.230000\n",
+       "Freq: M, Name: Adj. Close, dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last_day = aapl.resample('M').agg(lambda x: x[-1])\n",
+    "last_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-01-31    0.045940\n",
+       "2017-02-28    0.070409\n",
+       "2017-03-31    0.033823\n",
+       "2017-04-30   -0.007736\n",
+       "2017-05-31    0.039829\n",
+       "2017-06-30   -0.073528\n",
+       "2017-07-31    0.033035\n",
+       "2017-08-31    0.047890\n",
+       "2017-09-30   -0.049112\n",
+       "2017-10-31    0.094252\n",
+       "2017-11-30    0.022247\n",
+       "2017-12-31   -0.003357\n",
+       "Freq: M, Name: Adj. Close, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)\n",
+    "monthly_return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.02114094011940022\n",
+      "0.04775652864223314\n",
+      "0.09425168306576914\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(monthly_return.mean())\n",
+    "print(monthly_return.std())\n",
+    "print(monthly_return.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31          NaN\n",
+      "2017-02-28    16.029211\n",
+      "2017-03-31     6.615972\n",
+      "2017-04-30    -0.009919\n",
+      "2017-05-31     9.656274\n",
+      "2017-06-30    -8.704681\n",
+      "2017-07-31     4.810482\n",
+      "2017-08-31    15.751511\n",
+      "2017-09-30    -9.880000\n",
+      "2017-10-31    14.920000\n",
+      "2017-11-30     2.810000\n",
+      "2017-12-31    -2.620000\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n",
+      "Date\n",
+      "2017-01-31         NaN\n",
+      "2017-02-28    0.133743\n",
+      "2017-03-31    0.048690\n",
+      "2017-04-30   -0.000070\n",
+      "2017-05-31    0.067770\n",
+      "2017-06-30   -0.057214\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(last_day.diff())\n",
+    "print(last_day.pct_change())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    249.000000\n",
+      "mean     149.815713\n",
+      "std       15.065681\n",
+      "min      114.586983\n",
+      "25%      140.651400\n",
+      "50%      151.890000\n",
+      "75%      159.780000\n",
+      "max      176.420000\n",
+      "Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.000000\n",
+      "2017-02-28    0.133743\n",
+      "2017-03-31    0.048690\n",
+      "2017-04-30   -0.000070\n",
+      "2017-05-31    0.067770\n",
+      "2017-06-30   -0.057214\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_return = last_day.pct_change()\n",
+    "print(daily_return.fillna(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2017-01-31    0.133743\n",
+      "2017-02-28    0.133743\n",
+      "2017-03-31    0.048690\n",
+      "2017-04-30   -0.000070\n",
+      "2017-05-31    0.067770\n",
+      "2017-06-30   -0.057214\n",
+      "2017-07-31    0.033537\n",
+      "2017-08-31    0.106251\n",
+      "2017-09-30   -0.060244\n",
+      "2017-10-31    0.096808\n",
+      "2017-11-30    0.016623\n",
+      "2017-12-31   -0.015246\n",
+      "Freq: M, Name: Adj. Close, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "daily_return = last_day.pct_change()\n",
+    "print(daily_return.fillna(method = 'bfill'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "2017-02-28    0.133743\n",
+       "2017-03-31    0.048690\n",
+       "2017-04-30   -0.000070\n",
+       "2017-05-31    0.067770\n",
+       "2017-06-30   -0.057214\n",
+       "2017-07-31    0.033537\n",
+       "2017-08-31    0.106251\n",
+       "2017-09-30   -0.060244\n",
+       "2017-10-31    0.096808\n",
+       "2017-11-30    0.016623\n",
+       "2017-12-31   -0.015246\n",
+       "Freq: M, Name: Adj. Close, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "daily_return = last_day.pct_change()\n",
+    "daily_return.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# aapl.resample('M').agg(lambda x: max(x) - min(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              AAPL    GOOG     IBM\n",
+      "2017-07-03  143.50  898.70  155.58\n",
+      "2017-07-04  144.09  911.71  153.67\n",
+      "2017-07-05  142.73  906.69  152.36\n",
+      "2017-07-06  144.18  918.59  152.94\n",
+      "2017-07-07  143.77  926.99  153.49\n"
+     ]
+    }
+   ],
+   "source": [
+    "dict = {'AAPL': [143.5, 144.09, 142.73, 144.18, 143.77],'GOOG':[898.7, 911.71, 906.69, 918.59, 926.99],\n",
+    "        'IBM':[155.58, 153.67, 152.36, 152.94, 153.49]}\n",
+    "data_index = pd.date_range('2017-07-03',periods = 5, freq = 'D')\n",
+    "df = pd.DataFrame(dict, index = data_index)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     AAPL    GOOG\n",
+      "0  143.50  898.70\n",
+      "1  144.09  911.71\n",
+      "2  142.73  906.69\n",
+      "3  144.18  918.59\n",
+      "4  143.77  926.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
+    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
+    "data_frame = pd.concat([s1,s2], axis = 1)\n",
+    "print(data_frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['AAPL', 'GOOG', 'IBM'], dtype='object')\n",
+      "2017-07-03    143.50\n",
+      "2017-07-04    144.09\n",
+      "2017-07-05    142.73\n",
+      "2017-07-06    144.18\n",
+      "2017-07-07    143.77\n",
+      "Freq: D, Name: AAPL, dtype: float64\n",
+      "2017-07-03    898.70\n",
+      "2017-07-04    911.71\n",
+      "2017-07-05    906.69\n",
+      "2017-07-06    918.59\n",
+      "2017-07-07    926.99\n",
+      "Freq: D, Name: GOOG, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)\n",
+    "print(df.AAPL)\n",
+    "print(df['GOOG'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2018-03-21    171.270\n",
+      "2018-03-22    168.845\n",
+      "2018-03-23    164.940\n",
+      "2018-03-26    172.770\n",
+      "2018-03-27    168.340\n",
+      "Name: Close, dtype: float64\n",
+      "Date\n",
+      "2018-03-21    35247358.0\n",
+      "2018-03-22    41051076.0\n",
+      "2018-03-23    40248954.0\n",
+      "2018-03-26    36272617.0\n",
+      "2018-03-27    38962839.0\n",
+      "Name: Adj. Volume, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = aapl_table\n",
+    "print(df.Close.tail(5))\n",
+    "print(df['Adj. Volume'].tail(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close      Volume  Ex-Dividend  \\\n",
+      "Date                                                                    \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0   \n",
+      "2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0   \n",
+      "2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0   \n",
+      "2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0   \n",
+      "2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0   \n",
+      "2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0   \n",
+      "2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0   \n",
+      "2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0   \n",
+      "2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0   \n",
+      "2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0   \n",
+      "2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0   \n",
+      "2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0   \n",
+      "\n",
+      "            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \\\n",
+      "Date                                                                      \n",
+      "2016-01-31          1.0   91.581233   94.044912   91.156128   94.044912   \n",
+      "2016-02-29          1.0   94.084911   95.415659   93.880927   93.919781   \n",
+      "2016-03-31          1.0  106.576465  106.751308  105.760531  105.867380   \n",
+      "2016-04-30          1.0   91.297138   92.006223   89.859540   91.054300   \n",
+      "2016-05-31          1.0   97.337844   98.119674   96.575559   97.591939   \n",
+      "2016-06-30          1.0   92.295040   93.594832   92.158220   93.428693   \n",
+      "2016-07-31          1.0  101.823594  102.175417  101.325177  101.843140   \n",
+      "2016-08-31          1.0  103.816156  104.710177  103.796505  104.248477   \n",
+      "2016-09-30          1.0  110.497491  111.391610  109.849008  111.077195   \n",
+      "2016-10-31          1.0  111.666724  112.236603  111.224577  111.558644   \n",
+      "2016-11-30          1.0  110.182071  110.814166  108.908004  109.154917   \n",
+      "2016-12-31          1.0  115.209202  115.752409  114.004271  114.389454   \n",
+      "\n",
+      "            Adj. Volume  \n",
+      "Date                     \n",
+      "2016-01-31   64416504.0  \n",
+      "2016-02-29   35216277.0  \n",
+      "2016-03-31   25888449.0  \n",
+      "2016-04-30   68531478.0  \n",
+      "2016-05-31   42307212.0  \n",
+      "2016-06-30   35836356.0  \n",
+      "2016-07-31   27733688.0  \n",
+      "2016-08-31   29662406.0  \n",
+      "2016-09-30   36379106.0  \n",
+      "2016-10-31   26419398.0  \n",
+      "2016-11-30   36162258.0  \n",
+      "2016-12-31   30586265.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "aapl_2016 = df['2016']\n",
+    "aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])\n",
+    "print(aapl_month)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close\n",
+      "Date                                        \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34\n",
+      "2016-02-29   96.86   98.2300   96.65   96.69\n",
+      "2016-03-31  109.72  109.9000  108.88  108.99\n",
+      "2016-04-30   93.99   94.7200   92.51   93.74\n",
+      "2016-05-31   99.60  100.4000   98.82   99.86\n",
+      "2016-06-30   94.44   95.7700   94.30   95.60\n",
+      "2016-07-31  104.19  104.5500  103.68  104.21\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "aapl_bar = aapl_month[['Open', 'High', 'Low', 'Close']]\n",
+    "print(aapl_bar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open    High     Low   Close\n",
+      "Date                                      \n",
+      "2016-03-31  109.72  109.90  108.88  108.99\n",
+      "2016-04-30   93.99   94.72   92.51   93.74\n",
+      "2016-05-31   99.60  100.40   98.82   99.86\n",
+      "2016-06-30   94.44   95.77   94.30   95.60\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aapl_month.loc['2016-03':'2016-06',['Open', 'High', 'Low', 'Close']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close\n",
+      "Date                                        \n",
+      "2016-03-31  109.72  109.9000  108.88  108.99\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "above = aapl_bar[aapl_bar.Close > np.mean(aapl_bar.Close)]\n",
+    "print(above)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close  rate_return\n",
+      "Date                                                     \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34          NaN\n",
+      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
+      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
+      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
+      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
+      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
+      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "aapl_bar['rate_return'] = aapl_bar.Close.pct_change()\n",
+    "print(aapl_bar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n",
+      "2016-02-29  False  False  False  False        False\n",
+      "2016-03-31  False  False  False  False        False\n",
+      "2016-04-30  False  False  False  False        False\n",
+      "2016-05-31  False  False  False  False        False\n",
+      "2016-06-30  False  False  False  False        False\n",
+      "2016-07-31  False  False  False  False        False\n",
+      "2016-08-31  False  False  False  False        False\n",
+      "2016-09-30  False  False  False  False        False\n",
+      "2016-10-31  False  False  False  False        False\n",
+      "2016-11-30  False  False  False  False        False\n",
+      "2016-12-31  False  False  False  False        False\n",
+      "\n",
+      "------------------ separate line -----------------\n",
+      "\n",
+      "         Open   High    Low  Close rate_return\n",
+      "count      12     12     12     12          12\n",
+      "unique      1      1      1      1           2\n",
+      "top     False  False  False  False       False\n",
+      "freq       12     12     12     12          11\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing = aapl_bar.isnull()\n",
+    "print(missing)\n",
+    "print('\\n------------------ separate line -----------------\\n')\n",
+    "print(missing.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Open   High    Low  Close  rate_return\n",
+      "Date                                               \n",
+      "2016-01-31  False  False  False  False         True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(missing[missing.rate_return == True])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close  rate_return\n",
+      "Date                                                     \n",
+      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
+      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
+      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
+      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
+      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
+      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n",
+      "\n",
+      "---------------------- separate line--------------------\n",
+      "\n",
+      "              Open      High     Low   Close  rate_return\n",
+      "Date                                                     \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34     0.000000\n",
+      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678\n",
+      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211\n",
+      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921\n",
+      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287\n",
+      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660\n",
+      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = aapl_bar.dropna()\n",
+    "print(drop)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
+    "fill = aapl_bar.fillna(0)\n",
+    "print(fill)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     AAPL    GOOG\n",
+      "0  143.50  898.70\n",
+      "1  144.09  911.71\n",
+      "2  142.73  906.69\n",
+      "3  144.18  918.59\n",
+      "4  143.77  926.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')\n",
+    "s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')\n",
+    "data_frame = pd.concat([s1,s2], axis = 1)\n",
+    "print(data_frame)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2016-01-31    4.578210\n",
+      "2016-02-29    4.571510\n",
+      "2016-03-31    4.691256\n",
+      "2016-04-30    4.540525\n",
+      "2016-05-31    4.603769\n",
+      "2016-06-30    4.560173\n",
+      "2016-07-31    4.646408\n",
+      "2016-08-31    4.664382\n",
+      "2016-09-30    4.727830\n",
+      "2016-10-31    4.732155\n",
+      "2016-11-30    4.705197\n",
+      "2016-12-31    4.752037\n",
+      "Freq: M, Name: log_price, dtype: float64\n",
+      "\n",
+      "---------------------- separate line--------------------\n",
+      "\n",
+      "              Open      High     Low   Close  rate_return  log_price\n",
+      "Date                                                                \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210\n",
+      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510\n",
+      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256\n",
+      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525\n",
+      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769\n",
+      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173\n",
+      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408\n",
+      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382\n",
+      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830\n",
+      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155\n",
+      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197\n",
+      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037\n"
+     ]
+    }
+   ],
+   "source": [
+    "log_price = np.log(aapl_bar.Close)\n",
+    "log_price.name = 'log_price'\n",
+    "print(log_price)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
+    "concat = pd.concat([aapl_bar, log_price], axis = 1)\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                Volume  Split Ratio\n",
+      "Date                               \n",
+      "2016-10-31  26419398.0          1.0\n",
+      "2016-11-30  36162258.0          1.0\n",
+      "2016-12-31  30586265.0          1.0\n",
+      "2017-01-31  49200993.0          1.0\n",
+      "2017-02-28  23482860.0          1.0\n",
+      "2017-03-31  19661651.0          1.0\n",
+      "2017-04-30  20247187.0          1.0\n",
+      "\n",
+      "---------------------- separate line--------------------\n",
+      "\n",
+      "              Open     High     Low   Close\n",
+      "Date                                       \n",
+      "2016-10-31  113.65  114.230  113.20  113.54\n",
+      "2016-11-30  111.56  112.200  110.27  110.52\n",
+      "2016-12-31  116.65  117.200  115.43  115.82\n",
+      "2017-01-31  121.15  121.390  120.62  121.35\n",
+      "2017-02-28  137.08  137.435  136.70  136.99\n",
+      "2017-03-31  143.72  144.270  143.01  143.66\n",
+      "2017-04-30  144.09  144.300  143.27  143.65\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_volume = aapl_table.loc['2016-10':'2017-04',['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])\n",
+    "print(df_volume)\n",
+    "print('\\n---------------------- separate line--------------------\\n')\n",
+    "df_2017 = aapl_table.loc['2016-10':'2017-04',['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])\n",
+    "print(df_2017)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open      High     Low   Close  rate_return      Volume  \\\n",
+      "Date                                                                    \n",
+      "2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN   \n",
+      "2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN   \n",
+      "2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN   \n",
+      "2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN   \n",
+      "2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN   \n",
+      "2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN   \n",
+      "2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN   \n",
+      "2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN   \n",
+      "2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN   \n",
+      "2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0   \n",
+      "2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0   \n",
+      "2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0   \n",
+      "2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0   \n",
+      "2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0   \n",
+      "2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0   \n",
+      "2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0   \n",
+      "\n",
+      "            Split Ratio  \n",
+      "Date                     \n",
+      "2016-01-31          NaN  \n",
+      "2016-02-29          NaN  \n",
+      "2016-03-31          NaN  \n",
+      "2016-04-30          NaN  \n",
+      "2016-05-31          NaN  \n",
+      "2016-06-30          NaN  \n",
+      "2016-07-31          NaN  \n",
+      "2016-08-31          NaN  \n",
+      "2016-09-30          NaN  \n",
+      "2016-10-31          1.0  \n",
+      "2016-11-30          1.0  \n",
+      "2016-12-31          1.0  \n",
+      "2017-01-31          1.0  \n",
+      "2017-02-28          1.0  \n",
+      "2017-03-31          1.0  \n",
+      "2017-04-30          1.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "concat = pd.concat([aapl_bar,df_volume],axis = 1)\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              Open    High     Low   Close  rate_return      Volume  \\\n",
+      "Date                                                                  \n",
+      "2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0   \n",
+      "2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0   \n",
+      "2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0   \n",
+      "\n",
+      "            Split Ratio  \n",
+      "Date                     \n",
+      "2016-10-31          1.0  \n",
+      "2016-11-30          1.0  \n",
+      "2016-12-31          1.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "concat = pd.concat([aapl_bar,df_volume],axis = 1, join = 'inner')\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Close      High     Low    Open  rate_return\n",
+      "Date                                                     \n",
+      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
+      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
+      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
+      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
+      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
+      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
+      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
+      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
+      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
+      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
+      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
+      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
+      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
+      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
+      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
+      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
+      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
+      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
+      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "append = aapl_bar.append(df_2017)\n",
+    "print(append)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Close      High     Low    Open  rate_return\n",
+      "Date                                                     \n",
+      "2016-01-31   97.34   97.3400   94.35   94.79          NaN\n",
+      "2016-02-29   96.69   98.2300   96.65   96.86    -0.006678\n",
+      "2016-03-31  108.99  109.9000  108.88  109.72     0.127211\n",
+      "2016-04-30   93.74   94.7200   92.51   93.99    -0.139921\n",
+      "2016-05-31   99.86  100.4000   98.82   99.60     0.065287\n",
+      "2016-06-30   95.60   95.7700   94.30   94.44    -0.042660\n",
+      "2016-07-31  104.21  104.5500  103.68  104.19     0.090063\n",
+      "2016-08-31  106.10  106.5699  105.64  105.66     0.018136\n",
+      "2016-09-30  113.05  113.3700  111.80  112.46     0.065504\n",
+      "2016-10-31  113.54  114.2300  113.20  113.65     0.004334\n",
+      "2016-11-30  110.52  112.2000  110.27  111.56    -0.026599\n",
+      "2016-12-31  115.82  117.2000  115.43  116.65     0.047955\n",
+      "2016-10-31  113.54  114.2300  113.20  113.65          NaN\n",
+      "2016-11-30  110.52  112.2000  110.27  111.56          NaN\n",
+      "2016-12-31  115.82  117.2000  115.43  116.65          NaN\n",
+      "2017-01-31  121.35  121.3900  120.62  121.15          NaN\n",
+      "2017-02-28  136.99  137.4350  136.70  137.08          NaN\n",
+      "2017-03-31  143.66  144.2700  143.01  143.72          NaN\n",
+      "2017-04-30  143.65  144.3000  143.27  144.09          NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Change   Close      High     Low    Open  rate_return\n",
+      "Date                                                             \n",
+      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
+      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
+      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
+      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
+      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
+      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
+      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
+      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
+      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
+      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
+      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
+      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
+      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
+      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
+      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
+      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
+      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
+      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
+      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_2017.columns = ['Change', 'High','Low','Close']\n",
+    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Change   Close      High     Low    Open  rate_return\n",
+      "Date                                                             \n",
+      "2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN\n",
+      "2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678\n",
+      "2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211\n",
+      "2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921\n",
+      "2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287\n",
+      "2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660\n",
+      "2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063\n",
+      "2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136\n",
+      "2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504\n",
+      "2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334\n",
+      "2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599\n",
+      "2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955\n",
+      "2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN\n",
+      "2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN\n",
+      "2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN\n",
+      "2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN\n",
+      "2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN\n",
+      "2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN\n",
+      "2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "concat = pd.concat([aapl_bar, df_2017], axis = 0)\n",
+    "print(concat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.html
new file mode 100755
index 0000000..e1da695
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.html	
@@ -0,0 +1,122 @@
+<h2>Introduction</h2>
+In this chapter we are going to introduce some basic concepts in quantitative finance. We start with rate of return, mean and variance. You may think it's simple to calculate these values, however, there are number of different methods to calculate them. It's important to choose the appropriate calculation methods case by case.
+<h2>Rate of Return</h2>
+<h3>Single-period Return</h3>
+The single-period rate of return can be calculated as following:
+\[r = \frac{p_t}{p_0} - 1 = \frac{p_t - p_0}{p_0}\]
+Where \(r\) is the rate of return, \(p_t\) is the asset price at time \(t\), and \(p_0\) is the asset price at time 0.
+<pre class="prettyprint linenums">
+import numpy as np
+rate_return = 102.0/100 - 1
+print rate_return
+[out]: 0.02
+</pre>
+Let's say we bought a stock at $100, and half a year later it will grow to $102. A year later the price will come to $104. How to calculate our total return?
+Well, we can either deem it as a single-period:
+\[r = 104/100 - 1 = 0.04\]
+or as a two-stage period:
+\[ r = (1+r_1)*(1+r_2) - 1 = \frac{102}{100} * \frac{104}{102} -1 = 0.04\]
+Here we make calculations twice a year. It's called semi-annual compounding. How about quarterly compounding?
+Let's assume the stock prices at the end of each quarter are \(p_1, p_2, p_3, p_4\) respectively.
+\[r = (1+r_1)*(1+r_2)*(1+r_3)*(1+r_4) -1\]
+The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
+Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now!
+\[(1+r)^3 = 1+0.65\]
+\[ r = \sqrt[3]{1.65} - 1\ = 0.18167\]
+Strategy A has an higher compounding annual return!
+<h3>Logarithm Return</h3>
+We introduced compounding annual return above, which is a kind of <strong>effective rate of return</strong>. You can regard it as a 'hypothetical return'. Strategy B might never have a 18.167% rate of rate annually during the 3-year backtesting period. However, if we assume that the strategy has a 18.167% rate of return every year, it has the same cumulative return over the 3 years.
+As we mentioned previously, if we assume a strategy is quarterly compounding, the relation between quarterly effective rate of return and annual return is:
+\[(1+\frac{r}{4})^4 = 1+r\]
+More generally, if the times of compounding in one year is \(n\) and the annual rate of return is \(r\), the relation is given by:
+\[(1+\frac{r}{n})^n = 1+r\]
+Now imagine the stock markets. The prices of your assets is changing every second, or even every millisecond. If the times of compounding, or n, approach to infinite, this is called <strong>continuous compounding</strong>. The calculation formula is given below:
+\[\lim_{n \to \infty }(1+\frac{r}{n})^n = e^r\]
+From the above limitation equation, we know that if we assume continuous compounding:
+\[e^r = 1 + r = \frac{p_t}{p_0}\]
+Then we take \(ln\) on both side of the equation:
+\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
+Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>.
+This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
+<pre class="prettyprint linenums">import quandl
+import numpy as np
+import quandl
+quandl.ApiConfig.api_key = 'zNXvSaz2oX5afVGKjf6o'
+#get quandl data
+aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table.loc['2017-3',['Open','Close']]
+#take log return
+aapl['log_price'] = np.log(aapl.Close)
+aapl['log_return'] = np.log_price.diff()
+print aapl
+</pre>
+The output is:
+<pre class="prettyprint linenums">
+Date          Open   Close  log_price  log_return
+2017-03-01  137.890  139.79   4.940141         NaN
+2017-03-02  140.000  138.96   4.934186   -0.005955
+2017-03-03  138.780  139.78   4.940070    0.005884
+2017-03-06  139.365  139.34   4.936917   -0.003153
+2017-03-07  139.060  139.52   4.938208    0.001291
+2017-03-08  138.950  139.00   4.934474   -0.003734
+2017-03-09  138.740  138.68   4.932169   -0.002305
+2017-03-10  139.250  139.14   4.935481    0.003311
+2017-03-13  138.850  139.20   4.935912    0.000431
+2017-03-14  139.300  138.99   4.934402   -0.001510
+2017-03-15  139.410  140.46   4.944923    0.010521
+2017-03-16  140.720  140.69   4.946559    0.001636
+2017-03-17  141.000  139.99   4.941571   -0.004988
+2017-03-20  140.400  141.46   4.952017    0.010446
+2017-03-21  142.110  139.84   4.940499   -0.011518
+2017-03-22  139.845  141.42   4.951734    0.011235
+2017-03-23  141.260  140.92   4.948192   -0.003542
+2017-03-24  141.500  140.64   4.946203   -0.001989
+2017-03-27  139.390  140.88   4.947908    0.001705
+2017-03-28  140.910  143.80   4.968423    0.020515
+2017-03-29  143.680  144.12   4.970646    0.002223
+2017-03-30  144.190  143.93   4.969327   -0.001319
+2017-03-31  143.720  143.66   4.967449   -0.001878
+</pre>
+
+Here we calculated the daily logarithmic return of Apple stock. Given that we know the daily logarithm return of in this month, we can calculate the monthly return by simply sum all the daily returns up.
+<pre class="prettyprint linenums">month_return = aapl.log_return.sum()
+print month_return
+[out]: 0.0273081001636
+</pre>
+It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
+\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
+According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
+<h2>Mean</h2>
+<h3>Arithmetic Mean</h3>
+Mean is a measure of the central tendency of a data series. It capture the key character of the distribution of the data series. When we talk about mean, by default it refers to <strong>arithmetic mean</strong>. It's defined as the sum of the values divided by the number of observations:
+\[\mu = \frac{\sum_{i = 1}^{n}x_i}{n}\]
+Where \((x_1,x_2,x_3.....x_n)\) is our data series.
+In python we can use NumPy.mean() to do the calculation:
+<pre class="prettyprint linenums">print np.mean(aapl.log_price)
+[out]: 4.94597446551
+</pre>
+<h3>Geometric Mean</h3>
+The geometric mean is an average that is useful for data series of positive numbers that are better interpreted according to their product, such as growth rate. It's calculated by:
+\[\bar{x} = \sqrt[n]{x_1x_2x_3...x_n}\]
+Let's calculate the geometric mean of a series of single-period return:
+\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\]
+\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\]
+Now the equation becomes the form which we are familiar with:
+\[(1+\bar{r})^n = \frac{p_t}{p_0}\]
+This is why we said it make sense when applied to growth rates.
+<h2>Variance and Standard Deviation</h2>
+<h3>Variance</h3>
+<strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by
+\[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]
+In python we can use NumPy.var to calculate it:
+<pre class="prettyprint linenums">print np.var(aapl.log_price)
+</pre>
+<h3>Standard Deviation</h3>
+The most commonly used measure of dispersion in finance is <strong>standard deviation</strong>. It's usually represented by \(\sigma\). It's obvious to see the relation between standard deviation and variance:
+\[\sigma = \sqrt{\sigma^2} = \sqrt{\frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}}\]
+NumPy also provides us a method to calculate standard deviation.
+<pre class="prettyprint linenums">print np.std(aapl.log_price)
+[out]: 0.000142032804482
+</pre>
+<h2>Summary</h2>
+We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those comcepts intensively in our later chapter.
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.ipynb b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.ipynb
new file mode 100755
index 0000000..8c4c7b6
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.ipynb	
@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.020000000000000018\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "rate_return = 102.0/100 - 1\n",
+    "print(rate_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Open   Close  log_price  log_return\n",
+      "Date                                              \n",
+      "2017-03-01  137.890  139.79   4.940141         NaN\n",
+      "2017-03-02  140.000  138.96   4.934186   -0.005955\n",
+      "2017-03-03  138.780  139.78   4.940070    0.005884\n",
+      "2017-03-06  139.365  139.34   4.936917   -0.003153\n",
+      "2017-03-07  139.060  139.52   4.938208    0.001291\n",
+      "2017-03-08  138.950  139.00   4.934474   -0.003734\n",
+      "2017-03-09  138.740  138.68   4.932169   -0.002305\n",
+      "2017-03-10  139.250  139.14   4.935481    0.003311\n",
+      "2017-03-13  138.850  139.20   4.935912    0.000431\n",
+      "2017-03-14  139.300  138.99   4.934402   -0.001510\n",
+      "2017-03-15  139.410  140.46   4.944923    0.010521\n",
+      "2017-03-16  140.720  140.69   4.946559    0.001636\n",
+      "2017-03-17  141.000  139.99   4.941571   -0.004988\n",
+      "2017-03-20  140.400  141.46   4.952017    0.010446\n",
+      "2017-03-21  142.110  139.84   4.940499   -0.011518\n",
+      "2017-03-22  139.845  141.42   4.951734    0.011235\n",
+      "2017-03-23  141.260  140.92   4.948192   -0.003542\n",
+      "2017-03-24  141.500  140.64   4.946203   -0.001989\n",
+      "2017-03-27  139.390  140.88   4.947908    0.001705\n",
+      "2017-03-28  140.910  143.80   4.968423    0.020515\n",
+      "2017-03-29  143.680  144.12   4.970646    0.002223\n",
+      "2017-03-30  144.190  143.93   4.969327   -0.001319\n",
+      "2017-03-31  143.720  143.66   4.967449   -0.001878\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import quandl\n",
+    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
+    "#get quandl data\n",
+    "aapl_table = quandl.get('WIKI/AAPL')\n",
+    "aapl = aapl_table.loc['2017-3',['Open','Close']]\n",
+    "#take log return\n",
+    "aapl['log_price'] = np.log(aapl.Close)\n",
+    "aapl['log_return'] = aapl.log_price.diff()\n",
+    "print(aapl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0273081001636184\n"
+     ]
+    }
+   ],
+   "source": [
+    "month_return = aapl.log_return.sum()\n",
+    "print(month_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.94597446550658\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.mean(aapl.log_price))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00014203280448152512\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.var(aapl.log_price))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variable and Distributions.ipynb b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variable and Distributions.ipynb
new file mode 100755
index 0000000..d8f5a7d
--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variable and Distributions.ipynb	
@@ -0,0 +1,364 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4 3 5 ..., 2 3 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "def dice():\n",
+    "    number=  [1,2,3,4,5,6]\n",
+    "    return random.choice(number)\n",
+    " \n",
+    "series = np.array([dice() for x in range(10000)])\n",
+    "print series\n",
+    "from math import factorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gBAAAAMBQghMAAAAAQwlOAAAAAAwlOAEAAAAwlOAEAAAAwFCCEwAAAABDCU4AAAAADCU4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\nJEmSJElSp/4GZuC9GNWuBlIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109d98910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.hist(series,bins = 11,align = 'mid')\n",
+    "plt.xlabel('Dice Number')\n",
+    "plt.ylabel('Occurences')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4956\n",
+      "3.5103\n"
+     ]
+    }
+   ],
+   "source": [
+    "print len([x for x in series if x <= 3])/float(len(series))\n",
+    "print np.mean(series)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4956\n",
+      "3.5103\n"
+     ]
+    }
+   ],
+   "source": [
+    "print len([x for x in series if x <= 3])/float(len(series))\n",
+    "print np.mean(series)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def trial():\n",
+    "    number = [1,2,3,4,5,6,7,8,9,10]\n",
+    "    a = random.choice(number)\n",
+    "    if a<= 7:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = [trial() for x in range(10)]\n",
+    "print sum(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2367\n"
+     ]
+    }
+   ],
+   "source": [
+    "def binomial(number):\n",
+    "    l = []\n",
+    "    for i in range(10000):\n",
+    "        res = [trial() for x in range(10)]\n",
+    "        l.append(sum(res))\n",
+    "    return len([x for x in l if x == number])/float(len(l))\n",
+    " \n",
+    "print binomial(8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1     0.0002\n",
+      "2     0.0013\n",
+      "3     0.0087\n",
+      "4     0.0373\n",
+      "5     0.1041\n",
+      "6     0.2000\n",
+      "7     0.2674\n",
+      "8     0.2342\n",
+      "9     0.1153\n",
+      "10    0.0283\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "prob = []\n",
+    "for i in range(1,11):\n",
+    "    prob.append(binomial(i))\n",
+    "prob_s = pd.Series(prob,index = range(1,11))\n",
+    "print prob_s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.266827932\n",
+      "0.2334744405\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)\n",
+    "print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1fjrJoU37Did5b3dfl+S9i212AbEIdqHu/p3u/uDi8R9l/Q37VcudCna/qro6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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109eca950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.bar(range(1,11),prob)\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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prSOyuPS7nZIpJbXWhRiXAwAgS1AyAQCAtNYZiaqiJF8N5UV+R8FttNSFdO7G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\n1Tm3P7rui5I+Likk6TPOuWfP9GdRMgFA4giHndp7Qmrr7NG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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109e0d350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "norm = pd.Series(np.random.normal(0,1,100000))\n",
+    "type(norm)\n",
+    "plt.figure(figsize = (20,10))\n",
+    "norm.plot.density()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "spy_table = quandl.get('LSE/SPY5')\n",
+    "spy = spy_table.loc['2009':'2017',['Last Close']]\n",
+    "spy['log_return'] = np.log(spy['Last Close']).diff()\n",
+    "spy = spy.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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64YlDlcvkFVD5MyL6sddfGvubM2mkTGqX6j8N13JYML/FaMoA6AZwJlVgdnlA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\nog3rkmFu3OGZ1nlmKZP6Xvq7yN+7vIG1fNyrX3eJcHab2G2iLeIDWs+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piRpVNkrbEd5f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\njmbm9JdR9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fXktZ5uZVZBOmJ4dGg+0bshvpsqYxLpEmTE6PZzFErZNZKH6M20m7hk6zR2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\nn77jERxdtnIW7UYayM9o+0Ezk+4HsJP9vaP8LrqNMSYDMAPg8Jj7VppDq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8I2+zQ3U/5T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JNE9cBweGG3uTG/k5ds50sPfCuJ9gXQJ8IkoNecdACw6ar2v7DFcIHqoqrNqEw63Lq++qPl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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113aa8750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "spy.log_return.plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SWkuUR4kb1wCAeimPAADeoKqq7fKoBY6tJcmDJ4aURwBAbZRHAABvsLiykfWNrdaZPDox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\np+MBSMAYo5WnlGnpjEL9n4c26pbHtuqprQ269cq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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113c08210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "spy.log_return.plot.density()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11601ca90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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kp6Ra95FJIZYUzIAVKivHl5+f1GrmmNi1mvMqO9BSCFpLwaQOSwpmwAqVd2yN\nAuC0KtLTyavS9scUvNZSMKnHkoIZsEIdWM0cIyLoiFxyq2i3+8jn8eEVr40pmJRiScEMWJ1JCgCR\nvKHkVilZ/qw264kIWb4saoO1nQ3RmB7nalIQkTNE5CMRKRGRBS0c/56IrBeRNSLykoiMdzMeY2Ii\nNTVEqqs7tJo5Jjh8MHlVkOVrOykADEobRE2wpjMhGtMrXEsKIuIF7gPOBKYBl4jItCbV3gOKVXUm\n8BRwl1vxGJPo4MK1jieFhtxshlVDhqftMQWAQT5LCia1uNlSmA2UqOomVQ0Ai4FzEyuo6nJVjbWt\n3wQKXIzHmLhYUujIGoWYumFZ+MOQWRNst661FEyqcXObizHA9oT7pcCxbdT/BrDMxXiMiQt24DKc\nTdUOc6aaplVUwTgIhCIsXrmNlzaUkTcojcuPH89R44YB1lIwqadP7H0kIpcDxcBJrRyfB8wDGDdu\nXA9GZvqrUFnH9z2KqRrizDryV1RyoDbI1Y+sZOWWfUwakc37pfv5y+odfPfUw/j3UyeRnZZNWW1Z\nt8ZujJvcTAo7gLEJ9wuiZY2IyGnAzcBJqtrQ0olU9X7gfoDi4mLt/lDNQBMqK0PS0/Hk5HT4sQeG\nOH82wZ17+NrDb7FhZxX3zp3FOUccQm0gzI+eWcuvXvyYT/fXkTkyk5qQtRRM6nAzKawEikSkECcZ\nzAUuTawgIkcCvwPOUFX7OmV6TGj3bnyjRnZoNXPM/iwlLPDc8jWsGTmU+68oZs405/rNg9J9/PKi\nIygYlsmvXy5h0pRaatKquzt8Y1zj2kCzqoaA+cDzwAbgCVVdJyK3icg50Wo/B7KBJ0VktYgscSse\nYxIFd+/GP2Jkpx5bF2lgf46HA9s/5eazpsYTQoyI8L3TJ/PT86azvSJMdaCGmob2B6WN6QtcHVNQ\n1aXA0iZltyTcPs3N5zemNaHdu8mcNatTj91VVcmeQV4me+s4/bOFrda74rjxvF85ked3Lmfe42/y\n6Fc/i8fT8ZaJMT3JVjSbAUdVCe3ejX9Ux1sKkYjyXulu9uX4KNSadrufjhk3GoAVn+zg9yu2dCZc\nY3qUJQUz4IT37UODQXyd6D569v0dVDbU0JA3iEh5Gaptz3sYlDYIgNmHZnHvix9TWW/dSKZvs6Rg\nBpzQ7t0A+DrYUqgNhLhz2YcMygjDyMFobS2R6rYHkQf5nKRw2fEjqawPsfCNLZ2K2ZieYknBDDjB\nXbsA8I+gp3kZAAAXiElEQVTsWFJ4/M1t7K5sYHiOEMp1prKGoudqTXZaNgCjhgqfOyyfP769jXDE\nZlWbvsuSghlwQrujq5k7kBTqAmF+9+onfHbScPAECA4fDEBwd9szqWM7qdYEa7jkmLF8eqCeVz8u\n72TkxrjPkoIZcIK7doLXi2/48KQf8/hbW9lTHeC604qoC9Wh+bkAhHa301LwOy2F6mA1p04dyfDs\nNBa9va3zwRvjMksKZsAJbi/FP3o04ktuRnZdIMz/vbKJEw7N45gJudQGa5HhTlIIttN9NMjvjCnU\nBmtJ83m44KgCln9YRnlVi4v3jel1lhTMgBMo3Y5/bPIb8jqthAauO7WIcCRMfbiejMwcvHl58a6o\n1sRaClXBKgC+cnQBoYjyzHvNdnwxpk+wpGAGnOD2UtLGJrexYiAU4YHXNnH8xDyOnZgXv95ypi8T\n38gRBNvpPkr3puP3+KkKOEmhaGQOR44byhOrtrc7ndWY3mBJwQwo4eoawnv3Jt1SWPrBTnZXNjDv\npIkA8estZ/oy8Y8cRWhn20lBRBicNpjKQGW87CtHj2VjWTXvlx7o5Kswxj2WFMyAEix1LvGRNnZs\nOzWdlc8Pv7GZifmDOKnIuUJbdcBZl5Cdlk3auLEEtm9HI5HEB0E46PyOyknLibcUAM4+YjQZfg9P\nrEq83IgxfUOfuJ6CMT0lsM2Z+eMf035LYdXWfawpPcB/nTc9vmdR7II52f5s0gononV1hHZ+ir9m\nHax6GLa9CfX7wZcBY46GGV9msH9Qo6QwOMPPWdNHs2T1p/znGVMYkul34ZUa0zmWFMyAEti0CYC0\nwtY3sot56LXNDMn0c8FRY+Jl1UGnpTDIP4i0Q/IAaLj7DPw52yF7FBx+PgweA7UVsPkV+Nv15IwZ\ny4GhjZPQN04s5On3dvDov7Yw//NF3fTqjOk6SwpmQGn4eCP+MWPwZg9qs972vbX8Y/0uvnnSoWSl\nHfwziXcfvXYP6e+9AOQSaBgGX/8pTDkbvAnf+lVhy+sMfvk6Sg9sgWeuhbN+DmlZHH7IEE6ZnM9D\nr2/m658tbPQcxvQmG1MwA0pDSQnpkya1W+8PK7bgEeHK48c7Bfu3was/p3rZDQAM2vQq3hMux5M9\niMCIOU4LwdukG0gECk9k8LTzqEzPhtWPwcOnwwFnOuq1p0xiX22QRW/ZYjbTd1hSMAOGBoM0bN5M\n+mFtd9dU1Qf508rtfHH6SEZvXwZ/OBvumQEv/xfV6U4LI/vf3kDO/iXph06iYdPmNs+Xkz6EKg2j\nlzwBe7fAg6fBrrUUT8jl2MJcHnxtMw2hcHe9TGO6xNWkICJniMhHIlIiIgtaOP45EXlXREIi8mU3\nYzEmsHUrBIOkF7WdFJ5cVcrYwCfcsfc6eOprULkDTvkhXLeGmqOvACB70CgA0iZOjI9TtCYnLYeQ\nhqibeCJ8fZlT+PAZUPIi154yiV2V9Tz9ri1mM32Da0lBRLzAfcCZwDTgEhGZ1qTaNuCrwCK34jAm\npqGkBKDN7qNwRNn56kM8k/5jsurL4fz7Yf47cNINMGw81cFqZ0FatKsobWIhobIywlVVrZ5zcJqz\neV5loBJGzYCrX4Rh4+HxizixaikzC4bwf698QigcafUcxvQUN1sKs4ESVd2kqgFgMXBuYgVV3aKq\nawD7azCua/h4I3g8pE2c2Gqdj5+5k5sDv6E6/yj41utwxMXgOfhnUhOoie9nBJB+6KEABD75pNVz\n5qQ522zHp6UOGQNfWwYTT0b++u/8Kv9vbK2o4e8f7OzCqzOme7iZFMYAiatzSqNlxvSKho0bSRs7\nFk9GRssVXv8VU9fcyXLvCQydtwSy85tVqQ5Wx/czgoOtjvqNG1t93lhSSFzVTMZguPRPcOQVHLrh\nf7k353F+988S2/rC9LqUmAcnIvOAeQDjxiW3Z40xTdV/+CEZ05r2YEat+j28eCvPhk9g/6m/xutP\nb7FaTbBxS8FfUIBkZBCIdk21ZEjaEIBGC9gAZ7bSOb+BzKGcu+I3hCsqefuTKRw7qeOXCTWmu7jZ\nUtgBJO4lUBAt6zBVvV9Vi1W1OD+/+bc3Y9oTrqwkuH17y0lh/bPo37/Hu+nHcLv/3/ny7NYXtlUF\nquJXUwMQj4f0SZNoSKKl0CwpgDNtdc5PCZ50Mxd4X8f7zDchYjORTO9xMymsBIpEpFBE0oC5wBIX\nn8+YVtVv+BCgeVLY9Ar8+WoO5M7i0gPf5jtzpjIovfUGdGWgkhx/TqMyJym03lJoNNDcEhH8p9zI\n8nHzKa5eTvXT32m0d5IxPcm1pKCqIWA+8DywAXhCVdeJyG0icg6AiBwjIqXAV4Dficg6t+IxA1v9\n+vUAZEyberBwx7uw+FI091CuavgPDsnPZe7strsn9zfsZ1jGsEZl6UVFhMrLCe/f3+JjYi2LVpNC\n1GEX/JD/CZ9H9trH4YUfWWIwvcLVMQVVXQosbVJ2S8LtlTjdSsa4qn79enyjRuHLc/YrouITePzL\nkJXL04f/hvefK+P+K6bg97b+PUlV2V/fUlJwBpsbSkrIKi5u9jifx0eWL4vKhraTwpihmayd/B0W\nl9Qxd8VvIDMXTvxeB1+pMV1jK5rNgFC/fv3BrqPqMnj0fECovuhJ7nh9P7MLc5kzre0B3upgNSEN\nMTR9aKPy2GK4tsYVBqcPbnlMoYmvfmYiN9VfwZbRZ8FLP3EGwI3pQZYUTL8Xqa0lsGmTkxQaqpwW\nQk05XPYE//eBsKc6wM1nTUVE2jzP/nqne6hpS8E3ahSe7Ox2B5uTSQrHTBjGtEOG8q3qq9Gi0+Fv\n18Pap5N4lcZ0D0sKpt+rX7cOVMmYOhmeuBJ2rYWLHmFn9jQeeG0T5846hCPGDm33PPsa9gE0aymI\nSLuDzTn+nHbHFGLn+tpnCvmwvJ7XZv0Cxh0HT18D7z7a7mON6Q6WFEy/V7dmDQCZny6CT16Gc34N\nRXP4xfMfo8D3T5+c1Hn21u8FYFj6sGbH0oucaamtLT4bnD6YA4HkLr95zhGHMGpwBv/7xk649Ako\n/BwsmQ9Lb4RgXVLnMKazLCmYfq/u/TX487LwffJnZ2O7Iy9n7Y4DPP1eKV/7zATG5mYldZ5dNc71\nmEdFN8NLlF5URHj/fsIVFS0+Ni8jL9791J40n4dvfLaQf22q4P3yiJMYjv0WvP07+N3nnFlTxrjE\nkoLp31Spe/s1MgftgePnw+e+j6ry30s3MDTTz7dPbv/aCjE7a3bi8/jIy8xrdiy23UVr4wq5Gbns\nq99HRJPb5uuSY8eRk+Hjf//5ibPy+cyfwRV/gYZqeGgOvPZLW+RmXGFJwfRfqgT/fBOh/XVkHnUM\nnP5fIMLyj8pY8UkF151a1KHrI++q2cWorFF4pPmfTfpUZ/1D3QdrW3xsbkYuIQ0lNdgMkJ3u42sn\nTOC5dbtYUxptYRz6efi3N5wrvL10G/zpcutOMt3OkoLpn1Thn3dS95wzpTPz4h+CCPXBMLcuWc+h\n+YO49NjxHTrlrppdjM4e3eIx37BhpE2cSO07q1o8npuRC0BFfcvdSy255nMTyRuUxu1/33BwrCIr\nF77yBzjz5/DRMnjsQmdGlTHdxJKC6X9U4R8/hFfupN47C3w+0qMrmf9neQnb9tby0/Omk+br2H//\nrZVbGZPd+ka/WUcfTd2776Hh5t06uZlOUthbtzfp58vJ8HPdaUW8tXkvf1uTsK22CBw7Dy58ELa9\nCY9f5HQrGdMNLCmY/iUcgmfnw79+C8d+i5q9g8mcORNPejprSvfzv698wnmzDuGEQ4d36LQVdRVU\n1FdQNLT1q7ZlHVNMpKqK+g8/bHYs1lKIzWBK1qWzxzGzYAi3LllHRXVD44Mzvuwkhu1vwSJLDKZ7\nWFIw/UdNBTx2Aax+DE5aQPj4m6hfu45BJ5zAgbog1y56lxE5Gdx6zuEdPvXG/c4ActGw1pPCoM98\nBkSoXv7PZsc6mxR8Xg93fXkmlfVBbnhqDZFIkymv0y+ACx+Abf+CRRdDoKZD5zemKUsKpn/Y/Crc\nf7LTnXLufXDKTVQt/yeo4jv2OK5euJJdB+r59SVHMjQrrcOn37iv/aTgy8sj86ijqHrppWbHhqYP\nRZAOjSnETBk1mB9+cRovf1jGPS9+3LzC9Avhggdg24poYqjt8HMYE2NJwaS22r3w1+/Cwi+Bxwtf\nXwZHXg7Agaefxjd2LPNXB3hn6z7uufhIjh7ffOFZMjbu20huRi7DM9vudso59VQaNmwgsGVLo3Kf\nx0d+Zj47qzt3yc0rjx/PV44u4Ncvl/B/r7Rw6c8ZX3auJ731DacrqbZjLRJjYiwpmNQUqIFXfwH3\nHgHv/MFZg/BvK2DM0QDUvP02tStX8szY43hz8z5+edERfHFmyzOHkrGuYh1Tc6e2W2/wF78IXi/7\nnniy2bFxg8exrWpbp55fRLjjghl86YhDuHPZh9z09BrqAk0GtGd+Bc7/ndOVdN+xsOYJW8tgOsyS\ngkktB0rhhR/Drw6Hl38KEz7rJIMv3A5pzspkVWX7z37BvqyhPDrqaB68spjzj+z8Du21wVpK9pcw\nffj0duv6R44g57TTOPDnPxOpbdyNM37weLZWbu10HD6vh19ddAT/dvKh/PHt7Zx+zyu8sH534601\nZl4E1yyHnFHOnkm/PQZeuxv2dy4ZmYHHkoLp++r2wTsLnS6ie2bAil/DhBPhGy/AJX+EkQevphaO\nKH+/9xF03Qf8ZcYX+P03T+SUKSO69PTrKtYR0Qgzhs9Iqn7uVVcRPnCAPQ880Kj8sGGHsbd+b5cT\nw3+eMYU/XnMcGT4v1zyyiq/9YSVb9iQMMI+eCfNegYsegUHDnS2475kB/3eis+ht25vOLC1jWiCt\nbeDVLScXOQO4F/ACD6rqnU2OpwOPAEcDFcDFqrqlrXMWFxfrqlUtLxAy/UjlTih5AT5cCiUvQiQI\nuRNh+pfhqCtgaOMrpIXCEZau3cUjf3+X6/90G/VD85j2l6cYnTuoy6H87O2fsfjDxbwy95X4pTXb\ns+PGG6n8698Y8f3vk3vlFYjfz87qnZz19FmcMu4UfnnSL9vdqrs9wXCEhSu2cM+LGwmEInztsxP4\n9kmTGJLVZJX2vi2w7i+w8QUnIWgYMoY6K6SL5sDEU2Bw57vWTGoQkXdUtflVoJrWcyspiIgX+BiY\nA5TiXLP5ElVdn1Dn28BMVf2WiMwFzlfVi9s6ryWFfiYchModTvfGno9h5/vOhm+7o9tFDC6Aw89z\nBlJHz3IWbkWpKp+UV/P3Nbt46t3tpG0qYcGaJzmkupyJf/ojmVPbHwNoz5ryNVy57ErOKjyL/z7x\nv5N+XKSujh3/8X2qX36ZtMJCRnz/P8j+/Od5aO1D3Pvuvfzq5F9x2vjTuhwfQFllPXc+9yF/eW8H\ngzP80UHpsYzLa2Gjv7r9sGm5kyA2vgA1ZU75kHEw9hgYeywcciTkT4aMId0Sn+kb+kJSOB64VVW/\nEL1/E4Cq3pFQ5/lonX+JiA/YBeRrG0FZUuijVCHUAOEG53eoHkIBCNZCbQXU7HEubFNTDlU7nSSw\nf5uTEBI3icscBqOPQAtPom7CaVQPLqI6EGZ3ZQM7D9Tx6f46Pj1Qz7aKWjZu2U32np0csaeEL5Z9\nwCFlW/EMHsyYX/6C7BNP7PJL2lO3h8uXXk5EIzx1zlNJtxIOviVK9fLllP3ilwQ2bWLQ504k54Lz\n+XHJr9mZWc/Pz3+QCbmHdjnOmPWfVvLLf3zEyx+VoQqTRmRTPH4YY3OzGD0kg8EZfrIzfORk+MhJ\n95OT7iF7/wb821fA9redn6pPD55w8BgnOQwpgOyRzs+g4U4rI3Oo8ztjiPPj8Xbb6zDu6AtJ4cvA\nGap6dfT+FcCxqjo/oc7aaJ3S6P1PonX2tHbeTieFdx+FFb+J3kl4zY1ef0fLk31McvX31wUJhCJI\nQh2h5XM27njQ+K/G9Zvfb6m8o3Viz73njSyCB7yIarystbcAjd0VwuohjIcIzu+wOrcjeIioOOdS\nBRTRxs8vqngEfBohLRSInz592lSGXnAhQ87+It6hrV8w5xvPf4PyunIiGiEcCaMoYQ0T0Uijn7CG\nqQvW4ff6eeD0Bzgi/4hWz9keDYXY+9hj7Pn1bxoNPkeA6mwvwTQPEQ9EPG13J40aNAq/t/0N/EJh\npao+RF0gRH0oQjiibBk8ijuPuaLF+hl+D9npfgalexmpe5gU2cyEyDYmRLYzPrKd4bqXoXoAL63v\n8FpNFmHxoggRBOdfLPEHNFoef1+it5v+j216vFkdkRaOt1I3qToHjcvNwtPFbj1XnXSjsy6lE5JN\nCr5Onb2Hicg8YB7AuHHj2qndiqw8GJHQndDoH146X96N5/p0ZyX764JA4//AsToiLZQ3DaWlj/j4\n87UetyIJRyXhV8txK0JW/hY86fUoHtTjISIeFC8Rj/NbxUNEvIQ8adGfdEKeNOc8Ev3giL4Ejd53\njjkDqj6vF7/Pg9/rITPNy6B0P4MyfHi9HsTjxZc/HP/o0WQedRT+Uc2vcdCSCYMnkJuRi4jgFS8e\n8TT+4eDtNG8aZ088m8m5yV2EpzXi85H31a8y7OKLCWzeTHD3bvZuL+GDD18lVF6GJxDGE44gTVcr\nN+EdNoF0X2a7z5cOJI6khCMRJo44hBO+9hmq6kPRnyDVDSGq60NUNThltYEQqkOpZhJrgUb7vWqY\n7PABssP7yQpXkRmpJitc5dwOV5MZqcarIQ5+9Ee/3GjC7cT3JP4toXNfgKSVbx+N/zpaq9P6+zw2\nf2iTv88+JqP9KwR2lXUfGWPMAJBsS8HNKakrgSIRKRSRNGAusKRJnSXAVdHbXwZebishGGOMcZdr\n3UeqGhKR+cDzOFNSH1bVdSJyG7BKVZcADwGPikgJsBcncRhjjOklro4pqOpSYGmTslsSbtcDX3Ez\nBmOMMcmzFc3GGGPiLCkYY4yJs6RgjDEmzpKCMcaYOEsKxhhj4lzdJdUNIlIOdH7vYXcNB1rdomMA\nGOivH+w9sNffd1//eFXNb69SyiWFvkxEViWzYrC/GuivH+w9sNef+q/fuo+MMcbEWVIwxhgTZ0mh\ne93f2wH0soH++sHeA3v9Kc7GFIwxxsRZS8EYY0ycJYUuEpGfi8iHIrJGRP4iIkMTjt0kIiUi8pGI\nfKE343STiHxFRNaJSEREipscGyjvwRnR11giIgt6O56eICIPi0hZ9AqKsbJcEXlBRDZGfw/rzRjd\nJCJjRWS5iKyP/v+/Llqe0u+BJYWuewGYrqozgY+BmwBEZBrOVuCHA2cA/yMi/fVCtmuBC4BXEwsH\nynsQfU33AWcC04BLoq+9v/sDzr9rogXAS6paBLwUvd9fhYD/UNVpwHHAtdF/95R+DywpdJGq/kNV\nQ9G7bwIF0dvnAotVtUFVNwMlwOzeiNFtqrpBVT9q4dBAeQ9mAyWquklVA8BinNfer6nqqzjXQUl0\nLrAwenshcF6PBtWDVHWnqr4bvV0FbADGkOLvgSWF7vV1YFn09hhge8Kx0mjZQDJQ3oOB8jqTMVJV\nd0Zv7wJG9mYwPUVEJgBHAm+R4u+BqxfZ6S9E5EWgpSvD36yqz0br3IzTnHy8J2PrKcm8B8YkUlUV\nkX4/vVFEsoE/A99V1UoRiR9LxffAkkISVPW0to6LyFeBs4FTE64xvQMYm1CtIFqWktp7D1rRr96D\nNgyU15mM3SIyWlV3ishooKy3A3KTiPhxEsLjqvp0tDil3wPrPuoiETkDuBE4R1VrEw4tAeaKSLqI\nFAJFwNu9EWMvGijvwUqgSEQKRSQNZ3B9SS/H1FuWAFdFb18F9NtWpDhNgoeADap6d8KhlH4PbPFa\nF4lICZAOVESL3lTVb0WP3YwzzhDCaVoua/ksqU1Ezgd+A+QD+4HVqvqF6LGB8h6cBdwDeIGHVfX2\nXg7JdSLyR+BknJ1BdwM/Bp4BngDG4exmfJGqNh2M7hdE5LPAa8AHQCRa/AOccYWUfQ8sKRhjjImz\n7iNjjDFxlhSMMcbEWVIwxhgTZ0nBGGNMnCUFY4wxcZYUjDHGxFlSMMYYE2dJwRhjTNz/A/XD699s\n84ElAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1160538d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')\n",
+    "de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')\n",
+    "de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')\n",
+    "mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')\n",
+    "df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)\n",
+    "plt.figure(figsize=(20,10))\n",
+    "df.plot.density()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html
new file mode 100755
index 0000000..41e139b
--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html	
@@ -0,0 +1,167 @@
+<h2>Introduction</h2>
+In the last chapter we learned the definition of mean and variance, which are kind of <strong>point estimation</strong>. Point estimation means using sample data to calculate a single value which is to serve as a 'best estimation' of an unknown population. However, this it not enough because point estimations can be deceiving. We need to use more rigorous methods to test our ideas. That's why we consider distribution and hypothesis testing. Random variable distribution is the basis for almost all quantitative finance topics: linear regression, CAPM, Black-Scholes, binomial tree pricing, etc.
+<h2>Random Variables</h2>
+First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}.
+The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
+<h2>Distributions</h2>
+Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\).
+However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We we cover this later.
+For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It defines as \(P(X&lt;x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
+<h3>Uniform Distribution</h3>
+Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions.
+Here we use python to simulate rolling a dice 10000 times.
+<pre class="prettyprint linenums">import random
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+#define a function to simulate rolling a dice
+def dice():
+    number=  [1,2,3,4,5,6]
+    return random.choice(number)
+
+series = np.array([dice() for x in range(10000)])
+print series
+</pre>
+We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution:
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+plt.hist(series,bins = 11,align = 'mid')
+plt.xlabel('Dice Number')
+plt.ylabel('Occurences')
+plt.grid()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot1.png" alt="plot1" />
+Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
+<pre class="prettyprint linenums">print len([x for x in series if x &lt;= 3])/float(len(series))
+[out]: 0.4956
+print np.mean(series)
+[out]: 3.5103
+</pre>
+\(P(X&lt;3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5.
+For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as
+\[\mu = (1+2+3+4+5+6)/6 = 3.5\]
+Or
+\[E(x) = 1*\frac{1}{6} + 2*\frac{1}{6}+...+6*\frac{1}{6} = 3.5\]
+More generally, if we a assume the minimum value in a uniform distribution is a and the maximum value is b, the mean can be given by:
+\[\bar{u} = \frac{a+b}{2}\]
+Usually we use \(\bar{u}\) to represents the <strong>population mean</strong>, or the 'real mean'. Here we create a sample with 1000 observations, the mean we calculated above is the <strong>sample mean</strong>. Sample mean usually doesn't equal to the theoretical population mean unless the number of observation approaches to infinity.
+The variance is given by:
+\[\sigma^2 = \frac{(b-a)^2}{12}\]
+Deducing the formula is out of our lecture scope. It's useful to realize for a given standard distribution, we can formularize its mean and variance.
+<h3>Binomial Distribution</h3>
+A binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent experiments. Let's assume that the market has 50% probability goes up and 50% probability goes down, and we observe it in the next 10 days, what's the distribution of the number of days it goes up? This is a binomial distribution example.
+In general, if we carry out the experiment n times, and each outcome is independent, with the same probability of success p, the probability of getting exactly k successes is given by the function:
+\[P(X = K) = C_n^k p^k (1-p)^{n-k}\]
+Where
+\[C_n^k = \frac{n!}{(n-k)!k!}\]
+Under such circumstance we say X follows the binomial distribution \(X \sim B(n,p)\).
+Let's simulate a binomial experiment with success rate p = 0.7 and experiment times n = 10
+<pre class="prettyprint linenums">def trial():
+    number = [1,2,3,4,5,6,7,8,9,10]
+    a = random.choice(number)
+    if a&lt;= 7:
+        return 1
+    else:
+        return 0
+</pre>
+Each time we execute trial(), we did an experiment. If it succeed, it will return 1, otherwise it will return 0. Now we are going to do the experiment 10 times:
+<pre class="prettyprint linenums">res = [trial() for x in range(10)]
+print sum(res)
+[out]: 7
+</pre>
+Now we did the experiment 10 times, and the number of success is sum(res). However, it just means during these 10 experiments we succeed sum(res) times. If we want to see the binomial distribution, we need experiment N times. When n is large enough, our frequency will approach the theoretical probability. Here we simulate each outcome 10000 times:
+<pre class="prettyprint linenums">def binomial(number):
+    l = []
+    for i in range(10000):
+        res = [trial() for x in range(10)]
+        l.append(sum(res))
+    return len([x for x in l if x == number])/float(len(l))
+
+print binomial(8)
+[out]: 0.2367
+</pre>
+The number printed above is the simulated probability that we succeed 8 times if we experiment 10 times.
+for each possible outcome, we simulate the probability:
+<pre class="prettyprint linenums">prob = []
+for i in range(1,11):
+    prob.append(binomial(i))
+prob_s = pd.Series(prob,index = range(1,11))
+print prob_s
+[out]: 1     0.0002
+       2     0.0013
+       3     0.0087
+       4     0.0373
+       5     0.1041
+       6     0.2000
+       7     0.2674
+       8     0.2342
+       9     0.1153
+       10    0.0283
+</pre>
+Here we got the simulated result of the binomial distribution. Now we are going to check if the simulated frequencies close enough to the theoretical probabilities. Let's take X = 7 and X = 8 as example:
+<pre class="prettyprint linenums">print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)
+[out]: 0.266827932
+print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)
+[out]: 0.2334744405
+</pre>
+As we can see, the simulated results are pretty close to the real probability!
+we can plot the results as follows:
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+plt.bar(range(1,11),prob)
+plt.grid()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot2.png" alt="plot2" />
+Another good property of binomial distribution is that its mean and variance are simple enough:
+\[\bar{u} = np\]
+\[\sigma^2 = np(1-P)\]
+We will not introduce the deduction here, but if you are interested in it, we encourage you to do it yourself, based on the probability functions we provided above.
+<h2>Normal Distribution</h2>
+Before looking at normal distribution, let's first talk about continues distribution. As we mentioned above, we use a <strong>probability density function</strong>(PDF) to model the probability that our value is taken our a specific range. We define it as:
+\[P(a&lt;X&lt;b) = \int_{a}^{b}f_x(x)dx\]
+Now we can talk about the normal distribution. The <strong>normal distribution</strong> is most commonly used distribution in natural sciences, of course also in financial research. The PDF of normal distribution is given as follows:
+\[f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\]
+Where \(\mu\) is the mean of the normal distribution, and \(\sigma\) is the standard deviation.
+Generally, if a random variable X follows normal distribution, we represent it by \(X\sim N(\mu, \sigma^2)\).
+Specifically, if a normal distribution has a 0 mean and 1 standard deviation, we called it <strong>standard normal distribution</strong>.
+Now let's simulate a standard normal distribution using Python packages to see what it looks like:
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+norm.plot.density()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot3.png" alt="plot3" />
+Financial data is highly disordered and is considered to has lots of noise. Most of the time we believe those noise follows normal distribution. It's also widely believed that the return on an asset over a short period of time follows normal distribution. Let's check it with the daily logarithm rates of return on SPY:
+<pre class="prettyprint linenums">import quandl
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+spy_table = quandl.get('BCIW/_SPXT')
+spy = spy_table.loc['2009':'2017',['Open','Close']]
+spy['log_return'] = np.log(spy.Close).diff()
+spy = spy.dropna()
+</pre>
+we calculated the logarithm daily return of S&amp;P 500 index from 2009 to present.
+Let's first have a look at the what the time-series return data looks like:
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+spy.log_return.plot()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot4.png" alt="plot4" />
+This is a classic daily return chart. Let's now plot the density chart of the returns:
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+spy.log_return.plot.density()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot5.png" alt="plot5" />
+If we observe the x-axis and y-axis carefully, we can see the return of asset is not a standard normal distribution. The peak of the standard normal distribution plot is around 0.4, while it's over 0.6 for this chart. This is because the standard deviation \(\sigma\) of the return is obviously not 1.
+We can demonstrate the normal distribution with different mean and variance by simulation:
+<pre class="prettyprint linenums">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
+de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')
+de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')
+mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')
+df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)
+plt.figure(figsize=(20,10))
+df.plot.density()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot6.png" alt="plot6" />
+<h2>Summary</h2>
+In this chapter we introduced random variable, the difference between discrete random distribution and continuous random distribution, and most importantly, normal distribution. In the next chapter we will introduce how to use these distributions to test our idea or generating trading signals.
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+<h2>Introduction</h2>
+In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
+
+As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
+<h2>Confidence Interval</h2>
+<h3>Sample Error</h3>
+Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
+<pre class="prettyprint linenums">import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import quandl
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+#get data from quandl
+spy_table = quandl.get('BCIW/_SPXT')
+spy_total = spy_table[['Open','Close']]
+#calculate log returns
+spy_log_return = np.log(spy_total.Close).diff().dropna()
+print 'Population mean:', np.mean(spy_log_return)
+[out]: Population mean: 0.000443353825615
+print 'Population standard deviation:',np.std(spy_log_return)
+[out]: Population standard deviation: 0.00784267293815
+</pre>
+Now let's check the recent 10 days sample and recent 1000 days sample:
+<pre class="prettyprint linenums">
+print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+[out]: 10 days sample returns: 0.000845189915474
+print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+[out]: 10 days sample standard deviation: 0.00313558001122
+print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+[out]: 1000 days sample returns: 0.000462827047221
+print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+[out]: 1000 days sample standard deviation: 0.00766589174299
+</pre>
+As we expected, the two samples has different means and variances.
+<h3>Confidence Interval</h3>
+In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
+\[SE = \frac{\sigma}{\sqrt{n}}\]
+Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
+Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+Where \(\mu\) is the sample mean and SE is the standard error.
+This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later.
+Let's try to calculate the confidence interval using the samples above:
+<pre class="prettyprint linenums">
+#apply the formula above to calculate confidence interval
+bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+#print the outcomes
+print '10 days 95% confidence inverval:', (bottom_1,upper_1)
+[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
+print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
+[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
+</pre>
+As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
+<h3>Confidence Interval of Normal Distribution</h3>
+Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
+\[\%90 upperabnd = \mu + 1.64*SE\]
+\[\%90 lowerband = \mu + 1.64*SE\]
+The same for other confidence intervals.
+It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
+\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
+\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
+\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
+This can also be remembered by using the chart:
+<img class="alignnone size-medium" src="https://upload.wikimedia.org/wikipedia/commons/a/a9/Empirical_Rule.PNG" width="769" height="558" />
+<h3>Central Limit Theory</h3>
+As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution?
+The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed.
+This is the foundation of population mean confidence interval estimation.
+<h2>Hypothesis testing</h2>
+Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample.
+Let's start from observing our sample:
+<pre class="prettyprint linenums">mean_1000 = np.mean(spy_log_return.tail(1000))
+std_1000 = np.std(spy_log_return.tail(1000))
+mean_10 = np.mean(spy_log_return.tail(10))
+std_10 = np.std(spy_log_return.tail(10))
+s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
+print s
+
+[out]: mean_10      0.000845
+       std_10       0.003136
+       mean_1000    0.000463
+       std_1000     0.007666
+</pre>
+We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
+<pre class="prettyprint linenums">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
+upper = 0 + 1.64*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00039756352254768874, 0.00039756352254768874)
+</pre>
+Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero.
+Can we claim that with 95% confidence level?
+<pre class="prettyprint linenums">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
+upper = 0 + 1.96*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00047513689280089639, 0.00047513689280089639)
+</pre>
+This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level.
+We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
+\[H_0:\bar{\mu} = 0\]
+\[H_0:\bar{\mu} \neq 0\]
+If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis.
+Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
+\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
+Let's calculate the Z score from our sample:
+<pre class="prettyprint linenums">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+[out]: 1.90922032428
+</pre>
+We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis.
+one reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
+<pre class="prettyprint linenums">import scipy.stats as st
+print (1 - st.norm.cdf(1.9488))
+[out]: 0.025659656888
+</pre>
+It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
+<img class="alignnone " src="http://napitupulu-jon.appspot.com/galleries/coursera-statistics/3w76.jpg" width="663" height="517" />
+If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level.
+Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
+<pre class="prettyprint linenums">mean_1200 = np.mean(spy_log_return.tail(1200))
+std_1200 = np.std(spy_log_return.tail(1200))
+z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
+print 'z-score = ',z_score
+[out]: z-score =  2.19793023185
+p_value = (1 - st.norm.cdf(z_score))
+print 'p_value = ',p_value
+[out]: p_value =  0.0139770390655
+</pre>
+Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
+<h2>Summary</h2>
+In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/08 Confidence Interval and Hypothesis Testing.ipynb b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/08 Confidence Interval and Hypothesis Testing.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population mean: 0.000443353825615\n",
+      "Population standard deviation: 0.00784267293815\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import quandl\n",
+    "spy_table = quandl.get('LSE/SPY5')\n",
+    "spy_total = spy_table[['Last Close']]\n",
+    "spy_log_return = np.log(spy_total['Last Close']).diff().dropna()\n",
+    "print 'Population mean:', np.mean(spy_log_return)\n",
+    "print 'Population standard deviation:',np.std(spy_log_return)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 days sample returns: 0.000845189915474\n",
+      "10 days sample standard deviation: 0.00313558001122\n",
+      "1000 days sample returns: 0.000462827047221\n",
+      "1000 days sample standard deviation: 0.00766589174299\n"
+     ]
+    }
+   ],
+   "source": [
+    "print '10 days sample returns:', np.mean(spy_log_return.tail(10))\n",
+    "print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))\n",
+    "print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))\n",
+    "print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)\n",
+      "1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))\n",
+    "upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))\n",
+    "bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))\n",
+    "upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))\n",
+    "print '10 days 95% confidence inverval:', (bottom_1,upper_1)\n",
+    "print '1000 days 95% confidence inverval:', (bottom_2,upper_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean_10      0.000845\n",
+      "std_10       0.003136\n",
+      "mean_1000    0.000463\n",
+      "std_1000     0.007666\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_10 = np.mean(spy_log_return.tail(10))\n",
+    "std_10 = np.std(spy_log_return.tail(10))\n",
+    "mean_1000 = np.mean(spy_log_return.tail(1000))\n",
+    "std_1000 = np.std(spy_log_return.tail(1000))\n",
+    "s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])\n",
+    "print s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(-0.00039756352254768874, 0.00039756352254768874)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bottom = 0 - 1.64*std_1000/np.sqrt(1000)\n",
+    "upper = 0 + 1.64*std_1000/np.sqrt(1000)\n",
+    "print (bottom, upper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(-0.00047513689280089639, 0.00047513689280089639)\n"
+     ]
+    }
+   ],
+   "source": [
+    "bottom = 0 - 1.96*std_1000/np.sqrt(1000)\n",
+    "upper = 0 + 1.96*std_1000/np.sqrt(1000)\n",
+    "print (bottom, upper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.90922032428\n"
+     ]
+    }
+   ],
+   "source": [
+    "print np.sqrt(1000)*(mean_1000 - 0)/std_1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.025659656888\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats as st\n",
+    "print (1 - st.norm.cdf(1.9488))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z-score =  2.19793023185\n",
+      "p_value =  0.0139770390655\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_1200 = np.mean(spy_log_return.tail(1200))\n",
+    "std_1200 = np.std(spy_log_return.tail(1200))\n",
+    "z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200\n",
+    "print 'z-score = ',z_score\n",
+    "p_value = (1 - st.norm.cdf(z_score))\n",
+    "print 'p_value = ',p_value"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html
new file mode 100755
index 0000000..696c08a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html	
@@ -0,0 +1,127 @@
+<h2>Introduction</h2>
+In finance and economics filed, most of the models are linear ones. We can see linear regression everywhere, from the foundation of the model portfolio theory to the nowadays popular Fama-French asset pricing model. It's very important to understand how linear regression works in order to have a comprehensive understanding of those theories.
+
+If we are holding a stock, we must be curious about the relationship between our stock return and the market return. Let's say we hold Amazon stock on the first day of this year. In order to see the relation directly, we plot the daily return of our stock on the y-axis and plot the S&amp;P 500 index daily return on the x-axis.
+<pre class="prettyprint linenums">import numpy as np
+import pandas as pd
+import quandl
+quandl.ApiConfig.api_key = '_fgkxjSbt5389zGt4crC'
+#get data from quandl
+spy_table = quandl.get('BCIW/_SPXT')
+amzn_table = quandl.get('WIKI/AMZN')
+#fetch data from Jan 2017 to Jun 2017
+spy = spy_table.loc['2017':'2017-6',['Close']]
+amzn = amzn_table.loc['2017':'2017-6',['Close']]
+#calculate log return
+spy_log = np.log(spy.Close).diff().dropna()
+amzn_log = np.log(amzn.Close).diff().dropna()
+df = pd.concat([spy_log,amzn_log],axis = 1).dropna()
+df.columns = ['spx','amzn']
+print df.tail()
+</pre>
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="center">spy</td>
+        <td align="center">amzn</td>
+    </tr>
+    <tr>
+        <td>2016-12-22</td>
+        <td align="center">-0.004462</td>
+        <td align="center">-0.005543</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.001372</td>
+        <td align="center">-0.007531</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.000928</td>
+        <td align="center">0.000946</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">-0.005671</td>
+        <td align="center">-0.009081</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.002086</td>
+        <td align="center">-0.020172</td>
+    </tr>
+
+</table>
+We successfully create a DataFrame contains the daily logarithm return of Amazon stock and S&amp;P500. Now let's plot it:
+<pre class="prettyprint linenums">import matplotlib.pyplot as plt
+plt.figure(figsize = (15,10))
+plt.scatter(df.spy,df.amzn)
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot1.png" alt="plot1" />
+The plot is scattered, but we can see they are approximately correlated: generally the higher SPX's daily return is, the higher Amazon stock's return is. This is called <strong>positively correlated</strong>. We will cover it in the following tutorials.
+<h2>Slope and Intercept</h2>
+It's natural that we want to model the relation between these two rates of return. Intuitively we use a straight line to model it, this is called <strong>Linear Regression</strong>. In order to find the best straight line, it's natural to think that the vertical distances between the points of the data set and the fitted line should be minimized. Those vertical distances are called <strong>residual</strong>. Our objective is to make the sum of squared residuals as small as possible. This method is called <strong>ordinary least square</strong>, or <strong>OLS</strong> method.
+We use x and y to represent the two variable, S&amp;P 500 daily returns and AMZN daily returns. The linear relation is:
+\[Y = Y = \alpha + \beta*X + \epsilon\]
+Where \(\alpha\) is called <strong>intercept</strong>, \(\beta\) is called <strong>slope</strong>.
+Generally, if the scatter points can be represented by\(\left\{\right (x_1,y_1),(x_2, y_2),(x_3,y_3)...(x_n,y_n) \left\}\right\), then the intercept and slope are given by:
+\[\beta = \frac{\sum_{i=1}^{n}(x-\bar{x})(y-\bar{y})}{\sum_{i=1}^{n}(x-\bar{x})^2}\]
+\[\alpha = \bar{y} - \hat{\beta}\bar{x}\]
+Where \(\bar{x}\) is the mean of X, \(\bar{y}\) is the mean of Y.
+In python, we don't need to do the above calculation manually because we have package for it. But it still very important to understand the calculation process of \(\beta\) in order the understand the modern portfolio theory and CAPM, which we will cover in the future.
+<h2>Python Implementation</h2>
+In python, we have a very power package for mathematical models, which is named 'statsmodels'.
+<pre class="prettyprint linenums">import statsmodels.formula.api as sm
+model = sm.ols(formula = 'amzn~spy',data = df).fit()
+print model.summary()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot2.png" alt="plot2" />
+We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
+<pre class="prettyprint linenums">print 'pamameters: ',model.params
+[out]: pamameters:  Intercept    0.000012
+                   spy          0.492112
+print 'residual: ', model.resid.tail()
+[out]: residual:  Date
+2016-12-22   -0.003360
+2016-12-23   -0.008219
+2016-12-28    0.000477
+2016-12-29   -0.006303
+2016-12-30   -0.021211
+print 'fitted values: ',model.predict()
+[out]: fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]
+</pre>
+Now let's have a look at our fitted line:
+<pre class="prettyprint linenums">plt.figure(figsize = (15,10))
+plt.scatter(df.spy,df.amzn)
+plt.xlabel('spx_return')
+plt.ylabel('amzn_return')
+plt.plot(df.spy,model.predict(),color = 'red')
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-head.png" alt="head" />
+The red line is the fitted linear regression straight line.
+As we can see there are lot of statistical results in the summary table. Now let's talk about some important statistical parameters.
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-regression-table.png" alt="regression table" />
+<h2>Parameter Significance</h2>
+From the summary table we can see 'std err'. This means standard errors of the intercept and slope. The null hypothesis here is \(H_0: \beta = 0\), and the alternative hypothesis is \(H_1: \beta \neq 0\). This hypothesis score is calculated by:
+\[t = \frac{\beta - 0}{SE}\]
+Where SE is given by:
+\[SE = \sqrt\frac{\frac{1}{n-2}\sum_{i = 1}^{n}\hat{\epsilon}^2}{\sum_{i = i}^{n}(x_i - \bar{x})^2}\]
+The distribution used here is 'Student's t-distribution'. It's different from normal distribution but used in the similar way. The column 't' in this table is the test score, and 'p&gt;|t|' is the p-value. By observing the p-value, we can see that the significance level of spy, or the slope, is very high because the p-value is close to zero. In other words, we have 99.999 confidence to claim that the slope is not 0, and there exists linear relation between X and Y. However, regarding the intercept, the p-value is 0.923, which means we have only 7.7% confidence level that the value of intercept is not 0. We can also see from the plot that the line crosses the origin.
+The following 2 columns are the lower band and upper band of the parameters at 95% confidence interval. At 95% confidence level, we can claim that the true value of the parameter is within this range.
+<h2>Model Significance</h2>
+<h3>R-square</h3>
+<strong>Sum of Squared Errors, or SSE</strong>, is used to measure the difference between the fitted value and the actual value. It's given by:
+\[SSE = \sum_{i=1}^{n}(y_i - \hat{y_i})^2 = \sum_{i = 1}^{n}\hat{\epsilon_i}^2\]
+If the linear model perfectly fitted the sample, the SSE would be zero.
+The reason we use squared error here is that the positive and negative errors would offset each other if we simply summed them up.
+Another measurement of the dispersion of the sample is called <strong>total sum of squares, or SS.</strong>. it's given by:
+\[SS = \sum_{i = i}^{n}(y_i - \bar{y}_i)^2\]
+If you are familiar with variance, we can see that SS divided by the number of sample n is the sample variance.
+From SSE and SS, we can calculate the <strong>Coefficient of Determination</strong>, or <strong>r-square</strong> for short. R-square means the proportion of variation that 'explained' by the linear relationship between X and Y, it's calculated by:
+\[r^2 =1 = \frac{SSE}{SS} =1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y_i})^2}{\sum_{i = i}^{n}(y_i - \bar{y}_i)^2}\]
+Let's assume that the model perfectly fitted the sample, which means all of the sample points lie on the straight line. Then the SSE would become zero, and the r-square would become 1. This means perfect fitness. The higher r-square is, the more parts of variation can be explained by the linear relation, the higher significance level the model is.
+Some other parameters, such as F-statistic, AIC and BIC, are related to multiple linear regression, with would be cover in the next chapter.
+<h2>Summary</h2>
+In this chapter we introduced how to implement simple linear in python, and focused on how to read the summary table. In next chapter we will introduced multiple linear regression, which are commonly used to built models in finance and economics.
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.ipynb b/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.ipynb
new file mode 100755
index 0000000..c870f22
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.ipynb	
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import quandl\n",
+    "spy_table = quandl.get('LSE/SPY5')\n",
+    "amzn_table = quandl.get('WIKI/AMZN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spy = spy_table.loc['2016',['Last Close']]\n",
+    "amzn = amzn_table.loc['2016',['Close']]\n",
+    "spy_log = np.log(spy['Last Close']).diff().dropna()\n",
+    "amzn_log = np.log(amzn.Close).diff().dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>spy</th>\n",
+       "      <th>amzn</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-12-22</th>\n",
+       "      <td>-0.004462</td>\n",
+       "      <td>-0.005543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-23</th>\n",
+       "      <td>0.001372</td>\n",
+       "      <td>-0.007531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-28</th>\n",
+       "      <td>0.000928</td>\n",
+       "      <td>0.000946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-29</th>\n",
+       "      <td>-0.005671</td>\n",
+       "      <td>-0.009081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-30</th>\n",
+       "      <td>0.002086</td>\n",
+       "      <td>-0.020172</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 spy      amzn\n",
+       "Date                          \n",
+       "2016-12-22 -0.004462 -0.005543\n",
+       "2016-12-23  0.001372 -0.007531\n",
+       "2016-12-28  0.000928  0.000946\n",
+       "2016-12-29 -0.005671 -0.009081\n",
+       "2016-12-30  0.002086 -0.020172"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([spy_log,amzn_log],axis = 1).dropna()\n",
+    "df.columns = ['spy','amzn']\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'amzn_return')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b031b70>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize = (15,10))\n",
+    "plt.scatter(df.spy,df.amzn)\n",
+    "plt.xlabel('spx_return')\n",
+    "plt.ylabel('amzn_return')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   amzn   R-squared:                       0.044\n",
+      "Model:                            OLS   Adj. R-squared:                  0.040\n",
+      "Method:                 Least Squares   F-statistic:                     10.63\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):            0.00128\n",
+      "Time:                        14:20:17   Log-Likelihood:                 608.99\n",
+      "No. Observations:                 235   AIC:                            -1214.\n",
+      "Df Residuals:                     233   BIC:                            -1207.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept   1.234e-05      0.001      0.010      0.992      -0.002       0.002\n",
+      "spy            0.4921      0.151      3.261      0.001       0.195       0.789\n",
+      "==============================================================================\n",
+      "Omnibus:                       51.597   Durbin-Watson:                   2.255\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              570.651\n",
+      "Skew:                           0.405   Prob(JB):                    1.22e-124\n",
+      "Kurtosis:                      10.591   Cond. No.                         127.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.formula.api as sm\n",
+    "model = sm.ols(formula = 'amzn~spy',data = df).fit()\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pamameters:  Intercept    0.000012\n",
+      "spy          0.492112\n",
+      "dtype: float64\n",
+      "residual:  Date\n",
+      "2016-12-22   -0.003360\n",
+      "2016-12-23   -0.008219\n",
+      "2016-12-28    0.000477\n",
+      "2016-12-29   -0.006303\n",
+      "2016-12-30   -0.021211\n",
+      "dtype: float64\n",
+      "fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('pamameters: ',model.params)\n",
+    "print('residual: ', model.resid.tail())\n",
+    "print('fitted values: ',model.predict()[-6:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1d630588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (15,10))\n",
+    "plt.scatter(df.spy,df.amzn)\n",
+    "plt.xlabel('spx_return')\n",
+    "plt.ylabel('amzn_return')\n",
+    "plt.plot(df.spy,model.predict(),color = 'red')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html
new file mode 100755
index 0000000..02a930f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html	
@@ -0,0 +1,320 @@
+In the last chapter we introduced simple linear regression, which has only one independent variable. In this chapter we will learn about linear regression with multiple independent variables.
+
+A simple linear regression model is written in the following form:
+\[ Y = \alpha + \beta X + \epsilon \]
+
+A <strong>multiple linear regression</strong> model with <em>p</em> variables is given by:
+\[ Y = \alpha + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_p X_p + \epsilon \]
+
+<h2>Python Implementation</h2>
+
+In the last chapter we used the S&amp;P 500 index to predict Amazon stock returns. Now we will add more variables to improve our model's predictions. In particular, we shall consider Amazon's competitors.
+
+<pre class="prettyprint linenums">import numpy as np
+import pandas as pd
+import quandl
+import matplotlib.pyplot as plt
+import statsmodels.formula.api as sm
+
+# Get stock prices
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+spy_table  = quandl.get('BCIW/_SPXT')
+amzn_table = quandl.get('WIKI/AMZN')
+ebay_table = quandl.get('WIKI/EBAY')
+wal_table  = quandl.get('WIKI/WMT')
+aapl_table = quandl.get('WIKI/AAPL')</pre>
+
+Then we fetch closing prices starting from 2016:
+
+<pre class="prettyprint linenums">
+spy  = spy_table .loc['2016',['Close']]
+amzn = amzn_table.loc['2016',['Close']]
+ebay = ebay_table.loc['2016',['Close']]
+wal  = wal_table .loc['2016',['Close']]
+aapl = aapl_table.loc['2016',['Close']]
+</pre>
+
+After taking log returns of each stock, we concatenate them into a DataFrame, and print out the last 5 rows:
+
+<pre class="prettyprint linenums">
+spy_log  = np.log(spy.Close) .diff().dropna()
+amzn_log = np.log(amzn.Close).diff().dropna()
+ebay_log = np.log(ebay.Close).diff().dropna()
+wal_log  = np.log(wal.Close) .diff().dropna()
+aapl_log = np.log(aapl.Close).diff().dropna()
+df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()
+df.columns = ['SPY', 'AMZN', 'EBAY', 'WAL', 'AAPL']
+df.tail()
+</pre>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>Date</td>
+        <td align="right">SPY</td>
+        <td align="right">AMZN</td>
+        <td align="right">EBAY</td>
+        <td align="right">WAL</td>
+        <td align="right">AAPL</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="right">0.001351</td>
+        <td align="right">-0.007531</td>
+        <td align="right">0.008427</td>
+        <td align="right">-0.000719</td>
+        <td align="right">0.001976</td>
+    </tr>
+    <tr>
+        <td>2016-12-27</td>
+        <td align="right">0.002254</td>
+        <td align="right">0.014113</td>
+        <td align="right">0.014993</td>
+        <td align="right">0.002298</td>
+        <td align="right">0.006331</td>
+    </tr>
+    <tr>
+        <td>2016-12-28</td>
+        <td align="right">-0.008218</td>
+        <td align="right">0.000946</td>
+        <td align="right">-0.007635</td>
+        <td align="right">-0.005611</td>
+        <td align="right">-0.004273</td>
+    </tr>
+    <tr>
+        <td>2016-12-29</td>
+        <td align="right">-0.000247</td>
+        <td align="right">-0.009081</td>
+        <td align="right">-0.001000</td>
+        <td align="right">-0.000722</td>
+        <td align="right">-0.000257</td>
+    </tr>
+    <tr>
+        <td>2016-12-30</td>
+        <td align="right">-0.004601</td>
+        <td align="right">-0.020172</td>
+        <td align="right">-0.009720</td>
+        <td align="right">-0.002023</td>
+        <td align="right">-0.007826</td>
+    </tr>
+</table>
+
+As before, we use the 'statsmodels' package to perform simple linear regression:
+
+<pre class="prettyprint linenums">simple = sm.ols(formula = 'amzn ~ spy', data = df).fit()
+print simple.summary()
+</pre>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="right">coef</td>
+        <td align="right">std err</td>
+        <td align="right">t</td>
+        <td align="right">P>|t|</td>
+        <td align="right">[0.025</td>
+        <td align="right">0.975]</td>
+    </tr>
+    <tr>
+        <td>Intercept</td>
+        <td align="right">9.876e-05</td>
+        <td align="right">0.001</td>
+        <td align="right">0.097</td>
+        <td align="right">0.923</td>
+        <td align="right">-0.002</td>
+        <td align="right">0.002</td>
+    </tr>
+    <tr>
+        <td>spy</td>
+        <td align="right">1.0796</td>
+        <td align="right">0.124</td>
+        <td align="right">8.725</td>
+        <td align="right">0.000</td>
+        <td align="right">0.836</td>
+        <td align="right">1.323</td>
+    </tr>
+</table>
+
+Similarly, we can build a multiple linear regression model:
+
+<pre class="prettyprint linenums">model = sm.ols(formula = 'amzn ~ spy + ebay + wal', data = df).fit()
+print model.summary()
+</pre>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="right">coef</td>
+        <td align="right">std err</td>
+        <td align="right">t</td>
+        <td align="right">P>|t|</td>
+        <td align="right">[0.025</td>
+        <td align="right">0.975]</td>
+    </tr>
+    <tr>
+        <td>Intercept</td>
+        <td align="right">0.0001</td>
+        <td align="right">0.001</td>
+        <td align="right">0.134</td>
+        <td align="right">0.894</td>
+        <td align="right">-0.002</td>
+        <td align="right">0.002</td>
+    </tr>
+    <tr>
+        <td>spy</td>
+        <td align="right">1.0468</td>
+        <td align="right">0.170</td>
+        <td align="right">6.155</td>
+        <td align="right">0.000</td>
+        <td align="right">0.712</td>
+        <td align="right">1.382</td>
+    </tr>
+    <tr>
+        <td>ebay</td>
+        <td align="right">-0.0795</td>
+        <td align="right">0.058</td>
+        <td align="right">-1.364</td>
+        <td align="right">0.174</td>
+        <td align="right">-0.194</td>
+        <td align="right">0.035</td>
+    </tr>
+    <tr>
+        <td>wal</td>
+        <td align="right">-0.0865</td>
+        <td align="right">0.089</td>
+        <td align="right">-0.976</td>
+        <td align="right">0.330</td>
+        <td align="right">-0.261</td>
+        <td align="right">0.088</td>
+    </tr>
+    <tr>
+        <td>aapl</td>
+        <td align="right">0.1529</td>
+        <td align="right">0.084</td>
+        <td align="right">1.831</td>
+        <td align="right">0.068</td>
+        <td align="right">-0.012</td>
+        <td align="right">0.317</td>
+    </tr>
+</table>
+
+As seen from the summary table, the p-values for Ebay, Walmart and Apple are 0.174, 0.330 and 0.068 respectively, so none of them are significant at a 95% confidence level.
+
+The multiple regression model has a higher \( R^2 \) than the simple one: 0.254 vs 0.234. Indeed, \( R^2 \) cannot decrease as the number of variables increases. Why? If an extra variable is added to our regression model, but it cannot account for variations in the response (amzn), then its estimated coefficient will simply be zero. It's as though that variable was never included in the model, so \( R^2 \) will not change.
+
+However, it is not always better to add hundreds of variables or we will overfit our model. We'll talk about this in a later chapter.
+
+Can we improve our model further? Here we try the <strong>Fama-French 5-factor model</strong>, which is an important model in asset pricing theory. We will cover it in the later tutorials.
+
+The data needed are publicly available on French's website.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] We have saved a copy for convenience. The following code fetches the data.
+
+<pre class="prettyprint linenums">
+import urllib2
+from datetime import datetime
+
+url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'
+response   = urllib2.urlopen(url)
+fama_table = pd.read_csv(response)
+
+# Convert time column into index
+fama_table.index = [datetime.strptime(str(x), "%Y%m%d")
+                    for x in fama_table.iloc[:,0]]
+# Remove time column
+fama_table = fama_table.iloc[:,1:]
+</pre>
+
+With the data, we can construct a Fama-French factor model:
+
+<pre class="prettyprint linenums">
+fama = fama_table['2016']
+fama = fama.rename(columns = {'Mkt-RF':'MKT'})
+fama = fama.apply(lambda x: x/100)
+fama_df = pd.concat([fama, amzn_log], axis = 1)
+fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA', data = fama_df).fit()
+print fama_model.summary()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-fama.png" alt="fama" />
+The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
+
+<pre class="prettyprint linenums">
+result = pd.DataFrame({'simple regression': simple.predict(),
+                       'fama_french': fama_model.predict(),
+                       'sample': df.amzn}, index = df.index)
+
+# Feel free to adjust the chart size
+plt.figure(figsize = (15,7.5))
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])
+plt.legend()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-compare.png" alt="compare" />
+Although it's hard to see from the chart above, the predicted return from multiple regression is closer to the actual return. Usually we don't plot the predictions to determine which model is better; we read the summary table.
+
+<h2>Model Significance Test</h2>
+
+Instead of using \( R^2 \) to assess whether our regression model is a good fit to the data, we can perform a hypothesis test: the F test.
+
+The null and alternative hypotheses of an F test are:
+\[ H_0: \beta_1 = \beta_2 = \dots = \beta_p = 0  \]
+\[ H_1: \text{At least one coefficient is not 0} \]
+
+We won't explain F test procedure in detail here. You just need to understand the null and alternative hypotheses. In the summary table of an F test, the 'F-statistic' is the F score, while 'prob (F-statistic)' is the p-value. Performing this test on the Fama-French model, we get a p-value of `2.21e-24` so we are almost certain that at least one of the coefficient is not 0.
+
+If the p-value is larger than 0.05, you should consider rebuilding your model with other independent variables.
+
+In simple linear regression, an F test is equivalent to a t test on the slope, so their p-values will be the same.
+
+<h2>Residual Analysis</h2>
+
+Linear regression requires that the predictors and response have a linear relationship. This assumption holds if the residuals are zero on average, <strong>no matter</strong> what values the predictors \( X_1, \dots, X_p \) take.
+
+Often it's also assumed that the residuals are independent and normally distributed with the same variance (homoskedasticity), so that we can contruct prediction intervals, for example.
+
+To check whether these assumptions hold, we need to analyse the residuals. In statistical arbitrage, residual analysis can also be used to generate signals.
+
+<h3>Normality</h3>
+
+The residuals of a linear model usually has a normal distribution. We can plot the residual's density to check for normality:
+
+<pre class="prettyprint linenums">plt.figure()
+#ols.fit().model is a method to access to the residual.
+fama_model.resid.plot.density()
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-residual.png" alt="residual" />
+
+As seen from the plot, the residual is normally distributed. By the way, the residual mean is always zero, up to machine precision:
+
+<pre class="prettyprint linenums">print 'Residual mean:', np.mean(fama_model.resid)
+[out]: Residual mean: -2.31112163493e-16
+print 'Residual variance:', np.var(fama_model.resid)
+[out]: Residual variance: 0.000205113416293
+</pre>
+
+<h3>Homoskedasticity</h3>
+
+This word is difficult to pronounce but not difficult to understand. It means that the residuals have the same variance for all values of X. Otherwise we say that 'heteroskedasticity' is detected.
+
+<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
+plt.scatter(df.spy,simple.resid)
+plt.axhline(0.05)
+plt.axhline(-0.05)
+plt.xlabel('x value')
+plt.ylabel('residual')
+plt.show()
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-variance.png" alt="variance" />
+As seen from the chart, the residuals' variance doesn't increase with X. The three outliers do not change our conclusion. Although we can plot the residuals for simple regression, we can't do this for multiple regression, so we use statsmodels to test for heteroskedasticity:
+
+<pre class="prettyprint linenums">from statsmodels.stats import diagnostic as dia
+het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])
+print 'p-value: ', het[-1]
+[out]:p-value of Heteroskedasticity:  0.144075842844
+</pre>
+
+No heteroskedasticity is detected at the 95% significance level.
+
+<h2>Summary</h2>
+
+In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.ipynb b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.ipynb
new file mode 100755
index 0000000..3ae3a01
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.ipynb	
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import quandl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.formula.api as sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'\n",
+    "goog_table = quandl.get('WIKI/GOOG')\n",
+    "amzn_table = quandl.get('WIKI/AMZN')\n",
+    "ebay_table = quandl.get('WIKI/EBAY')\n",
+    "wal_table = quandl.get('WIKI/WMT')\n",
+    "aapl_table = quandl.get('WIKI/AAPL')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>goog</th>\n",
+       "      <th>amzn</th>\n",
+       "      <th>ebay</th>\n",
+       "      <th>wal</th>\n",
+       "      <th>aapl</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016-12-23</th>\n",
+       "      <td>-0.001708</td>\n",
+       "      <td>-0.007531</td>\n",
+       "      <td>0.008427</td>\n",
+       "      <td>-0.000719</td>\n",
+       "      <td>0.001976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-27</th>\n",
+       "      <td>0.002074</td>\n",
+       "      <td>0.014113</td>\n",
+       "      <td>0.014993</td>\n",
+       "      <td>0.002298</td>\n",
+       "      <td>0.006331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-28</th>\n",
+       "      <td>-0.008246</td>\n",
+       "      <td>0.000946</td>\n",
+       "      <td>-0.007635</td>\n",
+       "      <td>-0.005611</td>\n",
+       "      <td>-0.004273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-29</th>\n",
+       "      <td>-0.002883</td>\n",
+       "      <td>-0.009081</td>\n",
+       "      <td>-0.001000</td>\n",
+       "      <td>-0.000722</td>\n",
+       "      <td>-0.000257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-30</th>\n",
+       "      <td>-0.014113</td>\n",
+       "      <td>-0.020172</td>\n",
+       "      <td>-0.009720</td>\n",
+       "      <td>-0.002023</td>\n",
+       "      <td>-0.007826</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                goog      amzn      ebay       wal      aapl\n",
+       "Date                                                        \n",
+       "2016-12-23 -0.001708 -0.007531  0.008427 -0.000719  0.001976\n",
+       "2016-12-27  0.002074  0.014113  0.014993  0.002298  0.006331\n",
+       "2016-12-28 -0.008246  0.000946 -0.007635 -0.005611 -0.004273\n",
+       "2016-12-29 -0.002883 -0.009081 -0.001000 -0.000722 -0.000257\n",
+       "2016-12-30 -0.014113 -0.020172 -0.009720 -0.002023 -0.007826"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goog = goog_table.loc['2016',['Close']]\n",
+    "amzn = amzn_table.loc['2016',['Close']]\n",
+    "ebay = ebay_table.loc['2016',['Close']]\n",
+    "wal = wal_table.loc['2016',['Close']]\n",
+    "aapl = aapl_table.loc['2016',['Close']]\n",
+    "goog_log = np.log(goog.Close).diff().dropna()\n",
+    "amzn_log = np.log(amzn.Close).diff().dropna()\n",
+    "ebay_log = np.log(ebay.Close).diff().dropna()\n",
+    "wal_log = np.log(wal.Close).diff().dropna()\n",
+    "aapl_log = np.log(aapl.Close).diff().dropna()\n",
+    "df = pd.concat([goog_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()\n",
+    "df.columns = ['goog','amzn','ebay','wal','aapl']\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Close   R-squared:                       0.346\n",
+      "Model:                            OLS   Adj. R-squared:                  0.319\n",
+      "Method:                 Least Squares   F-statistic:                     12.51\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           9.49e-10\n",
+      "Time:                        14:16:24   Log-Likelihood:                 419.66\n",
+      "No. Observations:                 124   AIC:                            -827.3\n",
+      "Df Residuals:                     118   BIC:                            -810.4\n",
+      "Df Model:                           5                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept      0.0004      0.001      0.453      0.651      -0.001       0.002\n",
+      "MKT            1.2675      0.200      6.339      0.000       0.872       1.664\n",
+      "SMB           -0.4920      0.187     -2.636      0.010      -0.862      -0.122\n",
+      "HML           -0.4131      0.185     -2.228      0.028      -0.780      -0.046\n",
+      "RMW           -0.1974      0.293     -0.673      0.502      -0.778       0.384\n",
+      "CMA           -0.6478      0.283     -2.292      0.024      -1.208      -0.088\n",
+      "==============================================================================\n",
+      "Omnibus:                       20.018   Durbin-Watson:                   2.022\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               90.152\n",
+      "Skew:                          -0.269   Prob(JB):                     2.65e-20\n",
+      "Kurtosis:                       7.142   Cond. No.                         410.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = sm.ols(formula = 'amzn~goog+ebay+wal+aapl',data = df).fit()\n",
+    "print(model2.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   amzn   R-squared:                       0.351\n",
+      "Model:                            OLS   Adj. R-squared:                  0.348\n",
+      "Method:                 Least Squares   F-statistic:                     134.7\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           3.50e-25\n",
+      "Time:                        14:16:25   Log-Likelihood:                 702.38\n",
+      "No. Observations:                 251   AIC:                            -1401.\n",
+      "Df Residuals:                     249   BIC:                            -1394.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept      0.0005      0.001      0.550      0.583      -0.001       0.002\n",
+      "goog           0.8636      0.074     11.607      0.000       0.717       1.010\n",
+      "==============================================================================\n",
+      "Omnibus:                       67.564   Durbin-Watson:                   1.823\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1470.739\n",
+      "Skew:                          -0.374   Prob(JB):                         0.00\n",
+      "Kurtosis:                      14.835   Cond. No.                         79.7\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "simple = sm.ols(formula = 'amzn ~ goog',data = df).fit()\n",
+    "print(simple.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'\n",
+    "fama_table = pd.read_csv(url)\n",
+    "index = [datetime.strptime(str(x), \"%Y%m%d\") for x in fama_table.iloc[:,0]]\n",
+    "fama_table.index = index\n",
+    "fama_table = fama_table.iloc[:,1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fama = fama_table['2016']\n",
+    "fama = fama.rename(columns = {'Mkt-RF':'MKT'})\n",
+    "fama = fama.apply(lambda x: x/100)\n",
+    "fama_df = pd.concat([fama,amzn_log],axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Close   R-squared:                       0.387\n",
+      "Model:                            OLS   Adj. R-squared:                  0.375\n",
+      "Method:                 Least Squares   F-statistic:                     30.97\n",
+      "Date:                Wed, 23 May 2018   Prob (F-statistic):           2.21e-24\n",
+      "Time:                        14:16:27   Log-Likelihood:                 709.59\n",
+      "No. Observations:                 251   AIC:                            -1407.\n",
+      "Df Residuals:                     245   BIC:                            -1386.\n",
+      "Df Model:                           5                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept      0.0010      0.001      1.028      0.305      -0.001       0.003\n",
+      "MKT            0.9612      0.125      7.691      0.000       0.715       1.207\n",
+      "SMB           -0.5890      0.182     -3.235      0.001      -0.948      -0.230\n",
+      "HML           -0.1335      0.211     -0.632      0.528      -0.549       0.282\n",
+      "RMW           -0.4851      0.264     -1.840      0.067      -1.005       0.034\n",
+      "CMA           -1.5555      0.324     -4.801      0.000      -2.194      -0.917\n",
+      "==============================================================================\n",
+      "Omnibus:                       69.457   Durbin-Watson:                   1.937\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2012.675\n",
+      "Skew:                           0.241   Prob(JB):                         0.00\n",
+      "Kurtosis:                      16.864   Cond. No.                         399.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA',data = fama_df).fit()\n",
+    "print(fama_model.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a160d0128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = pd.DataFrame({'simple regression':simple.predict(),'fama_french':fama_model.predict(),'sample':df.amzn},index = df.index)\n",
+    "plt.figure(figsize = (15,7.5))\n",
+    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])\n",
+    "plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x103c271d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "simple.resid.plot.density()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-136-15922c3fe4e7>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-15922c3fe4e7>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'residual mean: ', np.mean(fama_model.resid)\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'residual mean: ', np.mean(fama_model.resid)\n",
+    "print 'residual variance: ', np.var(fama_model.resid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a0bcb26a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(df.goog,simple.resid)\n",
+    "plt.axhline(0.05,color = 'r')\n",
+    "plt.axhline(-0.05,color = 'r')\n",
+    "plt.axhline(0,color = 'black')\n",
+    "plt.xlabel('x value')\n",
+    "plt.ylabel('residual')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas.core import datetools\n",
+    "from statsmodels.stats import diagnostic as dia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p-value of Heteroskedasticity:  0.14407584284381386\n"
+     ]
+    }
+   ],
+   "source": [
+    "het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])\n",
+    "print('p-value of Heteroskedasticity: ', het[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.7248088754041377, nan, 1.7298240426802394, 0.18963839548692538)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dia.het_breuschpagan(simple.resid,pd.DataFrame(df.goog))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2668279319999998\n"
+     ]
+    }
+   ],
+   "source": [
+    "print((float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html b/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html
new file mode 100755
index 0000000..e5124ed
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html	
@@ -0,0 +1,225 @@
+Many papers in statistics and quantitative finance make heavy use of linear algebra, so you need to have a working knowledge of it in order to read and apply them to your trading.
+
+<h2>Vectors</h2>
+
+A vector can be thought of as an arrow pointing from the origin to a specific point. Any vector or point can be represented by its coordinates i.e. an array of numbers, such as \((x,y)\) for a 2-dimensional vector, or \((x,y,z)\) for a 3-dimensional one. We usually write a vector as a column:
+\[ \mathbf{v} = \begin{pmatrix}
+x \\ y \\ z
+\end{pmatrix} \]
+<a name="scalar"></a>
+The <strong>scalar product</strong> of two vectors \( \mathbf{x} \) and \( \mathbf{y} \) in 2-dimensional space is defined as:
+\[ \mathbf{x}^T \mathbf{y} = \begin{pmatrix} x_1 &amp; x_2 \end{pmatrix} \begin{pmatrix} y_1 \\ y_2 \end{pmatrix} = x_1 y_1 + x_2 y_2 \]
+
+This definition can be easily generalized to <em>n</em> dimensional space. Clearly, we cannot take the scalar product of two vectors with different dimensions.
+
+<h2>Matrices</h2>
+
+If we have a few vectors \( \mathbf{v}_1, \mathbf{v}_2, \dots, \mathbf{v}_n \) with the same dimension, then we can put them side-by-side to form a <strong>matrix</strong>. For example, the vectors
+\[ v_1 = \begin{pmatrix} 1 \\ 2 \\ 3 \end{pmatrix} \qquad
+   v_2 = \begin{pmatrix} 2 \\ 2 \\ 2 \end{pmatrix} \qquad
+   v_3 = \begin{pmatrix} 3 \\ 1 \\ 1 \end{pmatrix} \]
+
+can be combined to produce a matrix:
+\[ m = \begin{pmatrix}
+1 &amp; 2 &amp; 3 \\
+2 &amp; 2 &amp; 2 \\
+3 &amp; 1 &amp; 1
+\end{pmatrix} \]
+
+m is a 3 &times; 3 matrix. We typically describe the dimensions of a matrix as \(m \times n\) where m = number of rows and n = number of columns.
+
+A <strong>square</strong> matrix is one with as many rows as columns.
+
+Notation: \(x_{ij}\) refers to a specific value in row \(i\) and column \(j\) of a matrix \(X\). For example, \(x_{23}\) is the number in the second row and third column of \(X\).
+
+<h3>Python Implementation</h3>
+
+In Python, the NumPy package deals with linear algebra. The array we learned in the NumPy chapter can be deemed as a vector:
+
+<pre class="prettyprint linenums">import numpy as np
+a = np.array([1,2,3])
+b = np.array([2,2,2])
+c = np.array([3,1,1])
+matrix = np.column_stack((a,b,c))
+print matrix
+print type(matrix)
+[out]:
+[[1 2 3]
+ [2 2 1]
+ [3 2 1]]
+</pre>
+
+It is worth noticing that we used column_stack() here to ensure that the vectors are vertical and placed side-by-side to form a matrix. Without the column_stack() function, the vectors will be made horizontal and stacked on top of one another:
+
+<pre class="prettyprint linenums">matrix2 = np.array([a,b,c])
+print matrix2
+[out]:
+[[1 2 3]
+ [2 2 2]
+ [3 1 1]]
+</pre>
+
+<h2>Matrix Multiplication</h2>
+
+How are two matrices multiplied? Suppose \(X = AB\). Each entry \(x_{ij}\) of matrix \(X\) is the <a href="#scalar">scalar product</a> of row \(i\) from matrix \(A\) with column \(j\) from matrix \(B\). This is best illustrated with an example:
+
+\[ AB = \begin{pmatrix}
+a_{11} &amp; a_{12} \\
+a_{21} &amp; a_{22} \\
+a_{31} &amp; a_{32}
+\end{pmatrix}
+\begin{pmatrix}
+b_{11} &amp; b_{12} \\
+b_{21} &amp; b_{22}
+\end{pmatrix} = \begin{pmatrix}
+x_{11} &amp; x_{12} \\
+x_{21} &amp; x_{22} \\
+x_{31} &amp; x_{32}
+\end{pmatrix} \]
+
+Then
+\[ x_{\color{red}11} = a_{{\color{red}1} 1} b_{1{\color{red} 1}} + a_{{\color{red}1} 2} b_{2 {\color{red}1}} \]
+\[ x_{\color{red}12} = a_{{\color{red}1} 1} b_{1 {\color{red}2}} + a_{{\color{red}1} 2} b_{2 {\color{red}2}} \]
+\[ x_{\color{red}21} = a_{{\color{red}2} 1} b_{1 {\color{red}1}} + a_{{\color{red}2} 2} b_{2 {\color{red}1}} \]
+\[ x_{\color{red}22} = a_{{\color{red}2} 1} b_{1 {\color{red}2}} + a_{{\color{red}2} 2} b_{2 {\color{red}2}} \]
+\[ \vdots \]
+
+In NumPy, we can multiply matrices with the dot() function:
+
+<pre class="prettyprint linenums">A = np.array([[2,3],[4,2],[2,2]])
+B = np.array([[4,2],[4,6]])
+x = np.dot(A,B)
+print x
+[out]:
+[[20 22]
+ [24 20]
+ [16 16]]
+</pre>
+
+Since matrix multiplication is defined in terms of scalar products, the matrix product \(AB\) exists only if \(A\) has as many columns as \(B\) has rows. It's useful to remember this shorthand: (m &times; n) &times; (n &times; p) = (m &times; p) which means that an (m &times; n) matrix multiplied by an (n &times; p) matrix yields an (m &times; p) matrix.
+
+Reversing the order of multiplication results in an error since B does not have as many columns as A has rows:
+
+<pre class="prettyprint linenums">x = np.dot(B,A)
+</pre>
+
+A natrual consequence of this fact is that matrix multiplication is <strong>not commutative</strong>. In other words, \(AB \neq BA\) in general.
+
+<h2>Inverse</h2>
+
+An <strong>identity</strong> matrix is a square matrix with ones on the main diagonal and zeros elsewhere. Here is an \(n \times n\) identity matrix:
+
+\[ I_n = \begin{pmatrix}
+   1 &amp; 0 &amp; 0 &amp; ... &amp; 0 \\
+   0 &amp; 1 &amp; 0 &amp; ... &amp; 0 \\
+   0 &amp; 0 &amp; 1 &amp; ... &amp; 0 \\
+   \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
+   0 &amp; 0 &amp; 0 &amp; ... &amp; 1
+\end{pmatrix} \]
+
+Multiplying any matrix by an identity matrix (of the correct shape) is like multiplying a number by 1. Concretely, if \(A\) is an \(m \times n\) matrix, then:
+\[ I_mA = AI_n = A \]
+
+\( A^{-1} \) is the <strong>inverse</strong> matrix of a square matrix \(A\) if:
+\[ AA^{-1} = I = A^{-1}A \]
+
+Some caveats:
+<ul>
+    <li>A rectangular matrix will not have an inverse, but it may have a pseudoinverse (not covered in this tutorial).</li>
+    <li>A square matrix may not have an inverse i.e. it may be "singular".</li>
+    <li>If a square matrix has an inverse, then its inverse is unique.</li>
+</ul>
+
+Inverse matrices are computed using the <a href="https://www.mathsisfun.com/algebra/matrix-inverse-row-operations-gauss-jordan.html" target="_blank">Gauss-Jordan method</a>. In NumPy, we use the linalg.inv() function to do it:
+
+<pre class="prettyprint linenums">print matrix
+print '\n-------------------------\n'
+print np.linalg.inv(matrix)
+[out]:
+[[1 2 3]
+ [2 2 1]
+ [3 2 1]]
+
+-------------------------
+
+[[ 0.   -1.    1.  ]
+ [-0.25  2.   -1.25]
+ [ 0.5  -1.    0.5 ]]
+</pre>
+
+Now let's check if the multiplication is \(I\):
+
+<pre class="prettyprint linenums">inverse = np.linalg.inv(matrix)
+print np.dot(matrix, inverse)
+print '\n-------------------------\n'
+print np.dot(inverse,matrix)
+[out]:
+[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]
+ [  0.00000000e+00   1.00000000e+00   1.11022302e-16]
+ [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]
+
+-------------------------
+
+[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]
+ [  6.66133815e-16   1.00000000e+00   0.00000000e+00]
+ [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]
+</pre>
+
+Not surprisingly, we ended up with an identity matrix. We can form a non-invertible matrix by making one of its rows a multiple of another:
+
+<pre class="prettyprint linenums">singular = np.array([[1,2,3],[1,2,3],[3,3,3]])
+inv = np.linalg.inv(singular)
+[out]: numpy.linalg.linalg.LinAlgError: Singular matrix
+</pre>
+
+<h2>Linear Equations</h2>
+
+A common problem in linear algebra is solving linear equations. Consider the following linear equations:
+
+\[  2x + y - z = 8    \]
+\[ -3x - y + 2z = -11 \]
+\[ -2x + y + 2z = -3  \]
+
+If we let:
+\[ A = \begin{pmatrix}
+   2  &amp; 1  &amp; -1 \\
+   -3 &amp; -1 &amp; 2  \\
+   -2 &amp; 1  &amp; 2
+\end{pmatrix} \qquad
+\mathbf{x} = \begin{pmatrix} x \\  y  \\ z  \end{pmatrix} \qquad
+\mathbf{b} = \begin{pmatrix} 8 \\ -11 \\ -3 \end{pmatrix} \]
+
+Then the linear equations above can be written as \( A\mathbf{x} = \mathbf{b} \)
+
+If A is invertible, then we can multiply \(A^{-1}\) on both sides of the equation to obtain the solution:
+\[ A^{-1}A \mathbf{x} = A^{-1}\mathbf{b} \]
+Hence
+\[ \mathbf{x} = A^{-1}\mathbf{b} \]
+
+As long as \(A^{-1}\) exists, we can compute it to solve the linear equations:
+
+<pre class="prettyprint linenums">A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])
+b = np.array([[8],[-11],[-3]])
+inv_A = np.linalg.inv(A)
+print np.dot(inv_A, b)
+[out]:
+[[ 2.]
+ [ 3.]
+ [-1.]]
+</pre>
+
+The solution is <em>x</em> = 2, <em>y</em> = 3, <em>z</em> = &minus;1. However, computing the inverse matrix is not recommended, since it is numerically unstable i.e. small rounding errors can dramatically affect the result.
+
+Instead, NumPy solves linear equations by <a href="https://www.youtube.com/watch?v=m3EojSAgIao" target="_blank">LU decomposition</a>:
+
+<pre class="prettyprint linenums">print np.linalg.solve(A, b)
+[out]:
+[[ 2.]
+ [ 3.]
+ [-1.]]
+</pre>
+
+Of course, we get the same solution. We can check the correctness of the solution by substituting <em>x</em>, <em>y</em> and <em>z</em> into the linear equations.
+
+<h2>Summary</h2>
+In this chapter we have introduced vectors, matrices, inverse matrices and linear equations. Some applications in finance include: finding arbitrage opportunities by solving linear equations, computing portfolio variance, etc. In the next chapter, we will introduce modern portfolio theory and CAPM.
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.ipynb b/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.ipynb
new file mode 100755
index 0000000..06e6da5
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.ipynb	
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([1,2,3])\n",
+    "b = np.array([2,2,2])\n",
+    "c = np.array([3,1,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [2 2 1]\n",
+      " [3 2 1]]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix = np.column_stack((a,b,c))\n",
+    "print(matrix)\n",
+    "print(type(matrix))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [2 2 2]\n",
+      " [3 1 1]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "matrix2 = np.array([a,b,c])\n",
+    "print(matrix2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[2,3],[4,2],[2,2]])\n",
+    "B = np.array([[4,2],[4,6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[20 22]\n",
+      " [24 20]\n",
+      " [16 16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.dot(A,B)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-ac89428c1137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: shapes (2,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.dot(B,A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [2 2 1]\n",
+      " [3 2 1]]\n",
+      "\n",
+      "-------------seperation line------------\n",
+      "\n",
+      "[[ 3.70074342e-17 -1.00000000e+00  1.00000000e+00]\n",
+      " [-2.50000000e-01  2.00000000e+00 -1.25000000e+00]\n",
+      " [ 5.00000000e-01 -1.00000000e+00  5.00000000e-01]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(matrix)\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.linalg.inv(matrix))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.00000000e+00 -6.66133815e-16  3.33066907e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00 -2.22044605e-16  1.00000000e+00]]\n",
+      "\n",
+      "-------------seperation line------------\n",
+      "\n",
+      "[[ 1.00000000e+00  0.00000000e+00  1.11022302e-16]\n",
+      " [ 0.00000000e+00  1.00000000e+00 -4.44089210e-16]\n",
+      " [-1.11022302e-16  0.00000000e+00  1.00000000e+00]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "inverse = np.linalg.inv(matrix)\n",
+    "print(np.dot(matrix,inverse))\n",
+    "print('\\n-------------seperation line------------\\n')\n",
+    "print(np.dot(inverse,matrix))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.]\n",
+      " [ 3.]\n",
+      " [-1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])\n",
+    "B = np.array([[8],[-11],[-3]])\n",
+    "inv_A = np.linalg.inv(A)\n",
+    "print(np.dot(inv_A,B))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.]\n",
+      " [ 3.]\n",
+      " [-1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.linalg.solve(A,B))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html
new file mode 100755
index 0000000..4b8c325
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html	
@@ -0,0 +1,155 @@
+The Modern Portfolio Theory (MPT) suggests how investors should spread their wealth across various assets to minimize risk and maximize return.
+
+This chapter is mathematically intense, so don't feel demoralized if you don't understand it on your first reading.
+
+<h2>Risk Aversion</h2>
+
+In portfolio theory, the riskiness of an asset is often measured by the variance (or standard deviation) of its returns. Risk-averse investors do not want their wealth to fluctuate wildly.
+
+Risk aversion can be illustrated with a simple example. Which of the following assets do you prefer?
+<ul>
+    <li>Asset A pays $200 or $0 with 50% probability each.</li>
+    <li>Asset B pays $400 or &minus;$200 (i.e. you lose $200) with 50% probability each.</li>
+</ul>
+
+The expected payouts of A and B are:
+\[ \mathbb{E}(A) = 0.5 \times 200 + 0.5 \times 0 = 100 \]
+\[ \mathbb{E}(B) = 0.5 \times 400 + 0.5 \times (-200) = 100 \]
+
+The standard deviation of their payouts are:
+\[ \sigma_A = \sqrt{0.5(200-100)^2 + 0.5(0-100)^2} = 100 \]
+\[ \sigma_A = \sqrt{0.5(400-100)^2 + 0.5(-200-100)^2} = 300 \]
+
+If you are an risk seeker, you may choose asset B, because you can potentially get a higher payout. MPT assumes that investors prefer asset A since both assets have the same expected payout, but asset A has less risk.
+
+<h2>Portfolio</h2>
+
+Suppose we invest some fraction \(w_1, w_2, \dots, w_n\) of our wealth in <em>n</em> risky assets (labelled 1 to <em>n</em>), and the remainder \(w_0\) in a riskless asset such as cash in a bank account.
+
+Clearly \(w_0 + w_1 + \dots + w_n = 1\) since our wealth comprises all those assets.
+
+Let \(R_0, R_1, \dots, R_n\) be the respective asset returns, then our portfolio return is
+\[ R_P = w_0 R_0 + w_1 R_1 + \dots + w_n R_n \]
+
+Alternatively, we can eliminate \(w_0\) to get
+\[ R_P - R_0 = w_1 (R_1 - R_0) + \dots + w_n (R_n - R_0) \]
+
+Our <strong>expected</strong> portfolio return is
+\[ \mathbb{E}(R_P) = w_0 R_0 + w_1 \mathbb{E}(R_1) + \dots + w_n \mathbb{E}(R_n) \]
+
+Note that \( \mathbb{E}(R_0) = R_0 \) since the riskless return is known with certainty, by definition.
+<a name="correlation"></a>
+<h3>Correlation</h3>
+
+Before computing portfolio risk, we need to first understand covariance and correlation. They measure the linear relationship between two random variables.
+
+The covariance of two random variables X and Y is defined as
+\[ \text{Cov}(X, Y) = \mathbb{E} \left[ (X-\mathbb{E}(X)) (Y-\mathbb{E}(Y)) \right] \]
+
+The correlation of X and Y, which is always between &minus;1 and 1, is their covariance after being standardized:
+\[ \text{Corr}(X, Y) = \text{Cov} \left( \frac{X-\mathbb{E}(X)}{\sigma_X}, \frac{Y-\mathbb{E}(Y)}{\sigma_Y} \right) = \frac{\text{Cov}(X, Y)}{\sigma_X \sigma_Y} \]
+
+<h3>Risk</h3>
+
+Now we are ready to compute portfolio risk, as measured by the variance of portfolio returns:
+\[ \text{Var}(R_P) = \text{Var}(w_0 R_0 + w_1 R_1 + \dots + w_n R_n) \]
+
+Recall that \( \text{Var}(X + c) = \text{Var}(X) \) if <em>c</em> is a known constant, so the term \( w_0 R_0 \) involving the riskless return can be omitted. It will be convenient to use <a href="https://www.mathsisfun.com/algebra/sigma-notation.html" target="_blank">sigma notation</a>:
+
+\[ \text{Var}(R_P) = \text{Var} \left( \sum_{k=1}^n w_k R_k \right) \]
+\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k R_k - \mathbb{E} \left( \sum_{k=1}^n w_k R_k \right) \right)^2\right] \]
+\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k \, \left( R_k - \mathbb{E}(R_k) \right) \right)^2\right] \]
+
+So we have a squared sum of <em>n</em> terms. How do we expand it?
+\[ \left( \sum_{k=1}^n u_k \right)^2 = (u_1 + \dots + u_n) \, (u_1 + \dots + u_n) \]
+
+If we expand the brackets on the right hand side, every term has the form \( u_i u_j \) where <em>i</em> and <em>j</em> can be 1, 2, ... , or <em>n</em>.
+\[ \left( \sum_{k=1}^n u_k \right)^2 = \sum_{i=1}^n \sum_{j=1}^n u_i u_j \]
+
+Therefore
+\[ \text{Var}(R_P) = \mathbb{E} \left[ \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, (R_i - \mathbb{E}(R_i)) (R_j - \mathbb{E}(R_j)) \right] \]
+\[ = \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, \text{Cov}(R_i, R_j) \]
+
+The last step arises from the definition of <a href="#correlation">covariance</a>. The only thing left is to express portfolio risk in matrix notation:
+
+\[ \text{Var}(R_P) = \mathbf{w}^T \Sigma \mathbf{w} \]
+where
+\[ \mathbf{w} = \begin{pmatrix} w_1 \\ \vdots \\ w_n \end{pmatrix} \qquad
+\Sigma = \begin{bmatrix}
+\text{Cov}(R_1, R_1) & \text{Cov}(R_1, R_2) & ... & \text{Cov}(R_1, R_n) \\
+\text{Cov}(R_2, R_1) & \text{Cov}(R_2, R_2) & ... & \text{Cov}(R_2, R_n) \\
+\vdots & \vdots & \ddots & \vdots \\
+\text{Cov}(R_n, R_1) & \text{Cov}(R_n, R_2) & ... & \text{Cov}(R_n, R_n)
+\end{bmatrix} \]
+
+<h3>Intuition</h3>
+
+How can we make sense of portfolio risk? Consider a simple case with a riskless asset and only <em>n</em> = 2 risky assets.
+
+\[ \text{Var}(R_P) = w_A^2 \text{Var}(R_A) + w_B^2 \text{Var}(R_B) + 2w_A w_B \text{Cov}(R_A, R_B) \]
+
+Portfolio risk can be reduced by choosing two assets that are negatively correlated. This is the benefit of diversification.
+
+<h2>Mean-Variance Analysis</h2>
+
+We now try to find a portfolio \( \mathbf{w} = (w_1, ..., w_n) \) that minimizes risk and maximizes return.
+
+The chart below has risk (standard deviation of returns) on the horizontal axis and expected return on the vertical axis. The 10 black points represent individual stocks, while each green / blue point is a portfolio of stocks:
+
+<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
+
+Notice that all points (i.e. stocks and portfolios) are enclosed by a hyperbola, known as the <strong>efficient frontier</strong>.
+
+All portfolios on the efficient frontier have the maximum expected return for a given level of risk, if we only consider portfolios of risky stocks. Can we achieve higher returns by including a riskless asset? Yes.
+
+<h3>Capital Market Line</h3>
+
+The black line on the chart is the Capital Market Line (CML). It is <strong>tangent</strong> to the efficient frontier and cuts the vertical axis at the riskfree return. The point of tangency represents the so-called <strong>market portfolio</strong>.
+
+Every point on the CML represents a portfolio comprising the market portfolio and riskless asset in some proportion. Why?
+
+Suppose some fraction <em>w</em> of a CML portfolio is the market portfolio, and the remainder (1 &minus; <em>w</em>) is the riskless asset. Then its expected return is
+
+\[ \mathbb{E} (R_P) = w \mathbb{E} (R_{\text{market}}) + (1-w) R_0 \]
+
+Since there is only <em>n</em> = 1 risky asset, the variance of the CML portfolio return is
+
+\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \]
+
+Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight:
+
+\[ \sigma_P = w \sigma_{\text{market}} \]
+
+This equation can be used to eliminate <em>w</em> in the calculation of expected return:
+
+\[ \mathbb{E} (R_P) = R_0 + \frac{\mathbb{E} (R_{\text{market}}) - R_0}{\sigma_{\text{market}}} \sigma_P \]
+
+This proves that when \( \mathbb{E} (R_P) \) is plotted against \( \sigma_P \), we will obtain a straight line: the CML.
+
+<h3>Portfolio Selection</h3>
+
+Why is the CML significant? For any given level of risk, CML portfolios have a higher return than those on the efficient frontier, so investors should select any of them according to their risk tolerance.
+
+Risk-averse investors may give the riskless asset a larger weight in their portfolio. Risk-seeking investors may borrow money (i.e. sell the riskless asset) to invest >100% of their wealth in the market portfolio.
+
+Regardless of their risk tolerances, all investors should hold the same stocks in the same proportion in the market portfolio. In other words, they should not pick stocks according to their risk tolerance.
+
+<h3>Diversification</h3>
+
+What happens to the efficient frontier and hence the CML if we have only 3 stocks (IBM, GE, and PFE) instead of 10?
+
+<img class="aligncenter size-full wp-image-2155" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown2.png" alt="" width="1184" height="592" />
+
+Since we have fewer stocks to choose from, it's not too surprising that our maximum expected return is lower for any level of risk.
+
+This demonstrates why diversification is often said to be a "free lunch" in investing.
+
+<h2>Summary</h2>
+
+In this chapter we have learnt about the modern portfolio theory. It recommends investors to spread their wealth across many asset classes to maximize returns while minimizing risk. In the next chapter, we will introduce the Capital Asset Pricing Model.
+
+<h2>Algorithm:</h2>
+
+Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
+
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=71761bc8ce01e7c81b5ea91f54a82640' ></script>
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.ipynb b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.ipynb
new file mode 100755
index 0000000..70bfe67
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.ipynb	
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import quandl\n",
+    "from cvxopt import solvers\n",
+    "from cvxopt import matrix\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import minimize\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class stock(object):\n",
+    "    def __init__(self,ticker):\n",
+    "        self.ticker = ticker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tickers = ['KO','JNJ','PFE','NKE','PG','WMT','MMM','IBM']\n",
+    "stocks = []\n",
+    "leng = len(tickers)\n",
+    "for i in tickers:\n",
+    "    vars()[i] = stock(i)\n",
+    "    stocks.append(vars()[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.027400000000000001"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
+    "rf = (rf.ix[-1][0]/100)\n",
+    "rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    table = quandl.get('WIKI/%s'%i.ticker)\n",
+    "    i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
+    "    i.mean = np.mean(i.rate)*252\n",
+    "    i.std = np.std(i.rate)*np.sqrt(252)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         mean       std\n",
+      "KO   0.072506  0.188207\n",
+      "JNJ  0.102055  0.165611\n",
+      "PFE  0.080836  0.227700\n",
+      "NKE  0.149412  0.286772\n",
+      "PG   0.052920  0.177927\n",
+      "WMT  0.078416  0.194245\n",
+      "MMM  0.116281  0.221956\n",
+      "IBM  0.053190  0.222997\n"
+     ]
+    }
+   ],
+   "source": [
+    "stock_list = [x.ticker for x in stocks]\n",
+    "rate_list = [x.rate for x in stocks]\n",
+    "mean_list = [x.mean for x in stocks]\n",
+    "std_list = [x.std for x in stocks]\n",
+    "cov_matrix = np.cov(rate_list)\n",
+    "df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
+    "print df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def min_var_generator(rate):\n",
+    "    def target(x, sigma, mean,r):\n",
+    "        sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
+    "        return sr_inv\n",
+    "\n",
+    "    x = np.ones(leng)/leng\n",
+    "    mean = mean_list\n",
+    "    sigma = cov_matrix\n",
+    "    r = rf\n",
+    "    c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
+    "        {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
+    "    bounds = [(-1,1) for i in range(leng)]\n",
+    "    res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
+    "#     return res['x']\n",
+    "    return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 0.20992676,  0.27623188, -0.00295919, -0.10248824,  0.31125431,\n",
+       "         0.23690646, -0.10606759,  0.1771956 ]), 0.1455789711354554)"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_var_generator(0.06)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
+    "simu_var = []\n",
+    "for i in simu_rate:\n",
+    "    try:\n",
+    "        res = min_var_generator(i)\n",
+    "        simu_var.append(res[1])\n",
+    "    except:\n",
+    "        print i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rate      0.179200\n",
+       "std       0.240626\n",
+       "sharpe    0.630854\n",
+       "Name: 1518, dtype: float64"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
+    "port_df.head()\n",
+    "port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
+    "opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
+    "opt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Simulation#\n",
+    "monte_rate, monte_std = [],[]\n",
+    "for i in range(100000):\n",
+    "    w = np.random.dirichlet(np.ones(leng),size=1)\n",
+    "    monte_rate.append(np.dot(w,mean_list))\n",
+    "    monte_std.append(np.sqrt(np.dot(np.dot(w,cov_matrix),w.reshape(8,1)))*np.sqrt(252))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simu_df = pd.DataFrame({'x':monte_std,'y':monte_rate})\n",
+    "simu_df['sharpe'] = (simu_df['y'] - rf)/simu_df['x']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
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Sy+XSwoULFRUVqod7NVC/nvVVp35bKaKnHM6oQMcEUM5RQAEAAABAJeTxeDRv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y7oq2cekW6qrqSUiJN13RDAaDwfCDYwQog8Fg+BGpq6vj1VdfZeLEiWzatIkOHTrw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\nERERERGRZKUClIiIiIiIiIiIJKv/A907XhlB7lZHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x118f8a990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
+    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
+    "plt.scatter(0,rf,color = 'r',s = 30)\n",
+    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
+    "plt.annotate('risk-free',(0,rf),size = 15)\n",
+    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
+    "for i in stocks:\n",
+    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
+    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
+    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
+    "plt.xlim(0)\n",
+    "plt.xlabel('Standard Deviation')\n",
+    "plt.ylabel('Expected Return')\n",
+    "plt.axhline(rf,ls = '--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "###three stocks version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def efficient_frontier(tickers):\n",
+    "    stocks = []\n",
+    "    leng = len(tickers)\n",
+    "    for i in tickers:\n",
+    "        vars()[i] = stock(i)\n",
+    "        stocks.append(vars()[i])\n",
+    "    \n",
+    "    rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
+    "    rf = (rf.ix[-1][0]/100)\n",
+    "    for i in stocks:\n",
+    "        table = quandl.get('WIKI/%s'%i.ticker)\n",
+    "        i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
+    "        i.mean = np.mean(i.rate)*252\n",
+    "        i.std = np.std(i.rate)*np.sqrt(252)\n",
+    "        \n",
+    "    stock_list = [x.ticker for x in stocks]\n",
+    "    rate_list = [x.rate for x in stocks]\n",
+    "    mean_list = [x.mean for x in stocks]\n",
+    "    std_list = [x.std for x in stocks]\n",
+    "    cov_matrix = np.cov(rate_list)\n",
+    "    df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
+    "    print df\n",
+    "    \n",
+    "    def min_var_generator(rate):\n",
+    "        def target(x, sigma, mean,r):\n",
+    "            sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
+    "            return sr_inv\n",
+    "\n",
+    "        x = np.ones(leng)/leng\n",
+    "        mean = mean_list\n",
+    "        sigma = cov_matrix\n",
+    "        r = rf\n",
+    "        c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
+    "            {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
+    "        bounds = [(-1,1) for i in range(leng)]\n",
+    "        res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
+    "        return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))\n",
+    "    \n",
+    "    simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
+    "    simu_var = []\n",
+    "    for i in simu_rate:\n",
+    "        try:\n",
+    "            res = min_var_generator(i)\n",
+    "            simu_var.append(res[1])\n",
+    "        except:\n",
+    "            print i\n",
+    "    \n",
+    "    port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
+    "    port_df.head()\n",
+    "    port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
+    "    opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
+    "    return port_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         mean       std\n",
+      "PG   0.052920  0.177927\n",
+      "IBM  0.053190  0.222997\n",
+      "KO   0.072506  0.188207\n"
+     ]
+    }
+   ],
+   "source": [
+    "new = efficient_frontier(['PG','IBM','KO'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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Wac+fr/u7XaCISnfIRHSWMcx4AnB6KKAAAAAAoBw6cOCAJkyYoNTUVP3222+6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RAAAg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LQyZ3Tnafw2a4ehIwkhhBAvTn9/CZ5KluC9MSaGDmBIjRs3JiIiAgsLi5c67vPPP2fz5s0o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\nUwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115569390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
+    "plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
+    "plt.scatter(new['std'],new['rate'],c = new['sharpe'],s = 0.5)\n",
+    "plt.scatter(0,rf,color = 'r',s = 30)\n",
+    "plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
+    "plt.annotate('risk-free',(0,rf),size = 15)\n",
+    "plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
+    "for i in stocks:\n",
+    "    plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
+    "    plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
+    "plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
+    "plt.xlim(0)\n",
+    "plt.xlabel('Standard Deviation')\n",
+    "plt.ylabel('Expected Return')\n",
+    "plt.axhline(rf,ls = '--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html
new file mode 100755
index 0000000..392b745
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html	
@@ -0,0 +1,92 @@
+In the financial literature, you may hear terms like the "beta" or "market risk" of an asset. This chapter will explain where these terms come from and how they can be useful.
+
+<h2>Capital Asset Pricing Model (CAPM)</h2>
+
+As we shall see later, the name "Asset Pricing" is a bit misleading because the CAPM tells us the expected return, rather than the price, of an asset.
+
+In the last chapter, we introduced the Capital Market Line (CML) shown in black:
+
+<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
+
+All investors should hold a portfolio on the CML, which is constructed by investing some fraction <em>w</em> of our wealth in the market portfolio and the remainder (1 &minus; <em>w</em>) in the riskless asset. So the return on a CML portfolio is
+\[ R = w R_{\text{market}} + (1-w) R_0 \]
+
+If we let &beta; = w, then the equation above becomes
+\[ R - R_0 = \beta (R_{\text{market}} - R_0) \]
+
+Notice that &beta; is a measure of how sensitive our CML portfolio return is to the market return. Taking expectation on both sides results in the CAPM:
+\[ \mathbb{E}(R) - R_0 = \beta (\mathbb{E} (R_{\text{market}}) - R_0) \]
+
+Taking covariance on both sides instead yields
+\[ \text{Cov} (R - R_0, R_{\text{market}}) = \beta \text{Cov} (R_{\text{market}} - R_0, R_{\text{market}}) \]
+
+Now apply two basic facts about covariance:
+<ul>
+    <li>\( \text{Cov} (X + c, Y) = \text{Cov} (X, Y) \) where \( c \) is constant</li>
+    <li>\( \text{Cov} (X, X) = \text{Var} (X) \)</li>
+</ul>
+
+Hence we obtain
+\[ \beta = \frac {\text{Cov} (R, R_{\text{market}})} {\text{Var} (R_{\text{market}})} \]
+
+<h2>Computing &beta; in Practice</h2>
+
+While the Capital Asset Pricing Model is straightforward, applying it may not be. We first need to choose the timeframe for computing returns: should we use daily, weekly or monthly returns? Then we need to consider the number of data points available for linear regression. For example, if we compute beta using monthly returns in the past 1 year, then we only have 12 data points which is too few.
+
+Furthermore, the &beta; of an asset can change over time. The following plot is the daily rolling beta of GE stock with a 6-month rolling windows:
+
+<img class="aligncenter size-full wp-image-2161" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-3.png" alt="" width="1156" height="558" />
+
+The &beta; of GE ranged from 0.1 to 0.5 approximately. This is why you need to be careful when using &beta;. It makes no sense to talk about &beta; without a timeframe in mind.
+
+The following graph is the rolling p-value of beta. The p-value stays close to zero most of the time. However, during some period it suddenly increased close to 0.1, which corresponds to a 90% confidence interval. This might be caused by some mispricing or market turmoil.
+
+<img class="aligncenter size-full wp-image-2164" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-4.png" alt="" width="1163" height="558" />
+
+<h2>Market-Neutral</h2>
+
+A portfolio is market-neutral if its <strong>&beta; is zero</strong>. In other words, the portfolio's returns are uncorrelated with market returns. We say that it has no "market risk". Some classical market-neutral strategies are pairs trading, beta-hedged equity portfolio and other derivatives strategies.
+
+We have daily returns of Dow 30 stocks from March 2012 to Jan 2015. For each stock, its &beta; on any given day is computed using the past 6 months' returns. As this 6-month window moves forward in time, &beta; will of course change.
+
+Once we know how each stock's &beta; has changed over time, we can ask: which stocks' betas are correlated with each other? The table below shows the correlation between each stock's beta.
+
+<img src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-5.png" alt="" width="1056" height="578" class="aligncenter size-full wp-image-2174" />
+
+How can we construct a market-neutral portfolio? Consider two stocks A and B:
+\[ R_A = R_0 + \beta_A (R_{\text{market}} - R_0) \]
+\[ R_B = R_0 + \beta_B (R_{\text{market}} - R_0) \]
+
+Let's allocate <em>w</em> on stock A and (1 &minus; <em>w</em>) on stock B, then market neutrality means
+\[ w\beta_A + (1-w) \beta_B = 0 \qquad \Rightarrow \qquad
+   w = \frac{\beta_B}{\beta_B - \beta_A} \]
+
+As mentioned earlier, \(\beta_A\) and \(\beta_B\) will change with time, so will \(w\) in a market-neutral portfolio. However we can achieve market neutrality with constant \(w\) so long as \(\beta_A\) and \(\beta_B\) have a linear relationship:
+\[ \beta_A = m\beta_B + c \]
+
+Then eliminate \(\beta_A\) to get
+\[ w = \frac{\beta_B}{(1-m) \beta_B - c} \]
+
+If <em>c</em> &approx; 0 then <em>w</em> is roughly constant:
+\[ w = \frac{1}{1-m} \]
+
+Note: If <em>c</em> is signficant, then we need 3 stocks to get zero net beta. Usually 2 stocks are sufficient to cancel out most of the market risk.
+
+<h3>Example</h3>
+
+The 6-month rolling betas of PG and KO stocks have a correlation of 93%. The linear relationship between their betas is
+\[ \beta_{KO} = -0.0097 + 0.969 \beta_{PG} \]
+
+Therefore the market neutral weights are
+\[ w_{KO} = \frac{1}{1-0.969} = 32.3 \qquad w_{PG} = 1 - w_{KO} = -31.3 \]
+
+We tested this portfolio both in-sample (March 2012 to Jan 2015) and out-of-sample. It achieved a beta of 0.01 and -0.004 respectively. See the backtest below.
+
+<h2>Summary</h2>
+
+This chapter has explained what market risk means in the context of CAPM, and how market risk can be reduced. In the next chapter we will generalize CAPM to multi-factor models, such as the Fama-French models.
+
+<h2>Algorihtm</h2>
+
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8bac244b346cbd9bc3b39dca76192919'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5c75197259238b36a6cea72832c9e709'></script>
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.ipynb b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.ipynb
new file mode 100755
index 0000000..9990b04
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.ipynb	
@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from googlefinance import getQuotes\n",
+    "import json\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats.mstats import normaltest\n",
+    "import time\n",
+    "from cvxopt import matrix\n",
+    "import seaborn as sns\n",
+    "import statsmodels.tsa.stattools as ts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class stock(object):\n",
+    "    def __init__(self,ticker):\n",
+    "        self.ticker = ticker\n",
+    "tickers = [\"MMM\", \"AXP\", \"AAPL\", \"BA\", \"CAT\", \"CVX\", \"CSCO\",\"KO\",\n",
+    "                \"DIS\",\"DD\",\"XOM\",\"GE\",\"GS\",\"HD\",\"IBM\",\"INTC\",\"JPM\",\"MCD\",\n",
+    "                \"MRK\",\"MSFT\",\"NKE\",\"PFE\",\"PG\",\"TRV\",\"UTX\",\"UNH\",\"VZ\",\"WMT\"]         \n",
+    "stocks = []\n",
+    "for i in tickers:\n",
+    "    vars()[i] = stock(i)\n",
+    "    stocks.append(vars()[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    table = quandl.get('WIKI/%s'%i.ticker,start_date = '2012-03-21',end_date = '2015-01-01')\n",
+    "    i.rate = np.log(table['Adj. Close']).diff().dropna()\n",
+    "    i.mean = np.mean(i.rate)*252\n",
+    "    i.std = np.std(i.rate)*np.sqrt(252)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spy = quandl.get('LSE/SPY5')\n",
+    "spy = np.log(spy['Last Close']).diff().dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class rolling(object):\n",
+    "    def __init__(self,ticker,series,spy_series):\n",
+    "        self.ticker = ticker\n",
+    "        self.prices = series\n",
+    "        self.spy = spy_series\n",
+    "        self.df = pd.concat([self.prices,self.spy],axis = 1).dropna()\n",
+    "        self.df.columns = ['SPY','%s'%self.ticker]\n",
+    "        self.prices = self.df['%s'%self.ticker]\n",
+    "        self.spy = self.df['SPY']\n",
+    "        \n",
+    "    def roll(self, length):\n",
+    "        df_leng = self.df.shape[0]\n",
+    "        beta, beta_p, inter, inter_p,resid = [],[],[],[],[]\n",
+    "        loop = df_leng - length\n",
+    "        for i in range(loop):\n",
+    "            x = sm.add_constant(self.spy[i:i+length])\n",
+    "            model = sm.OLS(self.prices[i:i+length],x).fit()\n",
+    "            beta.append(model.params[1])\n",
+    "            beta_p.append(model.pvalues[1])\n",
+    "            inter.append(model.params[0])\n",
+    "            inter_p.append(model.pvalues[0])\n",
+    "        beta_df = pd.DataFrame({'beta':beta,'beta_p':beta_p,'inter':inter,'inter_p':inter_p},index = self.df.index[length:])\n",
+    "        self.beta_df = beta_df\n",
+    "        self.mean_beta = np.mean(beta)\n",
+    "        self.std_beta = np.std(beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in stocks:\n",
+    "    i.r = rolling(i.ticker, i.rate,spy)\n",
+    "    i.r.roll(21*6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      mean_beta   sd_beta  sd_beta_p\n",
+      "MMM    0.391424  0.106368   0.000174\n",
+      "AXP    0.260624  0.055665   0.000784\n",
+      "AAPL   0.095790  0.041038   0.095875\n",
+      "BA     0.190906  0.070859   0.023826\n",
+      "CAT    0.191012  0.063119   0.019710\n",
+      "CVX    0.280610  0.089215   0.011553\n",
+      "CSCO   0.132116  0.067679   0.115558\n",
+      "KO     0.181293  0.166132   0.309038\n",
+      "DIS    0.254268  0.063585   0.000340\n",
+      "DD     0.247352  0.057332   0.002610\n",
+      "XOM    0.292914  0.150067   0.026876\n",
+      "GE     0.256557  0.075137   0.010253\n",
+      "GS     0.237748  0.029856   0.000318\n",
+      "HD     0.219472  0.061830   0.012097\n",
+      "IBM    0.212634  0.112679   0.055888\n",
+      "INTC   0.152051  0.073508   0.057509\n",
+      "JPM    0.222741  0.057927   0.008795\n",
+      "MCD    0.243713  0.113179   0.189099\n",
+      "MRK    0.170320  0.062014   0.049013\n",
+      "MSFT   0.211149  0.104175   0.008382\n",
+      "NKE    0.115547  0.066427   0.252076\n",
+      "PFE    0.247528  0.092036   0.012432\n",
+      "PG     0.197096  0.159905   0.280305\n",
+      "UTX    0.318099  0.081374   0.000436\n",
+      "UNH    0.135254  0.032307   0.036826\n",
+      "VZ     0.170965  0.087304   0.082649\n",
+      "WMT    0.193440  0.062965   0.062210\n"
+     ]
+    }
+   ],
+   "source": [
+    "tickers = [x.ticker for x in stocks]\n",
+    "mean_betas = [x.r.mean_beta for x in stocks]\n",
+    "sd_betas = [x.r.std_beta for x in stocks]\n",
+    "beta_list = [x.r.beta_df['beta'] for x in stocks if len(x.r.beta_df['beta']) != 0]\n",
+    "sd_beta_p = [np.std(x.r.beta_df['beta_p']) for x in stocks]\n",
+    "df = pd.DataFrame({'mean_beta':mean_betas,'sd_beta':sd_betas,'sd_beta_p':sd_beta_p},index = tickers).dropna()\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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na5Nz9cvfzbaKjqcGN+Wld9YDcCpKrWu/HSxJNbL8K/NY+KncW7gb4CO2XtX5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Lc/ZtfJfI9dMYZGLHhMLRGpVeg0R94yYKB0N/CGzkgNLJmn2XDRPiJVUB4a09kcsEvJ2taevt\niNLR+MbkvwVPFxtUOXfxS6XOL+f3Zf2CWn45tgOLVt2fXyrc7VDpzUZSZRbh6WFvUObL5YcZOrAV\nB/9+iZ+/GsbcxXVHCAb0bsaFyxrKbxnfeFB6OtXyyzyUtWLlhUvpRPTVTtWM0PNLQRD48L0RzFr0\nl9H16uPpbI0qpyZOqXNL6o+VnbzZ/mE/vn6jG0oX7fVL1/MJa6vEykKOs50Foa08ULrY1Pnu3XC3\ntkBTUtPBpCktw93a4o7lh/h6ckjPbhZyGSvDg/m5dxCPeLkYXT+Awtkald7omSrvDnZo34gdc/rw\nzStda+WMcr57rStbZ4czY2SQSTlD6elIhlrPHzT5df3hcgYRfbUvHIP6BGFvZ4Wzk6HN27VtgoW5\nGUnXjV964Olui1pT84KgzizC073+jgwvhR3eXvYcOaFdnnY5PoeeXZtgZWmGs6MVoR28UHrYGa/B\nxhK13sCDprgMT5u6/vB0KyWRozoxtbMfHx6umQUU5G7PthEd2DKiAx/Exhs9+wHA093O0A6af7KD\nPd5eDnp2yDa0Q8dGKD1NsIOzNaocvdyZU4Knc604qbDHV2nP2jl9WD+vL2HBdTsAg/xdMTeTkaIp\nrHPtbig87FDpfU+tKUThbhgnv1h+kKEDAzm081VWLB3JnI/3VF8LaaNk1/rn2bluPO8v2G307AeA\nsvw8rFxq8rWlsxNleXkGZcrz87HUlZHJ5citrblVpLVdaVY2x+cs4NSiT8m/atoSEE9XG1RZ+u2Y\n4nraUo7adsziAaz/ZBBhuo7c05ezOHJOzeFVozm8ajQxp9JJTDNt1oFS4XjXnHH+cjoR/W7njGDs\n9doyjRROxGx9l7jo+Xz5/Z77mv0A4FnbPzKL8Kz1zH+1/AhDBrQkdvvz/PjlEOYuOXBfdQIo7CxQ\nFdXECHVRGQrbup3Sz7bxYv8znZnR1Y+5MXVnCkYEuLPFxA6I+8kZBho6NWHrMdOWLgIU5dzA3q1m\nQMrezYminPu7rxL/P2nIPSAsgU1AL1EU9Rf5BgInr+mAZwAAIABJREFUa5U9AYyr/QO6pRovAzgo\nB2DjbNpaoWGDgliz6RTLVx6kQ3Bjli56gl5DvqJdW2+qqqoIeXQRjg7WbFr1EtGHE7ielnf3HzWS\nqHMqth5PpbyiiqfCfFkyvhPPfBaNmUygUzM3IubvISO3hKUvd2FkNx/WPoA1a3U0xKnZeiJNq6GH\nL0vGduCZr2J5JsyP/RfUqPNN6yU3BrlMwEdpz5jZkShcbfhjfl8GvrWNoY/4sv9UOupc49cxG61B\nLuCjdGDMeztRuNnyx0f9GThxM4W6UR13Z2taNHUm5nR6g2mYtWgDSz54kqdHhHLoWALp6jyqdCOu\nbR95H5Umn6aN3dj661tcuJpO8nXTl8bUhyDAzIhA3llbdyr52uOp+HvYsWViD9LzSjmZkkdlQ6y/\n+Beh9Ql7xszR+eXcvgycso2hYf85vxzcrwXrt17gp9UnaRek5NP5A+j/xMrqpS/N/FyZNrEn417f\n0GAaZi36i8UfjDbwy8rKKl58JozI/RcM9oNoKKLOZLD1yHVtnHrUnyUvd+GZRfuJPa8hyNeFdbN6\nk1tYxumEnAb3ywFN3GnlYseEfTXrrwdvP05WaTmNbC35tldbEm6UkF5cdxbV/RJ1VsXWY7dzhh9L\nnu/MM58ewEwu0KmZOxHzIrU5Y0IoI7v7VO8p9CD5YPFmFs0awZPDOnP4RCIZ6nyDlzpPdwe+W/IM\nr0//7YGOANfHoL7N2LU3kSrdepODR1Np29qDNT8NJzevlNNxGipNWYtyj/x+ScXvl1RE+LvzakhT\nZkRrR33PZRUSseEkfk7WfBzWgui0XMpN6YW4Rwb1DWBXVD12+HmEnh2q7vIrpiGXy/BR2PP0/D0o\nXGz484NwBkzbUb3Uwt3Jik9f68rU7w43yJI9gMH9W7Fh63l+/PU47YK8+OzDQfQb+TOiCGfOq+g3\n8mf8fV34dN4g9h+8RrmJI72mYOnoSLdPPsLczo7C5BTili6j84ezMbOu+yJ4v8jlAj5eDox5V9eO\nWTSAgW9sxsXBEv/GjvR4TrtHz8oP+9LxVDonLpi+hPCfmL1oIx9/8ARPDe/C4eMJZOhyBkC6Op+e\njy9E4eHIr9++xJadp8nKMb5jyhge79+Cv7Ze5KffTtOurYJP5/VlwOjVDeaP+vx6PoNfz2cwuJkH\nb3RswjtRNXs0hHjaU1pRydUGbEvcKWfcxt3RihbejkRfMH45jsQDRjoCokE7IG4Bh4AXgEmm/IAo\nit8D3wMoA9+vN3yoNQU0Utb0yCo9HVBrDHvjnhregacnaNcxnzybiqWFGS7ONgwbFMy+2HgqKqrI\nyS3m+OnrBAc2MroDQp1fWj1KB6B0sq7eBOY2+bppcgBrYpKYMUI7qqTKK+Viaj6pulH73WcyaOfr\nAgcxCnX+TYPeTqWTdfWGl/VqOJjEjKFtAGjv60InfzeeCfPDxtIMc7mMkrIKFm+uu1HfP6HJLUHp\nVtNLr3CxQVNrYzR1Tgln47OpqBRJyywmKaMAH6UDIc3d6dTKgzH9m2NjZYaFmYySm7dYstq4tdaa\nnBKUbjUjRwo3GzQ5huvt1NklnL2q06ApqtYQl6AdwRvYw4fdR65TYWIjUqXJo5GyZgTFS+GESmPo\nU+rMGzz7+nIAbG0sebx/O27o1k+rNNqXvJTUbGKPXiWodROjOyDUN0oNZi0oHK1Q661xt7M0o7nC\nnj8n6KaR2lvyw3OdeOmX48Sl3eDDrTWbj61/rRtJWcavWfy3oMktQelqpF+qavllv1p++ZtxfqnO\nKkKpqBnJU3rYock0bIg9MbQN49/QzjA4fU6FpYUcFydrcvJKUXjYsezTwbwzeyfXTRzNUmnya/ml\nM6pasVKdeYOxr2v3l9D6ZQgFhaV0CvGla6cAXhgThq2NJeYWcopLypi7ZLNRGjR5pShda+KUwsWm\nbqws0otT+68xfXTNlN9vt17i263aJSCfvxpKssr4xmxWaTmeeksqPK0tySotr1Ous4cj41s3ZsK+\nOG7pvdjeLpteXMapzBu0cLY1ugNCnVeKUm90Welcjx0McsY1Zoy8Q844nU47P1cg2SgNKs0NvBR6\n/uDpVI8/FPDcm9r107Y2FjzeN7h6nwc7W0v+WP4SCz7fzsmzKUbVfRtNVjEKvdF6hYdd9dKj2gzq\nE8DcxTEGny1bcZJlK7TjGZ/ODyf5uvEdZJoSw9FMT1tLNCV1/eE22xOzmNO9GdTar/pafiklFVU0\nd7blfLZx0741WUWGdvD8Bzv0bcbcxYaVG9qhD8kpxscI7bOplztdbdDk1YqTuSWcScjRxsmsYpJU\nhfgo7Im7loudtRk/TuvFp2vOcibBtI041ZlFKD1r4qTC0x51luEzPmpoEM+9vg6A0+cytO05Jxty\n9LQmJuVSXFJOiwB34i4a97Jl6eTMzdyafF2Wl4+ls+EMRgsnJ8pytTMlqiorqSwtxdxOO1NMZm4O\ngL1PU6w93ChRZ+Lga9ySGE1OCUp3/XaMbf0560qWXjvmBj5e9nRpq+DMlSxKdHvjHDiRTruWHiZ1\nQKjUN+4pZ4x7XbsZqq2NBY/3C6mzF4w68waX41V07eTPlp2m75+jqe0fHnZoau1r9cTgQJ6fuAmA\n03FqLCzMcHayJtfEZSgA6qJylHY1MUJhZzhrqjZb4zOZ/0gzoKYDIiLAg63xWXf8zl013EfOuM2g\njt7sPpVudNv29PZo4iIPA6AIaEJhdk2cLczOx8617lILCYm70ZB9MFXAKKCzIAjv6X1+EehQq2wH\nwLi3XR1nzqfj28SVxo2cMTeXM2RgELv2Ge6qnq66QY9Q7TqxZn7uWFqakZNbTLoqn+5dtJ9bW5vT\nIbgxCUnGB4hzyXn4eNjh7WqDuVwgolNj9tRaL+6u9zIYHuxFgqpA991cHKzNcdGtWevWwqPOpjL3\npCGlloYO3uyJq6VBb01meJBX9QaVb/1ygh6zdhI2excLN8ax8dh1ozsfAM4l5NBUaY+3hy3mZjIG\n9fAh6oTh7vJ7jqXSRbcXgbO9Jb5eDqRqCpny5UHCXtlIr1c3sWjVKTYeSDK68wHgXHw2Tb0c8Pa0\n02ro6UvU0Voajl6nS9vaGmqS2ONhpi+/ADh1LgX/ph409XbF3FzOiEGd+DvKcA2pi7O2sQLw1iv9\n+W3dIQAcHWywsDCrLtOlg7/B5pX3yrm0G/i42eLtbI25XODx4Ebs0dt0qPBmBR3m7qbnor30XLSX\n09fzqzsfrMxlWOv2GOjRzI3KKrHO5pX/TdTxy+71+OXxWn6p1PnlVwcJe3UjvV7fxKJfT7ExOsno\nzgeAcxfU+DR2wtvLAXMzGRH9WrJHd7LDbTLUhXTTbQDq7+uijVN5pdjbWfLTV8NYvDSGk2eN3xT1\nNrf9sonOL4cP6nAXv+zHb+u0jY6Xp/xC27CZBPeaxaxFf7Fm41GjOx8Azl3LxcfTHm83W8zlMiJC\nmxBVa6aRQaxs70VChjZOyQQBJ12cbNHYkZaNnYg5b/xIzsXcQprYWeNla4mZTKBPE3eiM3INyjR3\nsuXdjgFMib1IXlnNRnr25nLMdesdHC3MCHJzIKnA+BGtc8l5+Hja4e2mi9edG7On1r01sEOIXs5I\nysXBRi9ntPKovmYMp+Ou4+fjRhNvF8zN5Qwb1I6dew2X4en7w6SXw/l9g/ZEAnNzOau+eYE1m0+w\ndZdpJyYBxF3MxKexI95e9tpns28AUTF1Y69fUycc7C05rbeRnkwm4OSofSloEeBKiwBXYk04FSUu\nqxAfB2u87awwlwkM8nNnb62TLJrq5c5eTVxI0S3V9LazQq5b/uJlZ4mfozXphcbPhom7mIlPEz07\n9GlGVHRynXLVdjj3D3Zo5krsUeNPoDiXmIOPwh5vd92z2bUpUScNn83IE2mEtvYAbsdJe1IzizCX\ny/ju7TA2xiSx8z6md5+7oMKniTPeXo6Ym8l4vF8r9uw3nMqeoS6gW2ftC31NnCzB28sRue5mNFI6\n4O/rSlqG8R0x9r5NKdVkUpqVTVVFBZqjx3ELMXyRcwsJQn1IGxuzTpzCqWULBEGgvKAQUTf7pDQz\nixJNJtbubsbb4WqtdkyYL1G1fHvP4et0aavdOs3ZwRJfL0dS1UVkZBXTuY0CuUzATC7Qua0niamm\nzVw7FZeCn4+7Xs5oz85/yBmTJ/Tjt/VHAO3Ai5WltjPG0cGaLh38ib92f7Mwzl3U0FQvjw7q25yo\n6Lp5tGsn7b5f/j7OWFrK76vzAeBcZgE+jtZ422tjxOPNPNiTbBgjfBxrOtYf83ElWW85twAMCnBn\nq4nLL+D+csZtHu/chK3HjI8N7QaFMfaL6Yz9YjoBoUFc3HcMURTJuJKEpa1VvXs9SEjcjQY9hlMU\nxRJBEAYBMYIgaERR/AlYDHwsCEJ/URRzBEEIAZ4DuphSR2VlFe8t2Mof3z+HXCbw58ZTXE3MZOob\nvTl7IZ3d+y4zd8kOlswdxstjuyOKMPl97RTmFX8c5YsPh7N/80QEQeDPjSe5dNX4TRgrq0Tm/HGG\nlZN7IpMJrDuYTLyqgMmDWxOXkkfUWRXPPRZA72AllZUi+SXlTP1Fe3RQlQgL159j9dthCIJAXEoe\nf8Zcu0uNd9Cw9gwrX++u1XA4hXhVIZMHtSLuej5RcSqe6+VP7yAllZVV5JfcYuqvJ+7+w0ZqmPvj\ncVbM6o1cJrBubyLxqTeY9GQQ5xNyiTqRRvQZFT1CvNj5RQSVVSKLVp0yGPF8IBqWH2XFnHDtcWZ7\n4olPzWfS0yGcT8gh6lgq0acytBq+HqLV8MsJ8gu1vdmNPGxRuNly1IQXm2oNlVVMnbuGDSsmIpfL\nWL3uEJfjVbw36XFOn0/h76hz9OiiPflCFEUOHY/nnTl/AtDCX8HnH45BrBIRZAJfLN9pUgdEZZXI\nB5svsOrFLlp/OJ5KvKaIt/o2Jy7thkFnRG1c7SxZ9WIXqqpE1AU3efvP+9vx/59YufRNenZthZuz\nPQlHv2b+Z+tZuWb/A62jskpk7k/HWfG+zi/3JRKfdoNJo4M4n6jnl8Fe7Pxc55e/PmC/rBSZ8/E+\nVn4zQns/tpwn/loOk1/pRtxFNVHR1/joswN8NKsPz4/pgCiKTP1Ae3Tr2NEhNG3sxJsvhfLmS6EA\njHttAzlGNqoqK6uYNncNG1a8gVwu47d1h7kcr+LdSRGcOZ/C31Fx9OiiPflC65cJTJ2z5oHZAHT3\nYtUpfpn2CDJBYH30NeLTC5g8vA1xSblEnc5gXN9m9G7XiMoqkRtFZUz7QfvSa2Ym8Of7jwFQVFrB\n28uOmDTlvlKExacS+SqsDXIBtiRpuFZQwoTAJlzKKyI6I5dJwb5Ym8lZ1LUlUHPcpq+DDe92CKAK\nbe/9ystpBqdnGGOHOb+fZuXkMF3OSCI+o4DJQwKJS87V5ozeAfQO9qKySiS/uJypK7THtlaJsHDd\nWVa/8wgCupwRbULOqKxixrwNrPvxFWRyGb9vOMqVBDUzJg7gzPnr7Nx7ge6dA5j1dgSiKHL4RCLT\n5q4HYOiAELp29MfZyZYnh3UG4M0Zv3P+snHL1iorReYtieGnrx5HLhNYv/UyCdfymPhyJ85fymJv\nTDKgHfWvfbSkmZmM35cPA6CouJyps/eYtOa/UoR5hxL4cUAb5ILAhqtqEvJLmNi+KeezC9l7PZdn\nWjeiayMnKqpECsoqmH5AO7LZQeHAS8GBVFSJVIkicw4lkFdm3Kks1XZYHMNPXw1GLhdYv+USCddy\nmTihM+cvZbI3Wt8OhvsKmJnJ+P374fdvhyqRub+c4Jd3H0UmE1i//xrxaTeYPLKt9tk8mU70WRU9\n2irZuWQQVVUii347Q35ROUN6+NCppQdOdpaMCNMO6kxbdphLKca9+FZWinzw8R5WffuE9rnYHEf8\ntRzeerUHcRfV7DmQwILP9rFwVj9eeKajNk7O3gFAp3aNeGX8CCoqKqmqglkf7SbPhGWlMrmc5s+M\n5uxnXyFWVaHs0Q3bRl5c27gFB5+muLULRhnWnUs/rODIjFmY2doQOOFFAPKvxpO0aSsyuRwEgRZj\nx2BuZ/zmrJVVInOXHWHFvD7anBWZQPz1fCaNCeF8/O12TDo92nux89uh2py1QtuO2Xkwha5BSrZ/\nMwREiD6Vzt5jaXevtD4dlVVMm7uW9T+/jlwu8Nv6I1xOUPPupEGcjrvOzr1x9OjSjFlTBiOKcPh4\nAlN1xzM391cwf8YwRFFEEAS++SmKS1dN70DX6hGZu2Q/K5YORS4XWLflIvHXcpk0IZTzlzRERSex\n8IsYFszszfin2yGKMH1O5N1/+G71ivBBTAKrBrfVHtV7SU18bglvdfYhLrOQPck5jG3rRffGzlRU\nidy4WcE7UTUDoZ29HFEVlZFaYPpSvfvJGQCNXG1Quthw9KrpszAAfDu05tqJC/z0yjzMLS3o92bN\nMbOrJn/M2C+mA3Dgl81cjj7BrbJbLH9+Fm37dKXbUwNRx6eweeGP3CwqJfH4eQ798TfPff3enar7\n30U6hhOhodZt1jqGszHaCYuTRFHcIgjCq8BktBvWFgJTRFGMvvOv3XkJxn8S63/DebXmD3/hkEzd\n8Ovh74oJm/A9aLIvHn7YEnAeHvGwJaD+87eHLYFGXQY9bAlUJTbMGltjyCsw/YjMB4VLt0cetgRc\nIozfHfxBk7Pb9FNTHhQFMUau5WsAXBybP2wJAAgvt3nYEhCXx929UANTGeB890INTNVl016IHyT9\nvu70sCWwZ6FpS5ceJLlXHuxAlCm4ODZ72BKoHB/ysCUgnH74bYgZ7zg8bAkAvNyy3//0G7rfGxv/\nY++0174e9q+0ZYPNgLjd+aD7OxXw1fv3d8B3DVW3hISEhISEhISEhISEhMS/in/x8Zj/KR7+cLqE\nhISEhISEhISEhISEhMT/PA26B4SEhISEhISEhISEhISEhASI0h4Q0gwICQkJCQkJCQkJCQkJCQmJ\nhkeaASEhISEhISEhISEhISEh0dBIw///PR0Qdl5+D1sCpBp/1vqDpsrL/mFLQJZR+LAlQEXVw1aA\nW/ueD1sCpBh/zvmD5t9wAkX60e0PWwKNOj18O7hlGn/k24NG1B1p+/+dxkO9HrYE0nM7PGwJkPYv\nyBdAxT7jjghtCOQvt33YEpD99fBPypHJrR+2BOa2f/h+uTs9+2FLwLlT94ctAVnyw2/HVF3MedgS\nkGUbf2zsg+bllsEPW4LE/xP+azogJCQkJCQkJCQkJCQkJCT+a5FOwZAmgUhISEhISEhISEhISEhI\nSDQ80gwICQkJCQkJCQkJCQkJCYmGRjoFQ5oBISEhISEhISEhISEhISEh0fBIMyAkJCQkJCQkJCQk\nJCQkJBoaaQ+I/40OiJ4dGzHztVDkMhlr/77C92vOGVx/75UuhIYoAbCyNMPVyYoOw1bTyt+FuRO7\nY2djTmWVyHe/n2HHgSTTNHRoxMxXQpHLBNbuvMr3687VKTOgpy8TnwlBFOHytVzeXnwAgKnPd6RX\np8YAfPPHGXZEm6YhrI0ns55qh1wQWBNzjeV/XzG4PqJ7U6Y/EYwmT7vT7q97E1gbk0RoC3fefzKk\nupy/0p5Jy48QeTrDaA09Qxvz/uQeyOUC67Zc4vtfTxtcf3dSN0LbNwLAysoMV2drOvb9mS7tvXhv\nUs1uzH5NnXhrdiR7opON19C1Ce9P6YFcJmPd5ot8v/KUoYa3uhPa0VurwdIMVxdrOj72IwDvvNGV\nXj2aAvDtTyfYEWnajuFhwUpmju+o9YeoBJZvvlinzMCuTZj4RBCiKHIpJZ+3vzqIl5st370ThiAD\nc7mMVTuv8kdkvOkantNp2HsHDaG1NCzV0yDoadhjmgaAsBAlM8d3qrHFpgt1dXRtwsRRQYgiXErJ\n4+0vD1Zfs7M2Z+fnEUQeT2PuT8dN1nEnli2ZwIDe7cjKKaBjn2kP/PdvExaiZObzenbYWI8duuns\nAFxKzuPtL2rZ4csIIo+lMfdH0+zQs3NjZk7qptWw7TLf/3bG4Pp7b3YltJ329AYrKzNcnazpMPAX\nAJQednw0PQylhx0i8OLUHaSri4zWENbOi5kv6OywJ4Hlf52vU2Zgt6ZMfDJY6w/Jebz9eYxWg5st\nC1/visLNBkR4YX4U6VnFRmvoqnBiSogfMkFgc5KGlZfTDK4/3dyLIb4KKkWR/LJbzDsej7pEe7rH\nkZHdSbyhrVNdUsaUg5eMrh+gs7sTbwb6IRNg+3UNvycantIQ5OLAm4G++NnbMu/0FQ6oanZpn9Cy\nKaEezgCsik9jn8q03fTD2nkx8/mONfeiXp9sysTRQTX34otYAK6sG8OV6/kAqLKLmbBwv0kaeoY2\nNozXq2rljLe6E9qhVs7o/RMAU9/sSq/uTZEJAgePpfLhp7EmaQgLUjLr2fbIZQJr9ieyfGvdezqw\nS2Mmjmirzd/X83jrm8MArJjWi5AAV05czeKlT6JNqh+gp7cz73f1Ry4IrLui5vuzqQbXn2ylZExr\nL6pEkZJblcyMiScxv4Qgd3vm92wGgAAsPZVCZPL97+h/tzbNey93JjSoVrvqid/uv96uTZj5Tpi2\n3k0X+X7lScN63+5BaAdd/rYyw9XFhg6Pfg/A1De70auHDwDf/HicHSbmTlEU+WrxZo7EXsbSypx3\n542mRSvvO5afMWkFqrQcVm54B4B9u8+yYlkkKUmZLF/9Ji0DGxutIaybD7OnPoZMJrB2UxzLVhwz\nuO6lsGfJvAE42Fsil8lYvDSa/bFJ9OjSlKkTe2JhLqf8ViWLvjjA4eOpd6jlLhqClMwaq3su9t3l\nuQAup+g9F9N1z8WV+3suAHp2acz7k7tr25VbL/H9r4Z5692J3Qhtr5e3nK3p2G+Ftl05sVt1Ob+m\nTrz1wR6T2pVhrT2ZPTJIez8OJrMs8qrB9RGhTZgxtC2aG9r29aoD11h7qKYeOyszds3sQ+S5DOas\nPWt0/aCL1y/qcmfkHXJn91q58zO93PlGVxSuNoAud2YanztFUWTBgu85cOAkVlaWLFo0icDAgDrl\nystvMX/+co4di0MQBN5661n69evOX3/tYfHiFXh6ugLwzDODeOKJfkbrkPjv54F2QAiCUAnEAeZA\nBbAK+FwUxSpBEHoB74iiGCEIgifwE9BYVzZZFMWBptQpkwnMebMbz03fiTq7mA1fD2bv4esk6BpG\nAB8tO1r997NDWtM6QOv4pTcrmLr4ACnpBXi42rDxmyHEnEinsLjceA2vd+W593ZpNXw5mL1HDTU0\n9XLgldFBjJ6ynYKiclwcrQDo1cmbQH9XBr++CQtzOasXDyD6RBpFJbeM0yDAnDHtGfdpNOq8EjbO\nCifqTAYJKsOjprYfS2Xu74YNvCNXsnh8biQAjrbm7F04kJgLGqPqv22HD6b0ZPykragzi9nw8wii\nYpJJTM6rLrPwy0PVfz87sg2tWrgBcPRUBkPGrdNqcLAkct3TxB41fCm4Zw3Twhj/xhbUmiI2rHyC\nqOgkEpP0NHxe81L37Ki2tGrhDkCv7k0JbOnOkDFrtPdi+VAOHEqhuNjYeyEw54VOjPtwL+qcEv5a\n2J+oE2kkpNcc49pUYc8rQwMZNWs3BcXluDhYApCVV8oTM3dRXlGFjaUZOz4dRNSJNDLzjDueSSYI\nzHm+E+MW3IOG2XfR8Mkgok4arwF0z8YLnRk3Pwp1bgl/LRyg1ZFWc+xWU4U9rwxrw6iZhjpuM/nJ\nYI5dyjS67nvl13UHWLZyFz9+/lqD1SGTCcx5qTPj5kVp78fHA4g6XssOSp0d3r+DHZ4K5thF0+0g\nkwnMebs7z721HXVWMRt+GM7eg8kkJOvFyqWHq/9+dkQgrZu5Vf97ycxH+W7VKQ6eSMfG2owqE07C\nlckE5rzchXFzIrV2WDyQqGOpde0woi2j3t2ptYMuVgJ8Mqk7366P4+BZFTZWZlRVicZrEGBae3/e\nOHAeTWk5K8NDiM7IIamgxr+v5BUzNvEMZZVVjPBXMDHIh/eOaDt0yyqrGBN55k4/f28agMlt/Jhy\n9AJZpeUs7xnMQU0uKUU1GjJLy1h4Jp4n/RsZfDfUw5nmjna8GHMGc5mML7u24WhWHiUVlcZpuO2T\nc/fo7sUdfHJ4G0a9t6vOvbhZXsngKfd3/G1NvN6KOrOIDStHanPGP8Xr5lqfbNdWQfsgBY8/vQaA\nP34YRuf2Xhw7ZVzHuUwQmPNcB8Yt3Ic6t5SN8/sSdSrdIFb6eNrxyuBARs2JpKDkFq56z+YP2y9h\nZSHnqd51G+H3rgE+6B7A+B1xqIvL2DC0HVEpOSTml1SX2ZqQyZ+XVAA81sSFd0P9eHHnea7mFjN8\n4ykqRXC3tmDLiPbsTcmh0vhHo0bPPbRpPvq+5oX42cGtaO3vanqF+vVO78Vzr2/S5u9Vo9kbfY0E\nPX/46LOaTqZnRwfRujp/+xDY0p3BT/+hy9/DiT6UTJGR+RvgSOxl0q5n8/uW6VyMu85nC/5i+eqJ\n9ZY9EBWHjbWFwWe+AQo+/Gwsn8zfYHTdoLXD3BnhjH11HWpNIZt+e4Y9BxJJuFbTsfT6i6HsiLzC\nb+vOEuDnys9LhxM26Ady80t5afJGMrOKae7vxi/fjqBbv+XGaxAE5ozXPRc5pWz8sJ7nQmHHK0MC\nGTU3koLiWs/FtktYWcp56jHTn4vbtvjgnR6Mn7RN2678aThRMSmG7cqvarUrm+u1K59bD4CjvSWR\n654yrV0pwNxRwYxdGos6v5RN0x5lT5yKBHWt9vWptDt2LrwV0ZrjCaYfuyqTCcyZ0IVxH+hy55J/\nyJ0z6smdk7vz7br7y50A0dEnSU7OYPfu5Zw9e4U5c75j3bpP65RbtmwtLi6O7Nq1nKqqKvLza2w1\ncGBPZs9+xaT6/2eQJkA88D0gSkVRDBFFMRDoAwwAPqin3DwgUhTFYFEUWwMzTK0wqIU7KRkFpKoL\nuVVRxfb91+jdrckdy0c86se2fYkAJKcXkKJ6/wHuAAAgAElEQVQLppk5JeTkl+LiZHXH795RQ3M3\nQw0HrtE71FDD6P7NWb31EgVF2s6N3Bs3AQho4sTx82oqq0RKyyq4kpRHzw537mm/E8F+LqRkFpGa\nXcytSpFtx1IJb9fo7l+sxYAO3hyIU3Gz3LjGLEBQaw9S0m6QmqGzw54EwsN87lh+UN9mbNtdd4ZB\n/0f9iD58nZtlFcZrCPQgJfUGqekFWg2R8YQ/4ntnDf2asW2Xtifb39eF46czqKwUKb1ZweX4HMK6\nNjVaQ3CAKynqQlIzi7hVWcX2QymEdzIcARndO4DVu65SoOvsyi3Qjq7eqqyivEL7ZmdhLkNm4jSt\n4ABXUjT3oGF3w2mo1nHbFhVVbD+YTHhHQ/8eHR7A6p11dQAE+rng5mhF7FmVyRruxsFjl8nNN34k\n3xiq7aDR2SE2mfBO/1k7BLXyICW9gFSV7vmMSqC3brSwPiJ6B7Btj/b5DPBxQi4XOHhCO0pfUlph\n0vMZ3MyVFFUtO3Su5Zd9mrH678s1drgdK70dkctlHNTZoORmhUlxKtDFntSim6QXl1FRJRJ5PYtH\nvAxfnk5m3aCsUvsMxOUU4mFjWd9PmUwrJ3vSi2+iKimjQhTZm55FD08XgzLq0jKuFZZQJRo2FH3s\nbDibe4NKEW5WVpFYUEIXdyejNQQH1L4XKXXvRXgzVu+8UudePCiCAm/nDF283p1AeNg/xOu+zdi2\nWzuqLSJiaSHH3FyGhbkcMzMZObnGd5IG+7uQoikiNauYW5VVbDtynfBaOXj0YwGsjrxKgW5gIEfv\n2Tx0QUPxTeOfBX2C3O1JKSgltfAmt6pEtidmEd7U0CeLb9X4urW5vPrvm5VV1Z0NlmYyxPvoeKjW\ncw9tGn0iHvFj2/5r919voCcpqfk1+Xv3VXo/4nfnevs2r87fAX7OHD9Vk7+vJGTT04T8DRC7/wL9\nIjogCAKBQU0pKrxJdlZBnXIlJWWs/TWasS+FG3zu4+dJEx8Pk+oGCG6jICU1j9T0G9yqqGLbrsv0\n6eVvUEYUwc5WG5fs7SzQZGlz2MUrmWTqZoVdTczGytIMCz1/uWcNAbrnIlP3XByu57l49HYb4g7P\nRen9PRdwu11ZoNeuTCS8p88dyw/qE8C2emau9n/Mj+jDqablLR8XUrKKSc0p0bavT6bRRzf7515o\n09gJN3tLYi4bP7BXraG+3NmlVrzu24zVO+6QO2X3nzsBoqKOMHToYwiCQEhISwoKisnMzK1TbsOG\nPUyY8AQAMpkMFxdHk+qT+N+lwTahFEUxE3gZeEMQ6mz3qQTS9MrWXa9wjyjcbFDpTcFVZ5fg6WZb\nb1kvDzu8FfYcPlO3ER/Uwg0LcznXM+ommbtrsK2loRhP3TSn2/g0csS3kQN/fjKIdZ9H0FM3pfRy\nUi49O3hjZSnH2cGS0CAlSvf69f8Tnk7WqHJrRkvUeSV4OlnXKde/QyO2z+nD1692Relc93pE5yZs\nPWradD1Pd1vUelO61JnFeN7h/8VLYYe30p4jJ9PrXBsY3qzeBHJvGuxQa2peJtWaon/QYI+3lwNH\ndC9Vl+Oz6dm1CVaWZjg7WhHasRFKTzvjNbhYo8rRuxc5JXi6GNra18seH6UDa+b1Zf2H/QgLrklm\nSlcbti0ZSMx3w/h+80WTZh7Uq6HW/fZV3kXD4oHEfGu6Bq0OG0MduSV1ng1fpQM+Xvasmd+X9Qv6\nEaZbLiUI8N7YDixaZbiE5r8RTxcbVNl3sYOXzg4L+rJ+YS07jOvAopX3ZweFuw2qTL1nI6v4zrHS\n0w5vL3sO60aTfRo7UVhUzjcf9mXzTyOY/lqoSR1TWjvoxYicO9nBgTUf9Wf9ogGE6ZaE+Hg5UFBc\nzjfTH2HLpxFMH9fBJA3u1hZoSmoayprSMtxrjWDqM8TXk0OqmtE2C7mMleHB/Nw7iEe8XO74vX/C\nzdqCzJs1M+2ybpbjZn1vnRwJBcV0dnfGUibD0dyMdq6OuN/jd/XxdLVBlaN/L4rriVMO2hjxUT/W\nL+pffS8ALC3kbFw8kPWL+tfpuLhnDe62hvE685/itdYnb8frM3Eajp7M4OCO5zj49zhij6QajIre\ns4b6YlTtWKmwx1fpwNoPwlk/tw9hRrx83JMGW0vURTU+qS4uw9O2rk+Oaa1kz+hOTOvsx/xDNTky\nyN2e7SM7sHVEBz44GH9fsx/g3to0t/HysNW2qx5AJ7HCwxZVbX/wqD8Heyns8W7kwOHj2qbk5avZ\n9Oyml787eKP0tDdJR3ZmAR6Kmk49d09HsjNv1Cn30ze7GD02DEsrc5PquRMKD3tUmpoRY5WmCE93\nw/+XL5cfYujAVhzcOYGfl45g7sd76/zOgPDmXLicSfkt4182PZ3reS5qxwdlwz4XUE+MyLpLjLhj\nuzKAbSYuyVE4WaHSawOp8kvrb1+HNGLHe7355sUuKHXXBQHeG96WhRvrLpcwhnpzp0s9ubORA2sW\n9mf9x3q5s5Fe7vzM9NwJoNHkoFDUzIxUKFzRaAyXfBUUaO/Xl1+uZtiwSUycuIjs7JrYvHv3IR5/\n/E0mTlyISpVlkg6J/34a9BQMURSvAXKgdlfwN8BPgiDsEwThfUEQvOp+GwRBeFkQhBOCIJy4kXbg\nvvVEPOrHzpikOlOP3F2sWTL9EWZ8Ev1ARg/qw0wu0LSRI89M38Fbi/azYFJ37G0tiD2VwYETaaz9\nNILPp/fi9OVMqkyZ23wPRJ1R8cj0HQyaE8nBixqWvNDZ4Lq7oxXNvR2JuaBukPr1GRQewK591+re\nC1cbWvi7EHvEtE4QozT0DWBXVGK1hoNHUzlwMIU1P4/gswV9OR2nobKB7oVcJsNHac+YuZFM/jKW\nBRO6YG+jbcSockqImLqD3hO3MOwRX1wdjZ+Vc88aFHoaXq6lYdoOek9qWA0AcrmgtcWc27YIxd7G\nnGf6NWf/qXTUeh1r/8vIZTo7zI5k8uexLHhVZ4f+/3k7RPT2Z+f+mlhpJhfoGKRg0TeHGf7yXzRW\n2jN8QPMGqVsul+GjdGDMrF1M/iyGBa91xd7GHDO5QKdWHiz65STDpm6nsacdIx71v/sP3gcDmrjT\nysWOX6/UTNsdvP044/acZdaRK7zdzo9Gtg33bNTHiex8jmTm8U33tsxu34IL+YV1Zkk8KORyAR8v\ne8bM2s3kz2p8EuCRCX8xbNoO3vo8lpnPd6SJCZ21xjCobzN27a2J1028HfD3cSYsYiU9B60ktGMj\nOoY8+Bcg0NnB046nP4xi8teH+OjFTtV2+E/y20UV4WuOs+TYNV5rVzO6fy6rkEHrTzJy0ykmBDfG\nQv6fm98b8YgfO2OTTZ7SbXK9/ZqxMyqhut5YXf5e+/NIPv+oH6fj1A3WlgKIv5xOeloOYY+1bbA6\n/onB/VuyfusFuvdfzvNvbuDTDwcanOzXzM+VaRPDeP/D3Q2mQS4T8FHoPRcvPZzn4jb/2K70czFp\n+cW9EhWnJmz2TgZ+FEXs5UyWjO0AwDNhfuy/oEadb9ogjjFo25UOjJm5i8mfxrDg9a7Y25pjJhPo\n1FqXO9/ZTmOFHSMea7jcWVFRiVqdTbt2rdi48UvatWvJxx//DMCjj3Zm796f2Lp1Kd26hTB9+hcN\npuPfjCgT/mP//Vt5KMdwiqK4C/ADfgBaAqcFQXCvp9z3oih2FEWxo6P3I/X+ljq7xGDGgMLNBk12\n/RurDOrlx7Z9htME7WzM+eHDvny+4iRnLpnWE6fOLq6lwRZNTkmtMiXsPXKdikqRNE0RSekF+DRy\nAOC7P88y+I3NPPf+LgQgKd34WRia/FKUer2hCmcbNLUCXn5xefXU+jXR12jT1Nng+qBO3kSeSqfC\nxOETTVYxCg89O3jYornDBnHaaXJ1e6MH9PYn8kASFZWmNRw0WUUo9BrCCk+7O2vQm857m2UrTjJk\nzBrGv7EFAUhOqTvqcVcNuaUo9UaLFK42aGpNDVbnlhB1Ik3rD1nFJKkK8VEajnBk5pVyNfUGnVrW\neTRM05BXj4aTDadBq6PEUIeLTd1nI6eEqOM6HZnFJKkK8FE6ENLcnWcHtGD/N0OZ8Wx7hoX5MnVM\nSO0q/ivQ5JagdDPSDhm17PDdUGaMbc+wR3yZ+ozxdlBnlaDUG01UuNveOVbqLb8A7WymSwk5pKoK\nqawUiYxNJrC5KX5ZglJv1oXCtT47FBN1PFVnhyKtHbwcUOeUcCk5l1RNEZVVInuOphLob/wMhKzS\ncjz1llR4WluSVVp335/OHo6Mb92YKbGXuKXXoL1dNr24jFOZN2jhbPyMtezScjysaka43a0syC4t\n+4dvGLI6IY0XY84y5egFBCC12PilEZqcEpSu+vfCtm6cMvDJmnsBVJdN1RRx9LyG1n7G3wtNVrFh\nvPb4h3jdJ4Btu2p8sk8vP86cV1NSWkFJaQXRh64T0tbTeA31xah6YuUeXW6sjpUK00bX69VQXIbC\nrsYnFbaWaP5hL6rtiVmE+9TdcyExv5Tiiiqam+CT+txLm+Y2gx7Q8gvQxhllbX/IrH953CC95Re3\n+e7nEwwe8yfPvb4ZQYAkvT0r7sZffx7k+VGf8fyoz3B1sydTXfPdLM0N3DwMp5BfOJfClYtpjBrw\nEW+M/5bUlGwmvvDdPdf3T6gzCw1mbyg97dBkGe438MTQtuzYrd2X5vS5/2PvvMOiOr7H/d5dOixV\nqqiA2BXsomJJxIolmmhMjFFTTVOTmJiiRo1RExN7YoklluQTS+ydEgXsHRQLRZC2gPSmCNzfH7sC\n62J0Fwjm+7vv8/gkMLM7h7ln5sw9c2ZOCsZGcmytVXrs5GDBqkXDmDrjIHcSdV/DAKRmVTEuqljH\n1Oa4gCrmCPt/mCP8qj5+MbBPYwJC9F9XKrPvaUQMO1ub/vP6+sRt2jRUra/bu9vyeq/GhMzpz5fD\n2zC8c0M+H9ZKZxmqtJ2ZVdjOs4/YTme17bz9iO3UYb7+/fcDDBs2iWHDJmFvb4tSWXGXhVKZUX6h\n5ENsbCwxNTWmX7+uAAwY0J3IyJjyMiMjlZNq5Mh+XLumX7SzxH+fWnVACILgAZQCWjeniaKYKYri\nH6IojgXOAT31aSPiZjpu9S1xdbLA0ECGf28Pgk7d0arn0cAKSwsjLlW6xM3QQMbPs/zYHRDN4dA4\nfZpXyXDrLm4uVrg6qmXo5UHQaU0ZAk7F09nLCQAbS2Pc61uSkJKHTCZgrVAtPJq52dDM3ZawKsLH\nnkT47SzcHC1wrWeGoVxgcOcGBF3WvIzLvtIutl9bF6JTNB0dquMX2n33tERcT8OtgTWuzgpVP/h5\nElRFv3o0ssZSYcylCO3zcIP7NtE7TA4gIjINt4ZWuLqoZejbhKAqbjwulyG8ItpDJhOwtlI/C087\nmjWxI0yP/giPyaCRswJXe3MM5TL8uzUi6Lym5z3wbAJdWqkWyzYKY9ydFSSk5uNka4qx+rympbkR\nHZvZE5ucp9XGU8ng9AQZziXQpWXtyQAQHq3uCwdz1fPo7la1HBp9YUlCah6fLjtBz/d20fuD3SzY\nfJFdIbdZ+Hv1Lv+rK7T6wbeKfnhUJ1zU/bD0BD0n7qL3e7tZsOkiu47fZuEW3fsh4kYabq5WFeOz\njydBYfFa9TwaqsfG1YrxGX4jHYWFcfkdOV3b1ydaj3D38KiH/WBR0Q+P3NAeeCaBLq3Vc2V5P+QT\nHp2Bwsyo/HJOnzZORCfovriOzMyjoYUpLubGGMgE+ja0JyRZ8wxrU2tzvuzoyadhkWTdr7jETmEo\nx1C9o2BlZIBXPUtu5+oemXIjJw9Xc1OcTI0xEASer2/PiVTtc7RVIQMsDVX3R3sozPBQmHE+XY9n\nEf3os2ik/Syq0kllHpbmRhgZyMp/36G5vV7PIiIyDbcGlebrfp4EhWpngaqwGRXzdYoyn87tXZDL\nBQzkMjq3d9G4vPJpCY/NxK3SXDnYpyFBFzTHZsD5JHxaqPvBwkg1Vz7mxVgfItLzcLM0xVVhgqFM\nwL+xPUF3NMOaG1lW2O/eDW2JU9+276ow4WHAg4uFMR5WpiTlVe+ujqdZ0wB4uKrXVTV0SXBEZKpq\nDeFiqdaHpgRVkRXMo5HNY+y3qo9U9rseYVXI/DhGjO7O+m2fsH7bJ/R4rjVH9l9AFEWuhcdjbmFC\nPXtLjfovjOrGroAZbDv0FSs2vE+DRvVYtu49Pf9yTcKvKXFraIOrixWGBjIG929O4LEYjTrJyjy6\ndVbdy9HY3RZjYwMysgpRWBizbvkIflgWyoUrumcyK5ch5pFx0fUJ40JR8+MC1OvKynbLrzFBYXFa\n9crniKtVrCsf45h4WsLjs3BzsMDVTr2+7uBKYITmkSP7SuPTz8ul/ILKj387j++Mw/SceYT5uyLY\ndfYOP+zRzjb0RBmqsp1ndbCd5vrbzjFj/NmzZxl79izDz8+H3buDEUWRy5dvoFCY4eCg6cwQBIHn\nnuvMmTMRAJw6dYXGjVW6Wvm+iODgszRurN/xvf88MuHf+/eMUmtpONURDauAFaIoipWvgRAE4Xng\ntCiKhYIgKIDGgF5vvqVlIrNXnGL9/AHIZQI7jtwiOj6byePaE3HrLsFqZ4R/bw8OPOKlH9jLnU5t\nnLCxNGZEf1Uaq2kLQ7ge83QLQQ0ZVp5i/dz+yOUCO45GEX0nm8lj26lkOJNA6IUkfNvX59Dq4ZSW\niny/7hzZefcxMpTzvx9VCUDyCx8wdeFxSvUIZSwtE5n9+yV++7gnMpnAjrDbRCXnMmVYKyLiMgm6\nksK4Pp70aetCaZlITkExn6+vSOVX384MZ1szztzS/zxWaanInJ9CWbdksOpZ7L9B9O0sJr3diavX\n0wlWGw1/P88q01vWd1Lg7GjOWT3Sf2rI8EMo65YNVT2LvdeJjs1k0ruduXo9jWC1M8K/XxOtFF0G\nBjL+WDMCgPyCYj6bGUipHtEgpWUis9efZ8PXzyOXCWz/O4aoxBwmj/LiakwGQReSCLmSgq+3M4cX\nDaa0TGTBlktk5xfTvY0TX77eHlFUnR1cu+86txKefhdHS4av1DIcU8sw0oursZVk8HLm8E9qGX6v\nJMPY9oioLupdu18/GcrlWHeODV/30eyLl724GpNJ0PlEQi6n4OvtwuHFajk2XyQ7X7dMNNVh4/KP\n6NG1BfVsFESfWcG3i3awceuxGm2jtExk9tpzbJih7ofgGKIScpg82our0ZX6oa0Lh5eo+2FTzfZD\naanI7MVhrP9pkGp8HrhJdFwWk9/sSMSNdIJPqJwR/n0acyBIc3yWlYl8//MpNi4ZjABcu3WXbVWk\nZHuiDGUis389y4Zv/FT9EBSt6odXvLkanUHQuURCLiWr+mHZUFU/bLxAdp4qOmDBxgtsmt0PQYCr\nMRls1cNZWSrCDxdjWNazNXIB9t5OJTa3kHdbNeR6Vj4hyZlM9nbH1EDOgq7NgYp0m+6WZnzZwZMy\nVI6AjTcSNbJn6CLDkmux/NilFTIBDiakEZdfxBtNG3IjJ5+TqZk0t7Lg247NURga0M3RlglNGzL+\n+CUMZALLu6nCvgtKSvnusn5n/lU6eZYNM/toPovR3qp56uGz8Hbm8NIh6meh0sl2zeyZO7ELZaKI\nTBBYveuaxm3sTy1DqcichaGsWzZEpZP7bhAdm8Wkd9Q2Q+3AVs3Xmjp5ODgGn4712f/HaERRJPT0\nHf6uwqH2VP3w23l+m9ZbZTuPxxKVlMuUF9sQcTuToItJhISn4NvGicM/DKKsTGTBH5fLx+afM/rg\n4WKJuYkBYcuH8eWaM4RG6HaMsVSEOSejWTewNXJBYMdNJdFZhUzq0Iir6XkE38nktVb16VbfmpIy\nkZz7JUw7rtr97uBoyTv9W1FSJlImisw+EU2WHhftafXJE9Y0oIp+OKBn6vIq2y0Vmb3wOOuXD0Uu\nl7FjbyTRsZlMfrcLEdfTCFY7I/z7N+HAUW37/b9fXwRU9nvqjKN62W8Anx7NORV2nVeGLMDYxIgv\nZ48qL3tj1CLWb/vkHz8fEhzB0gV7yM7KZ9pH6/Fs5sJPK99+6vZLS0VmfR/Exl9eRCaTsX1PBFGx\nGUx5rzsRkUqCjscwb9Ex5s3oxxuvdUAU4bOZhwB4fXQ7GjWw4aN3uvLRO6od6HHv7SAjSzdHafm4\n+EI9Lo6px8VLbYiIrTQuvB4zLmY+Mi5+PUNouO7He0tLReYsCmPdYn+VLu6/qVpXvtWRqzfSCVaP\neX8/Tw4GPm5daVG9dWWZyKxtl9n4QXdkMoHtp+KJSsljin8LIu5kExSRwvjejenj5UxpaRnZhQ/4\nbPN5vdt7nAzltlMusD3wH2zncrXt/K2S7fztApvmVM92AvTq1ZHjx8/Tt+87mJoaM2/e5PKyYcMm\nsWfPMgCmTh3P558vYt68tdjaWjJ/vqre5s37CA4+g1wux8pKUf57if//EMQaPDtaRRrOzcCiKtJw\nfgZMUNeRARtEUdTO41KJJn3X/bsHDKvCoO49SWUuNRvepg/ySP1TCdUYJbV3tvNpKXPX/eb5Gqfu\nVRL+5bO/VZF0pnrpAGuC+p3861oEBD3yetc0Yj3ty7n+bWxeefzN+f8WZmZ1PziT1tdMaHx1kCXq\nFz1V05Q2sXlypVpG3lv3zFQ1jbjzGQh5fkwI/b9JWKh+GTJqkq7dbj25Ui0jtKh7nZTH6XdMpCYp\naad/1pKaQpZUu9m4nobo3T51LYKapnVvQGsRt+mH/rWFc9zcgc9kX9ZoBIQoio/N9SOK4jHgmPr/\nFwILa7JtCQkJCQkJCQkJCQkJCQmJZ5daO4IhISEhISEhISEhISEhISGhpk5SQDxbSF0gISEhISEh\nISEhISEhISFR60gREBISEhISEhISEhISEhIStY3wTF7L8K8iRUBISEhISEhISEhISEhISEjUOv+Z\nCIgH6fqnh6wpDB3s61oEuFe99Fo1QVlDyydXqm0ZFEZ1LQIGV2om93l1KB7apK5FQLbral2L8Exk\noEg6V/eZOFwdutW1CCAzr2sJSIv/99K4Pg7DU0l1LQKFt+r+ln0To7rPPgHQ8NWGdS0CCTv1TwVY\nUwgmdb/se/Cg7jOjfHCq7rNYGZha1LUI8CxkoLinW4rQ2qCshV1di4BoXvfr2o1RNZdWtzqMa9K0\nrkWoXWRSBIQUASEhISEhISEhISEhISEhIVHr1L0rXEJCQkJCQkJCQkJCQkLi/zpSBIQUASEhISEh\nISEhISEhISEhIVH7SBEQEhISEhISEhISEhISEhK1jChlwZAiICQkJCQkJCQkJCQkJCQkJGqf/xMR\nEL26uTPzcz/kMhlbd11h5YbTGuUuTpb89K0/lgoTZDKB75cd41hYrEZ5wM63WLIqjF83ndVLhh4d\n6zP9PR/kMhnbDt9kzdZwjfKvJnbBx9sZABNjA+ysTegwYgsA677rT9sW9ly4mso7MwP0av9Reno5\nM2Nse+Qyga3HYli977pWnUFdGjDpxTaIIty4k8XHP5+qfrvezkwf3xG5TGBbcDSr90Rqt+vTkEkj\nvRBFkevx2Xyy/AQu9cxZObUnggCGchmbDt/if4FR+snQypGZr7RDJhPYFhrLqkM3Ncpf7NaIL0Z6\nk5pVBMCmv6PZFqq6+dfF1pT54zribGuGKMIbS0NJytD9huYeXRvy9ae+yGUytu+JZM3GixrlX37c\nHZ+OroBaH2xN6fj8WgCmftiV3r6NAPhl3XkOBkTr3P5DejW2Y2b/5sgFga2XEll5Mq7KegOaO7Bq\nZFuGrD1NREpu+e9dLE0IeK8bS47H8OvpeL1k6NnNjZlTeyOTy9i2K4JVv53TKHdxUrBw9gAsFcbI\n5QI/LAvj2Inb+HZpyGeTemBkIKe4pJQFS0I4dS5BPxnaOjP9jU4qvQyKZvWua1p1BnVryKRRXojA\n9bgsPllyorzMwtSQw0sHE3A2kdlrz2l9trqsWvguA/u0Iz0jl459P6/x76+KHl0bMn1qT1Wf7I5k\nzcYLGuXOjhb8MLsvlgpjZDKBH1ec5PgJ/XSgMtV5Fje3vcrNO9kApNwt5N0Fx/SSoZebLd/0aYJc\nEPgzPIWVZ6v+uwY2tWfVsDYM3nSOiNQ8rE0MWDWsDV5OCnZcVTIzSP8sEz3buTD9DfVcGfi4fmjE\npJe9EMWH/RAGwM3tYyr1QwHvzj+mlwy9u3vy7Rf+yOQC//vrAivWhWqU13eyYsm8EVgpTJHJBeYt\nPkpwaBTD/b14f4Jveb0WTR3pP3Il124qdZZB3/nB2sqEn38YglcrR/7aF8ms74P16gOAzvbWfNjS\nA7kABxJS+SNGM4OJl60lH7Z0p7HCnDmXbnJcmVFe9m7zRvg42CIT4Hx6Nssj9btFvmdrR2a80k41\nV4fGsvpRu9W9EdMq2a3NwSq75dPMnq9Hty2v19hZweTVpwm4pHvGjZ7tXJj+ZqcKndypneFoULdG\nTBrtXaGTi1U641zPnPkfdMWpnhmI8Oa3QSSlF+gsQ69uHnwzrS9ymcCfu66wcr3musTFyZJFc4eo\n5yUZ3y/9m7/DYjTKA3e9w5KVoazZdEbn9gHyrl0ledufIJZh070HDv0HapQXRN0ieftW7iUl0vDN\nd7Bq3wGAooQ7JP3vd8ruFSHIZNgP8Me6Yye9ZOjRpQFfT+mOXC6wfd911my+rFH+5aRu+LR3AcDE\nxAA7G1M69t9Al/YufDWpIhuSRyNrPv4mkMCQuH9NBlDZj+++7IWzgwWiKPL2p4dIUuqX/eRZWEP0\namjDzB6eqvEZmcLKi5rfM6aVM2O9XCgrg4IHpXz59y2iswoxkAl8/3xTWtlbYCAI7LyZyi8X9JOh\nMj2b2vPNsFbIBIGtZ++w6liMRvmLHVz50r8Fqbn3ANh0Mo6tZ6vfriiKBKz5i5jzkRgYGzFkyhic\nPBto1Tu2aT8RwWe5l1/IZzt+LP/9mV3BXD56CplcjpmlBYOnvIqVg2215ZL471HjDghBEEqBCMAQ\nKAE2AYtFUSwTBKE3sAeIBcyAVOAHUZz2khoAACAASURBVBT369ueTCYw58t+vDbxT5Speez9fTwB\nx6OIjq1YIHz4djcOHL3Blu2X8PSw47cVo/AdtLK8fPqnz3PsRGxVX//UMsz6sBvjvziM8m4Bfy0f\nSvCpO0SrF4gA81ZVGMKxw1rSsnFFyp+128MxNTFg9KDmesugIY8gMGt8B8bN/xtlZhG7vu1H0MUk\nopMqXizdHC2YOLQVo2YFkFv4ADtL45pp941OjPsuGGVGITvnDyDofKJGu42cFEx8oRWjZh4lt6AY\nW3W76VlFjJx+hOKSMsyMDTj4oz9BFxJJUy+2nl4GmD2mPa8vCkGZVcju6X4EXk4mOkXT8B04l8Cs\nPy5pff7HNzvzy4HrhEWmYWYsp0zUox9kAt983pMJH+5FmZrPXxtHEhRym5jbWeV15i+ueLkdO6oN\nLZqpUrz27t6IVs3tGTZmK0aGcrasfoHjJ+MpKHiguxwCzBnQgtd+v4Ay9x573/Ih4FY60Xc1F4Xm\nRnImdG7EpcRsre+Y3q8Zx6Lv6tx2uQwygdnTnuf19/9CmZrH7i1jCDweQ/TtzPI6H7zVhYMBN/l9\nRzie7rasXz6cnoPXkZldxNuTd5N2t4Cmje347ecX6TZgjV4yzHq7M+PmBKn08vuBBJ1LJDqxIv1Y\nI2cFE4e3ZtTXmnr5kCmveHM2svbSrm7efpxVG4+wdvH7tdZGZWQygVnTejP+g90qHd30MsEhsURX\n0tH33+zEoYAo/vjrKp7uNvy6dCjPDd1Y/Xar8SzuFZcydOrB6skgwLd9mzFm2yWUeffZO7YjgTHp\nRD3iaDQ3lDOhfQMuJlfIdr+0jB/DYmlWz5xm9fRPoVfeD7MDVf3ww2P6YURrRn11RNUPViblZfeK\nSxn6afXSvspkAvOmD2H027+Roszl4NaJHPn7BlGxFSmvJ7/bi31HrrJp6zmaeNizZeVYuvRfxK4D\n4ew6oHKyN2/iyPplr+rlfKjO/HD/fgmLV56gaeN6NPWsp38/AJNbeTD1zDXS7xWzytebE6mZxOdX\n2J60ovssuBLFyx71NT7bykZBaxtL3gxR2ZLl3drQ1taSy5m56IJMgFlj2jPuJ5Xd2jXDj6Cq7NbZ\nBGY/YrdO30xnyGzVxoWVuSHB8wcRei1Vp/ZBrZPvdGHcrAC1Tg4i6GyCtk6+2IZRXx7W0skfJ3fn\nlx0RnLiSgpmJAWV6GE+ZTODbr/oz5t3/oUzNZe8fEwg8FkVUbIUN+ujt7uw/cp0t2y/SxKMeG1aM\nwnfQL+XlM6b6cSwspqqvfyrEsjKS//wD90kfY2BjQ8yC77D08sbE2aW8jqGtLa6vT+Bu4BFN+Y2M\naDD+DYwdHHmQnU30/LkoWrZCbmamkwwymcA3U32ZMHk/yrQC/lo3gqDQeGLiKq0hlp0s//+xL7Wm\nRVPVGDhzMZlh43cAYKUwJmD7K4SdSdS5H6ojA8APM55n5caLnDyXiJmpAWVlOotQLkedryEEmNOr\nCa/tCUeZf5+9o9oTcDuD6KwKm7HnVhq/X0sBwM/Njhm+jRm3L4JBnvYYyWQM+N8FTAxkBL7aib23\n0kjMu69fhzyUZ3hrxv56BmVOEXs+6kFgZCrRafka9Q5cSeGbPTWbJj3mfCSZyelMXDOD5JtxHP5l\nG+MXfapVr0nnVnQc3IOV73yr8XvHxq68sfgzDE2MuHAwlOANexg+bUKNyvifQDp/UCtdUCSKYltR\nFFsBfYGBwDeVykNFUWwnimIzYBKwQhCEPvo21ra1M/EJWSQk5fCgpIx9RyLp17uJZiVRxEKdX9fS\nwpjU9Aqj3u+5JiQk5xAVo/9Lllcze+KTc0lQ5vGgpIwDx2Pp0+3xOccH9/ZgfyVv5anLKeQX6v6C\n+Ti8G9sSn5pPQnoBD0rL2H/6Dn4dXDXqvPy8J1sCbpGrbjcjV//JsLxdTzviU/NISMvnQWkZB07G\n49dJ0zP6ch9Pthy9RW5BMQCZ6nYflJZRXKKyUEaGMmR63hDr7W5LfFo+CXcLeFAqsv9sAn3b1n/y\nBwFPZwUGMhlh6hfNwvul3Csu1VkGr1YOxCfkkJCUq9KHgCj8erk/tr5//ybsP6LaSW3sbsu5S8mU\nlooU3SvhRlQGPbs20lkGgLYuVsRnFZKQXcSDMpF915T0a+agVe/T3p6sOnmb+yWaK4R+zexJyCoi\nSo9drId4t3YiPjG7fHzuP3KDvr0ba9QRRbAwV71kKhTGpKrbi7yZTpraWXIrJgMTYwOMDOW6y+Bp\nR7wyj4TUfNXzCIvDr9Mj48HPky2HtfUSoJWHLfWsTAi7kqJz20/LibM3yMzOf3LFGsKrlSPxCdkV\nOnr0Fn16eWjVs7AwUv/XmLRq6MFDqvssaoK2zpbEZRWSkHNPNS5upNHX016r3qe+Hqw6G68xLooe\nlHE+KUdrrOiKt6cd8SmV+yEev86PzJV+Tdhy+GZFP+Tcq1abj9KujStxdzK4k5jFg5JS9hyKoP/z\nLTTqiCIozFUvmZYKEw3b+ZAXBrVhz6EIvWSozvxQdK+E85eTuV9colfbD2lurSCp8B4pRfcpEUWC\nk9Pp7qi5I6csuk9sXiGiqPlSLYpgJJdhIJNhKJNhIMjILNbdlnt7aNstv3ZPZ7cqM7CDK8cjUvSy\nW95NHtXJOG2d7NuELYduaOmkp6sVcrmME+o5svBeiV4ytG3tQlxCFglJ2ar13OFI+j6ynhOpmJcU\nFsakpVfMm/2ea0pCUja3qrGeK4y7jZG9PUb29sgMDLDq2IncK5o7/0Z29TB1dYVHznEbOzph7OAI\ngKG1NQYKBSX5uu/6e7V0ID4xl4Rk9ZoyMAa/Hm6Pre/f15P9VURKDnjeg5BTCdy7r/sYqY4Mjd1s\nMJALnDyncnwUFpXoJQM8G2uIto6WxOcUkZCrthlRafTzsNOok/+gQt/NDGWUzxSiiKmhHLkAJgYy\nisvKyNNjbFTGu4E18XcLSMgs5EGpyL4rSfRt5Vit73xabp2JoM3znREEgfrN3blXUER+Zo5WvfrN\n3bGwtdL6vZtXUwxNVOO3fjM38u5qb3xJ/P9BrfpgRFFMA94BPhQE7Rs3RFG8DMwBPtS3DUcHBcmV\nwrpSUvNwdFBo1Fm8KowX/Ftx6sj7bFgxim8WqHYLzEwNmTjeh6WrwvRtHgCnemakVFqcK9MLcbQz\nr7Kui4MFrk4KTl2uvZcZR1szUirt5ikzC3G0MdWo4+6kwN3Zkm3f+LFjdl96ejnXQLummu1mVNGu\nswI3Z0u2zunHjrn96eld0a6znRn7fxhE6C/DWbMnUufoBwAnG1NSKnmlU7K0ZQAY0L4+B2f15eeJ\nXXFWl7s7KsgtLGbl+13ZN9OPL17y0itTjqO9BcrUikWRMjUfR/vH6IOTAlcXS06fV4X83oi6S4+u\nDTExNsDGygSfjvVxdtRvp9XR0oTk3IqXlpTcezgqNHf2WzkpcLY04e9HohzMDOVM7ObO0hD9d5IA\nnOwtSKk8PtPytcbn0tWneGFQC04cepv1y4Yz+wftUOqBfZpw7UYqxQ90N9yOtmak3H1kPNhp7ki5\nu1ji5qJg63f92DG/Pz3bqvRSEOCrcR1Y8MgRmv86Tg7mpFTW0bR8HB009WzZ6jMMHdiM0AMTWLt0\nCHMWHq92u9V5FgDGRnJ2fT+QHfP749dZ03HxtDhZGJNSafcpJe8+Thaa46K1gwUulsYEV4qkq0kc\n7cxIyahkMzIKcLR9ZK50sVTNlfP6s2PBAHq2q9iBNTaSs+uHQexYMEDrJfFpcXKwJFlZsXBMSc3B\n+ZGx+dMvwYwY7M35wKls/mUsX8/TjroYOqANuw+Ga/3+qWSoofmhOtibGJFeVFz+c/q9YuxNni4i\nMDI7j8sZOez068Rffp04ezeLO/m62y1Ha1NSMiuNi6xCHK2rsFsd6nNgVl9WvFdhtyozuHND9p3R\nL8xaNTYr6+TjxqYlW+cNYMeCgeU66eZiSW5BMT9P68XenwYzbVwHvTYRnBwUpCgrokdS0vJwctTU\nhyUrQxju35rTRz/kt59HMXPBUUC1nntvgg9LVmkeI9KVkuxsDG0qHFCGNjY8yNb9Jakw7jZiaQlG\n9bSdm0/C0d5ccw2R/k9rCAtcnRWcvpCkVTbIz5P9AfodZa2ODO4NrcjNL2bFvH7s/u0lPv/AR+9N\npWdiDWFuRHJlm5F/H0dz7TlibBsXjo/tzBfdPJgVonLGHIy5S9GDUs6+0ZWT43z49VIiOXo6Yx7i\nZGVKSiWHtDLnHk6WVcwXbZw49HFPfnmtA86VopWqQ35GDpb1rMt/VthZk5eh7YB4Gq4cPY1Hh5Y1\nItd/DkH49/49o9R6EIgoirGAHNDeelVxEajy7IEgCO8IgnBeEITzeRn63c0AMHRAS3bsvUrX/r8w\n4cNtLJ47BEGAKRN9Wff7OQqLai764EkM7u3B4dDbeoUn1iRyuYCbowWvzg1iyoqTzHurEwozw9pv\nVybDzUnBmNkBTFkaxnfvdClvNyWjkMGfH6TP5L0M7+WOXQ1NmI8SdCWFnl8cZNCsAMIiU1n4RmcA\nDOQCnZrYM29bOC/MDaKhvTkvdXerFRke4t/PkyNBMeX6cOJMAsdPxLN1/Yss+q4flyJSKdU3dvEJ\nCMCMvs34LuCmVtmUXo1ZdyaeQj2Mta4M7d+MHfuu0X3gr7wxaRc/fTtQY85s4mHH55N68PV3gbUm\ng1wm4OasYMzMAKYsDuO793xQmBny2oCmHLuYhDJT93tA/usMHtCUnftu0MN/A29N3sePc/r9K7bs\ncc8CoNfEXQyfdoiPl5xg+oSONNTTOfdPCMD055ow92/9716pCeRyATcXBWNmHGXKokf64d2dDP/8\nIB8vDmP6G7XTDwAvDPJi256LdPT7kbHvb2b5/BepvJfQro0rRUUPuBlde8eTnjQ/1CX1zUxoaGHK\nyKBzjAw6R3s7K9rYWNZKW0GXU+g17SD+swI4EZnKwjc7a5TbW5nQ1NWK0Gu6H4V5WuRyGW7OloyZ\ncYQpi0L57v2uKMwMVbazhQMLfrvA8M8O0MDRghefa/zkL9SDoQNbsWNvOD79VjD+g20s+W4oggAf\nv9eDtVv+3fXc43iQk03ChnW4jh2PIKvdZba/nydH/o7VWlPa25nRzMNWr+MX1ZVBLpfR0duJ71ec\n4sU3/6KBiyUjBjWrtfafhTUEwOaIZHptPsuCU7f5qJMqCtrbQUGpKNJlw2l6bDrDW21daWBZO2vb\nygRdT6XH/GAGLg4hNCqdH19u++QP/Ytc/fscKdF38Hnx+boWRaKOeBZOoTx2KSGK4hpRFDuKothR\nYde5yjqpaXm4OFV4Q50dFaSmaYa8vTzciwNHVZcwXgxPxtjYAFtrM9q2ceHLKc8RdvA93hjTkQ/e\n7MrrL7fX+Q9Q3i3EuZJn2MnejNSMqsOV/Xt7sP+Y/vdNPA2pmYU4V9q5cLI1K7+46iHKzEICLyZR\nUiqSmF7A7ZQ83JwUj36Vju0WabZrV3W7QRcSNdt11mw3LauIWwk5dGqu+86BMqsIZ5sKGZxttGXI\nLiguP+6xNTSWNo1sAEjJKiIyIZuEuwWUlokcvZREq4Y2OsuQmp6PU6UXAidHi/KQwEfx79eE/Uc1\ndyhWbbjAsDFbmfDhXgQgLl4/73Jq7j1cKhk6Z0sTUit58S2MDWjqYMGfr3ci7KMetHO1Yu3LbWnj\nbEnb+lZ82acpYR/14I0uDfnA14PXO+q+06pMz8e58vh0sNAanyNfaM1BtRPkUngKxkZybNW7f04O\nFqz6aShTZx7mTqKe/ZBZiHO9R8bDI+f9lRmFBJ1T62VaAbeTc3FztqRtU3vGDmzGsZUv8MXr7Rne\ny53PXnu2DLk+KNMKNCJrnBwsSH3k/OjIoS05qL4I9nKEEmMjOTZV7MrqQnWeherzqrGckJrPmWup\ntHTX/fIqZf59nCtFAjkrjFHmVxoXRnKa1TPnz9HtCHunK+1cLFk3wos2jtWbHyuTmlGIc6UoOSc7\n8/K/rVxOjX7IV/WDSxX9cDWVlh569ENaLi5OFSGyzo5WpDwyNl8Z0YF9R1RniC9cScDYyADbSvPr\nsIFt2H1Iv+gHqP78UBOk3yvG3tSo/Gd7EyPS7z3dsR9fJzsis/IoKi2jqLSMM2nZtLLRXU9Ss4tw\ntq00LmzMSM3+B7sVEkvrRpq2yb+TKwFqm64PqrFZWSerGpsFBJ1L0NJJZUYh1+MySUjNp7RMJPBM\nAq0a66OTeTg7VThwnB0UKFMfXc95s//Iw/VcEsbGcmxtzGjbpr56Pfc+b4zpxAdvdWPc6A46y2Bg\nbc2DrIr7BR5kZWFobf0Pn9CktKiIuJ+X4zRsOGYe+jlhUtMLNNcQ9v+whvCr+vjFwD6NCQi5TUmp\nfhsY1ZFBmZbP9agMEpLzKC0VCQy9Tatm+t3T8kysIQqKcalsMyyMSS14/Byx71Yafd1Vf++wpg4c\nv5NJSZlIRtEDLqTk4OVQPVuizCnSiGhwsjJBmfvIfFH4gGL1s9969g6t62sfh3hazu8PYe1H37P2\no++xsLEkt9KxibyMbBR2un337cs3ObH1KCNnvIOBYe1vfD6TyIR/798zSq07IARB8ABKgcdtkbQD\ntFM0PCVXrqXg1tAWVxcrDA1kDOnfkoDjmpNxckou3bu4AdDY3Q5jIzkZWYWMeuN3fAetxHfQStb/\nfp6f151i01bdQ60jbqbjVt8SVycLDA1k+PfyIOjUHa16Hg2ssLQw4lItXmYHEB6biZuTAld7cwzl\nMgb7NCTogqYXPOB8Ej4tVGfGbCyMcHdWkJBWvTPo4TEZNKrUrn+3RgSd12w38FwCXVqq21UYq9pN\nzcfJ1hRj9dk8S3MjOjazJzZZ97OT4XFZuDla4FrPDEO5wODODQi8onkbuH2liduvrQvR6qwP4bcz\nsTQzxFZ9vrRbC4fyMl2IiEzDraEVri4KlT70bUJQFTdQezSyxlJhzKXwih0rmUzA2kpl6Jp52tGs\niR1hZ7R16Wm4kpyLm60ZrtamGMoEhrRyIuBWhe7l3S+h/U/H8F0eiu/yUC4l5vDW1stEpOQyauO5\n8t+vP3OHn8Ni2XRe99De8GtK3BpY4+piiaGBjMH9mxN4XNMBl6zMo1tn1W5BY3dbjI0NyMgqQmFh\nzLplw/lheSgXruh+o3u5DNEZNHJW4Opgrnoevm7aenk2gS6tKumliyUJqXl8uvQEPSfuovd7u1mw\n6SK7jt9m4ZbLVTXznyIiMlXjufj3a0pQiObt/cnKfLqp72do7GaDkbGcTD2ORVWmOs/C0twIIwNZ\n+e87NLfXuCDvabmSkoe7jRkNrExU46K5AwGVjiDlFZfS7ucwfNecwnfNKS4l5/LmznAiUvW7wb0q\nKvpBbTN8GxH0yO3sVfaD8jH9kKB7P1y+moR7Qzsa1LfG0EDOsIFtOPr3DY06SSnZ+HZRvUR5etir\nxmam6iVEEASG9G+t9/0PUL35oaa4mZOHq7kpTqbGGAgCz7vYczI188kfRHU5ZVs7K+QCyAUBbztL\n4vN1j5YKv61tt4IuP53deojq+IV+tgIgPOpRnXTT1skzCXRp7QRUHpv5hEdnoDAzKr8w1qeNk146\neeVaMu4NbWhQX72eG9CSgOOaDvrK6zlPdzuMjQzIyCxk5ITN+A76Bd9Bv7D+93P8vPYkG/+8UEUr\n/4xZIzfup6VRfDedspIScs6fw9LL+6k+W1ZSQvzqX7Dp0rU8M4Y+RFxPw83VCldn9RrCrzFBYXFa\n9crXEFe1Lx0d/BjHxL8hQ8T1dCwtjLCxVumsT4f6Ghcc68KzsIa4kpqLm5Uprgq1zWjiQMBtzeN5\nblYVTtHn3eyIy1HNUcn59+nmqnIWmhrIaOdkSUxW9SIqwxNzcKtnjquNKYZygSHe9QmM1NQB+0oO\nE7+WTsRUY33fcXBP3lo+jbeWT6NpVy8igs8iiiJJN25jbGZS5V0Pj0MZk8ChFX8ycsbbmFvXnFNf\n4r9HrabhFATBHlgFrBBFUXz0GghBELyAGcBb+rZRWioyc8FRNq18WZU6ak84UTF3+fi9HkREphB4\nPJq5i4JZMHMgb47phIjI1G+qd3u4lgxlIrNXnGL9vAHIZQI7jtwiOj6bya+3J+LWXYJPqxYF/r09\nOFBF9MMfP/nTuIEVZqaGhP4+mi8XhRJWxXk+neT57Ty/TeuNTCaw43gsUUm5THmxDRG3Mwm6mERI\neAq+bZw4/MMgyspEFvxxmez84id/+ZPaXX+eDV89j1wmsP1YDFGJOUwe6cXV2AyCLiQRciUFXy9n\nDv80mNIykQW/XyI7v5jubZz4cmx7RFQhMWv3X+dWgu7nLkvLRGb9cYmNU3oikwlsP3GbqORcpgxr\nRURcJkFXUhjfx5M+3i6UlolkFxTz2QZVSqcyEeZvv8KWqb0QEIiIz+LPEN2jVUpLReb8EMq6ZUOR\nywV27L1OdGwmk97tzNXraQSrnRH+/Zpw8JHzmQYGMv5YMwKA/IJiPpsZSKmeO1qlosjMwzfY9Gp7\n5ILAtitJRKUX8HGvxkSk5BJ4K/3JX1JNSktFZn3/Nxt/flH1PPZeJSo2gykTuxERqSQoJJZ5i44z\nb0Zf3hjTAVEU+ewb1c3ir7/clkYNrPnobR8+etsHgHHv/6Xzy0dpmcjstefYMKOPSi+DY4hKyGHy\naC+uRmcSdD6RkMsp+LZ14fAStV5uuljt8aALG5d/RI+uLahnoyD6zAq+XbSDjVuP1Vp7paUisxce\nZ/3yocjlMnbsjSQ6NpPJ73Yh4noawSG3WbAklLnTn2f8q+1AFPliVvXDV6vzLNo1q8fcd7tQJqqc\n+qt3XdPLAVEqiswMvMWml9qqbEZEMlEZBXzS3Z1wZR6BT7jALuydriiMDDCUC/RrUo+x2y9rZdB4\nun44y4aZ6n4Iilb3gzdXYzIIOpdIyKVkfL2dObx0iKofNj7sB3vmTuxCmSgiEwT9+6G0jK/n7eeP\n1eOQy2X8uesit2LS+OyD57lyLZmjx24we+Fhfpw9jLdf7waiyMfTd5Z/3qdjI5KVOdxJ1O/FQiWD\n/vMDQMj+N7EwN8bQUEbf3o0Z9/5fGrfjP5UMIiy9GsvCzq2QCXAoMY24/CImNG3Izex8TqZl0szK\ngrkdmmNhaEBXR1vGN23IhJBLHE+5Szs7K9b3bIcowtn0LE6l6d4fpWUis3+/xG8fq+zWjjBtuzWu\njyd92qrsVk5BMZ+vr0hFWN/ODGdbM85UY04vLROZ/etZNnzjp6mTr3hzNbqSTrZ14fCyoWqdvEC2\nOqpuwcYLbJqtOqZ1NSaDrXrcPVBaKjJz/lE2rRytSmm++wpRMXf55P2ehF9LIfB4FHN/ClKt517r\njCjCpzP1TqJWJYJcjsvoV7m9fAmUidh0646JS31S9+3BtGEjLL3bUhh3m/jVv1BaWEheRDip+/fQ\ndOYcci6cpyAqitKCfLJOqzJdub4+AdMGj7+U/HH9MGdRGOsW+6vWEPtvEn07i0lvdeTqjXSCw1Rp\ng/39PDkYqO1kqO+kwNnRgrN6pGKtCRnKykQWrDjNxmWq487Xbtxl21799hmfiTWECDNDotk0rI1q\nLRWpJCqzkI87uxGRlkdgXAbjvFzo7mpDSZlIzv0SPg1UOXM3RSSxsE9zjr7SEUGA7deV3HhMhPRT\ny1Mm8s2ea2x6q4uqT84lEJWaz8f9mhKRmENgZCrju7vj19JRtc4tKmbqtprZOGncsSXR56+x8u05\nGBobMXjKmPKytR99z1vLpwEQvH4P146f58H9BywfNwPvfl3pOWYQwev3UHyvmJ0LVOlarextGDnz\nnRqR7T/FMxyZ8G8hPHqrc7W/UDsN52Zg0WPScKahSsO570nf69Z2Qd1emgAYOuh+JKCmKbPXLZ1T\nbSBU8wb4mqBMYfTkSrWMwZXajWR5GoqHNnlypVpGtqtm0zzpJYP7466Y+fdIOlezjk19cHXo9uRK\ntYzYSPdjSzXNAx+XJ1eqZQxP6e9ErikKb92qaxEwMap7fQBo+G2XuhaBhJ36vxDWFEJmzUWP6MuD\n2Nq/l+BJdFrsW9ciED49sq5FeCYouVf3dyyVvaF/xEpNIdypuSg7fZn19rNxJGJck/7/p9/QGy0M\n/tfeaeM/e/6Z7Msaj4AQRfGxOW5EUTwG6H8QSUJCQkJCQkJCQkJCQkLiv8gz6RL4d3kWLqGUkJCQ\nkJCQkJCQkJCQkJD4P06t3gEhISEhISEhISEhISEhISEBonQHhBQBISEhISEhISEhISEhISEhUftI\nERASEhISEhISEhISEhISErWNIEVA/GccEPlFyroWAWsr17oWAdHa+MmVahkhPvfJlWoZg3jd087V\nNHez9UsrVZPYXq77O10zcvXPNV5T1Eszr2sRnokMFIlpJ+taBFy8R9S1CLi1qPt5Mq6s7jNx8Axk\nwSgr+/fS2f4T8SH6pwutKQwj/znF67+B+AxkkHpQWveZOF5qVPeZF8LrWgAgNfViXYuAlYVuaUpr\nA1lSfl2LgPyWbimEawNvW9u6FkHi/xP+Mw4ICQkJCQkJCQkJCQkJCYn/LNIdENIdEBISEhISEhIS\nEhISEhISErWP5ICQkJCQkJCQkJCQkJCQkJCodaQjGBISEhISEhISEhISEhIStY10AkOKgJCQkJCQ\nkJCQkJCQkJCQkKh9/k9EQDzfoznzvh6BTCawZftplv0apFHu6mLDsnmvYGdrQXZ2IRM/20xKag6t\nm9dn4ayRKCyMKS0TWbwygN2HLuklQ8+2zkyf0Am5TGBbUDSrd1/TqjOoa0MmjfJCFOF6fBafLD2B\nSz1zVn7WC0EGhnIZmw7d5H8BUfrJ0NKRmS95IZMJbDsRx6oAzRvQX/RpyBcvtCE1R3UD9abjsWw7\nGVdebmFiwJHpfQkIT2bWtiv6ydC+PtPf6azqh6NRrN4RoVVnkK8bk15tiyiKXL+dxSc/hgDw+YQO\nPNfRFUEmcOJSMt+uOauXDD18ppHAkAAAIABJREFUGvD1x77IZTK2741kzWbNZ/rl5O74dKgPgImJ\nAXY2pnTsuw6Azz7sSu9ujZDJBE6cTWDuojC9ZOjTowXzpr+EXC5j87aTLF0ToFHu6mLD8vmvUc/W\ngqycQiZO3UiyMru8XGFhwqlDX3MgIJxpc7brJUPPts5Mf6OSTu6qQie7qXUSuB6XxSdLTpSXWZga\ncnjpYALOJjJ77Tm9ZADo07Ml86ePRC4X2LztJEtWH9Uob+Biy/IFr1HPVkFWTgHvfvqbdl8cnsHB\ngCt8PnubXjL06NyA6ZO7qfpi/w3W/H5Zo/yrj7ri006VtcDExAA7a1M6DPoNAGcHC+ZN64mzgwUi\n8NZnB0lSVu/G7B5dGzJ9ak+VPLsjWbPxgka5s6MFP8zui6XCGJlM4McVJzl+Ir5abT6JVQvfZWCf\ndqRn5NKx7+e11k7P1o7MeKUdckFga2gsqw/d1Ch/sXsjpo30JjVLNU9tDo5mW+htfJrZ8/XotuX1\nGjsrmLz6NAGXknWWobO9NR+18kAmwIE7qfwRk6RR7mVryUet3PFQmDPn0k2Op2SUl73bvBE+DjYA\nbIpK5O8U/TIb9HK35Zs+TZHLBP68kszKM1U/34FN7Vk13IvBG88SoczD182WL3o1xlAu40FpGfP+\njubkHf0yPPTu7sm3X/gjkwv8768LrFgXqlFe38mKJfNGYKUwRSYXmLf4KMGhUQz39+L9Cb7l9Vo0\ndaT/yJVcu6l7tqpe3dyZ+bkfcpmMrbuusHLDaY1yFydLfvrWH0uFCTKZwPfLjnEsLFajPGDnWyxZ\nFcavm/SzGT2b2vPN0FbIBIGt5+6w6lhMlfUGtHZi5diODF0WSkRSDoZyge9GeNGmvhWiCLP3XeNM\nbEaVn30SPXwa8PUUX+Ryge17r1dht7rh0/4Ru9VvPV3au/DV5O7l9TwaWfPxzAACQ+J0l6Fjfaa/\n54NcJmPb4Zus2aqZp+GriV3w8XZWyWBsgJ21CR1GbAFg3Xf9advCngtXU3lnZoDWdz8tz4JOiqLI\nodU7iToXiaGxIS98MgYXzwZa9QI37udK0Dnu5Rfy9c6FWuWRYZfZOm8D7yz5lPpNdcv00KNLA76e\n0l2lD/uus2azps36clI3fNpXslk2pnTsv0GlD5MqMjJ5NLLm428C9dKHPj1b8f2Ml5HLZWzaGsbi\n1Yc1yhu42PLz9+Ows1WQlV3AO5+uK7fdmbdWce2mal5NTM7klXd/1rn9h/Tq5sGsaf1Vc+Wuy/yy\nXjPbk4uTJYvmDsVSYYJcJrBgaTB/h8Xg6mJF8K6JxMSpxuSliCS+mntIPxma2jNzcEvkMoGt5xJY\nefwxc0QrJ1a91oEhK8KISMrBQCbw/YtetHKxxEAmY+fFRH55zGefRM92Lkx/S72mC4hm9c6rWnUG\ndW/EpNHeqveMuCw+WaQaP871zJn/YVec7MwAePPbIJLSCnSWQRRFflu8m0unrmNsYsR700fj0Uwz\nQ+D9e8Us/noTqUl3kclldOjeklffHwzAxqV7uHZRlTmt+F4xOVn5bDj6nc5y/NeRSdv/ujsgBEFo\nAIQAHURRzBQEwQa4CDwHmAPLgfqoois2AXNFURQFQRgPbAD6iqIYqP6uF4BdwEhRFHfo8wfIZALf\nz3yJlyasJDk1m4Adn3A4+Cq3YlLL68yeNoytu8+xdfc5evg0Ycang3n/898pulfMB9O2EBt/FycH\nS4L++pTgsBvk5umWIkomE5j1ZmfGfRuEMrOQnfMHEnQ+kejEilSRjZwUTBzemlHTj5JbUIytpSpN\nXHp2ESO/PkxxSRlmJgYc/GkwQecTScvSUQYBZo/y5vXlYSizi9j9+XMERqQQrczTqHfgYuJjnQsf\nD27JuWj904TJZAKz3uvCuOlHUWYUsnPxYILO3CE6oVI/uCiYOLINoz47qOoHKxMA2jW3p0MLB/w/\n2gvA1h8G0qWNE2cidFs8yGQC30ztyYRJ+1Cm5fPXhpcICo0jJq5igT5/acVL9tiRbWjRtJ5KhjZO\ntPdyYshrWwH43+rhdG7vwtmLur3gyGQCP8waxYjxK0hWZhP012ccDo7gZnTF3/LtF8PZuvssf+46\nQw+fpsz4dCjvfbapvPyrKf6cPKefkXoow6y3OzNuTpDqWXw/kKBzj+iks1onv9bUyYdMecWbs5Fp\nesvwUI6Fs15m+LhlJCuzCd45jUNB4Rp9MefLEfy560x5X8ycOoyJUzeWl381ZQinzuqf6lMmE5j1\nSXfGf3wAZXoBf/06guATcUTHVTg55i0/Vf7/Y19sRcsm9cp/Xjj9OVZuusiJ80mYmRpQVqa3KBXy\nTOvN+A92o0zN569NLxMcEkv07Qodff/NThwKiOKPv67i6W7Dr0uH8tzQjf/wrdVn8/bjrNp4hLWL\n36+1NmQCzBrTnnE/haDMKmTXDD+CLicTnfLIPHU2gdl/aL6Anb6ZzpDZqhcbK3NDgucPIvRaKroi\nA6a09uDTM9dILypmdQ9vTqRmEp9fMeemFd1n/uUoRjeur/FZHwcbmlpZ8FboZQxlMpZ2bc2Z9CwK\nS0p1k0GAb/s2Y8zWSyjz7rN3XCcCo+8SlaG5IDQ3kjOhYwMuJleM26zCYt746wpp+cU0rWfO5lFt\n6fLLiUebeLIMMoF504cw+u3fSFHmcnDrRI78fYOo2PTyOpPf7cW+I1fZtPUcTTzs2bJyLF36L2LX\ngXB2HVC9nDZv4sj6Za/q9aInkwnM+bIfr038E2VqHnt/H0/A8SiiK73Ef/h2Nw4cvcGW7Zfw9LDj\ntxWj8B20srx8+qfPc+xEbFVf/3QyCDDnhdaMXXsGZU4Rez7sQWBkKtFpmk5GcyM5E7q7c6mSs2d0\nZ9VL5cAlIdiZG7Hhjc4MWxGGKOoog0zgm097MGHyPpRpBfy1/sUq7FbFS9fYl1rToplqjjpzMZlh\n41ROaitLYwK2v0rYmUTdBFDLMOvDboz/4jDKuwX8tXwowafuEH2n0jy56kyFDMNa0rKxXfnPa7eH\nY2piwOhBzXVuu7IMda2TAFHnI8lISmfS2ukk3oxn/4rtvLPkE616zbq0psuQHix7a65W2f3Ce5ze\nE4Jrs0Y6t69ax/gyYfJ+lT6sG0FQaLymPix7RB+aVtKH8arltJXCmIDtr+itDz/NepUXxi0mSZnF\n37u+4mDQFW5Gp5TXmfvlSP636zT/23mKnl2b8c3UEbw7dT0ARfeK6THkW53brUqOuV8NZMy7v5OS\nmsu+P94k4NgtomIr1qqT3vZl/5FItmy/SBOPevy2YjTdB60AID4xi4Evr62eDALMGdqK19adQZl7\nj70f+BJw/XFzhJvGHDGojTNGchkDloZiYigj8ONe7L2STGK2Hu8Z73Zh3DcBqjXdwkEEnU3QXtO9\n2IZRXxzWWF8D/DilO79sj+DElRTMTAwoK9NxklJz+dQNlIl3WbrtS6Ku3WHdwr/4bu1krXqDX+1N\n6w6elDwo4dtJq7h06jrturZg3ORh5XUObQ8l7laS1mcl/v9AZx+MKIoJwEpggfpXC4A1QCqwF1gg\nimIzwBvoBlRezUYAoyv9/Aqg31a7mvZejbgdf5f4xAwePChl14FLDOzTRqNOs8aOhJ5WRRWEno4q\nL4+JSyc2XjWJKdNySc/Mp56tuc4yeHvaEa/MIyEtnwclZRw4EYdfR02P4Mt+nmw5fIvcAlVO9Mzc\n+wA8KCmjuET1RmNkIEOmZ2oWbzdb4tMLSMgo5EGpyP4LifT1cn7qz7duYE09hTGhN3Rf0JfL0LQe\n8Sl5JKSq+yHkNn4+ml7/l/s3ZcuBGxX9kHOvvMzYSI6hgQwjQxkGchl3dXTCAHi1dCA+MYeE5FyV\nDAHR+PV0f2x9/75N2K+OOBFFUSWDoQwjQzkGBjIyMnWXoYOXm0onE1Q6ufPARQb28dKo08zTmdBT\nqp3f0NO3GORXobPerRpgb2fJ32HXdW67/Dse6uTDZxEWh1+np9NJgFYettSzMiHsSgrVoYO3G7Hx\n6ZX64gKD/Lw16jTzdCL0tCpaJ/T0LQb6VfSVd6sGONRTEFyNvvBq4UB8Ui4JKXmqvgiKpo+v22Pr\nD+7jyf5AlcPD080auVzgxHmVkSwsKuHe/RK9ZQHwauVIfEI2CUlqHT16iz69PLTqWVgYqf9rTFq6\n7jsVunLi7A0ys2s3F7q3hy3xafkk3C1QzVNnE/BrV//JH3yEgR1cOR6Rwr1i3V78AVpYK0gquEdK\n4X1KRJHgpHR8HTVznyuL7hObV0jZI2+SbhZmXMnMoVSEe6VlxOQW0sXeWmcZ2jpbEpddRELOPR6U\niey7nkrfSk6vh3zaw4NVp+O5X1Lh9bqWlk9avmrM3rpbgImBHCO57najXRtX4u5kcCcxiwclpew5\nFEH/51to1BFFUJirFrGWChNS0/O0vueFQW3Yc0g70u1paNvamfiELBKScnhQUsa+I5H0691Es5Io\nYmGuGguWFsYaMvR7rgkJyTlExejvOPduYE18RgEJmSrbue9KEn1bOmrV+6R/M1Ydj+H+g4pn0cRB\nwSm10z6joJjceyV41dddHyrslnqOCozGr6fbY+v792vC/qPaTtkBz3kQcuqOXnOUVzN74pNzSVCq\nZTgeS59uj9+1H9zbg/2VIkVOXU4hv/CBzu1W5lnQSYAbp6/Stk8nBEGgQXM37hUUkZeZo1WvQXM3\nFLZWVX5H8OaD+I7sg4GRoc7tq/Qht5I+xODXw+2x9f37erI/oAp9eN6DkFMJeulDB293YuPTiEu4\nq7Ld+8/hr2W7nQk5dQOAkFM3tWx7TdC2tQtxCZncScpWzRGHr9Gvd1ONOiKgsFBtoCgemSNqRIYG\n1sRnFJKQVaSeI5Lp10J7jvi0XzNWHY/VmK8BTI3kyGUCJoZyikvLyNPjeXg3sdNcX4fF4ddFMyrn\n5X5N2HJQe33t6WqFXCbjhHo9V3ivRC/bCXAu9Co9B3RAEASatm5EQX4RWXdzNeoYmxjRuoMnAAaG\nBrg3dSUzTXv8nAy4RPe+7fSS47+OIPx7/55V9A0CWQz4CIIwBfAFfgReBU6IongUQBTFQuBD4ItK\nnwsFOguCYCgIggXgCWjGlemIs6MVycoKb2NyajbOjpoG4dqNZAb3U73U+Pf1QmFhgo21mUaddm0a\nYmRowO07uodPOtqakZJRWP6zMrMQRzvN73d3tsTNRcHWb/ux47v+9Gxb4RxwtjNj/4/+hK4awZrd\n13SOfgBwsjYhpdLnUrKLcLQ21ao3oG19Dn7Vh5/f6oKzulwQ4KsRbZi/SzucSxcc7cxIqfSipLxb\noN0PLla41bdk6w8D2fGjPz3VIaWXbqRzOlzJqU0vc2rTy4ReTCImUXvCeqIM9uYoK3mllWn5ONpX\n7VRycbLA1UXBafXL5eWrqZy5kMyJ/eM5cWAcYWcSNHYcnhZnJyuSUirppDJLSyev3khicH9VOPng\nft4oLEyxsTZHEAS+/XIEM7/fpXO7lXG0NSPl7hN00kWtk9/1Y8f8Cp0UBPhqXAcWbLxYLRkAnB2t\nn9gX164nMbjfw75oi2Wlvpj71YvMWLCzWjI42ZuRUlkn0gtwrPcYnXBU6cQpddSLWwNr8vKL+Xlu\nP/ase5Fp7/vo7SQsl8fBnJTUR3TUwUKjzrLVZxg6sBmhByawdukQ5iw8Xq02nxUcrU1Jyaykl1mF\nVc9THepzYFZfVrzXFWcb7fLBnRuy70yCXjLUMzUi7V5x+c/p94qpZ2r8D5+oIDq3gM72NhjLZFgZ\nGtDOzgr7p/xsZZwUJqTkVjhfU/Lu42Sh+T2tHRW4KEwI/oeQ/kHNHLiamkdxqe67Wf+PvfMOj6po\n//d9dtNI78mmQEjokEINJRQlSAnF3lBRwfZiAUEQX7ooKCj62rADitIEaUpJkCodAiGUJBBSdze9\nV3bP748TkmwSILsBk6+/c18X18WeM7vzyZyZZ+bMPPOMp7s96ZoaG6vW5qNytzNI89GXe3lwdDAn\nI6fz05dP89/3d9T7nbEjAvn9j3P1rjcGD3c70mt56am1hXjU0bB8xSHuj+jKkV3/4cfPH2XeEskL\nxrqVOS8/25dPV5i2Te4Gng6tUOfVPAtNfhmeDoZ1rquXPSqHVvx1ydAj7KK6gPAuHigVAj5OrQj0\ndkDlaIWxSP1Wrb4zo/jW/ZbKjqOn6q8cjgpv3+CLaGPwdK3Tf2eW4OFyEw3utvh42nEkumkT1PU0\ntIA6CVCYlYd9rYlFe1cHCrIaPx5JT0ghPzOXDn26mpS/h5sNmtp9ROZtxjE3rQ/tqhdYjMXLw5E0\ndU715zRNHioPJ4M05y+lMGa49AI55r7u2NtJfTeAlaU5+35/h8iNbxMxLART8XS3I11T84KrzijE\nw6OOjfjqAA9EBHJs9+us+uJx5i3ZVX3P19uRP9ZNYv33T9One/1tNI3Bw96K9Pxa4+uCMjwcDNu5\nZCOs+OuyoY34I0ZNaYWO47OG8vfMe/n2wFXyS42fqJPGdLXaZ3YJHs4NjOm87Vm3eAQbPxjJoKpt\npX7e9hQUV/DFzMFs/Xg0Myf0NHkck5uZj4tHTdtwcXMgJ/PmbaO4sJRTh2Pp1stwYjlTnUOGOodu\nPdvf5Jsy/3ZMmoAQRbESeAtpImJK1eeuwKk66a4AtoIg2N+4BEQCw4FxSB4Td515H26hf+8A9m6e\nTv8+AaRr8tDVGrB5uNnz1dKneG3WL4jG+k42EqVSwE9lx/j5e5jy6SHee6kvdtbSzLg6u4TR03cw\n9LUtPDDEHxcH4wcwjSEqRsOguTsZ9X4Uhy5lsPSZngA8NciffbEaNEa6hJmCUing52XP+Fk7mbJ0\nP++91h87GwvaqOwI8HUg7Nn1DJiwnn7BKnp1db+rWiKGtWfXX1eqXdFa+9gT4OfEoLGrGDhmFX17\netMruPFeJMYwd8lm+vdpx74tMxnQpx3pmlx0Oj0Txw9kz/5YgxgIdwuloqpOzt3DlOWHeO8VqU4+\nNaID+06noan1ong3mbNkEwP6tGf/1lkM6NOetKqymPTUIPbs+2fK4gajhwawc19idZ0wUwr0CvJk\nyRdHePDFTfiq7HhwZIfb/Mod0DGiA5u2XWJgxI9MemMbyxbe16Jnsu8kUdFqBs/8g4j5ezh8QcvS\niX0M7rs5WNHBx4GDsaa5VzeFk1l5HM3I5YsBgczt0ZHYvMJ6XhJ3AgGYfW97Fu29+ctDe1cb3h4c\nwKxdl+54/je4f1QQ67ecplf4Mp7+z098tvghhFoVsXugD6WllVxOaNpWrVsxdkQXNm49T7/hX/Lc\nq+tZvmgMggBTXg7j+zUnKDFhMG8MggCzR3flvR0X6t1bfzIFdX4ZW18LY+6YrpxKykVnomtzY4kI\nb8euv67Wc6F2c7GmY4Azh46aNjFnDKOH+LPzYKLJbtxNoSXUyVuh1+vZ9e3vDH/h/n8kv1vWB39n\nk7ZfNJbZizcS1qcDB7fOZkBoB9LUueh10up/t0GzGHL/+0ya+h2LZz9K29Zud03H2JFd2bD1LKH3\n/Y8Jk9fyyXvjEATIyCyi7/DPGPXYd7y7bA//W/JAtTfVnUQQYE5EF97bUd9TM9jXEZ0oEro4ioEf\n/sWkgf74NjCpfidQKhT4qewZP3sXUz46yHuT+2FnY46ZQqB3F3eWrDzFA9N34Otpy0P3BtwVDbXR\nXdfxv3k/M+KRgXh4uxjc+zsymtB7glAo//8MhiB7QDQtCOVIQA10A4yJOLQWeB1wAKYB79wsoSAI\nLwIvAti434uVY2C9NGptPl6eNbOyXh6OqLWGs3GajAKefe1HAGysLRhzX3B1nAdbG0t+/foF3lu+\ng1NnTQvyps0pQVVrddnT2RpttuHLmya7hLPxWVzXiaRmFJOoLsBPZU/MlZrVrYzcUuKS8+jd2Z2d\nR5ON0qDJKzNYKVQ5tkJbZ0Ihr7hm1W/d4UTevr8bAD3aOtM7wJWnBvljbWmGuVJBSfl1PtxSP2jh\nrdBml6CqNUvv6WrTcDlczpTKQVtEYno+fl52hAZ6En05k5IyyTVt/8k0undy52SscYMIbWYxnrVW\nkz3dbdHexH09IrwdC5bVBLgaNtif6PMaSkolDQeOJBMS6MFJI7chqDX5eKtq1UlPpwbqZD4TJkv7\nEm2sLRgzPISCwlJ6d29Lv14BTHxyIDbWllhYKCkuKWfhMuPm6rQ5JahcjayT6VKdDOngRu/O7owf\n0QFrKzMszBSUlFWy9GfjnZXU2rxGlcUzk78BwMbakjEjqsoipC39erdj4vhB2FhbYl5VFguWbjFK\ngyazBFXtOuFmgzbrJnViaDvmL69ZUdVkFHMxIZuUqhgFew5dI6SLBxt3XG7w+43Sk1GMyqNOHa2z\nl/SRsV14/nXpmUfHaLC0UOLk2IocE7yjWhLavFJUtVZtPJ2sb22nDlxl5sOG25cievuw53Qa101Y\n9QfIKq3A3apmEOpmZUFWafktvmHIzwmp/JwgDerndO9ASnHZbb5RH01hGSr7molmlZ0lmqIaDbYW\nSjq62rD2yR6SRhsLvn8wmImbzhKjKcTTzpJvHgjizR0XSDZx4liTUYCXZ403ksrDAXWGoevyEw/2\nZPzLUuyRU2dTsLQww9nJmuwcqf2MGxnI73+avtKszSjEy7NmNVPlYYe2jobHHghiwn+k4LOnz6Vj\naWmGs6M1IYFejBrWiVlT7sHezhK9XqS8/Dqr1xnnuaXJLzXwWvB0sEJTa7XT1tKMDp52rH2xHwBu\ndpZ8+2xvXlh5gpi0fBZtr5mY2Pif/iTexLbcCqnfqtV3utvcvN8aZthv3WDk0AD27E/kus60IDWa\nrDr9t5s12uybaBjiz/zP/27wXlNozjp5bNtBTu+SYgF5tW9NQWbNxHdBVj72rg1vtahLRWk5GUlq\nVs6UYhAU5Rbw68JveWLuC40ORKnNLMazdh/h1vhxzA1GDg1gzwHT60O6Ng9vVc3WNG9PR9RaQ49Q\nTUY+T/1nBSD13WOH9yC/amyt1krldy0li0PH4gjq4kticibGoskoxMvTvvqzyt0OrdawTjz+QAhP\nv/IrAKfPpUk2wsma7JwSKqracsxFDUkpufi3ceHcBePGdNqCMrxqeUWp7K3Q1to+bGthRgcPO9a+\n2BcAN1tLvnumF5NWn2RcsBf74zK5rhfJLq7gVFIuQT6OpBjZl0tjulrt08UabU7dMV0xZ+NujOmK\nqsd0muwSLibmkFLlVRN5LIWQDq40Nrz5rt8OEbVViv8S0MmXbG1N28jOzMfZreG28c0HG/D0cSXi\nsUH17v0deYbnpz/YSAUy/0ZMmnoSBCEEGAb0BaYKgqACLgA966TzB4pEUaz2nxJF8TgQCLiKomh4\nTEMdRFH8RhTFXqIo9mpo8gHgTEwy/n6utPZxxtxcyQMR3dm513ArgbOTTfUM+RsvhvPLb1JDMjdX\nsvqLiazbcpJtu0wPRXEuIZs2Kjt83G0wN1MQMcCPqJOGs86RJ1II7SrtGXOys6Styp4UbSGeztZY\nWigBsLexoFcnd66mF9TL47YaknLxc7fFx8Uac6XA6J4+RMYYGlm3WgPe8CCv6gCVU1eeJGzOTgbN\n3cXizTFsPp5s9OQDwLm4LNp42ePjYSuVw6C2RNVxk448kkxooCcATvaWtPVyIEVTRHpmMX26eaJU\nCJgpBfoEenAlxfiV75iLGfj5OuCjspM0DGtH1MHEeun82zhib2/JmVpBLtXaIvr08EKpFDBTKujT\n3cukLRinY5Lw93OjtY8L5uZKHozowc4owwFR7To55aXhrNkoRX5/adoqggbPJeSeecz9YDNrNx83\nevIBGqiTYQ3UyeN16qSXVCenfXqYQS9vZsgrv7Nk9Wk27080afIB4PS5JALauNcqi578eYuymPry\ncNZskAaBL05bSeCg2QQPmcOcJZtYt/mY0ZMPADGXMvDzqVUnhrYj6lD9yUb/1o7Y21ly5nxNHJR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10Pej0eA8PwGTXC4L6+spK473+kOCkZM1sbOr70AlaurgAUp6Ry5aefuV5WhiAIBM9+B4W5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\nr6xEn5dLfWUFzhHmb4+cz/+DIJPhMWIE/rOv7XI2hAZtJQqP1nu18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yUilc8fEUy4uxOP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f9ifI14EzsZlM/v6wXuVDjV28W2MXWy6zcHkDu3i7CbsYqLGL914PoX9vDwDmLT7Ljn3x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\n1RqB+SuDWf+VeFzS9mNxxN5VMfP5zkTGZ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bLt0A1Wbw3XzvvNnvTq7AaA3NQIB1s5XZ/6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\nGW1fmMiVxb8gaDR49O+DpYc7sbv2YuPrjXNAJzwG9CVi9XrOfjwHYwtzO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qOt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2cAX3+9HkdHWwBefdWfF18corMMR9bu4PZ5TVmMmDoWp5aVy+L4xn2EHw2lMDef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\nwWAwGAwGt/N/S3w2JE4ATzkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11663fbd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cov_matrix = np.corrcoef(beta_list)\n",
+    "cov_df = pd.DataFrame(cov_matrix)\n",
+    "plt.figure(figsize = (20,10))\n",
+    "sns.heatmap(cov_df, xticklabels = df.index, yticklabels = df.index,annot=True,cmap=\"YlGnBu\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v448vrefjt73Eaf/5MN97YAlrtjYYJkmSJEmSBpxh0gAaWpjgv99+DL95/8nc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8rx/ApMeh2v3DyKi/qHdehaAyJVCHbdmDgGRbu3ukqInqQ+THJLZZLMTBJCCOEeEiYJIYRoV6Up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\n7f8DDi7hGT7PuDIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1164b3110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "p_df = pd.DataFrame({'XOM':PG.r.beta_df['beta'],'PG':XOM.r.beta_df['beta']})\n",
+    "plt.plot(KO.r.beta_df['beta'])\n",
+    "plt.plot(PG.r.beta_df['beta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   beta   R-squared:                       0.870\n",
+      "Model:                            OLS   Adj. R-squared:                  0.870\n",
+      "Method:                 Least Squares   F-statistic:                     3363.\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          6.92e-225\n",
+      "Time:                        12:43:08   Log-Likelihood:                 704.82\n",
+      "No. Observations:                 505   AIC:                            -1406.\n",
+      "Df Residuals:                     503   BIC:                            -1397.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const         -0.0097      0.004     -2.285      0.023      -0.018      -0.001\n",
+      "beta           0.9690      0.017     57.988      0.000       0.936       1.002\n",
+      "==============================================================================\n",
+      "Omnibus:                       11.655   Durbin-Watson:                   0.068\n",
+      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               11.842\n",
+      "Skew:                           0.369   Prob(JB):                      0.00268\n",
+      "Kurtosis:                       3.133   Cond. No.                         6.50\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "-3.19434626171\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = sm.add_constant(PG.r.beta_df['beta'])\n",
+    "coint = sm.OLS(KO.r.beta_df['beta'],x).fit()\n",
+    "print(coint.summary())\n",
+    "adf = ts.adfuller(coint.resid,autolag = 'BIC')[0]\n",
+    "print(adf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAAAEAnFgEAAADQiUUAAAAAdGIRAAAAAJ1YBAAA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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116b40890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "plt.scatter(df['sd_beta'],df['sd_beta_p'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GWZdSzCwCAABgLtq2CxUWQQYzi8ZtaG17TAEAADjM2rYPFRax0CYfx3Fl0XzWAgAAwOL4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\nvRfl+z5tPf3cvb6Zy86YOOTfofBk3LiaMi5fPDq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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e3db810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (20,10))\n",
+    "GE.r.beta_df['beta_p'].plot()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html
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+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html	
@@ -0,0 +1,96 @@
+In previous chapters, we learnt that the Capital Asset Pricing Model (CAPM) treats the market return as the only factor affecting the return of any asset. This chapter will generalize CAPM to multi-factor models of the following form:
+\[ R = \alpha + \beta_1 f_1 + \beta_2 f_2 + \dots + \beta_n f_n \]
+where each \(f_i\) is a factor.
+
+<h2>Fama-French Three-Factor Model</h2>
+
+This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns.[ref] <strong>Fama, E F; French, K R</strong> (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref]
+
+The 3-factor model is
+\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \]
+
+where
+<ul>
+    <li>MKT is the excess return of the market.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
+    <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
+    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
+</ul>
+
+Data on these factors can be downloaded from <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">French's website</a>.
+
+<h2>Model test</h2>
+
+To test the 3-factor model, we use it to predict returns on NASDAQ US Small Cap Index and NASDAQ US Large Cap Index. We estimate the model with daily returns in the past 6 years.
+
+Results for US Small Cap returns:
+<img class="aligncenter size-full wp-image-2206" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.20.02-PM.png" alt="" width="1342" height="830" />
+
+The coefficient of SMB is positive, so when small caps outperform large caps, the Small Cap Index will have higher returns, which is not surprising. By comparing the t statistics of those factors, we know that MKT and SMB are more important factors driving the Small Cap Index.
+
+Results for US Large Cap returns:
+<img class="aligncenter size-full wp-image-2205" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.18.48-PM.png" alt="" width="1346" height="822" />
+
+As expected, the coefficient of SMB is negative for the Large Cap Index. The coefficient of HML is quite low, which suggests that value and growth stocks take approximately the same weight in the Large Cap Index.
+
+<h2>Factor Returns</h2>
+
+How do we find out the returns on a single factor? We can do so by constructing a <strong>tracking portfolio</strong> and computing its returns. For example, if we want a tracking portfolio of the HML factor, we only need to find 4 stocks to construct a portfolio that has MKT and SMB coefficients of 0 and HML coefficient of 1.
+
+Consider stocks A, B, C, and D whose returns can be explained by the 3-factor model:
+
+\[ R_A = \alpha_1 + \beta_{11} MKT + \beta_{12} SMB + \beta_{13} HML \]
+\[ R_B = \alpha_2 + \beta_{21} MKT + \beta_{22} SMB + \beta_{23} HML \]
+\[ \vdots \]
+
+Let their weights in the tracking portfolio be \(w_A,w_B,w_C,w_D\). We write the coefficients into a matrix:
+\[ \Sigma = \begin{pmatrix}
+   \alpha_1 &amp; \beta_{11} &amp; \beta_{12} &amp; \beta_{13} \\
+   \alpha_2 &amp; \beta_{21} &amp; \beta_{22} &amp; \beta_{23} \\
+   \alpha_3 &amp; \beta_{31} &amp; \beta_{32} &amp; \beta_{33} \\
+   \alpha_4 &amp; \beta_{41} &amp; \beta_{42} &amp; \beta_{43}
+\end{pmatrix} \]
+
+The tracking portfolio is determined by the solution to these linear equations:
+\[ w^T \Sigma = \begin{pmatrix} 0 \\ 0 \\ 0 \\ 1 \end{pmatrix} \]
+
+Economic interpretation: The 3 Fama-French factors represent "systematic risk" which cannot be reduced by diversification. Investors earn those factor returns for taking such risks. Any idiosyncratic risk, or firm-specific risk, can be diversified away and so investors are not paid to take such risks.
+
+<h2>Other Factors</h2>
+
+The Fama-French 5-Factor model comprises two more factors:
+<ul>
+    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
+    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
+</ul>
+
+RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
+
+<blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote>
+
+CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that:
+
+<blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote>
+
+Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+
+<h2>Summary</h2>
+
+In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
+
+<h2>Algorithm</h2>
+
+Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.[ref]<strong>AQR (2013)</strong>. A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>[/ref]
+
+The paper recommends picking stocks by their value, quality (profitability) and momentum.
+
+The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
+
+The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets.
+
+There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
+
+In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015.
+
+You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
+
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=480128b21eebc545b1321e0b8caa26d1'></script>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.ipynb b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.ipynb
new file mode 100755
index 0000000..ab5d1bf
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.ipynb	
@@ -0,0 +1,338 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.formula.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats.mstats import normaltest\n",
+    "import time\n",
+    "from cvxopt import matrix\n",
+    "import seaborn as sns\n",
+    "import statsmodels.tsa.stattools as ts\n",
+    "from scipy import stats\n",
+    "from scipy.optimize import minimize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fama_5 = quandl.get('KFRENCH/FACTORS5_D')\n",
+    "fama_5 = fama_5/100\n",
+    "fama_5 = fama_5.rename(columns = {'Mkt-RF':'mkt'})\n",
+    "spy = quandl.get('LSE/SPY5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spy = np.log(spy['Last Close']).diff().dropna()\n",
+    "spy.name = 'spy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mkt</th>\n",
+       "      <th>SMB</th>\n",
+       "      <th>HML</th>\n",
+       "      <th>RMW</th>\n",
+       "      <th>CMA</th>\n",
+       "      <th>RF</th>\n",
+       "      <th>spy</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2017-06-23</th>\n",
+       "      <td>0.0024</td>\n",
+       "      <td>0.0077</td>\n",
+       "      <td>-0.0050</td>\n",
+       "      <td>-0.0035</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-26</th>\n",
+       "      <td>0.0004</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>0.0069</td>\n",
+       "      <td>0.0025</td>\n",
+       "      <td>0.0017</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>0.000738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-27</th>\n",
+       "      <td>-0.0084</td>\n",
+       "      <td>-0.0025</td>\n",
+       "      <td>0.0130</td>\n",
+       "      <td>0.0031</td>\n",
+       "      <td>0.0032</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000779</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-28</th>\n",
+       "      <td>0.0102</td>\n",
+       "      <td>0.0082</td>\n",
+       "      <td>0.0021</td>\n",
+       "      <td>-0.0018</td>\n",
+       "      <td>-0.0007</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.000205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-29</th>\n",
+       "      <td>-0.0083</td>\n",
+       "      <td>0.0007</td>\n",
+       "      <td>0.0135</td>\n",
+       "      <td>-0.0025</td>\n",
+       "      <td>0.0036</td>\n",
+       "      <td>0.00003</td>\n",
+       "      <td>-0.005262</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               mkt     SMB     HML     RMW     CMA       RF       spy\n",
+       "Date                                                                 \n",
+       "2017-06-23  0.0024  0.0077 -0.0050 -0.0035  0.0005  0.00003 -0.000615\n",
+       "2017-06-26  0.0004  0.0005  0.0069  0.0025  0.0017  0.00003  0.000738\n",
+       "2017-06-27 -0.0084 -0.0025  0.0130  0.0031  0.0032  0.00003 -0.000779\n",
+       "2017-06-28  0.0102  0.0082  0.0021 -0.0018 -0.0007  0.00003 -0.000205\n",
+       "2017-06-29 -0.0083  0.0007  0.0135 -0.0025  0.0036  0.00003 -0.005262"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([fama_5,spy],axis = 1).dropna()\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                    spy   R-squared:                       0.340\n",
+      "Model:                            OLS   Adj. R-squared:                  0.339\n",
+      "Method:                 Least Squares   F-statistic:                     210.6\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):          3.73e-110\n",
+      "Time:                        16:03:03   Log-Likelihood:                 4457.8\n",
+      "No. Observations:                1229   AIC:                            -8908.\n",
+      "Df Residuals:                    1225   BIC:                            -8887.\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept   9.267e-05      0.000      0.503      0.615      -0.000       0.000\n",
+      "mkt            0.5700      0.023     24.636      0.000       0.525       0.615\n",
+      "SMB           -0.0753      0.039     -1.942      0.052      -0.151       0.001\n",
+      "HML            0.0658      0.039      1.670      0.095      -0.011       0.143\n",
+      "==============================================================================\n",
+      "Omnibus:                      142.776   Durbin-Watson:                   2.657\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              609.967\n",
+      "Skew:                          -0.472   Prob(JB):                    3.53e-133\n",
+      "Kurtosis:                       6.319   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = sm.ols(formula = 'spy ~ mkt+SMB+HML',data = df).fit()\n",
+    "print model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  small   R-squared:                       0.995\n",
+      "Model:                            OLS   Adj. R-squared:                  0.995\n",
+      "Method:                 Least Squares   F-statistic:                 9.912e+04\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
+      "Time:                        17:13:18   Log-Likelihood:                 8506.2\n",
+      "No. Observations:                1517   AIC:                        -1.700e+04\n",
+      "Df Residuals:                    1513   BIC:                        -1.698e+04\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept     -0.0001   2.29e-05     -6.254      0.000      -0.000   -9.82e-05\n",
+      "mkt            1.0407      0.003    403.477      0.000       1.036       1.046\n",
+      "SMB            0.8707      0.005    181.700      0.000       0.861       0.880\n",
+      "HML            0.1050      0.005     21.744      0.000       0.095       0.114\n",
+      "==============================================================================\n",
+      "Omnibus:                       84.277   Durbin-Watson:                   1.984\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              260.380\n",
+      "Skew:                          -0.213   Prob(JB):                     2.88e-57\n",
+      "Kurtosis:                       4.985   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "small_table = quandl.get('NASDAQOMX/NQUSS')\n",
+    "small = np.log(small_table['Index Value']).diff().dropna()\n",
+    "small.name = 'small'\n",
+    "small_df = pd.concat([fama_5,small],axis = 1).dropna()\n",
+    "small_model = sm.ols(formula = 'small~mkt+SMB+HML',data = small_df).fit()\n",
+    "print small_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  large   R-squared:                       0.998\n",
+      "Model:                            OLS   Adj. R-squared:                  0.998\n",
+      "Method:                 Least Squares   F-statistic:                 2.425e+05\n",
+      "Date:                Tue, 01 Aug 2017   Prob (F-statistic):               0.00\n",
+      "Time:                        17:12:41   Log-Likelihood:                 9634.6\n",
+      "No. Observations:                1517   AIC:                        -1.926e+04\n",
+      "Df Residuals:                    1513   BIC:                        -1.924e+04\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept     -0.0001   1.09e-05    -11.144      0.000      -0.000   -9.98e-05\n",
+      "mkt            0.9891      0.001    806.811      0.000       0.987       0.992\n",
+      "SMB           -0.1468      0.002    -64.463      0.000      -0.151      -0.142\n",
+      "HML           -0.0076      0.002     -3.305      0.001      -0.012      -0.003\n",
+      "==============================================================================\n",
+      "Omnibus:                       97.595   Durbin-Watson:                   1.959\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              365.063\n",
+      "Skew:                          -0.190   Prob(JB):                     5.34e-80\n",
+      "Kurtosis:                       5.373   Cond. No.                         221.\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "large_table = quandl.get('NASDAQOMX/NQUSL')\n",
+    "large = np.log(large_table['Index Value']).diff().dropna()\n",
+    "large.name = 'large'\n",
+    "large_df = pd.concat([fama_5,large],axis = 1).dropna()\n",
+    "large_model = sm.ols(formula = 'large~mkt+SMB+HML',data = large_df).fit()\n",
+    "print large_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html b/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html
new file mode 100755
index 0000000..c946c4a
--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html	
@@ -0,0 +1,128 @@
+<h2>Introduction</h2>
+In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options markets are organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
+<h2>1. Definition</h2>
+<div class="page" title="Page 1">
+<div class="layoutArea">
+<div class="column">
+
+An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
+
+Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
+
+</div>
+</div>
+</div>
+First, we give the building blocks of an option contract and will discuss them further.
+<table class="table qc-table">
+<tbody>
+<tr>
+<td style="text-align: left;"><strong>Style</strong></td>
+<td style="text-align: left;"><strong>American Option:</strong> the holder of an option has the right to exercise his option at any time before the expiration date
+<strong>European Option:</strong> an option which can only be exercised on its expiration date</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Type</b></td>
+<td style="text-align: left;">Call option, Put option</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Underlying Asset</strong></td>
+<td>The security on which the option is bound. The underlying could be stocks(stock option), stock indices(index option), exchange rate(Foreign exchange option) or even futures(Futures Options).
+Note: The options in the tutorial refer to the stock options.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Premium</strong></td>
+<td style="text-align: left;">The price of the option. (Premiums are quoted on a per share basis).
+The option premium depends on the strike price, volatility of the underlying, as well as the time remaining to expiration.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Strike Price</strong></td>
+<td>The specified price at which the stock can be bought or sold when the option is exercised.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Expiration Date</strong></td>
+<td>The last day that an options contract is valid. All options expire after a certain period of time. The right to exercise the option will no longer exist once the stock option expires.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Participants</b></td>
+<td style="text-align: left;"><strong>Holder:</strong> People who buy the options(have long positions)
+<strong>Writer:</strong> People who sell the options(have short positions)</td>
+</tr>
+</tbody>
+</table>
+<h2>2. Options Contracts</h2>
+Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
+
+Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
+
+In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
+<h2 class="p1">3. The Value of Options</h2>
+The option's premium consists of two parts: the intrinsic value and the time value.
+
+\[Intrinsic Value_{call} = max(Current Underlying Price-Strike Price,0)\]
+\[Intrinsic Value_{put} = max(Strike Price-Current Underlying Price,0)\]
+
+From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
+
+\[Time Value= Premium-Intrinsic Value\]
+
+For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contact is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
+<div class="page" title="Page 243">
+<div class="layoutArea">
+<div class="column">
+<h2>4. Option Moneyness</h2>
+Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
+
+</div>
+<div class="page" title="Page 54">
+<div class="layoutArea">
+<div class="column">
+<table class="table qc-table">
+<tbody>
+<tr>
+<td style="text-align: left;"><b>At the Money(ATM)</b></td>
+<td style="text-align: left;">The strike price is the same as the current price of the underlying asset</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>In the Money(ITM)</b></td>
+<td style="text-align: left;"><strong>Call Option</strong>: the strike price is below the current trading price of the underlying
+<strong>Put option</strong>:  the strike price is above the spot price of the underlying</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Out of the Money(OTM)</b></td>
+<td style="text-align: left;"><strong>Call Option: </strong>the strike price is above the current trading price of the underlying
+<strong>Put Option:</strong> the strike price is below the current trading price of the underlying</td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="p1"> For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.</p>
+<p class="p1">But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.</p>
+Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+<h2>5. Option Exercise and Assignment</h2>
+When you are the buyer of an option you have three ways to deal with your options.
+<ol>
+ 	<li>You can close out the position at any given point prior to expiration (For buyers, write options and for sellers, buy options);</li>
+ 	<li>Wait until expiration date and out-of-the-money options will become worthless;</li>
+ 	<li>Exercise the options which are in-the-money, resulting in a trade of the underlying stock (The seller will be assigned the obligation to sell or buy the underlying stocks)</li>
+</ol>
+For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
+<ol>
+ 	<li><strong>Exercise:</strong> Exercising option means that the option holder executes the right to buy or sell the underlying assets at the strike price.</li>
+ 	<li><strong> Assignment: </strong>When an option is exercised by the option holder, the option writer will be assigned the obligation to deliver the terms of the options contract. This is called the option assignment.</li>
+</ol>
+<div class="page" title="Page 10">
+<div class="layoutArea">
+<div class="column">
+
+To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
+
+</div>
+</div>
+<h2 class="p1">Summary</h2>
+<p class="p1"><span class="s1">An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.</span></p>
+
+</div>
+</div>
+</div>
+</div>
+</div>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html
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+<h2>Introduction</h2>
+In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
+<h3>Option Payoff</h3>
+Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
+
+From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
+
+\[Call_{payoff}=max[0,S_T-K]\]
+<div class="page" title="Page 55">
+<div class="layoutArea">
+<div class="column">
+
+On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
+
+\[Put_{payoff}=max[0,K-S_T]\]
+
+</div>
+</div>
+</div>
+Where \(S_T\) is the price of underlying assets at maturity. K is the strike price.
+
+We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Contract Name</th>
+<th>Type</th>
+<th>Expire Date</th>
+<th>Strike</th>
+<th> Premium</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> GOOG170714C00940000</td>
+<td> Call</td>
+<td> 07/14/2017</td>
+<td>$ 940</td>
+<td> $7.5</td>
+</tr>
+<tr>
+<td>GOOG170714P00960000</td>
+<td> Put</td>
+<td> 07/14/2017</td>
+<td> $960</td>
+<td> $19.5</td>
+</tr>
+</tbody>
+</table>
+If you long these two options, the payoff at expire date would be as follows
+<pre class="prettyprint linenums">import matplotlib.pyplot as plt
+%pylab inline
+price = np.arange(900,1000,1)
+strike = 940
+premium = 7.5
+payoff = [max(-premium, i - strike-premium) for i in price]
+plt.plot(price, payoff)
+plt.xlabel('Price at T S_T ($)')
+plt.ylabel('payoff')
+plt.title('Call option Payoff at Expiry')
+plt.grid(True)
+price = np.arange(900,1000,1)
+strike = 960
+premium = 19.5
+payoff = [max(-premium, strike - i -premium) for i in price]
+plt.plot(price, payoff)
+plt.xlabel('Price at T S_T ($)')
+plt.ylabel('payoff')
+plt.title('Put option Payoff at Expiry')
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-call-payoff.png" alt="call options payoff" />     <img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-put-payoff.png" alt="put options payoff" />
+
+The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay premuim for purchasing the contracts.
+<h3>2. Put-Call Parity</h3>
+Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next we will demonstrate how to derive the put-call parity according to John Hull's book.
+
+We consider two portfolios as follows,
+
+Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
+
+Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
+<table class="table qc-table">
+<tbody>
+<tr>
+<td> Payoff</td>
+<td> \(S_t &gt; K\)</td>
+<td>\(S_t &lt; K\)</td>
+</tr>
+<tr>
+<td width="33%">Portfolio A</td>
+<td>\(S_t - K+K=S_t\)</td>
+<td>\(0+K=K\)</td>
+</tr>
+<tr>
+<td>Portfolio B</td>
+<td>\(0+S_t =S_t\)</td>
+<td>\(K-S_t+S_t=K\)</td>
+</tr>
+</tbody>
+</table>
+From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolio must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
+
+\[Price_{call}+Ke^{-rT}=Price_{put}+S_0\]
+
+If the dividend is paid during the option holding period, the share holder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put–call parity relationship is(D is the present value of dividends):
+
+\[Price_{call}+D+Ke^{-rT}=Price_{put}+S_0\]
+<h3>3. Synthetic Positions</h3>
+<div class="page" title="Page 57">
+<div class="layoutArea">
+<div class="column">
+
+Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in an underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
+
+[ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017 <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
+
+There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
+<table class="table qc-table">
+<tbody>
+<tr>
+<td><strong>Strategy</strong></td>
+<td><strong>Content</strong></td>
+</tr>
+<tr>
+<td width="33%">Conversion</td>
+<td>Synthetic Short Position: short call + long put
+The actual stock position: long the underlying stocks</td>
+</tr>
+<tr>
+<td>Reversal</td>
+<td>Synthetic Long Position: long call + short put
+The actual stock position: short the underlying stocks</td>
+</tr>
+</tbody>
+</table>
+Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
+<h3>Algorithm</h3>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
+<h2>Summary</h2>
+<div class="page" title="Page 264">
+<div class="layoutArea">
+<div class="column">
+
+In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
+
+Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
+
+</div>
+</div>
+</div>
+&nbsp;
+
+&nbsp;
+
+</div>
+</div>
+</div>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.ipynb b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.ipynb
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+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.ipynb	
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%pylab inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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d8q6oOOYM1Zd31NcoSrPWzNoChH/XRRyPSErZtbeIH02YwaIvt/HIFVnlFgmR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\npUIhIiLl+v+FudKxQidIjgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106d33c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "price = np.arange(900,1000,1)\n",
+    "strike = 940\n",
+    "premium = 7.5\n",
+    "payoff = [max(-premium, i - strike-premium) for i in price] \n",
+    "plt.plot(price, payoff)\n",
+    "plt.xlabel('Price at T S_T ($)')\n",
+    "plt.ylabel('payoff')\n",
+    "plt.title('Call option Payoff at Expiry')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Lsft2jrosEclwOuspzbTObcUdp/WndNft+fX9b1L+3vKoSxKRDKdGkYYKW+cwfuQAenUq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\nAriyzQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106d06690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "price = np.arange(900,1000,1)\n",
+    "strike = 960\n",
+    "premium = 19.5\n",
+    "payoff = [max(-premium, strike - i -premium) for i in price] \n",
+    "plt.plot(price, payoff)\n",
+    "plt.xlabel('Price at T S_T ($)')\n",
+    "plt.ylabel('payoff')\n",
+    "plt.title('Put option Payoff at Expiry')\n",
+    "plt.grid(True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html
new file mode 100755
index 0000000..3ebeb9c
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html	
@@ -0,0 +1,180 @@
+<h2>Introduction</h2>
+Last few chapters we introduced about the basic principles and mechanism of options trading. We already knew what an option contract is and the basic relationship between call and put options' price. But how do these contracts traded in the exchange are being priced and where does the option premium come from? In the next few chapters, we will discuss the pricing of options.
+<h2>Stochastic Process</h2>
+In order to value the derivatives like options, the most significant part is to find a model to represent the underlying stock price so that we can price the options based on the underlying price.  We usually use the stochastic process to model the security price.
+
+First, you need to know what the stochastic process is. We say any variable that changes over time in an uncertain way follows a stochastic process. The price of a certain stock at a future time t is unknown at the present so it is a random variable \(S_t\). Then we can think of the movement path of the stock price is a stochastic process since\(S_t\) is a random variable at each time t in the future.
+<h3>1. Brownian motion</h3>
+Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
+<div class="page" title="Page 327">
+<div class="layoutArea">
+<div class="column">
+
+Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables {\(W_t\mid t\geq0} \)} indexed by time t. The one-dimensional Brownian motion is called Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
+<ol>
+ 	<li>\({W_0=0\)</li>
+ 	<li>For \(t\geq0\) and \(\Delta t\geq0\), the increment \(W_{t+\Delta t}-W_t\) is normally distributed with mean 0 and standard deviation \(\sqrt{\Delta t}\) .</li>
+ 	<li>For any partitions \(0\leq t_1&lt;t_2&lt;\cdot\cdot\cdot &lt;t_n\), the increments \(W_{t_1}- W_{t_0},W_{t_2}-W_{t_1},\cdot\cdot\cdot, W_{t_n}-W_{t_{n-1}}\) are independent random variables.</li>
+ 	<li>With probability 1, the function W(t) is continuous at t.</li>
+</ol>
+Intuitively understanding of the definition, Wiener process has independent and normally distributed increments and has continuous sample path.
+
+Next, we simulate the Wiener process and plot the paths attempting to gain an intuitive understanding of a stochastic process. Each path is an independent Wiener process.
+<pre class="prettyprint linenums">import numpy as np
+import matplotlib.pyplot as plt
+%pylab inline
+def wiener_process(T, N):
+    """
+    T: total time
+    N: The total number of steps
+    """
+    W0 = 0
+    dt = T/float(N)
+    # simulate the increments by normal random variable generator
+    increments = np.random.normal(0, 1*np.sqrt(dt), steps)
+    W = [0] + list(np.cumsum(increments))
+    return W
+t = np.linspace(0.0, N*dt, N+1)
+plt.figure(figsize=(15,10))
+for i in range(5):
+    W = wiener_process(10, 1000)
+    plt.plot(t, W)
+    plt.xlabel('time')
+    plt.ylabel('W')
+    plt.grid(True)
+</pre>
+</div>
+</div>
+</div>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-wiener-process.png" alt="wiener process"/>
+<div class="page" title="Page 328">
+<div class="layoutArea">
+<div class="column">
+
+[ref]Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.[/ref]In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
+
+\[\Delta x=x_{t+\Delta t}-x_t=a\Delta t+b\epsilon\sqrt{\Delta t}\]
+
+Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
+<h3>2. Stochastic process followed by stock price</h3>
+Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
+
+On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
+
+In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
+
+</div>
+</div>
+</div>
+<div class="page" title="Page 84">
+<div class="layoutArea">
+<div class="column">
+
+\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
+
+Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
+<div class="page" title="Page 338">
+<div class="layoutArea">
+<div class="column">
+
+Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
+
+</div>
+</div>
+</div>
+The discrete approximation form of (1) is
+
+\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
+
+We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
+
+\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
+
+Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln S during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
+
+\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+
+\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+
+Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
+
+\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
+
+Equivalently
+
+\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
+
+If we change t to 0 and change \(\Delta t\) to T, we get
+
+\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
+
+according to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
+<div class="page" title="Page 333">
+<div class="layoutArea">
+<div class="column">
+<h2>Monte Carlo Method</h2>
+Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
+<h3>1. Monte Carlo Simulation of Stock Price</h3>
+We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
+<pre class="prettyprint linenums">import quandl
+quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
+data = quandl.get('WIKI/GOOG')
+close = data['2015-01':'2017-07']['Adj. Close']
+annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
+annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
+mu = annual_return # 0.39644
+sigma = annual_vol # 0.2344
+s0 = close[-1] # 903.5
+T = 3.0/12
+delta_t = 0.001
+num_reps = 500
+steps = T/delta_t
+plt.figure(figsize=(15,10))
+for j in range(num_reps):
+    price_path = [s0]
+    st = s0
+    for i in range(int(steps)):
+        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
+        price_path.append(st)
+    plt.plot(price_path)
+plt.ylabel('stock price',fontsize=15)
+plt.xlabel('steps',fontsize=15)</pre>
+</div>
+</div>
+</div>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
+<h3>1. Monte Carlo Simulation of European Options</h3>
+Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
+
+The essence of using Monte Carlo method to price the options is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
+<ol>
+ 	<li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
+ 	<li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
+ 	<li>Calculate the payoff of options according to the \(S_T\)</li>
+ 	<li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
+ 	<li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
+</ol>
+There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
+<pre class="prettyprint linenums">def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
+    payoff_sum = 0
+    for j in range(num_reps):
+        st = s0
+        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
+        if option_type == 'c':
+            payoff = max(0,st-strike)
+        elif option_type == 'p':
+            payoff = max(0,strike-st)
+        payoff_sum += payoff
+    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
+ return premium
+mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
+</pre>
+Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
+<h2>Summary</h2>
+In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
+
+In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
+
+</div>
+</div>
+</div>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.ipynb b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.ipynb
new file mode 100755
index 0000000..ac941c0
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.ipynb	
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%pylab inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def wiener_process(T, N):\n",
+    "    \"\"\"\n",
+    "    T: total time\n",
+    "    N: The total number of steps\n",
+    "    \"\"\"\n",
+    "    W0 = 0\n",
+    "    dt = T/float(N)\n",
+    "    # simulate the increments by normal random variable generator\n",
+    "    increments = np.random.normal(0, 1*np.sqrt(dt), steps)\n",
+    "    W = [0] + list(np.cumsum(increments))\n",
+    "    return W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UcH24dtztd4ecV2YtA2CMZQxTk6ZS016D1+/tcr1gpWJi4sQuz30r41skRCRoP/9qzq/4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pDGYwn2XjfPr6vhYEtgIKS4bFZ32OiwOcnwVc3DiZ69182mGqnKvPTtBWh9fTn/78Ip/OCcoQXH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G3OHErn3wGAf8POR4SGqrq776biu98jUFlJsG4b0L3q6L7GMWIEobo6Gv/xOLW33U7bv/4F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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a205c90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t = np.linspace(0.0, N*dt, N+1)\n",
+    "plt.figure(figsize=(15,10))\n",
+    "for i in range(5):\n",
+    "    W = wiener_process(10, 1000)\n",
+    "    plt.plot(t, W)\n",
+    "    plt.xlabel('time')\n",
+    "    plt.ylabel('W')\n",
+    "    plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import quandl\n",
+    "quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'\n",
+    "data = quandl.get('WIKI/GOOG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "930.5"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "close = data['2015-01':'2017-07']['Adj. Close']\n",
+    "annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1\n",
+    "annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)\n",
+    "s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x119954cd0>"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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zadQoHhuQ12XFVNHrO0IitP89yH7kYUz9+1Pzm98Qbt13hfl4JkFRCCGEEEIc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7AAAg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2zKMsyEXVKTm2TgejQhvI1iWgVPkxNvtWRIUWBBnM/4GAKx7E3OvAoHeRqjoAHidC5BAmiGos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hUIgoB15YWgKHg//AhrUjKm9AwAZly+ilFdBf3SVTy1z8XYYICZK/PxBKMM4qQ5Ywcqk8Kc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1HIemj0hMwlc0EgVM0TRz3H8FDYOPYAib8Ew7g88okCKoOFgSQS1GaJJzyBLb8Gg7KXaN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\nuVztpbbzcdriDZynkXDBMQ747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guSlERM4jEcyx6ndXouze+cQgESGTbCJgtLwgd4tOdJ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\nJwK8VfM0qZp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g0aXoqrPxdWEVbZkwTelSNmoq58QcBlGmsTiDvSjJdLuJy8vn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efZt30T37r9MQKVlZjOz2KaDuJAxz0YeFaV+fFikrTbhD8eZcu5k6w/d5CU3sa6kSOsnTuB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s5OSSnVoid9XLTEUp6aJ8BvNPs37QTOXyCl11Vbxr28DO8adJCnNMaALsLjqFfTXFBbLc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PPO5mcIrC6SW/URWW5kzXICeyZJ083L1M6NcbvmPJdU/exVXeA3tu/T65oiICzhyS0RlAQq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\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RUt5GNiss3HvnHZy5FPayyJ2wGX94Ek1cIO5uZDqqp9E+izUiYo+7MRTsdNz9GaaWKKf1\ng4SanJQbLFzzbOOuqRG1GMWdvcqHdae4ZBqgKhTHq0txpXcbps0EqkFCJ2XRSFpaW69QbVpAnhR5\n8uXj/Ojwwwy193Os/ZdYnAnW5Rpedu6nJz6F6qzAFpxHZ3XSY3yT4Wg1EacHjZKhaXaY8y0HGS43\nY7uxQm4zzcaMnaDpAJmUg99s24dWjdAaHGHF2Y2kKDw0dZUzvYdoCAeoTMaZqW3GuWAgbbmAFEoh\nz8tQVOg0hvhO2oRbvIWZZvKGU1QpId6ouMPNylZ65tLM9tfy0s4WMko1ca+bpvlpKoJB/niPjqZy\ngQ+oQY5IE0wIbVwQdnNQuMYu5SRLb+uI3TUwE2rgwG89zz/Xf4r3xGN0TtjB+SDDmlViaoF7rq9S\nNTXEXO0AG7o0c63dLLu8SFN5Dio3aZfPoBNKPFd6mLygoSu+yBlLLzUdlTyqUZmXZMIqVPt/zhcW\nHyOp1PKCv55jq5vML6VpKAQZ6epnNL+Tu5ZW9s4O8/S7b7FrbZbre1o4ycOsSh4SZjN/9fPv83Ti\nKf5tcC977hZ4fDhPXWScopThbpWNuLWCDXxcVA/S6rmNeytKtEbCH8+R0Zv5Rtvn0eYnqUtc5i90\n38fHBuUtD7MlL/e57rDXe473xQepSbWxf+4cakbi/MHDrDgraRLKfG65mrw8TLEzxFxukPuES+ST\nJVKTZiJdIuvbtCxuNvEr4R4+P/Em+/RzJC0awjesmCYFkjotqU/n8Y4ncW1labk7S2UwgvaTS+ic\nGSwk2SeeQ1W11KZMXFXh//7IX3J6x0Hu6XsTjVREq8mjFsH+/2hJfbCI6ZKIJbWKZ2qKTV8jlo4x\n5uwmPNOt3Gj9f9m3rze7DgLB9mvvfXLOdarOqXQq51KpFKwsS7JwxDYGN20TB5rQYXpmmpn+Gm7T\n0/fyQXczDJ3h0pjUYMAG2zhLsixZOZRKpco5h1N1cg57n31f5qXvxzs8sP6L38Oy8/lfPIffFiWz\nqMcaKKK3yJwzG0jpwnzkTAxjwsQr9m/QNR/n+KWfcb27nwvNB7i8ay9vHThGSmfj/fYL/G3/M5wf\nOEz9xjpf/N4/cNXYwYi3hc80/ZDDg/e4lNNgOCZTU6ewOh7ijD7DT/dl0GpX+cPidYZtYMg10Bp3\nEChGuBzYjb20QqLSgd89RtYq0jOfRFstM5PZx+pqDzpRxCtEeSO6i2mjjajJSE6TxUgVq7X1bEk+\nhsVepmnBWCpxrmMPy24fX5z/Jnv6uvF42n7D2viP/Q6Kv6bfVig+fyPMlMfNZ9fPId/Rc3PvQWbd\nfnozTuo2NBy2/A0ZjUTIuMHcTjMbhh1UUeBx5wS5GSPajEqs2kAlnmc05kZFR7mhhaK3jmypirKq\nktansJXtqKV6RP0ac6UZZBPsL7Yz7/olpi2Rbb2fvxZMaEopnklbaNBcw67r4AdNryFrJbwbcdZK\nSwQTB8naRGZqHqd2bgytUqbG1sd6Mc9c0kwpt4Kiu8q91BCH3P+ClixXCgcYyg9Sl1vgxkwjrbYI\nIbuXnfKf87JkBkEgTA3hJifGQpE/jz/HEes7HOY6DbZZPKkENckcF+paUMsl7K9D5ecuLpVbaJyJ\n0LuYxOwuobfJzEbc1KhznFDmMEVEAkoYJV/moDjM91ofI7Oxw2SlEZ+o8inrDqeWz2LedRuTOcv8\nq3WosgQqtMlrDJu6iahm/sH8bXbyJrRalf+p/0NkRaAgGRC1Kh/zvcaU0EosXo+34RY5fRSzd4U7\n0knSRisR1cJ4xc1BDKSqrxHp/R5GKcOt9UN4R8NYHQViJoGt8p9iUB1sVfJE9CXc5hj3WnuxFyI8\nNTeJvvIwxkoVp/R5arecyBoNGVFiwb4Km2VqxSI/cWrZl5c5canMpkMk69hPY2yHaH0n7s4lqiQ9\n3qt/gS98hLQ2jnX+YZbnL2PfuUOkWCGwNMFtSxcN83kyfXM4DQUC/gUkJLwz91M25dH5pljIaBkq\np3jPvc24usEd/Q6W4ComsozX9CKIJupzO1RkI+WMj4pnknLDMPrwRwguiNhi1VS2lukVLxO8t41z\nehu216loXcxLrzBjsKLTZSmXjewTXiK0fpYqW4Tp0gMUqGe91ItbiJCTBTrlF5ndbubmrn3MNzaT\nNLtZlhrQSCLhgp+GeC+ydgNFytHaMEw0Wcf9obNMLHXyJscwmzIMLM8hlSTu1XdSNBjIWizUR+cw\nz1fIHzOQHYgRntuD3rlF2WimEvUSSEaxFItks270hjStLdfQiQX0FCmYUvjDXXSW/agzt6he2eFm\ndwpZUDC1HGB3wzDmG+B5PU9wZIPYAQubkRYa1HH2CPfIKXZUxUyp/Bju8k18WxW0r1aRzv03zJY5\n2naGeUce4OfJJ2myjjFhAsk2hEXVsvpWLWWjixXbNrerZjlff5txFSJlCb9OZrMsMiOUKSgl9is1\nGBP1CI4U7s1+/iz9Cz4XGaG1lGJK6San1qCpaJjWb/NozTgNlRxXk7s4L+/ictNevnb3J+gfW2Vi\naQ/a0jqq2kim0ssjajXLxkUUKniDU1wPHUNxm0h4/GScLoSiwuJ4gntmhWMU8Gu/gqT0IYpZlvVe\n1tU4g3EXwUiB9VALRzIJ8qMpXJtbfHHPH7CpcWFxqaQDDl4+dppNq4eOrSXCazrWtW4e19+kRbPG\nO95DVEWTXGvooXNziWByh5qlTc73H2Ra8fP3oZOcr+tHatTzVN0lfIEOZn37mXElUVUtvyh3MSx7\nmVZ8jMnVLFY8uMjyZNOrlBMmdKKWrYodOaPjacWAWTuBPt5BTlvmTdzoNEWqBJnwdojtBQ/jrXux\n5ou0VBboi8/xw+on6PPcptJeQhnNsWdohNGWDi47+smWHehkDda1GdZ21VMrRWjZP4uvL4q9PkOx\nycVF/VH+/MffprS3zBVvDy+tPkr3wBwaQ5LUho1vFP8YOa3hfca7vBPr4InqUbKSEUcoTHVXFLsj\nzfZdF+5gnpv+CtXntpByOq70RDmYkrBGL5KOVWjajmPtz6PZk+P57WeZd7Zz9NpbtIXXUVtkVFnA\nZttGFBTaFu7QdXOL8ZZ+tgI1dI2N0Tc2QcJkYKSuirFgNTPWELZSkobAUSzoiW9HqSpI5JIr6IxO\nMjYrgsVFPrvF0x0/pbdqgtWlHqbmDxNPBhDFMoJGJm92kzRqGRidoHVpBmsuz41H61itWHCXcjQ9\nuMrCzMeJyE281Z1ltN1L23oZfaSarzc/wupagIHae6wnHuL8PjOPjVzEmweHCmQzNGzcJayr4rnG\np0nnZBxaGSFvo74cw55qRVcMIxUnuHeqD8N0lHXBS9f2DA/Gvo5m/jV+7+0ZHqsbos0+yf28wxm5\nm1C0gEnJsjqlJ6DcpF9OYTakEFMlxKoiHaYxPHMlUg6BG00H6LCN4qwN42tZ4aHcBVITJfIWJwmf\nF7Fyj6ApR1E9xKZdgzm2TippRpb0DLcN0Lt+h5pCnOW9NRQitbzdvZ+00YpajpLTGyhLDhRJw6qj\nChWBR6++jlYSmaxu4NNJgemkQLYUpEZM8sfaN/FWRGY1Yb5ebmRQ0WLLh7AavsM/O8wEi4eJHDIT\n78nzZkFgTZcnKHjY8PjYaOsjtFxHfiDDNYPCS2I1M/lHcBHnsHqV2282YS0UePfYMV564DTzvlpa\nwytM1VTxy54ORqU3ELMljg9NUT87wUxrC5e629EYzIyE2ilodQwuTfJT+RBPSxfJqha+qTxOBYlp\nXQ1fEHX8XmWWEG9yAD2PqvWcTPVxvlnha22DdKdn+du7WSYjy6xnHXz22r9zufcQOZ2ZL3z32xgb\n0wgHthj0XuF64ggbPj8n713HgszFgWMsWDTcfyeJsSiR02/QkFpFDo9zYt5HwpZl01TFJY7jblvF\nNCFQVh3slad4VStTyf8MjXSYNu0WAdMyW2vt7FKS2NMeWhvfZjPVxa3qBt7oO0HeZ0WnqAy3+/hK\n/ktUpy8TSC3RHI0RZIsg2yjdTxEbX0HMayjudvOD/DF2ch6+XvNtTMYyqxoT17XV7Gi0aBoUxNM5\nCjfN2DYrKDoJQVVRVq1cMvw+a7Faig6FBu0cuaobTDa0UgxbeH/q5zS75thJVyFOqUgvt+Bdi1IK\nqVRMoF0TKFn12Icy3DrewYDrHZyNb7LvJ6NIBaggkFwwMWuqpaVhi/sXysyly5weVrl930kuuZo5\ncO8n1KgV9j70Ag+JL9OTmsSwU+bN3lOkixp+WT5EtsNDIqHjPX0nC44gX+aHHCldJpVwUaoInKgp\nIokCN3UZztlF3r8aoH/cyD/2wvsbwlzWr1AsmgnG9cStJq44vXxx9dtsNRhwFPJMxfoYKu5DUAWs\nlRSLQiNDlW4yognF4WHGPkMFmUA6SM/wNTzxHaJOFwsNft7suA8fEWpY5YMrLyBbD1ET6vkNa+M/\n9jso/pp+G6GoqirfDM+g0eQ4fknHsuVhvNse1l06Jr0eHl8a5+JajAUhhK2c5IrqpSQJ7HevYrKX\nWMq6CGzkMHdnWE+4kASRSn0zZY0NS3aR3oMXIS+SK7iY0ijUFi6Siowy4WnDoI3TWvFjwcpPhBC9\nmRXum36J4/YMPm0SjbDOj8sKrwbm8W/uw5OqQltaYLO8Sc26SHBtFh1aYns+wi+6glRRxtuwSnlL\nIpkzYFRlbKJASI7Rm1+gTZ3l9dU2JKHCwUCYG3yWc9o1MoKBp3mNBZOPy40D7Nqc5/ORf+e9WD15\nl0ooncGdK/Bj+SSWK0VqXsoRq7bzJx1f4LG+s5geTdBUyBCrGBg5aGFpsYNkEjRWLTnVQqe0zAO6\nOwTFMH2FG1w3C6zmd/Gxvu9gbH+BzK5V9PY8S2eCFOIGJEFDr3OQrdQykmWTCbEft26bUd0T/MRw\nnLwo8oH1V3BmypwL7qVzcAinfYe1cBsaBN3BzpwAACAASURBVKy+aUbvPUBacZNTJOyaMlbmqatZ\nw7lxBJ16m9mlTlbttaiZOYTLZuw2I6LpGQQE7Oks85pt1hxByqKOmE7gwFiJcK1EUmNlJbdJzYWf\nMmRapeyqxyHbuNwUwXdZ4XazyMPVAkOBB1jz2VF0GUSNieZkkn5TC/7pj2FUTUymzMQjDaxLW3Rf\nfY182sGVwQF2Tc8jFcPko3ns/k6UxiFUfZbq613ciEZJ4sJavUDVeITtfB1SRSJiNGEtZWgODlER\nRBrEFZo0k0iTZuwzEbSOHeZ2+nG6N3DpVWJTE+wd/jHN65NYZhSEnEjFXEEu54muLXG1pQVXJkrz\nxiobtgDGMYHASJjkgpmwkCJlc3JS+i732X9EakqHb36eKwcPYchnGIiNsWjysSp6Wc3VsDddh6KN\noy82UDauEksG+JX/JL3ZGR4bvcBqJsCvgkcpSDoiPY+SdhRoW94k4rQiZfVIfTHiJ2wIGoV/GP19\n6pyrVNm2mU32cHjweYLBSfzbG7Qu6Gnb/jJqfpRyVYSviP+TukiGlngT8x0XCUeLjDcmUUR42BXG\nXSrg+ScNls7diPIK7YZq8oY6Tgi/Qk8ZoVxgQ3IglZ9BUd3YhFvYfTE0gpWabTCIC/xf4ifxNK3j\n0ybZJSd5Q6fnRlngXl2Saec8a5YNREFhUGPkA540D0g6DjmzHDbDCanIkysJNMl2MtjpCrzLs9N3\n2M8k1w169heKVDCwSpDj5RhhRcdd0vxT/DabSgPDSj2yKNF35B3WlwepFG3cd+MauuwmBVM3JkGi\n+tjf4vEu8l3NJ0no3PyPF7/FM/m36S7MIK3n2MlaSJRzvKI6qRE3OKL7B67LES6Z6jEqcFwYpNbY\niKn1RxgMb1H7q21eb9nP3dYQ33F9hZ1cLTsWF3tu3uHv/uWrXDJ2sij4eCJ8DYdNIqSb45zvCFt2\nF0WtnqbtNYS4ypGL1/nVoRNs2j08ELnCSd0sOknLuNDONh6mNGEMQp5lk4sPZG0cRYsSv0bZbqTN\nsc19/it8f/6DzCtVNIkxfGKWUcXL68YyOiWHPZvl3arbCN4hPtF+B5drhZXtEEW9GVNkC/fOHA8Z\nrqK7VKYyZcAxWUAdzOELrWDZKPHSwGk269oJxTMosW3aNEu0hSYJtc8i6SqoFQGDo4zFHGP3yDh1\nt2NcP9zE/7v+fqo0cR7c+yrStEQ8amWnvQ0qek4rV3EUE7yj9CHFsrgOxhDTXuSSCWdzBLkoYpQh\nN2mjqFNYro7y8Z8lGO914hdTNDelWdhrI6Fx8Jz9s3St3OUDdS8QO+fGY8ogvOXEeaVEcGyTeK6B\na4cOEPd4WFQclP02Jjo7iFfX8C+dH+S6doBKzwCB9DqP6o/RaGwnYt4iHF1AEDU8YnsKq2BhRRuj\n6HQT3wmwPd/BTrkWh2mVwsYOltVlpJ0YFZ2epD+IJpPGm0gw1N1HLCVjrJbpNq1xPfwMscJxAuZ3\n+O59h9h39xJts9ew0oY7usC7gwNcMx4i5bLQuzlKdSqOTAEEAVFnQJA01EglWtw5eko26rVhnNoE\nEWMz/riL0MKvmAh5uWYN8YfpF5mM1/PJ3Av82L3JqZEO+vILmE7GeU85TQPzqJoxEtEGahMFenSz\nHHONkYwGCawuYL2tcE/upr5pFrlLIVS9yi8NjxNIJDh9cYS8LKJ2l3EGM3wq8S5xxcqCroERoZ2I\nCRBUrKoPjfB+GlnmdoOffI0NJStxufkUWwY/Kx4/jsUtFLuLnN7PQ+dfJO9wsWNzc/rWJPVTF1nX\nN7IRrGZIVKlsSewyjrNP3OSYMkKX/BRjUpigFOWmauW06KKovct1NYSt5ORuxcOLq49hTRuRgm8Q\nLdYRNFj+zxtsRNi2sdliobi2C0NZoFgu8pj2BuvzHsoGPQvNjzLV3ExUV+CpKy8TSiZYdnpZdfdx\ncuwCPTdvozbez7/13o/PmEbV67ga6sOfiqLbyjGnVjOjBLmqdrGuVlFBwxEEPiUt4tJ9gzIJHNIV\n4pabfH7XMd70DfDs0q/41uRfkby6iHd7k9dD+4lUnHz5lX+hb36C2vQYhc8VUYIq8ZnjXFB3I8+n\nOLQwQqzGy6/aW/CvZ7i9k8VdUVnyXsJW0eHNu7Gp63RHpnCks8x7arguHUYs6SinjRzkDil1mXGD\njaz/g9SxTKNmnuR2ABdRHtW+wLLJw32mt+l5Ywx/UsZeKnG9sYPWxFf46PoOJZ1IqbSXZ5SP87Xy\nh/jf8lP863Y7I/5m9o7N8dcH/h8mF5zcZ7jHU+IVKrLA1nsOLj1Z5rtdMn1tBWrOSbjvVFBEEU1J\nJV+3m+/Y9/OCdjd30iGuLO1jaG2QssfASc1ZDmsvUm9bRl9WqJ3VciC1QnJeizZVZsdvRzmdxXJe\nYtrdhUHJYxwFtUFL7FINgfEEekeZfELLnQe8/Ln5j9mTGKElEGVgpcJSzsCX3vdpHHMzPLR4hc1H\n9UhBgZH8cZo1I3S7h7ignuC2rRe7OE3eE+Sjsy8glSu4fTmO6l+juZJksBDD51Mo2jSM5SXusymc\nXlNgshmpkmXcITNdFnjWW+FGZQtHpoGG1A43Go7RUnUbh3GH9HyI69mD2KUkBx1nebLwHnptmnml\nkYrezLhjnLQYZ89YHaKmk7QtxZ5bt9EJBb75wMf56OYr/Jd732Hw4jDOt1W0jT68++7/TZPjP/Q7\nKP6afhuh+KW/+DPOth3ieHYEcdzBm7udaNN38ZZTDDcGeKL0ffq195jddjObdBE0pXBpsxgkGUEQ\nuK9lmfSGAeMkGB9LsmnqIy+5MGws0GydwtyU5H1zU1yU9yOJCtOmakYdHbTp48hyCUtFy+5yC5eU\nHF+88E8E/VoU7+dxav+e7YKFyVkjtdEMvXonUf0uLvbNMlG7TXu6HQsi5lM6btbpueHqxSdHqJ8B\ng6xBIyRIlo1sFo3cLvSRN5qY3HaymbWyLxTjJc2jbIg50rosRVeMD+WukjZYeK3mGM9NvcT4aoap\nuI/FLS8TWhejunZ00xLtsws4HkuReV8BsWShs3cYz6UKNWKen5nfjyOwRKxQh7xR5Jw8wDnTPuqP\nDvO/N57BaY0zUFjjfcIy7+o7eazpLUaXB3AqCTavBUgsmzGIZtwaL7s9R4mWdrDE15k3NZIxWRir\nuEgKZr6Q2eERHDTee5NXmg+TVsz80fYF1kunWMuYkRUtkUgjoco0/1o6RTC2ib9+B3vbGcyr91NK\n7OGqVKSt4ODnrav4wzksucPo/P1sq1FcGi/TxVvkTU4URUZKFHjR3ox5ax2t1U7OYGY5VE9aLKMr\nCxgkD+POKe40g6mso371OOWCHZs1hVKwo5XKRExagukuzIqBq5kik+ZxtkwrlAwxqpVlfLNpmlc3\n0JdlzPk4sqjHNJVE3uXGtNWK7jvjBGY2cM6lKLapiNVZOn60xnxTNya5jEcv4a8bYmz2BLmloySv\nHqPt5SsYpkWkITOOxQyiW0cxNEvPxD7C+XWyx4wkn5ZZtJ9G/8Ex4rusDBn7KepMHLp9icDEOhm7\nhcVQI7XHx8lFrMw1tpP0llDXEhgvpnAklhg70kvE6mEqt5dv2+5jqLmbud4W+qNmThTzdJisbKdl\nzEKamCiQV0w0Xtqk+doWLYKOolzmrdAemqzTVJU0DFx5l/nGAeIOCz/QHuZQ51neWTvIXDJEhyFM\nwDePz72IWZfFuXoQ99k1zDPLqK5GdNffJnmogjZW4h/r3sdHVlIEBSv/0HyZsgj/9VWVpr0J9Le1\n/OXRv6DXDdX+R1B1p2nlHh7pIgsNBsY6LEwuDlAQQljlQUya8wiCyKwo87y4Gw8BWrrfoDM4Qa0r\nTa2jwsdnYjSW9Qzk83SUSvxxPMGg2UhfbRyHVsVgKIIo8P3ys5yY3mB3JsG7ylFaWObR7HVqWOJ6\n6oO8bR+grzBPB3NcYi8lpYYH3xinoS7O3lyWDZeTUtHKWs6OJavFmNfRZXbwj7v3cOrqG+Tqj7NV\nBHPDFdZNHl42/h7WpSjfkv+G7vwsIzuneCCn5Rf4+GvN9/mK9jkOS2NcjHXzl4ZP0ihusLtQh19w\nE1l7ldzeC7i+rSGnGPjy4H/isGaFQN8YTblVAjNZ9mxO0DSzxJC9BZNb5r/feI6u4SlyBTPX2naT\nMNvQVoocHbmFRpRYbgqR0RvZqfbwQPgORdXMmVIrblRkKY1GrdDPGOfz3YjY+YhgZ9Sxzl/xTZ60\nneXt2EFWy9W8ov9L5stN6LQq0YqJSFnHmuKgMbHO7coRPtt+Eb9O5vxKI7veXeHf94wwHtTTPV9g\nNFfL7cB+Tt+8gmWrSCphQ2gokT+i4NXEMcybMHgj7B08h3lXGrM5ixx2Ep5rpGMmzHZNDTpdGncx\ngnRXSzzVybyzxNMD57BJWaSXPUSMWuad9bw3cITOlUn26ue4ueSj4lKp6o+RHm4ilyxT8MtYtCW0\ncQO5MTfXumMcmZVpM5bRfziJoTNPrknGbM7wzdIXyIh2vqj+NVZzlrHtHmrOpjFuVYhqfFwbOMJa\nXR2SnKer513ejg9SX8mgESTSuoPcsJixhcysBw3U2bo5ElG5nbtDlesEOq2Jjc4CL/a0cF/CQcrR\nRC5xjQpmVEVAUpbQzu1gKOWRghVaH1hEzshkilVsB6rZ1AlkdDLFtiYKwi6i8geRSu2UdAla7C+z\nYqyibmEdW2wCnSWAJ+2lfzpJ57qG9o0cNYkYGlJUNFqMK3NU1DKyowpFbMIR70dfdGPM+7lb044n\noceeK9E7/gOEqiaqkgluG4O0GIrMWBLYk5007gzCyUlEm8zXl/4Iq5jhgHWRmZkkNXIOResjlagm\ni4b4vJYTc7don9kkn9MyZtvDZrqWlGBkztjKf93+CdsvexGjoO4r8abmINmEnyJ6amWVGnEOsaJS\nSTyAYlkgrFslpN3gbe8xsnYN6/oQG04vHdPLROYFOpYm+fj88/gXlwiWc8yk3MxFDKQ0Nqa0DWCS\nyPgt/M3KAnohQV6bIVZ4jF14yFiusKrx4KmsYqVETO0gqYhY5BrenxvguFKhu6xgyt9PvFBFULOD\noCh0joyxXtWINlyFTjEw4bzGM6/P4XWnKE1ITHb0UGc9zrf7LBy9c43P/PwlojVdWMsxxoLNBDe2\n6Jzf4ErHpzGVnRTLRjRqHVc7PHRuLNGYziJKRc4rXYyr9YSkKO2qg/PAHSGGjMhHyn9O78wUXzrw\nF8xaa/kvw9eI2FUa3ltFXExT3XAfXc4OfmkwsmCr59np63zpA3nWM/UE0418T95hpX4fpqUwd1pb\nGe/sRqNWqN/4Ef6cCbtljjH/RXzZWuwCICh8jBfILW0yMHKVfJUPV7RITqej23iTvnSRcEDDH5le\no0FaYqHcQutqnAm1jfvVK9TFUmx7dawmB6CigaYYdmGF/3v9Kusaia81VrER/u+8oDp4THuJ08Jt\nQvYlLst9nA3soVSUSeUNHI7tELo5TV53nPJEGG1QZsJUxZMbKZwvSYSbmrCmstx1NfCF3c8ybQ/w\n+PJV/vP4K1Slt5m3Bbi53s+JN8Yo7VOoSmUZuJfClU+jiCIXB/fxt/sfxWl8EGfDRbbyNbSPLvDC\nsffx0okHSApGHv7RDRz1OagXKC9rSXTUUL86ynLegcmZI+RLcq9ay8XsIT49+hYuNU3+kzEcMx8l\nMH2EoRUvW5EA4wkvuToPZreOAgZeXPwfOBIj/LIpw1lrgqFkHRmjjLlFRJeQaZ7MUl0sk2jXYqyK\n8I4ssV31cQquIxxfXuHDO9P8yKEjmAqQ1+vQ21PYd8qMrB6mVlrnI5WXaMpFqSCw0CHibJjlLXbY\nNG5x+vYJNNkxdtjgpZCFTNDAmd7HSNgc/MnET9jLJIX39Lzy5BGUk0/TGQj+ZsHx/+t3UPw1/TZC\n8cL0Ve56W9kzP4wuOkHr+Dl0mTXWdQJrgSZe9x9CMxHFkMogO918yH+LRnMCnzFLoyXB9+UHqHhE\nLAt55iy7CVtreZh3OGIcpl8bRh8XcBaKlPNlovoAd+UaFCRaNVE0ksCwqmOf6mNXfJ3RwBodwc8h\nKaPYdGdJ39Hiny3Su6xib19g1OzhevUw264mPqHO86jlXcyFAk2aeW6bdhGruPjUvz9H/fIKdcUE\n6So90YIFSjlWokYSBQN9zh1y9irWBT8fVW7zz941eoXHcBTnaK8sMCUe4MTiv3J9pw5aYdJ7GOfa\nNtntCt7YDv1HV3EZini3S2gaosyMDXK6OMYMXi4p9xGomUFVJaKLVmoKW9Tro0y2t3EqcIayq8i5\nWz3Mr3voLkxxZukoo1utvLtykKvKIDZdDebcDBP1ETpKZ2myPs5iepGQkuGK7jBJ1czXsbJXsiPP\n/RJrLMz2YAsXYp10OKE3dYRxaZVUuoo68zw/iBwkK5p4zPoWTQMjKEKFrW03oVIn2uIGU7Fpjm2l\nuOfJsMv8OKogklx5B7unnyFxDFlrwbwywz3quO3Yw4S9lc6tBQS7AUlQUKw1mLNZimYLteUqZqwL\n7F73YSo2ozdmGBx8De9qH4dK+1jQbDIlbbKaUdiyrFM2xmlikYjsI2apwpAskTcayRuN6AtFTMUk\naj7FzsputkSJV+sHSClVeONrmLM58odVjHMVqsczGPMJPA3DSFU5Fqf3UEw4OTD6Xcaae7ly5DAz\n7W0s1jcTUR34a6fRmptYE6wYHxshMbuXyNoHCcn3iHkLrIR78Ltm2asbJdiepMm7xk36iVR8TPr7\nkCUN/s0wi6EmSn6Jnz/5CFeCg8TEKq443SgDLmS3AXOxwnytyEci8/jkKjxqisViCLvzKvq0wMCt\ne2TNbWx19/DElR9CmxuNVmU8omNveJSZ+oMoxjS19eM0uFboH3ofsjGNUhCpDUxikAroRwT8Ix9H\ntRoRZmcpBDaIzQO7S7iFMK/qH+fo4l0CuQESwSss5gs8ktKh6S2Sl7s5tX2ShnQLZUFFHvsppsDz\naFQH0+hRasHauMbWZjuNSj354gBZ9vOa5KIoaFhUvbRWQmwMQUzvw+rcwZ3UcCy+Q38xz1lxD6ez\nm3Rno0ScOsoGEQSBTNrJZrqXT228zKT8OUYkPbWWKdqL69z0ubiz+iVOl9ox0IFFOk9UbmBZY2K3\nf5zTrPOC1Yyqt0LeQ1g0Mp2rpcZf5N0WN0O+bi7sO8qFDjuWjII428L3/cdJC2aevTVMuxojmv8I\nIWEQi8bC61KGLcXNJzRnmJ/xkJhuQ2iwkxdUgsO3sLhD5AJ+TLffwTgi8fX+38fsU2kSk8hFA7VN\n96ium2Qyvp/GmUVc2jSnFodx7GQo6XT41yNEHA4mGlv5yOsv8/FXXqAxpWCPLqMTZIZC3azYA9Rv\nzWDMzxHQ6zCoOhShyKoQJCCl0Ws2sVaM3Ge6TndlmHDJw19nP8kHNOdZt0fJVdrRqRqqpRhVYopJ\n1c9wTTu1VUk+ZB9jYamHttct9M9Oc7PDT8yyxqrbS+uCSt5tZdjcwg/bHuTtqsMMLw9gmymzltmF\n1pqhPXiX/Dosqv08NDNO92aE/vIyOYuD/xT8KseFc4hOheJeifCYhfguD/fXXcbzE8ik/KS1MmQq\nzCn1ZAp6nta+x3bBgqungujKMnPeTjjh4xudf8n9whmUpEBh1oQxV8BQrseyN4nGLhN9PcT3tE9x\nwXWacV0fn0z8hDb3CNdu3U/U0cqiVc+2zcD0wF5KOj2WlWmirmr0SoLWdIKE3oukKrywp51snZOc\nRUtFkmhPlPEVVL7VG+AjayrbTRs0dT1PizjM2uYFzuxyMq2E6Bp+D01yBylRRBBE9rxvFld/lGIq\ngCuQITuVp2CuoehwUnLXYEp2Y023oAoVBOsNjJnbjNi68EcTGLUiKbeb1sGzLO7sQigUMMtpTAoY\nC9VoC0F0WYWSpCfnFggpfsREN0Xi7AxkWNS52DNfxpEXseQnqV27ydWBTpJOGwZBgyqKaGUnro12\n7G4d9YOv88biSSbnm9iI+zhVfxFXRoNBm6JpYJTCTh2v5zvZ0dh5aOkaUkXmjrGOsLmbbM5FbSTG\njZoeHotd4E66lTvWPSxWNdBiH2dxox9F0NAi3sOnbDCW+wwSKeKOGSRRRzhvYS7QSNRYhSxpeGT6\nEn84/k/Y5QTvaPazUvDSrFljUV/LfLoajaCyo/diLmfIqkaUWgv+zC2EgsIeDjBd6+OGM03JvsK7\ndbvZtzPGhFhhSyigR8vjxR5i5Rw2jZ4+nZsDFTMF3TQpFERthUJeYN/oMCvBOvBPccm+zOBUESFS\nhZqWme17nBsBH6MBM5956efURHcwxyqUrbspiine69lFbdTMGXcV9nKZ6rKd+SoL07V6gpNLBNUI\nPjFLsxRBQaToeQe7uM4HK7e5VOnh1cpBWhKrBHs2+WHLh9g7FeFiXTuW5R3uP3MVV1uGv+8/wlPF\nAPW5GC/avJyvP4CsT7FtXcCwtIcpt42iLk6+rgXF52fvTpYj4xcJpQ3USBlMVNCoEpp4PzaxRE+0\nSFKYZn3Lg65f5VLVUQaXZomLZi7ZFnk6FWd3FKybWki5cW/LhPLrDNPDbX0nM3ILc+F+yqKO1tar\nVPvm6bWN4k2XmbtuY1Yf4Kel3bQi8T3NX9BnneahwggPVG7yTmY3mzjQCvDly88jiEHEjs+TT7+D\ne0bhhLsJ68+3WQ3o+LOn4+yyPsPXqg9jLKb56tVvc3L5NlY5SfWH5jg4PMMlyy5mzQ18wH+W/ok0\nubKelbNeKiaRQXGCr/b+Ce/UBghWlvDXLzA61cvjwxc4PHOdw/dGEPOwOujnWeuXeHz+ElfKXfx7\n85PknQv8sDXOsXyOgL7IS8lDfG7kdZIPwHhbC3OL/RzIB6jOR/m6QSQWC2Byx0gYAzSwwMD22/xJ\niw2BBCn3p2jdbuMJ1zWyXonW0WP8SjLS/kKWam+GRKPIMWucTyzd4PPrrxDM73DWfYirXjv2FISi\neURDkcR0CzbbDrWhcbbbi7ykPsxfWT/F+loPd8rbzBjCfLoqS41LJrpVQFRyGH068o79nO/bxyde\nfYFjN67jCmXYbLXwZu3DPLu7FZut6jdNjv/Q76D4a/pthOL6rVF23TxDOmFCctrJOf1QlqkLL9A6\nP8ZMUzdD7fuRLUaqJRUR6NIuoooin6v8ZzRtOzyQU/lRw3FiLi8Ns3ME311GnpIwWMv4jDlisoc2\n3RIjHKBZWiGoLnNT6eWRchPfrVioK+XZZa7G5QhiVuxol7+K2ZumIsNzD2iwL5lhVss7RxKs66Mc\nKDbyp7FzRPMuatihMbbJsZXrPPzKGbSJMsGDcdZSftqiYZRq2Clacal5astJHqib5l31AOZ8jpOG\nN2iNfJTd236+0+nhqcg5GqOjXFmuwWwu8inbbZqIEJMC+Fa3WHI5iMsmzpmP0Sks0ZSMYNYWqK7E\neVV9kE2xSN5WhGGVSgIEVMiUsE3FMNgKTL7bQjkuYTIqSGWVH9g+TFzjpGxQsEouEuUETblFXjv0\nEEMth/lM9C+ZKj/Atx27KQoa/rQ0DxtvE19+A9fSEsWuQXZZTvJzZMYy7eSFMn7hJhpznvqea/xi\n61FObV5h4PQdDIY88yt9PFz6GWqpA6fSxUr8FmpsjY6wlqqWZ1CWr6CZOY+u5TSaZJq15A3mfUmG\njQ9TK0RRBB03zO1oClmcBgVBgJLRBIChoMWs1XIwfT9JsYAi6xGRadX6sCRbWIr5KOqSZC07VKQC\nrZkpYpKGOEbKGjuLtdVcburldlM3x0dusmyt4u/2PMO/Ne7nnNDOpLaWK/5WJl0N3D86SvqwQLGx\ngvdSEv/6NvIjKZSoEd1NMzWbUxQrRkb7+vB6FzEbq4npy5DTY7eHwTNJa+RjpLy3Wbog4De6mdl5\nmNWigCgpDCxex3gdZIeGK8ouDJ2bNHXcpJQz0BG6jP+VCKqk41snfp/rwV42HF5W/B4qVUaacvC1\ne5f41Oa3eKHmAe44fBx/8euYtWby5QleD3ZhkAvEqoIMdx9nv/gNEsUONupr0Co2XtK2c6WugyHR\nR4d2k4OdZ7DGuxiJtJMjwVwwQJ/1OoII0ssN6LYzvCGohLLLVHQZ4gfKaHdUtF1FuKXll62DPL4t\n0qiBhFLA2WLDm+ikefYzOMsV7Jrn+EL96xyZvUcgkEWlwPPhD7FjNlHt2EJvCePdPI5SlpjXapjR\nRDgqXWFZrSGRdbFfu4SzagFd7D6UjcNUc4OETcNzgTJVAZH6WIGqSJGETYMpV+ELui/z3YmvIsq1\nnKmyksza2Vs6yOs9YxhrKoymzWQTKm06IwuKkd36N7nOLoy6Iq+V9/JdbwQxrsVYacRJgQnFz4St\nltVAPX0LORZqrJgVlTuNBpYcblZdDk7fzdG+5Wa9uA+j2coZ7RD3NFt48houiiEGb07wqv9BkoHj\nFC2bhPNB3tN6qCtsUnvrLRZKJ7jb9Qnu+FwMaBew23aIJwI0n9lCteqZz3ZSL8/QMLONJZNhbW+I\nSk5laPdu+qdGSRptnLpxmfKHkyihHYJnElRaarhRqGa73k/a5GQgFUeLhsOeHM7eV9kX32B/eYjL\npT5iUoJjJZhtmOMry3/AquTjaHKZ1t6PImynWDWILFUFmKxrJNlejVqq8CdrRvYuPIpmx0LD9V9w\nrl/g9f1toBSIRR8iSiPVsXWer30Un85MTKdFa6jg00rkZYnnM/sRRgpUlrX83aH/zlu5IzwXeR9O\n5UH+qPYJPuP+R3yVHbRbKopXwbw7SpdrGsO8QtmjIp2KoFw3Ikgytw29tKsGzFKWkUoz1bsmKIf1\nVBUlYjsirniUgBrBXpvAEFUwzrqp6Cq4TkR5N/EgFytHudV2kE0pgITM5wx/hyMMN0onean/OKvB\nZkLFPFpFxrI2QwH48YPP4liBeU8LZzr30bWxiDea4EDEyd1qA+ayyj2Xljv2Mh9bFXAVK1ycexun\n+RE8ugWszWt0lkd50/QhOieGENUKekKMqAAAIABJREFUADOefcTLj5O98ziphZPE546DW4+qTyCr\nBqzpJoz5AHOuITyOC0Q0dnIWI6ogIaoVpEKJksZEw+YGVcZJdiwyRUucom6DoiGOVJQxKEEM1GPL\nNqMUHdib32QhOcX3Dj2EZCjy4UiZdTFOcPl1TMUc5wYFRrzTBHL1bBu3sZVtzGpt7Ot4C8kUIznT\nR7e4RZu8jb2yiqs1hS2YI7eoJ3q9zIIpxIrRx+nl6yw6qxhp34veKLI/nWJbJxAKb/ByzXF+cORJ\nrvXtYUTXR+fmIsWkkyYWGKedRHEAU8rHVtU1kPQookTE5WHJWwOoPHLvMqHwHLd1HTyte49sRGbc\n3Ml1024W1WpqiJDCjIhKQTLwSPosK/WtbBp9JA06nutpYsijZ8Rp5rqjj5jByaB6j+q5LWJmG4bc\nCk1vfQe57hqT0SVqLJ0siXFmtatMW5oxGosIeg22+TCDI8OEuz1MmbeJWQPsv5lmI1BDsPYEX2tR\nUHVG/tvz/4YA6OQ4eU0zx6//lJePneJOg4eNzSJb2iy7iiaGmiV2rFriUyrzZQ9+2UpZm6NVjJAU\nKnxeeoEnKuOYq29h1QhcVvewbGkk7XYSNQukzVq+/NxzCIJE7dEwUa+N6EKWQ64BEqVV7mjMfCw1\ngcb7UV7YdZSkqx9ZHwKxwmdHynxmQeY9dxp7PktMF8OgGDCWrbzYfZiou5Fo/RbKbJyipcgPDn+e\nPQtL2PJ5ZqrrGDaDe2MdWRvEXcoSyq9RVwhzs7qJmGxFRaKkGOhVJ3hYOc+tyj6W1prosYySM0iE\nlBJXC6e4KzTyd8IsIe27jLdbUNJWguo2B26MktV4OSKa6Zp8jaqeJYxOgZyjC3FsGs3UFmWXhFSr\nINvuI1o6yWWNhs/nf044ZCOwE0ODgpCV8Izl0VbKvFl3H5+S38aqFhgSejDeq1BJlPE1pamav4pp\nTks8rWOg4R6TlVbuBeoZWJlHjWgQjxb4m+YncW7Gac2s05Ve4o3mQ2zTw6PhNrwlH/3SCMH5Ao7t\nOIlny2yt7+OPE/+LZcXMO9r9nC97cJumeGLZzHiDlX1cpVGbwGlLccJWxqK28xnjd8lUqxgTPoqr\nf8DzwiscuS7wqwEdZlmD7BdZtnm4lDXwi0ItplY4aF/mbDFJY6oOKWJAb03T030WozXFlaidf/b9\nEVveNqaDjaybG3HnvBzYceFtuom1PYG86UMSOzjXOYhQkfjo2Unsm3PYurKYNRVsqTyVlieo8Tp+\nU9T4tf0Oir+m30Yo9u3dxy/OX+f33nyNVaOLiNeC0WJD0VkIrq6ykbOz0djAmq8OKpAsOrljb+WV\nyH2ciA7haY9wNnKQklZH9coSloU4iiRiVXJklo3kI1pcvgRGo0xJqDAnhPiw+DYXlHa+hwsFld+/\n+X08dfdhqbhQ5r+Bt3mCZF7D15tdvOQzMF+t8PBQiUIpxXCrg18sXUVWTIwV38+1rafIlDaxLmeo\nzApsHqxmJtjKpfoDbEpeLOEC9d4wS0UXNmuRWleCs9IxajZX6TAv4o4N8aXqLt5sP8pnV3/G6IaL\ncMHCU4ExKlKQamGBQcMELd4IJkOJO7EA2xk954tdaNMF8nGRlOjA+m6C3rEZRh0axEU7aq0brVek\nEi8jiCrxGQdCQSVW04Le52SXbZGfVe7nkZrzKJrn+dzVa6A1IKgFbrvi+ExRFsIh/lF7GFFVaE9P\nkElsc2r8Mr5Yih/vP8436p7g99CDIDOBwFUELlc6uVloQRWhR79MyJ2hpn4GuSBReM/AfboVNupW\n0aZOEdR4qbl7EY9vF1r/HqZTN7BuzlFoGMCkdTIuDCN5TYwW9/O/tP/CbsdVLuhMrJV7eMh2hj3y\nLGGtk3JFiypJBNJ+EmKR2oqH3lKAeKKKlnwbaEo0SxaWhTXymhIycFFo4pfKEebVKioVgWpNnuWy\nh1vFei7X9PB8+ynSOhPH8pN8euJ1Hp25hisf53zdIHs2pggb/Djatyj1VNCuCmRPV4jP1mOarqFm\na5L3jh3BaIjT3HKH0nwvLzQPEopNk8/4qApOoiu68My/H9fmRQ7XvsllqYOCJCMhsu32MaVxcNN7\nkJKrQHPXXVD5/9i3rz/LDvJe888KO+dQtUPtyjlX59zqJNFKKIGQDBwyCDOADfYYg49t7DFgLGwc\nhG3SAYkgISEJCcXuVueojtWVc67aVTvntNY6F56bmfFcmws/f8L7+d18L15slnX0zhK29TLDugZe\n33GEbXOjHL6ywFhY4rMjE3ztwk3qDDsxF/bhz1d4ttFMuZjgiqPCtx95kJmaWtbsfhqSi+CKEBjO\ncLFnM27NxgOlNpqFMq+LdpqEVTa5l6gOjRKfuoPJUpFpTxsHJitYAuOI+hyTi1/BNfkOS+iocZYx\nTqYxGm1YB4tkD6rYZ9d5K3iI+ydv4yxvxVA9idM/gnfko0TVCj7DV4jLt/hnt8hn9RlIQtYpcLpz\nL9HKI9iyU+gsMYzhTZgFN6d0w4i6OAcsJ3EqeW5rPhS1l87VD2KPbMdcacYgXkHW4vjbZRS9QL7o\nxJ/LULdSIJqupzO/wub0GMeyjzGhOHEUYKuyjcSEgtw+Rs4zRmWlHidX+Urf71HLHGtaNZNCIy9v\nPox/Q8fVQJzeSAOOfI640UwirsNuUnFIESI2B1+9eAt9eJKbLY14UhXqBtMcMLzDAlWM2IZALVMV\nXkf1CNSJcUZcTUgeOyXzGqJioDHawoKQ5a6bP2fD2sFcw90UJJmeggFZKtG67SUic43EZC/ltJWM\nzso2x2UqK3rGd3UwWNtDzOnEmUwx2t1F3GdgflsjrcZ1an7jQshXmOo+TC5TZKlsIVpfjaDT8UCs\nhhXfMFlTjvM1HTzl/hjHu3Zy3RNiAysr4U5e1brpVUaoi45yY1WjybeTf9jaxVxVkIos05TT2Aia\nuaZo7CnlachZGc4d5xuf+CYpz90wX48WqSGm86JpKgdRudeY59iOOu5evo6oVKiTb9LXewPzSJZZ\nfS1JfxtrtS4StV7OKPDH5u/QaJ/C/SMJ5+teZnYasBmzyKKC5gI1ABjBIFeIrNnptHZxUK9wNZPG\nI4xR1RtnPL+FD6XOETZYERazaIUKnq4kOn+JhtEWkvURbnTs5H/ZPslyTQBFkjCVy3y09BMCuiW+\nwre4VLuJ2twG9966hAiQjGCKb9DlXOdEyyFu17czXV2LO5ukaS2BS0nQkyszZdPzxHiCTQkdq3KK\n982luOhXCTenecXbzt7L2wjFtnBxeSeTOh22XIy0xcHrBx/m4JwHS8pFWYuQtYcRRQVdsgVZtWBB\njy7dxIb1GB/M/Zy79VfRiQp5wcQRTrMkBCnoLMiCxqpUQ1Rw0SlM0sQCS2INAW2K/clfMTD3EtGa\nceoefpps4BihxlFWzUGuOndRUQtYY5dRdOtsv3qZ5VoRtX0CW2YbuoqNfMVAFiv1hnWaOk8Snj7K\ncacLVZlDF55Dv2LA2xtDy1WzddRCVO/AVNK4aWhk2W3FJoeRXFYQBBzRi9xnHuOcqQ9bukBa07j7\nepI5vwnvfBGPfp0uwxWWSs0YjQt45FMk5BAn2zcjFaAptsxO/5s8uvQ6hbCHX5R3MKI1cp02HhUv\nU0xkmTU3IooqKcz8jfwjvhz4KYZ4lPrVefIhL8PVbaw5q9gydpFtGyfYKz3Jl15+nbLPzrPV92Be\ne41TjTd56K1pxN8TSN9RQLYfYEps4tfeOMFciYdKNTxe+DMuCZvJGkU65+aYSdaT3m7nYtOfI2gK\nDosXHO/yStth+saHud3aybvbd9I2PE7b8jmsuSzH+nYQ8VdDUUFf4+bsZhthp56GcIoHZySW9Tmu\nSA5imhm/mKZFFXhf+Tr/ZnTS2AZ7dCNcWN3KfMVHtTGLIIr887f/nND6AtF6P2pLji3ZKT6x87Pc\nNbZAn6mWXwkV9GWJM1u3sj1a4S+vLtF07B+wG67w5bUdfFuaYjxjJeN3c9m2iitjwq1KaDWDBKtW\nmdsIUrUwyIu7wnSEPbRGslyu87EjPMahCxPseCdDfrHIzPYqWgvrFEQDjZl1CmqGYtnDVsO71J9b\nwGtKsl+6yb7KLarXy4Q2CswYfHy9/HE+IJ3mY7rvocgKNy0hfpWUOKokCZtMbLs0wUBhGCWfRe7p\n5iX7ddqkR8lWTiHmQX5fgeIelba5L/F/qSqyZYz3OV9iXbeDZNBKlbSMYR7EokAwuMz5ml4+X3od\nCYWAGuVva57ga00f5ID8Ll1qil+s38E1sZujTcfxBhO0No7RZE+Tt5s5eaSOI7Xn2HZiBkuxgjOW\nJ3hgmuOZPQTXwuy8fAZbfQHDUIpCNay0D9Af+S3/avfzdX0zF/J13IHEZ1QdB6Rn+UzkB3jKefQ1\nc9SkTNRm8nSabxKIZ4l4DSiSxgXRTvXyPFX35PB2a+jtKmHVjdMUwyoHaGhcw2uIsyC2UmecZTpn\noM6gMNVj50JykWRO4s3i+0i4tuBbfRGxPEjJ0EjcvZ0z5XaWUhcYMFeI0cUZ3w6mq2vZM32dwLbf\nID+cRFlrJFRepoUFZFHG2nX4v9Qb/+/+G4r/Sb+LUASoe+tP+Jl0gMduHOOl3Ydon3uXoitA0VPF\ng5dPc9tSS7zGi1Qs0xVZolIwYjRIrNtrWF9voiiKHFPbOP3AXt7s2MPrls3s+PgJ7Od0qGmBxJQF\n2aJQ4wxzTttCTHDzx9pPSM0V+cjtk9S4J5kLVFO7/kv8DeeI6mU+H6rhnFPkg8k0O4ztTItBDl1d\n54BJpd4a43JiC8azK9TPvIN+roC6LjIbqOUj/8eTpAp2cpKet4xtdB69ibE/gzMmMRtx8EroHgyK\njIdFArNR3M1Z/nnTh7hr4RLNK2OcjzSw07tAxN7Es+IRkrNeqleX0QUrTLZVMVzqwphK4y7ESRVt\nhAtmphNO8i1udK4+UuECrrYU5Y4ShXIzVc5pcmEzIKDJOuK+FgxygpuVem6rLdxTnmTXoImeiSWi\ndo01WwO3tftIRYJcqXTxgHSBoyPHCSgrvO/WNZAk/uHQI7zm2ssuyxCKmObxSi2PY+AMJTK6Igat\nxHC8k6RmZlPjZTyWGKeW+thbHsSrKpwt76Rm9QYG+11IhjLW9mbSeDlmn8L2gSlMNjuezBbutP+A\nXxQeIS8IPGH7BU3lDS7UL5CO7UGxK3yx+BKNvhEWDTZyOS8lSUMVNDqzGp1SNy1aEJ1iQVJMzOvm\nGJVXSZZ0nCw2sSxU8dHaF/Fl0pjWVrFY/QTkCFukUbIVHbsSQ/zpmZ9RbNzPUpuJfGMV9o0Rbrj6\nyVqtyFY36bSXqvpZ8rtUkEB6w0FOVBjq7aVoNFJRZVZXWtHmUjhia7hnRqgYPVg9cVTfTaonH0Py\ntvKKYCFukDBmfFgT7aSlPJK9gnlxnPaDI5SzMounA3i7EhRzOuItBp7q/gPsxQRffvY5fu7eRW1B\n5Ws3/pLF4iqZlRFMkp7uuX9lUW/gxT1Hud3SwdErF3jsrd/wxu79WCoZQqsxljy11CoudtllZixv\nsrO4haNUeKDqBGrNNIpc5Np0N2FzHQMTNUiGAp7ONxBEUJJBlqS76Q3+hvJADss1hXyPyPVKAEdb\nDsFaQbtSJi7o6ZFakJzzVOzz1Ew9TjH6Os2203zfWcVj8Ub6tBlWrzi56O3iPiVLKexgOuFgdbWd\nGWmdaSFCXMxSVvVcL27m5/VH2SeOMlUUWNAtENA9R1A4R1a5G4dwlmXFwt+X9Wx3VTPrN8DGHYSU\nGwxkxkmU9qPNjDJc1UxTMUtVxU/I0EMqs4yvbplXDGtcdjzMpYY2/GfXOEcbTUKclMHEtfZD5O1H\nGAwGkLNZGtILzEt+7JEkGx11eNNRAutDNGcu8vFnXmW1qGfapHBUTjNqXUdSZESlTNdQDp25Qtoq\nY9FrGEtmDEUXwakRNGMXA6kEaqXATNtjzMkqv6hX6S/EMGdr0TJVOPU3WDE0Ete7mfY24Hbk2F9z\ni6zdyKjWjlUfYGAty1jAQUhYwJ7xYJzrI3jpGJn2Pq75jQQMVdxal2jXydys9fFyg4O3nZs5pn8P\n16StJDUTxtUMJYuRoRofVwN1qEB7IYLVbSdnlfj37f1YSlkCyys8MHqFXSthFmw6wiEv5yWRD8Z0\nfPM9dzDlCGK+eRNt3YvRMo5oH2JV3Y6gzbBRDb0L4xhUBX18A1VqY3+km+XIBLqqJrbWvMzvW5/i\nPvFl7rW+icMdo3jKRaL8OGrfnXwpvIse9yQuY4pSWcL7I5Fom4yxuUg+ZsCaqjAthzEtTxPYFkfv\nKDFzoo/4XJEd+lmyRSNjYhP1zUuIFo2hRRlzZ55/93yBWm2ef+D3uTN1mj+6/hyt9kFO545w3H2E\n5tgKf377X1kmwKrdRbrTwWcqv8FdnSK7Ws26zUXH9BCPnHmLD+vvIidWGJeW2RYpsjNrYrU4iDc6\nw5S0hj43xoHkVT4Y+Q3HF2M4dFvYV2ygfnYYbe06G1V1LNX0sm3OyJR3HcEyjkoUc3QQZ+0K5Vg/\nQr6KTPMbbDY+wy45TnTBzGi9jr/b9EUOl89wOPsuOioUkCkIejZte5UoTkbSm2iQ53hUeo1aaxxH\nXYFiI6zZJb67oaOz4qbFP8viWhPvGRtCn8ty6NR5zJkozjvWuKec51ZpL1WymXJokR3159lYa2W6\n0oDh1j0smGc5kH4becFFo2E367kBdJ47ub2eYMrYhtVcZFrXxJwuSLMxhOAo4SmtE9soM1uoI67z\nYhWK9EzOcihxkll7kYZ4iajTQkvfFURUVhLtRHU+Fl3VLBtsRJcV2sUYQVZQl9xcUZpZUD1sE0aZ\n1YK8LW9l2lqPUSrzF9v/lpCUYH8+Q19+hGApw3CiCk2owWRI8GnjP/GxJ9/mfMcSZySZoNHFwG9n\neHPnYaaDDrombnGgKc+ZrVv5S77Bq+5uTgRNzFZVI2Djw2tWFJa5SgDVZKRleoKcy8Yze/+Esk5m\nqLmdA6kMdY4z/Dz0Xg7mrvBS/1Hmq308aDmJJxpntKmRF8VdKHUWVL+ZslnCUlBJWUSU8mm+GPtn\nvmJ8lsHMg5gzQ9wpXWVDF+CyuY+cowaHMcPF8fuxShqT5SpCsQ0ORYbYNHwbQ7lMWIY11UybNcIp\nwxbeqLby/piFc2KBGZufvNvAngtX2K7Vo9dK7Khs5X9JSd5SqklpJpYCVZQK7RQTRlrkKPtXA+wV\n/w3vu8vcatRTKnaxPWZj2Z5g0D1IYyTF/SfGSZrBWlRpqo7yhPYl3jLsp8EcZm9xnM3qENLFEoV5\nPQmfAUNNEVNZoYyOd3Id/KXpf5Au2/ii4bfUi4uEq/Wklz9MYqxAnX+dkF4hMSJTSeXQ1d/Bqw4r\n3+tcZkBcRbfVS+pwDM/8xygufJKfqjKTqNRUv8irVTlWPJPs6FqAbQrZHSpitYfMg2n6s4sM5Bd5\nzrGTr2U/z1mxmX3yZRYkBw9JN/itaRdCXGNv6zWMhgyOTInWtSzPy/upuipgbQ8jtZbwHFMQVAGH\nN8ZqyMkZetkcv4EtuIFyw4i6rUQ0GOTLJriRei+l+D6OLF/hM0YLm+QpGnS/IJl5GEP2veTqTrKo\nryWU36BpOctcvZlMOYRR0eOuvkptUw7VqlFC5kRGotuYBVVCtiQQdRqWsInxyAGOmTups5/gjoZJ\nfil9hMpSHV1vpzi/46P4kiofPe1n0PsvmDLvYC32k/L5KYh7SU9v4uXmO5jzBtmSv0rPxDI+3yLD\nBQuh2VpCuimmtVqKO/4AT7D+v5ob/4/+G4r/Sb+rULSvXeR7Yh/dC1M8cOkMrXNhboRWUa3N6DWF\n99c9wzvsYS0Yoml9GUOlzG/79rA5U6Y3F+IfSzXUtiSYdbWg2Iyogkj/cIysdB/+jcvo7AqpBTP+\n5gSCBMNCB63SPIdjt7HO5DHlVFqlM3ibplgvufl0oImYZCPj+Bj35RU+FDnOZus0yQUzttky1zz9\n6G9kMJtinKg9TFVuHUEUOHdgHzPeGsY8dSSnS9zT+Ba9VcP8hfIdXm67m31zp5HSOlSLjRc67uTO\nV89i7izznuh5jsZP8dJSD3ZdHktA4QulL/CeUpGBcyeIGnWMPWokPH4Pm6Vxvmn9KE3OLj7kOEpN\n6zR5T4Toskq4lMRuttG0ex6HbR3ZvEp1Y5r1ySBFkwNdIUOoapRHkhd5xnkEY1Hhe/LfYaquMNzW\nSCQl0lm3xKqzCochyd8J/8hnpGPkZ0VWTHYKgpmv7/4kt42tPNb4KjZ/mkxUxSRZiJnsPB9MUYrK\nlDDwYO1JRpONXN7YzJbQTUYyYT6dXOedipfewUXyV9ep+KvQ+w6jEGRSXqdiu8n6LR1Gu4nq3AAx\nKcw3K4fZYhuhvnQX3cotugcFTjhqmUn28R7vGeqiabyONRIRJ0nZja2SoFK7yKp9jHKuhFsNclWa\nZrg0xoRcxbFKBxaxwL94n+Sf+p7gYvMe3MUY+fUEmWA1lqKKRZ/koZ4XcZ5TKcpuinoTSYsIJoW8\nwUWVo4yjnEVL6knHvVT5FpA3wPtimXP791EyGf9j2JoAiGhiGldkFr3OQPveq6yEO/D65rlWiqOm\n25mWKshyBmlhBU/Ohr7gJ21Vadw2iNWVZv6tILZMAEGXxejL8fr8B7gc2MZf/PzvCet28LbDx59m\n3qSjTeXNsolYY5bgyXcYEUUOXrmO5hX5q/BTfGQjhn+0TFTMc3xgN/WxNTbNrnPAeIho+7MozTe5\n7irQsjKAQWkg3fk8kaVe1GQv/sUWbFW3qTv0HURJQyrq8djHWJ09SjrfQ2LHGP4xAWkjg+e9LegX\nFeiIUrkAQ/W72J8cw1Gop6ivYItuoir3DDpLnNrY3zPA05S0Jm7PuijF7Ljcn+S8PEmdZMTXeho0\nlURJh4ZG2/oQQl7EXcpTFbqFt2qB1VQ1w0o980Uf5ahAnW0cV9TBLYeHSqyRsemd/FPHAb5f9yC9\n6yv4V28w3VzLvBbiw/qfYFGjlMUduPPbSNScwaZv4unqh9k5dJPmd8d507eNdv0GfRPjPPTWW8z6\nizweNuIxt3Kps5sNo4RW1kg0utk6P4k7myTUPk39mRWebr+P7dIMawYBSTHgjGwia4uQsTUiGhvx\nD52jyCHEUj0nxDQfvfA8xmKC5dpDJLx9RER43lri7sIqkmWanMmFLtyAPbRCImtGFeB0ez/7Mpfp\nzM4yn9vFtM6NBSOeuiLptQSlsoV733iO2hsXQZY4dVcDBdXOQtCBSVshtirzlZRMTfkm1o0YW/Xn\naY1cYPrdEJ81/4QHoxNcW/BRMBlR6qzEq93oVIUT/buxlVTaj7/L0LY+7l3WWNeWOBI7x6C5kbDf\nRQYVS8VIaOk67rhGWLXTXXYyYHqNJVlEUJvxKUt4NA1fNE+t5iWsbpCMrVMqJWk6OE2rf5zCwgE8\nq1Xop5YZX2nhK8Uv8PbeRp5zVKOtwVXnPm6atvNj+VM4Z9JsRAPUVyewtkRZndGjhFOMmTvo3TPC\nZLSNpeEQ7pUM7qEiTx/8IM/d+0HsG3mabeMUszqudu/lqriT+6cyjMv1HBga5nn1QZwRjW1vj1J7\ne4m5oge9oKdNmOHjys94PHICdGV+bPbjFqpwLKVo2Bhlp9qF1xDEInnIaRnCbDAlblDSSmyrtNJQ\nSrMs5pgQWxlVW2iUV/i57wrzcgJ1fgzBU0sDdppmUui1alLeORAKSOkVvHkjiVKcM3eEGKyu5lx9\nkG8svcB1pZP/sePPeb7pw2RlMweWX8cUjLErMYXgiTGZ7WZjrZl4qgZjzk9d9xU2OrOsz1uw5yqE\nlCRXSndyTr9CZVFPb02W1vwcscUqbDMjlNU8m9qXqPUWuGToZlxtp6fxNZrrRxiik0i2FkeqRFGu\nIhSuRlqJYBAtXOlJM6NlSSwVSAsOjBTICxaO5qY5KdZh1kfxyjkelN5hlGYqqSjOVIaixYhiM3LG\nESRVKhNUJV7uPIB/8H6M0SZiQgxFkLjQ28FXw39DS+04OkUkHmkkrDg5VWllnzjIU7p/oqc0wWvC\nHlR0fLDrF2jnm9APr/KG92Ecrg0GlAnOZVoRcnE6B69hjOQw+QscmUzySrON0LCOw9dXUCSZ6913\n8dDiMLd39/IDfp+2bIRH5R9S/26SsqhnLFDFw8tLUN7LohylanGKqMnO1z73f+KLRvjWU3/Lm7sP\nIFiXiemcXLd1smgO0pRbpCgZeMlzmNfYzEvO/Qiqhhgt4ljJ8YUrJcxFlbE6Ax3lAl+LPMMPQw/h\nmhe5l+d50H2NlOJgWQkiigLhhU7QBAYbWxELFeaLTuqIkQx6aJibQ6ooFAsGfuh/kFOxHtYFG/P6\nNby4GSlWkJZyjEgufq0VKesCXKfE23hxEyePCSmdQ4gr5JDplMIk5RT/uP4oWklm3O5jLXYnRTlD\nxTrLSs3n6b81xsDUAjcH6qkaSPNN+WOcU/uYL7qxCnn2lwepVCQWr7n5/L1f5Addj+KpnydXn+OC\n2MOTyU8zVwjyoY7nOZi/zbVtJiblevrnP8VaZgol56TNPkExo6OU0FEZeIBnOl4gqQZYSw7w3NpR\nXl06ynU1xDDwFhUGDFGeTOyHwlUebU1g0MFcWcRj1Cg1ZDHHOzm0cIuSJvB48uvEBRN/tf4jvur6\nFUcYoYyOXbkhXjTup15YwOWN0zqbxZRRsb+awhOPMqZYsG4r8kqPicA1M8YZEcO2BDeK3Qy1NLK7\ncA3TuEBtKMkVk4eb4U+RybVwKH+RlsJFUpVL7HK8wkKxgdcSXyBf0VOrl9FXXcSRK+NKVVgMmfFN\nvZfS1EMsW1apGo1gGhQ463qcd8xtXI5O06OXcWYa0UUq5AN5aj232G8Yot2oklXgudIu1r2dJBx6\nVn0tPHwhiyunMVl1FYQKKKfYtuJk0t/KRMCPoVzivuEXOJB6m2SunqlwF95UiofEt1BUA5ISYs1z\nmGDrf0Pxd77fVShKRiOewR/3wzxWAAAgAElEQVTzPfMDHFq6jqH9HobaHMxZoojGeoKX8mQCEuOe\nNgp6PV25CS7U9ZPTe0iFBYY1gVyPD3smSzC8TqTRx2Q+SFCYYzpUi+os4Z5N82t1H3v8Q8wI9cxX\nguz3XsPeVqC6K4XJU2Y54eMHNfuxpzpoTbfQFs2TjVp4Xd7J8cpm2t1xhIUSpvksF/bsZqa1nn1X\nL2KolHnzPXchZEs8cuw1Tu/YQbzRz4B1kMncEc5Z+7n/3ZMcPXaRmeYWyqKOdbuZ/VNXEESNWluU\nF9d7ieZNDMysU3WtwF2JsxhXx7ClckQ+q2Cd/jh9ySM8pWxjHIHbGJhnnMeUH9GiRrnh20ZV3RI1\nWyO40jso+2aw24q8sXAnlyv3c7BhCYswR2zGSVmTGM3U02CexS1v0CDEEFJZZjIeHnEN8jHtBO9R\nB+nWVomMWlEnZBY9dkar67ji3MJj7S/yJ4mX2bq2zM9cCsWCzITvNgMX38JXWCFEI341y8G+lzi5\ntJ+MWODr2hUcmQq3ZT3uMwZSXj2fuXuSu+LbMQkWrssTNK2epjyvZ/vkDYTgXYyUAySx8DfFFqor\nIYqlKoLG0yQm7Fzy9GMwaOxiiKpkiQ55jjBeHhSPsyM3SGthkqpKK2WtlnLqO7xi3sIZpR2vmOC3\nhj9jtMbP69Ld3HHjMqd2HGQ9FGDc30Dnyix2QcXatEz5XSu5QgoK0+QcTko2H14xi00sEV0XuOvm\nOYbqNmEclfC/neLi5v3krNb/2LRUAgQ2CessG6spuaoouLyY7GmuzPfQWTdMvmzkdtKIhEggbyZC\nkSPDT+Pzv0NN+y0MTWnkk2Z8dRWWdc14kjGO1dzB875HOTx8hvsuTfOdznvxUOZbjq8zvClNsmBE\nr++kEpEIbqxjLpYZqGxQripwU3DS5N9N95XrnOhoYdpbw+emZAxGDydya8ws9JNcdTAiLZJuOIHV\nsQqXv0AlVodoGaX+yFNIZRUpLGBdupNMwygt4VvMlu5jbbILnbVM9dg86X1hTOdTFLZrCJpAjT/L\n9ugzOI0nIPI5tIqJauf3SEybCIvzhGwz/HnTV6lKG2i7fYmZYIAlcwVNFSmjoRcqJHNeOheH6Ht3\njNrFeaJ1bpbSXTida7Q2X8M/f5gxQ5a0SYepqGPZqBBL7SaTc6EhkjaYORr9Dg8kBzHYyvy6fDfO\nVJ4+0YPD+BZldYSKupPU2Ax/0f9JDGqJP5r6KR2+AWQxh71cpmCVqV0ucYhd3Jlxsymh8L6lCvcn\nVc415Ijb7OwonsEaVlHnLFTnYjwTfA9HWKMsqwiqiCXXgK6coWhcpmGugieZJe3dRlYd5TOn/42y\nJPPUe++hylHAVBR41qygCiL1hgUEo4FSKYozWyAX3YG/4TrXDf2M11Tz5OR3yBVE7FKSq2IPabFE\nLDqAKBRI2XVEvUGanXuZPupkGStmc5K9DS9T0mtcXutln+nvcAprGMMSfdZpXpvejUGDTzVfohip\noicloq5t0JBZZckbYCJQh6FSYvvkdZabatF0Jv5yykBEWWcdM03RNeJmG2dr3Yw6JCQROvLL7JQ0\n9pVHCKhGgoqJZjmGDVgviZyuhJgrZ2iIjFIqpfDvjuNrXkYc30HH1Cfglz8ntuxlSuhk0FGHEs4g\nxgvodUk+ET2OM3eOIXuQQzE3j6l3sRGrRwlcwtacQmsTSQ74+Knh00wmGrlSbOdkaBvX79jM2R27\nqVlf5ZxvN3XM4/eFeUr6Q5pLYb51w03vappXBQ+aqLJIiD7POMpUCWtApCSbeL/2DhbVxbv6AG3K\nOjVqltG2Ndb16wTzW9gnb2U2M8QLvMYBrZ+sWMGj2ThaaqKUusRvtQRv7z9A00qYYDHMjLkNV8FB\nJZ1BSiXI1DaS1Gu4830U5AyKcYFjnTt5X6GRfbqdfHWvhWFvP43p89wdmeRQ9jx/2P8njHi62Dk0\nyh//7F9xXspwdaOWoDNObSXNxoSLDbefanKYYz0I6gyu+CqF7Rppu0Roo4BWLHFJLzLuKtCQ9NEY\nnCe/oaeUMrLXM0htXYZhr5mTlSPYK1l2xEf4w5m/5u4pC96WM8TW65BNc8hZA+XsONX2OiadGa5W\n3aAxW49BgJ2ZNBGdgFW/zt2cY1GswSgofKvwAVakXi7b2xg3B1nRVVOnSzNUrqdeBUUQGQq1crbB\nyon6Ksb99UxVhbhv5i06um/gtcZJjFtIE+SmEmJVdfBt3fdpkVa5vlpNh2eGvrZRrpan8I1WkFWQ\niys8ufvzXHUOUDMJhuwiTr2PTKbEStZJLm/BIhuQiw7+6tN/wXitB1Uyc7VmF9fl7bhyJbpHbtAl\nj6KsVdF76woXe7cTEVY5EvXSpYRY3LjOVz/zBwSjEf7sp//O4PZ+2sQlfh06wpwxiClTJG6xs3Xw\nEl9OPcvzdXcj6Iw8kfoVD+ivcS0X4o6cGaumsVCXY85jJaJ3kxtPIc+Y0Cevszc8z+pVJ9aJKIuh\nOoqahYRfz7GWbiZ8bQgWETVcIpmTCNnybLg9NM4vUVaM/H3jY+wWh1krOpguWpgVVVChR4VvrIyj\nS69ywuZmWrLRXpkh2/AjSvE9aKqMjgq57X6qVyNYqDBSDiBYJJZz3QhAVHUQLnqQZqB+KYpbX0Dp\nr2ZKCPIL5QgfHX6NtN7Cea2HgZsTNDWs8/2ee3lH2Y6SgvHFFgTBwOvx3czlA9zlvMY9Xa+RtVUo\nGCRenW9gR2obFVcLJuMWqsXLSJ4Y4Y06/ujOIUbjj5PZuJ+FUh05sUBZMxCR8kxoEjIVilqZ/X1P\n09QwiSiCJINd0FiMV7Ppxh9gkF7Hl44SUVu4qh3kk1u+g9C0wR8ZzWybVKm2FHAY8tRnJwn+Oo0t\nWaGJNCMlD9M1VgqfzPMtt0SdKDNgK1IzVI0ulqBKKTHaHGIk1sYdp0dImSx8o/sjPJu7D0chzQdK\nL+GJzZG1lPhk9QgFCb5rugtnqpecIrFscBGyXaJ/NM5EsIqMRSR0+wnSbc9SftdH3QtzTB7089et\nXyCm68GfOMHJQpH9xU3ob9UzOvEpMhvNzKftjAhuLsaTlPIpEq7DRDyNNIej7B0vgSaRti5QFd3N\ninOYDFM8dLMXqzLHjulhbifaCacP0EAZnZjgU8rrJGWV8dIemnXvsmF5EH9f83+hNP6//TcU/5N+\nV6GIo4b6s1/mW+YPcbuvlftcB2kfXef5tjFC+Xqc4Si36jeTL0ks1gRpi09j969xQd/O0myKbt8S\nk6E29i5dY2/iKkP6JqJ1VTRYbrOj+01Mm8KMDXWxZX6Cb/V+kNWaILqMgLsYx2+K8ILvTt6pHSBq\nbmUu48SgGDjf3EN1UsCeT6Lq9OhkHZPGbqYaBxjrbKBgMVPGiC2bZHnP/dwrbeZ7e1p5aPI57nnl\nHFcGejhlP8JtQxN9sxP8z+99lxmfymrTAPpMgqvRWrbah7BcKPF6cCdrEZmGWJKacBYR0OdV7Kky\nqcOQEz9Cf+55fuyw8au8l6MNJ9huH+S15HZeV3dwr3SJTcVJFt0BrPXLSGkbZWeYZMHOi7c/xpc6\nX6AmNYh+TxR1TsdizkkgF8YZT7IYcTOYq2Y260MUNbo24pQKTjy2CMWUzPJ5F5ZQkZIiU5JVevrG\nuddzGu9aIx5lnhbFwpLLgrrhRjA5aSxYER0ioi7NWLCBYhKSaT+fz7xJwiHjfdNAqSDzw70Opmqy\nXKg5RF0sRkJ/kzuNV7jAdva2TVKgGzNtHEVPqrTOkmsaZ3k7FsNNArOT/MJ/J8WilVxAJt6bZMbn\nJBssU2pKMGxy8W3zx9iS3IJenOb7Bi/H1c3cL53nacO3kKwlPlr/tzz+66e55/IpXOkcl/u20rt0\nnfvczxKN1rO62o5UKdA/N8lEwE3dcpS4vxlBKpFH4zfSFh7OR3FWzTFk7mM61ErBaKJv7hYFR4XD\n1ceZzHYTwYD6Hy/x+O3zLIW7mFfqcdqXqHEt0TIbZ1v+HqqlGmrT3yf5xSLFPg1F1GM6bcD7mzIn\nqu7k+YH38HTn+7mp30ILE3zM/W8M6T/E22UrfyS9SIttksmQBVsoR/XzGsGVOWRNA0DYyLD+4SJD\nyzsISwm63ffQcuMNnt+1B3M+y42gl5+093P41AmyLgeaoNLafo5E2sfZxP1M+CTO1ovc8/opypsV\nLKdkcqcSZPdqSIEkueu16PU1/NDh597ZswhKhVKTSjkIlpocm2drqGIEAQVZWiKvHsQsXyQ+Dm3t\nk5wyb+dG7D5s/i7qZ2/zblBHwWykAmhlI7GcC12pQndhiMTvZxGtBuw7J8hm3CyvdaKTygRj/cyK\nMfKSwpjOywwNuIjze/yGUV0zXYlZvpg7SWFdz8/TdxN1+ukXujCohzmRLdCir0YvLqIZe1mVG/nO\n8CQN+QdwqHVsVz20JN5hyOZCc7exT6r7j7sioKIyJs/wYkc9ukSBW9ZujGoZi6ageCTOCl106pZA\nFOgdvE7S0Qqal7QjjGcjw0LoftzZJQ5e/AdmA7X8yZ4nWNNbeHjLk7xp9jAer+EPpTjIYaRCkM8p\nfdTLNpYqAkqiicWWAP3501S4ynVLiP3Fea6X91ARJIqmMNN+L5I5RUWzMu8ycCXegUHMEXCv4q6e\n4rc8yPqKG+MwmDETM5o5W1KYSQzwPx3fJx55gvFCgn3FZpKGGZw5lU2RMnU1p9kUvY1zVeN2sJG/\nfvM8VfoGJpIBDNlGrNlqelenCUYW0HQqU74Qi24fncs3icsiKdUEosj/PVEqop4hali01PCB97yM\nvllPoHkZ4/JOzFOPsZAbRQmP8emvfpMz23ZRaXCi11moS0g0VY5jkzzo87UcGD9GjfN+/rQXRopJ\nri0143VGOGc4yqvmhxAUWHPVYQmISHaJhbYGHp4M81e3LvBSvY5L8l1siD5mhDY+O1qmJV3iLd0S\nRa3CXa+/ScTnZdjcwQH3DW4Y+zHIBRqLTzAjt9PIr3hX20SDmmE6r5CMbWJveRMe1c4vhZ9j30ij\nZK9xv8VFu7rEubU3mczHsG1r5HX/XhRRIJCKcp96nEWhEVXvpuz2oQkCUtmCKVdL2bpE1CRxo6WH\nKbnM0tqbnO27m8bCce5+6yTvz5zEquU5V36EmNFF57KB7bfPkDDnidjsBOby1NUkiRotdB0bxbSs\nsrfldfYIFzBcFdnwGCjVClSSGr25dRrr4UzCyohWZAsWaurmyIkCRw3zTFesnLAfIp6sodF7k725\ncXSKn4ZNg2wUNJLJIIoiUzZlKbt9xA0aFUkmkA/iKDmpsaVYW30IS9MZFnJNjPhF3GUzayYX86pC\nRbEiCkb0yBQtkzRSYos4jyyI6NQSY0teSJYJxUoYbRVqx+c4vt7DptEJ3FNlFtYbSBgcnFHb8Cpx\ndDo9JcHEWMSOzRil2pzEN7gVMZNgorlMIFzGlF7FGG/DGb4ClPFX7aKrbZKYp0Q47URXsvHru54g\nr5cRtTiSyUhOtiJVKugrJSZqGwiMlPEkYtxz4iRzwQDvtG/CPXMaceQkf/ahDyFrCp979ZcMDA8i\ntSpoHo1zzi3kJCM5o5nts7f5cPUvObJ6gzW5mqs13ax7glyq7WOlo5nRVgsXOyzMeu10ZGfZ0LtR\nchrtI5fZNzePsi6RrrJyvHMr37/3cW40dDBrrsGcKbFr8AZD7Z3sWD3HhNpMzeoKYsjKK637eNu3\nlays5xn9N9mhjvC2/w5KqohcKtO2epN73/0p7ZFRbK026tQEf/rbH5K1llnUd1MQ7PRuTPClyz/n\nlruVoJxFJytMVzyImkRASJHULCDl6Ygvc9a/hbO1/WwShvm78gewCzlEi8wTyi+5pu/hueBhtttH\neCr9ENZymr8x/gtX6OdMYgvxgpPPdD5NV34NWyCMahRZWexhPR5AVzbzlGLnDQ18hQgD1jGe3Kxx\nPfk4xWw3W80qxRY32XUVveckf7PrB9TVxHgkcY5XC/t4b/2zVKwqtmyFprkcfeNp9IkUg/pqmhPj\n2LUsFyI1zJjy5N3D9PtyjFe82E8H+WHIxEEhQasxzqzTzpyxiV7TIj9JNvHq9gI7PWXmNzZzY/E+\n9tVfYSOs4ZytUE7CUw98gq7kFI9dOcGFvnauWzv5H9Ixvr70Y5ovbjDSn2ZffYLuSopbmpVw6vew\nGfJoqIjpCttTx7CqeSbaXZhjvehWdzLd8jyhpdsYJkTWHnZxXncYb67Itsk0k/YZZrUohvlPUvEM\nc0lep27qYayN09xRPc6dzmWus4UkTh47XWbQ9yqBVCseWxQp3s6ErCGZ5+lIu3DlU5iUMncr9XxS\nc7EoLZMWC9SyxgnzTl5wz9GUqWWyqZXu9v7/EmL8//XfUPxP+p2FoqRDO/vPRNJ23mArR9QcrpLC\nmq1MhgIVSzW7IyeRNDNjNY2sCV4+b36S4+P7qaQUSp1ekAR2e9+mITBMT/4650v7GPF2YVVSvLF6\nH+eKfTwwc451T4CKpuJLJpmw1vMrfz8/a36Yk9Z9GKNRXGmJ83V5goESm656kIoDGPN+9IUyshKh\naDRRLEm0Dd1GkjSmWttRBA9vaQLGtSQnq3opJQU+bf4hSkCBsI1v/uO3UOzVzPbuI201YkzE6NkY\nZt7mxZrIsizLmCplehci/NM9DxIvO2iLLVPyCMx0d7I5+16KQpBf50qERQefa/oxbTWTtFtmeTu8\njw3RSu2WYeRQAnkFUlUxFpJ1vD15hM3SC7R0jVAOFhAkSJsETntbeEX6Pe70jJIwutFSJSgrFB3V\nZKurCS3NYAsUmb1QDSXw3pGlJhDnZqKGuuolfmSFy5UeWgpBetUrvOW7g5PVW2nOLJOxGhHlHI3l\nNXx1ZwgaE5xZ3sNOxnDP5EiPmHnuIFxqV5GVKtKOg9g3brDg8/Bw7iRioMSzpTs4o+1mn6BnTlA5\nv/ozLhk1+qhFU7vxel7lR/b7saRUjuTqcad2EFrbj2XpQY4Zu6lznmeHZQb/7MP8OxZeUEM8aj7J\nl9wvIpgKvB3qx3IsjDMVIWwzs3N2gQO5N9jf8Rbrl1wo82kkg4mNUD1Jp5NgPEPG66ciK5R1enae\nvMTrdTup5CL8vutFbiitlFQd+wZP0To7z5Guq9RlUsxpNWSwsp9LzAt19JamWJEMeIUSRrGC3z/D\nfDzI9GIVfdYg0UO/RV5T+XH047ys/xy+mc3ErQGevmsv8x4/PctT3LUwySeqvkFlrZEXlg8DFb6r\n/ybTdhvXzrVQ1R1Hr0uykH0Ue2aOqD3E0EAXri0zpKYdrMoBnAuT9AQe5IJhg0mbxOMTL3LLP0DD\n0gRli5GgaYqqhnnmZjZTKS+hd48z0tROsziJszqC8xmJZU8j5aQBfVcUY9U6rck1NheHEbRmbLeX\nULxgCGqU9CJNG7dRBVjwu/GlZykL1STLj1IdehNKRUaHHgJdM0cVibOtjYQteUAgVDRyx+DbuN9/\niYb8KJbZHOOGD6L1L2AUMnT8eJlMSyMLiVoWpShGVU9ZUEAQkAQFh+qhVreOtRJlhFZ8pxaIjtlI\nWd1EqqrYrfbw7eIygnEPHdoWiloHOrUGe3wOT9lMRTrNctUvcCheDPLdpEzLLIpJJEXEj5O/qimy\nYrjBDWOZW3WtiLNpDPEsiVoXPWvzhCUrgqAnIKex5RX2XL2BvpAl7tyEpBgo6vuxpecZuPFd5NBW\nvv35hzgSfJVrSwPcDvdzO9FOrV6hw32CfNHKy0odS80OdgxewC3rmNNc9K+qOOZkivN3Imzso8Vy\nCeN6jnjhfYwH9QTSSxjyIgHVRUzMYtbHAUhk3ERm2pg0txGWbazhQjaIZGMy46Ut1FpXeaRxhKsb\neuyqkU2ZKupKdmqsBoLb/54e8QZN5jHS8WqsGxnel3WwgI6NigHkd6gurIDQgjMVxJ8ex1mJMRRs\nwlrM48okEUQBDVAREAUwoLAzcIG7ut7G54hitqQ4y36WFt7PfHmQWVOO49t2MxGsZ//0TSzFPPtm\nTOxfg9pCMyVTGEGARa2fn/UHmXfbWfAHyBjqWE3t56Kvk8OLJb56psjWpWe4FOom6XEiTyUprN7i\nMf2zrJfHmffuYVpoY6B0ky/fdnJTd5wZSeVgpZ+CKFJ76wqTza3cNnQiChp7w6e4eeg3rGZPEC4c\n5joDTCsNfKx8maGcg4PaPbzhOsdPewoM6Bv5hOENZPEqX7UkyZYM6HUmhOE1GrQZjrftxLeyQM/y\nKDvso4TjMmmDB0FVMedrkMsOzjX9ind9x9g6L3O5bQu3Wvvomr7OHzu/y8JcI/fZB4nG6tmjfoBH\nFpLsWZ1lw2klrqxStIuYNpJUWYsEnBu8y2b+N3v3+WXZXR56/rv3yTmfU3Uq51zV1V2ds9RqZckS\nksiIZIxhfO0xjC93zAzG9vX4eowvYEBmEEkgkBDK0N1Sd6uzOndVV3VXzunknMPee16w1ry4w32N\nX/j7Rzy/9VnredbvoP0iPu8mhbgGe2MB8R0dyqIa+V0d1rYCtpiG1dAAk7VhmJZpqSvzyOYy2bSJ\nlz2PEU80YhHmGdf78OWK7NedY6UlxZ27hxDyRRSNBm0piTpZQhBMiIKEUTHT3DhBefoh5FKC5uZf\nEsx1ocup0MhqbrV2EOtv4Avr6wwnnGg1VT6x68esrfVQUhlAEPiQZGNYruVOsUogWaKpUmUhosVT\nDDEzsJ0fD34cV7bAWM5DSjTzoH4WtUrDqlCPWQ5Tjgok7X0ko0lKeh3z3QWSqjQDCyWE4i0QNBx7\n4Fl+tnOY+mKEX/Q/wamtjzDRvp2UxcZDZ77L4ZVLfMb/Mg3vbeeTL3+NL772Eu5qCUWrJqXT0hVa\noW96jtcOPUCmnOTk0CBRu4sDY2OMt2/n9J6tHB86yG+8h2lbXyFuc+DIpOiNTvPPLf+JQtbKVzee\n45c1DxNR2xlMTvGRwHG8cw20Bzd5ovAmT60c43a1kbnWPj7/5hvYCnmcR9J8/xMP8vzWP6Y5sME/\n/8vf8idv/oonzp/i9MgeQk4HH7j5M1RVLRdsQ7RmAhiNMpfFVnrVEYLqZiSVgQO5Kr0WG+X1eRYs\nDdwnjbKxw8WGpZlWcZ4JQzNSpIMJdzcVQUNBbeATd08yZmnFbqkyU3ETlF3sVK8wLzsRJRkVEs8f\n/zZZk5oNi5d3pe2oqZLTGNhuXufPzK8TskWYkrZwtjjEhuzjW7rnOKqa40h1kHQ5xv2qZYa3vEOL\nYZW8Xo0kq/j7TJYlyypL2T5mKk5k4LjQjUuVYDF/iKnSCA7HBUq1v8S8vJ24oGCo+wnvaZ7hvPET\neKUC27SXaWpawLtopnbiyzyvVXPSnOL+XIYt5dvYlSyKAj/XdXKya4xATs0OvcROQ4FwycCbTXlG\nK3oeqGZpNafp1Gwyp5g40VKmL6XC4tBilLXcXN9Nm30BS2eC6LSDmliemmScPYlJvBtRTJ9a57Z0\nm0dMcwybY+iayuzJ5unSphAEsEhOZpMfY0T/Gh3qqyzm76FGPcN1jxd1XRjDzV7CE2MsX7bQNJUA\ni54201foSl2hPzFJf/+7WKNuzGv3Yc018FLHj4maZ9iSHEEf9eBsu8jFtIGN9Dy7p3fgjReYrDtO\nTaaNGjX4bOts1ShoUrUYZR1dbe/jdm1gcU0z6pijkEmjVSSiNPFrrZ555wy/tm/hc/f/BTV67R8Q\nGv///gOKv6d/t1AEhJnf0pa7wS/lo4zrijysb6bxzm1e6VvBW23FPxcj6XERFGxs1NViS8WZmmqh\n0mEjXeekrNZyS9jBae4nnXNTvpujYLcxbtpKKGIjV9Cw1TrF/iu3GKxMYAiXWa5rJ5tx0xGIUpNO\nURdNMmmf5EjzGPdrL7JsusjQqSjG7CrbR3/FnSYTxuIh8iY733MNMaPyoVGDXhVlE4grIgVFz1JN\nA7t2XWbr2CRPffs9Kp1GfEN/AwYHK6oINaublJUMunyRiNWEUIVtCyHKn8izpXaab2g/SdBpZ8fS\nDL5AGKlRZFo+wHcVF4+1H2fvbxYRJtzo9oVJFSy8l93FrrprmENlJK/C6bWDPDf5af6Xd95gcOcS\nCKDKCJRkN7mlpxnL9lORJb6m/xmTum183HGW/c45Fkw9LOubse1ZJhVVw3UNi4NtNNcGMKkqBAtm\nIht2agxbedt1lXdtae4t1nJ/4hS/arifhXoPgWUzYdnMbaODB1rOECfMwvpuNmUX289NItco+IoC\nj5+SeWfXIwyEs1jzOUqBCFcMw3w181lGlQ4mPXrW3Bq+M2hisHqXv+EFxis23MJO9Lo03RWJD6v6\naVJcFBQ3Wl0QJBNbN2q5vaEwtf44w7KDF6Q4I7o5hhaXWRbaUNyDXL+qwh5PMpwQKMtFlu06nDvC\nhK56yGyaGVwNsWv6DqpClYX2dgINDaTtVoyKyL7MbUr9ObI6Fe+Y9lGMa5hor2FNa0Oy6egaWyU1\nYySzZsAzGqL7zjS1qRBynZqb4iCPaN7jrHkHmrSaJv8dgjhZsZnQWa+hapineLwe4bbMra19NBRW\nGW9ycLW9i4NTcwxvTuPdaMdWN46oz/GT9f18wnqOffJNflUYoU0KYV+pkj8kY7+WwBxJ8v8MPER+\nm4E27x2SY/eiVFNs2PV0rUUpeNs53uHjw7/+Cf+r8luuW9rwr2xQujeFWl1hdGMP5hLs7jrOI5o3\nsHujBDN+XvV+nJzLTkRTj80TwG5dZ3vsFlPSTs7VNNK7uoZuoYSUFFC7TbRFY8w2mwnknsAtz2MU\nrqAlgl49x9poJ6bVcfy5ALrgJHEjRIwyyArbz5zAtJEge1RGqZWp1oG9aQpbqpnJje20XlmlYT7F\nRq+HvKxFo6ioiL/7RqBLrmFJTFNffIp/berBk45jCiVJiQoL/Vtwqz1EFDOjKit/peiZKc/z7KEG\n9m1c5aImwISwwQuBGton5sg99Q6irKUrcpiQJsysKoRF1vN6fRV/aJU79a1ELC4+cLfAdKrCp5I3\niUsigqjFJ+RRCRI7lDVkidkAACAASURBVB7yI1Wcs5fYcBxGJdtxJMbZMv49cve5iH5wiX7hGk4p\ny5arixw37AUZDplvIuRMCGU/V2Unc/a7vF/7BnfavKxa23GFq4iimaKQRRZtbJQG6OU3zKez3PI3\ncT7dxD2iTExMICODAAhgzDSRFwcQxQ1mGptRgmVWZQfrahde1HxZW2ZTv0Q85eXe8gAmlRWdI0tq\n5z+irepw3foSFec0Vu88uY0mdKomJooiADF3kIhdIG1IoC/ZMOSb2SXludFUZdHaRN/mEhSqoBZ5\np9yFWc5j12cYHjqDqAi8t3aIbzq/wmawlVWTRH1sk7xKg7eQoS28SF0yQVcqRu1mA+MmmaxxGZ06\ng6ioyBmd3OhsQj2boieQIFprY92jY89UngM38uQkcJRq2Jl6CXc0zUYqSizfQVTlZ8W9B505R624\nwYH5O4wWgwRFNZmMniMJFcXadqxLtzjm6sNmktjJLXaXJtBaBaLhe1ighablZQLOWtRU2KHahyA3\n8m+mn3LkjoUvib9CUgReXRniQqvETHuCj6mfRZ3VIK/M05UNIotZNuJVZmwHSOm9IKqYrNhoLNZT\n1WTIGwMYZBWffO08iqAnZZT413/5JtlbLtpUUdrdEd6N9eBaWEMly6gcLVhtzdwqjOHqTTClE2me\nlPG3ZDG4E/idaaKTZpZuOrHUF1B3FviK9lFEYZP6RQF/Q4omVYGxqp6bDRJbQznaM1qeNz1DvmRD\nbZknshribOEh1tVuPsBlIhttzNLJUCmNTWkgrANzoYCxeB8+yxX6R46jlGxkl+8jo19lUvLjkBxU\nEEGBIf/bzBgGsWy6cGVgU1DxdqGR9uIAJtUiBkr835URfqWRSdQZqFPuEkiYAIm9pWuc3XMUcz7P\naEsPmXURnzpLrzrCVE0jRY0WQ6EEFYGybMYc2aTivYcPVJ5mL3uYzU9TNLj5+ROfJOL0M5SUeN0/\nREp08DBvcVc7yEdPvAGKDmswjSHyWYSEkbR7hDSLZL1uMiYrL+x7mG7LKL2XVgm63Fzcto+43Un/\nwhwXhreR14JKJdNS3eTT777E53/6IlW1lq2LEnUJB1tXdLzb1IO7ss5//sW/sfXSVWqiq3xOOE5L\napEHCq9wYPM6ttsZmm9v8OahB5hrbuHO4V6+1/tRzpkOs2f8Jn/xs+/w3lA3mlIeb6aARpKYd2jo\nnV7goanbnPNvI17WM2eooYSGj3b/DL8hgpw1sS5oqZYCtG6sct4zxGPpC1zqPEilquLfKvdyzdbL\nbU8H5lKRA7ExJm2tbA3OYewuMksDo1IjnaoIaGfYlNupEeIkcNOdWeSZLSd4VnWOE9XtNBRy/LXG\njEG5SFg288/iIf7C9WuOpY7QSJivq39KUu7BpX6Th/Rn2CLcIBBuINdRAkVApZJpnXqG3ZFDvFFq\npFYd5wnVGknFxa+lHczLjdxXvcJq29vkS27CsR3ofCqwjqMtb3L/SpoZ8VU+2jaHNVfhe2v9/Kbh\nMmeNK8zo1BzXOaipKjSVyywJNl6uT/O3K2n2rLtYX3LiaE+yUp/jVlnF4xV4zr6fh7Nz6EQFh1jm\nsVye/eU0mzkH3vog5/OHWE342Nt8nWXRx8nWB/nAmXdwB6KUmxRW6o0MGCTme/pJ1yiEuhUUswxX\ntUzL+zitfRypWEN3y09Y0uVIZXYwVdmKvuMGRn0C17fXMMU2qask2NS6eKXlABZbiM31vcixFkor\nI+zPByjEHsZnvMqT6QuMbKootRcpru4HQSYXHUJc62Bo3U/Oq2UuvY3mchVd0UHTgW+TLWuQ4i3c\nct3C5l1G1ma5ooqwrF3ho7k5tFWRu0ITk97zVAQBo1HHl7Z8GK0o/oGE8fv7Dyj+nv5dQzG+iG3z\nNPpZG6/ZeigLIrvX7nDFv4i90kjUU4siCHRfn+B63xYmLCPgVoNHjyIIPKn8iiOR88QKfqY8XRRq\nbAhFCUEGucZAY0yiJW2jef0GmrDAqeYRNpvqqamk0ctlrMU8S9YgK+YxPuhOAZDTidS8E6UhMs98\n/zY2mtuoKgbsWTNl7RIRlQOAOkHCpYlRI+aoU6VpUKUwWBJsFBsINobRPZPlfGOEOwk7qnIWfWCB\nd3YGmehK8OexTVpvZgkccCLck2YuBDtOx/hB5xOUmwSGppeori/wb75GMkaRPzW8iONqD4a5ADdK\njTT1BrgQ3IN828Lum9Nkdgt8f/xTNNtX2H/vbxGaBfSX1GyqHyBw+fOU0n5OqQzUiTKPms6xV7qO\nSSjxVnqEeLJAye4hWKil4ViEkkbPrf4eVpUG6jVhGnURxmJ1WMtGrvs3KIoyb/me4un4BB8OnuEV\n31HmCm7u8R+nyX+BgWqCw7croAj8Sj7E4aVbFD6boWrWYR9TSDg7cColNIkws9UBjotbeVB1FVt9\nlXtDHmwxhdvNWr6S/CF1lSh1qnEuW57Ant9OjdLEFBJ3xLfYKB3H7DtBrfAuutwePHI3imzDR4kD\nlf8NozaAe88ssj7K/GgBdbpC64EyrS1H0O4YI5kUiEy4KGe19GxE8aQL/OzgPUjFGL2SiH/nOQYc\nEfpTt1g1GdHs3qA3GSW3buY150F2SMtUew1ktXZ2vz+KooJKXo0oywgolDNqnGtRAi11zCvttDtn\nuUe5TEkvYHakiC52UTfwHtFcLVcqR9GbVLTHA0hKgeMD29FWKxyau4kgyGgMMzRr5lHVxLm2tpt/\nUr5FvGjEez2N64bMuuJC3y1hXE2hXheY6q3hvt4LKBqF6N3P0LR6lrXGGmx3L9BRsfLyQBP2WIqB\n0au0N6ywnLPTNjzB3eQI7ZYLpKOtlKbc1IwFqNkoYryoYsnVScpgoqhV86HYacL1ajLY2ZEb53i4\nk+HFCfC0o11M0G4PgUHNa+lP4UuMoDYZcJbG0IlrZKQPs1gZwLA6idHZgW7ow1y2BJCQqZVtbKk0\nUhi0kO9b+t2MEPQ0X/t7nOtHqM/1EG3eweu7diPm02gEgYJYpdW2Qr5qoiKpkJDIimUOZmrYkNfI\n6swkfR8h5UgwWGnku4qRb1RUKFKc4IP/naDWQGldRvrdZSSDayWGSxfJ7VXIee6wtNRK3eAviGVd\nLCgpGhMRCh4417gdQznLA5FJrpVtXKv4AYFmVRKVICFS4Rv7u3mh8SCnd+6mdWmBjuVbtC69xvUj\nwxgfu0Mg1szk3YOEw200BuJ4pTDY9LRKGcypThxJN+OGMJWCSLolx3TdRwm6KnyodJ0DciNnhs2k\n5BXUmTZ+23SU7z5+L1G3nnbDDb4U2krWILFizKMrS0iCgXK1C1daoH/qFCcP7GRPMcl/Tev4Qllm\nm05NT8nNaCnPus3MhaZu9peW2Rj5Z8SqgYmNryJfOE2L+CTZxrOYLHFuRexY0p1U1JuUDEkEBRRZ\noGgMYy47SWU96DJpxlt8qGWJulyCiWI9Qxkn9rxI89BpbJYYd28f5Xx4O8Nrd9g68T3qKzbKejtO\nx37GjDM0pCGjSdMd2UtGUnPSkqJPu0AVEXPVytWOWuJGG4/eSHNoXctj59+ld+MSj+35Btc1CRyh\nQUTBiMoxTq5o5j7mmKzUE5HNJCp6BhY32RJawBD30SGb+XW1hstiE1s23uZSnUSkro03bL30VZN4\nUsu0OEK8k3qEDWoZmrpCSI7jLGqYtvTRU9mJUX2KT1aOcY9xioKk5SeZA1zvtbOlI8LNnMxva3s5\nNngvrYVN9KINg2IEk5eq6nfvmoYqR3QL5JPbCbrex1G184HAMnsdSzxsusBTiRPMSFra5/N0t29S\nNqiYmHWSF/JktUWymTlWVUnihQDt9ix1V3K8OKJlX6JEjTlDZl3Pwu06DOUKP+4ysF+bZVgVR/BZ\ncDSFsZRk3KoMO5U0U1otfxYu81P5KYoqPS7VbS4Xo9xpMBPNHSTsGqWmzs9oYginEOdj2heJqDZZ\nrTaCQ8Kq1lJK7EZlXCR88wFkxYVO8uHIdCCKVQraFJqyDefqbryRMRo2mymLRfxVLXWKlU8LJooo\nrApZ0v5mNkUNqmCBbNFFVRF5MvIW47sPkDeZ+ccXvkFuU2FD7aaw3YWVCrscabIeB/pogarNjaLT\nI+Yz/NC1j5VeOx/ZEJhy9vP80X0YFIl/vFnmi4sSsUqRKY+eWaGH9tAi//nX3+XCkSFaF9w0T9+k\nd/pnZC0dLHf2kbVJ7FYuc62hjwt1+7AWijx44Szv7DxAz9I8t7t6eer0Kf7Pt77JB958hyM3L7Gn\n9i7RjIeK7gOo1Abstii6jIbt4wu0nj2DGFZwpvOY7A7SpgfoNI4jbiZZueCmnFTj0CeZdjew0N5O\nVmNElzXx4PnjfOKtnzPR6MVYzHOzZ4jRxiEev3qGQxOjVESRzmAcvSfJu659FGQtjxdPsdd5mbI3\nzsOpIPvLV7ibaaXgs7OAh9HmIRrLKW5WG/ggZ/g7RnAqt/jK8j/SNRXg9bYDNNcHGdQs8f3qg3iE\nHA+px9ix/g5PjF2jQ7PKHXUniQYrn9Se4faCmacrJkLWYb5NlbNyP7+Vd1OVTDRmqowr7SiizEdV\np9ELYebSLibTW/Eb8rTJKyQTJoLZdgyuOKmMg9PxvUQQ+ZhpnUOVJnKymQkknhHO8nX9C7xmdKHN\njJAte3lUegVNnUyssMyKfoqPWYu4TTLtExX+LvlXRNynUQsC9UU3YU2eMZPIpzJpfuqw8oF0hb1y\nihZ9lMmEn4GNBN/TmKjqZB6rL6MeheMaA25djreETn6e+SR9ShJdIU62A6769jE508qIbwx7U47/\n3vbnPHbhJIZSheKhKg3ONHKXTFN5HUEWKCaayTdkyaW0mE6WWK17DJ1rCau+kzFsFABzvp7GLS9h\nHIWFyiBnDxykq+XjrHUOMCV2Y8q00eg4Q0fjy+Tj7cznD6MRijzt+FtaNEm69Ely9VHC2WHyq9vR\nxlvw5evZ1Cd4UVGIqxS2EUeXq0Xw3GFhcQS7e4Vpx10uF1TcLAqslkWs+Sp/Gg9x0e0hUqzFV/Ax\nW6xH0d/gIfpx1Tb/IXjxP+0/oPh7+vcMRTQGGP05rrSDlG6AV9TQaXDTHjjHlWYN7rIPVzyJKJ9l\nPjGELl9Eb5fJGcwMy+N8Wvk+4YltfDpkYcqRIKx3I2YrfPJSjgW/loxLw+5FO7bEXUzFFIsjh/jJ\ngcMgNzE0V8dv2y+wZL1MUbWFhuU2wuE2euoXuCxrsUf1vPb4ITYNBmoyCqZCLXbdEh36Rf5CGmTJ\nfZ5KZ4EfdDxFd7WZmsokG6Fmjvm6ebvZQ1OukwHre4QCDYS0TkyxVfxRE4Kc5oOVOKqOCsn7qhgr\nFb4ujHB1x0fIGB1MZxq47Wjn4NJtBtan+aP183jPVhCyUQQU8oZ2ouv9rBotbIhOngnDb52dXE/1\n84Wmn+OqT1HJOZhd/DLZ9Z34dFVqTAqvijJDJQ3p5P0kvQrjHitSc4nGLcuYVCmc78nUrQVYuL+G\ncvljFAtbmLIotKiCGOQ8yzERo15NXq8jLi6iDwyxW1zmQ8E3WK5vwao/wUFbjK2jOaxShZp4jJ9r\n7kMWtTT0plGaM2wudmGvFkg4nNzO13PL0spOeZK22B16F+bYqtlJIa/w2cQ4u6ov8a36j9BRWMWt\n3GDUdJgfNcHr0WVuVQf4K/V7DBZn0AhZXlG56az00YKavGGSLu3btMgx4rN2wvN2AqKf1N5mdtuv\nozGeo2yTqL+tIVHQ0BKM0xpNcb2hiayooOjUPNoyQ9WbI+uJsVxj5oamg17LInsXQxhtErFVEyd1\nW9GHS/xL+FuUp1UoskhOZ+DyB4bJpjQUdL9by2lcWmW+tQN7tsC28iQX2U7WJ3LCdS+vGJ7hTcMT\njNe3kzaaGQjcIdTk5Kavj3umb9JZWUBRBMroGakkydQl8OdkDufH0OcrJHImftWnZn5HikOrfriR\no9KsUPfsDIIpj5hSiC88SOPyZTbrnazVOnHNn2DG38l0YxsP/+YMyqSEoy9ApUum+UdpbANJjDMS\nQXszh/PjtNnieBxZgrKX5sErPCX9hqZYlgldK9XaNN5YgW5jGHlSYra9k7a2PtzeG6zO1vO+vZ9D\n5Q7M5TjB5nHKsoq16rN4jFvJ77JhtT/GZnaKu+YkiqAwGBapqd1P2Weh6LiLLl2LY/Mo5vgASXUG\nk6TFIZihFGBdFaej9SLtLVexNy7gDA+yIFWpl92sqSL4+39BNtZOWmtE0RSoqkoYiy3cJ0rUikZm\ndz6HzrxGfSxCKOxn19Qmrs1F2uO3kQfypO02VMYqXqOMZ/0xVAOvkAg1UXSo+UX3w2gkic+tTjMo\nfZ/W+kX6au/yZP+rhEOtSJIGfUMIn3OV9qUptJt6PnLix2iqAS4cOkjM6iEcbiMSbgFFBYJAxOtB\ntBnwiinGXGPELCusGd9izVWkmtpJoWE/atUq9678NQ/PGBnzb+G+APy2NsymzcXgoprW7DR/GrDw\n+fU2yqLID3dGeKTheaSSlnzegidcjzUfZWdwnC7zIM9GjVikCvn1r5F74nW0uRrUmSbSdaMsO0t0\nN3wbRInpxS/zE5uZz7zxIipVAzpDL0rjJSjYqYaH2Hbrp8hinqTLiSwoCKKCXZulImvxxV0EnTLT\nfh+mtMBQoA6/rMPtjNM09Cbraz1sxttpIs5dwxp4vDgqNnZWGjgat6CW48yaZrEVPaQ1ceYw06Wf\nQStKOEsDbFe7+EF/HR2bKQ4viEi5GXbf/hGvHKyl1R2kxRtkIzmMkjajVGx4dNdZVVo5wjWOydvY\nJi3TrEozVazlRrUbg2wnqJjIIOHz5jFJIkmdQqeYwxfeyqphkBuii4xo5nDwPS5oFFxJD50b66y1\ntBIX0ngrl9ksbWW2uJ87hT9BtrnZ9N2k1yNyqeCD0hT7l9O4KhaWjSVCRheuqoSgyCCIyKgQS0aE\nQj1fNP09Y0IPOZ0ZnxhlXN+EQyox4EkQ1qhpaMowFfdyV/JxZnszPxu+xlsdKzStZHFSi9P4ESx3\nrvGv99voGq3iSMOV6Ie42t/HqnuTl7bJaBUj+8oRrLJMATsFWcGpLmGTFQ7mVLyiPEYCO5ViCX1s\nlT8ub/BlaZr+zCSOlQrRoJmSxUc4t4xWTHNYtcZKSk9U10pCt4lCmXRwGElwIqvKzHqu8V7bi9Rr\n8/R2XsVpj5Bc3o012YFKUdCYA4gVGy2yBrFujN+oMsTNNi52t7NLa+CrGTVvDxhoKixRnw9wavfD\nHI5f40OR3/AT5Sg6bRlzX5Vbnn7E1RKNC3OI6irqWBRZb0TWGYhvqaNFGqM96OXljiL3Tl3iobkc\n95Z9CEB/TCEZjVCxqvjr5/4Fl7ZEfUSh//YVjPkA6LVk7FXWGvXo8x76WKBcSTDqGeZ8/07udvYx\nMDfJpeHtPHbuJH/2+vMM3bOCyVsisWgis2bAubXKhPoxlv1zrPS+T8vCHXa8/zaSysRav5Z6fRr1\nbAandoNKJEzolh21qxPjfi1NnQv0VG7zD6vPs/XiFR596R0G1pa52uwj5KhS0Th46fHPMdHfz3i9\nlaM3x2iMZwAImDTQpJAu2Pgv7/0CZcxAbNKFJ+Cjxj+DezXEhLObWk0WQ6WEXpBwMc9M1xk60gP0\n5JxM28e5p3WBs9UBrqr7eV3ai6cS44BxiqfVbzGoTVG9q6EhGMfaVOJV7SFeYhsv2R7lRUMfk4KM\n1nKbWvcrSKoC5WIr40oHgjHKLmOefyoc4fnSo+RjVfLeLbRUP42szNMqLZBdsVBsU7hccHEyPojK\nc5yJmje4ZJng/cQ2dkZneWr+DO7GAC84jRRMS9gKFZYaRqlThVAQuM9SYZtDonm1gDMi8SNDK4Jt\nAkGGRxNWDlTX+EyigE+u8MP0Z/giZ8kpOiRAsCfxGLJ8z2XGXGhEe7qFmXI/Ac09BBOfRp88iqfc\nxEzxXowqiXL7Mkq1wmxaTypZx77aK2wtXse8L4SmtUSXmGCxx4icVtNzM09/MIE/nkDSSsQH1RQ0\nNnJOD7WDL1Btvorbs4xndRWVTsHUOcHS0jBz3n4EQUCPlju6ZroSMKqtst/4Q3ZVxmjWbeLT3EQo\nTzCV6ELWfRmj+gQ1pRyv1nVwq9POkZ6/Y9YSoLC8SIMmgMOex1aTwhJqIFFREHRpBgZO8f7ik5gr\nWg6o1HSvHeaL2XnMQpq/dAnEdRk60h2oJSMbsoW+tkP01vj+oMz4H/sPKP6e/l1D0ehCufANjKZ1\nPMvtjDv9HNcbedByP+e8p/DlaxnzBTjTHcWT7CKUtPO1Xz/H3tJ17m15nXCgDSl5D7sLzWzLvscJ\nSyPtCTUDaxWu6MoUGs3sufATBlfGkQQ1DcuTXKcVOaWhtlriRuMLKKIOVJ+jNVCgWLRgc6zgXi8w\na2jnh/d+lt61NCZhGVOmlUJpnl6HH69kx1GO4ipf4u2aJ/jKTA77ludYi7XjTQvsuZ3j0TMFBPtu\nptMaEnV6zrZZGJqJ4C/Wc8Z9gCH/LPEmhXLMw0/r/om83sb/cfZb7N5xjnvlSWb7bVxVbaFoVhEy\nVHl/oErvqoIlWybjeZD6cp4LNg/NBhe/rvZQY13laNsxKhk/a+e+jFyx0jz4EttyO3lDjHAbHV9S\nC5S1YSKb+yiEt1KzsobvRAT/sQy+SIRonZVZzaMIYh162wbl2FZmtQpHLTeYTbtwRE3cO6HlVvsG\nR7uCJBoqOGMGnoq8Q08uT99yjky5gfn8XtTvb7Kiq+F04wjz2Uas1gRnG3czo7RyTW5kQevHJBZp\nIEVnZpJH6pw41COk4pfZ7j6GWkzwzb7HuCIN8EzybfTKOZ4O/ZgluZ/3RR+6YjOJpllez+3mO7lH\nUFuXGC55iHce44TPRUsySo8xwpAjSJc7xsPxKzTEMnjiZZzBRoT0p6i9dQJnroQkwmSjE71SZvtm\nAUfNk/hCfkLNk6AIdNiXsE3aqCvG+H7z1/ijmyeJSxpuG9q4mu2iPbqOWq7ynZ3P8Cvtfk74d/Ob\nxn281bif27Y2nkme5o6tn3F6qBQM/LLxCda19WzJ3GbwZpZaxcqNOjfXmwaZsrfSFlhl6+osk852\ntpfuEMLL2dxhautv0amax5SsoNHL+L0ZUk4bEyaBnnCK4jYZySGjmxQRSgrVBoWmdy7g3AyTMXuI\ned0kGaGgFBkd6KMhFaZTt0TyySpyQIfneAXTeRV14TCHmm/gNqe4yDZsYo5dyijmRIbmZI6wzcD0\ntUH03TECNgs7g8tUug30WWdwOW9SlbScyOymrLdRoIJGPcWKJU+kR6DiH8Ue2Ic5103aMc5Eao2k\nQURdqWBc1ZLxqmjMN5FL7OeCcpgdq+0EC6dZEFa44NTwqjdDIb/IstvPRf82dpvOYjktUH/WyGZ9\nAwkxjyIolJHQIJKtaqmqS1gUPS5RZFjxs1J3kkrTeX5c+CKWRRX2cIotly7iisfR54ro5wX019UY\nWveQbryA5+bDVO0RrvRYecH3QXoKC3xl7jn6nMeodubx64KUjEGuyx3kyi1os+DtmmCn+got0gJd\nr1VpDi3w3z75eRxSEa02T6lkZMVZg72YJavOoSgKM8brVNVl1qwxVi1LhGwVtkVtrMhbECoKe6uv\ncFuzzmsNTtZVyzwS78agqaVWkLGlZAwJOyPZFNPSEm/vv8njxu+TzEFFspGJ19IUO81+z150TXup\nKQqcrdEwlRjH9sgZMMhkTRu0rz6Krv4YO8VLiMY81UUvX2t8mmduHcecifHc48+wddlF2HkHp2eJ\n1Nx+Mu0mAmY7BQ2oUFALFVJCFUGXxqAx4QlquN5hxFK00LZR4d2BHAc7f4CAwkvTn8InJxCAmqod\nV9mKpC7xSGGErJino9SAXjawWVKjFiWM+gB6QWJAauSIXM+JeiPve/XsXrjEmKThwNjL5GprMHu2\n0RM/ilJ3Cq1lHv3qPvJlO2v6MhUBCqKHv5DdpLWrGJUcXlnFacVLqXYCx9BJesMyJkHh3ks3qam7\nhw1xk5IuiaRbR1WVOTy1wo+683QvaBlcDlLf+QG0ljqmNEEmdFa6y08RV5pYL1nokn30Vzt4yfwY\nsmCnIxijNVPDhDvH1aHHqWrMtEc2EatVyqLAsnUJjeRGUUcJdN6kZbHAkqqNUcMgq3Ibr2gfoKW6\nxFbPOoIIp8PtZCUd9bmD2FRenkkGyK4baar30yvs5Lv9e1hRJ5lpV7Gif5yi9iDLmjf49f4o7elO\nPpNO80vLPj6f+TKbqVpOeI7QeHcdiz3Hi+JTRAQXiiAiqLWojS6GdAskyzpGLKugcrBoG0BWKugC\nqyTCHhpMaXaZ5zmnceKoVpHUUDDkKBsilAxhTAi05JqgbCSerGVOE0KzPo5d3EqdTsMhnRtZkVmQ\n8ixYxhCLIpZSgd7AMtvDGdYcduob30beELjbOUzS5GAl4eQt9R6ikpUjoTGuD21jl/Q+rQshYhYL\n651GNOEyhlIe2Whl14KBrmUz57waDIVZzOUiGU2OMlWCVgutZRXZEIzcnmJo5iQB83YohIg4G7jS\nXo+r+QiXW83Y02l0mUHWKttRFzb502vPYdWVmbR1M9bVw5GrF/nSiz/Adl8Wq6FMsahmMurFWqhQ\nDcpEd2X5RddhPvXDi2wZm2Khvovpvr9E2BJg2fYQjalF8gtZ8mEdst1BeuhJnOYd2DTH8Cl5KmjJ\nndaT0Wi52FqL6LERut/FyyNfQBZFnlx5kw8vv80dlQ+zUIfdHOFajZ+mwhJPP/A2UykfpYqGrath\nlHgKdb0KT12M0ZADzE6sYpmopMHpfZmzOj0Xbcs8kznCQjBMpW4ZdHChupPe2DKfm/8xL4+ESRvg\niXKQBbuTvoYgQ85FTha3ICgm8vZZhjU3Mda8hs5xne/MrvJfxCuctwrEDTE07kW+Ez5MsPAzloR+\nxq39mIUYlfwGt4JF3KYK3eYF3jOZeHXpU0hCgS2Gu+wutbNmm0ZE4q/PnCFolfjekIawBlBU7NDM\ncVSV44MU2O6H0RnJMQAAIABJREFUZp2MKq1ny2yMaa3IOd8a2YqXr4bV/EnpFoOlEnGVhp9Wj3K/\nepR6Yjxb+Ss+qD5PrVzm67p2FiwS3bf6ecv5DBVNCw9kvRiRGTCcRlLNo1eyrJUOEzWpGbSd42Ju\njYX1J9lde41aQxBTuMrWUJLpVjMZh5bbl3cwMeugzpvAreQIVpvI1xRRWnNYG26hiVfRHXOjUhcR\ndqTRN04jlU2ECg/wkcJPCct7WBKSOMK1TNZrOKPKckrYhsYpMFidRc8i7+Rs+PcpXMwY0TUu0R5N\nYglINBgaKNtv06ydZ2PZj7mQoSc9wfDINRJzD1BEoq7zPDpzhp/f+RSxzCCf6fsN1oSKo+UbnDa0\nE68pM0kBjayhq+gkY7tDv7Gewdb+P6wz/of+A4q/p3/XUFRpEK4/j0rOMNP8ae7LGnlPlHkVBac6\nz6j/BFFjiPzmB3EU/GRRMVfj4otvvY26onCz/Al+Mezl2fVVxPJWfO+/yh+dP8mv7Uaiiolyk4Ur\ntT1UM3rG63azY/0aeUXDWXcb46Y0onGJiqmOoVQtjlwGSyZD96vrNN6q0LoeRas28YVCD7sq3WyW\nyyhaO0eUFt5wnuFE+w1uaDy0Jxt5WJoiX3+R1xyPUBvIoFKJhLRFQkIjGbFEXd1Njtd+msZ4HGM8\nwAsPfpxaxxJ2bZjh6TgXLCP85cpP6fVfZbi4zq70Cs9tPsLb3nu4ZB/mfM1OEvpD3Ko7wIJeR7Sy\nScrYSJ4SpywW0oqGZ1vexVbVs37hLxHKEl0jz9GYPoCUd/GOomYNhQ+LFbZQR41aRClmaD73IlLZ\nRb62k8k6LSumekTzg9isSxTzr2NX9lDMt0N6jWHXNFNJD2F/HffoZAZMq+jW1ThJoMoq6OIiar3M\nieR/Zqp4lJzKxz3KMYLOLUxWbbwf3MFqyscGDjymCI+3HePB5tO8F9rHPduvUBN+HFW2wqDxqzgM\na2w662mw36Jy14ZuIcmAY4Nozsj7xQ8xq9VxTWPhROow4+Ve7KU0X7z6Tc7tzPBKTTtz5iEaW66y\nUaPntqkbU6SCeqFMQDZSyqtxGTcp6R+mksigZEOgQMhvxOWrsMXzGPXmr6MTp3nF+FmabddRVWBk\nMcQbvnuxBw+zU29jz8XnqMnGOFO7lTfb9vNGxwGWTLVskWZ5ettr+Gc0jCzd5lzDVnSBAjXOLFnB\njM0V5U3fA3ycH5G7ruJMrp31aIE9wRJVa4WsXseT772GQXDiqQb5kPwmY8oQb0htdJo3MdbEuN7Q\nylddn6QSV+Ef3KDXL1PpUFB0CqUuqHQqSF5AAsFWRrIIGBe1rDT70VlljEaJm3VtuDRhYgcFPNYA\nLcEspR1VjDMClbgGERUv1zzCqNDPfKWegcwUNZoCkiwyMWhFby1SzrvQ+qIEyg34chmqCYVSzMd7\nxQ9xt95NWasirMkzLdrZyHaxutnHe+zDl72M1HqbQNurTCQHkQWR+lCApr53Ke8+w28MuzmwYaMz\nVeWEaYZRXYawRkWlGseai6ErFfEFVnhj8AHsCT/D1zaoyDPIw1Y28ibckpVIwUiurPv/Rk0FiUOV\nXjKGTTIj3+FnfJKmcIhqyMH2zYsYA2Wuf3onl7t2Idlk6heDxPI1qFvyuCJPMaHJ8w3f02zjOn+u\n+W+YfUEkYxXXDQVmRJyiE78jTu5mEyGziTeUKbJCiSFLmYbXokQ8Xr711LMMheYx6RIcMY4RTDsp\nGyIkaq/izrTgKjfRHFToDXupKTRSl69nqfExIkY76vU86kQtrZJEd9pIiE3Olhu5mRZ4OifiNM6w\nnndRVWtoaV/B0/AChU0f/1Cs4pfMkKrF7PbTUG3kZvYGx5pCfHOgjSP1/4ZDF6c45kLdHMac6MGq\nBMjXLkHEwqWlh+gJrLBTdYfamhrqShe5gAh5F56GO4iSnllJQK0RQZAQBJAVFXuEW3QKe9le9eMU\nZQLVMtc7zKRNs3S332DQcIW1sY+i2ewl5kihk8po+N2taVGjZmepla8pRXpUecaVIPb4ELOSk6V2\nI+e7hpH0XnbFFf6vPiPaUpodK7M0JyvsnzzNqQOHyGkraCQtFYq4aqcIJdpQ0j6MkgWnTkdOSLKo\nDiMqQMmHJdlPUVVmtmRGI1+mJ1/Hqn6W/ZencDtG0OjdbGqDSIrAw3cTnHjwj3kmfQC7qZ8LHQ+w\nzdaEefICQaNESWPGohL4p/ZvYi56KOdcdAsW/iiopj1UpSQphHQxts0cYOu8ivrMHdIGDUnFwVMn\nTjFal8Zd7SenzbD15Bz+W3kaugY4U2mhOdlBY9LHYm4vd6qDlMpVbhUUNFUjKHmsCZFcOIuigued\nj9Ct6DlS1XOsVGWbZEAnaIkZNjnZdwVLxcQD0TZWIo0MvzdNzGhjxlDH4s4u8pKX20IPBYxoymU0\nmnlUio48Zm4xwEvCfYSFOqb03SgqDTc9Y6RsJrqSdUwnRXodMQ4V59gcT3F16A7ty8+iqlg51fQ6\n3tI6SqWRnGgmVTHgyPnQS1YeM/Vi0k/xN00y+fhJCo4k1bKBsKBwpW0AvSQhFEOIlXn2+18lPO7m\nws77US1mEIoy5aiCqVzgb97/ATePbKccttIe2WSpuZbzrp1MtPajk61482FUhVr0QoW3+qz0hhZp\nnU1hU3TMmtMUq2k6ql6K0jyGjSncyUWmumqY2DJEsM5DxeFkyVzBnJe45+I5lsxZNNph/KubdNy6\nRdudNK2RRj507DkevfgOqvtl2uwxMqKRuxebCJkMTDUOUhcN0Ty9xKNnT2PNZfmHZ7/Aawf3s3Uz\njm7BR/f132Id+TIUQ+Q0ETSJArrNO6ha/gijcAq1qkBszEhCZ+BibQOSWsUxxyHudj/Osxun+NbM\n1/lI/BRdmihbXFWcte2can6YeCNoF9PEpywoWQ3blwOIOivmA/87lzzvM6Ak8RFhdcNByuLgC9Wf\n8c0aA7LiIK+KsaLd4MF4P//SMcs/pcdx4qa1vMzI+BwjCxlONu2ho5Bmt20ZrUni+mYDfUUPtZEz\naGpvcLduhYq6yHeDUYbVBZJFIy95BDLaCir1EsHwMLWbE4zERxn3tjMjN6DJziA4k/xrR5CnchmM\nCQs/KTzO49bbPGSDjrZzHLYXOVqzgHJvjnhLiTcEDc9kc+wsFjhp1vPdQAR/rMwNrYVvZnU8OR8m\noRL5qL+WvErhkUAtf1wZ5ZzYy+Oaj/Fa/TKYSvynzB1es5h4UT2Er6ylX1jhJzYjQsnIRulphuQM\nj+dtmPRhWo/+AwOc54I8jMa4gbesIrsxgqNuFFPORVC2Mxrayn3eS1RsApZMlYVWM+mggwcSC4Sy\neu6E/XS5QmhEmcnYExSibSQW9pN+v4m60XmkyRaCrgb03hCppYPU2lvxZ8exFz/EfLWAoeihwftb\nPpJ4lV9xGH9plQeEy5wNtnHX0EOCWrS6PK6+CZQNPb3iEiHPXRJTJlzdKYLqfuqbE7TtnkEQqkTm\n7gVRprhlljr1JndCXagyMpFCDY+VL2KSyvxXwwD6mkY+71zB64iTifupzzTT2u6ns6nvD8KL/1n/\nL3tv+WXpdR/oPu97mLnOOVV1irmrq7ugmUnUki20wJZlxwyxkxUryfVkMlbiCRlGiiljTAwySBa1\nWlKrmRmqupgZDzO8MB9yP82adT/dWeNZK88/sPe33372j/5DFP8X/EGLIsDEKYhN48ldYjn7AB/V\n6HGJJzlT7ATHbUprB+ha2UyPro+SqGdUqMRovUnnhRy9gSI6FtmgvI1hQKSq711erd/NydAGNhRf\nJ1WmkvNVMZJ0M2hz05RZ4ODMTTL1OoZ1QaToTkxiBTsis+jXljl84hT6gkz4SRX9tJaOhShyVSe9\n0bOYDeWkC3Zq9PD1lkVulX2GOcc+vjaRItv2XVY0Dn4jPkO69AreSAVmQWLVpKDVSnQ1nuWKuJtB\nfxsbB6/hiUfZUX8RS0bBN6fw3MpbbMgNoTfKlC8VODvdzoveZ+iJ3mJ99hp5q5P5umqWSjKTlgBD\n7mqGLCaSOgMyIhZB5nOSmdH+w2CM0blyl/dKH+L1rJN3hCwDaKkU83xY9aAKCmZRQ2nxTUwrk9zo\n/jOi/t1s8+9mSaxFFgxsF8340h5aXBX0qYvE1P1szB0jrYdsqsSybRON4QjrsjPMlgzETruJ3bUx\nu9LEsPcx7MlJop52NC4bim+G6xs6udf3Hv5knt2mIf6092doclnKNyxSEEUWhzrZIfQQnj2K5JnB\npivSt15kKVpLZqUF/615oiMGXnI/SpnQwf61aYJaB5WGZaz2ab5w9g3qiPCNA8+zUV1ij3icvEbh\nuHgPH1w5S0MqQrRay1zQxGq9hWA8D+oqkulDLMb7EUWVKkueyvWPUSG8g1pcA0GgJzbGpHETvhkd\n/vwCi7p6dqZ/QMj0JjqTRLWc58BgPytOH7OhSv7i+k/4+M33yLrt1I1VsmXlJkkBjtZso6oYw0KB\nI/5dJEwWKidHSA94eMY4RYtYyTtFCfuizPPTi2yrlriWa+NRRjGU/oiTSjm9GOnQzlHjH+FF/gzn\n/Ci7Q71o5SJl7ynkjvownNAj7c5TUPVck7dhWChi8aXJ9yho6iOkBCfJohFKMssBG6sBJ88Yfo6g\nqGwcThBIF7D4CiRSZrLzWu5Ut3Ojfh13AyHOhdZzVepiOFFDesVM1OEjXrSjExTUQIw3Vx7lpqGD\nqUKAsYANVAXD6gLGpWk2xhNEjRZkrR53MseSYmI8aSYcrkaWDQhAaNtV9PVRtBqZk5ZqhBWRy2Iv\nCTXFWtAE1i5+1tXDR199hXW2nXQa1mN39RH1zHF6Yxuntm6jz1tGSdGhS6Yol+xY/SPcsq7jYsNG\nfMkoOimPdst3uZPeQGK0DseygiiU6Lx4l1KLRH5PiuXlJpK+HjwLY9iX5li1PUtQDfJ3m1Viop0n\nR48SX6rEGYbV6Wburm7C2LOG+f0yVjJedh65Tlf/AFbzFlZsEpt7E1j7YaB7O0OhStApOFZlYlaV\nfMbO5o7TdPkiVIYG8HrnqI5MEpxLcS60C2tC5FT3dtZl5mmOzzAk+9koq3hlPaF8GSWlxIhq5KSQ\n5NrGWvKKHm1CZbTzDNU5BWPv85zyt3AueIjNsTEUiqQVHYOWMJp8noelV6hxzjB46fMk5j9EcmYL\ncdcAxeo7FNFxtf8wyxYvqqIQVsqY1+iZlquoUNx0xXcRtY9jqrhFcqSOp9WDpJMLxJQ46A2UScts\nKP6ccysa/HaB5nXfZFoXYNDZwQd5DXvERGzwIJaSDTXr4BgO/NokekFGzTcQV8z05ovkijKWTDmo\nGu4EwtzsaEURRQbcRn5ZoydiFPmjsTRKbpbQchLEFLP1NciqSFRNsJLwUVraw4upap7CQKxgol5y\nM+3MIMgZJFmHN9qJgIZqSUdMn2Rb0YKqyKwIYW42pNnSt0Ztxf04YiN03hrhpY98lt/UGBg1KDy9\nYqBb1BGRFHrj1+m5dZ6Vhg7GdFHmA4e4VWOnfcZIv2mMS4Z+VjVRdCUr2yJ7UTBSFBMULXPM2xqp\nDdcS925hwVdNZcyNZIpSPr6GLZVhMRfEZNiEoZAhZ55FFHQohWpmpe1MBWvxpYoo0gTICTKW9fzW\ns5+IxUFl9C6bjJWYNQsUBZW8mEfRFRi3jVOVCeBP1bPrzFlcyQTbFvoZt1fgtRYJZZYRVLCkM7jH\n3uMnW1dJxfZRIeSZF6FBzCBjICQs8GFeZ96yxhPRRYLyClMZNwvFAJ2uaabdTYyWDhBMtZEzLzHo\nvc0N4YvcTYUYlMqY0ccwSVZsZolFKcmrlS588Wl0Oh2qbMAT6cE/e4u6iSv8dv9j1M1No0FATsHb\ntQ+SN5vZG3mH+dkAggqbdb0cvNvLU/phFtM2DEUzrUNNNA2fwCBWcq3BS/viJDlTjGi1jC6dxpHJ\noZEOUD41SpUKE/YSUTHNVk0r+cGfEfd46evciFqaos+Vona5Cp1chimzDltilVatmVJmEXdQwmUd\nR5PKE5o6izsVwbMlTVUwigB8dN3X+c7B50gbNLx2z+PcbGvn0JXzWO0FGvYss1+4Sm9gPRu4QPux\ndzGGNmGs6MTo+gWFygKDDS7KtBkMSgynqw9BUBmTyzhe1w3JAoiQbwzw9eg/8+Tam2hlP8ni55nN\ntOIxDGHR36BBtVJTeBxFpyWVjqBV5mhcLiFu+SKykkc8cx5dg4q/kEeRYjSHetFp4/zSaWXzgB13\n2smt4DgXg7PszEfYm8/i1UWo1/0pC34rrokR7rsxRTpVwOMpMTxeweee+B6vdHfy8Py76GbspFxF\nvpZapi6l5cxyLbE+M0c3pnmouMaopcBwopNe62a2zUzy8OwRroY6GDQ2M1p3DY0aRx/1c1iYRuvI\nsLX7BCbPGMOROs5NdjCsXcCagiq/Ql9Mw1MRhY3WEjXFAp2pAiLQEs2wjMhj6Qy/sDq47t5BQF/H\nM6kTtEl5/qTwJdrdE8wb5ng4tcrWfIEflrswihGOxT7Ks9pjlIkFfmtowxDdyQfTDgTtGk/a/4pM\nMM1ymZHZ1VaSioMnjT9isLCf5HwPrevfxWOOcHJuH5Wei5QbCywFjGhl2D+4QFqycMXYSTFeYDLz\nJEOpT5IJN6PR51iQcgzU1/Ofn/00brkDZamd5avT5OMfIjIVpeguUpPfyrgso5XgsZpVXrOeZViv\n4x9LJ1BK8MbaBjKBVlC0hFrPY9LluDT6LD3iVfSLGq6HaylrSRDyjeB2LTGY0zE73oqYaEGULTha\nLuAUY1SuLNAwOs6sppYv647w3+WHuM0Ozlc/zrniXt7X7WfY14kvmyC0LsS6sv/Yo/gHzx+8KOYT\nMH4cQ9N2sulhLOpmeopXuFesRBPdw2PXhikzd2JRFSwI9IkWFgJ2epbuoomZ+J1vK+/kWyktzXO3\nopOX67ejddxmC1F2lbq5Uu7Fqj2J1fIGXlsVrWNzNK6sYK12UKCa1ZSRXeG7PHj2DNPuci7v28Xv\nthxmusPH5vevctMSY7zSgr/rdZIzuzhZI3GuoQtD9ga7F66yo+JVcqY435f/hAeHE9y0vk2yZOXD\nJ24w1dCIqCsSqhzCLMlcNO8gmMqzMXced1ucsXQn85cCtNunyFm0mHMyacXAJ3geY0ni3vBx9k5O\ncunep1mqc7AxdYUfv/xtnlmb4dDIMby6IH0uD0VVwL5WhlOXo8e/ylvGbn4lSGSEAhkF3KKej2HF\nmlvg3OwvSRslKm6eQipr5Jf37cWzAguFInnBgleJ0WDzYLUEyM5f5Kj3t9jkTuaU3XQa32Y07aNK\niHDZvJ30TS30BTDG0vjWJ7nu/TAFjZe2gX/mdkcNSmod/lWFy00BamdyVBdi6FMF2q7NEVyxU/QZ\nqW3up+f4APKtK+QLY6xrXmFZMrPWrGN6qpPy2wvUz81wZOsBTtp2saVkojx5mVbfHPrERg7tfgmP\naCLTepgPKOPUikNoXb28J32AJwau0xGeIydqsUgy4y0OVIeEtiShvRwlefsGBk8dkS0fw1+qwanX\noSlOM3vcSHyuCmdVidpIlmCxj7zGQFtumKzWwE+LT1Nn/RKCZTfC0JuUGWpZrMpx76G3MV3WUnE7\nhi9yF1kt0B5f4WTlBiaNfurSCxzbvhf9WJzP/+JNnlRucG/1WVqkFIf0b3CksInjgpX70tU8qNox\nK+2kCmn+RmPGI6QpNyapCwzinIviFfLUlU2SermCdJmHqmtraNfnGA5V8uuB5zjtuZdP/ONRwlPl\n6LbGUW1gGSuwqDaSVHVIJYUBXzvr1TvIg3b65S4G5UauRjpwlLdjHJvgre0HOLN+M1mDjRlTDXcC\nLZxr2sLJ2n306jsgYkZYNVNePoLJmGEtUo9s0iHmcpimh2jRNlIoLRETDSTLAgTmdbgS8yTtVhRB\ngyzrEQQJt3uJWKWGn2a+QL1mlA1LE4yFzdjiUYoGM+/U9TBs17Np4C4Pnz+PffICWV8dP99g44Tu\nHubkatIZB+P6JobdDaxaHWhUeC20k7uVDaSMZrRaB+ZoH7mclrWZRmyFPGmDiYrlORqGZ8jskdGO\nCUTFdpwymE31eCdv4CpZudnSzk9D1fTMDuGel1FTAbbMH0S4eIbpuloWY82kSzp63u9DsmrQCEbq\npsaoXx/F8bpItgbmRT9SmY/FgJ7qxSjZrJNlxySTaR+lpWYKeStGQxZ9a5yIqZy75g2c7NqOLZ/l\nT6d+wr3OcwgFNw6xwBVbPWpGolKTpEkT5rqvkek6H9tSy7iW9Vwwb+E1Sxc/XF9JtSXK34l/RdhQ\nRnrBR0Qsx7tSjU2nIEl+ojefQVNwENr3Dfwb3sDsH6ZUsLJ86TPI6SB6lqmem6Xrzm1mqqsIKCF2\nFFqZX51jtVCLp/EsdQMTmNLrcHg28PWeBtbNDLJkqAZR4X2jD+/234Euz+RQJTlbFaeFvajjTuot\ntbzm1hCUdGxKGBmTvZhQ8eWNlDJmmmQDBkXLglZm3LrKeHeQolbPk9dPEoiLjAbcaEtR/mZQy6Sw\ngLYQYz5UTlRn45ZcTkiT4lqxiqNFPw5NnB5dHK/sYbKooCnKzNhHCa3uRFS1qM5J1JIRt20YBBVR\nNtC0/AFc0lbeaDzF7kQLLoL8+NBBXq824UiuMO12sBSXsIYl+jRpmtMz2BdHqJyZZKKmivJkGlU1\no9HPY1AjaFVQFBj2FnioZGKdXsOM/jg5wULjTB29gsKcBrriHpJGM7JhDkOhRCpwD3OBgwiFfmKx\nWyxIeQgmKBmTWPMBCoodi+hFzfciWFux6w6hWq00WPSURzR4dHoGDdNImiI3vLeoS9XhE6IM2Zc4\neHmN2oUYVzY/hKaQxuWR0RlEFGREQcQ8s8rPQw9R8t/ErOgIFe2ErZNc8V9myTaNPrWBHvppKaa5\nVnk/R4s92FMJIkU9stPJPm0vlut1hKaPk04ucML8aYopA3KlBbv/TepLJ2kfNzBtqUJnjODIRtHI\nEieauhj0V1KzrLL/xm9pWlhgyWVjtqaedWo/a8lazrVtoaDR84jtdW7PtqMg8ulNPyN4Jsekyc+i\n248+W4exZMWslhNcKTKeSaN3q7iyKU7VbqB7ZgRLshyjZCPubKZq4F1kTYxZt4WokMa8kuPWphbM\n+RxfMb6CmqlFTd1PWLOMjELatR3b0lXWF45g6h9DWVExmnJ429KUb4mTqbAwZ/BiSBc4mljPYiDE\naP16tKrIvpur/NUfPUt7JIEtt4jBWOL+xCX+vPqLPHD2FKZtH0PlLJWOE5QZM3jsKv9lz5fZI76O\nXhURVJGSR0NiVuaaqYftso0PBI7SmpliOv8JSqUvcTkywrDdSVD+JDMzkyi9d8lUCNTr76fB0Ejl\ntVNIwRacjYf5VTDBxusXMWUUXA1Z/rnByR5nguOKgTt6I2WpnezuXSJvgR2aeT4fTqNTFRzEect9\nijJ/G+WeT5O16ElGlpHv6Pj2oU+jddajkRP8p9y/US5lOJTM0SMmOLbQzETOzcaxNWqsDTSUkpzw\nQvfKNGK6kvert7FluZ8rO8+hJNsRwpv40zPDvGp4gvXmaQ6odxh3+Bm8u5sXpz9C0/gUUiLBsTKJ\n3VaJxjmVyJkq3jZ/ii9Kx4jbtJDXIqCwN59HBtTjdpLep5izd/Dl5TeZ02nZIk7ynHSRIaOWL0US\n3DAa+J7Nwd58hDHbCooxylOpNGeSH2JDwYFH1rDX/xVGzDk2JmDWr8dtWmI8sR67I0q9cIuZxF7y\nq600tx5hJevic7pzVC3kWS0zEBgtUnrHzN3pAD9suB+NXI9PuxGt1I/J+XtC+9/GYlhiLOZCMgcY\nLbOzaT7Fk1U/R1K8pNV17Fa/i0P/GkPxJ2gznaa1+H0aSyX2CkN0ynFOL9WRcTSB2ciCM8+Omiuc\nmNlLe80443Mutlmm+Hn7M/S527CqCX67luN4WssmtQwpXoMx7yEz3o697AJ6c4HFCT/P2U7gMWR4\nQXqMkbouZIeRr8SjjCT+mjbjKktVPfx1533/MfX0/wb+4EVRZ4YbP4HoJGZDhGKxDUXbhF/zJm3K\nQfzzbxLWBMlr6zGXLCyawywXq7nb2sYbvoN4MmDK6Tgb6qLXXYWoX+He7CWO7Pwi414vjpKCX6zn\n8cEUqsGMyy5SNjaLP7yCr6aZ8XSGz154lSVfGX/6ua9i1kicDW1hwNlOVWKB7t4+xhubuW7YjS3m\nImoUqLG8TzjxO/7SnERxTXA3soPX7R/gyZtn6HVOk7OtYdO2IJvcnAks0CxraTL34pzZy3MZGd3O\no6g5kReNX2GwqZlHs+9ybZOLY8Ugny19gLi0nk8M/56T+3fxiw88x6LPgaGoMO+v4+CN09jDC5il\nIo1rg5TqtjKFjgmdwuN6lXfI87JkoSszwX7zBOmAhadM5UyXGXjFssCz777FsFlD1eI0he6n2KkE\n+f56A3ULMkZUdlqNqPkYRUEgc+1H7OiPYEsvsli2l0LGTUaeoUKziqyvYjxURcXiDE3tM0gNDq4o\nn0JW5/jNvvVcaKnlmfd/xbJ7G5sm8zjkCQQpy+VCLYdVG+rCHUx9EokODdntCumCgUb9MjZ3gbV3\nXWjualmLlVE9OYLQpeB8fA5HwYkxWkPz0GtYO0aZS92DXi2h3XEVqbKPrGcATc6FK7XC4f5hmnJT\n9HvKmXCU0ZAMc21xL7HzVXiPJigs6dCWFWEpgsUcwFKzA4EokcszyKll1FycTGI9jqpldEKYqLaM\ntdJXkYrP8mOlgT3YEZb7kJZuM9m+nX17f4oku5iMf5awtYbTPc18teWjnNmzi8fWFrio83NzcyeK\nXuRD109wT98lrOsex2k/i1U3hk0p5zH1JG+KXVwWYjzk/3sk7RVeHNcy4Kxms2kVe76E0z1PyLVA\njXsWywmRgYVySpVQ2ZfCV5vhT1J/zmhrK19461c0z03xZ1/8LwhGiTrNJJ7flyilDPz84AcZq/z3\nX76zwkE4rt7fAAAgAElEQVSO+e/lWGgHQ8YatDqBJa3K9fp1/H7vfezPXOQjU/9KfX+UzsVJ6tcW\nsOWzrDg8zDt9PP3qGyTLa/GWD7Gw2IzOkEOcnsOsaNgT2MWq8yKxMASSWbZfPUFoYZFE3S6GfFbs\n+Sw6XZHO0Ru837SfK9btHD52nuHcJsRiCXFpAsPaPJ0D1+juv0Ll4iSTwWaEUoyzVQaOrH+A3anT\nrCkBHj7+HrfaNrB58C4TwRB3y8uRRQ3Pji5CboW+8krqVxeQUzZSRjNRi51Xu/cRNrvZdesq9l4J\n25hC0WRh0mtkt34LulwaZfoC//DoYVJa2D94G51YJOiewHrqPbxra4zX12PLJtl19gJFvZ7z925D\n2h/DM5ZErVTIHlQorZe5oeRp1OoJVt6lrLiK2bYG1hi1JSvzeR/nzbuxjhlxuxcw+MP83vooMYOT\n+9dOsbP6CNmKApPRDiRVz4nubYS9bh6+eJaox4niMbKo2nl+4lfkrAKBOSuLthj3T6/xrOmXiMYI\n5eZpLJIdZWILIhrUvA8SQTLGJC2tP8YYmqAvsYX5/i5m59ahZMowZSuw5L0knSXKFhcoT1vY4djH\nTCHNhGokK5twWq4gtZewvh3BGdyOUx1gsX4Z3apA1OJky5bTaHUSP7ryEWxjWXrCHm7X2uivqOX9\nChNL5Ubu1BkIZtJ0RfQ4C25E2UTJtMq0ZY1Z6zwGyzzhagsDFfU0jY4TSESRlGFGK1rQ565RFY5R\nUXIwaytSMBooqSKLioNyMUkJDVOyB48uwi3ZxDGngQrNGs5UAH+qAUHVkrKPkTYtUzCGEQSBVDaE\n3hhFzS9hlRrwZlsZ8l1goG4X/9Jspm75No+8/So3NuxgMqjniYiMw2akzN7KoqwjaQzSODZMzGXG\nKeUxynlEyQCCgIiOprlN3DSYCetHWdSL3M9p7uY386YFlrUK23NaSloN+uIwMW8ZJXU726y/pGv0\nN0SVWpYrD7M9pWfRGcaQ9WOTVDJ2aNz+BmujEqLoI1R040soJE0ycc0icX2cOmk9SsKLVcoSyBpo\n6o+x91aE9zs19K33sVbVStLlor3vLit2N1mtiWPmdj5nPEbYnGTNHKYyHUKvGBh1jpARJfLKAvdd\nz1DvS/Ej4xO8vfkwgzUd3M01MapU8lHdcQiv8RPPfn7afJiSUU+Pdhix3ErK082Tp4a5ZyFB1+2z\nHAltI5rTs2fwCldbNlK/OMvnf/e36KQ8ILBpqJ9X9t7PbttRYpFa9KUiSlZhfLCSRMnGJscdOsUM\n+REXg/X1yDozpsIkotSErBH5N7dEe36BT0hXmFdslCWjGIpF7Ml1HHb8AxGpmkX3NrpuHSXpD7Bg\nkVmsqEDSweORI3iMCRaSjxJVfOTzx3m/+zjbZqtZ8O7FvDRNJpBmsO5PKG6pY+LgKj/wfIY3Hfv4\n7MrvuZu9n2M1T9Bz83VqluIcGHLhWjlFSNFzyWMjO5tDJzTTaB5AMWjp9RyiQ1eP0fYPzJv8/GXb\n59kTu8lzi2/jkNI8s/7vuGbbzn2RM2xyzmI2CfQEb1NWWqNXfR5HaR83Iyd54Z6t2J21XNKO0XXk\nJHJGg25qnNO1JUrj72KPJrF0fxZJq+Xv28w0Gm/RYllCtgmUFfQECkVedNgILoh85bUpatdiHOpZ\n4aFUCj0KSlEAjUBnIYtQus2lwADl2ucw1x7mxT3dfHBimU/GKjhw7as02Gf4deMHeLB4hYG4n9ux\nCq62RNg1XIS0kf6im4mKNIJY4pOXB7ka6ODyegXVOcJnz49w1dfDueAOVkxejJEIB8yDCMt+LpU2\nMFN0sye7jCuSZqIsh88MNW6ZqdEAbaYhunRzvFblZCov0iQX+ba1hh3FOAl7CwxMs2/hBFsdS5ww\nWVmnrOGSZO5LZbGqKi/a3UwatQwZdCi6JHdS9/GR0ghOpYSc3k3KeZNHNCf5td3GrYlW1LgWT2uE\niOBmzlDNE4m3WDPFicT3kgs3cmDzj1g3kkSfFukf0fNO6WneLc+z8+4aDwxdJV3zQUwK2JRfUEjk\nyOYNeBvXaPMP0JaM4tNf43HLdwlmEiw7TCzEd1GmW2A0U8uqsoUZfy8LSoggy6wvZZiTTJxZbSQV\nqMGhjbC1+y3ykpWVrIfZgJ+XKr/A4yvHORC/weXYduS0i15hAkkRaFzdglmXQ5OuRquYEGwLOGum\nmFn285TlLoNxF7ZRPafu2Utgdpktr3yHUoeVlfwAj8bNNLXuxaLV/h9VjP+Z/xDF/wV/8KJo9UH3\nx2Hff0LZ9secuzmBv1CJRXwLMFLMBzGNH2OpfAcuQ564ZYxJuZxYyU4Na3z33W/x8MR5NoWvEA7O\n0ZMYZqjrKSy5OA3D19FkzfTVuAlFl3gw20qDaS+CyYBh4jq6+AQHpq6jK5X41yeeZ7YmwJQngJxX\nEYoyt1o38MTJdzDmC1xpWUdedtG6KLO14hV2mK1Y3MPMz3XwR+PHeM1/iPFyOxVT14jaoLq0mbrJ\nSbwRG3m9l4qKUXSpKYyNbyCiMPRmHTWpJG81H6Iu3I/VFueH4W7mNnwGm61EtNzF7fXbKeoNdI3n\nqVuIMV1uI2ZzsOf2dcIOA9Z8gbJUEdHVwU2DzAVjhhsFO5VylC/1v05wYYEr7Z2MGWOsKWkurO9g\nscVN960hCno9f/vk0zy2qOIQ4L2yCI5EHxsFH7KmyInkMHPBR9CqGWRlhVf376csGkJUlojl83zY\nc5YRZR3j1bXMNJbxI8vzVK+IbLn5A4zlGzhw/DdUL/RTnrlO2OVjzZvDkgzRmCtj0tbAdPW9zNV0\nkVo6QFS1IRnbmCo9xqX0swy6tDimYrRNj8PuAsmnikTzDuzhOorJckKP/hbf6hoFpYqVyGZSsg/3\njSmCPxSoFCeoyyYpCTrWjB9mfuMAkiVP1VKOpvQk1lNxMg4Lazt8NDVtIJs0wcRFtL52CjMDSLNX\n+fYzH2e4roUNN95HVGIkK7ZzfXYvXqmWhF6DVRqmVawkP3kKJbtG8tOTiNoCv7jyZU7ofMimSU5Z\nN6JHYmvVCPN2D0UFYq0BumbD1OXnsOqceFx70WQvYDBlyCq7KTNcwjOa4LeufSyIdsLuEr/hHvR2\nAaHKRG18CZtnAac1inZWQLilRzlcoGlhBf2oiF5bYvPIEB8+c5R1MxMkdSYmqgXeCT3BvbzHeFUN\n2343SCC8imUB9tW8Rkt+CveUxO67d+lvbOJ2qIG8Wc/Jjm1s6b/N37w6wCXTI0wUi+weuYl/fprD\n586wotoYbGulp/cm7lIIQ/MghZyFzBjoUnG2+B6izPAmR8UuTNEVfNEigXiYmfVP89K+PVxtqMFS\nyOFOppi1N6Jb09GwNkehaEdVBKxLQ6xp/eRtPga71zFR24g5m8eSSzAVcPGzRz5JozhC8/QcN4Mb\nGa1uRNJo+MZL/5WKoUW8QootcyM0OI7giGq4FWhEo0hUxiPoZIn32reikWHF7eZc91Yme/z89r4v\ncN/EG0y7apgwjxJpWWNeKePlXQc5dPsS7mIGnaZAIu+ifG2OrMNE2Otlz8kLSDodF3c8Qt4lsJqu\nJufdhmnnTRSnimIHZzCH0zZFo34Sr2cBr2eRGkcEr2+WhvJBdmnPUy0t8nPxI3Q4ruEVw1iWBT5l\n/w5Ro5Ufpb5MxVyGOb+H1RUjiXIXl9o6cGVT1ERXCC85MNf1URfsJTW5m0B2CbPlFsG6q8xObEFa\na8HbdAZNWT9TtixZYZmRyhw6cYZg/S0yqpXp21uRSwYEsYRNuUKVTyGTrESbr4SKLjrszcyWJAZz\nGhTRQF63xDVnkPbgHVLtYZK1F/G571Lj6CNUM0yochhRVRm8ewBvXofXYCZvitC0NkN5NE370hjt\n48uINh9nG+wkjf3syMq0GUQumGaw6yIYBYjqnJxq78SVTHDf5A3mFRuVqkDUUEVtpAJh4Qj70rUM\nW5IoqsAlqYZGTQRt0U6ZLsyw7Ccmu0noLYSKS4T0kwi2GMaShUzFHfJiCk3JgKItguSnIl5LzLSC\naNCRNFSiiFbebmvktrtIKDrB3tO3sWu2MVrtp4CCfjKNrwRrqTwL5kaizibmK9vIWWQUbRFrogF7\nshVdwUHOukjWksIgR4npY+wstXBTjvKW2IaEQH1JpEnSYskk8MWmCfsNjJX5SDdEuNy6hTc7HiDq\n1zFon+dUew/t86sYJQtnnUXM5av8OvEIjfF3ELJXUHKX0GZ7iZYZ8MlW3MtrKGsjyGKOjC3IwZtL\nqLLEpZ27cEplaBBZ1dkwFtrJezJoMlb2L1up1uUp007hHMox7YfKfDWT2SYqSwZ2356mYRBMzQVe\naP0cBUVL3mRGDpjxDK0wrK3ib13PcddVj1JuQtekJ2H6e+pnh5kNHSLuCPDAyTcQ3OVcr/Bx0bCe\nsZ5mxuobmC4P0TU8gDuTRiuXAC27b1/l7dBeltzVtC7PMDFvJVk088fR9+iZ9rOcOkTE7yLmVbFO\nLFNm3sUGo5WATuRJ5TSfM32LanmIq8oGtIqKPeXEmrUQOPEmSVuWiG0zy+U7kJRWBNYoGvNYUnXI\nJVCWoVf/LJW6k5SVrJxrvJe/nPo6i9otrFjbaBw4R1CO4vMl+HzbnzFqauEbI0cISEOcTPw51Yt6\n3DkX3qwJk2REl7+AMZngYMrM9rKH8Jp2IJJkU+Zt/rnro9iEU2zNXOTbno8TmI8htM5hy4TI5b2U\nS/O87H+Gb9U/iqUo8HDuFKog8Z3gP7Bpyset5C3a3dv5TZXIXYeZ5//lvyGKOvRk0RlVyvsnsEWS\nvLJvL9/YUova+wsKVi2fNp7GZ06jR6FczRBVFL7ndtK6omf7QIGRA630WEZJFayYNEXeGe9GvCJh\nr83iEBTWZ1c5QgmbfhMPRs0EjCEkKYvPLJHTr7AjfY1lrYeTMzXEtQq3mrPcrYWYOc14TRB9Icui\nL4tOU6AiE2OydgZBNnOrJYFDnyaTbsUnZtmTvkQuJ9JlG6dRmGVJH8AtxdEm43Qu6Mhb2whWzhBO\ne/iw8Topp4YrmXKuGlQeyaY5bvFyS17HB3RXOJWpYHtwArNOwVqsxa+EmZPNeMUSkkZgTzaPuejB\nnISum0HGLfcgyAKbSnmm8ztJ+H/HDnmWtkKJf/OUuPdkgVKFiC80w1C6g3vi1/AWEkQ3rBAbOwQr\nFXSo5/k7+XHet6S5W77ItDfKxTaR1uV68o69+Ap3aNpzicPiEBeUNuZvOjBbi/hC46zPTdA6lWJY\n28lq1xKx0YOktO30UYWxpOWm/jUKriC9+T2cseWYH6og5yinZLfj6H6HMp2Eopipc07xA/mvqMgZ\nsUZi7JBvUCwYKS+LcIoIwXQVwXQ1dtUImQoShgRmyYWr9jLds2sENBmOzbbyzvb7WPBXQm+CL1w+\nyoHTYfZdUfFfmSTb3EKg/v/e0tM/GMUVBOHLwKf497XIP1JV9UVBENzAb4EaYBr4kKqqMUEQBOAl\n4AEgC3xMVdVb/0cu/v8ntgAABqDraRnlRznS+f24zf9EdMaHLS3iyr9Kwb6NCgrs0k0yKznYNnwH\no1ziREsPt80N5AsOhPo1MkYTEe0UHYkpzOYsvUoF81YP13WXsS2doa1mgv7+CsqXllAEga/v+AyJ\nsJHKlMSQS4fz5gKKRiC5pYLvfvIZnv/ez/iLH36Xlw8+QcrRhCCAxzmMefoQxdgpdMg8FH2D71V8\nngdqu3EnZa6UXeFYcImSTmBHpob6lJuqqgEEScE/ambKVaA4PMVXx19kw80FCjYT4U8cBlHAIErc\nbu8BoDJc4oG783Tc/AF3ml7gYscmFEHg6I4HsGeyPHL2GJGD+5FdZcRmQNTCE4uXqZubIa/T862X\n/is5g5GU24f5gU8x6wnROjPBS09+jPve/QXxvJ99ygHaTvwT+nyG7zyiQ1+9m+YFDbllC3f2NfPy\nhifJqCKPnblIxFJLUh/mU21/QdZgY+PALFPRVpqSIshr2PNh9o2Mw0g//+2pj2OyFqibncJQtKNP\naYgZi1SrRRJSgpJiR5ZdMHQ/STFLSDdIVmfEoN/D7c+tsTGWgK4cxhsi1b8ucWPbJpyWBKmAjkSl\nkeZzR5lZ6aHCMEBtKkIuayU3bGS62czpimaq111ASJczuaZldzFGdubf++HObduNJWihde0gto29\npOJz5K9+E7koc3zzDvZfv0yNzYqluUB02IpeqcakGSeceBfTWgnHo/9ErKCQDA/h8zTSsNjC8ykP\ns0UTWm0Wo5ogolrYoZ3EPe9DI4YR61sw5XN87cR5rjQ4GGhsoPr8L1Fqn+Zl2woP6Y6zrNTgjnt4\naOoSR2q3czPdjAB8sNCLdkYGjUw+5kPILmL9pZ7hp7wEqmcRf2WkpIHEpAVHIMPb6w+wo+DH77Iz\n632ZhtECKVMH1f4+sLnYfec6TY+OoxhWGBzfw67lVnapCb5640n+c+hLvFp1iFBykW++/w+Upgzc\npzVjHnsXQ7FI3mBkwe3gj8+8zMl797F0uIpHxo5xI+En6BwkEwmhsfmx2RTeSnvRuPWg97LszuK2\nf4wblZ1MBnQYCjnONm1EK0sU7WCNlaiKLKOIGub0Ok49+Tw5gxWAKnWKFQIsN1Sx4+oUZ5raQFV5\n6MIrlBJ1GGvyFPVatIrK3Q0baZ0c41RwL+3XztJ2dYrQl4aYmrLyfu1+KmJRkgYdMYudL/3251Qv\nzfKfPv8Vzvp2s1m9TOkjc/jHx8jlbIxlW3nrqV34I2t8+V9/yGhLC0W3yEJDOee37MGZirLn+AVk\njYbTew9QpvipSJkYMKaY9kao1KusDdyPvfVdHFoRjb5ALmfjNcOjVNwp4pfX0AsFzK4YvooJdK1x\nDihvciO7g63mc7Q6h0iZtLyQ/0dqVtdQBHiy+ntsn9rAzdtbONvew42aVh65c45nHZdoDV2ib2Yj\nWm0CSzpETcMbKCUDxbtPk1E1rOlKtNadozLlZk5Yh0CGjA8s9hjLE92IKsyWuXi87ocY9AXgJjV5\nK8Onv0w0Vc37goyiapF0WZKOuzji60iWzLxXOsweyyk0QgFdvIE78RCRgpVN3pu4hDxdiWlu5NdT\n0i5gVMCYF7DlpxBlA4hF1l2ZwtVTz/mGzTg1szTP3MCtCujybnKGKDfWN4FGIG/UoYgaTCYRbU7l\niWtLGAs+vn//l4jMDVITBlFQeVA7jsGwQDK9H8m4SoMpzM1d3fiSSXruLLCg2HELKXSBi2TzNso9\nwyxGWliz2PGml0hqy7GnQxQc45xvTjIeCLJnrI/Gybl/j1cVLlLMc+B6kbc1VbyhCJyT8ny0pCMQ\neZXloJ6E24mmpBIK+7jaYGZxKoE+s8ijvuuMr/VQNIJMI1ZtEXP2GRa0RQ5mdYRkgSIqY0YtbYb7\nQL2M5I7xjcqn0clFbFGVhCQhZlrQXknzrzobD0t5utQ+ckNNBHVxTGoGVRBBVSn4K0GjJTN7g6l8\njoSjjOaZOSa8QaYrvGhM68gqIe7IPkKuDJZsOylvP4IiUpZZR96sYSTlYsPFa2xL5pgdj3N9c4ny\nUg3y4nq29Y/y0sY9nAzuJ2Er4y9+/D2yGjc/ffAwo65qRoshLJYSmhYrZoPCh44f4d3ND7NNukTT\nkd/wk8ef46tf+EsePv0uj166wLk93YxZKvjwjdd5peMwv773IZ7um6dx4Cj6RA57Oktv+WZ0UokG\n5tlrnsKXilBwWhkLxlE01wHQZ3LsHBpEv+8pbFoBpZihQvM2K7124qNa6teNM9jWhqbQgHftMhG7\nm+beO2SbXicWvJfa+deonj3NmquMvvUNzGmeor+8iC+bY5/1fVKWl/jerSX8nhg96V9w2vw8N7Z+\niB0Zgb/uPMSaYOS/30zTmT9JQelhn62MnC5DOuslq2qw6WWs1o9h1JgBKEp36Z28hTeZY8NmA//P\nzLdozM5wx9pMtzrMSKGOvv7DKJ5bjKwE+HL0CN65b3Kk4mN8t+mPeLH2MUyyxNbJUzzdcYzuTCPe\nxTY2rmTZfv5X+KNriHoborWBpa0d1PT9mmjcyKWNPiarm3np43/JN8e/iS8V4zvaR+gpTbJV6CWu\n0QDg2JDh75ae4XMdr6JfUpFTEimTnlHFRC7URvHkOI07F9FbFbZb7vD5igQ/ODuLxduOSB6NSYOT\nJSLU8tnWb9E99WMmg9OkrXnCsp7hUAFZM0zVspGCHjYEYpyR04imJQpLD1KMbyenydGCyAaDwIq7\nldSMyBVLJ39iO8IL6o8YSW9idTXGaNCDsV8iX2/hkG8Uc1hisMqB8qaLgc1FwoKGvdlF/riyyLv5\nStYXo7QY4vzE4eax1AyjaoBvOLSkrXZWtJr/95FawJDR80RNnAd1p/m33D2E8iuYdGGeEvtIrZgo\n8+f4VXYZdgELoCxCXeW/IakCKyaFnamTrNhVzkY/y+90f88Row8qbyNrkuxc2cHF8kV6Ox7DH0vR\ncu2XvGWw0RA0s9HQy5e6HHzsjJ/oLRfPuPuY15r4Yf5DdN9dxVw2ylqyDGvJS87UyyGzhN14g3FZ\nT3GikZy0wnSFhKVlkD2WAgNzrWTnGolqzKxTzrBtchZRtbIYCnCQy7wwdZhCzSiVmQqKQgl5eQeg\nMOscISZ0Up3X0mIMMxnzElYsBFYmePDULIbFFG/0bMTultGtXKeqGOTR7u7/7frwv5M/iIyiIAjt\nwN8Dm4HvAX/zwgsvXAK+DAyqqvrkCy+8UAEc+trXvnbihRdeeAC4H9gK3Aa++7Wvfe3H/19n/MFn\nFP8nLPZGlkbeQpvcgl33FpFhA2mNjmnLNG+uSxM2rdAo62jRLvPoueNcKF/PP234MGKLwod2vMaG\n8hscS+4l6W5mtKGbB9SzJNJl3KptJheH7296mvsSF9GHi2TyRt7cuo1wsIZrdUFWK8yYRhMczPXT\nlF9mVONnpKEVr2OZiskou+5cpn71FDpjlPnFpyg5FzgcGeKaUcdLpgxa827WTfjoHu1gyHeZqkUT\nzmyWG2Vx1sUEfLY81ZN55lugyhpHGDSypXeMOw2NJLRm3tjxEOZCnvuvnmC0qp7mpTCPXFGYLEsS\nrtRjKCmMBUOMV1ZzYUMPg5VLHLg1TPPMHd5+6CEEBcRqPV964+fMBiv46Ne+jaNo5mxXO1mNho+9\n9TP23LqKpNVwsmc7j5x8i5dDbYTyeewTZ0laHWztvcLZSgc/3dHDU8kC73lMhKbG+dxbv6Ni6Q4l\nMcaLz/4xU85qFs0+JMFAdXQWg5Jn49QIMy4Fz9hlrq7bwM8+8CQb4ioSOTbdukr36FFSxTybR45S\nO3WOTGmCFWM/Yu42z1V9B6tplExjmORiN5aMF8fWC7hvSty5VUftUpr50AO0aC8gzT9HZWwjNsMl\nJuPbcaeKuO8MImU1FDM66jesslGZQ9Fnsc5+kbmMnU3yVZZvOMjYrCyuqyPj+ADtCYlfBc9QsbkP\n4ZqOmUAlf/2ZL7PHcIGN9WeosKZYUrwwMootlWCtxo5nJU9V7YOcLkRoHTqKtNGPS3qKzflFahQr\nJzUWRvT1lBXD7JFGcUfnGK2s4XZNC1989306Lr9Ff0s3WU0KWVpj3PsgUdMI93Gdrwof5XVdB/co\nA/Q56oirZh51zLGp5U1ia1VYTAmi0RCdPx2nr249t7RbScsS7cfDaBWVYq2O1l1L/Nmev+WzsRA6\nrY/74zsxrgQx5zxY6s4jdVahil3k7rtDdKWS9HQrT2j+BYf2DEqxCXnpAI7YHRwjGrY6L8G4jGl1\nhIjHw0jzOqrmZ5itr2fHugF6y5q46OhG1zeHUErgaY6xFGsj6QoxqEmSNNmxJMspaJKImVVOWf1M\n1ZeYD5Tz3KvfZyXYRl9VLbOOCkKRZQL9V1n+H9y8Z5SlZ3mueX3fzjnv2rtyzqmrOudutVoRhCQE\nEiYYsIkOYBsb28w5VTYHg23GPpjh2BLZBCEJodQKLXW3OsfqULEr57Rr5xy+MD+M1zpzlmf+zFrj\nxVx/32c973p/Xuu538dsZa65l177dXYKl/kI32c9VMO8tZ64xs1IdT0pi433z/8Ux9UwrnCaaz3b\nyRnNaJAYr6/ndvNeajfcTDcaueeNKzjfVjGEJN7efZCozcJMsJaGjTU+fPIlmpaWyPqdLAb9TIot\npDfL0C5ZiettvLljF2G9h/2TY1SHN2iZnsIWyVAur6MVc/SfGUEATh85QsZiQjLIhHUFsohUBu7+\n20KAiYN47CFu3TnO1lYNi4t9GNe1qLKOYtGMLOkJSRVMr/VSTNmpdt/Fp0QRcwYM1hTPb/4uw7YO\njk0MIaDgcm5SUXkXY17Ge6OOXQkbTnWN6s5XMRjyaEWVzXAQm2rBv+t50rN9SGipOfBPnM/vJiBv\n4fMvMF7owhWXaQzcxmndYmRyL6V8Dk9yP/HqEEHtMrdKvTyj+xRN+rNk8hJOawLBtsyWeYEhexX1\nMRdhl4264BmqdMusvFbFP219nFPhPt634kUJL2Jr3aIvOU02vItFeQ3RZGELNxqdik4tAJDRhfGF\nZ0kbjNyoqiFhtlMXDVHQ53irZx+bDhcfP/EKweVVMJvxFeKgiCREAUfeiyk3i6u0QlFrxCYJmLEg\nyj4m7UtkJQdBbQZnNsnOxVkEJE4VmrBSwqwoBMOLxK0u1JKGCaON5byfCsMC1mQjWdMGzkKC5tAy\nVfEtTOkqRFlPUZsjrppwaxO0CiE0bj2B0hYW6ySRMiuiKNE5OUzbxDjP3LOfC/YKwnGJdbMTw4yB\n8vZrFLI7cISrqTcI/LUMBkXggZxAyaQnohY5aZbZHY2QsIhYchsoI2GkRZnMegnNZh5tXqIUMKPJ\nlxjXqPiFLFYhTyVxNmUjsXY3pryekjdIAni3ey9vH3wvt7r24MoUaFhfYq6untO2es6XGoiqFtIZ\nPfvjRXK2ORwJmV2Xn+VmdTfLGoE1NcdtXwO9y1P0jI6wfXUCS3qLb257kpvlnRR7POwcv81HT/0c\nXXcA+AwAACAASURBVCLPOU0rJa0WQ4uBCluSDqbZc/4aT516g/fkJnFOSOy5M4HB6eZsVy+v7znE\nWwcPI6zlCERKHFmuw5xd50x/J58uPM023yjJOSMnDx3gxK576Bu5zlwxQI9mCatWQR9XKF9dopAR\ned3QxEdn3iRT8Si1znLWQ1ex3fkr0gslUms61irqKVteQiM3UDA20jPyXa7vewhTKU/T/BDOxDXK\n1+6irzuOzlbJhbIIWrUcR95IrHKDe8WfsZqzE9AbcZjfxmtZZlHqISX0cKu/hZ83mtmzFqfXPUBr\nZI4b6Y+SM2uRpC30koDPqEevz7GRD7OUHOZW5CS3YyMo6S0a5ucJ11joFebQqjJlhTje6O8TVPxM\nClFWYwHyBg8BNunQjfClxFHeu3kDiQJK4SZtmVdYK/Uy5RrDGJ7i82/MUDs7zIbDgi2TRt/9Qb7V\nOQp1S9xbHWZneoslqZxWaY6vLD7DOfq5pdbTmn0Mp2cNR36dq0479/sL2NVWHkyfZlnw4DJkWA5X\ncEVXy/cf7uZSczlLYZUeU4IqKUxZbh2buANrcYqg5YuYtTcIF+pIF/6aR9bNnG0s42rgNM3hSv5m\n9nO0nh1mqgJyhgJ5PbgkBwHjEuN6PUdv28hb4ghWLcWik8uSHpemHGtmhu/ZH8coGOnVTFLvnCTe\n4UcqQZmyyf70ElXZFCGvnmmXg9vRMiYq4+xc17FPTbGQ6mVLn+Tj2lVqSxJ/77Lz0WSMnzh1TBo1\n1JQUbDofDdYMxnkH664Cq2V53n85zD23Z1jwPU6X6Q3Oip3UTi/iCmZRVFAE+Pdfeb5k8d8GC+Qx\n5xQKwjK1ukVGcvfSVrQQ1xq4v3qUfWUbHFH0yJMPUm+BUvY87TN5llsq2SMvsi1j4BHHFH6yePQ5\n/qbuv/JiSw+HLq6j8RYpRZrQqFrq9z+Lp3URnbOENhklexcUh53J3js86VtllG6mRg8SHB/DtL6K\nP7yOVRGJ5VfYsNSwUz9GTBPnvF2kL9KHIpkw5wNELKvYCw60hdv0CwtUZiJ8of1vWbB66L57E28s\nhE6QcRZivNl9P9da5xkPJnm47r1YnM7/JJv4j/mNi54ODg4eAkyqqv5yYGBAGRwcrAGagaeALw4M\nDKQHBwdnga8PDAx8e3Bw8E+Al1VVHRkYGFgZHBz80uDg4C8HBgbS/3d3/KaJoijqSEvjaKYCaIV1\n9DVaZpN2qjczvLZDJWwNIasy3lwl1csrrBz5DBq/TEdyjpdrjrNXvECrdYpz2UPsNlyhdi6GLxRj\n1eInbndQL61Qld+gJzdJctHEu9u28e7+AxT9VozrOX48qXCpVmTO7+foTIHRoI2hqh4W6utwVIbw\nxGPYLoF3Ypb0ag8NZdOcD2i5qNVSl9fTNd6FSdLSEO2ma72NJ0+dY2+gwN43suheMLC6aOLHGgeR\nYQ27bqeJWQxM+axcDLQzuaOPmtU5rmzbw+GZOxwa1SDKJp7f6+SeGxdR1F5u1zvYdHv4zE//kUcu\nn+WFfXD/UI57r13g3LEDPHH+Lfqmxvjy5/+UvNlE2FnN2d5OLvVs51pHL93TEwy1dpHs1PHf7/89\n7tQ00jx0irqteRyHv0Imv8n+K29z+MZFyq+/wNHLp9g3PIQtk+BHjz/OPz3+2zgKSd7/6o94xHCD\nX9Qfx1C04s1P0+7spbAxjD6f4Z33/zF3ymwcnLxDSVTYe/4CZk8Rl7xJ/OMakk9mse6P4u2K0ujb\noD4RZ1r+S5rDD5Pz3Cay2Y542Ued4Q71ti3mkt3EvTvBuEkgKRHOlnDnuykKi6zkewiMn0WudaOJ\n5rjb5uW6pR9jWE9FYZbt8mnWEg4y43r+7nOfwiS38eSmiaKa5VKmGc2228R2GJkLfpCPuL9Do32U\nloUMq5sufl7/BC11jeiiMvbZRRRRRKvRM62fpm5xlsv32GgueHEJRtyik0VUlgQNn8++SP+1YfQF\niWce/ABNyRKfGl7GsHidHm0zKbLMVJYR3jHGH229wJrVxo/FYwQKBXRBE7sXhlk2tbBVcvGQMUvK\nECKZCAICRVHLL5sfok4IId600bY2RdbkZbrvKbqNZ9CWWqlJBtCiQa/qaNFYqZJ8xCvOs6Wxo2lY\nQRYKDI8d4ejoKda9Xq5mnuJ86gOkSlYUwzwuociIsZp99tucqj/EdMd2tOp9+BKzVK0vE67+MjPZ\nFFcaOqibGEYfKuHriqARykgmvSiAImk5dP4EjeExVqx2PNI6rx95hEAiQv+di0hIzFc3AxDcWKV6\nZQo9RmrzafbHL1IZmGSAv2Ha1syxiWEUb4YEdh5bvM7B2heZ3dyHNrNG9/h1/OF1yqNhRmvaqd4S\nOTSWR1Pw8NPDHRweus6pPUdY8/qI2D0YixJ/cnOe+qHTzLRup+gN0BSaJGHxcc3fRdyh41JVP0X0\n/E74hzjXVFaD5SScDpI6E3WjK5TPbCEaJN75UDOTaid+rY64SSFv9KIvpairvoMqGyhbMpPKatgq\nltPadp5MvIJlUwBJo+VGTRsnOvZyvbKDkMnLgqYLadNGvecOigjjI09wV6hl/9QIBkVCk+wmmbCg\nNyeoqprAp8nRkLFj6voZVnuErUQlXvs6RXuUXMUIfssWRu8qOs8MxlED46E61uU62oK3aXSNoi0L\n47fPMarZxj+bP8qav4pHAz+lwX6RyN17mTv3OHE5wSuND7FtbRSdb4KEIiEBb/TvozG9yr2N30Qp\npflvo3/K69IhlGKG386s8B5rCxXKLmR9iowlQlvmNi+bdmBHwbWc4nLnEeo276JqNSAI6CSJzkgS\nk6wyVFnDsqeMOVc1G04Xf/CLH3D41iUKQRf2VCuyNosqFlG0ebL5IG5hBVFIc6Gxg4jdhyOxRI9U\nx3NljSTiIi3iFv5UGp1SwGFyECoZKBfiuPNJViobEdMihMJcy7USku10ZD3ErAVceSMaTQhLqQCy\njKzJEdOVeLvQzLBSgYkCFaJMWWELl5hjSXZSE5qlP3CampNRfvLkQ5zsOoTv6gLVoo7dG8O8UdZD\n9coicsscnqVt/DywxVTexP1ZPflqBf+WSAUjnDJ6uG41ENEINGkihK1G4l4nmXIXn9pZyVdnsjiK\nK9QK0ywpHu7KPvKKhqtKPSOmNqbyjWyag+hcJd7sO4ioCHz+4iI102nMk+NE9JWctjQxqfhpl6L0\nx4aIWDwEbXcRVS3hVivPavt41+ZiyiByx9fAiLeBmMWJSSyR0gcpF5x0KzYWdtYRtWv4x59/lUiP\nkz+t/TyOQoZvnfpHfuv8a5gta3w99wM2zcv4NzVoZhQsyRKu5jQPl53k06vPUp8IM63swJJIk8yX\nuC+zhC3nZ7RJZdFQQe+qxLs7Gvn6PZ+hbnqe8VCQyoKdzngFRy9+h6qpeco2N2lemWPv6h1eb83Q\nYPogPr0e8eK3EclRTGvYuuf3SdY1Yp+6ymLdR1GVDRoXz6BtupdxaQabouCNJgAwdHdhLe8mvzxE\nKXcbrH5Ob2tmf/oUZs0IHu0baFD+TQR084xnj+OTrmPMzPPY81+numwBW8HIrzIf4qvtf4NpOs7C\n1g1cXevM+H7I6O0VwoUVCkoOk1Bg98wmgk7hrW334FJS/Kz6IVpTc7iES7iEBUhNsWBowEqWvdZr\n2KQs66obv8GGLR6jM2bGkO2hvGCnJdHMm7XD3H92HY1oRNFoUVWFN57wccJ8mVR2O+WZKnrFYQ5n\n3uXB5E0SgoGPV8k8USbTHrqP6aJIpXiRY4kCuTINFcoYdZEs58x7aS/OckI5xKsdHtLa10gZ5hgP\n5njdauC3kimapWU2zKep0J2iJAp82+Xkl1VarkVH6Ch2UqvoecNxhmNjjaSKMTY1GbRKholaCU/G\nyJrNwIReYUdOpnsrT3UkykLnGQ7KXVQUnLyjity1t1EU9Ag+M1OlBoYCrRyNX2OneZ5mW5iYamO2\nTc9CjYl/WTNxPRAHTTltk3b67KuUiWk+EknQqWaZ1WkRda30Z9f4vtXJj9c3qUwEyST2UD6aJ7i5\nnb74R5kzbjLqMrNttYGYu53D9v9BjWaBusAGC3odecXKLwO7+IumL6MtTNKcjxKzaLCUVIqigF2Q\nedmb4G33NJ7kDrrijZhD2ygkAyTnd4FkYbfJQqrhLs4766xVBWg1LlKhZBAFcOgLXAlXMR3b4lr7\ng/RaNfRvOVjNmhF1WUzuCwR/nibXAgZ3kczWI1hM27in4zJy1k3qzc/i2YK0JcCbB45Tuf19VKzV\nkyzeJpsXMQQ17C8ucJUmfPlaXMlmIq4wtoIOW64CXehljtqnyVtVMovHqEbBUVogV1CQt/vJbyjY\nI1FKeok1T4hDlioqKnr+k2ziP+Y3URRV4M8GBwd/Njg4KABfBTLAXlVVv/Lrmgzw1wMDA18fHBz8\nDPD6wMDA0q/PHgWuDgwMrP3PfQVB+NTg4OC/DA4Ofkqn05V/8Ytf/P/0Xf9vMfuriV8fQS1W4/S8\nxLzeQmAiQzBwnI6532VveIuQWYfeVc9xTSc7MlCIz9G8+RJbUoB2z13s4gbHiu+wPLeNvtUROm6N\n8NqB+9i9cYsnIq9ysXonf/TwX3KtexdliTifHy/ylQWZZyji87zGpbLDROwyB06+y92qRpYrKni3\naj/zOj9v7L+XinCE1rmLTPrvw7F/ju0aHfaJQ1jjfnLmInpZw80DN1nY1UbX7RDipMTFboGqNTh8\nq0TnkgII3LpnO35TCz+q76JQVUbU5aIqusrRWwIGyYOkXeBUbz2GVIILXVXkDQYknYG8wcBCdT+q\nvh/JKNI1M0Plylvcd2OKsMPOSGM7C+VVhFxGAuFNZEFg0+Pnzb2Hudzdz6SrFTWtILsNDHc0URtJ\nojFYcNXci14V0UslTve3sD0wh7hd5S9/60u83XSAh9cv8NiZc2ii69xXnCar1fNmUz/eRAw1u0z/\n5TOsb3uI96jt3NatUBZfxWPupTZ5CmVVg8FSxNabI7BURJ814pCSNK8niMu/jbl4iII0QWBNhxy6\ny6pvDz+qf4QHzL+ixbtCXnVQN53CMPQzvMZa8t4eFFlkuRTEt3UHjVxCUSVi8QDZcht9TOAVZkip\nBkZG67lT08r37/0gK3b4ndWzOMWbmHVOvl/bwRHbSQLBSxhLKh3DObYEB+nX9WxZXAz1NLHL9hhr\nLVl0YQEWLlC3OItiVtisCbJP/y9YNOfJKA/yAC4eRUdXqQ79+Bt84zNfZtXp4B9uZKjVVyOt3USO\nTNLQfpxK3Yt8YPM0Bkr8ytjD3FobXdYtPOEIxy6dJWYpZ8jux7DcynapmiXNFuZMhtVgBee27WL7\n7DLHrr2OrlRirf8PuFDfzF71FcpTTWjlZs5Q5KSSYEsKM29IcrVGoN10EVWfYPzufmrnN7FPxnm6\n/gnypgJZ8waj+hKqrMMiyGjQMWOpJOz08aQEtdomrBYv0vxZZvPTFOQc17p30Sh56U7WovOGMThX\n6YiF+UXVLnzJNHO1VTTML/Bu707WvRWMt/bRszpHThVpnRvjAf8rJDecXO/YxXhjD5ZCCUUVeTdw\nkJf1T5HS2vjKWJ7dawnqDC9yy97NjLOMV4XHuNXcyYavlpJWpHFpiqrlaWxyBUlTjI+8/DVETT0x\nVznffvwe4lYbX/rXpzm9Yy+f/eVPODo2gxhb4kpfB8uaasxqiprNFeyuTW46txHMlrj/znlssRKq\nqkEVRGJOJ0/f9zjjLa3sWb5C7LNFNNUlQqkWtKUcGllGlDJsmixsqztPJFLJWLKViORDoylR13Cd\nZzcPMWms4m5TN+VhI0VBIGsSSVvMyBotd72VrCYb2Ga8gq9sHEe0gBKpxpitx5F1kTStsRlpRkSi\nrO4aVJ1Db8iQSPoYu3OMvGSiqWoEvyXMnVAnJ2+9H2XKyy+kg1yW2wlmt8iZRMosW5jkDGZDiRci\nj7LsbqZsaZqHan/KO7n3Mzz3CJUpPZXRFNfaA5SHU4gJLZJkoKPjXeqFWbpqXyYrCXz1ypeQMwLd\n1jX+91f+EX1qkmuaCAFbC2WRnegTBzGTY04w45bTPCheoW9+iqbzl2mYWwBJIbCwxFG1m+NpH00Z\nkROVRuIWMx8+PUr7+m2u79xJRrTjSjXhXZ8l6gOdoBISwWwKURJUXKkVLta3UxtLkpI3WS6VsVQ0\n0CWuI4gqpkwlnqAFIbOEW5NnI6PFk9uiqDXwC9t+ZNXCZ/NpVvxBfn7YT8uUBpNS4pojiJwpEpNF\nTkktoGr4GlbKSypCsgJzppJ1SwOn82aWLQEMgpuSqkNNC1iiEabkSj6ZPs3h/jMsiC7elQ9wvPIs\nmWgvp3MW/LLIPgnqyl8nH2qloe97WBbiKMZGmhQFqy5CpUXH5dYuvjg6h/UKLFrGSIhrBP1zdAXX\nWdxsYkG04hSSbNetEhST3JXLmCfAgeXzpOczvKWtYkyj546vkRFbkCx6DukiDEoe1g0b1JhixFUT\nU2olZ6LVxLQW+kKTNChWHs1ZSerCXHPUoTp34nT2E3O3cbfWyqleD4dG4pQlR/my6zME8jG+pLeQ\nEKu53LyHFyy7eUr/Jv2kcFZkSG+YEMpUrD0PkZY/iFa4wXroCGXzQdbqQqwlrTxiOkE6t4OyzA1e\nad3PbMbEj3Y8zvbxOxTGC6Ax8UjGBBixRedp2TlJvs5IcsWA3VvGtjtR7MsXkApbEJ9EzgsU+lqw\n++7HevLv2PT0EfHvQMxdxx1fxn/3HLWhKEW9EUOxSNjjwatdoLL8hzS7rLTYu9hr+Ve2b9RwxZBi\nvzSKBoU0Rr5j/iN2y+8QU32Ekrv4feVvadFvUqUvMJ55ENUYImUyYF9KgQqBTD+zcyG0BhmDJOGV\nMzTNJbDlS0QO6rlr6Wdmw8hK0sUz/R/mqdCLyGqIN9ZasWmXeKzsDGWZPDNKJTvEVZ71yKSFHIGi\nAbPGgEVbTU5JsXdcomlhk5RBwpIvMlRv5QctU7iyFrbFd6Lb8pE2HcEn3MIuxfkTdzXebCPiNRt6\nt463rav8q3eND6QKBLeyeOIlZKORJqykpCK/tD/IUHACc0HiqfWPMenrZ9m9h+7oEjVSDL9cYk6v\n4/mUi0u6KoZseYrIiEWJfdJ21jUa3DPz5IoRBCHJofE4b20XEcy1JNR1CqLAlyIRvJKbzbiZdUeJ\nj9DHnpKJm64EmwULqlNPs7KJvVig+9QoVzz30yMuISse3rQ8zXXvZdbUEkMxMzHvk6RcH2KsrJuP\nbL5JFUkMGpVfGN7PS/b38WeRX6Ag8IrNxOPZNOfkPtIrMaSMiNn7MJRcNIb72XRsYTJuRxUUAuaL\nNIsLhDUOpnYZGaj+DD/0f4p94ls0Ja/SmSkyqbNSXioSt2m53m7H9YKZ4ysJzhwMMR7txpezQ6IK\nteDGbMzQpDVQMJyitWGGdssCkqpl2NTBD6ufomHjt1jL3iS1aSVX4+REoI0e/QiFpIQia8kuH8Ix\nu8hswoerK4YtuI6l4TKimGfurSNIaQWNsRKDWkXLhg7jbJoU50g7wBCLoMmlqLdHEUrNhNUAQqGJ\nF/f76NhawpQpo0GZoNs5ymyFBckVIj71XmRdL1JhAZMlR0Jy4U+ucZcDKN4Cu/s+RqPd859sFP9X\nfuNEcWBgIDw4OJgFvgV8EBgBSkDvwMDA139dw+Dg4J8NDAx8Y3Bw8EPAhf9JFD8GvPO/iuLAwMDQ\nwMDA0wMDA0//9Kc//Y2aKAJotRYiW5cQ11qwyifRbWtCPruJb3aa6tULlC1MEfW4mCuzECJOp1JD\nta6W2lU3IxYdK/YZDpgWiYaqiUWrWPXf5tjbyyQtNl7uOc7J4G5+FngfgWKYP/rBd/ncy8/Rq2/g\nLVOUt0SZG5Fu+qeGGelrp0/Wkk5pyc6mMJhhprmFsugykstKz+gIUbmeJm2Izvg6c+EPkLJJPN8f\nYNucwlBZFzMaC+97/k3e2bWfHz3+F+wODOMcTYAKGhVSRidXqqq44+tE8RqxZVJ8cvIH6GJHAJG+\nmz/iUnsDt9r7yFis3H9pjKWgj01fGVtON6v+ct7eeYjJmnruuTmLJZ8jZney6TIx2tRF88IsabsZ\nSzbDx19+FmcmTdfsJP1nbzGTcvCl8DCTZjMvHjzMuTITP2nw8t19Xby2dzepGiO3a5v5i64/ZtFU\nzh/f3eQTEzZajNuZTgxRzMHnxBc5I+3gRmMHNVvzuJNpOms+yJo2RDY9warLz95cHSfLRQ5znWB3\nCm8ui6lowJ+JYosaEIq9pIufIr3wNPK5F5GWr+KIjJAz+tjNNN3iOTSiSpPpEtrsFJkNA4XoDFvN\nvZxobMW+IWEWVAzbDrBa2KRmeYHDLbdIaKxcZAcvScfou3qTv/vIp8jYrMSNNrrzz9GbfxmNcYKv\nlX+aI5sR1hItCGN6etVxvqv5PJGSyI7ROyQEDRV+Ky6xE3P5UbI9MXS6WYLb0+zWT3JC3kWLOE9R\nMPFT5xrhooetjVc5c/BxXu3p4MDMJIG7V5hz5ImaEvSW3SDgfY0yNcKY0MLwbCNbqXrsHgFbOs14\n1EVjbBVXZoORhmbqzIuYo0mKQhwVUPRa9o9eYfe1sziTSVI2K3f6azgftGAsSqwX6xnSrrCunUXQ\nr5MyRMiImzRIS9jc6yRClURW6rD4N7lRsQ+rpsQdqZyiYqRGt4VRU0AQJVSNTF5rwpTYZJ82AUIv\nlzy38K2OY4vm0dY8RSjgZiLg4g/XbeQUGWou8Vrmk7zeuAshk6Asl2Oqvpl9N+/w7p57SFjtHLux\nQHXUhpCfJLVmpm56kvLQMnPVLUw0tjFe08iay49OKvKxkQVQc4SkaTQZO33lrzMttNKQXyWbc7Hh\n97AjHiOY2YOcHcaRSvHJE89jz6YwZ+ZQjQfJmDN8/hdP07C+xKFYjl3XL0J0njstnazXVYOYQSeo\nBMumOe45zRNXj/DEssxKbpWSADpdDlnWAQpq+HvcMy3ROLaIdktA3ZNFX7bFbbWNfalWKjQLZB0J\nav0TLK904CmkSKlW3J41HJ51DlZeRRBKDDn6KWmzhNwWdkwX6L2VZOfkIk9H/5CrYhe/cLyfJmGK\nJv8wLmcMebUTtWjHmKvAoBgJ5UWqYpVonSEUbYHxscOUSmaSKQ+KIGK3bfHC2Pu5mmnlplhDFiPf\n0n6be4oSd7N7qay6jEarkimZuTC3k3BZJYmCA0tE5ZpyiHGzgavpHIudFQQjU0zU1tOxsYhek6Cp\nZYhK/RJZ2cwz575IHANPrT5LTWSTkr0H0dBJ2tnFvKwja1nCZY8yVdrDhC7MrmIP5ZpHcBYcMHOB\n0wcf5Eb3UZ49di/PNfl5clnGszxGujhLU3gFT3GViNfLjK8CKdpOWVHDlN2ASZRRtXks2iQ6ycpE\nupW7TVeRim+giPcTSG5QUwqxK69BMvzbhEjSpklmYxhQkVW4TAP15ixDUhVq3sGH8jqSZis/OehE\nSEkccP0rttlDJLJGzug8LAh+XKUE3xx5i1VLD8WiFUlTRLCE8EdclGlSXBEdiEYNoteEatKRTRvI\nlkQeeuhfWTT58aRzTFrbOCscIRZMsxHWUWE1sq/iVTKL/ejkLJ7ml6jdMcOuxrdoazxFPm8mHTPy\nMcf3sGqSeDtfYSVjRCPKtDZeIWBcpHzqMAcMY+wsP0VLNowo6vGLGZbzdualSkqqlUcXzvLnQz/m\nE9UeatQYDdowLlcT/mKGWfMW+oKbmxk9Y2I5qODJJ7h35Qphmwarxk9L3sa4XiYuKuyKXWehNsTz\nuzpxJ1LsvCXzf7i6qBU2eM7637hk6SYndWEWPPQUjAznH+F/lKX5gd/JtMnFfE0PncoH0IuzoE5w\nNvlZbjYY2HnjVYasTbCZwW0IsmIIMFVtZrG8koalBVrPDnPD18aDWT33rz5NyNKNXKVD0m3R6Z1H\nJ8vkGp5g0rGPMjmNsnwdZCjbnuJdx58xpQ5T0KislX+QlKHArts/ZstmZtltQVOhR5eQ0EkyZ48c\nptd7CZ0IOmEdi+4aWiGOXThLvzyNqoKkwI9L7yOsWrlt0PCk8DrDmQeJK366/WdBERhJHmaEFaqX\nS5iSMlM1RUzhAtlcntrjy7Qtx6i8nceoSiQ/KDFp2EtKtVJ9dxxdPsZ4WSuvGQ8wEfZgiCVo3ieT\ntQepiqywhZ16YYlv+AockO/y+4WLvFD+FE2nX6B+dYWapQ3yeoHf+7TKW7vdJMobaUw10ZJoxlYQ\nEGw+bmtTLCi7uSzWkVCqqBzbQl8oobOvct1/mZhGQJPaR7Myj00tcVGqoqE0zlW6eaf6CFH5JR68\nLlC51kKPbOb4ioY92T3Yta8jAmthP5mJGh68kSJs8zFZFWfYu8x98b0EVTujcoihrr1c62nm8XPj\nXOpQKVhEJDmLUrJRubUbwWAhG1fRFVSyNes809jOcGMLTnea6swafxVdY1wjsFZZwYcYx6kZRiMm\n8GS24QofZyIa4ImJRo6FAjy1KPNkrAynUkIkTrT011QVDhNQqrFxAoNaQM6YaVczvLHRhpDNUuGo\nIaP0U9n+NOaUgCtyBEFxMFV2laghhSyIvMBxXvbXccn4FHtyb/KPV8aZdfSjFiYI5CV+Pt3PVysM\nbIp9lMd1+G9n+NaeCPO5w4QFFZc9hGyYo0/rwm74J9rUy8zJteSuwo+9f8wX932OOu8OKpVR2gur\nTCRUupZSjDf2c8JRTf2Sij6xgLEosh48gCxsJ5eoxNN8Gp02y+ypBq4Z51goS7PsWmU4eBF7wY6z\n4OWuf5V3Gt+hecPKZtHEpfo89mwzRdmGZNTwBWEI/7uvErPux6eLU6MdYg47amuUxNJ2VNmIxlhH\nZn2UZEcZjrUQK4YaVM1BPu8tx1bp+s+Tif+A3zhRBBgYGLj1a6n7weDg4D5gHuj890ipIAhB4Ilf\nR08PAbGBgYFRgMHBwb8E/un/T9HTf0fvdZC9GkMVTAQrS6wHD7GYTeMyVaC1ljN8zEkpZcChHfGy\nSQAAIABJREFUuUNJDeJXHbxZOckPgi+zFSujR3WxMLeNrJjnlfK7mEWFD74zwRu795PS2Hl45jw/\nGflToikXntUYpcUL1Exdoye7QEkR+OLFZ1kps/B6bz/xMhNVoSSJhSJCuQHZZKZtcxFvJIIvPMeK\n9wH0JYHp9L0Yq86w3/MscqSWYEjDa9urKEuskbR14UsGOWMt557rl5iprMGSy1Gxvs6ouYrbvV0I\nBoHP/eoZDMUjCCUnplyY5tkXma6sZramgb7xG/RtVbNroohLk+Shl/87nzh9Fk8ywTvb9/DCPQ/y\nkwce5VdHH2CssZPuyVH+7rvf5L8Y/5l3Vtr4xIUTHJ0bwr0eY9/MENvr+jkwvcWxl77NnN+DSRFx\n5Y30JmZok+4Q0gc549lOSdTyhed+wgOLBdAUUYd+QdiiZVVRaLaFabkzz3M7H8aayaBY9awYMkxo\nVjBh5u3GTt6sMfPX4R9S41okH9WxdqOCLfP/hqJrwyQuYdSMIuQvkrm2jODtRd/9IXSt76FR+0u6\n/L9krdjBryJ/iyUXorJqFqFGIb+g47Ymzws9jewf26Rkd9FubcKvzCItL7Pi+RhL7s9iTNfTNLfI\nokHhxw8/wYdnJkkbzJyy7+a6q5mPbLyMPmXmwuQOfhFt5a8032NRDXBDbmKysp69t29QtbzCaJWL\nGnoRANnzXZqt62yIbiaGa3g9vBOft0i9eJPnPAd4uruMEzvvZai2kmA8woNXTrH3whvE2nI8ansL\ns6NIZMZHzPQt3lKLTPuaSRqtVEzOs/3mDdz5DKcr+7h/8RqN0gzDx1xMymb2DN9kpbqK2rk5tsoC\nbAYCNE9PM9e6k9FKF43xVaKSi5ggkdXkUQC9qsFDnJRgJpl2Y4pqmNjYjuwpEU7W4Q5HMN/d5CXf\nbmKSm+qMjWWxhAYvrtA6utAMmngE6CJoytMh/C1nk/VUh5No4xtIyhpv9/Sz++0fcdJgpLZ2Em9S\nYLzQzZrXzbF3zyC5rETdLk7sPEz32iSfPPEPbMhbSFoBvSQSa2qnYm6CnXOz1JdyfGXSziPnXqdy\nY5iCkKRU3EQSFI5mdqCU1nmv5zsE3zlC30qSnYlpAtNNGPNpsuoch0eGsBQknr/nAXZM3CTs9BBM\nVNA6c4aXdxzgwMQUgtGJkouwYXEyUt6MW8zjDq/TtO8C4Y12GuYaWN4YYtOWRlBlShgAARSViphC\ny8IW7lQMnzODPGrA2B+nz3oXm22RcsOb6LQOjK51UjN1LFCDqAiUlU/yjdufw+i6RqO7wBA7SRqt\n/H7yeR6NTfJi2MFFvY0rW51cyjeS2dSxtlhJMBLBV7WAu+E8yUgNUs6Ly25FUTfpM/pIVp1nJdzI\n1loz16ubOd+8DcuihvhCHWXpNMdL5/Dq0rxPvUMm/iEmi9sZdb+GO6Pg9iUYWe1hd22crJhlyd5E\ndMTMscgwkbCWjKDDVJlHKxdYDtbgC6sE786RatdiETPcuPBh3pUrOJTX06zpRa/vI29tJGd0Yc1l\nyRkFwgqsNL7Dn7cfYjTYwILDxq5bF7H4ejnX7+Tv73mEiXIPGYMBVavHpWhpufBzfBszZNx2WiYm\nWbT5eWnPUR4YL4Aio7MuYUs1kjdtgahQSnTQVFJJ6tsJVbyX919S0JdcZI0bSIYYkmpEREIVQJP3\nouqyiAg0UAABWqKN7Mjq0ah5nn9IoSDq0FyJsV1/ncCMFafWzdg2lfunX+U99RdZ4Dii6qLeqyVe\n9lOiiohGNuLL+dk0w4jexXvv3kGvpBG8Rursa3yn6jOcMD7C1bLdZMscSE4jsU0NYrzEyj4vPXMm\niFTQmV7D+7Pr6BdFcqlWSnfKCcfdZPVW4lE/te3vsLLeSCxeTod3keax36F85n5Sa5PklHa6N5/D\n3qjn2qaZtfomHps8S9PGAl++8hO22cdI/raGUPYA0XgT1mwlpqjAlHkcQdUx7tvFDZ8HIVniwNYN\nvnH2aaS6KrzCKmLyDnptJVY5zy2TkfLYBuf39RC227j3jWd501KNAQ1fbi+wlSuRWL6HMvMIZ7qK\nzCct2BU9NZE+ppxj3KldwK8pZ1+6n6Ia5EbmEFHZgGvzBA9ffZtzFd2MlrXTaYriSrmZ12/BQoGw\nxcVc0U9zUeRPdT9ktb5IWluBlK1kwVOgoEBbYInU2ArF6o/hq97BVmKWxm2TaPwdXM8fI68V0BWr\nKRhcnG9O8vClk6Q1Gt59bwwpIlK/mkNfkllvt3PEOMwbFgNNpSJ5VYNOUNEKMvPrfiLvWrig7mQm\n0AKqgpyyUJmbxW0ocjd3P5Q28ZVWaHZfo31ug+CNJMF4hoK7jWx+C9Voov5OAfetPIVaM7EvZFnc\nqmJRaScQDrN7KUfaniKwOM9cyza6Js4TqKynPvUUf7a+C1XIcq94A1FQuWwyUB2/nxHvYRRtH3tP\nP487tIEpn2e0uxevpo+GdAvuohOHakVFS0kjUhAkHNpq7lNm2MUleuVZttmK7PEsUaMf5e88dp5M\nFbg/F+GF9Q7iGR3kZOotCV4QH+C1yjDa4jRfeFliTLBgbD3OAyk7Ju2PMYnzlGSR12fqCZstbNpM\nuFI69JpOVm0ZqvV5dkd388O2t9ls6WXRtYd3du+nLX2OVW0cp0alW28ktvUkNytrudK2g7sNBzhX\ndYQVs49D8Yv8uXOA/fbLCEsNxGJJ+jRJevWvsFTchl6MsaafQis9yP5cC+XmBoKCE72qsiREed3W\nwGvWg7xS14TOmKY3LODS/hwRmVYhx2gyyHzEQpMnSUbzODbNBvt5lh3Gt7iq85HCysnGn3DdmWBB\n6UerNvBG7WPo8pPUhF/ju2Vp3mKcpCjyeDbNtBSAsJ13q1Qe0X0ax8QQt+rdpO37CWUVdqT0KNZF\ndgknqRfP8qbmENfCf45esLHPdQRvXsY0GeH+wu+xXicRSCSYjOnYtxiGxDJZdYnA2nnyyhyCsRVT\nIYFj7gKV5kdxrx3E0jXG5aDMmGmRpGURryCjFxfQuyxUreygPdpHxCFyQN3Ekng/c0Yj3cIIzN5g\nbjJEx3SIhKeKmLmNBpOeWWcfon8CsXqMzPxuKDnR4kbduokWAaec5Ly+m+2eGE0tjf+pLvG/8hsp\nioIg+AcGBjKCIFQDX+PfFtmUAc0DAwMXBgcHfw9YGhgYePvXUdXf/XVUdTdwVFXVf/h/6v8bK4oW\nN3KySG7ZjyX2z5T98Xd4ezFFr/chFt09yLY6lqVxEthJFMNUaQI4FCsLxRL33P0UkdUusvo4lrSP\nxsbrXNBqec+1Eo0rWZon3sGzuETZjQzOpQRaq5aETkUrK1jTCQ4uj6IKAqebDaT8nSiCyEeGihR1\nYTZKRlLVbo6lzhNX3DTPTrFgOsCMeAizNs4rjWV88uwJ0loDxUwnYZtAwdJC41YQd0bl4XefQdLI\n/PZ//SbPHn8vXdMTHJq4zrOPPErz/BifiFaQleopIFC9/A4CMfY6j2JZfJffizXjFi0kMwp1iRT+\nfJgpU5bHTEd48NRLiMkNWiIRqmcusPP2JXrGLpM2m/DPpnhg7RpqVOD8gzsI9XppurlEeb5EIjIM\nxRQd0Ry/M/ZDfsf0Kx5Nv0jjRo5Hlo/wyIqAYX2C+27eYDU1wVJpEYdhBN3BFNFNOwtJJwc1Uwzp\n2hipa6EltEyWLKIi8x5pB4HZ67yP59mXusSCIUgh9iCZyU2yW9dwuc6z6uznmmEvbep5nA15vnX0\nOHbdGdyOl6i0jBOJ+DkZ/gIZnQ/DxCwmtRqvf5LVqjLqzy9Rv7aEPQsb7m56zG8hmx+jOPc2pqIJ\ns6MBq2rCPHuF7zz4AFveAH8/bMZZ1PBKtZUpYzX9qXEeSpxkLXOUB80vsVMc58+lT5MRLJQPz9G5\ntYCKgG1piWJFHS7L96kuDnNS2c6PZo+xc2aCZGuQ29YObgqNvNR2FBELH5hP8Vsjy/g3h7jV3MSH\ntSfYYZ+gGNMwda2C3IyA6GjEHszxshxgEgfBlRBv7N6HsdHJWoWTJcnLoakRyu8m6JibwZ7Os1Rb\niTYvsX08TfetM6iCwNX+Ts41b+NOVSPv29QTJYosquTMJqrzBiKCDllQUREJF8soaHSIGT09mTtY\nb6/QszbPKw17eSqjIyA5CTflMCYdONfeQTX04RQNrGcm2e4+xVRSz1jRj0X2Y81GURQ4uXsfmyYt\nplyR83W7qXDcovFiG4+feIZdd2+x6A1wfttOVt1+Hj/7NgtuH2opi13noaQUiBvd6PNJ9nW0si3W\nTlAyMa/acMyeZt/ly1wN1pHVV1CWrmB9vQ2TdwaDfYPSRiOaUDXOsiu03/wepqhEeSpG2uwirxfJ\nGk3UrN1iy7ebtYpD6E0lWuzryNN3CdX6ublrFzXZdVRFIOlwIhWNxJY8GG68SVyfZDNYRvftYTbK\ngzicK2QyLgSjgx1D17FUZ8j3gvmWgGbKjBDsJORcRPZL6G1hxIJI3UaGaakeASiZVFwLPTxXaOFc\n5ZNIqo4/XPsG3YZhNFVDPCR5mFmQmLEFOBC9y+HSLWqUCMpkgti0BUd9Ck/9VW4WVL6zvYfP5kZI\ndv0IRTYwPHyMpMHKpdo2Dl5+k4axa2iSKYzhVaSMEb3ZSFLrwhbXcrbmNh2rJZjPoa3QE11u49Cu\nT3MmfIUVQxfZkg5bKsSoUk2dJsbh+BQJVUVfyjNWW83ehWZYcRBouMS8aia0WcdDOSOqKiEXZ9Dk\nL2M2RIlJ48QrchRNcV7z38uG3YNWkVkza+g7+xzzbdv4Lzu68ERDfOKV5xhq6sAni7wR1FLIRzl6\n+Tyki7TMTOPMGGlfjGGmirRzCkFQMefKuVtuwVkMkdXIeLNVvLrTy5PX8phzsGwOodXF0AgqGkFG\nxYgoSKi6LCgKogyqTiKn1NK0vIg3Os6N2iSXm/r4A+Wb3JlpI3Anz4H5y4QCe3jk0iu4axQqd94k\nO/VBfBotsn6K8j3PoQ5bqZ65Tsizk+a8lhm5yO6pd+mZHEMjKETd5diTBVoXbXz0zHnuuxbh6M01\nJop6RFFhpyxRP2tnNQid86fRxVYpJX24h5dZMfpZrK7HnMmQ11nYWGghkfPTI2npCB1DzCtEL30N\nc3KVtYojGJZk/KNXaKqWuP+X72BJZYhXeNjo8WHGzsz6l0mmfWxqS5w3KgQMcUqmNSoz9ehkA8Ox\nHLqghq+dfIaiqZwJe4l7fU7+T/beK0rSu7rXft63cs5d1aGqOuc0Mz0zPXlGk5VGQiiBLAkZE5Z8\njH2MAQMGhDE+BmPLRBNFRkIajbI0I2lyzp1zDtVVXdWVc3jPha98lr+rb/nz4Vt+Lve++V/uZ+3f\n2n+5thebLI0j1c+4TMM1ez1BhR7rmJ9JvMhkAo/ENdwSTUTWNqKQChy0fJ1OqZ9ntU4mJCMNUoS2\nle0k1EE257fh19k5Z1WQ8ssIayLcf/IHDFc6mWuuZa5gI1RTzrqFApv8F3lw1cf1qAkRkT/L3WaP\n+pd8q+Ez+LQ2HCt62uQXOCffQHdkiLLqVSatXrxrx3C7zqFz5pg9U8JnqEYQK8krnUyZpzAlF+gZ\nHcSczWK0W1h3IYEowZrVzHr9JCZjksZ8gVCpjF9NdvB7zd00RyZJn1EyX+/lWttGPHNzqBIxElY7\nl+UG6qUpApnNLBR78S9ZWXPkaXYuoKrNk1hQUzO1SEUkiTcUw+GPUnUkSsqrZ2ShGZ2skhWZGXVg\nhSZlDS31XkayCbyjt5AKIj26g3wVI1OUMMok9gn9CEKOkExFJNmFNummOHaBpqkREjodikKB252d\n5NV6ajM5upWvYOmcIRSoRjJvZ1/GxebCj6mRvYkkqZGQUAoJdJKV53W9XNX7+fOVgzQKZwnG1dws\ntHDAMkRM0vHduieJFo9iy9rYMKKjRmam19iAVf+3mLjEaMyGU5Mio+1CKDUTKQRpXAlhLaulPN2E\n3T6LN9LJobJt7GxopiL0NS4ZtiKoIZEZw23ayRPmAVyOeX5c8RiSSo0+FqI2MkPP3BR/ufhtJvNy\nTGV5bInt1I1G6HG+SV4qI1z4K4bVE/SUxvmqxkS/M8+u2Od5YXaUyeh1Qmv9yFZuo10aYJ/5Ct7k\ndwkqeqkpvcoIDajzcV5dbCOn1jNS04w2sh5t2XHU8im+YnUj5BooNywTVPmJyrOsaBeZNs6hzPRR\nnrvAYsFHppgEYEGh4CORGBZ1nrklJ72KbrbG25GVteJPR/hY835eXYmiF5TcJ3+HHtl7XKOXK6H/\niUqrpMfSiFgqUZMoIsavkrENEBElOopB5lIuwokIztAKtogPRyxB0F1L2JlCig+xazGE2fYA8rQd\nfWQDFbXH2Kqz8RlJyz0jk9S3N5BSjSKfSKKRWdFlOlhjG1FRR1oboJebdHpSlJVtR9k3gqZHxXx2\nM1Z5DctrRYr2fkyGGHm9gDxfoJBuRZHLIdfPoYynkJXbePSR7ZjV/33M5v81zzzzzDvPPPPMX/Fv\n0dNPSZI08swzz9wEPvfMM8/8DWADPvWVr3wl/cwzz0wCW/i3qOph4GP/Z+z0/+QPVRQBFC49iYs+\npCJobQHUkXa0URgvP058pp55/SiWgpEz5dcZ181wX3gPFdE68s0vYaw7zVrYjT7Winqmm8mKaXbe\nSiC3yImYH6F89SbOQBTblkaqevpRW/Jkp9W8tLOS0YZyVt3t9Ms2c9e152kJObHFtUQNCyxEtegs\nEuPaatqSC1ROLSLPJ/CV7aJ28lU2LZ5Ccy3Ol+7/KJWxMPXBBbwrJmZsizTMT1E7d4ZTNR0EbRlK\niRjnyj1Y0yLntmzhg++9gScRJ5JSkFMaaB39JfLOe8HmYQet6EUtaiFAUiwSTRsxLl9FmVnF1n8c\nTXCW2tACOy6dQ8qnaJvzIcocGGN+TMsFcnEFEgI/3vkQv113HzURP+6BC8jSMRJlXno3r2L1zJNP\nCPiDT/GzfCNpjYLWgoVNWQslRyMfe3Afb2/eQ7dymY7kH2OX1TEWmaCgEWm5ucgruw+iS0fwJIJs\nPX8Rm30jm7Qv0pk7x7c8f8IzNV/mA8FmVGVJ5IuDxMfVhBbSxPxFhJEssQ4TD669i1vyMa71MqDu\n5vH1f8ORd3+BOlekIMxjMu9DpZPhVt9gZdVCxXyAySo3eW0rUdlNRIUNVTxFydfH5dII0tw5ErkY\n//ToUzy0UGBnsMiqQuLtShXOtRC3jM08EXgNo2qeLbJb3BBaMJ7PUL60zLb5fjK6SjS5CFmFAvXC\nBSrcI/yEu/imooWDqSyD22qIa6w45AtkNRrer97ElplRKny3WWMVUYAvx76DXpfmwnwrnxM+ydGG\nvazzj6Bc6kfteopvY+bDXc+T0dYSyW2kV/saps1zfKf3U4zW1nH3pcvYI0HG3V48viW6B/owROdY\nqK7lwtYtZAxGvOEV6kMh1koBECQkSUCRLyATolQWq9Cls6S0SUolBXJJIqzRsU91ltdN29g0PsZi\nm5szFTU0JwTkaRuuhbeRSJEuv49N+irmI9cJFA1MRe0gc5E0NtPX/DhTlvWs2hKM1jYwUlvPvFjN\neXEX7arL6Jc6+Nd7tvLO1l3MOCrpnB7jgfdfJ9q8mV2G/TgUHuYT/ejjq2TK69mVO4geDddSBfor\ng/zU2k3P0ihbJ4cIiTbSijo8ShG70o9Odo3KVwZwchrN/WfI6RR4r0SYLDOTlxVQdaiYVnfRMXmb\neVsSdbEMTUqJte8kJXmJE3sOoCjkSWiNdPbfJl0rJxIvJ42RBa+HoN2BrKAhpRHIajRsVp9gINWJ\nWg7mcJR8dwrN2wJV28Ikr2hITan5RPHT+CMVbKi8gTqf53JhG8msDrEkUK5JczbaTKCukozFxP2z\nBazHL5DJ5MmVgdo1xJFBHe7ECDKPkfXKNurkNSxqqslrvSRHjFhqZvB45/AUslRUHkWesdA/uZFs\n0sRNdwNb+i/jTkSQC5VIcoFsWQVbFL1EhSwpnYiDBChAszxHQW5kLruVUkHBxNg433nyy/x8+DpJ\nm4WVpQQyzSjlJRtrST3NkxdQFzOMNHaTFJepn9GSLFvBa5/EMLgdZX6CYnYKqbhAvrBCKjNHqtxJ\nSanGnytyvmsrdxZe59DCKeZKNRzbdYATlTpqkxLP/uRf6ei/yN6oiUfj5fzeIyNsMdMz1E/d8gIy\nScKQDJAz9JBVGVnQzaMttLBiyXO014Mqn8edWkIoyWlY1lIWLnG9IYOpOI6AwCb3NeSmEHV9iyTk\nOnIqFZ6ZGYrBWabKa1HF/dx57td87oMPc35rL3ddOMX+weuMRTysqi30uvwEpUbSGhdb3vslkeXd\nBNWdCMoY1t3fwHosT/37PhTJHO/tPUDFGtiKIi+5Pbzt3szd02f57d57aPPN4UgsYg7ZSRjrKSj0\ndBXUVJCh3ackrU4yVLWIbu465WsR1Lk4ka1buNzQgHdujvrRIgmTQFqjwhSJ0PPeaVSCgszwUZSp\nMEqbm5igYsW5icr5qyxF9Hz3ocf4zoNPcqlzA+frN3GicTtrpiKx7AKvyTUopTxq7Qw6QeCw1My3\nChlMCPxRdIbOwXMcv/8RmjIjhEx3cavMzaFiBV59K06tlhMCyOI5dLk0DjFFh+IqBlkFNX412pxE\n80EdA1olB/xvccnQymTci75wGp26mib/evq1AS5anaRTApWhIr3Xv8dUhZ1PffbvmXO4kS8kiTiU\nbFkq4lV5OGdwc12UuD8p8nHzVwneUhMPavjlzp1sHYtjJ059wY5vqZ4K/Q0ahXNotTMUcmquKnpI\nj2XQpZIE7VtQJaf4xYEGnnz7GJpUCpWQx55JIUYlbnd3c6UlwRH9DHmZgpSo42elh5EieoxSI7b+\nKfJaOee27MBaiLLu3FX02Tzz1dWo0zLGsjJ0WQ+izMRCRZ5/aQ2yIXqESvEigqtEZlKJulBCmcuz\nfE+cZnUKKxE88jUuCo1kJT1CYJZkyk9/ZTerBiWyeISizsgtcznvSQZ2yvx4ZQmcxX1EVOdozGXp\nODdN2dVbNMxOkKpuQr/qY7i9i0VPFZZSEVPXJTQtIeSqDE6VxLqZMqplX8AlTnBZ2ITfrkGWzvJ1\n0yd5tu4BRjXn0ETzeMY9WHXLtOiWEZU7adNe5lSqiV83t6BKnWa9socZpYJ7HA+Tk5+kXHiDk756\nLq546bD5KFPaGZHMZGJBOhaDeOJJvvHBJ+mu305Z4jyaaTcTwpeotgwwX6xmTLUZTew4C7JaVGuH\nSDhSXBV7+XrgDXr6XuDzsZc4ULjIWGkD4tqjmKwzaAoXcKiH0ZHiOd0+3qs6xWVBxb2ZJarCAR7I\nvs53XXIKwUrkBYndZVOY1AUCKR0LGTVfa1exLZykURrk/cJnuLW6lbykJeAxYFnbTEwd5EdNL/Ka\nUUuX/xDaTJEu+WleNgvsWdrNqr6CqMGBObmKoaTgyfUf54T4QVot9YjhRQpSnDvyYQakDg6KDzCp\nWcAuVNCat9D9iR2cvzxPpXiWTyh+h6/UzUn/ZwnZ1RyWieRlAlH/Scz6etTSFRS/n0N/SY7KVGRz\n5TyxmZ2s903TMruAQlbBVFsNQ5W1XGls4eHFEoLZyxnVMA0ZN4XFJgr2c0SsQRZsWgosYP2JguaR\nm7hnzmFqvkCLdpSUboWQqEWsD9K9Ooorc41pRzVNZSMMxQ+RkavJSkVu54vUVM5gME5h9FwjHXRS\nyvWicyTIhAJYZTlo7KSpzPlfrRL/jj/IfxQlSdrxH9RCwN7/oC4BT/9/8a7/G5Bb1GjXO0leP4jh\nyrM4Ip9nVlxhWYwgrz5Kua+Dgn6RmkQN3rydJeU8LVSgdzTy6pwCQZZCrlnCEm9m68STTLv+F+bw\nPLmGCA0DMQRDOQHrE7w9nWSPawylocD9AzFOrnuaxbIhWqQQUdV62sMK+vRLNIlLCEKEy9NdLPZ4\nWI2pmKiro314mCvKGMNNMnaeCjDZ0MTO2UEK2hIFIKkPcaK7h+rZVxAoEampIqT5BZSUJNc+ww92\nHwFgd99tHOEQMz0d2GKzLG38MA5HFxWSiCRJLGZm8a37R8pz3eT6nkQud7Nh6gYJpZxZmwWDaKUk\nRNk87SdurAH01PvW8Os1OJJpEhoNX/vhPzPlqeCbj3yCTdeuos1msFXI0cgGAYjNyYgNv8GjQNJs\nJNZ4L7ryrXy2x0pEr0VVKPKK92m2X1qlrCinWWdkKCGwMb9IQ2ya0doGfh34AupdeUrSRxCFIgOa\nJuxr9/OReI6SUGRGmKF7S4qFy1YM6Ry2yBjPfuhPeHvzdjav9XHT3EpSrgPAHV5mbLOH3VeP8pcf\n/mv2XL/MI8NmajvkVGxQsPpWnuFqM5XJEoopAzLxh9ga65F0QXbMpEgXVXz6Tz4LwMNzORaFGD+q\nMaHI5/DbHVQPLPDTig/w8eWXABCuFli/3I8kiIhSkTm7FnlBS0V7kMUJB1fPNpBtVLN3wwnMrlro\ns+FODOJv0fKadz/yUoFnJk7QV8wQMVoxkmSyWM7n819kuqMaVbqAPhnlRz138ZV3f4ly7C2+0V2F\nO9KAI7EXEQVvVpTzmtiLspDjQy0/RHbvw4wPRugaehkRidUyJ+MbdqLQVtAoaPAXI/hkYeSFFIIE\nbs0MmzLD1DDPn9vL2b/yJFvFbfza9woaVwZZVKIE/FzzCG/t6uGBc+fomR3ljQOH+dZ6ie6xWfbO\nzBCr6+TH+xwcT5RYt9YOsQGgQLtzG7nxVxjybKU2KnLg1ZPYVWF2375CXKvn85//C37iOcBD7WFK\naiVhvQltOsldL72FI5TilHsHXzWIFOIFNsWN6FMxBMnJx1WLCFkbm4si6emrHEkFeXHXJp4YPE1N\nT4zAVBqrTEul734ma5a4friVHcvjjJ16kJ5z3yPrFFi0GFDLC9R2D7DvH2aZKa+id/AGgnQDRbFA\nCZGB9o9iXa3EERnGb4/Qv66bzqZ3MAznULyl5/SegxQUUFSkidpsWNbWKE5YMLbFUce12pFEAAAg\nAElEQVQkRlpbsMhKiFIAw1QWe1sMqX+WR5LXqVKFEARIqWUkDAVkK4CsyGi4knG9RN5tBOBIfJwJ\nb474rJEZVTvl684RPNKP+con2ZKuwijKyRbT3CpcRlhLkissMfzedjz33WaT5lU0wQ5CymXW1irJ\ny+Q0DA/TEN5KxD5M1pimIGooS3uwGyoomzlJyFTHfH4ZISEiK+RJO93o16KYTGUEhCTfe/Y7bJMr\neL3LQ95WjbRYxgCADEar9uCoXMQzP85ArYFs6FWcq9181DVIxvtN/NNGrFklBpkFXFUUSlbixjwz\nBhsXGjuxR1I8kD2O2bfGrh/086WnP4dYLPKtMS2Wtu2c05vZ6uhFFr7CR3yr/KjqIZZcZVStrlCs\n7yA3P8eqvRPz2nU+LPycyB0avm96EmVBxb3xZ7lieAqHNIU+USBojVATi5IVRDpbT6OzLFEtFDBY\nZWhuJ+hb303H0DCyXJY1ewMPnHqXN5q2MdVUD+kCu0+epcoXoKlrmdeqt9CjPIsyuYX+VDO37/wi\nubAafdaHe+PfYnyhhO6qnBICJrmGv3zvGPMyG0L5TnRKK4mqRX6+6T7GnV7uvDRPXqcibqzDGBni\nQlk1RUS6skYyYpGc7AZNQZHFzg1EPHUo4hGSbi/6WIyasSlmmu9Ele8CrqHKSsjTEXJDLyMTRLA4\nkS8NUhtd48aGdn507xd4ZVsV+myGBwNj7I4t8lqqgbBaiyj3c76xiva+Kf786m84v3c3l/Me3pJF\nCBYUfLekokvWwXjHAf5l+3Y2rtQyprfx0o0iV83wixoV03otShn84vIq0cGjXF7XiiWU49e7jTx6\nSuJGvRJDWYjH7/kK0//0Jl+Ovc0htnOy/n5O1xt59MwELb4aupbyAKhSMyizAb76J/9ASRSx6vOU\ntHKyMwleNglU5gTOU2BdVsYHVK8hSpXEpsPsmjnNjw/fz7xNiTLYy4ftX+enm76C8/mX0DiKRINq\nfn7gYd6rfpFHdu/GFNtJScyS1M0gl+ppnpliqr6OdeWDeMuDLCzaOV+2iXY1yEv9yMnxTvZeFLEt\nKAwG3AsnUcVTvHvPAeRCkerwTc41VeGMphBKJWqWVkikQ6RUx1CZnkKXtHHvXAPvzyUpFsvZWL1E\nqkbDWNRMXTrGHlWKYG49x9JFHjSP8DHpKN/XbKTM2kk6dg1x+hIbau7iyo5DnNcFSQ/l0JKjSr5I\nV0WEs2EwKVTc4ysxv6hGa82SDivRzY5RUioZa6jFi5Xm9tOkHPNMT21AFAtU1/ShmZ/BkI4zIn6U\nXM/7FFQxupcbuGnbxJI8jNU3R5e0nQWXibfCbTzlOIXT8B7FtMgXDnwZWfS3yEpKaqbtHJLvIyrP\no1M9TzSpZSDoIKcqMiR20cM1dpr+B5fjChbuaqPu1Td58JUX+elHPkG5KkK3JNCycB8L9m+zx7Se\niykdrRo1K5mLHJm5n+/r2jBZI5RZf0OHLo+6VCBTgv3CSUryS0i388jEArlSJSOF+3ldOMOcrEiN\nqGOIRtoMkwhCkVNaM8rOJHdcVlGURPpqOyiFAqhCCTwLaSrWzpEz6siFG8iJoNB24F39tzn0QsUL\nbA7eiTOlQ1Eqcbmtmm9XPcKe/t+QUTmY9D5MdcjHwblrmMLtjGgbydtL5IaOs2/yjzna+jWeiMVp\nr5rlPeEyb8tP8GB4C+u1e/mnb36LrbIwfyo+x3Kpnhnhy/zz3UZ+0JfFlAWlTkHa0UUhFUKXaSHF\nCU733sWuxTPY3eN0FF5jSe1hrqKZvs6DKPCzddTGCxsWKFVtILc2yYJ2gAG9kY5CI3NvHGK5bRmn\nOILj10mETITT3W6cqz72CXNkypJMSk24kgG2xQcZSRtoiibYUtEPRciow4xnbPxIb+HOiIdYzI5a\nlWR5eg8qfRYxnEbKt6PP3ERIr3Bnc+t/tib8p/J/zUbxP5s/5I0igMKpJXFphUy8imLOgM8jMR4K\nk8rqqG46C7Iiylgl23fMUyu3UwqWs7pswCfFSFcUOW15E3fahqCKI0vP0zqbpMncimLqEv6uvfQL\nzUzp7LyrcbMhO4ZiIU911RIzQjt3qt/kMd2rbNK/xDbFWbYIN7AS58XEDvT6Apd7NvLOpp389uAR\nJl1FZi2VGBQlpurrKQsEOMhpNiv6GKMGb2CVHZf7yGgrWHMdIatbILG2nWSmFktVgaJcwDEXoikw\nj3P1JvqKBmpc6zAjYyY5xinfr0nlw7SZt5MT4tT1B7COH6MkiKiKRezJDMpMnKJMhqqQR17IUbY6\nAYAgE1DlS3zqL76E3T9P69wid184SURvQJvJkAvGedfby/RsBl2/Cl1FBntLHClQojQzSCJ0i59v\n38r2WIndi1H6FFH2vvB3SDNncDfdT7Bwk1mVmbsUN3nBcw+T8Wqq/D5iqgpkhfv4vfVpfl+t5SWv\nEgGBQ4vniQw6kXZ+BkvNIWSuDto0LRyrUjBhcJMXFSAIAGhyOY4EL3KiqpfBuiYW7OXceew5Mt4W\nyo39zFsf4Iy5kntLb7Hb8wJO5wrjziLvdO7ipe3388/3PM5clQcB0EcLBPwSHh90Ti6x4JAxXl3D\nFcMGasIB1ItxZDfzqLsfR0r4SAHmuA9XVwRXfYyH732W399xF0+99hLbz8couxKkZmYO63IM52CU\nnx14kN5oPx+Lf49m9TTZEhwvdHJxqZs/mz/G2OFWFqvdfMr9HQ5Xv0N4zothqh+P8x4M6U0kbYMo\n0y7k9jm+az7IB2ZS9GjeJaKaouLNKRTFDKc3bKGt488wyfS0lWpIZwV+XlvPQ2kvHRkLHvsKzop3\nWR+a5SXHIX5c/iDjqhe5J7yboKMd+UA/+8+9z/fu+xA1oRVaAoukBDnV8+Pk0wnax8bZMHWFkiDw\nr0c+QmNCYNwoI6V20TZ7i7zCwV7rbmb1XiJyDWm1gje3NtHX3sAj775JRqemNTjBUEcNF90V+Cwa\nDPkscbWG5mSS7uGrfOGhI8yZ1KwaFehTSWzhNUzCYc6KKhbEEt7aE+za8D4Wl4QpUIX54RF07lFy\nhTCLgXbK5UVc0W2kkg5GpS3oJi9TuTxJvrpELqbEb9RT83YOZbaALpVluqqaMW8N5asrvL/3IUoy\nNdoURIydlAXiRJ0JVqNeEj4HA+3dlOQCJGo4LVioEGPkNCoW7HUIRTnrB24xXVPHQW6xmFWimSih\nceaY0NcSrizDUDuPxbLC4O392OQhYjEnUzU2nKEULqOSNUWRLcM3EGXHcZh8xGYNmH1Jmm7ESWwL\nY7EsUVSvMVK8zXD6GGr1EmpXBJWtAnX5R1EH1+MNuigqo9xMVpCPmElIMiriXq7WvUpSFUIQBFRF\nNeZYI2Oq6/RcOMl4UyOyZBytfxlJlJFz1eFW9pKYeo2KRI71J9+jc3iQs+t7Kdg17FgepEe+QAVR\nxkpejMl+4rbnUcdOEQg+RDirYb/nAsumLIqJFAljhmh9C73iZka1qwgIzDi3M+vQ8cCJd6m/KGC/\nvYjCUODgzWscOnsBk1eOpNyO1+pByiWInfo+BV+W0+u3kpcpuCM1wo0//yYvpDqokDT0eReoOjiA\nTJ9hk/IqR5Jn2O+fxWdwEE5VIikDpIUSc3It+3p+j0kZxXvrs0TUA4Sr1fgmqmgaH0eTzSIv5GlZ\nmON3h+7lxx96HGQizv4IGw4fIBPPErD10q+U0z6XoabgYVJhRVYwkJRpaDeaqQkeQFPspBRfgUwY\nAGltCt3aCJPuXbgkFTesakabOnng5Du4lpdRilvIK2LkVH2c1ZmZUErcOfJbvu1pxxSYpKgqMlDX\nhpoSJZ2eYqlEvCDjuTvv4abXxJouRlJtQ6HI4A2X+JeGffym+QCn7I3kjTVEPEfQpGZ57lALbdEi\nv7qSZY/PiCNUSXhtDWu+D1d8jabYEmZ7BAE1OYWSqYiBRaWV+6fOsu/i91FUbUJha+GXjQaWjSZ6\n1iTu8RW4vXSUl9a1EVcr2D4S44GwCnMkQjI6ybLbzbLZwuJWDyrjLD+ihr6+i9xWVPL42uvMSFsZ\nj6o4MtdP3dyrRPXDvLHhBHVTt9l5432O7nuCC53N/M3yRX44+wx9TUaSmSzZpIlReYmyYoEPpoq4\njdeRFT7NtK0Bw/wVagIRju7aStusRLnyCj9orsU7tIxhPs6L2zbx3D1P0D1roWFFi0ADKd04SXOG\nj7/4K0zxOJmNatY7JklEVDjK4uQpUF/yUSmsEo+bOB/7IjJlmkJuiPUDL3Bm1wGiBgPrGCA/KaOs\naEbs3clqJkVRqWLnjRGQa4moiyjkbSgWb9E+eRHHZAZNVR5ZjYTcVaLcGUOlyRMKfxyn4V4+4b7O\n3RmRzZkJipd91C7Fcfv82CNRypqPU6pc4eJsFxvkfto1fTTO9vAj9xuYUjGarolkYwpq9gVYq7Fx\nU17PjEFBwWJnXfcN0vZBzKMf4pVYG7qRHG7lDOuiy4xrq/D1jCMiQ5LneEn2BCd0tTQuvE5KmGLK\n8ylkkoI32nYjKEQOr51nWulmLuggoHiH7rCXp2MPoZTk+Ff+lfWmEeYnTIzKnFxt2YcGkY1CH+g8\nGOWH+dK29SSlHB849Q6H3n6FsrMjlBKraCz7Mcd62Xr3Hr6/4MeSCRDJTbHOOMHP7XtZ19fPh2Yu\nUU6Yd1YauRjw0qQLkPfDSkGLX/lpgvmHGRk7z2F/kPuu5dHO2/A5GtgoG0bwbsO8NsUxe4kdAZiL\nOBhcv4PO+DCxhAzvqoadtlnGCruYyWzAophn2TyJWIojsIZWIaApgEISycgVvNe4AV1qnh2zMd5s\n2YKo1vCli++wKpeR14aIpAPUBlZpDLgIGvSgNSITRtidWeIzFTMsGTKcc01wRXULpRjhr/KvE5aM\n/EPuy8jlMawqC3cvF3A82kxkAzwW+zQDmmE2SwfJ568za/0Yl5rsdBS85MrvxldziFzDHsScj3md\nhfJwGRsCWtpUFgoTJyg3d3DOsER1voJGfQtlsRZc19MEhCIvHWzg2MYpLrdJfDwX4mRkHct4cJDk\nUqSBL+af5kKxmbUqA4PU81ZyM215BYvKIgmZBXcpiaNinGDEgkX7IMiuk/S3YC47g/fuCXSKLgyW\n6v8aefh/4A8yevqfzR+6KIpaBYVAjKxfjUyVoOZj27lxtY8GTxv3P/hHGHUDzMxoUCg2svWxp8gU\nipzxXUCSiXzx6b9m7twSl80z1I9O0lRqxuKbpBiaICOXcbv64+QFGLWGKdgMJAIJ6gIhbPI5Dte8\njUdaZIZKMpIShxhBJhXRKnI8Jx0mHyzRGRpFnoTN0/30N7TSNTeBWiZxu7yWPzr6POKSQG3NItNC\nJfbxJTTpHNP1laxWpDGkK7mZWkelGGWlvRp3dI09JQ0Lhi1YQ1MYZ88gpSOs+C8SnT+FJlYiJQXR\nSJ0sh6JoB06hLBVIa9X842Of4HsPPMaPjzzCq7v28+Cla4iFNGKpgCCCMl9i3F3Ni937uS1W80j1\nnUjSLfTLIZJeHUGDjZarU9h9MO+pRx8I8+wuJeu9T5LVyynNz3HXxTPsz9WwfiXKnpf+kZxMRJVO\nIKjdtLjOM5y0Ec2YGPG0M+nwYExbyKU3YSit46ZdzzsVCjyJIqddSlBvpVe/BaWg4bkGHUcbHewM\nCczqFUwbZAAIkoQ9kyeo03PL3oNMX8uwxcCB8GXkqzlk4yuU1aYp02fYLPRjM43y8fr/xV83/U+e\nqzrCWUsPKZUCBA1pUcb9g2nc41niWhkrZhl1AYn2RTnqnMSiTcExz05GS9vZmDOQ09mRT51E6HqQ\n6c4EGysmeD2xnV92f4CMSsP5TV2Y0lkyBi0/vuchfnjfh6j2LfL6rgM8+dqLWKNBBvfp0Qf+ks7c\nOnrbL5K58wbr5ReZDjSx9ecT1D8fZspTT1koTDE8zbU6A/ne72Lyb2ItV82xcgtfu5VCPHERx5ur\nKHJZFl0uPvUXX+KCcpm1oJYdgprjJXh5k4kx+QplqUGMG37J8uxhfm/axrONj1GSO+m5dpNgaZm7\nsw1Yp26SLcS51lTH2bZeHLEwVv80Aa0cZ9SPPeYjK5N4a9e96OQmXMkoiwmBcLUJuayWmfI2LpZr\nmau28XaLjAutBhImJfmsxNbxfpzBVT7f/gkOGV4nqHeRyRh46t0kD51+k0IhwInevejq/DzqO4vn\ntpW3N7czXtfD+ukSTiFH2BAikDVRFqlDWtxN7R2/QpJE8mkN6jI/E1ED81kZLozUKbSYxZdw3jhN\nxKvCNFEk1ikjlNXjCieYrWugv24Tl3o+wg+P7KJzYpSq1Rkqlm9TP/Uu2YY0Af0umkdOMl9WThEF\n+Vwl1mgbQ3IZemWIMlmShbyJg5dOsnX1IqbJGDNN1UyrqllLmciaJJbv0VLaGcNZNYnVEiCbtDK/\n0Ekk6kIuz/FC62HqJvvwDp+je/ga5b4ZWCoSnzcAUBQlUlkVgzV6HKYV8mVDqN1TWBqiOCoUNGQP\n0Bq9i4aEiCarISVfIVr9NssXd5FXljCUdFz2vMOEZYRl/TJTpilmDbNoExXc9e4xCgYbYx4n2uVZ\nBCQkmQL0bWhWzaxbvkj9yCAJvZ5YbT0+q4nxymruvfAuG0duUlZMMGuqYKHQxh8NR1nOHiJSciG0\nOdmaGaFZkWTLz4p0TxdwLExxwhtDJrch09dzrK2SLQM3aB05hTVejm1lksijBVLbU4ymn2aIDdSr\nsyBo+efGIF2Xz+FaDeJ3VnBi0zYel79MeuoaU5a9BM0ZNlh/i8UV4sKVw7RfmyHflmWlQsVR0z0k\nlwpU+4IU1EkOb34JRbaI+TsatPaHWdXFGauQ87z3j/jpPQ/xu4NHeHXXQY7ecZhr7d20LOR45EKC\n+mCR5wMxGjUtVBTVKHwXOXzzOCX/INTsY7UooCglaAodg1iEwuwFSqFxXm29k2e2PsX75e282LAb\nv1pPd05OKA1rUg7TrIhV04OEhCXwMgs15Vws1bBzuY8jw+9yYOY8bYEFto4M0Do2zpXO3Vyqb2S4\nooZzHT0YRANBo5GJKjcJRYnmwBJR1wZ+Y2qiPTTNlLWWcw4PUXWaqS0upuxGPnwpwHfXaThqLeIP\njFLUz7JW0nMmX02ZMYk5XiBhMJCU5MRdRiqKSzy0cJUy92FkhnI0KhPXdGlWDGoWDDKOVor0VTqJ\nGcx4Vpbor3XQtrhMtbmTtFzHpQo1rcuzXDXZcabsrF97nzecm5hTu3ly4Tp3CO/zanEPARR8w/Ez\n7JkokvlD3Hv0OO92F/nR/kexiBq+3K/gm4ZXOBkXaZUp+Wk8gCZaS2dWSVnFFRSKAGJqMxatg0Rs\nAvfsbd7atY+GZZBKCmp1l1hKmckY9GjqK5H8AS6su4MtY3okWYZU7hoxi5U1l4O66DTrmyaZkqq4\nT/waqZKSJxUncIurCAK8H3qapKSiefsX6Hxtiv7mFhY9FWjjNSwKevwuJx1CHXFhA7GCSMKUpWY5\nTuXKFBWrU6w41yFXtlK7cI1Vh42i4368mrOUqZIotQVWJsz4BuYwW9ex/UqCfP8yzto4kjnHm5VN\nlLIixrU5DJdiVBfnyLdJPJZ6mYZjEV7eLnLVNMwnyz6E+OooCi+kqhWYZVEuxFxkVErERBBl7Sy5\n5X2cnVHy/I4DoNCwY3QEr8rPXBuEcy6arv8VOeEM37Y+iCqtR0j8ktpsJQ5dFx+MH+W+0El6/ENY\nhRh6KUd/WmLItsbnVz6BZWWF7Pl/ps48hMpa4LX4RkpoOVF9AJOQpbs4TEi5zKd3P8pNeYmHkl70\nmRwLJhitb8JpjsDYACrzNvI3lkiUipCvwpGewz+TYLl6C98Z+nsaFYvczjfRwBL90XIGYy6K4zIi\n7V/EXGjk7PKLBKQYfp0Wv72SVEFEIS/HqQ1gzCxh3/kl3vNfYdaUZ8OqjM9IL7FOvYAI+FNGdjtn\neD/0MUqCmV0GG2WiFm0xTEZYoGpylBVXBWGNAV0uQ1WwD1tsmpi+hiFPPU/N5NgXh0EhjAYFEZUc\nSzqMmhBXGsq57t3CI/PdNBXeYG9YIDJaTV5XTkob4Jn4LQxSjo9X2LhqWMYxN8HT8TbU1hTaPXY+\nefEzhDNrLGnCXDD20RVq5Y0OE3clDPhwsybmsRdNuFMK6otliGYL37CIuBUqerICqf7fokulmLWa\nmFInSSWzVKcSKD3bON8m4/eed1GWBJIKiZ5UDvGikTmPl9vKKo5lNlEvLLOisXE81svpTBd2Q5yG\nuAYPJd5RyOiwVqKVD1LmnGNwRsPOu+tZGkxRvv84pEqYxHuxuKv/6wTiP+C/RfE/4A9dFAHkLgPJ\ny0sYxd9h3HkH2/fto2NdC0qllfLyg0iSwPXr16mrq2Mx76d/aogtmSYcbTVsiUU5FSpjQ9hGt7mL\nwvRJxEKeaU8FmY4uYtIyWuUKA84ahHyaKr8PuV9grLWRn4QP0X1tFGGmyLCtmmOz9byu38uwug4x\nW+TOidepS4VpWl0mJSoYqGnkA++/zYO242i1ecTxHMPhWuqnwpxr6ORLn/hLznT0EFVWMltysWbR\nUGaM4HNV0ra8jDw+R9QSx29Zhzw+jsk3gTqyglHIoE0VqAmFyYoTXKtppXZuBlm+yJ/9xVegUuAj\nyy+hGVxiqLGbpMlK763zKE0ljIfSnDRs4c1du6lbmqQtbmOHpoxPHV7HH6tfwO0JMtem54qijOvr\n7+WLH/kYcYWOluEgv2mo4LWuI/x+x27uu/Ye8vEzFBcuI+hKPPHZf2Tj7CCypWFe2fkYjpKfRUsL\n5ZEIw5U19DndfCBkZU4r4+vtWrYFIrh8g8zay7luUxBQS/ywXk1SvM1CZoTna1IMONw8OZ3lsyNZ\nokqRQYuS+kSJOYOShqTA40PzhFZXCMsVbBwdZbjSTYN2gCGTlwfa/4UJvZuW+Ty7hsN89N3XeTb1\n13jCcdw3zXhXNPjMAr/cbaQ6cJGH3vgGC141xkKUav8Sq8ZyhqtNFJQuNpz9AbJSlube81zTdnEx\nu57fu/eT1OhQRX9GyHoHPqcTSQ5t41M8+N6bvLV9D4tlLv7sF8/BrICQ/wgusRN1eBZDdifaKTf6\n5+c5/OZxzPEYSY2O2vl5VJUbKa0MUJU3E6uRIxeV2INtuG6+Qevb30K9moKiQK4KAk/JmTO4WTG5\n+MJClmxewcvlKmYqVTx08hpl7gG01iVOznQxoLuHnsFb9N58C48/jhCLMBm7hWiyc7Xeg1Itp7+q\nnq6bpzFGApQl83Quh5n3tvFPj/85vcNDfO3bX8WyushARwtxvYG5chNL5QamLHJW1SK2nIQ2ksUR\nB3MsQHkpStPEJCWnipOVvXzZ+BXqllfYfeI83smzNPimWP1QifvUR3EZRtFKPqqvZzDEAgTlLhoS\nCiZLKjwJKw05Nd49/4AkFhno24FrfBZVS4J4ykC0IDKmmCdAEM+tSRQxPwpNjkJWxjdbHqM2NoMo\nCQz3bGKkvgp7SMdkpYLZSjePvfEyJze1YfjgCuqtY+jso0QWD7Dzyq+ozgaQRxoo5CN80OgkLEWR\n5El23R4nkc/iml8DCXTtOaaoJeNR4d3Xj9YaRphVk89VU4xbCE8exDqSxpidw5n0M+5wsenS+4St\nZby/9TCVvjmUhTzq8gJJYwM5axnpQoYrrhzPZ5MIF+rglpOm1f9BxcyDKGPVLOnHCXU+h6/+KJRf\nJLDcxt7QA5Bws6Dwc6XiFIejMr4eWiYfcbOgkzHkusSq0ULBWIkUXUPMZREAoVTEhINNV36MNhpi\nsHE/P7p7PymtFnkqy4zFyY32dh49/hbV83O0hmc43r6VC4fuIIKFmtgNljvbWT8xi9M7SmKlg4G6\njSjyWUKVHRhDPo6t340SOZ+7Ok0oPkbH7AglQYkiniNxh4Smqh/d/Co35K18f52ZtUSJjqyf1x78\nDOezDtLlal5wHeZozUFG3RpGK3XsM79DJGziZ44/pvPGKt5X/KS7BNZrLrP31ds0Dk/iuncUUVNk\n9bUmXt31SZasNn7jaucV9V5UShmfGC/hWZxClVjCHV3mUy/8jK7NR6mrfZVKVZZDkSbaRDW1vkts\nuP1bEgotqnwSjVbPgsaDd/4trDPvU/APIcVXmWip4m+bHiXlUqEo5nFGQjxYAhVqrEU56kiRrUUz\nsmKaY7o8581eBFHHgqRHqDdRuzKCX21hxuAmpxQxZGIcuHWTrsGbePxBLrd14M7KiAkJNt46i99W\njjmXIS1FGRXK+BvfAI+bm0hLOU4rNKx0uKgJF+keylPhC+BMDaNUrJHKOHlPsrFbOUtdc5DrpmqS\ngp7Xe3ay7CpHb87Ruec94oEAjnwXJYWOnzYZ0OeL/H1fhiFZkIUyF3svvMn6oevYUgV+0tOMJ7nK\nz92/YsnWgjuSpzawxPHaGiqHJ9kTGOBPRxXYi21YFcdoE8f4afEgUamMh7WnUL86R1qp4R8eKBIp\n/wCHgkPIU+/wW1OQzNyfEoxU8VH137GYXU9cLvF3u1uZkm2i36XhUKjIK/UyBlvraArMkVToySca\naUmO8+0N0yTNXop5G7achmqfgDOqY9U6wbFNm+gQJ1GmSkx567nIBvqEDurlIZZkHpKighZpmiF1\nC7ejT+FXTbLhxX5myysZaO9CEY1hSHVjShsRCjZWSnUkshJ5VYS8Moa6vJVKUw/L4Wnql6+wXLGN\nRc82Jpq1JKKLtOlGkYkSCTR8JfIE22dvU5g9gyzqJ1W2DqWlkXLLMC/UZxg2WChUq1HqlNgu5qht\nmkDvr+ZyQwe/rj7Do5UPsn9MT/rmTQz3hJnfICc8ZGYhZca8o0Qyq2BsoZGlgp3xCjdLNheZ4RR/\nonobuZRnTWqk7HtRvLWXGVNU8pzrAxyaP8Wk4gIHVjbw7fzvaFm9gTkaxCaY8Gfl5BVpnqnOsznW\nRfWUQDQ6hl3vpqx1kDHJy0BAT7Wxk0qbwHFlI9XFZbZnr3Iy42W/yQ6TN9hf9o95SoAAACAASURB\nVBa15TM02udxOcYp1MXIXE8iM7exZQ32hdTcGfPQqbnKZxe/R6U8yNV0J7bLATIyJ+GgjLhCibz3\nftpzLbwffRl1fT/O9UHyAQ2JNRFlsUR9IMbZyk10FycwTL9JewYmdEU+pZgnJegYL3rp0U/j0CTJ\n46Yv/TCXW5TkNUWakiJeuYcaWTXHvQokochr63YQ1JvpWAlhyul4s3MbBZnIorYAxnKElRF8Oj1H\ne3bTV+Gh3j9ORXSepxegO+ZhzjhDRWmFSakHG/V8Lj5MfTHMt7VbmS0ZCWvDzFWu0JSpxbb2Y34T\n/B0n8st8evkJ7jjZz9mmEu9WL7BvIU+71MVJ5SALYpJx+TLDch/lJTM5lYk3AnE+lpWTUIucVfup\nH75Iw8QQdt8klUPvsRrx86seeN75G9wyD/tvP8WY8yIypQflmpew2URsPMeivowfqr7HF5S/5X7x\nHPerz7BOf5rVyGaUkoIxpUCyaGRnQxOCcBLIcrIvz7rWY8gMUeZvb2WjuwKVt+2/2CD+Pf8tiv8B\n/38QRZlOga42garvrxA0ZgTv1n/Xd7lc3Lp1i6WlJYaGhqi0V7Ax7CZ23Y9oqaLm3G3aXL2khCyF\n6dMUZCLf6riP93VukqzhkiXwRgNEjA40UT81gTBxv5LeoUHEdOl/s2+fz3We98Hnv/fpvQKn4QAH\nvREACZAESBBiFSlKpqhuyZZly7Yc1ySO4zhP4k0sJ9l0K3Hi7liWVWzLomSqUBJFir0XAETvvRwA\np/d2n3tfZOaZ3ZnsM/u82Gd3MvnOXG+u3/z+gM/MdZGLKwhlbBQ9MvJrSfr0bUgI5LUa1KokUVs1\nnoSfG/VtONfX2XR9CvNalExahSmeYF1n4C+f+RquVIAkKwQtHkJuA0WHlo1SF4a0yOEbSqyxMnLq\nEHlTkIW6Sl48eA/ND1whdX+BW4oDVAQWMC6lqFiYxxRL8KPHPkGjzsdzo8fYkTlBnT6AQhJ5pfkQ\nd2euITQ7eGDv9zi1ZQd/uPISn8y/S0H2IC7/OL8u+QkdGgv2YpQntnwXY67A4/mtxJVyjne0cG77\nYabdzazptexKarhb2cNavg+lIkugxUJ/ex03HO3cf/EUF8vbWLb70OQLlIxepz5bYNRVxjGfljMO\nGSXZIv96K48xGWRP1MxZl4oxs5KUIs83Fp/HtpTiw+aHMMZvsW19iX5HOcZYPxXRea67fegyQcat\nBs57rUTMDi5aN6E0CmxkbLzUeB9/1fQlSvIqvnUrxP6FImuihEKsxKTcoLN4lpnYI9jX+vnF/lLq\nVqf541s/w10RoLlshL2K0xzmAz6//hJj+krerGqgaXyQDtsosyUdXJU1sWIo5WT7Lh64cApHYgJb\nwcmgtxZBUGCS5zCH4vz8yCP03LnFztF+UjV7KfPcQ27uItnrPyC/0ktx+DLyWAylo4V3Hvw8f/b4\no7TNzlI6c4vVUjf6xTtY0vcy7GzAdepFqgbPIslg1dPNyr4Kip+eRikUqLqzypTpIJ+eV3LMneZk\nnZmSmEj7gor1YorFpc3IRQWeyCru5Xl0sUXiSgfd0xPkTU6WpA3k6SCmtUW237mEORYmWlbNSw99\nFpl2Fy/ctxtPLE3X7TAqZZLG6VEO3rpKUdPGVJkWUSYHQSCjEAhrFMQNKjIqifVSG2GlHl9wmT19\nN7m77wZ5l5bShgly2/346yrJ7Yvgdi4inDWymCujvHaMxr4xDrx1i0tbqvHE7ZSJchq1ISr2/A2C\nkKd/8DAV5QOojAm0URGDax3xLTlbZoZw+weQO2dIKpsxLm4wXttCs6saxcoIMa0SqzGLvKBGKVui\nKBMYraxkh+YivkOjFCxwobiPGmMf8vINxBEf1ul5nBt9+Py3YepDFIUQcslER/8Zqq0p8lGRkMbI\nNVct+rIM7ZtOoipm8b/bxPj6p8gtHqLu7UuENJvJyKrpuvpLHHN+5KlVipLA6x/5FCmTnb2am0jh\nNHlRR6q0EkmpRshlcBYclNjn8fRZaCy9j3KpDhkCN8wTHNPnealyL7vyl1DLMyjvfJIzsg269eO8\n6D5PWp7hk/LnCHV9lfJ/fZ8STTtJtZyOiQ0OXZ5FmUkTNZloXlqnciNC/XQfCjHHknsHfs9h5EIY\npSLJQNbK/uH36G/ZyWqJAxNqnOuLvPHYAxRUSgSdnMdn+uga6mP/e+fI7MsjVrWzsnSQ6XoZMjFL\nEDUXNm/jT15/ne0RBerlAazxKEKxQKxQxalyF5WWNdQds4iuIT6wNbF10EwuV4YqcwbN8iR6uZyE\nxsD2mTwPTwxzx2tmTeGi56XrVIwMsVFSgiSqKT0Xp9AiktwjEWtRIHkKrJ2q4hfbnuZCQwO3bQoy\nMgWfyL/IU6uvoppo42NpO03VL7F94y1qPgiz4XXg1rsoVl5CXnEZRpeRXXmXBaOT/3bgD3g0lUQ2\ndYr3tnoZ9Bipn59Hl85wbO+9/J33Y7jtIoEOD4lKK58cPM5Ohx+nsIW1HJSLcqyhPjrufBeLFOKy\ns5V5jCiEIss7fExU1qGNLfHT2o8Sbt1FvtaF5PoI7WX7CeaX2SfV8GufirRKw67ACHvPHme+chfu\nxApzDT7GqytpnZvgLm0VL5cpyLp1/MHQOqHiVXLGdRSiDG2shmNKB0dGLvGJe3t4e2yVh5t+xav2\nh4nKzTSMTzJRVs982MfOTe/hT9xhUN3DW9UGvjmcwZgT+XWtlafvrHJ/wk6PrJF78+WML0V4y/sc\nEeUaytwEB8bcpLVgSaU53dHDI/MKunJl9EVG+VFbD0cDbYSKLl6Vytgim6Ixs8hQooz3t23BaGjg\njTu/y5bCAN5QA2+JPeQEOctSFXs/0cz3c/9IQVXPrKscKV+kOuxniQUcwSjWcJhlh4A2pySQ6qIy\n78JqGOKWYZXSlJ0KfxV5VZRzrUaWXS6eH/3fcObj5AQ5lcISWo2KREGBUhBpM4ziygd4LfVnZLMS\n2++8RMRk4+a2TuzJCEP2ERz+Aqg2ocrJSBjXiJtGkaWnEVUqQqkVwoUMX2/5OEoyKJWjKBVb0GRN\nuB29NMsnWMPOZbGd/rQNv8bDoLsezfaHqS07RE6+GbXsFj2ZNb5bLXClNE+frZRNKwKl13NMPxzi\nh6XrlBc8/OPBv2X1j76GqiKNw5vlvcwDBOZy3Nq+m+dbnuFK037uNHcy7K2hfbyPfLTI9uIGH5eO\ns75chezNIJ59cbTqNb7U9CxqMUlq9ftklCJfmZvCJ04xlX6KgfxujMIXkLiff7FPM6mN8Y3JAgsx\nGX6iOCtEyjTDXJlzE5KMWJybqVoN8/H3/pUzlp3s0d3ElMgQXAzyO/wCnRBBLHYgCEayxlVc8QgG\nV4qc1IJCWCFt/Cle4UeUFDcYV2yhkPommlwbmYHbqF0fxREJkzHBTtNjXOQcO2THMNeqKFjt2LfM\nYfSksMhFyq5v0N+8hRuyZmJFC14xxiPJMFlBxQfzXcRmVOR0BZpMG1yIPUOsWEp/bZJlYYW3nSKn\n3DqObCgRpCzLqWUMs0GiwTwGIceYr4aFEjfyfIiUvMh1kxZtIYMrFmLW5sIY+Db7InWQE9iW8XHM\ncZNfV2ziqfUPCKhdtBZH2SyMs9JrIW5pQ5FrxhjagsK4znHbWYYSMt63xdkbquGjw1XY7lxne3kH\nk1oFH7qHSObkgI49mhuopQzr2JiWrxDIzlElT9JZ1HEOOf98fw8BZwmOWCntjVdR1MKrjc9wxv0r\nKnNuHpv6Ei/KJVLadeJFCZ26BXUsyTHbLj6/p5ZHPv+nCDu+gNFdi378VRrSIcKldiKhFrY69RyL\nJ9ihL8FXLkOuvEJnepysN87ayH0MzD+CoXsTVS7b/zdw+L/pv6D4H/SfAYoAMqsDYeEKTJ2Gzt8B\nmfy/zxQKBXK5nN7eXiRJ4hNPP0VBpSM6G0O9kcFuqSAvwdcbkrgXBgh6nGz58teJZIvU19eSD82Q\nlymw51LkdQbcS4uUxOLovFniuw28JdtF1/QQIZ2HK5YGFnQVCCYFG9gYr2imZW6Ihy6d4kplGzfb\n27n/vQ/Qijl0rhwBhZOfHHmMWU8Z7/R9hcMrZ5kZ2kogkKM6k+Grt/s5NLyBsiAgSlpuVduxp1eo\nUc/wacvLrObbUGvCHIzfJLDkYKK8Du/SMv2tbaQ8Dj67Vs2sYiuFYh1qxQ2qssvc1m3ipbZHeaVq\nP+p0kr/73l8Ty6jYZR4hpwyivfkBT9rnqMuv8787Kjnv/gSirEhtOMYnNko44ZDjWL3Gn//sR3z5\ntZeomBkjr1UzXObjnfot/GDPJ5nX+fDbS+keus3ugV5mq3ycat1F+doKxtVJfPkcfp2RhEbPI+ff\npFOswCc5qE0JbArnCKllFMQsvyq/h+tVXejSY+gD/8q52s+yKkTodzqIx/8OVS5K1NyFOjvP/Ssy\nQnojAY+O202tXGvdyritjh1D/Xx9JESmuI5JNcxvtmk41lHBrRIfn954nYRsFeet6xzNned3Za/h\nKQmgNIqsyM3YpDjvFzs5re1EphOIZay8vXMfjjkVGzEFCZOZdVsDUyUWtq9MYC84MchXGHRsYs1i\nY9xZRUKrZtJXzRfPXsU3M4xe56Qwf4X8zGkEQMrGkWmMyBvvReHegspcjkyu4cd7OpGLBbqG+hGA\n4sog+pHbKMMLFJQaTtz/FDH9YZTZNapensB4ApRTSR4STShVZfxJh4GwToVj7Tw12T404+MoE0Eo\nSMgSEdSxOdCZ6Vpc4ta2HayVl7FXf5AqbT0TNgMhFWT1ZpTWUraPTfD+1k1EdRqePPEKQnqckPsz\nRCwm6mZvoRNXMZFlxOXDEo+gXMnx1aUiH+udx7l2jWWLk1mHm1Pt3QRcdhrjEzjOFFDN6lj21KGp\nm6FogJKfyrCeKfITxUPUGeaQ74xTWNNQM7TAoKuDMm0c175/AEWewaFDiKKCusbrKOwFOG1G3p6k\nYmUDZUWS7MfiZLZLLHhNFDcqqW/6LDeFYWrHR1mxGnGX+8kYtGi0KbZbz/Oo/kW01VGCASd/av57\n7gidTMtq2KV9j0yTnM32efy1OrRLhX9/bh4KUjk/DkA+KiKpQWpNo7w3g6/iDomMCd/f5Ik4PkdB\nrKAkfIuy1QuoEilWPT3IW+fIrKZYM+qZKa/jdtsuOlfH0a3LkJNDCiQp164Rlpci6o1oVvz4snKI\n6dhpvZdrhiEqci78ilmuNaoYVTTjvlkkutxIKFTOPtWLvKMVuGYf51Dgbop9GYKX3iBmlijGN2ia\nzrB/IEBOIac0kcYVimFO5ygqlOhyOcIGPY7gDCF7kJhLy0JKx7aFs3SrNmOZv8PxvYdQig38Zt92\nEgYtm2/0slJVQXnQj1Yp4C91o7SEUJlGmA2WIkgKlGIl/3ZoN1UrE3zl1y8hLt/AmEoDUFAZ0CU3\nCCbMrI5aqFnIkmvJ0sN5ut4bYMV4F/JMNfLcHNekGrpmcuxYVdFQvISyboxTsntxb5ThWe4jn0/j\nr6wmrbeTu6RG1pRG5clg/LWSyg/jWCIRLnR0AQJppYyv9H8PVd0aad0d3OlGdIHNvOlep+52mJJs\nJ24+h+LcHXLFEObjSyDA1R0f5Yy+jEydEfnGBkdufkj3aD/rVjuj3Ud41XeAaEYk16GjWjHN1wd/\nSnVTkHz9FN6le5kpiuhDM2y78wMudR/mxYcf4xu3LzGuLqFJXOOh3tO827Ofvi3dCLECK0mRkY5q\ntizmaFDoKddWUZJIMqj4LVXxZWSmAl/NzPLjaA8VyjUEpYzLtQ3own52iC5e2KzFsx6g3H8DeTHN\n5qF+vmfYy3WnlVy7mfG8k7mlCBXj09R1jJCS6ViNqGkb/xEtiwLnm3q4cbWZc4WtvNfgwKxQ8M2R\nLMoinCxT0+8xYxUETIFVjAqJMcf7zOjGqBOP4JcNYU/FqRXaUab8lCoMfMk/xkI6zEhhle91P0pb\nwMV9GTnHFVmO5bsp2mUE7XXYXY/w2Y1/oTk1RV/+fp4vHiGNxOPCAr+RGnlUuspF2RhrJU/iikhE\n1UWs67ewiWruXSvBc+0Npmo9lGffIamqRButQZaqoTJYiz7RgExSErWMcLvMzZemX2Zfuo8SWYBK\nMYJNlqa7cJ6sXsFSzoMulyXi1JMc6WTH7e+Q0au53NODUspy6J2TeGNGLNYqNhJXEFMXkTJjKKKr\nKGIBBL2ZtNFCcWWAkNrBl27/FndWwuPuYFU0UJOdxKpa4Gebvsh8qBRHPsByRRXv2LbxgUyLXbbB\nmCpOrzNGT2qU3UWJ3mApSx4DFzdZ2dsXQBgQuNIs8gX5BoVXfoNiPIPiMSeO1CqF2s9waybI6wee\npDM/y2dqm1FdPYdfb2Kkspl9vR/wuOo6PhZ5tvlP2FVxA4cywpcrP8l55z52Tv4jk+YAe4IFPics\ns5rpQCl8Gbe8irQUZVw+w4+8Z/loQOBJJnDYimzE02wxTyEWC5xfrSTv9JE1GGgdeIfEniDVqnFU\nGhnNTLNNGEMtFVjPfZukeIRcYQ9Czkm60I1efhGD/LcYFB9gFsNMKh4im/4jNLl7WZcHMcpqKVrs\npG+/hJBYJ7/nM2gkDauu07SySHlIRDP/t/hix7hqrUZdlsF0W2SiupEiMtYFB/00syiVcbPYyraL\nvTjXgizb9SwVqpjIP4Ekn8eanaAkGWWgvI6KUIjaeJaqQgmR+Stc7dkFRhP6ZIwbTW04AqukdTZk\nMi2iAIpikfr1JZz+ixxKb+K+aDdDigXWbEWeXOtEnYxTWhikRRilUlhhOOVl8Z4ismKU5Uw999LC\nQ+EeTqn7mXeFsOblfG3yEIy+S1GbYLh2E8+sP4U+LiEVjYxYBxlwuvmL4Ov8vPExZrW17AhJSLIo\nM3I/1/NW1iQZwco6nix8SJ15EJMuxVVdkV7LCk+vHyWd9nJaVsRJjK0pJ4Kk4qphO6JCw+fLglx8\n5acMX7jAnbMTrIe3I3lX6YxfZl5qIhkyE1JIXAumUcbO47H4SZXAUszNmcEnaMnoqeypwFui/1/u\nhf9R/wXF/6D/LFAEQGuB2z8HR9O/n/9TTqeTmZkZurq6aGxsxFxnwbzDzc3ZOPOrSWJeI291uIg7\nS3n8sUfZ3dnKgVYX30lGSSUSeCMBAES1hrgEHp+f+rYNXtF9jJ8YD1AVX2LrzChzBi+iDJpMQVJ5\nJdmQxKi6nEmjjxnBTr7SzGhjMyWWLLvLbzHY3M5zXU/y8ZNv0XZtjBJ1mn/WHmIzU9zZ2kwP7/Dp\n/D9TpholtfU2r9XsQJsqIsVUGBJ+1Dd91M/PQa+MK54dTNTW8+xTX2LBkcAbK2KUtJz32slG85yS\nyjmrvhtDRmDWZqF2cY4vHnsJXaHAD+sfxCwlOCC7gLkyjlqU8YWyEs5olThSW1i1enBujLMl70W7\nmuaE0YLf/VvuGingWV9Ds9DH3z/4GJc3b6N6ZZG6yAD36F9hVr6Z7b39aIxVvLKjk0W7mZqFBcxy\nkcbYLHt7P0S7PM90rA+loGJJn6Dj8rfZl2xg73qEpcxxtkshvv3h99DMZNk6OkBc38Kyy8M2fxlf\nmB/AkQnT69zOuEnG0RWJBydTBNLT/MWPn+Oe6xcpSwYZsxUIyeLMyyTq/Ovk0DBrc7EpNE6rYpxC\nrZoW8yzJVRUrVyz0ap4mZBZZKVr4Yu6r/N7VQX7W8xm+9IvvcrVtGyfbt5LTyNmyluTFzk7ca0Ns\nnZ/FFE/gWVxiuGY7T0+nkELTXNq8FWsiyR+teRHCMxRX71BMB4ltkjHb4uNG004889PIVgb5baPI\nT9obueSxsG05znBFBz8/cghDRkfl8iTaXJqCoZTkga9zR9lATiHwVw92cHHTbnzBZcrXg5TUPkXM\ncYdN9ufwDatRKo9ROqhAIEdKnUUfTqLKxNDlIVTTzEJ1FVmtluGyKl6pd9JacGAxVvPrrk7ONzeQ\nUWgYL6tgzO3mywMh3qsuxbcwCPlV5NqDKLIrVM0NEXA70RdzzJe4+b3jP+dA73XqvHtYlK3jmzNw\np8qEUszQuTSJTn2I2saP8r3qHXyn+Sjnsnfj6S9QdX4ZVUEkK1PTdWaG4FYN0l0pVC0RFKWTOJtP\nolRlGB68G+28SEamR67JYTCEWbjSTKnZT6ZHJF8nQkhNZMlKac0iExYfYxkJ0/oCjZMz+E1GNkI6\n7OYF6jvvoNdHmBOq+QG/z0uGp+m6Pciz/j9nSV3NSd0hurXvM2f0UvpiEimlQCaTuLSrh7d27Ubo\nzuA+PMfK/XJy24toEhlOJe+h6++XiMt8TNs7EeQKfF3fxXQljy69wYp3BxG5gzUW0eQLnO0+StBm\nZ9e5M2i0SnKCAXV4HWPtBhEqkSQFglSkuJigrbEGZ76F7zt+TUeqEYsuyb9V1dAUyVMTWiOVh6wm\nyKTgYyY/yuYpA66pKMX8NAjg0lVTZtmCb/wWikKe683teAIB1IU8b+w/zJbJMT7c3s2zz/wuG2Vl\nZGxGaiYnefDCSVIl1WgSs3Rfv8QHPQfprzYSNpv41k+/y76hU7zdsxdNTRkPhcIMqjSoBAmne45Y\nwowmUMOJzc2EjBqE5N/TY32GRKaAsnyR5U8JhJtc2G9HsRprwFrFzdoefl52lHuFE2hTIdoWxljX\nNiCothIVFGwt6FGQQ9d+mQbLRa7l97DmKqNqqRR5ZgR5MsJiWS0/dhzhoWM3WFn08Lulf8pqQwk/\n/tinqV5e5C++/x1Odu9BlwzTUDqO3hRnYiWEouUSXtc4aU8ljlEzuhoDGWOS/Bt+FIC24SH03t0c\nJ89I3sJSvQW9kObnlUc43ribh1UNvJrO0Wlc4o+rv8V+4UNMrjVkpjCB2W50gVYqFXLOyA2YxHka\nJvvoLC2jS72FRxVW9ieyiOMfoE6skhYNNISX8WeNpEr1hNUqSkIxhgQ5fyHkWQuVINd40c+HeVU8\nxAwqOpX9mBN57nhr6a/08YpPiSsZ4vDYDSQRDs6eZN5ZwildJ49OnmFoawsFpx5zZI3RrZuoME1j\nF4Jor8bprR7mT1+d5fjee0jqDGQCCnLNNj6SvMaeJScXbVnGbRm0miVO2b28XVPKJeUlRnTvY+QQ\nwxUf476FVU6XjXEgqGFNYeDz4R9ToXiHYHoCm3AfC6Uq7goUcKVEWm/9EFEl8hv9fmJqK1brKt/w\n/4jrwQZO+et509jG/ej4ojDBB4KaiwEt2aoewikLJf3rZFfWWc6biRcc9BtcnLU4WMlZeGnbIfZb\nnsOcMJPOeinKIGwaIK3bYNmmZdvAab7OcWQCnDdu4wXxLxAjPlo0Z/DrS6jMrHCDdsSVKion32ax\nysVgeytxrY4T7bs4nD2FfSDOmYP3ccehwxtYo0azjCAIdNoWuNzQjT5ZIK03Ui2LsFBbi2d5BvvQ\nW0Td5awr2njXc57AkhVtQQ0mDbXloxypGUTwxvnppt2sVoZ5qPRlZIJA7XoCU8LL6y3PEnDczbiv\nkUfOXeT+axIlV2QoZvNEa3TMtR6hIX6FaMHKT3x7iVjsvHb79/HoKri8kWPUW4Muk2K4oZ1vzH2f\nFaUTt2yVpswkTzU/y0nX3bTnxhDjx7krIPHX2QAJOYQz3yaQ2WAwch6j3MZPKk+QkCIcGPBQ3lpP\naWqYzcZF9Io4N6Pl6KbjFHUG/DYTxiO3kepy1BCjLJ5EBsglCOSeZUqVINP0T5SGM2TzhwE1xeIN\n5tAzJDXxtvlP2RTdRVamAanAy55vc1c8hGC4D0kqcqNWTqf2CM9XqXjZ1ULd+AZN2n6QdMRy9bwY\n28Wl9R4sSpG42YRAEZ28SEnBypJMRfW6RFghsOwtQxYoMq77JILcQLBkEpkgURRAhsCg20P92jz1\nRQ8v39XNjLaIqISxmjoUxSLf/Nl3cK5OYNQ42boaxrEwii4XxyCzsTfVTrCwTrAQJC+K1BQdiIU8\nE/I4mxljlGqGvTI0ZTmUdqj2H6A+U86gconXIj3s8VzjiVwc1zvDqIIJ1u7TMChsZj41jiKRIa+U\nMeBYIiau8EgihkxW4BdiD5qwgU8WPQzLl9CQZiJsoEU+SlvyGpf8zazJrIxb55hRqXlo5Umsgood\nih9SJ9aRAU7posxF6zkknyVx+lcsZlZIxiLsMTxIlbGDmzN5litS7JVOM5XaS60ockGh4FKsg3De\nTL11imOTR+nZ8i4ai5/mqv3Y7Nr/xVD4H/dfUPwP+k8FRVsNDPwGNsag46n/y0gul7N161YqKir+\n+51SLad2uxONx0DtTjfVJXr+qaBlTGviTt/bON/7Pe6bfY39ZTYW/QmyqNHKJY6OfEjj5lX6wx6W\nVrdRKqvgTImBrvVhdqxMcM/8LXoGbvHw6Dl2r9wmaLFw216PJiehVhVY8HkYtDRSzJl5sW4vRWQ8\ncPs0nuEVfqb+CAP2Gv5h8AeseSt51XuYylUZncJplMYwcb2CN0vuZfvsOPFgKZsvXaQwIyOcNXOr\ncxt33DXM6G2Iahstq0usKONoYyssK/zIRQ36QgBTREPbyhDmdJCbm7cTddopFmQsiBoekN8klDaz\nctJMX91W9qe7qM/5uOAtJS1ItAdTdIlG5qMic9ZFTm8PEbUe4djOo6Tteg4OXyUuvMWc+jxrcRlN\n8TI8S6vog3OkNxe55dxFu6cf96BExGileusQqUgreknLZHGOJV2e66VFxr0Z9iV3svX4b2iUjdJl\nnMZ1VkfLTIRDN85yvqMTv9HM55Z/xc5UP/mhNBMeiZvuCm45VbjiCSJ2E3FXCVGzmf7yOt5r3Ynf\nZMOajFMTWqR1eYb5go9eqRWHEEJ5K8l6v4l8RkFos5xLnlX+NvEFuv29HDE00RiK0XjtOHGFiVCp\nlzu+Si5V+kirlHxkLsO9gz+g4eY8voU1jvYN0tp7hv0X36Vlepx7rlyhGBxCUmqQK3SceeBJakou\nIN4bILbkYKR6M/MVPkJOHxfrt2BMS0w59KhleUoD67y5p5tLm7chyVQYEOMEbQAAIABJREFUyh+k\nWuMiGBVR26/RKV7gumUnb+7qQec2sC1TzWrwCurWfkzxCMZlJ0V/ir6mCPWaED2XI0TNNv7h039I\nb3Uz7cFZrPFZblU0MFdawSmnjHe9agRAU1SyYC9lw2Tl4GqegEbJsLMEXzyJ1T+AKAvzTlkPO5f7\nsIZCqCWRz//6JVrmp8llNpDVHcCCngJTWNcd9NcaWTCWMOEr5zWfmou1NtxrS3QMjmPK5iEawB1N\nUB1bIaYy8DX9H2IrFClJhrA51ikqBZYvdND84SRtg0NETWbmpGZGllupvT2HaypKqkxJ60yYC6N7\naD22RLhNh8M3gz9SgnfEj2MjwDVbB12V7eh7LpGJqJg+U86MqoXbup3smlnnq7/5KaabGc7baikd\nV3K+egc7DR8Sr3GynpajiipxW6fxHBnB7l4mqVNwTd7N1bX97P/LYdx38vhdR5itegSQIRbex70l\nwPykAa0yiyIrZ12uIi8toRaMHD/wEZBAJpfTMjlC7dwcopAmmVHj6VgmH7WRVlkhnaDbvptMQYGj\nvovl+ChXrFu4UGJne7KXTatb8eSvIxMyJNeXuO/qKoKowVnSTZttH3OeA7zTs4XF9BwHbl0h2/IQ\nDYEwQmyJpFZL+8QoAZOVlw7ew6aQH1cmzu3yek61dGJOJtjVfwPbxga3a91U6YYZsG+lfHSOr5z4\nJXMeNxda5KwIDZR6qzlZUY1fUNOluozRtcbL9XcxbPLxRPEVHlXfJGQbo6RtjNReEZmpiMIZRhqp\nxrq+gbT5CZ7tasU3scr+pdsktuVQv5mgYm2Y6+6ttBb0KIU8ru6fYS6/RWF0N+3RPn7l3Y0zbsQc\nngMpgSweIywr4f7JK9yw1zDrrmZgbzdN8SKfvhFCM3+HDaedqbp69ginyMUVpHU5NPZVdKFGCr5J\nTMrHkUVrEKZDKJb6UNceQtN4lJjlBuHIJDN3tVHu2uDubW+yYnHQG27hHYooKHKk6zhuWQD/7ccI\njTzAyPJBnkt3UJaMs1ml57fGIsfubuOjZ0/iiKpJeq2kgyvorXVcE1ZRxPxU5BNknBZWE0aE1TQb\n0RynZHBJKJAQQFYQkDaShAsWlDITrbocn4q/z4Sqgu6+64xU1XNwfID2+VGiRS3SdIadfX183/Mw\nokzO4ckrPHDzDO/u2cdyhQd7NMLht6/Q+sECqaTErDND93opZ+2biVfZ0GoL5PQaHo8/h6uoZ9N6\nBedLjfRo3qZucIpU7Bph9RkKqkpWPV9GksnpTBfxFwfZMIziSdSxJJQzrF5l1ppm2OKgznyR6kUn\nysu/wOyf4NK+/fyR+A7vFhvYiArskd/hauRZhozNzKiK7EqoSCoLHFKc4RfFHqIbOuTLafJSHjtZ\nkrI8a0gsCCLLOiPrSjv5qIYH77uHh2e+xppujRGzARV5kOcJSUX+nFfQKfJcMN/PTpuEM3+BkfBR\nnOoR2nIDlLocjIbsrOlVLPjKCdrsrEkmYnqBAe8m5suq+MWeRzhbU8tqqZf5qlr+MvM8utt5bghN\nvLD/47QtTyOpNMiLIqJSyVpVAy5DAZtnirnUfpSpIsbVZfTCKtWN/birZhnWV/Ki5jMYpRhLMh/R\ndSPVrjuUZ8pwF5f5efkDfLx3EpfGzXRlBXd8LuR124g9OM4vTQ+SihbYKfSxGE3zvW3P0LU4Rcf0\nRSo3TvBPm55CGUvQee4qZpfE5wNvcELexcHYZd4v/xjPuw7w/bG/4S8nfsQTiQT7C3Hkkkgk6UOU\nP8yvtK9zoryX0+XXmdKu8fCgCDkDt0fgmsVKv1WBO+/E+dQP0W+cx+eaYpBWGoLrGNa0VEfXCQRq\nCRnc5LJf5YJczTV5AFsoQa3mOLNViyQqfoslFuQ0dzMkNOGw3OTrbbsZdvo5tKxnUgVjqUtoEmWU\nlu1Gb9tJEfiFT2DKaqLPUk19Yp5G6V2KRROZzXsxBhcpaAwIhQwtS7dYMnuJkwMgrJXh97gJ2kuQ\nZD2gqCRhGKUoD6GbGQKZHLOY53JtK3dMWrpianYE8jw2sU759HmeCBr58oKBpEWBZ3mFzeE80uR7\nWG2L7Nw6Tczfwph8GZ24To1iM9MKP5csfcQLBTJSGVNCCVfw0NA+g3RtHzXDX8Ze9DIqX2HcdpPR\ndBmF8Tqe+uVtshYVvY81MSh1gyQjq1WR1uto6x8gm88wVhYgom3isbU+ng8fZMKu4oDaQFWFHX9s\nGlEscidSwVvFbs5qO3hb1slc2XVU0UrMyU4OICcvn2JYrmdRt8KCeQD9ehM7Z95m1Z7C7KugNlxO\nvXkrCVkKt7GGy+NzjAd1NDjnWUvt5GFpkCPSIuZgN4HZ/ezZfBy3aQyLdRKXshqT67/+KP7/vv9U\nUBQEkIrQ+wuoPwxG9/+DFQGrS4/WqKJepyaz1M8z1/+Yp6ZfQKtQYDdY8U4fZwd97KCXvdJlrL4E\nktzArwoPIgYHSFvDdPhvMFriYdbiJKspEtNq8NstDFa58Vi3My+TyAgKsuEC2wO3ibitnCjfgV9d\nStfMCK9u2kdp0cBL7i5agxPc338NQ7SKU9tqCFq83FV8k/KVDJ1r47yhuYfH3zvOTF0tG01OzOUw\nVVFGQFfKmU3bKI5n+cz5E3QXEswYtCgQsCTK2X/zEsPeauTKAPpYBSohiisRRFko4BQSLOnieBWj\nVCUzhEaN7MrvosZwN1uLf8ZpWxtzljJKwot0ZEpwpEZIKxOoBQN5mZ7ppmb2j/ahFUWMeTPWhIA3\nsx9VTsLatEjZtTDNuUGuN2/hun4n+0reIhIqo4iARuZk2aijYLJhXF+hYKujdqkfhWUPx7wytr93\niyljBXM7tHxnex4pb8S7keZc5x5MExs8qLrC6cxmakfn+dSZk5i1RioC02iLOSZcXkbt5dQmFHzS\nb6Asn6VXLWPS5aWYHWRNn0UnN7GYr0AW0GMNrKFwtvBSzyZeVX8OQ1DB91IiWtcWFhffwbK+zF89\n80WODlxDWZCYLykF4Am/iuTkBOq2MLpZCXkmgZiLI5Mk9PIUlYEFzMV1enccYaqslN+0NnHLvJmW\n7BDGlmXOq/ZjyGYZcjWxUFLKxy6u4o1OMFVmY8nuxJBMYMmlsEgFphyV7EopeaNFS2v2Ns+Enke/\nUMUNp8g3Z5sR48tw8U0iPUrkxij+ixYElcBMpZXHXwsSN5j51hf/GxGzFUkQuOFtZMnjZc7oQJKr\nkIppBEngd8Y3+JtBkafnChxcTHDPSpqfutM8fjlPRtSgMiRQBCfRCGGU+i5q565SNT+PKJezatJS\nkkgz7bFTr25ii/JHjEa3Y04WSJp0mAUZyUKWtsErHLr4OtZiAiEaIS0rUBlOohBF+tq3cM3QzOVo\nG6qgkZ4fjpEacuEd9fPs5k/SvTaMOp/lfFk70QUZj06eI1Ew4TiXJd2npWpqhajRxF9pn6HL1U+J\nZx5Dbx7TRoYz7buo67yJ0uJn6XQVSb8C7VwEhz9K5cYoixo3SUWCtsUsR90fYTAjcKfUS5v9MpTa\nWdulRr47hGZRpL43yang3XzX9CXm4mXIgwUK5Y+RMHjRmFd5mxV84VuYNnZxUuZjaZeVzTdvM2+O\nETN7ee7p38MZCnLX0BUubulm961ZqleSrFmVZDLgblrEUzfOymIDcq2Zjtx2Fn3XqTh8lPzCm/xt\nVRc5ucCaSsOXIy9zQPwNo50H2fTaDcrDMSqDMRLFApdL3Nx2hnH4Z/niay+Q0OkxrI5CdInf3vdx\nXr37IzSOjnFs51FqpCimfIZEMsXZcAN6q5V39+4i7K5AU4zwtmcf3plpbJPLdMRUOMLTOIPrXGvS\nEyjdQa8EiSKsat0sjtbwtukpZhQ+Hpvv5YnYIil1iFLNBqoIJKa3YViJIJblSWpELDdi/MPRIyya\nNPztiZdwlD5EwnOLfF0pmrMRolKOQkkNFT3fw+gexDj1OPqpB9AE/Ax59dypsJBxWpHbBbaK4zRN\nTbNm0RK26Og/eC/GopxnryzSJXegrejCJbdgqHgDH3MoNEUMxhQvjjyO5boLW/UKCe0ZNgbyaAdO\nIDN60LR/mpc9Bf5oSzOzdY3oJZHPDy4izizSvrWXJYOP1fUS6r2LPOp5Hff4xyhfOcBFuYkf7asg\n5zOSEmIciWgxmNUoJR1daxEy85f5TkUNcatES7GMRG6Z+UKCaF6F01rDqkXB9slRVpu9ZMv0NC9O\n8g8Xvs/TI++xZ72XJ4dOcXjuKqtCAWf/POseF6JJw6aVeQyZOOXFEvoSHobUZnY5O/iRo4q7F69z\ndOYqzkiIx0+/w57e6zz93huYV9MoY0UcxQxG9X7mnA/jisuY8yhJWI3INjJ4TqyzUfkBxkInH1lR\n8Ot8nDnrOULyEVxBBYeHa/jO8iamEHmrqpJvTFzlDVsKilpUWRevlM1zzaBgWD/DaGqNnpO9mNbW\n0HY8TZ1zB825FPuU/8YLhXvoE7dRmbdxwrCBOSfSU8gRz5Qx7+nmuk2GpiBxd0Si0f4+W4tR6qPr\ndC6eoT3cj0M7w4MDA1zwdDAZW6djqZfdmlHmQ3JWjOXoMmkeD71GnTFEuPAEvzS2cdD/AmLLE9xa\nSyNP7EGrvpfrK5uoOXcCQSajQlOPX5nCZq/g2LqPXJmBRWMZDjHEn/z0++zuvcEbe+8lGNZyT+s+\n/nHnIZbVepRz6/yO/re8JzXiXpggbbUxL8hYWYgiV7cgy+lJ6GeRx3JExk0sjPl4vfQxVMj42MQg\nyaLIDVcnJRur2F1P4Fl6m+bgFLn+KR4Wr3BU+Ut2aQa4XelEZpVhVkdIxhy4c0vUqle5bNpM5/vH\nWY4YMKtSPJS5iOLGAj6tjb+OvoRMiPDkjud4TWOmr3iSlyeeZ1d8lufNJoajVWDJcyPlxR98hHd9\n/Rz3XqWoK1IWMXF3b4H7ZV/hzsMCTiFEekiOLO2hpfkxbP3PUcId1JoMw/lGTrv38uzmz/Oh4T40\nQQ8lhUfIyIzkjK9iKMQZoImsaYP+ywZc3gauGEuYj9bTVnkRR/kkN9ItbPafx1XUsSXeyrfrznDR\nbaJc2kR9UkItwf1+aN0I8WaVlQ+cuxAlB7dterZO1VImupiSrSDL5ek834e2a4W1XDUIUBQEDs9e\nRxSOkC26SGknSBv8aBcn8awH0WcLRJ0uDNEgQ1UN1M4M0pUr4XrgXbrt9+GR7CglCYfci9e+Dbeu\nmpLS7bitOrzLB6gQS5lQzRORa6jTppmVEmjzGvKAXTSiKvrYo3bhnngKR24r0XyYyK2fEMoMMG+q\noklaZ0pTRrbMwmRTG5FCKSDHW7STFxIUBBlVMzN4VlNca4YZSYsuXM9FsY38Zitpl56wGZQT/XjV\nKT6bf4NtI2P07mlnS+CXBBwrONY7qEtlqJc7OKmM4jDa2AjfYMEV4COTy8T1KTwhLcq1NPt8H0de\nokU8akM/WMTcchej6XICqW4CjvcRY7uJ5H0Iohwhr8K80I0Y60dpi+E1fwad0/v/Kgv+Z/svKP4H\n/aeCIkBpA1z/8b+Dsf6e/6lVQcyz5/WjuMU4HPw22od/iLrrGWh7nIzMgGSp4FqkhCVbD77P/Rxj\n6x5unztHaWSVgdadGKMx1ko9SBYZEYVAVKtENNs40dXDoeQyvXkTsiI4g0GeGX6BaqOforxI89Qy\nVUurzBTbmVYreNDUR21wCm00zrS3ieteNzK5RFNoDqVU5J7jZ7EuhamrXmRIVceEtpKQtoQFq52x\nEiXaoSylwRAqrQdNtoWjQjW+hQEUUx8S37ebGZkGlWwFZEXCRQ0zRRORop4mSeK8rAmTmIaggnlZ\njHMVaUJFNbpIjjF3JTc8Htr9AWoEJyWilWxexryrkc2LM2Tzci7lq6glg6XoQJ/PM1viYcuVGOZs\nCs1Ulln1IoO1h2k13ELj1xONl5IV5bSLetbECFmtHn0qQ2/FNr6/yc03XvgekiDDMhfDtZZiSSvj\nN/vyVKwpydrLuVzbxX1zZ6mdXGHHrRkmGxrIayTqRAdZfZg15VWqV8f5Rmg37tASHQkbh4ZG6OeH\nPHlyEq/fyL9UH8WrShC16CnVVGNwbWd/vovScIHfR4FCZ+G3XKD1xgXGfCXc3NJJ0/osdUsWjKEN\n5stKyCkEJn0N+C5PYROV5BUqkgq4samdUw9+kX0ZJZJlD9Wm7USVi+jCCww76jjp3M9hTtCduca0\nv40PN+3EFc6xrPpLticV+FbXUefy3DN6k6qQH5WYZc4ioydhJaSVM2GqpTv+a6ZLVrCGiuyPbuKM\nboo5h5qJYor8pJtiIEfEXcPRc3OYkym++YWvseDyUrqcorJvlWypkTW9BWQKlOk7iOoKXOFxzlRU\nkglcp2lijDKFkyH5HPrgDOcay5j1aamJbyAqlZhDixQ0UziCORa9Hi7u2cNARQ0dE2MEhTgORzvZ\nYgeLwYt4YjV8dk3i0ZUMmdB57GIKWT6POryOMpfGY21j1uehbH4OYyZPsMaJSijSml5ALhOonFvg\nzUP34rOk0aZSVC0sMFDbxKduvo2gALkooioUKKoETu3dR8j7OcLyFPPxMurdIyi3RSjkFBSblTir\nb2GZvRd9YgeLhSUUmTyWxBrKRBihGCatUpKVJShV+ThrVXPKvYO6UBS3qw+NPsHtyE5a/2UDsVeN\naq6AfSPAdLYEvaEJNzo2jEP8qruUSUs1dSvjvGDs4I61noFsI77cKHmlxIsPf4bm+UX+6Z+/xWfK\n3+WMbhfV65sI2bcgZoaRpDSpGRvJUA+COEtS7UaFDKntecRvPc/Ju5u4qWrFng4RURo4aasgU97G\nmUEVR69e5dWDH+Fy+4MoVHvRFp3cc/0091w+hlIUEYCL7Tu40XWI7997mKdX9bgd21jSraJGpDlR\nz8/Ka8knZKgqjOTVAmNuNyvWcZpW+lHn4OyOMfqrrrNmSbN/oIhGruVq816QJBpmZwjY7KyUuknJ\n1DwxMcnvTJdzW72Vn2rbeOzPzjMY9jKl+ANKOs6SWlOgqk5yKbObX921nz8/9S6l918mVn4BTaya\njGueM7GdlLusOA78Eo1tAdXig5wckFM7+ltW5Ac5Ir6G36ilt7Se22XtnGg4zFR1HUv2Uvp2PkRM\nq+V3T98kMfoC13OF/4Od94qSLCvvfH/nnDjhfURGukjvfVaWyfJVXaa7aW+AxjshYWRgkJBGmquR\nQ1cWIQFqGWBANN0g2ntbXV3eV6Wv9D4jMjO8t+eceeDhvujq6o5ZaxaL38tea6/v28/fb+3/3rQV\n0tSV13C2P0sk2YXZGEadvotvrx+n19jGUKaeeNsFrMElpG2FZG+Sl+8I4RgZJYoLm1bg6NwUbWkv\nDRcWCTTKHKp5j3l/H1+o/A62ZDX68U/gEEUCVQYuRLO0pEPMNvrJ6QTu3ixzKKZSruhA257EWwiy\nWuOiveBFEvT8iXsXM9Z2VlMlvpGXecTawScSEh+78DonrzxBwWhmrtaKvmoXVa2PEIpOs2N5DFsh\nz5S3hrjdw5zoJdO0h89tVZNdPMPbVV1cqvCSK6t8SN1m2pYGdyW+jICnoJBpPcBHfvMrHHefwn25\nQLdpkCFvL/vQ4ymLnKmU+cJSmauihw++dJnfevQuHggI7MjoOKu/yFc3ImRDR6kMbpB1NvCxmJXX\nK+F3t57EXTJgzfagqkZ6l3R8t3CNjwVPsvP1DJWbCcQ9IlrDF8ih8i9mjQHTFUIGF5dyzcQMm2zg\nYVf8ElWRNzi/cwc/2eHBX47xiWkJiziKXq9HiK9h2VqlIq1hKqnIxTJ2UeK8uw8UiZ2ZMVrNUQb1\nKzS8tcDA7Sl6+4OUqCde/m12ZbfYYJE/DQ7jMxc4LPYilZ14yzLS1hitjY8hN3VDJkOouM6So5Zi\nSUBcSOJe2eY3o09SOR5mc7CCl1tPUjfQz7+oeowLCSrCOlqVFXyVaaaL1WiiiM5iZ6S1F0MJbKUG\nlpxm5lzNmC06lEKezoUJ9lhMnDh8DMPiD1ix+blqPMKVrQxRh5uPhV+lxxmg2rDEzbyfRWMb9ydP\n0RHaQMmaWdJ8TKesdDoifDz4CqGEgaJsYC7mYZduniP2eY6YzqORYFkvcEdwHL86w++H1qhUVB4v\nNbO44afjjt9m9u1VgmEbzbWH+Dv/T6gTanl7cYJdUx9n0PdZTIZKLmRHedE1z+H2NFWrA1RmDpLJ\nHWOreC/LkQGmbVaeGjxEX1Jh3mXhaMpHd0ZmPvcC0h3nceRKrBbqyW95kLajuIXHGM/o8IlrdCxN\n4f6exvGzV2nrP8zI9hY9QjsnEwc4mO3Hn1NICim+W5/FlY4ymPfiv/wEN3vaea1qN4rSx4dXdWTV\nJAtSkFd2HOSVA3fwse+/jKlXoCJQImqoIaw7QrngRpJWibnWMaZTHLl2C4feyvKuw+g0HRWpOHdf\nuoB/7B0qqw9Qb+1CL/4sRikgIAB5UUArZTGtj6HdHiEQHOFadRhn7TqxlJM1XRSPZ41U2ofdvok3\nV8vhcg86UWJJGmdy/Qpj0TMsWcGWgaGxMcKVPmosWTSzTAkJSdDQ1DIlsUgRAYtmIFDpZXBiimWP\nlZXqEDcTDzCgbnFYC3NOL9Dz/D/zfsMlZvTtKAmZkPM+zgw3UrLcwFAIcSpxioP6y7wrDLEpJtBm\nr2NOlphsTuFgmFrjMCfbD1GntOGUKvC+v5Oqviby21nMM0migoNE1zgZXZiH1C58FoWAbZwMRXJ5\nB95bEYqjIsb7P4zX7fxfrgH/M/xCFP8Nfu5EUWeArUmYexP2/iqI4n+8d+pFGHkSPvAD6P8giLqf\n7ZtcyK1HkLvvZVmt5tTkNr27DtBU6+N7GzbOGJsJ9bYyuDKGJ75B6z1zxOccoAJmOw6zyA927UWO\nFyGrsGnwcNq8j9FIM+E1C6veKg6uKLxuNeMrbNLQVkWD6QqukRgG8SDX24uMG4fJbsg0bqyjjmmM\nHe7i28MdnLe8S126AU2ycLpzF674O/gSlWzIFfTna1Ao0JgLYna1ErzPw7Ff+TL/KSfSEQzhjNcy\nU3ZzjSo2VRs9UhCnamBV18JcUxsBvx8FiRhOdGVo21yjdXuDRGmZSd0aG1IMQdJR0Olwp5O8XWwn\n4/UxnnRhdaokrRYat4IcWcljaLmHUnSW/vkIb+8eJqC3c5/3p+TTVTyavJNA+0usFtYRhUbEdJof\nHn+Yz770E3bMTJI88VX+9kAPu26Ns3O+zOFxjXYpzKHYZQ6+OY77Ugw5XOadowfZrq1lxbrMUcXN\n4dR+TPlt9nIEc9lM+e0/RaGMqfkE7bNbeANRvj78Aco5PV9W60kQ4bZbwz7xDqtOP8dkL2klxhvi\nJQypGF2zsyx09lFRyICkYo5WUBvTM94qsa0zcueUSLB+B43eNr74gTt5+fAJJlo7iLhreTDWgOys\nJ3f7ReSR18j67HgzaWrjYVoKqyi1Ga4lP8lovQfP9rMk9SM0OqKYtuupTkbJyEYefvFF4jYLFkGh\nQrPTnDfxjU4LnZE3GEinaEv/GknynDVuYVAKKOUGpIVNykaZey/exJNI8lcf/xVG23vQqyUq0otM\n97VR1jQaFzfwylFCti788XnedyHNzc5anOEQJ1/5Ln+1q5aaucvEK12M1/tpiAfxJaOUTAYyNh1y\nJMeGy8KWw0LRW41sEvFtR6gIbTNiLVFj7qPV0s5krY7hoshSEb7fW4sqGPAWnWhqBAQBm/8wu/IX\nEaQ+LGsTdK3N05NbxZFNM9nTS2UgQP36Bi+duIOqXIiW+VWq8hFqt7a43dZBXTCIsyVDIaxHrjnG\nQWcLJ0Q3g4VqmHChY5T80TJuT4Bczkp87PP4tDr+s6WbkiZSl99g1D2Avb6Zw8IOlgpzZCOzuCsM\nJE01vOPYzeAExCbu5TPbP8Lfss2ktwElruPw7AiVZhWfro9pvcpzbT5iOMEis5CtJY0FQ5NEfXaF\nlsQcl3ccxiA189iNDZrmz1GI6VDSe5EEJ2VJw2pyU0zNoyklcqU6pt3ddKpGZqQAFdXTpPaI/I3+\nKyjoeOqVEWZrnmDBfBcX5DZ+9/vfwlhSOH34V+nZcJKTUriSm0Q8fdTkA6y0dPFeZzOPf+iXudzW\nTM3mKsrUEhnzHCYBfImdJHNOLKkSC3aBWLcLOf0Wis5F1NbDvlvjjNZCSvLx4IiKrP8wvStzuGNF\n/vXE+9AkEVG0kzGK6FQQ0Rj1VfAvLQZOV8r82nPP0LS6zNcfKnO19zByeIirzhqGbLe42HGML1x1\n0NHzCkXbBtbQAFnvJKom4mgo4Ri8hpDTOHfpJAeeeY3uiVm0TJaq9XPEM72UC2UGgzd4eG2BPeoF\nZo31jLUMETfI/OE/fZ3D55+nPpqiIzCJtHYdbf4Wqkumc+2/oC13Mq4OkBY26eI0XaUc4cQipYNF\nsneolHoVao1ryIkkx35ynStyOy4lg0NQeafmMJGUD49/jqPyuxiEAqbxL3N9y8ZCjch9YY1YKsrx\nxDK/s1bBOeM2W8kgfZqbvAjmumFsM+cZs5jwKQLV1k5ui7McSK6jF218UKrmamod55m/QA3egro9\n2Pf8Kr6tbWrq70bTFFLB05gKZTJ6mct9n+ElrQ4zZr62CXIuyZXwJDd9HWTKKv2awBdMbYSFJA31\nD+JpvgfRXsezO3s421jFA7oocmIeZXKWl7UNGq79mLOVFxnMK3xuw0+rJvDscCNXGtoJ5Z7iwehu\nGvM7eabxIb4UbmU+cZ1ybhWzZzd7ossM55+iNLObtpCfRXMOi6mOwXyQ6Jl59NEi33xA4N0Ohb/f\n8SDfa7Ex4q/kqep7mK5px76ZJ1gyIaLxx5mneWrwHs7vGKBjLc1jl8CgSCj6G5SNJmbME5y8Fadl\nK0lVPEXGqCNihIRUSbEg8lnHKSrEOJTA7c9iqSmgM2iElD9DFTUEtYVMQ4CarrfoXnkIZ9lMdup5\nZFczhqY7iNtsVCTKFK0VtOSs3BAKRDc1qgsagbKZZ+2H6KtdprUVAsyPAAAgAElEQVTpOu/ZjjO3\nqfCJhTy/s6LxmGBnUXTSkTlPfW0SoUIgVbRhK6rkTOBOVGAR86TlHItNu6jJDOFMZ8jHZ7m9vkEh\n7aRmK82BcS96xcGE309PchyhLPJE7Bhryc8RSh3lpnQ/y24d9+ROMVvTwF/u+zKn7bv4wMYb7HKt\nMexaZbd7DZehgF5UQAAZFY+i0qBsMZiLUdbc/CR5jLdrH+TK4AOYr15AF91mRzDL2ztLzJiX+KMf\nFRE9v4fsGgJBQBBKdOfaKaRkPmINUZU/Qk5QWNCfQpfR47V0Ui/WM2vK8xuja5xcCnAw5eRbbXqu\n5jcYtMyjNCYoUGY710Hbdow5YwJFb+LoC+9iT/ax2X+S884ca6sLFJMBSgYjG756tk0im4Yt/qlZ\nwB5dYU2XpFepodWu8aWt3+Cu50/zSPYAgigyolshJWS4QzzHc413YU3kaFuIElZOIpU7EcpGPLo8\nYfc8QilLwWJhpakZT9/d7E91su7SEVOjSJF1DhRdCPkisqsZVVNIl+LodSZUQNFKXE28wTVnkrHm\nKlZrfOQEM8lkBSAilAykYmakchmbUORI/jDbYoI3zOfRb0tkw1cJuyvo13XQtRnHGF0jbTGzUmXj\nar6fdcVOv7BIQTJjQ+Oe4l58UiWzuk0QRZo20lzsKJAXJf4ge5GPZX6K9co8dwpz9FQGCG9Zmfe1\nUc4FGW7vYjT5HVz5Lh5L6InmFd40NNCqVNGnNVMjN3ChapaYOcTuhUfpum8n7lUBrVhAiV4kef4M\n78ZMbJZXKMnbHI22cigzyIJd5nueeeR8iEbBSFIRSNgH0dcPYRloosbzi89s/o/n504U4WeCN/Ij\nqN8L7ub/eN9rX/3Z+r6/BOHfFkyPx8OVK1fI5XI0NTVxtK+BYiZH3dR54nY3tu11ikIV7uYgiaCN\nnChh0BsZkK+xq3GBkZUmXJqANx9AJ6vkJSP5lMBVs52iKNGfmuClfDfXy13cN3cZRWdlwbvMWmUf\neZ2D/S9cJ2TS8ZVPP8qa415MmUtsWFYYafokcVOBw7NjDPefQFoU8Gl6dt/4OyyZCPrGg1zqjnOg\nez/vhpNcN1Wwa16hoWDjprFIp7DKX3/qKOOBIHkhT0IJcOd7t9BpZSJuLwIgqyqyonC1qYuU0YI3\nHUcBXLk0k+V6FmwVlPqsECqykrbjtRbo3Z6jseJujPZ65IoehIUz7FlY4adDD3BYPcXRtQ+i6qIY\nr/+UHe/ESetkwn4/RcnAbzz5PbbrmtG3dHCmpZd5vwt3JkV1KIl9U8O+WMKTiBFyulnp9DHd1Ids\nULiv5RwtyduI+RM0KxU4FD+P19wiVtb4ft8kfbkOalx7SXr7uGqu5LcEHZ1lA9/p8SPnQ4SrfGSK\ny7wjpghKc+T1MrZghOpoiO899nH0ap4q2wz1sXaympfjC1P0zdkwZlXMGYlvHK1l02WgqJeJOF00\npzTu39RYyN7mico8Y61d1M+MUl+VIlF0sp5swpj284z/CIqQxhT9OzxWM9v5Mv5kOwKgVxV0hRxD\nYxPMtregR09P0YulDKd1LejTh+lQ/JyRpzhTbGL/7Dn00U3KKOxZ2CRhMfHbX/qvhD1GIo5KHLkM\nG756GiObPDBxno7EOs5AgjvTZ/ja6ncJxx5F0At89UffJG6zsdzRjCBISJqCLxWjKh1H0jRypjjG\nso29I9NsOi24TH7iTjOSKiApCo2rayxYyswV52i176A/L6MIAmdLZcylKPWxJIohimyrRHXXERZT\n9L11DVP1MEpkFlHJULEdZWRgELOQY7j6MrpJDSmn8PePfJSHT7+JO5og0FiJNZnFTI7qIwlWNqqp\nqz5E3mRmSs2R1gz4l25jeGOWdMMdFF2bjC4eoZCzskvxkiGNXvNj1OaoLoYYqthFi9BKMHqDiKGE\nLVJLyahyq74Gf7QG06qTs/Jx1oQEd7uvceHhZiblbmpCTWQt1bxgzZNLqYiRAppORDSIvG/xNfyx\nVfrDExT1Bi70H+e/PPUsYdcBKMbZ1GpI+e5goSbLSLOTxqCFDZOKqbiBOa8nuPc+PrzlYFJcJpKo\nYcLTRtXNFL/93FP4F6aobtAhRXI0rAR58Mx7vHn4N2iIuJHlGM2jf0NVdIVA7VEyHpnF4jhyLo47\nr7JWUcXdp5/D6pQw6NNUpYcoZC3oBA1nWUbITZJxR6lfyHFkScdkYw1T7TtYTQ3y0YsLtBYP0GXe\ngdvhxbxwHswebtc3IErQEcsRNer4swtLXKl2oIgCDRtr/PpT32X2wDCvdQbJ6RWu+e8kTCV7izfY\nq07iz4skGk5hf16PPfBprMUuEhUjGA0pIvNHqP/rdQbHF0iULXx/8EG+03cvrkyS/pVL+IMZDtb+\nEi1KO/vjf85jm2Oc/PEoxy+dwpgOkNmhUbqzQHqPRvqAhhwF03mBp/cc5kCmGVP+Kl+QvsUB6Rp2\n5SbK6wYUQWQx1sifVv8B1bmr1FTnGFtvZq/cw/t0XbypOXkRGFe8zFb2ccRwDvvaCSZvDxNXV3kh\ns0Gn7OEEJmrUCkQ1TyB7g/vVTsJofJEMj8gGjJVDGOdOs2ox0GnpYMFyk4So8vGSE0sqivHiX6PL\np8j6jHzmK3/Evu0SYv0u4hYz8dtPYg5vogEGRSHd1MGhoo2TWKhHz+2pp/nTvvvRBAEQyAhwj2Cg\n29SJjJ7V2BmE7Rn+dsiPyWomG/fQPPwm8gUD/tg2+mKeOu8dbGY7ueUe53ihiQbFy4itQO/Vx+mf\ny+L37ObwZgmLzkogt0iqECFapyeWvkT9rSDCeAx9YBLF6mDda8NxbQt9JEXCbeVSG0x7ZQIVD2Es\nlrCiUb2l0bJZYqHHhnMlRLWYIjRQzVs9Jzh04zyfmhklV66jLOVIu2LkhBhX/OPsXnVw7cAJNuqr\n2Dc5gSrCWEs387Kf998+zWLlCZR3QrjbM+gtKqHFXsq2R3Hr/oycVI8c34s+mKKqtJ/i9CuUFt6g\n2GnEIHZgVgUA3AVwaGZyRT0JscB+ywo+n4fFpMbrxr2c3t7PLiXO4xNGehIqtymiF0t4sfB16zAf\nsJzBUjOKKu4jEytSlKFU9mLL27EIeTrXKrEUBCS5ESE1g6NLoanpHMwew5yz4kuqZNUIqWwLk8oR\nClEfel0jWnEByl5y0QHG9T38cvZxmrfmaNqOMKyMIgkaggAFQYeoaWgILOec/G3Dx/nMwJ9xU3o/\nOzc/jFJ+hJx7Dz/qayJmtWLOZHi/fIjm2pPoMdATrcfnuR+PrpHC7MvYvBsoWg3V0jfoFG/hTqRJ\nKZ8k03yLCms12Wf/mT94+B4ORgUe3lCJl8MMaC0sC1v8pKbEhf49eC7HaWyeIx/2kQ/YCNTUkrWY\n6ZtZpmprC/nwV7jhLzBW68cs69n5vo/wrKeZb3XZMGXPUdf5I9Y3mqmOb6NYG9iTjaBTOliNT7OV\naqOqci+aAOflaeqUCsZUOzYhQcC9H2dsCF0pR8E8TdS7giE5QsRu5PiGEeam2G7qYKOwzpwUpC/j\nYUUKY9Xb8dQN4bT2o5WynN18ho38PE2WPkpCmVd1VwnadZjMCtUN4zRN6dh59hz9FfexbEziU+20\nzExx8NwZrC2fwo2dK5VvglrBiq5MoqqRjqKX7uvvocVX2Ojaw0hXMw9vJPDYXDjlVbKY8GwH+LHh\nGLJqoJiRMZeirNRY8QfjXKqpBec4a/K93Je/wQ5rkCp3ks1NGxPWD5DQF9Hnw5gDFxnxrlNl+jTH\nNh/kOc1EURI5WerHItmpMjXhKTt5132JXbKdjmtGyovnyF/7J+aXp3nV5WSbIFtigqSYZkJe5Wm/\ngfNVObrW5jHHYlSZe2lOBtiU7GTyOpwteprqK/6HR/3/HfxCFP8Nfi5F0VkPV//5Z/HTznv/Yz2h\nGXj79+HAl6HxwP9rmcFgIJ1Oc+vWrZ8JYypOdH4Es8XMk4NHcCglbJPL1O3dRHVWos2nMVUXUaIe\nHjr8QQp5K7fCaZI6G4ZyAc1hpDoN5tImhyPncftsPKS/TLdhC0sojSu2QaHiMJPeC3z0jQl6F2f4\nm4dEinYzMccujEInYecQWYsfd/QF7i23MTtXpj9pYVMOsztwBteHHiCbdRLLRunet5trl4JccUu0\nr4VIlw2M6AXul6/Tuecu7jl5D8cOHKPdVs3S5cv0j03SNzFJzGSks+8st8z91IRSlC1mTHXNFBMJ\nUhg4U2hAaXPwB9Pr7MnGuaqakZM6Pqi5qNTXowkgGeyoBgu2mdMcunWNpO5zNCdd5M5/C/18DlSB\nuuAmAhr7p8ZwRUOcObifgFzCnFzBrJSYaO9gpaWZZF2e89VxmjYFNqr89N5eREKluXcUky9EvCqP\nb0NCUXch5seIjT7FH37u94i5dvDwT/4BsfYAHqubuwU9VeiZkkN8q6+SmGyhaWwBo0FFsIKhUOB0\n6yCfevkZrg0O8cTR+zjifJ1AqAtHYQWl4KGgVpGpifCD/dX0LxcwFRXe3/jHHNW9w1VhH1sGgVT4\nElOZaWoD0wilLP/y8K/wId1PeX/hHUaTj7JQOMi5HgvO9E9oqDrKLdvnaQvE8eYMXGjuozoRIaXm\nMaUSVMYzTNba0BA5nLAxUPBSq9iJCmkWrdcIWyV828vkKdIQSRKtb+IPP/MVMnqRTXc9nnSciM1F\n99oC965NQz6PIoiMR0zsqfNzNPE8W3o7d13dxrx6mcDB40QMAmW9jra5OZJeD5KmIssygqCjbTWO\nN7zNxX3HER0WFDQ0NHJmE53TszS4JJpaJjnd8RFKZfipX2DePkdlZoGSLguCSklQUCmjUkIfX+NG\na5lOcQg2p8l6DYz2DuE1zvC1tmHqkxaGR27StrxM7fY2qiCw8lk9LW+HCXbUMlrZS9w0QLNnH5HA\nKFOqn1gBqsa/jyzJVFR/Ffv6vTzvqcScmqNBqWYImXXLFXRqAVu6xCHTHWwLWYqWMOXtLXJCkMZS\nM+O1lSx7zNTlwRSTMXsULuUfQJ09hKflJqH0Ceo3zjBq0hOtcKPb4aJQZ6VUZSVW6+fg1HXMuTjv\nDd+FtlTkPXs9A9kCWWcneXsTJUniYUlmXxYmJRV/yY4udp2oVWWndTdWVWRp8zRFUxWdFzdoigRZ\nqa9l1u/FNJXH6NRx4PwlSromkt4TGAxJIuVXGJpeoFyXp+w+yUbiHUoGGdQy3u1ldk1HkKyN6PR5\nvOIKpehOMqLASauO1SJUlDV6516naGigdeEGLSs3mWrbSa7RQUPVKrZqDy5DDT/sb8CwNcvJq+d4\nQNrJpzckqvPwqt/Au/UuTIrGb99e5qGn/wF9scgXv/B7FKU8xvRpCqa9fPjKLDVSKwbfGbIVkySX\nvFT/KMdPLDa88jFyCwdYmzuMq/EijoqHMdV9hJW+fbQee4ljXW8yKZ+gpGugcfUqZXGWK+YyDbKI\nMTNObiSNKKbZ/pIJx54IsaKdRMlI2QYpvw7rTbBvTGGoCzNk+A75sspzaztJjcoYQhoNVUl69SFu\nSN10j2Rxtq2xW67iiPCvXM1Y+DtdDUOayB69HqPdz6O3DnBxvYt8XmLMsMRNSxtS/BqHjA2YkIhk\nV3FkSzTqmzkbO8frxhri0evs1Vfj8/TzjKSwX+9jw7bOq4bd/Eqpiuy5P0fViphOHEMam2Wl3sWf\n7+3lGb+OV6tFYlKJdVEh5DDRHIohZQV8lXvp1htIjz3FH7cP0WmN0iaFENE4KM9xW8mRwszvUuRZ\nvYMZu4PRXX3kNgrMzyjs8U8iVeXxXi+C00s2XCDuuQMhZ2TKucGOop6qxCQ9F66hFkOYRBd6ZyMT\nuVE8kofN3CLlzUUeDI2RmTHwwuGTnNkxzL5rl1lubmS1sprq5hgv3ncnL3Xcj5w7jyvvJm+spHv+\nce651YMiCmAIcjw+RYMuyrXqPjqzK9x/8z1WXRWUTFGarC+yLVSxe1NHp7yDtyrH6VdNPGx/E39t\nGN2sTBE9V11dVK5FaLkxQtYis3GnlXltCJ3wKLLiJD/3Y66lDdRZBrGog5RTyyw6f0LwYR8N8c/w\ncrXMm9k8jXfUIaylUVToQySrvwVqEl1yg8GNSxQMNmKCg0eTFto1mY+KEc4b46QVmWOqA32vHsX/\nJIr+MGdveXmz2MFoSwfxSit9qyrGopsNt8rLe0QaQhoGqR0l+h5B+W4swVbeGjThTCu4U1Ye1Cpp\nV3wEyhVU6BTszjDh0LMIOgdKvgvUKU7orrNTGSdeMPJt5QHOu3ZQ3LPFin0Xry/s5q/aP8Trbffw\nuatT/Kd1FzOmJX7oT3P/to1H5sM4w2v8UrqN6rKROf0SFizsLvTjEm3kJ57B0OYgcO9xLPMyOmGR\nCukCN9X/jE2rpOGLH6Dwysv814N3crGtiYd2+JGmA9RplQSFGLJtkwdWPZyTN3hn6CSNL8U48uRN\nbMkUK83N+NfWGLh2GalumOsNNmySj2/u7CfgreUJh5dZp56hwApdSys0uKaxJ/Nkc3ZkAqwI69SW\n/YSEfhyuIYw6PUExxowuwK5yC1ON/ZTSLo6OlIjZ0xy6/X9TNzPObGcbSbuVCqGS3YY9JCuHaC1V\n06+4WBHWmZND2FQjWb2EqJnJ2Yqkr/035O0Fls0KNp2bC+YlYroCh4VO9hX62C7DuMFES+09+Ax+\nAoVlNg15jqu7iNuMNLj38qr1Mnvve4z33f8Aztk8VZev0HzldfKSys2WOiYGemlKaozZJOL6LPZU\nkSe1Ic4Ze0hrAtdQuCmWePjWiyR9Dtbr66hUdSw65qmLlfDcdNLkC5AuGfh2/gQ7bo9TEiHh9XHa\newkBkS+EHiNbTjBt3KY1lqJN389fl+f5Ztvf0bBuJ6/L8Z7rKsMvvo5xZYyEtZaLh/Yhq1m8AYn2\n8VGeG7iJvVRBdSpDdWQbKZ3gtm2e5xquc+drb5MlRNnRTtdAHZV1v4ie/h/Pz6UoSjoIz8H0K7Dv\ni/9PhPTf4+xfQXAUHvln0P/7V+FtbW20tbWhqipTU1PodDp+6dOfJiQbOGWtYOft66hJL/6hMcIT\nbjy5DKq/l9vjs/zSgwc4VFtJ+2iKoQzsjiboLVloif+UMhLP246CaEBnsCFLEg0L02TN++jYPM29\nZ8f40d338caBo7hynaRzJtJuL6rmQjcdoi6wSWWigD9aS16TeduRwbi/mR0f/yjrwWWaNnxMF3OE\nz0eIVckoVjvhTJRtVWK3bGVtc5qhoSEEQcBdXcv43AQrTgsD77sf53tncd4qEL1L5ce17+ePDu5m\nT283T+jsjAQNlMoiO0wh3jcboEk1065z84IgsEOqoBGJBd0WVtXAlkVkwV7AGQ7ROPYuyuoVhHQC\nxS6g6VVmWlromZrFEw4z29HBen09BUHFpAq4PQa+13+UwdgMKbEWUfYx3+on4m/GmYhTv7KK52IG\n47hIaVChJXIDSVFxGp7AsG6n3nuUU9UVzNTWsGfsHczObvJo5LQkFYKLV2pk2q3XGAv40W2luW/0\nDO1T02y5fOybvMXjH/oINmeSWKaFGxWdfLj0FxjabxGemsOqP8/tlmOUEelfKnGmugnpQpjDCyPc\nqOzlSmsv4107ud63jy1vDQW9xmm5lhvZRua9nbw5VI2mZXj/yjivVD6CvZzi4EKYNZeHS22DbNrt\nDNw4w7VOhZyxgEV1ERRjTNpUnAWIRie45kzz9f0PsFTTyM6JsxgLRSozGn/0ud8lZLejSXqMpSJJ\nk5nqVIAlXyM9t0cxlO2gK7BDivDZ3/gK0xefp1kto1y6Rcrp5mxPM3vKbTixkc1skLFaUSUJVVUR\nyiIxu4XFlhZMKJTR2FtyI2EmIau0h7YQE2Gau2PkCrf5x7r91K1fw54Jc6u+A5u5m1JxC4MCmqaA\npnKz3cCpunEeVT5Jee1dVnwNBGtq+KedH6V7WyTlsFEVmaFteZNzA7to3AygX5QxZgq8N3yEba2B\nOmsLlXkd2Zv/DEqBtBFal0+T1js4XXOYHlGkeUtmybBCtmCiS/SQ0wxsGlPsoRuXoZJoxT/RrC5h\nCFsIigq5/BwtgTAXutuYqTHxbq+Zt/3t3KyuwRZTkG9eoVwcQS1tkW/xMX2gF2spw+/9t29z/OZF\n5ur8NC6PE3H5ODpxm/m6RrY0B9t6Pa2ag5JsZWDq+8iiiMdcS8EgkcroCZpNWLNz9CnVTNZbKS7d\nwJdKs1bnJ+pxg0mmoDOw6qrCYOpk8OIVRgd+DYO+SMB1E2cqRMtyEOlQF9ZSE6upi5gbdrDkdOJK\nSlBaxJgXsRZ2oysMIOmz/Ks9RUgzcFTQEVGsUNjCZMuiGjX6C3vZvWIi7Avwbs1B3qus44U6Pbed\nOvRmlf1XLmOaeZukTsHgm+PVihZcpRz3j1/GuXiD/pERIgM9VO7aw+B6kTnhBuZCmHN9H+QNVz2H\n1HNoqsTXbv4qrmSGk0tXeaLuIMasDb9oxNHzEsXq2+jlekp930ZnCWMoOqlvOU8Bhfl8DTT00tSm\nEvOvkRlPQkhH4v8SkGxpItu9OOe3mb/lJzbnILrqRHO30ro4jTM3jq6inbfzXyC2tUXncoDp9m5e\n6z5OnT7Ip7ZfIhspoGsoUbavURNMYDCsMaKcJIlKd4OLRzbK5PMiy0kdYwaVU2Y3laLI7+vrkTWF\nzdwCVaZGaowNxIpb3A69xtbOZnRiHHHpMi2OAQ4YasgIRWrL1QhOF4PLc6iL7zJdV4nl5EmES1e5\n2r6DmaYWFATyeh2hkIH1rm6C7e10BcK0mOuorO6iuPQe3zcq1NXkcOuKbFfZuLljgJTZiFfZpqAt\n4BETJIoikzWNKH4rnaOjHF07jao3Uzu0gDxaB5EY+lKK212tmPNV1Htf4KD65/xYiXH4WpZA/SNo\nehsWJY/X3MCWsEIutEBjMYFjukCmUsJefYJA+z7+8c47qQiskXLY2ZD8eOJJ6iISvZFGOsMl+jaW\nMOWNrNpvk7XMMRSIEnBXkDOYGNhYoDIcJW21YxJybHjfZVn0YCq5uFN3nK5CMzd8G+w3vElbOcOz\nuvfxZO1d9E5PcbOhE0UUObZ0k5o7wix56omoDjpSAxRjAbLXr2ELB0nZLJgddYz2TyAPX6Zu8z70\nySaerFtAS87z5GqGcyWJh9AjAjoEGGxHm76C5qvCby7QLEZ5UGki45qmf+83uafpNYzOOaoDh1nf\nTqGve4ez40O8mNyBKkj4fBqqmuDg+ByRijQjDVkmGhsIVEj0rahErcM4A5Ws+gQ8kRcoGjUqEx50\nCEynNiiIFlpteXrFBm5VuxADp5CMgxTU3dTYw8TKIk8vtVLZDU09V/FuPYB//GP0iEM8GKvg4bUs\n+7NutqyL/Fbd3/PbiTvxOh3IGZHBgou4WOLXd1l4xvVDyolxdOOTxEsxbOMv4fr0p2i66zihK8uk\nCl0Uyg9iUetQga1z8zxFmqcPHOQ3q+x8qK+BsLfAtdu3SApZar0Ztqst5K7/gLijnjd23cnl7gG2\nnB5coQhZQcetzj7eba3lmf5O/rXdjyqKiKKRzuASO5bOMe4O0hqVSaabyCZtGI0pSnkrBXQsZw3E\nM9UESjJBpcwS6yiAM+3DvalREVCZqtPz2j4PTX2zaBNpFJOVstvN8PDduJZyVCHiRWRr/iw9F15A\nMrlotA6SF4osS2FW1DBLdTUEGptRTTaW9TFSBjhW6mM9dwuHWEtLrpVe6nEJLq5K88zrw6iigFdf\nQ4N7L0ktzdOGpwmOx0nEY/wouMhdb71Asr+ft4b3EKusRNDp8OirCcl5To4v03XlNPs2JjiwMcq+\n4Bi93k4m1SKnK3v4+N37+faiETFVxsksi54YkuUol8uDjMV87Bubw55IstDQRM5uY9OWoH/JyAPF\n/bxknkBVy+yqseBI1dOdXOTRlW6G7McZKvTyivs8c/Vemmt+neCJOq6LM5TbbnFEd405srzbUaQh\n1MxI8yBNq3NkMjOc61tDkZLcahFpW1IoFCYY6m/H0dzyPz/z/y/kF6L4b/BzKYoAOiPc+iHUDIG3\n7d+vLWbg+c9D5z0w+OH/z6MFQcBut9PR0cHw8DB79uzBZrNx3G3ns0212IwGbr93lR2HvoIUSLO+\nGeOR93+I2eAm42Oj9HVUs7w9BjE7kuRBNpxHSa3RUZVmVNfFpFLNjl17+PznPkjsh09gTIfom1ll\noc7CX3zkYdC3E7JXo1j1iNECjvkwzlCIYL4NXcFJc95BoLLE1bKOptIy46Mj1LY18vyyin85zzFJ\nx6PrJY6Hy/yjpjIk6HgwW8FKSEUxZGho/tkvVKIoMnbuXbp+6Zdx+e9E3ShSMXeef9nzGOO5In+7\nus1WUUObyUCNkYdGnsWiXOBIxRN8Wz/A3nIFO5FRrDLtXxxkenyRVcXLX9pbuHx4F/ZCmoZwiHTD\nEYzBOcIfEfjSfV+na/oKBklh4UE3V3OdVCsmymKWxlQlkqmCp1s6+ez6swSlJmyaQHtxEZ89iHmh\ngKmqyEyxgT2lNYymAtN1q1RG4mRa9Xyz/hG+NJPlrrSHFksPo5Zpvu59gaQs05OvYX8yiKx/gVNb\nRzh24BSN/bMkHWZ2vTND0Ovj8Yc+zudvx3jJ18qRibdo7b/FsiHHxWKZqgU7Heplrre10riqxxXQ\n0EITyNsJdm4nGJLfYs/IWYbPnaV+Y4mLu+9EED1sujqYrPdTmYxw/+Rlrlbvx1HI89Unv0PC4+ZU\n1x7yUpaUxYc7vs1qTSt5jwdHVkDSBMqZDcJrZwjlltHiy7TEQngyQfxLC/RshLHlS7x2/D4ykoih\nXKI6pxI3CpgifwJyFwm7n/rEMgAlgwkxEOSvqgY4fnaTYmCZC/t2UxDMfOC3Ps7ljQJu1U5QTtM6\nN8fOsdtUpko0T49zvbOfaqmAXkkSkdLsL7VxWxciJgZoXEgy2zzAfuEqwWU9armIzVDFUm0vb9TZ\nGKtrZbbKhjm1iLsoYy1b+eSMwKytBnf1dSZ83QScPha8fr0r3G4AACAASURBVA4sjrPq8VNX6mPd\n7+LHH97HvgtjuKIppFqVhR11pFQLh0sdpK99B3N0FW9slqb1ywjAUlMtSfMwdkmgRtYxJQRJGQLs\n0daoVHu5ioET+nYWUyPUGV9D1OXxWbM4FoyUijkKWpyd45dQZCsnY246FwoEHRJZ5mhbnmDbW4W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4tVlZq8REKZAEOT56hZ8rP5J08i69zI+3fvoMiCp/YopXY9lwoFZIZe7nfYcAi1zBYmGHel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j9jlfYj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\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8mzfdSbV0OwlriomZn2Bu/jrJ+UlaXKvYNVXPrN3HBVMRy5JinFYtNbtKuZnL8l1rlm3L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\n/zDdcMSI9R44AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x119944910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mu = annual_return\n",
+    "sigma = annual_vol\n",
+    "s0 = close[-1]\n",
+    "T = 3.0/12 \n",
+    "delta_t = 0.001\n",
+    "num_reps = 500\n",
+    "steps = T/delta_t\n",
+    "plt.figure(figsize=(15,10))\n",
+    "for j in range(num_reps):\n",
+    "    price_path = [s0]\n",
+    "    st = s0\n",
+    "    for i in range(int(steps)):\n",
+    "        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))\n",
+    "        price_path.append(st)\n",
+    "    plt.plot(price_path)\n",
+    "plt.ylabel('stock price',fontsize=15)\n",
+    "plt.xlabel('steps',fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):\n",
+    "    payoff_sum = 0\n",
+    "    for j in range(num_reps):\n",
+    "        st = s0\n",
+    "        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))\n",
+    "        if option_type == 'c':\n",
+    "            payoff = max(0,st-strike)\n",
+    "        elif option_type == 'p':\n",
+    "            payoff = max(0,strike-st)\n",
+    "        payoff_sum += payoff\n",
+    "    premium = (payoff_sum/float(num_reps))*(e**(-r*maturity))\n",
+    "    return premium     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "151.57654346653473"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,10000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Option Pricing Black Scholes Merton Model.ipynb b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Option Pricing Black Scholes Merton Model.ipynb
new file mode 100755
index 0000000..a8507d4
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Option Pricing Black Scholes Merton Model.ipynb	
@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from math import log, sqrt, exp, pi\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class BsmModel:\n",
+    "\n",
+    "    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):\n",
+    "        self.s = price  # Underlying asset price\n",
+    "        self.k = strike  # Option strike K\n",
+    "        self.r = interest_rate  # Continuous risk fee rate\n",
+    "        self.q = dividend_yield  # Dividend continuous rate\n",
+    "        self.T = expiry  # time to expiry (year)\n",
+    "        self.sigma = volatility  # Underlying volatility\n",
+    "        self.type = option_type # option type \"p\" put option \"c\" call option\n",
+    "\n",
+    "    def n(self, d):\n",
+    "        # cumulative probability distribution function of standard normal distribution\n",
+    "        return norm.cdf(d)\n",
+    "\n",
+    "    def dn(self, d):\n",
+    "        # the first order derivative of n(d)\n",
+    "        return norm.pdf(d)\n",
+    "\n",
+    "    def d1(self):\n",
+    "        d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
+    "        return d1\n",
+    "\n",
+    "    def d2(self):\n",
+    "        d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
+    "        return d2\n",
+    "\n",
+    "    def bsm_price(self):\n",
+    "        d1 = self.d1()\n",
+    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
+    "        if self.type == 'c':\n",
+    "            price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))\n",
+    "            return price\n",
+    "        elif self.type == 'p':\n",
+    "            price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))\n",
+    "            return price\n",
+    "        else:\n",
+    "            print \"option type can only be c or p\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.1573056407749949"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)\n",
+    "a.bsm_price()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html
new file mode 100755
index 0000000..51eabcd
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html	
@@ -0,0 +1,140 @@
+<h2>Introduction</h2>
+In the last chapter, we modeled the stock price with the Geometric Brownian motion. Then the logarithm of return \(\text{ln}(S_T/S_0)\) follows the normal distribution \(N\left[(\mu-\sigma^2/2)T,\sigma^2T\right]\). It means the logarithm of stock price\(\text{ln}(S_T)\)follows the normal distribution \(N\left[\text lnS_0+(\mu-\sigma^2/2)T,\sigma^2T\right]\). Based on this basic assumption, in this chapter, we will talk about a famous option pricing model: Black Scholes Merton Model.
+<h2>Determinants of  Option Price</h2>
+In different kinds of asset pricing model like bond pricing, <span class="s1">enterprise valuation, </span>the most commonly used valuation method is to calculate the present value of the expected cash flows of that asset. But options have some characteristics that are different from the common asset. For example, the options value depends on its underlying assets. In addition, the cash flows on options are not constant in each time period but depend on the occurrence of specific events which are not predictable. In fact, the value of the option is determined by lots of variables.
+<ol>
+ 	<li><strong>the underlying price:</strong> Change in the value of the underlying asset is the most important factor which affects the options price. For the call option, holders can earn profit from the price rising and put option holders earn profits from the price decline. Therefore, call options become more valuable as the underlying prices increase while the put options will become less valuable.</li>
+ 	<li><strong>The volatility of the underlying asset:</strong> In tuition, volatility is a measure of the degree of fluctuation of the underlying stock price. It is a forward volatility, which means it is a prediction of how much the stock price of the underlying will move in the future over a certain period of time. The higher the predicted volatility is, the higher the probability that the underlying price will move a lot. Thus the greater will the value of the option is both for calls and puts. We will further discuss the different type of volatilities in the subsequent tutorials.</li>
+ 	<li><strong>The strike price of the option:</strong> Call options gain profit when stock prices greater than the strike, it is easy to understand that the higher the strike price is for the call option, the fewer profits the holder can get, the less valuable of the call option. Thus for the put options, options with higher strike price are more valuable.</li>
+ 	<li><strong>Time to expiration: </strong>If the current date is t, the expiration date of the option contract is T. Then the time to expiration is T-t. For v=both call and put options, the longer time to expiration means there are more changes for the function of stocks prices, and higher probability for the option holders to gain profits from price movement.</li>
+ 	<li><strong><strong>The risk-free interest rate: </strong></strong>As interest rates increase, the expected return required by investors from the stock tends to increase. On the other hand, the present value of discounted cash flow will decrease. Thus the increase in interest rate will increase the call option price and will decrease the put option price.</li>
+ 	<li><strong>The dividend yield: </strong>After the dividends paid, the share price of the stock will decrease. During the options holding period, the decline in underlying price is unfavorable for call options holder. Therefore, the increase in dividend yield will decrease the price of call options and increase the price of put options.</li>
+</ol>
+<h2>Factors in the BSM model</h2>
+After we get an intuition about affecting factors of options price. Now we will introduce the BSM option pricing model. The Black-Scholes model for pricing stock options was developed by Fischer Black, Myron Scholes and Robert Merton in the early 1970’s.
+
+First, we introduce the factors in the model. For all the factors listed below, only volatility is not known.  There are many types of volatilities. Then which volatility should be used is a critical question in option pricing model. We will further discuss this part in the next few chapters.
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Factors in the model</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> Stock Price</td>
+<td>S</td>
+</tr>
+<tr>
+<td> Strike Price</td>
+<td> K</td>
+</tr>
+<tr>
+<td> Time to Expiration</td>
+<td> T-t</td>
+</tr>
+<tr>
+<td> Interest Rates</td>
+<td> r</td>
+</tr>
+<tr>
+<td> Future Volatility of the underlying Stock</td>
+<td> σ</td>
+</tr>
+</tbody>
+</table>
+<div class="page" title="Page 58">
+<div class="layoutArea">
+<div class="column">
+<pre class="prettyprint linenums">class BsmModel:
+    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):
+        self.s = price # Underlying asset price
+        self.k = strike # Option strike K
+        self.r = interest_rate # Continuous risk fee rate
+        self.q = dividend_yield # Dividend continuous rate
+        self.T = expiry # time to expiry (year)
+        self.sigma = volatility # Underlying volatility
+        self.type = option_type # option type "p" put option "c" call option
+</pre>
+There are some details we need to pay attention to about the input of BSM model. Firstly, the model works in continuous time, rather than discrete time. Therefore the risk-free rate r has to be modified to the continuous form. Secondly, the time to expiration should be converted to year. The volatility is the annual volatlity.
+<h2>Assumptions</h2>
+The market assumptions behind the Black–Scholes formula for pricing European options are as follows:
+<ol>
+ 	<li>The volatility of the underlying assets is constant over time</li>
+ 	<li>The underlying asset price follows lognormal distribution, this means that the log-returns of stock prices are normally distributed</li>
+ 	<li>The underlying asset can be traded continuously</li>
+ 	<li>The underlying stock does not pay dividends during the option's life. But the basic Black-Scholes model was later adjusted for dividends, here we demonstrate the later version with dividend yields.</li>
+ 	<li>There are no transaction costs or taxes</li>
+ 	<li>All securities are perfectly divisible, meaning that it is possible to buy any fraction of a share</li>
+ 	<li>The risk-free rate of interest, r, is constant and the same for all maturities</li>
+</ol>
+</div>
+<h2>Equations</h2>
+The derivation of the Black-Scholes model is far too complicated to present here, we only show the formula here. The basic principle is based on the idea of creating a portfolio of the underlying asset and the riskless asset with the same cash flows and hence the same cost as the option being valued. Then we get the Black–Scholes–Merton differential equation. The solutions to the differential equation are the Black-Scholes-Merton formulas for the price of European call and put options
+
+\[c = S_0N(d_1)-Ke^{-rT}N(d_2)\]
+
+\[p =Ke^{-rT}N(-d_2)- S_0N(-d_1)\]
+
+\[d_1=\frac{ln(S_0/K)+(r+\sigma^2/2)T}{\sigma\sqrt{T}}\]
+<div class="column">
+
+\[d_2=\frac{ln(S_0/K)+(r-\sigma^2/2)T}{\sigma\sqrt{T}}=d_1-\sigma\sqrt{T}\]
+<div class="page" title="Page 359">
+<div class="layoutArea">
+<div class="column">
+
+N(x) is the cumulative probability distribution function for a variable with a standard normal distribution. It can be calculated by the integral of the probability density function of standard normal distribution from 0 to x. In Python, you can use the <code>norm.pdf(x)</code> in spicy.stats library. For the following chart, we plot the probability density curve of the standard normal distribution. For example, N(-1) is the area of the left hand of the red line under the curve.
+<pre class="prettyprint linenums">import scipy.stats as sp
+mu = 0
+variance = 1
+x = np.linspace(mu-3*variance,mu+3*variance, 100)
+y = [sp.norm.pdf(i) for i in x]
+plt.plot(x,y)
+d = [-1]
+plt.plot(d*100,np.linspace(0,sp.norm.pdf(d), 100))
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-normal-dist.png" alt="normal distribution"/>
+Then in our BSM model class, we will calculate the European call and put option prices by using BSM formula.
+<pre class="prettyprint linenums">def n(self, d):
+    # cumulative probability distribution function of standard normal distribution
+    return norm.cdf(d)
+
+def dn(self, d):
+    # the first order derivative of n(d)
+    return norm.pdf(d)
+
+def d1(self):
+    d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
+    return d1
+
+def d2(self):
+    d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
+    return d2
+
+def bsm_price(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    if self.type == 'c':
+        price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))
+        return price
+    elif self.type == 'p':
+        price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))
+        return price
+    else:
+        print "option type can only be c or p"
+
+a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)
+a.bsm_price()
+</pre>
+</div>
+For a call option which expires in 90 days and no dividends paid, the underlying price is $42, the strike is $35, the risk-free rate is 0.1, the volatility is 0.2. The price of this option is $6.157.
+
+</div>
+</div>
+</div>
+</div>
+<h2 class="page" title="Page 354"> Summary</h2>
+This tutorial discussed the factors affecting the options price and introduced a famous option pricing model including the input parameters, the assumptions and the formula.
+
+</div>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html b/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html
new file mode 100755
index 0000000..1ee40a4
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html	
@@ -0,0 +1,229 @@
+<div class="page" title="Page 87">
+<div class="layoutArea">
+<div class="column">
+<h2>Introduction</h2>
+Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<div class="page" title="Page 425">
+<div class="layoutArea">
+<div class="column">
+
+Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<pre class="prettyprint linenums">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)</pre>
+<h4>Impact Factors</h4>
+Stock price, days remaining to expiration and implied volatility will impact the Delta.
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+
+In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+<pre class="prettyprint linenums">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+The color of the graph above represents delta value.
+<h3>2. Gamma</h3>
+Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+
+Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<pre class="prettyprint linenums">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+<pre class="prettyprint linenums">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+
+The color of the graph above represents delta value.
+<h3>3. Vega</h3>
+<div class="page" title="Page 438">
+<div class="layoutArea">
+<div class="column">
+
+The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+
+Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<pre class="prettyprint linenums">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+<pre class="prettyprint linenums">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+</div>
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+The color of the graph above represents Vega.
+
+<h3>4. Theta</h3>
+<div class="page" title="Page 432">
+<div class="layoutArea">
+<div class="column">
+
+Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+
+The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<pre class="prettyprint linenums">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+<pre class="prettyprint linenums">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+<h3>5. Rho</h3>
+Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<pre class="prettyprint linenums">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+<pre class="prettyprint linenums">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+<div class="page" title="Page 438">
+<div class="layoutArea">
+<div class="column">
+
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+
+As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+
+<h2> Summary</h2>
+<div class="page" title="Page 84">
+<div class="layoutArea">
+<div class="column">
+In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.ipynb b/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.ipynb
new file mode 100755
index 0000000..b99ba7d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.ipynb	
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import log\n",
+    "import scipy.stats as stats\n",
+    "import numpy as np\n",
+    "from numpy import shape\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib import cm\n",
+    "from matplotlib import animation\n",
+    "%pylab inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "class BsmModel:\n",
+    "\n",
+    "    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):\n",
+    "        self.s = price  # Underlying asset price\n",
+    "        self.k = strike  # Option strike K\n",
+    "        self.r = interest_rate  # Continuous risk fee rate\n",
+    "        self.q = dividend_yield  # Dividend continuous rate\n",
+    "        self.T = expiry  # time to expiry (year)\n",
+    "        self.sigma = volatility  # Underlying volatility\n",
+    "        self.type = option_type # option type \"p\" put option \"c\" call option\n",
+    "\n",
+    "    def n(self, d):\n",
+    "        # cumulative probability distribution function of standard normal distribution\n",
+    "        return stats.norm.cdf(d)\n",
+    "\n",
+    "    def dn(self, d):\n",
+    "        # the first order derivative of n(d)\n",
+    "        return stats.norm.pdf(d)\n",
+    "\n",
+    "    def d1(self):\n",
+    "        d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
+    "        return d1\n",
+    "\n",
+    "    def d2(self):\n",
+    "        d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))\n",
+    "        return d2\n",
+    "\n",
+    "    def bsm_price(self):\n",
+    "        d1 = self.d1()\n",
+    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
+    "        if self.type == 'c':\n",
+    "            price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))\n",
+    "            return price\n",
+    "        elif self.type == 'p':\n",
+    "            price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))\n",
+    "            return price\n",
+    "        else:\n",
+    "            print \"option type can only be c or p\"\n",
+    "\n",
+    "    ''' Greek letters for European options on an asset that provides a yield at rate q '''\n",
+    "\n",
+    "    def delta(self):\n",
+    "        d1 = self.d1()\n",
+    "        if self.type == \"c\":\n",
+    "            return exp(-self.q * self.T) * self.n(d1)\n",
+    "        elif self.type == \"p\":\n",
+    "            return exp(-self.q * self.T) * (self.n(d1)-1)\n",
+    "\n",
+    "    def gamma(self, ):\n",
+    "        d1 = self.d1()\n",
+    "        dn1 = self.dn(d1)\n",
+    "        return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))\n",
+    "\n",
+    "    def theta(self):\n",
+    "        d1 = self.d1()\n",
+    "        d2 = d1 - self.sigma * sqrt(self.T)\n",
+    "        dn1 = self.dn(d1)\n",
+    "\n",
+    "        if self.type == \"c\":\n",
+    "            theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \\\n",
+    "                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \\\n",
+    "                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)\n",
+    "            return theta\n",
+    "        elif self.type == \"p\":\n",
+    "            theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \\\n",
+    "                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \\\n",
+    "                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)\n",
+    "            return theta\n",
+    "\n",
+    "    def vega(self):\n",
+    "        d1 = self.d1()\n",
+    "        dn1 = self.dn(d1)\n",
+    "        return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)\n",
+    "\n",
+    "    def rho(self):\n",
+    "        d2 = self.d2()\n",
+    "        if self.type == \"c\":\n",
+    "            rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)\n",
+    "        elif self.type == \"p\":\n",
+    "            rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)\n",
+    "\n",
+    "        return rho"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s = np.array([range(10,70,1) for i in range(23)])\n",
+    "I = np.ones((shape(s)))\n",
+    "time = np.arange(1,12.5,0.5)/12\n",
+    "T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contracts = []\n",
+    "for i in range(shape(s)[0]):\n",
+    "    for j in range(shape(s)[1]):\n",
+    "        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))\n",
+    "theta = [x.theta() for x in contracts]  \n",
+    "gamma = [x.gamma() for x in contracts]  \n",
+    "delta = [x.delta() for x in contracts]  \n",
+    "vega = [x.vega() for x in contracts]  \n",
+    "rho = [x.rho() for x in contracts]  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = np.array(gamma).reshape(shape(s))\n",
+    "delta = np.array(delta).reshape(shape(s))\n",
+    "theta = np.array(theta).reshape(shape(s))\n",
+    "vega = np.array(vega).reshape(shape(s))\n",
+    "rho = np.array(rho).reshape(shape(s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BoinHpfeIDsZh97wrioJWq8VwN6CD7tnvVbSuV4DvVbQuHuCTVrSOiA6eCGFzP+IrdGSz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Bu823iWgxSPsu/vZBW4WCxI05SzszOyLFtbBZ4PAjwpq8D74Vk99is8y/PnPC5K0OJJ4lkq4//M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\nFwTZNl6SY1E+jNA7blsobHiY5JiiYDXeZ9/XmRd9VlQYn5dbR+5BGShyxb6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qg/OU8Id/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vazgrj2mN45GqPIufRQF/FeL8MZeHqtdSoQzolw82iMUSVJYCgrXXtn5++CW4cTRmHJtSig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\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RzzLatY83PABACE63A76f1rgzGLIx86J6z6SovKwVuU/oBgneexaYsCxyTmpLM9LEgUKI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\n5OWkRcofRxECVTi02GkSIZ1YQD9qH73rC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YubJRHUic2OxTvy+JmCyVTJZcaIg+siFI1o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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bcdff90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z = gamma\n",
+    "fig = plt.figure(figsize=(20,11))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.view_init(12,320)\n",
+    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
+    "ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)\n",
+    "ax.set_zlim3d(0, z.max())\n",
+    "ax.set_xlabel('stock price')\n",
+    "ax.set_ylabel('Time to Expiration')\n",
+    "ax.set_zlabel('gamma')\n",
+    "m = cm.ScalarMappable(cmap=cm.jet)\n",
+    "m.set_array(delta)\n",
+    "cbar = plt.colorbar(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GcTr//vTnv8V/81d/tqPz1MvifACFQkFhXwaXRStKDyBjzD+m1U5v5dv+qxX/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bIgK+SFy8/8LBIVSwTkwj6RZnFZNqhkLVKGCqRYaIxY1Up8Yg03hP6Y1Z7PnaTMBbXL9Cvl5qMw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v4M7MZ/lbv/5Ffv1zhz7kEbkbRVGwbRvbtqlUKjueS9N0hwlqq9XaDnRumaBms1mSJCEIgj0d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\nAvKagi5naEr3NSwHMUamcmLMRr9jvrfSCqmpBrsklT1DObIswwsTl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PLy8gMlAb2/ZyUJ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zKTk7GCCEQpMluuZxyLKZnrSRSnF+I0QXHgdv9eV3wpyhLZjXRudYWB/SFhl64XK4UaPR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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10a0b9510>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z = delta\n",
+    "fig = plt.figure(figsize=(20,11))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.view_init(40,290)\n",
+    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
+    "ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)\n",
+    "ax.set_zlim3d(0, z.max())\n",
+    "ax.set_xlabel('stock price')\n",
+    "ax.set_ylabel('Time to Expiration')\n",
+    "ax.set_zlabel('delta')\n",
+    "m = cm.ScalarMappable(cmap=cm.jet)\n",
+    "m.set_array(z)\n",
+    "cbar = plt.colorbar(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Aa02/35dQQAghhLgNJAAQQpwKJ6HP/yjFao//9KFt1v/0LTzw6AXSQRvX3V8ubRkGoQnd\nQBGYDulyCUNBLX+OhtcjU1sj4to4lgEa2l5AOZIjTNsUGmuHVgcA9P3hHfzcfeTKRXLlp+iFio3s\nEqnGZVJhh4htjoOAQGsabY9yfolYt87Z6vJgy8ADlRaWqcia8NVfeBeVpz7CP/349/LSv/sd/PjL\n7iclbQEnyo0GAJM8k1Bg1EYgoYAQQohruWtDAI+RU376Qoh73Unq85+k0u7z6392iXe/7T2c/+jv\n8cLmBjH76i34/FDT6vns5M6RaO0wX1smMgwIMq1Vul5IM5phN5HHbe6iwpBuKkehUiTuWpgTqgNg\neAe/H1LOL5GqbzJfWx4ODjSJArFGkZavWM0O7vAngi6+H1LOLuDYXeZqw4qCI+rttB5UIoQa7nv0\nj7j4xIf5e4+9lp/8nm/gWx4q3MKrKW6nWxEATHJYKADsCwSAcXXA3mDgJP3/LoQQQtxuEgAIIe5Z\no4X/E088wdzcHOl0+m4f0nXr+yH/7aPL/N7//jjTf/omvmb180QscB1n3/NCrWn1AnaTs5iWN57A\nv3fBbZkGcUNh+S2MSo9a5gym10VpjUYRaI15oIg/GG7pt5NbImZUmautDO7gHxgc6JgGjgmxepF6\nYFCcepBIr02+WiIZsQ6tKBjpeAE1K0knnWG6UiLu1NDtDab/50/wC49/N+/5nu/ldS9/gHzcOfJ1\nxN13uwKASfbuLLD3/WGwG8Go0kdrPXHQ4HGf+SGEEOL2UIB9ylfAp/z0hRD3ooN9/qNBYyeB1poP\nPrnDL77jMwQffJiFi58mU9vAcUz2jtfTWtPxAspOhn46PhjE50y+k+8FIc1+yG5ueBe/UcQ2FZ12\nSCOSZCeeJ11dJ648HNOg4wXsJucwbY+ZemlixcGV4xgu4t0MvWSCmc0n6TsxtnPn0LuXSEVsDOPq\nRWHPC6kZEZrpaaYqRabDFnbkynvk8fnaT7yZ9af/gu957J/wY9/zV/n2r5qWu7nH2MFdKu60a4UC\n/X5//JxRpcAoGJBQQAghxGkhAYAQ4p4x6vP3fX9fub9pmuMJ+sfZE5sNfu6P/5LSH72V+x/7I2JB\nhzDUtKMpyok8KvDJ1DawCOnYURqZBQrlFRJBHStiX/V6QahpdD12c0tEVZWZ8iUi9qAtwFCKuGMS\no0OvVqStDdbTixD4mG5IrrZO4hp38LteQF1FaKXPUKiWmA3qWDEb3+8Sr65QTUxRUyaF9vagKkFd\nmSVQyyyRr66SbxRx3MkLr5hjcr56kfwbX8dvPf6dvPM7v4+fe9XzmEq6t+yai1vruAU0R4UCvu/v\nCwWUUuMqgVEocNJahoQQQohrkQBACHHijYaEHbat394t9I6jnWaP13/gK3zwwrt48GO/z/PbO0Rt\nA2UOfkQndJdcc5VW16Njx9jOn8P0u2R3V3BNrrrDHmpNuxewm5gBO6RQGTzPckzYs/XagCIIoe/E\nUWhS1XXq2bM0o1nMfm1w9//A+qfnhzQCk0Z2iVylRL65iuNeWWApBTHbIBZUaXZ9NjILxNsVnH6H\nWn6JTGOTxfoKrjPYDeAoodYQhMx95dOoX/s83//Ua3nd//nX+dZny2yA4+ZOtgDcjL0L/tHPiesJ\nBfbOFDgJ5ymEEGICBcg2gEIIcXKN+vwnLfxHTNM8li0APS/gf32ixP+68Gec+dM38bVbTxJ3r57E\nH2po93x2c0vEujXmd54EoGO5rKVmsPw+mdoGEdsg0Jqqk6abTpKvFHFNsB1zfyAyen8/pK4d6rk5\nsrvLpPt1LBsSjRI9H7ayi0R6TdL9KjHbwA81TQ8quUWytQ0W6sXhQMCrF0MaMJUi5lpky0XKhfvA\nMJm6/DSZqIVpHr3w94czCMq5JTKqwrn6CrZpUHjzT/HLX/k+/vz7v4+ffPkDxK4xY0DcOSclAJjk\nRkOBSYMGT+q5CyGEOF0kABBCnEgH+/yP+gB+3CoAtNa85/EtfuXtj+K+78284OmPkHAMrAPb3oVa\n0+4H7MamUI6iUFkhGXUwhn3+Me2Tbg4n/EeSXE7NopUiu/UU6W4Z19kzBV1rRh3aXhDS9BXl7BKZ\n+joLtRWciMkoEre0xjZDIo0SHS/kcm4Rs93Aj6dJtXaZr60Mt/w7fBEfhppWp89OdpGY0WC+tgIK\nyok8LWUy1d0hOmHxHoSaVsdjN7dI0myyWN+/i0De0HzDx97IpeXP8fef+FH+4/d/A88/m7r5b4q4\naSc5AJhkUigA+7cU3fuc0fNc15VQQAghjivFqV8Bn/LTF0KcNIf1+R/lOAUAn1+r8VNv+yzdRy5w\n3+f+mBQezoGFv9aarhdStRK00xkKlSIJ1yKwBr37I0qpQeuDMmklCmR3VzD9HvXMGRqGRa66SpQQ\nxzRQDO+q90N2ckskmzucra0Qsa8uwx/d4URpjGAQG1imQahAhyFhEOARYgyvvVIKZQzeI9DDO/eZ\neRynz1xzbdBGMFzsz4U1mt2AUm6JQnWVlA2WoQa7GXQ9yul5Inafhcb+r9sr7po8dPkLTP1//5J/\n8eXX8H/8wN/hn3zTEtaEYYPizrnXAoDD7F3sj2itqVarbG1t8eCDD46fJ5UCQgghjhsJAIQQJ8K1\n+vyPYprmeFuwu2Wz1uUXH3mKTzz8Ts588QPEvR6+VvSCAA045mBR0A80jcCkml0gXylSaLWwo4MB\nf3ubGPwwpNUL2MmdI97aZa58CdvQWFGLdG+brhfQcBPsJgrEG5dxuk2q2QVcq81cdWV4V33y9RtX\nHiRmse2A2foaMcfEb9ZoaoON3H3kykUSFpgMZwr4Ab1AU01Mo9ImU/V14hOGCBpKkYpauI0SFSdO\n1YmTrW3QTM5gpgzONNaJOVe3QRxkmwb5oM3Xv/tX+ODm03zq6R/g5//e85mWAYF3zWkJACbZO2xw\nNHBwb1i593mTtiQ8rddNCCHuuBNYAaCU+gXgO4A+8BXgH2mtq8/09U7Y6QshTqMwDPF9nyAIrtxx\nvoEPzHezAqDdD/jvH13mLW/7AIsfejNfW75IxDbwfE3PD6jFC3QjSexuk2jtMvWZ+0g1tgZl+c7V\nC4Mw1LT6ATupOWyrz0xlGVtpTMvANK3x8+OuRTTsENm5RCU1Sz1zhmhjl1S3TMS5erAfDLb0a3sB\nlWge7dgUGuskXAtjOODPMQ1yJsRqRWpOlFIsR2F3BdMyqUVy+HaEfHWViK1QFgS+TwDjXQfUnoWO\nYxok+018ZbI98yymdy6RNUOcCdsYHhSGmlbPZzs9T8zu8qLPvYud4uf4geUf49+++pt5yX3Zm/um\niWfkNAcAMPg5tTeUnPRzalIoAIPgwLZtCQWEEEJM8gjwr7XWvlLqvwD/GvjJZ/piEgAIIY6tG+nz\nP8rdCADCUPNHX9jk1y78BYlH3sSLLn5iuJge/Ng1HYg4JsmgRn17m3J2nmZmBq01bSeJXW+hsXAt\nY1zq3/U1dTeNl4qQr63hmhrDVFiWve+6aK3peAEVJ0M3G6dQXcXtXqbrhWxn5rH8HtnWDjHHwFAK\nDXT7AVU7SS+VZqpSIu6amBO2FgSI2AaO7uJWiuzklgjsCNOXnyIVMbFiDuFw4KIxuhMahuOFjw41\nvUDTiGbwMhmmyys4Xo2qm2TNjjLbujxxNgBcqUzYic9g2SELjfXBOUQs4q11Um/6af596Yf4jld/\nNz/80iVMaQm4o0ZD806r6wlADgsFwjCk2+3ue3xUKTDaklBCASGEOJ201u/b88dPAN99M68nAYAQ\n4th5Jn3+RzEM447uAvDpYpX/9I7P03j3W3jW5/+EpBmOy/jHhovZciSHl7EpNDaIOSaBV6bnhbSi\nKXbiedxei0TjMr1Iinb+LPlykYipMUw9XhhceUlN1w+pqwiN9DT5SpHpoI41fG/HNIi1NwaL6PRZ\njNAnVd+k78RpZacp7K4wE7awo4f/atBAzwupK5dmbp5CpYgKQ3YL59DVddJmeNVowNF8gL4XULfi\ntDNTTFeKxLw6pq0IdUiuX6PTqbNSOM/s7gopR423NxxVJpSjeXTEZa5SIh61MfdsPWgZioLy+Wvv\n/3U+tvFlHlv5Yf7zq76KQsK5mW+luEGneYF6sALgeh0VCvR6PXq93vhxCQWEEOIWuDsr4IJS6tE9\nf/5trfVvP4PX+SHgD27mQCQAEEIcK9ezrd+NMk3zjlQArFY6/Px7v8xjFy7wrE++jSW/QcQxQO05\nh+EivaYiNNIF8sMBf+ZwEKCBwjYNErpLsl6kEZhcnn0IpUOS1Q0MHWAaJtaecn+t9WB2gDappBfJ\nVoosNErDyf77WYYiFbGwm+vUQ5vLs8/GbdWZ3l0m6VoTWwNGen5IIzCpZRfJV1ZZapawh7sHxOol\nKlaMUjRDoVYitme+QM8PqePQyiwyVSky0yjiuPuHD2rADkOc8iV24gUagU+uW0ajqEVz9FMJZstF\nEhELKzZ5Ua8UpKI2L3zyg6y+/it8/1f+Gf/xh/4aX7eUue7vobg5p3kx+kwDgEmuFQp0u92r5g7s\nnStwmr8PQghxTO1orV982F8qpd4PzE74q5/WWr9z+JyfBnzgzTdzIBIACCGOhZvt8z/K7W4BaPZ8\nfvvDy/zBH76P+z7yZl5ULRJzrmxdN+L5IQ0PdnOLZKqrzDfXcA5WBgB+ENL0NLvZJRLNy8yXnyYM\nAnra4HJ2EUNrsrU1YvaghL/ZD9nNnyPZuMx8bRnXMVBq8kJkvAVg7hzp2gYLO1/GCzSXs4u0WlUy\nfmO4xd8V/SCk6SkquUWytQ0W60XcAwt4xzKY1l2atdKgN79VJtXv0lE2jcwC+do6U8NjO7jrAAxm\n8ijDIOYanPEqVD0oFp6FE/jM7l4i1itj2BAGPn54ZecBY8K/EctQ5HdXeOA9v8nPbBT5B6/5Ln7g\n68/KokjcVrcyAJjkqFAgCAJ6vZ6EAkIIcS2K0a7Hx4rW+mVH/b1S6tXAtwPfqm+y504CACHEXXWr\n+vyPcrsqAIJQc+GxdX79wsfIvP9NPGt7hX4kyU58mnT9Mo4FrmmAgmYvZDu3RLy5zdl68erKgOHr\ntfo+u6mzOFaHmeoKjtKgwIo42J5HurNJxwtouim2E3lUGBBVVeaqy4PJ/ubk32p+ENLsh+zkFkm0\nypwZPh9MojbEmms0vZDV7BKZ2jopw8dQ0OqH7OaWSDe3WagVJ24bOKIUJCM2Zn2NmoqwWniIwu4y\ni+P3OnpxNG4vMCLUszPMbl+kmj1LYEewTB/LGMwr2DtTwB/+DjSUIkTR9uFydpFsu8JDl5/kvvc+\nyR9vL/P4D76Gn/3bzxoeh7gdZAbAnR+CeK1QoN/vjx+XUEAIIU4mpdTfAv4V8Ne11u2bfT0JAIQQ\nd8Wt7vM/yu2YAfAXl8r853d+js4f/x7PffwRkpbGMhReo0IvCOnZDluJaUJloEMf2+pRqAzL7A8s\n0vVo271YgdC1KNTXcU3QKhx8UN/zfMWgJ77nxrF7LaKNHer5BVRboft1orY57p2HYajQ89nJLOBa\nLeaqxeEWgPuPwTIVGdMk1ihSx6KYmMcIQxJGnfn64Gswj17A+8Fgh4Ld3BLJ5g5ntr5EOX0W1a5i\nBl2cI76+54c0sKlmFpiprjJdX8GJGqRaa+w4SVqGzWy3TMQ2UMO7rKMzCEJNs+eznZkn5rU5V76E\naw1mCLhK8Y2PXeDJ7WVevfZj/NI/+FrmM9Hr+A4LcWPCMMSy7v7HqhsJBQzD2DdTwDRNCQWEEPe2\nE7gNIPBfARd4ZPgz+hNa6x9+pi928k5fCHHi3Y4+/6PcyhaAYrnNz7/nCb7w9gs8+Om3k/Eag7L2\n4d1821LYpsL0PHq9Bm0nQaq8Sjszy+XsedrtXeLdBq5lYJuKrhdStRO00xnylRIxW6GNEEMZmJbN\nqCl/tAtAw03TT0XI19eIOxZGzCTdXKXdD9nIzmP3u+TaO0Rsg64XspucxbADpuurxGwTdciEfRgs\npLteQDM9R6TXROmQrh0j9BqD2/OHrAvGIUN2iYRZ40x1BdcEK2oT72xRwWYldoa5+hpxd0J7QWBS\nzi4xVdvg/toK7p4qA8c0mA2b1LpwKbvI/O4lEhEbpQa7ArT6AdvJOWwnZKm+NtgVIOqgtR7/5yjN\ns4uPsvknAuoGAAAgAElEQVSGf8Wr136Mf/HdL+abnz1LNBqVxc4tdNqv5e1uAbgZh4UCWmv6/b6E\nAkIIcYxprR+4la8nAYAQ4o7RWuN53m3p8z/KrQgAGl2fN/z5Jd7xtvdy34ffxItqa8QcE/beSdea\nfhBS0y6VzCy5cpF80MBKOGhvl15nm54fUk1M0XcTWK0aXiZDtrxCLmiizBC0wravbOuntabrhdSs\nOK3hLgDTjoG1Z4s+2zRIRw3irXXa/YDt1ByB5WL5PfL1DeKuhelah5Zoj7bX203MYtkB08Pt9QCa\njQrruXNkGluk6eNYVxY4QTjcli8zT9Rsc7ZeImqbaNsYv5dlKgraJ1IrsZY9R76+Scb00RqaPuzm\nlsjVL3NfdeXQ9gJDKbIuOJUVVrNLFBqbRMI+5VgBHXGYr68Td0yMPbsC7P23NdhjHRa72yT+4N/x\na5dfy2Ov/EZedlbjOA7JZJJUKkUymSQSichiRzwjxzkAmOSZhAKjNgIJBYQQ4uSSAEAIcdvdiT7/\no5im+YxbAIJQ89ZPr/GGCx8l/4E38+LSZwZ33t39Pz79IKThaXZySyRrG8zXVwaD8obnqZQiYptY\nhiJs7bLrJHC8DvRtqtkFwsoqMaUHA/iUAq3pBSGNcFAWn62UmK0sE4tcqQrYa7QTQCuaRps2uZ1L\nNNJzVONTGJ2dwVDCCV/T7oeDhbRrU2hcvZBORW0izRJ1bbOSXmR6d5m4Y9Lt++yk5rDtgLna4M77\nqLrgYM4wmg3gNkrsWglqbg5tOaRbFc5XlwfnfI32AoCYYzJbLbKVWUDbURa3niQdGYQb18NQEPM6\nvPDjf8AXKmu0f/CHeN3LztHvtKjX62xubtLtdrFtexwISCggrtdJCwAmOSwUAPYFAiChgBDihDqZ\nLQC31Ck/fSHE7XQn+/yP8kwrAD5+scy/e+tn4P2/z3O+8D6StsKJ7J/aHw4H911Oz+P0msxWV4ja\nBkrtL7Xf24sf6TU4U1vBMSBobeJrRddw2EyfxfK6pGobBIZFJb9Eur7JfG0F1zHwvAmV+KNtBY0o\n7fQUhWqJmaCJHXfI9Hdodj0uZxdwu00y/dp4CF7HC6i6GbxUjHx1dVwlMIljGuTNgEhlhd3UDNt2\nBNfrMdPcJHYgMNh7XAfPv+trOskM0W6Tvh0l3q0TiVwdTEzS8QJqZpxWJseZaonAsNjKL+HWSsSP\naGsYHUrHC9hxs/jpGIuVEkuPvo3Ht1f40a0f51e+74WcP58fP7/X69FoNGg0GmxubtLpdHBdd1+l\ngOu6stgR+9yNIYB3wt5KmpFJoYDWGsMw6PV6pNPp8aDBkx6KCCHEvUYCACHEbXGn+/yPopS6oQnl\ny7tt/st7nuAv3/EwD3zqAqEyqOQXqfk+qcZlItrDNhV+qCnH8niuQ6G2Rsw1MZyrB/y1+gGV+BSh\nYzBdXyVqKQICwMBxbFyliKNJ1Es0O322556DVorM7kUSRjgouz+4sNhTIVDPLJCrrFJolHDcK9fZ\nNBTpmEO8vUHT02xkl4g1LqMNm34qRb5SIh7UsSZsRbhXqDWdfkgtXsA0LLK7JSqFJYLruKT72gTs\nLgu1EjHboFutsJk+Q7dbIx92sc3JC6euH1LHpZZeYG53mTO6jR0x0VrjVoos584zv3uR1HAuwL5L\nBHS9gIqZoJXOMb97iWRgj8/3haufZvk3Xserd/4Vv/iPvolnzyYAcF0X13UpFArj1xqFAvV6nfX1\ndbrdLpFIZFwlkEqlTv0U/NPuXqgAuF6HhQK+7/OlL32J5z//+ePnjOYI7N194LRcJyHEMXXKNwSS\nAEAIcUvt7fOHO1/uP8n1vn+94/GGP7/EOx9+L/d/+M1X+vwBv9GkH4T0A81uYppeNElgWGQ3niBt\nG0Qdc9+e9KPe/aqdpJNOk6+WiNsG2tCEQYhtWeNp9oMqgoDdxAym43Gm/DSGUrQMh7X0GeLNbVL9\nJtbw5T0/pBkoKtklMvVNFmorE7cVHDENhWtoEvXLtFIzoBS53WXi7qAl4TBaQ9sLqDtpvEyS6coq\nccfAjBrEKstsJ2Zoe11y/mCo4V6hhk7PZzd5BssJmK+tDtoQhtczapssdLYoqwjFyBRn2lv7tujr\n+SGN0KKSmWeuUmKmvoIT3dvjD8mIxWJtmfX0WXrNHXJWMD6fnh9S0w6V1Fnmy0UWdAs75uw7Rtcy\nuL9RIvE7P8U/2/nn/MT/9Qpe9uwCkxwMBbTW+yoF1tbW6Ha7+L7PxYsX91UKiNPhNAUAkyilCMNw\nvNiHK5UCnufR7/fHP4uVUuNAYDRs8Dj8rhBCiNNAAgAhxC1xt/v8b4YfhLz1M+u84eGPUvjAG3nx\n6mev6vO3zMEe9N0Q+m6cdLmIE3p0Y0k24wVMv0+6sYlraBSK1nBLu3y5SCFsoiwIAx9rz7Z+o+3/\nym4WP+mSq6+TcO3x+0a1T6q+QjuArfQCVr9NtLFNo3CeRGuX+eHgvIPtBiNa6+H2eg719By5aol8\nY4UwhEo0xaoTo1BfJ+5Y++6ej0rm61aSXirDdKVIPGxgRa68T8wxOdvfoeYblNILzJSXSURsNMO5\nAokZlGEw11gftglc/evGMhVTuku90WY5d4653YtEbZNWoNjNLDJdW+fBenEwS+GQRoGobbLY2eSy\nk2RNGRR6VbqY7KQXOVNdZbZR2hccXHUMhmIubPCKC/+BX93d5Cuv+R5e+02L1/y3q5QiEokQiUSY\nmpoCoNVq8fTTT5NIJKjVaqyurtLr9YhGo/sqBRzHOfK1xcl02gMAgCAI9lUFHNU+4Pv+oaHA3pkC\nJ+X3iBDihJAZAKf99IUQN+u49Pk/Ux+/WObn3v4Y/T95C1/1+CMkLfZN2IfBHfpG12M7t0SsXeFM\nfXDHXSmbtO6Sa5bo+SEdw2EjPYtvOSR3VpkpXyJqGYRBgDIMHHswwG9cHWDGaKey5CslEhETM3pg\nYagUjqUwDQ3lIuXsArXCeZx+h3i/iescMnRLa3qBphkaVNNLZOvrLDZWcJzhlH0TZsIGrXqVy5kF\nYs1dsrqLYxl0vYC6EaWbnmKqUiShW1jRyb8qLEORdzSRWpGt9Fla7Sq+GyOMRpiulEi4Vw9LPEgp\nRTpiYlYusZWeRxsWU43LPFBfIeKYTNoV4CDbNJjyG5QDk4uFB5grl3ioXiTiTt5V4CBDKdJmyLe8\n/w18qFVmufyD/Lu//ax9Ox5cD6UUpmkyPT3N9PQ0MKwE6XZpNBrUajVKpRL9fn8cCowqBSQUOPnu\n1RkAN+J6QpC9C/7Rc48KBfa2D0goIIQQN08CACHEM3ac+vxv1KjP/0vveJgHH71A2m/hOvt77Ud3\n6Hdi02BrZmqlQRn73q3/lMJQw5AgO02sUyPWruJbDtupRUy/S6a2QcQ1MYG+F1APLWrpeXKVInk6\n2LHJPfij1oBychblBOQrRaKOSccPuZw6i+N1yHYrgzvsw+Pu+yFNHyrZBdKNLRbqK4PyfGXuG8xn\nKEXStYg016kHBqXMIrHKJv3MWaYqJWabJezItZvktB6E6a7XoZUsYIcBs9VVIrbCOKK9YCQINc1+\nyE5mkVS7iqFDem4M1e9d82vHX98LuJxdINeuct/mk2wU7iO1exHXdq45YDAczmjYTJ0h4fb5tk/9\nPl/cWea1u/+C13/vC8kc8r25XkopotEo0Wj0qlCgXq9TqVQoFot4nndVpYBt39x7iztLKgCurgC4\nXpNCAbgSMHueJ6GAEELcIhIACCFu2HHs879e9Y7Hb3zoEm9/+H088OE38tXVtcEE/L2L+uEd+oqV\noJVKU6iWJk7JD0cl/LEptGMwXVslZpuEjiIMe6Raq/R8TTOWYSeWx26W6SezpOqbzDeLuJEJw/24\nEjxUYnn8iENuWKYfDhfVqYhNvLtJsxuwmZkn0muS7lbxQ0UltzhoD6gVidjDhf8RAq3RGBhAEE0S\n6TaJmhr7Gh/iR20CNTtJJ51hqrzCGb9Gx9Os5c8zvXOJjHn4HdEg1LT6IduZeRJmk3P1EjHbRCtN\ntd3hUuYsC7VV4pN2GGAYjvR8ttLzJM02D9YH117FHSK1FVZyS8xWVslEjH2zGfYef9sL2I7lURGH\nB5rDLRCjNi8ufYqnfvOn+aHKT/Ir/+glLOWiR16LG7U3FJiZmRkej6bT6dBoNCiXy6ysrOD7PtFo\ndN+WhBIKHF8SADzzAOAwo98tk0KBUbvZ6HkSCgghrou0AJz20xdC3IiT3OcfhJq3fLLEGy58jKkP\nvJGvXXmMbjzLdu4cOzok2dwm4ncwlKKDTSW7SK68Qk63sA9MyR+X8FsJ2ukMhWqJhGOCpfB9D9M0\nx+X+hhESdGq0oxlMQiy/RzNewKmtohyFYzIOAbTWg+35nNRgcGClRMI1MIctCWFw5RhMwyAdM4i0\n1ql3fNbOPodIu8ZUZYVUxIJrfAjv+SHN0KKWmSdbW2exXsQyFPW+pphbYmp3hfQhi+fBlnwJWqks\n0+Uis+GVa2SbYFWW2U7O0PPa5IMWtnnlw/uoqmF7uCvAufoqUfvK7gkKRTYCdqXISu4cZ3eXSUWt\n8b+zUeiyHZ/BsUPubwwGNe7dfSHmmNzXXGMtOUW/16SgeuPhgKOdAcp2kk4my7nqCgnXxIpc+XUY\nsQ2eXb1I4nd+ih+p/AT//rUv48WLmSOv581SShGLxYjFYvtCgXa7TaPRYHd3l+XlZXzfJxaL7Wsf\nGA1cu5tkBwQJAODOXINJv3cOhgKjLQkn7T5wUn5nCSHE7XL3PzUIIY69e6HP/99/qIL1Z/+Trx71\n+ccd0E38ZgMvGAzLq8YLtJIFzH6bzPZFHEuhMIZ17mrcW98IrcGAv0qRQquF6Rj4voehFLZtD6dh\naxqdPpXUHKbpMVMfTMEP+91Bn30kzU4iT7q6TgwPBdTNKM30NLlqkULQxD6k9x6Gpe+dPpXMPI7T\nZW77STxlsZU7R7C7QtI1sMyrP4j3/ZBmYFDNLg52EKgXcW1jcJ5AJqKI1FbYSRRohT6Ffm1QSQDD\n+QAxGplppneWmdEt7NjVxxi1DWbamzSMKMXEGebqa0RsY9BOkZjFdkMWh7sCHNw2EQbhfDJqs1gv\nsp6eo9eqkDN9ekHIbjSPjjgsVlZJRO1DZwy4lsFib5fLRoxVK8uZXhkNVJVLNbvAucoKqXbr0Gts\nmwaL/TLf8Yf/ln9b2eWHf/i7+I6vnjn0+3E7KKWIx+PE43FmZ2eBK6FAvV5ne3ubixcvEgQB8Xh8\nXCVwXEKB00ZmANz6CoDrdT2hwIhpmti2LaGAEKeZbAMohBCHO8l9/iu7bf7LnzzJX779bTzwqQtk\ngwN9/kphGgzK/TNnMP0eZy4/iVLQdVy2EtNoZZBsbuN6bXwMKvkl0sOt9xzHGFREaD3eykrrQWl6\n2c3ip1zyzc1BaflwoWoqiLsWMd2mV2vSDA3Wc+cIDZPMziXONlZwDyl7ZxgsNHselcQsphUw3dwg\nZpsYERutNbFakWo0RcVNMF1fJ+4OSmE9P6Tp6cFsgOb2uEVAHRiSpxhM1j/jlal7UMwuMrV9Ec+N\n00gN5gMUDmzJN4lpKAqmj1NdpZQ/j9trY7oGZ2prxCP2Ve0Uk0Rtk4XuZS5bcb7i5LEtk8VykUTE\nwoxde2ieZSpmdZvdTpen0/Mo02KxvsZ8Y2XQfnENpqGY1n1e9e5f4Hdrlym99gf4kZcu3dXFwt5Q\nYG5uDhgsdFqtFo1G46pQYFQlkEgkbmsoIItfqQCAuxcATHJYKBCGId1ud9/jo0qB0c9xCQWEEPcy\nCQCEEBONFhWjD3Mn6a5/o+vzGx+6xDsffi/3f/jNvKi2iq1CrGFZPgBa0/FCyk6abjpOobY62Ppv\nWAqeICTb2aDrBXR8zebcQ2ggWVkn4nfQBnje4MOuaRhooNMPqFpXJvvHI/tLy/fyA007NKjmFkjW\n1rG8LtXcIv1ug0yvOhg2uOd6h6Gm7YVU49MQMSk0t4g55r6FtFKKqGPi6hatep2t9Fmi3QZuu04t\nt0iytct8vUjENlETqgP2MgxF1AxJVNfZmn02idoWC7UisesYDDi4vINS/Xosj+v3sZQi3q4Sc23M\n6/h3NCrVr5pxuok8U7urNNMz2JaBeR3DBQH8UNP0QnZzC0w1d2gkC8SCLo57fX30g3aDkJ3kDC/9\nwv/mi7+6w89Uf4Sf/dvP2tfWcLcppUgkEiQSiXEoEIbhuFJga2uLp59+Gq31VZUCt2qxJgGAXAM4\n/iHIUaFAr9ej17syfFRCASHuUTID4LSfvhDioL19/p/85Cd5yUtecmI+9ASh5m2fWeM3Lnyc/Pvf\nyNeVPjNY1LsWnucNnqQ1/SCkpl2qmVly5RWmgvpVW/8N+vwDdiM5fMth5vKXsU1F1xssCH03Rqq+\nSdzvYqiQlnKoZebIlVfI68Mn+/tBSKMXsJtfIt4qM1ddGdyJt21SrTVaXshWZgG31yTTrRCxzcFC\n2M3gp6Lkqqskog7mIcECDCb8R2yDTGWNSuEcrdQMU1tPkXYNTNs8cjK+ZtAm0MCmlppnqrpGfvcp\nWsphIzHHbHNjEE4c9vWawQDDSA7fjjFXKZGI2mgNO2aMVSvOXK882JngkPfveSE15VJPneVspcRC\no4gdMajX1vhKboml8iVSB7dM3CMYDgjcyCyQsVs8p7ZC1DZpVZa5mD3P0u4l0lF70vzF8Tl0vIBt\nJ42XS/Bgc7Cl4bkn3sUnfn2bH638OL/8919A8ojvwd1mGMY4FBgJw3BcKXAwFNhbKfBMQgFZ/A6c\n9msQBMGJG1R5rVCg2+2O//7goEEJBYQQJ9Hx/fQihLijTnqf/19cKvMf3/45eu/7Q5792XcTN0Mi\njjXeik4xWHw3fdjJLZGqbTBfL+K4+z/Aaa3p+iFVI0YznSdXXiERsTCG5f5xW5Hyduh3BxUEm+kz\n+JEkycoq0+VlEq458boFoabZ9djJLOCaLWargy391J4eeMs0SJsG8dYarX7IVnYBFXgQtShUikQs\nAytiHXkH3A9CWv2A3ewiMavBmcoySil2E3naQKG7O6gAOPB1o4V/MzSpZhYp1De5r14ctExgEtUB\nbm2Ntdx5psrLpCPmvgGBo0Vz1UnRSyWZ2V0hHXMGsxaGZnR7OOF/noVqiZi7/zh6fkhdW1TTZ5mr\nrHKmUcKJGOPnpKIWZnUwHHC+UiJz4Hs3GhC4mZghYgc8q7lGzDHGcwYSrsUD1RWWMwv4jU1ytr5q\nq8KuH1LFpZKb5/7GMul+A2s44DDmmLxk7ZN88b/9DK+p/iS/+uq/wlw6cuj34rgxDGN8539kFArU\n63U2NjZoNptorUkkEuNBg/F4/JqhgAQAAo5XC8DNuFYosPff+ygM2DtsUP5fEEIcZxIACCEO7fM3\nTZMgCI71QLFSedDn/4W3X+BZn3oYp9OglSxQdxMov0+quYMb9vCDkEpuHqfXYq66QtQ2UGr/Xei+\nH1IPDcrpJTLVEvOtVeyohR8EeGE4LgUNQk3X86nkloi2q0Tr63iWy2b2HNFujXSnQtQeDOEbTb0v\nJ2bADpkaTa0/pAdea40XaDpOHI0iUduikV+kZ0dxwi4ccu99tPAvZxaI2G3O1EqDgGH4Pmf8KvW+\nppQ7x1R5hZStMA01Xvi3AoNKdpF8bYPzoy0E98wHUApSURunXuRyYoqO16UQDib8d7yAmpWgm8oy\nW14hGTTANcaT90cMpci6gwn/xdw55nZXSEUM/FDT8A12s4vMVdd5qF7CdQ3UgXNVQMI1OV9fZTU5\ni9cukzd9DKVoewE7kSxh0uV8fTRn4OqFSNQxua+1zmo0T7/fYprBDgFeEFL3FFv5Rc6111jqFnEm\nDAiM2AbPrz5F8vf+Da9tvI6f/8d/nefMJq563klxWCjQbDZpNBqsra3RbDYBSCQS+yoFDm7NJsRx\nbwG4GUeFAkEQ0O/3x48bxiAwHlXUSCggxDEiLQCn/fSFON2uta3fcQ4Amj2f3/zzS1x4+BHu//M3\n8eLqcMEbs8j5Fbx+mb4f0g00W4Xz+JaD3aoSaVXQlsIPNZYxOGc/CKl3fXYL54k3tzlTXyHiDBb6\nfc8fb+sX6sF8gXJyFsPyma6VBuXwURt0QLpRpOMF7KTmCAybTKVEJ57FS0bI1ddIuIdPrWdYeVBX\nERrpabKVErnmKnbMJN0oUcdiNXOe/O4lUq4aT/gflbuX02dxnD6zw90G1IH3MQxFxgW3ukw5lqep\nNZnOLp6yqGSXyNU3OV/bvyPAJBHb4IxXphqYFONzxDpVuqk8s+UiC2ELOzYYRuiHk79eAcmIxVJt\nhfX0LM1WlXZqmunGZR6qFYk4R7//6BgWu1ts2Uk6WmNqTSeVYam6QjJiY11jQKBrDSb8bxlxioZL\ntl1mI7/ErFfmBd0VIq4JRxyDYxnc393kVQ//DP+y/uO87rWv5KUP5I58z5PEMAxSqRSpVIqzZ88C\ngzu7B0OB0eyBZDJJNBq9y0ctjoN7pQLgeh0WCvT7fR5//HFe8IIXjB83DGPfTAHTnFwtJoQQt9vx\n+1QvhLjtrrXwHxkFAMdJGGoufHad//rwx8l+4E183fKjJFzrqoV1GGpaZpRaZpp8eQWbgBBF37LZ\nSkyhDZtYexenVaNWWMA0O8wMe8W1UvS9K9v6wZW+9n4qSr6+Op4tMKYUtqWwTIXV3KRqp9jNn0Mp\nSFfWcCxzcs+5HmxB2AgtqtkFMtU15hslHFeNBxY6lkFBB0Qrl6jFslSdGIXaGgCV9BlMJxhM/J+w\n8D8oapvke2Xqgcna7HPJba+wVFkehCfXMdhOA14QEphRMC26sTxzO8tkYg7XOZtvsO1ioAkMm8BN\nku7UyNPDta/vzqEGwhBs3aeamiHpdXmwuUr8OnYGGDENSPXqrKbn6SbTPKe+TDJiodT1LV4sQ5Hv\n13nlB/4rv9Sucfk1r+IVD6Wv+/1PGtM0SafTpNNXznFvKLC+vk69XufRRx/dN2QwHo/fs3eExdVO\nWwAwiVIKPdwZZnQttNbjYOBgpYCEAkLcYVIBcNpPX4jTZVSu6HnedfX5H7cA4NGVCj/3js/Res/v\n81Wf/xOSlh73Z4/4QUjD02znlsjU1llolLAjJoE/ON+ECZneZZo9n1okQ6NwDkwTQ/XwvADCEMsA\n27JQStH1Q2rDeQD5apEZ38CMTBhyNb6D79LInCVXWaUQNgm1pqksVlNLpBpbJMPucPs96Aeapq+o\njGYS1FYGi2A1YcE0HOxne1WarTIbc8/G8j2mdi6SilzZveAoXqBp+ppKdol0a5v5jb+kkpql1lNY\nQRvniABgNJyvbkZppKeYrpSYaZYIQs1m5gx+q0LO8q8q+9/3vQk1rV7A5ew8qU6d++slHMugEpis\nxGdYaG0RPSIEGLUr1LVFOb3AfLXE2XqRdqC4mD7L/c1Be8VRRrMKttwsOuvw7HYRv2dyKTXPg61r\nfz3DY6hqm43sAvd1VvjBj/8Kb++UKX7f9/KymdNTCr83FCgUCoRhyPOe9zwajQaNRoNSqUSz2dzX\nZpBKpYjFYhIK3KPu5RaAG+H7/r7KucMqBY4KBUazBSQUEELcahIACHFKHNbnfxTLso5FALBW7fDz\nf/JlHrtwgYc++Vbu85tEnP0L5UGvvc/l9AJur8HZ4XT9g4vpnhdQw6WWPUu+UmQ6bBBq6PR9WpE0\nnXgeu9ckUd/Cd6LUsgtkq6ssNFexJy2ytaYfaBrapJpeIF1ZZb6xuu8OvqtDko0VWoHBWmaReGsX\nu9OgVjhHornNmdoKEcvg/2fvTYMlS9P6vt979tzXe+vuS1UvNd0zZpgZwBEytsK2kPTBNigCBcg2\niJAGgsAQxgFmiZAdSAIkjCVAYQfgCAgYRizDsAxgBhAMaPAAntEYmll6urvq3lzunuvJ/Szv6w8n\nM+vuVd3T1V3L+UX0h76VefJsN+95/u/z/P9cs/osZRRJ10nfgKTG0tEXCDSDk9IWqrlLzjEuGNrN\n8APJIIBWYZ3soMlGd5eEqYNjkByf0FEW1dQKy+4eqXMF8Kzw72k2bu4Gi+06i1PzRBCgw9r4mIaZ\noq6nWfbaWOcc/mdjCsf5NVL6kG13j6R5zwCxLCSme8jdwgabnSop+6JJoRdK+oHguLDBSveAF9zK\n1KBQwzZAb9d4tbTNze4umSu6IMa+pKkn6BdK3OztkpUmetICBYZb4wv5bZ7t3CV9RcJAKBW9SUil\ntM2Kd8i7/AqWo4GCr/3Ln+X3h01+8u9+HT/2gnykYgLfCmaCoq7r5PN58vn8/N+CIKDf7+O6LpVK\nZR4ver5T4HEucmIPhIi4AyDiQc7D/USB08SiQEzMm8xT/jUVCwAxMU84D9rufxlvdwfA0Av56Y/v\n8ou/+ofc+pOf50uau6QsHTMa3gei4xt5Ic1kCd82KXfrUQF5rpANpGIQQLO0Sa6zx1q/jpUwkFIi\nw4CUpZHVRozdKgNf0li+jUDh9BqYMri0Pd4PJX1f0SpskukdRYW8dckKvhBYho4mJJzcpV3ehlSZ\nZPeQrJhMi//Lr4mUiqEf0kmWkY4VxQA6JnoimrVPdCs00wsMlKQ8aU9Fj2hbwXz/NsgMWqy2drA0\nFRkZhiGaEBiaRkn52J069dI2i80KOVugTbsfeli4+WUW2zUW5oX/2eMzdS1y+B+N2M2usObuYWpR\nwTycFv4JY8yWu0/ilCv/qdND3tHR27vsFG+y0bxL1oli+vypieJRYYOFfoMXuhUc8+KMfjZhstmu\ncKe4zXZrh6xjzE+pF0i60uCksMb2oMpNv455Kp2AqcHhTXeXV/I3udXZmX8+TNMFJiHVzAppZ8QL\nXiSgzE0kReRr8Hde/QifGDb5H4TF//73/yPS9xnHeJK4LgXAMIxLRYFZp8Du7i7D4XAuCsyMBpPJ\n5GNT5MQpCBFSylgA4GIHwINylSgAnBEFlFKXpg/E3RcxMTEPwtPzdBIT85Qxi/XzfR94fYX/jFmh\n+P3nr8QAACAASURBVFYjpeK3/vqQH/u1vyD3+x/gfXf/gsBO0iyu0xA6ttcn02ugCRiaKXr5MuV2\nlZSlozsXvQDckUejuEli2GbZrZKwNBRifm7MUwZ/jfwazmTAaus1DE0w9iWdzBInpkO+XSOhKQxd\nMBgHtIobJEZdlrtRqsBVK/hhKOmPfVqFDRJan5VuFUMX9JXGfnaDfKdG2lBnVs7VNNKunSgRJhzK\nnXqUXGBp8xhAIQRJy8AO2pHDf2GDcqtC0hCMQ2gW1skMu6y1dzAFmLYxn09VUhJKiZpe34QhWGnv\n0EgtMAg9LH+Mm19nsV3nZvfeavtVzB3+OzUqxW0WT+7iFhcxzYANd5Z8cH1hkHFMNju71PPr+O4B\npgw4zK9T0Ho8361FRfc1xUXK1rnZ2aWSW2eld0BGjzofDkrrrI8O+RKvip24+hjStsFzvQp3chus\nd/fIO9H1P7LyTAoONyf7pGwdzbi4DwJIWTp/o/6nfOYDbf6x+738xDd8GYsZ+9pjflJ4vQWwYRgU\nCgUKhcL8Z77vz0WBRqPBcDjEMIwz4wOJROKRLLRjASAiDMO4COXN7YQ4HTc4YyYK+L4//zsGzH0E\nYlEgJibmOmIBICbmCeP1zvlfh2EY886Bt4q/qnf5wY+8RPe3fokXXvpdMnqImTQBn/LwAD+UTALJ\nQHfolDfRZIAz7KCkwgskln4v3m7ohTSSCyhDUe7WSBgC3dAJwhA5jfUTQkTxeYkFlClYmLbBCyv6\nerQMnbTfYDKUDITGQXYVGYKt91m4ottgRigVg7FHO7uKaXgsTSMAZ0Z9RV2R7FVxDYd6qkypXSVp\n69GKtVPAy6Ypd2qkLA0jYSLDEHlJp7GuzYrvCm0nTyNdItdvsNauYAqFYWj3zKiYikG6fqacV0rh\nyxBn1KVbXMcMA1ZP7pC29Qd+gFRKITSBFYw4WX6epcYOJUNivo6VcMfUWGxX2Vt8low34hm3Ttoy\nrjzH50mYOlu9OvXEAvt2gqWww7vGVRKWfrm3wvn3WzrPDvbYSS/S8Yb08kVujSpklIbxAD4Lli74\n0u7LpH7l+/mm7vfy4+//z3hmIfVA+/4482YUwKZpUiwWKRbvJSrMRAHXdTk5OWE0GmEYxrxLYJZA\n8HYX3/Hse4RSKj4PvPEOgAflfqKA53nz1wgh5oLAzGzwi3kuiIl57IlNAJ/2w4+JebJ4I3P+1/FW\njgAcuWN+9A9e488+/Jvc/otfZnPSuXKG/6SwTmLsstF8FU1E8+FjO0UzVUIJjVTvhFDojLI5iu0q\naccEU5ufH13TME2TkS/pmilGmRzFTvQ6/ZJiVQChUgxTRfTQJ9fZZ5Bb4iS3StipkzR1TP3ezH80\nrx/SSi2imYqF/uF0BfzctoXAMXVs5ZFya7QTBRqJHDqSUrtGOuhiJM4mDXDJrLEfSgbjgHZxg9Sk\nR/bkLt3yJqlhi2TCvHcfXDGnPAkkfUw6hQ0WOvvcau/ghZKD8i2WGndImYpIOogeJjUhEKdyraVS\njDxJI1kGx2S5u4/WO6CVWkAGHovhEEO//mFTTjseTlI3MCzB8407uFaGlp3DCXsPNFM/28ZBdpmk\nCLD9Dpo2ncd/wGfdIFQMJUycJJqtWPAbZCztWnPD8/swDiWBrvNf//EP853jHv/LP/rbfNlm/v5v\nfox5WDPwV4kCruvS6/U4Pj5mNBphmuaZTgHHcd7SAicWAGJO83bE514nCgRBcKUocNpTIBYFYmKe\nDmIBICbmCeCLmfO/Dl3Xz7QXPgzGfsjPfKLCBz/8MTb/+AN8eeNOtEpunn2IGXohzUSJMGuwOG0p\nn60K26ZOBo9Mr44rDbqFVcyhi9IN3PQiqncUOftrAssyIyd5X6ebW6PUqlKWg0uz45VSjPyQjpVj\nnMtQbNdI2QZ6yiLvNRn5IW4iTzORJ9+ukdQlgVR0nBIybVLs7ZO2zWsd+mef4ZpZ/ESWYnMXt7jO\nxHBIqMm1X9J+KBl4IZ3CBinNZdWtY2mgDEmivcNJbpXJuEtJTS4toOeFf26Dhc5e1Opvagg0bFPD\n6OxyUFin6B5TMGXUWSElUilkGBJKySSETmoRmbW50amTtg10x8D3fVb8Dg0cqlaB1UkL27i4D0rB\n0A9pJEoox2a9U4/OsWOQkkMagcmuXWJz0rxgLnh6GyM/5DhRRKVtnhnskbINFHAUOuyaeTaDduQd\ncQVy6jVQyW1QUF1emOxgahqHIklFy7Eedq5PSVAw8ELqiSVMS/FiWMWWGgt/+k/5oWGXb/6mr+bv\nvrh4zdV8/HmrigfTNCmVSpRKpfnPPM+bdwocHR0xHo8xTfNMp8DDFAViASDmNEEQ4DjO270bZwr+\n2f15nShwenwgFgVinljiDoCn/fBjYh5v3ow5/+vQdZ3xePymbe80Sik++rlj/tWvfZLk732A9975\nBGlLP7sCrxSTQNIRCdxcmYXOdM7/3Ep6GEpcX9EobJDtHbHWrWAZGp7bZuyFjOwMrUwZEQYkusf0\ni5vkunus92qRi/v5czaN9OtqCfq5BQrtKuWwdyZyUNMEKdsgqfqMul16ukMtv4YRehSbO2QTJvoV\nbvKz4x/5EtdIMcoVKbVrLKo+ZsokN9ynq3RqmXUWGjtkktY9h3+lpiv+Pu3SFimjx2q3hq0LQiHR\nNB1dM7GEwJ4c01Ym1cQNlvoH84i7e4X/OgvdA252d+eF/wwBJC2d9eEBJ1aakdC4EbhYhoamFENf\n0nIWCAyHpU6NhKmhGSDDABlOW8JRLDCmM/LYyayw0bsXszcr2lt2Hi+bYb0duf+fPse6JlgQPu1h\ni9eyq2z361F6wSnGfkjTzDAs5tnuVcj4xr2YRgFLakxj7PNq+ga3xkdRzOLZS83QC9lLLqIlFc96\ntUhcmq6iLcshDd/kNXuRW/7xBRFjlpLQ0JK0CiVujXbIaiba9P2LwuMffepH+flhh6Nv+Hq+8SvW\nnsgH6rd7Bt6yrAuiwGQymXsKHB4eMh6PsSzrjNGgbdtvyn6/3ccf82jxsEcAvhguEwXg7PNELArE\nxDzZPJrfTjExMdfyZs75X8fDGgH43IHLP/vNz3DyW7/Mrb/8bdJ4JOyzsUZBKOl5ikZxk3x3j41L\nYviUVPS9kJPsCqYxmprxRQ7xMgwRUpK2dVJijN3YpVnawk9kEEoySBYx3QOUqWMZ03Z2pZiEkp40\n6eTXyXfqrPVqmM7l8+fzIt7MMUxkKZ28SmjYNMu3UO0aaUNeWLVWSjGeFv6DXIlSu8aCHJ75DFPX\nKGmSRLdKM7dEP/Qpel10DQYTSae4Rkrvs9qNCu9QSJQSWKZ5RswwdI2yFmB399grbVNsVlCGhZtf\np9w9iFb8jbOF/3ksXWNJDuj4GrvpZRa7dfrJEl42yVKrRsYxoii9swc5vzeVDEmj0Do17ha3WW/d\nxdR1OlaGQb7IRqtCJnTPjjqcQhOCogW6u8drxU1utiqkbJ1JIOkKm1Zxje1elexkgHlKPDj9/rIZ\nYvSOeTm3ynODeuQHQCQenBgZ3EKO7VGNlNLRzxkVzkQIY9Tg5dQqz43uvd8PFW4A1fwWW36NVVlD\nszS0U9dAE4KCBd/4mZ/kIz/VYL/zfr7nq56ZGzk+KTyKBbBt29i2Tblcnv9sJgq4rsv+/j7j8RjH\ncc6MD1iW9bqPJe4AiDnN4xiHOHuOuEwUOO0FFIsCMY89gjgG8O3egZiYmNfHmz3nfx1vtgDQ7Hv8\n6z98jT/+8G/x3Cd+iWcGHfrpMq5ho/tjsoMTLELCIKRZ3CCh9VjtVi56AagoGq9lF/GyDiV3n5Sl\no1lRrF8QBNPcZJOBF9LM3ECYAQvtXdKOSTjoMvYlAydLI13GGXVJD1oEQqNd2iTrHrI6bYe/1Dhu\nWsR3hU0/u0ixW6ckexhJE1RI2q3QMxyq6QWK7ep8hnzsS1wtwSC/SKldpdQbRh0IlyBE1GHg+C26\nk5C9/AZaGJDSB6z29kiYOlKHcLrSJK64DwTRiES2s0dj8VkywzYb7d1olfuawv/8vjgEmJMBBzdu\ns1b/LLmUHR3v5W+A6UMi04dgUylo7lBZfBZHStaaO6x6XUxTQ0Oh1NUt5EJAztbQW7vcKW6z1KzR\nXNhkY7jP2qh65TmcMUso0Lt1Xi5scau7w0S3OcivszWsshYMLohL599ftEDv7/G57CbP93dRCnYz\naxS0Hu8Kd0jYOkoJLvt1EUDWMfjqnQ/zRz/X4ju738GP/L0Xp3GGTwaPogBwGedFgVnm+sxT4Lwo\ncLpT4DpiAYD536SYR7sD4PVwVSzhaVFgZvx4WfpAfD/ExDyaPP7fTjExTwkPa87/Ot4sAcALJL/w\nFzV+5sN/wtof/TxffvSFyD3fESz4J/hjFRn5hYrD8hah0DH9EcZkSEA0BmAZGoKofb2rOXRzURG9\nGAgMx4hc7KejEIZhMA4kbSPDOJei3I1ax5URnTNDF6R1jbQake1WGIwDDlduA4p8Y4eUkFjm5aMB\nk0DiCgs3u0ShU2O9X8e0Tz34T439HOWRdmt0nQxtJ0ey12CUX6LYrlFyq1iWuLj9c8xa/TuFDZLj\nLkrTUQg0FEHgRw9c51b9z+9rT5l08+uU23tst15lIGwOMyss9/ejWL1rPl9O2/Tbdg4/k2apXcX0\nOuwvbqO16+SdB3vAm/iSrrBwFzbYaOzSyS0jNAPLiMwFpZRRtwCnTAZnRoPTbYShIlTRsbeWNrnZ\nukMxYSAewNkfpjF9psaN5i6fW3mBzd5dXvQq2PcRD+bvn4oQG50dPlO+TZYez/l1UoaBeMClhJSl\n85/W/5BP/HrA+91v4cf/wXsppq4eE3mceFwEgPMIIbBtm4WFBRYWFoDoWGadAt1ul3q9jud5l3YK\nzIgFgOgcPG6r3g+Lx7ED4EF5EFFghq7rmKYZiwIxMY8YsQAQE/OI87Dn/K/jixUAlFL88SsNfuTX\nP43x0V/gPV/4EzKmQD+12iqmrxvo0zn/1i6OoRFKhacUndQCEzuFFvg4/Qb9wgbZzh5rvRq2o8+F\nESklhq7jSWgrey4QLIQuhmOipOT0o4mUiv7Ep5FdRbcnLDVeRROCoWaxn1kiOWqRnfRImPo8aaAX\nanQKm+S6+6z1q1j2JSLBKYQA3fdQSYNJIoc5GWATYur6te+LzP0krcI6Sd1lpbdHwtIJfUnXh3px\nixvNHTJXiBTjQNJXJt38BgvtetTqb2mATkL5WN19qoVNlpt3ySasC5uYFf4dO4c3LfwzQQ9z2uq/\n2d9jP3sDf9ihpPtXtrNHAoRBO7fGSrvGareC5WjkB/vU0wv4oy4Lmn9mpWw23iKlRIUhoVSMpeCw\ntEnJ6/LCsIIAdnJr0D+gYHGm5f4ypFIMJiHV3Bp5p88L7c+ym9si29vF0rX76TBAJGK1lcVReZ3n\nBl+gltsi8B7893ASSBrKoZVf4T/v/DF3f6/O+/vfy7/+xr/BRjHxwNt5VHlcBYDLEELgOA6O45wR\nBcbj8VwUqNVqeJ5HIpEgk8mgaVHKyNNMGIZPvQgy40npAHhQrhIFpJScnJxwfHzMrVu3gFgUiHkE\niE0AEa8zuufh5PzExMRc4K2a87+O0WjE5z//ed7znve87vfeORnwQ7/zOXZ/40M89+nfIKcmF+bh\nz87518lq8oJTeygVvXFAo7CBOe4RWA6h4eCMOqSGLQyhsE0dhUY/FDQL6+Q7e2S08MznzYQCwzAY\neCEtp0Sg6xT6x6QsHXHGZC9KHejkVxEoks19eje2yXYPyOBFRnBXXYvZyrs06OVXyXf3SGsBhqbR\n90Ka+Q3y7iE5LbhwPmZxfq3iBslxj7zXJWFFq/RBENzLeVYax5kV0sMWReFFDv/Twr+nTNzcMgvt\nPTJ6eHZfT33fD7yQw8wqpf4ReT1y+J8X/lYOz8mw1KqQsc0o4vAcXig5NrIIpVg8F9PnBZJuqNEp\nrLHS2SenBRfM8/xQcmjmMMOAJTW8ELMXFe0Bh7lVUsJneXSMrd+LQQwk1NLLlMdtykaAfsn1mCUM\nHCQWwBJsTo5JWtHD5sgPuZNcY31Qp+DoV17OUCp6nqSS32ItOKCkeViGxtgLedVZY32yPxchZmLd\n6Qf/UCpcT7Kb2WA73KNkR7GEfqjY1Yr82/d+Lz/wD/8L3r2WvXwHHhOazSbtdptnnnnm7d6Vt4yZ\nKOC6LsfHx/R6PQzDmIsCs04B07xiVOYJYzgccvfuXd75zne+3bvytvPJT36S973vfXFhS/Td0Ol0\nuHXrVtTpNf3vNLPxAcMwYlHg0eGJvQDvKwn1qb/91n+u+EX+g1LqfW/9J1/kKdc/YmIeTd7KOf/r\neCMdAN2Rz7/52F1+98O/y3N/+kG+tH9E8tws/Wz1/SS3hqP1WOlWSJx7zTz6L1kmtHQW3cidHa/H\nZBAy8UOGyQLDVBHhjQkNi+SgyXJnl6SpX5jdVwrGgcLVEwwzOQqdGmnHPNONAIAQmIYgJUB29miX\nNunnbyACH90bRg7z17Tc90Mdt7BBvrvPmjvzEYhaQXOOQaJfp6vZVFM3WGhXyDgmoVLRin9xg5TW\nZcWNjOaEpUeeBtN2Uk3T8H2fpG2wOjqkI2xqzg1K3X0805kW/jVuulVs+97nXkbK0lkf7HNs5RjJ\nkOzYpefk8TIZltoVMuG9Ff/LsHSN5bBHU1nUnAVWRycIoB9qNPMbLHX2eYdbnXYeXLx/TV1jJXA5\nJkHNyLHqRzF7UimGnuQ4tYCe0HhmtE/S0tHte0WUAgyl2BwesmeVCPwBJUYY2r3RgUkgaRgZ+vkc\n24MqKcM4Y/CXMHWeHe1xN7FE6DUomfJMJ4Gc3n/V1AppZ8w7g93IYHJ6XzmWznPeHjvmIp7f4Ybp\nn3laUkTpAlVrkWTG491alYStz8+FqQtuyhb/+C/+V35g2OHbvuG/4r+8XeZx5ml7YBdCkEgkSCQS\naJpGOp1ma2uL0WhEr9ej1WpRqVQIgoBEInEmkvBJFAXiMYizPG2/D1dxuhviuk6ByWTCeDye//t5\no8FYFIh5U3nKK+Cn/PBjYh4t3o45/+t4PQJAEEp+5T/s8xO//O9Z/5Nf4H21l0g7Brp1r+hSSjHy\nQppOkSBjseDuR14Ap14zW8nuaEl6uRLldpW0HUX/zc6PjoqK94nL2ExC6JMaNBnklhnJHIXOHo6h\nYRsamoBxIHGFQ7ewSr5dpaiGGFcUt2Eo6Xkh7cIGju6y1K1iGzrjYUg3VaJjpci3qmQSJsZ05X0S\nSPpSw81tkHMPWHOrZwr/01iGRll5JN0ajewS3cAjcLKk9C4rnWpU+NsGSkr8IEBoWlQsCHFmBd/U\nBGl/TDhsc7D4PAtHr7HdreBY1xf+pzF0jfy4TSO5wH7mOdaOvkD+PoX/aXRNUFYeeu+EndwauqZx\no3fEbbeKLkJM4/oiR9cEN9SIpuez4yyy3D+kkyjg55Ns9eukhXEh8hGiZQkhBI5psBF2ONDTHGGx\nrHrIUNENdU4KG2z2d1ibuJhXrPDbhsYt74iKWSYIeizqHroWdQccGTlG+TTPTOokTR3NuHhObUPj\nVnhMVS8Q+COW9TFq6llxohK000WeDyqkLRPtkmuia4JlMeJb/+pf8DM/2eLwH3wd/92Xrz7QuX/U\neJJGAN4Is+JXCEEymSSZTHLjxg1gKmYOh/R6PZrNJru7uwRBQDKZPGM0+Li3jD/Jc+8xbxzf968V\nvO4nCpz+bpmJAafNBp/m752YmDfK4/3XJibmCeG0gc7b1e5/GQ861/pnd1v88K//fwT/9wd51yt/\nyiiR57i0RdsbkRk0sJAIYGA4uPk1yq1d0rZxYfXdDyW9QKNR2KTQrrPer2MljPn5CcMQTdfxQ0VL\nyzDKpim5dVKWgWYLwkGdcSAZGRat7BJa4JNwj+mVNsl26ix2dkkmrij8pWIw9mnl1zDNMYvd2nQV\nPtrHpG2QUD2GvQ7ddIm2laLQrCANC7ewQbZ3yGqvOk0suP4hOJAKPwRpOIjAQ2kaiVEHxzaidv/p\n2Mdl7v4KGHkhPSNJP1um3KlTaLxCK7uAGPcoSw/jkpb908hpfGHHzuNlUyy1qtA/4aS8RaJTxdCv\nNwec4YWSvg+NwjpF94R+bpF0MMRxTHz/wYQjTQhS0mPoj9hdep5nGy9T0F2MSyL9LsPUBCuyz5G0\n2bGKeKkMq+MD3ulVsBI6ctpuKmU410+0mcGgEFi6xrbfpK5nqUsDJ/A4zK3zzKRCRvYwrkkHgKiT\nYTPscKAS7MoM+aBLLbvBzbDOpr6HaV0vpmhCULIV73/l/+RDP9ui3v4mvvtv3XrsYgKfdgHguuMX\nQpBKpUilUiwtLc1fPxMFTk5OuHv3LmEYkkwmz3QKPE6iQCwARLzO0donnpnY9Xq4ThQIwxDP8wD4\nmq/5Gj72sY+9afsa85QQxwDGAkBMzNvJbBbuUWj3v4z7PdDXWkN+5Hdf5q9//dd4/pMfJh8OsA0N\n6R0TjBR+KJlIRTO9yDhdQPM90r1jNKJiHzR0AaGC/iTgpLhJctCKov8sDSG0M7F+Uuh0lEMnt0ih\nXY2i95x7haKua6R0DTsM0ZtVWqVNJk4mcvU2ExjjCWEo0fWz4whDL6SVWUJYch4pKC5beRaClKVj\n+F16wy6NpefRA49ya5esY6Bp1/9F8QPJwJe0CxuktA4rbhXH1Bl3ujQyS4z8EblxG8eK5iDPLFsr\nxcgP6WppRtlFyu0ai/0ali1AGDhek5awqVsllsaNSyPmlIra0ttOgSCT4EarRibhYkyd6I32LtXC\nFivNHXKXmAPO8ELJwFecFDYo9k941q3iGDqD9i67xW02mjskHuCP69ifJjoUFtjsVtCaXfYLmyR7\nNXTrwUSIcOoV0Cksk1ZDTDWkqPlzf4V7bf36/CTMRYEwRKGQSpDz2uwsPEuWPi/6uySdB3860DVB\nwe9zx16nl87zznCXTNLgstGHS49BKnxh8J7Wn1P/tRO+s/vt/IuvfiEaeXlMeNoFgNfb/n6VKDAY\nDC6IAqlUat4pkE6nH1lRIB4BiIjPw1nu1wHwoJwXBcIwpNPpfNHbjYl5Gnk0/4rExDwFPCpz/m+E\nwSTgpz++y4d+9Q945k8+wHu70Xy+mBadGmBoUYHXya9hewNKJ6+gCfBDRT9ZYOjkEEriDNpMrCTC\nVCxM2/2Fqc+FEaUUaDrdUKNZWCfb3We1V8O6pEALpaI38mgV1nG0PstuDcfU8Ps9Rl5AJ7tCSzPI\nd+rY06SBTmqB0DYo9PajToKrVnyn5oD9ADqFDTK9Y1bbd/BDSSu/xmgyIO+5Uy+Ds4W7FyoGgaJT\n2CA9bLHiVqMIvml3gWMIFgYHDLDYL25zo10hbWvzhISRH+IaGUbZAqXGDkuMpzGC9+4ZU9dYkB7d\nfoNqYZ2l1g7ZhAVCoKRi6Ie0nCIyY3OjXSedMOeF/4yMY6J3q+wX1gm7hxTssw77XigZBIKTwgaF\nXoNbbpWEoSOmq35p22CzU6Fa2GSlVaFwhV3CJJC4yqRVXGejW2VjXMNKGtFsv1vj1dwmz7iVSIi5\n4h6USjHwQvbTSzgJn9thNPbRnmi8llji1uTwgvEgANOYQQClaQy8kFp6hYTu8eLoFXp6gqq1wObk\nKEoI0Kavv6K4nfiSYy1FN1fgtrfLZGLyWnKN2/5e5BlwDUrBwAu466yxaLu8T6/wHneXT3y0wTd3\nv5sf+/r3Uk4/HjGBsQDwxRd9QgjS6TTpdJrl5eX5dofDIa7rcnR0xGuvvYZSai4KzP57FFbe4w6A\niPg8nOVhJSJ0u12y2cfbPDUm5u0iFgBiYt5iHrU5/9eDlIqPvHTAj3/4zyn8/s/z5bufJO2YaPbZ\nGLeRH9K28ni5xL05/1Ovyckeg06HrplmkCpgD7uMUwVO9FUm/ROscIwuFKZhMAwkJ9k1EqMOS52L\nZoEwNWybhDSTC4iMYrEXGcfNvAUsQ2AZFs7ggACNvuHQLG2CDCkcv0rGMc50EpxnvnJf2iLdO2a5\nW432wzBIAMnRIb0A9gqbFDt1MrrE1MS08I8Eg/Sgwcr0fbPCf3YvoBSOaeAIhdOpcpxbYzRskfCH\n9J0841yexVaVZTlAWeJCesAMTRMUHA2zW+WgsInf2ccSAZ3kAso2udGpk3IuFv6nSVo664MDDlMl\n/EmfsuahlKLvK04K6xSGbW51KpGAcclDbtLS2erVqeVXUb1jCpaaF9xeIOlJjUZhg/X+HquDCraj\nMTMbFkDW1tnq7PBaYZtbnWhU5DJzvWO7gF+w2R7vkZpGSgEUTYU2POKV1CrPjuqXdkJAlHZwbOQY\nFtLc9OqkdB3NMEkpn6bX4Y69yi1vHwuFrxSo6e/qVBAIFbi+oJrb4mZQZUtEBpGJMMAc7fOZ5CYv\neJGIcRmTQHJEGjeT40VVI2UbiOl9/Z9MXqL48e/nW9zv4V9+w1fyzELqyuv1qBALAPKhFDkzc8F0\nOn3ms2adAudFgdn4QDqdfsuL0LjwjXjaIgDvx5vVAXCeVqtFqVR607cb8xQQxwA+7YcfE/PW8ajO\n+T8ISile2nP557/xV7i//Yu8+NJHyRjyglmcF0i60qSTW6PcrnIjdC/O+QcSNxQ0C1sU2zVKgygC\nLuj3mfghEwntzBK+nULJEG3cp9CqXjAUnO3XyAtpWzm8TJJid++CIHHqxfhS0XcyDJIFCsevAIJO\neYvRyCU36ZA0T8UBMo3l86Yt+4MmyzMB4txDrqFrFDRFsluha6ToJApk2nX65Q0y/cY85WBudqgU\nQRhGRYOuo53aXtLUKXXqtLLLdArrLO2/zErYw5z21E9HH69GCBKmTrld46R8E1NJVhs7ZBIG+gOa\n+9mGxorX4kRLUBNZ/GQmKvy7tWnnwvUP+Y6psdqrc5ReJhh1yeMxDAXHpU1Whke8c1TFsS9PB4Co\nE+Fmt8Kd3CbbrR2y0/0e+yEtPUm3WGJ7sEtGmRjnPB00AQVboA32eDm7yXO9yplWei+UdKTJ0bXi\n2gAAIABJREFUYW6Nm5MK2+rsnL8mBCVToo+O+EJqnefHdRKnBBspJb1JSDW1RsHu8c7gDo6hTUUO\ngUCQtzWeGe7ymdQ2L4x3yJzafhQLqNhJb/KsqHHLGGDoZ+9X29B4V1gn++l/wncPvovv+e+/iv94\nu3Df6/Z2EgsAb13bt6Zp85X/058/GAxwXZeDgwP6/T5KKdLp9Hx8IJVKPdQCPW59j4iFkLM8LEGk\n3W5TLBbf9O3GxDwNxAJATMxD5lGf878fXQ++9YN/yWd/53d4/s9/mW2/O3Wav3cMkXO+5KS4Sc49\nZOOSFv2oPd/npLxNut9grVuJIuKEhpISFQbYukDTBJNwjB+a5Fo1AjtNq7hJJ5iQdQ9xDA3L0PBD\nSRebfm6FUrvGouxd7uw/TRVwhY1bWqfQqrIqB5iJ6Osv068zmISc5FcxAo/8sIGlC0YBtArrpESb\nZbd2aeF/HiEEhj9Cpcv0imsk+01y4RDnVAF62tPANM150TTzIuikygQJh4V2DboHNBZuYjTvkkuY\n9y2wQqkYTHxamWU0E1ZPXmNsp+lkFkl6TfT7mAPO8Kcz/oN8keRkgBNMKMvBmeO4H4YmWBoec2gV\naaRXWRke8+JgF8fUEQ8wG5+ydG71quwUNlnt1AlMi5PiBluDXTaC+rVJBQLI2Rqb3QpfyG3ynFvB\nMTV6vqKa32DFP+Rdsop9hVHBPRGhzufTm9weRiLCJJAc6jmG+TTPB3VSpg4YKCWRoZx7CwRBQMoU\n3B7s8PnkFs+NK+RsjaEvqZqLJLIB7xGzWMHLr4mhC7Zkh2//3D/lX/1Ui6/7uq/m7717+UFO/dvC\n02589nYLIFeJAv1+n16vx97eHv1+H4B0On2mU+DN+nsUhuETGW/4eok7AM7ysASRZrMZCwAxb4y4\nA+BpP/yYmIfLrNgLw3C+4v+4rJJN/JCf/bMqP/ehz7Lx/3yIjXGfoZNFIkgPmpiGhqEJvFDSSC9j\n6mNWuhWSp7LSYWps5YU0UosII2SpM12R1fVoJXw65y+FRg+bdn6ZQqvKetiPWtXVhOKgziSQjA2L\no8wNwjBEGRa5do21aVLABZRiEkr6oUE7v06uU+dG6w5J2zpTdBm6Ri6pkR4d0h8HNHMr+FaS1KDB\njW71zCjBpSgVueFLHTe/TqZ3zEqngqkLulKnnt1gsbVLytaR00jF04V/OC382+lFcAwWunskLR0j\naYJSWN1djvOreIMWRc2/dBeCUDKcBDRzq5hGwHL/iKSlo6dspPTojDx2M8us9/evnUuPCn84ya+R\nH7a56dZwDI3ORLCbWWbzPu+f74+MRh+OSxuUJx304TFK1zCE9kDGfjNMTXCju8fdlXew3q/wTm8X\nO/Fg2xBA1tHZ7u7yhdw2jpyQTox4IayQsPV5u/2V7xeQc3S2+7t8Ln2T9X6Vg/wWz/pVsqKHcarL\nRAgdNNCUIgwDdF1HKYmtw3ODu9xJbFL0WnQzZV4Id8mY9zeMhGnMojHh/Xd/gl/6YIda++v49r+5\ndcaX4VHicfluexg8iqvfmqaRzWbJZrOsrkbxkmEYzjsFZqLAzHvgdKfAGzmWeOU7Ij4PF3kY3w3x\nCEBMzBsnFgBiYh4Cj/Ocv1KKf/fyCf/bhz9J4qMf4Mte+TgZx0BMDfz8UDIxbTrJEhM7hdQNCkev\nkSBAMzSkilZQUYqxL2kbaQbZHOVONOes2UbkxB6GBGEIQqMfCprFTTK9I1bdCrZ9ykhPCExdIADf\n85GaTrp3gqYkveI6I29IrneEYxlYemTW5geSng/t4gZp95AVN+o28P3Lr0EQSvqTgHZpC3vcJdPd\nxy2u4wqBdpmxX3SimASSAQbd3Do59zBKLzDudQoUNYnTqdDIrTAYdSmoIc60nTyUioEX0M6soFuK\nxf4BSctAPx1/JwRJy2B1eERTT7Knpyh5J1in9nswCWnmV3GMMau9A5KWfsbIUNMEBUuhd/fYLWyx\n1rpL5lzb/BlX/0GTW24tWqmfCh8FEzT3gJ38BltTUeTS8ygjseeouEHe6vGOfpWEFUXxnYQONTPP\nut/G1K8vLgKpGPiSemGDst/inZ3PsZdcojcZY+kPttI6i0tsJErYYkKQsFno75FImg8sQsxWtXV8\nauUtXhy8Qv6+YxQzoU8HoZAhKF2jn8izER7gGBAGASFM/QQio8GLkVeR18Edc4VydsB3tH6K3/xI\nje9qfhs/+N/cfiAh5q3k7V4Bf7t5FAWAy9B1fS4KzAjDcN4pUKvVGAwGCCHOmAw+iCjwuJyDh03c\nAfDW0G63KZfLb/duxDyuPOW/ok/54cfEvLk8znP+AF846vNDv/UZ9j7yKzz/6Y+QFT7CEugChKah\na9GM9EQKfMuh3LiLKWRkiJYuMbYziDAkNWwh/And4gaFdo11NZjnuqtpVwRCMAkFJ7lVrEmfpZlB\nnrg4OtAf+zTz69haj+VuFceMVoEL/RpjX9JL5GkkiyQHDYyBS7e8RXLQYGn6WsTlxVIYSnrjgHZx\nA9sYsNiNClYtZZEdHtD3FfuFDbK9I7JqMneVnwSSvjJx8+vk3UPWZoX/uc9RUmIKyVJ/n55wqOc2\nWWjtAtDOr2GaPkvzov3q1lnT0FiUI7rDAfv5LRZbu6DpNPNrpPQ+671oZf6qBAMhBDnHQO9UqBW3\nWWruUkgY+FIx8KFRWKfQb/DMNM7vfMeDEJC3NbR2lbulLbZaO6RPrYAH0y6Go/w6OWfI8/0appBY\nVnRMuhAsqjGNSUjFKbPhNbAuEQGiSL+QvfwaGTXk9ji6HkI3uOkds2stIP0OC2Zw5Sq4ml6flnBo\n5hfZHlXIKI3JQPJqaotbg93pOMWVpxulIpPAA7NImDV5h6wjxvBKcotnhhXyyfu3OY/8kD2jiMga\nvFurYmqCKjl8bNb0yPdCKomSChmEKAWI6FqFStCUDsfJVd6lV0jZOpqw+Nrh7/Hxjx3y/tZ38a+/\n/r0sZOz77sdbhVLqqS7+Hufj13WdXC5HLpeb/ywMQ3q93pWiQDabJZlMnjnmeOU7IhYA7jEbe3wY\ntNttnn/++Yey7ZiYJ534Gyom5k1CSsnh4SGO41x4MHrUaQ89/s3H7vJ7v/LbPP+Jf8t7ho25237g\nhyiiGfXeJOCksElm0GC9O3Vwnxa9+bCL12vTHwe0SlsY4z5CSjr5Vfx+g8RoiC4UhoAAjZZTxE+Y\nlNw9kraOdq7olCoqKlvJBZQJ5d5eFAt36nWGLkjrGk7Yx210aJc2CZMFtNDHGvfRLzMDBEIp6Y+D\naRE+ZsGtR0X4qdcbukZOUyR6NXrKZC+3Sa5ZQeoWg/wyue7B1YX/zN2fe+3+WugjGrscrryDxNDl\nRqdO2r4mdvAcmoCUKQiaVfaX30Gue8TGdL/FFcd5BiFI2zprbpWD3ArjQZNBbpli/4RbbjVa8b/P\nw3vW0dFaO+wWb7LRvEvS0iNH/twqqYTHc8M6yanZoe/Lc/svKGs+rVGHneQiW+PjuaAyu9YH6SWM\npOKZ8R5JSztzT1i6xpZ/QtUqI32XRcNHP/dg6YcKNxDUC5usj/d4MajOvSiSls5zoyqvpjbZHlbJ\nT7tazuMFkpa0OM6s8ay3S0bX0aer7be9Gq8k1tgaHVC4YhQhkIq2p7GXWecdapesraFP2/23pMt+\nkOC1MM9N1Y7SHDSA6N+lVPQnAa8mNtkyjni3voOpCZSUSCFImBp/M3yJhU9/H9/c+15++L/9Sm4v\npS/Zi7eeuAPg4RU6bwe6rpPP58nn8/OfBUFAv9/HdV0qlQqDwQBd1+eiwHg8fqz+7j0sYi+Eezys\nBACIBIB4BCAm5o0RCwAxMV8kp+f8m80m+XyeVOrRj+2CqIX8lz+1x0/96sdZ/qOf5yv2PxutNp4q\nvGaxa43UDYQhowg862yxqKYFXCO5iLBCltxa1Po96uIFiokf0E8WGSXzoEJCYZA/uUvR1rFt/cyD\ncxQjKOmYWcaZFIVOnbRjXlrMy2kbfTOzgrB8St1oZn0cSHqpIu1kjlx7j5QWYk/HE7ojn05+Dd2Y\nUJ7GBV6aGkC0GmvpkPI9ws4B7YVbGIFHubFDNmFc6FY44+5vGGhC4IeKQSBpF9ZJ6S5rR59naGVo\nZZYwh8eXjxec2+YkkAxCjXZ+naw6ZuP4C7TSNxgEFo4KeSBvv+l2hlJHGjajZIli75hFzcN4He3k\nadtgvbVDrbiJ5Xskk5JnhnskzYsiznk0ISgZIVq/wZ3sMjcH+4QKTpwiXjHB1rBOUtPR7cu3Y+ka\nm16DmlFAhiOWtAm6JqIuEU9Sza1TDtu86O3i2DrnUwYSps5zozp3EmvI8UFk9Dc9d6FU9DzJbnaT\nNXnEl1CNvCVOnduEpXPbP+A1axF/3GTBlvNOBIWiPwnZSW6wZLZ4r1nBNs4eh64JVo0RDW/C57Ql\nbvuHUYcKMPYl+2SZZJK8R596TwhrbiI66y4SSvECVb7j5e/j+3/yf+Jb//5X8bdeWHrg6/ewiAWA\nJ7/93TCMS0WBWadAv9/ns5/9LKZpzrsEMpkMyWTyqbo34g6AezzMc9FqtWITwJg3hmCmuz+1xN9Q\nMTFvkMvm/E3TnP//o86fvtbkBz70Kaw/+CXe/fk/JGOKM3FoM/f8tkjTzxVYaFcvRuzNXqOnGOQK\nlFuVM68RUmIQopka0hswTBXJtvfQZUAvv0zXSmKPuqQHDWxTRyno6Q5udoFiu0r5Cmf/mbFgK7VA\nYBsUegekThXyaV0jpXqMOl0Gus1edo1E9wgvtYCmQkru9YX/7DPGvqSnOwzyS+Q7ddbad/BDyUlx\nA693TF74mIYWeRpISRCG6JqGZZrRXH0AncI6mWGLtW41itBzTNJyRLc/oFbcZKm5M/VYuOgxMA4k\nfWXQza5RdA/ZcqtoKsROWCQmDU7MNAdKcSMYRPtx+YEwCSQ9ZdDJrrHgHvCMW8HQBMdGhiNpcENG\nLen3Q0rFwA85ySyTCiZgmiy6dVKOcXE1XEAkH4lzPxYUTIXqHPDywja2CNnqV0j7+hljvaswdY2N\nsMOeyLAnBQVvwH5qCSMheS6oRVGOV4x8QBRR+Mxkn7v2EqF3QsmUjH1JzVnEyYa8S1UigesKk0Db\n1Hg2OGbXKBB6A25YPn6oOBIZRrkUL1Ah41hXvl/TBAu2wpwc8ZK1zguTCr7SuJPc5B16jYLdwzhl\nEHh6jGjWXq2UYkP2+J9r/5z/4wNN/uxLv4Kvvp0ml8vNC6+3ugCJBYAnXwC4DMMwKBQKFAoF2u02\nt2/fRtO0eadAo9FgOBxiGMYZT4EnWRSIRyHu8bA7AGIPgJiYN0YsAMTEvE6um/PXdf2RFwBqrSH/\n8ndf5jO/8WvcePX/xek3kUJn5AeYUmHqGlIpej40ipsUWlXW+gPsc4W4H0hcqdHKb1FsVSip4bxY\nn835SwTjQHBS3CQ5bLHS3cWxNYQwyPsN/JFkEkoGTo5GqkxgmGQauyx2qiQd/cKct1KKkRfSdgqM\nssmoO8A20C9poxdCYBsaoTdi6I3wnAzSsLB7J+iauHLRfdaB0NMTDPOLUeHv7mJNvQQcU8fp79EV\nFtXUCgvtKo4RrW6bpokXSDqeoJvfoNA7Zn3qbYB1yjVei7LirfYuR/l1vN4xBT1A17V7woNm08ve\noNzeY7tXxZ76I3heiFIKy9C4IQe0Ap2aU2Jl0sA5vZI/FRB6yqSbXeNGZ49np9sBHQHckH0aOOzp\nOVaCzpXmfKcLfysB2/0DkqaOP1JUMiuowSEFSzyQud44kHQ0h87CDdZ7FRr5VWxdPJAAMUPXBGWv\nSyW5TCO3xPODO2QeoANhhm1o3PIO2TEX6CgfL5fmWa9CWn8wd37L0NgOO9S0NDueSS9T5nlRJW/1\nQGrXNnTAzFNBcHO0y6fTL7CmH/Nl5jQW8AHOYtSVI6lZ63zH8Kf5yF/u8UHjH/JtX27SaDTY2dkh\nDENSqdR8FTaTyTzUouRpFwCe9uOHe4XvaVFghu/7806Bk5MTRqPRXBSY3aOJROKJOIdxB8A9Hua5\n6HQ6cQdAzBsjjgF82g8/Jub1IaXE9/35vOf5FR/DMJhMJm/T3l3PYBLwUx/f5Vc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ntpPp8nn8+P/y0Mw3Ec4ebmJr1eD03TxsVWLpfDsqz3lRSYGL892iMA7wZCCNLpNOl0mrm5uXEa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Yk1uVfN8Xa68FRdOkyd+IcKd/sZruZLS9/rTufgXjnHW+kddFXjw2qOzXyFRHBRq34SEre4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\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TaJpEeA0Mn+R+cZFrS3uEvMZ8trxSNLqCWi/KWnZwQfEveVhcmJv8n7W2Yj1MLDJhITzG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/zlL/n1r3/NsmXL8ro9CwuLAkKd6B04MSRJ+kfgB8Bs4PeSJL0jhLgI2AosB26TJOm2999+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\nhBBCCCFEB/D/Ac5v+W5NxDg+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1041f4790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z = theta\n",
+    "# facecolors aren't normalizing as might be expected\n",
+    "# we need to normalize it to avoid all dark color for value under 0\n",
+    "norm = matplotlib.colors.Normalize() \n",
+    "fig = plt.figure(figsize=(20,11))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.view_init(35,300)\n",
+    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
+    "ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)\n",
+    "ax.set_zlim3d(z.min(), z.max())\n",
+    "ax.set_xlabel('stock price')\n",
+    "ax.set_ylabel('Time to Expiration')\n",
+    "ax.set_zlabel('rho')\n",
+    "m = cm.ScalarMappable(cmap=cm.jet)\n",
+    "m.set_array(z)\n",
+    "cbar = plt.colorbar(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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18o1Go63FPpfLwbbtgbfYE/WbDs/baIv/3NxceLtq8XccB/fu3Wtr8Y+u62eLfzt1IdA0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\nf8brs6/zzvw0PjPYXBCjyGNL6DxeQIuACDrVJNKHQGFyN6in12QYNDm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EexOPmdaYSKNz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AgrQa1cSoMoHCoCjIY7Mh8FoplOGh4/tiSejUHM8hUaRx3pEkycbW4pxHxA7HqlEb0h+e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\ngW0DjFJEdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108c51d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "z = vega\n",
+    "norm = matplotlib.colors.Normalize() \n",
+    "fig = plt.figure(figsize=(20,11))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.view_init(20,45)\n",
+    "ax.plot_wireframe(s, T, z, rstride=1, cstride=1)\n",
+    "ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)\n",
+    "ax.set_zlim3d(z.min(), z.max())\n",
+    "ax.set_xlabel('stock price')\n",
+    "ax.set_ylabel('Time to Expiration')\n",
+    "ax.set_zlabel('vega')\n",
+    "m = cm.ScalarMappable(cmap=cm.jet)\n",
+    "m.set_array(z)\n",
+    "cbar = plt.colorbar(m)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html
new file mode 100755
index 0000000..299fc4f
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html	
@@ -0,0 +1,191 @@
+<h2>Introduction</h2>
+The change of volatility can have a significant impact on any options trading strategies performance. In addition to the Vega we explained in Greeks letter chapter, this part of the volatility tutorial will discuss the concept of volatility, specifically, we discuss realized and implied volatility, their meanings, measurements, uses, and limitations.
+
+<h2>Historical Volatility</h2>
+
+<h3>1. Definition</h3>
+It is a measurement of how much the price of the asset has changed on average during the certain period of time. In common, volatility is said to be the standard deviation of the return of assets price.
+<h3>2. Calculation</h3>
+Here we discuss how to estimate the historical volatility of the option written on specific underlying asset empirically.
+
+\[r_i=ln(\frac{S_i}{S_{i-1}})\quad for\ i=0,1,2,3,...,n\]
+
+Where (n+1) is the number of observations, \(S_i\) is the stock price at end of it time interval
+
+The  standard deviation of the \(r_i\) is given by
+\[std=\sqrt{\frac{1}{n-1}\sum_{i=1}^n(r_i-\overline{r})^2}\]
+where \(\overline{r}\) is the mean of \(r_i\)
+
+If we assume there are n trading days per year. Then the estimate of historical volatility per annum is
+
+\[std\times\sqrt{n}\]
+<pre class="prettyprint linenums">import pandas as pd
+from numpy import sqrt,mean,log,diff
+import quandl
+quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
+goog_table = quandl.get('WIKI/GOOG')
+# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016
+close = goog_table['2016-01':'2016-08']['Adj. Close']
+r = diff(log(close))
+r_mean = mean(r)
+diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]
+std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))
+vol = std*sqrt(252)</pre>
+An asset has a historical volatility based on its past performance as described above, investors can gain insight on the fluctuations of the underlying price during the past period of time. But it does not tell us anything about the volatility in the market now and in the future. So here we introduce the implied volatility.
+<h2><strong>Implied Volatility</strong></h2>
+In contrast to historical volatility, the implied volatility looks ahead. It is often interpreted as the market’s expectation for the future volatility of a stock and is implied by the price of the stock’s options. Here implied volatility means it is not observable in the market but can be derived from the price of an option.
+<h3>1. Definition</h3>
+
+We use volatility as an input parameter in option pricing model. If we take a look at the BSM pricing, the theoretical price or the fair value of an option is P, where P is a function of historical volatility σ, stock  price S, strike price K, risk-free rate r and the time to expiration T.  That is \(P=f(\sigma,S,K,r,T)\). But the market price of options is not always the same with the theoretical price. Now in contrast, if we are given the market’s prices of calls and puts written on some asset and also the value of S, K, r, T. For each asset we can solve a new volatility that corresponds to the price of each option – the implied volatility. Then the implied volatility is \(\IV=f^{-1}(P,S,K,r,T)\).
+<h3>2. Calculation</h3>
+Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
+<pre class="prettyprint linenums">def bsm_price(option_type, sigma, s, k, r, T, q):
+    # calculate the bsm price of European call and put options
+    sigma = float(sigma)
+    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))
+    d2 = d1 - sigma * np.sqrt(T)
+    if option_type == 'c':
+        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))
+        return price
+    elif option_type == 'p':
+        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))
+        return price
+    else:
+        print('No such option type %s') %option_type
+def implied_vol(option_type, option_price, s, k, r, T, q):
+    # apply bisection method to get the implied volatility by solving the BSM function
+    precision = 0.00001
+    upper_vol = 500.0
+    max_vol = 500.0
+    min_vol = 0.0001
+    lower_vol = 0.0001
+    iteration = 0
+
+    while 1:
+        iteration +=1
+        mid_vol = (upper_vol + lower_vol)/2.0
+        price = bsm_price(option_type, mid_vol, s, k, r, T, q)
+        if option_type == 'c':
+
+            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)
+            if (lower_price - option_price) * (price - option_price) &gt; 0:
+                lower_vol = mid_vol
+            else:
+                upper_vol = mid_vol
+            if abs(price - option_price) &lt; precision: break if mid_vol &gt; max_vol - 5 :
+                mid_vol = 0.000001
+                break
+
+        elif option_type == 'p':
+            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)
+
+            if (upper_price - option_price) * (price - option_price) &gt; 0:
+                upper_vol = mid_vol
+            else:
+                lower_vol = mid_vol
+            if abs(price - option_price) &lt; precision: break if iteration &gt; 50: break
+
+    return mid_vol
+implied_vol('c', 0.3, 3, 3, 0.032, 30.0/365, 0.01)
+</pre>
+From the result above, the implied volatility of European call option (with premium c=0.3, S=3, K=3, r=0.032, T =30 days, d=0.01) is 0.87.
+<h3>3. Factors Affecting Implied Volatility</h3>
+According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
+
+<h2>Volatility Skew</h2>
+For European options of the same maturity and the same underlying assets, the implied volatilities vary with the strikes. For a series of put options or call options, if we plot these implied volatilities for a series of options which have the same expiration date and the same underlying with the x axis being the different strikes, we would get a convex curve. The shape of this curve is like people's smiling, it is being called the volatility. The shape of volatility smile depends on the assets and the market conditions.
+
+Here we give an example how to plot the volatility smile by using the real time options data of SPDR S&amp;P 500 ETF(NYSEARCA: SPY).
+<pre class="prettyprint linenums"># download option data for all expiry months from Yahoo Finance
+# provide a formatted DataFrame with a hierarchical index
+opt = Options('spy', 'yahoo')
+opt.expiry_dates  # list all the available expiration dates
+def IV_plot(opt,option_type,expiry_index):
+    expiry = opt.expiry_dates[expiry_index]
+    if option_type == 'c':
+        data = opt.get_call_data(expiry=expiry)
+    elif option_type == 'p':
+        data = opt.get_put_data(expiry=expiry)
+    r = 0.01 # risk free rate
+    d = 0.01 # continuous devidend yield
+    s = opt.underlying_price # data_call['Underlying_Price']  undelying price
+    expiry = data.index.get_level_values('Expiry')[0] # get the expiry
+    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date
+    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
+    premium = (data['Ask'] + data['Bid'])/2 # option premium
+    strike = list(data.index.get_level_values('Strike')) # get the strike price
+    IV = []
+    for i in range(len(data)):
+        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))
+
+    plt.figure(figsize=(16, 7))
+    a = plt.scatter(strike,IV, c='r', label="IV by solving BSM")
+    b = plt.scatter(strike,data['IV'],c = 'b', label="IV from Yahoo Finance")
+    plt.grid()
+    plt.xlabel('strike')
+    if option_type == 'c':
+        plt.ylabel('Implied Volatility for call option')
+        plt.legend((a,b), ("IV(call) by solving BSM", "IV(call) from Yahoo Finance"))
+    elif option_type == 'p':
+        plt.ylabel('Implied Volatility for put options')
+        plt.legend((a,b), ("IV(put) by solving BSM", "IV(put) from Yahoo Finance"))
+
+    return strike,IV
+k_call, IV_call = IV_plot(opt,'c',23)
+k_put, IV_put = IV_plot(opt,'p',23)
+plt.figure(figsize=(16, 7))
+e = plt.scatter(k_call,IV_call, c ='red', label="IV(call options)")
+f = plt.scatter(k_put,IV_put, c = 'black', label="IV(put options)")
+plt.xlabel('strike')
+plt.ylabel('Implied Volatility')
+plt.legend((e,f), ("IV (call options)", "IV (put options)"))
+</pre>
+The current date is 08/14/2017. We plot the implied volatilities for SPY options which expire on 12/21/2018.
+
+<h2><img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivcall.png" alt="implied volatility of call options"/></h2>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivput.png" alt="implied volatility of put options"/>
+
+Plotting these implied volatilities across strikes gives us the implied volatility skew. For the shape of volatility smile, it should be a symmetry convex curve. But from the above chart, the implied volatility curve slope downward to the right. This is referred to the skew, which means that options with low strikes have higher implied volatilities than those with higher strikes. The smile is not symmetry. The skew of a distribution is a measure of its assymetry. Although the volatility skew is dynamic, in equity markets it is almost always a decreasing function of the strike. Other asset classes such as FX and commodities have differently shaped skews.
+If we plot the call and put options implied volatility smile in the same chart for the same expiration date:
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivputcall.png" alt="implied volatility of call and put options"/>
+From the above chart, we can see the implied volatility for put options is higher than call options. Usually, put options trade for a higher price than call options, because traders place more risk in the short put positions, which raises the amount of reward they require to sell the position. Higher option prices signify an increase in risk and are represented by higher implied volatility levels derived from the option pricing model.
+Then we scattered all the implied volatilities of contracts across all the strikes.
+<pre class="prettyprint linenums">opt = Options('spy', 'yahoo')
+r = 0.01 # risk free rate
+d = 0.01 # continuous devidend yield
+expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]
+current_date = opt.quote_time.date()  ## get the current date
+s = opt.underlying_price # undelying price
+num_expiry = len(expiry_dates)
+IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]
+
+for expiry_index in range(num_expiry):
+    data = opt.get_put_data(expiry=expiry_dates[expiry_index])
+    expiry = expiry_dates[expiry_index] # get the expiry
+    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
+    premium = (data['Ask'] + data['Bid'])/2.0 # option premium
+    strike = data.index.get_level_values('Strike') # get the strike price
+    num_strike = len(data)
+    for j in range(num_strike):
+        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])
+x= [IV[i][0] for i in range(len(IV))]
+y= [IV[i][1] for i in range(len(IV))]
+z= [IV[i][2] for i in range(len(IV))]
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,10)
+ax.scatter(x,y,z)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-volsurface.png" alt="implied volatility surface"/>
+<h2>Volatility Surface</h2>
+By fixing a maturity and looking at the implied volatilities of European options on the same underlying but different strikes, we obtain the implied volatility skew or smile. The volatility surface is the three-dimensional surface obtained when we plots the market implied volatilities of European options with different strikes and different maturities.
+By using the interpolation method, we can generate the implied volatility surface of SPY options for both put and call options as follows:
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-call.png" alt="implied volatility surface for call options"/>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-put.png" alt="implied volatility surface for put options"/>
+<h2>The Reason for Volatility Skew</h2>
+The volatility skew shows that for Put options, implied volatility is higher for deep OTM options and then is decreasing as it moves towards ITM options. For call options, the implied volatility is higher for deep ITM options and then is decreasing as it moves towards OTM options. From the demand and supply degree, the skew reflects that investors are more willing to buy deep OTM puts and ITM calls. Why there is volatility skew in the market?
+First, the majority of the equity positions are long. Investors usually have two ways to hedge those long positions risks: Buying downside puts or selling upside calls. The increase in demand create increases in price of downside puts and decreases in price of upside calls. The volatility is a reflection of options price. Therefore the volatility of in-the-money put is higher and the volatility of in-the-money call is lower.
+The second reason for volatility skew is that the market moves down faster than it moves up. The downside market move is riskier than the upside move. Thus the price of OTM puts is higher than OTM calls.
+<h2>Summary</h2>
+In this chapter, we discussed the historical volatility and the implied volatility. The historical volatility of an asset is the statistical measure we know as the standard deviation of the stock return series. The implied volatility of the same asset, on the other hand, is the volatility parameter that we can infer from the prices of traded options written on this asset. In contrast to historical volatility, which looks at fluctuations of asset prices in the past, implied volatility looks ahead. The two volatilities do not necessarily coincide, and although they may be close, they are typically not equal.
+Now we know the constant volatility assumption in Black-Sholes-Merton model is not applicable in the real market because there is volatility skew for most of the options. Then in next chapter we will introduce some volatility models to capture the volatility skew in options pricing.
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.ipynb b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.ipynb
new file mode 100755
index 0000000..a877f12
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.ipynb	
@@ -0,0 +1,438 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from numpy import sqrt,mean,log,diff\n",
+    "import quandl\n",
+    "import scipy.stats as stats\n",
+    "from scipy import interpolate \n",
+    "from scipy.interpolate import Rbf\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib import cm\n",
+    "from matplotlib import animation\n",
+    "from pandas_datareader.data import Options\n",
+    "import pandas_datareader.data as web\n",
+    "import datetime\n",
+    "%pylab inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1010,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1011,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = quandl.get('WIKI/GOOG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1012,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21326449960467742"
+      ]
+     },
+     "execution_count": 1012,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016 \n",
+    "close = data['2016-01':'2016-08']['Adj. Close']\n",
+    "r = diff(log(close))\n",
+    "r_mean = mean(r)\n",
+    "diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]\n",
+    "std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))\n",
+    "vol = std*sqrt(252)\n",
+    "vol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def bsm_price(option_type, sigma, s, k, r, T, q):  \n",
+    "    # calculate the bsm price of European call and put options\n",
+    "    sigma = float(sigma)\n",
+    "    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))\n",
+    "    d2 = d1 - sigma * np.sqrt(T)\n",
+    "    if option_type == 'c':\n",
+    "        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))\n",
+    "        return price\n",
+    "    elif option_type == 'p':\n",
+    "        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))\n",
+    "        return price\n",
+    "    else:\n",
+    "        print('No such option type %s') %option_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def implied_vol(option_type, option_price, s, k, r, T, q):\n",
+    "    # apply bisection method to get the implied volatility by solving the BSM function\n",
+    "    precision = 0.00001\n",
+    "    upper_vol = 500.0\n",
+    "    max_vol = 500.0\n",
+    "    min_vol = 0.0001\n",
+    "    lower_vol = 0.0001\n",
+    "    iteration = 0    \n",
+    "\n",
+    "    while 1:\n",
+    "        iteration +=1 \n",
+    "        mid_vol = (upper_vol + lower_vol)/2.0\n",
+    "        price = bsm_price(option_type, mid_vol, s, k, r, T, q)\n",
+    "        if option_type == 'c':\n",
+    "\n",
+    "            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)\n",
+    "            if (lower_price - option_price) * (price - option_price) > 0:\n",
+    "                lower_vol = mid_vol \n",
+    "            else:\n",
+    "                upper_vol = mid_vol\n",
+    "            if abs(price - option_price) < precision: break\n",
+    "            if mid_vol > max_vol - 5 : \n",
+    "                mid_vol = 0.000001\n",
+    "                break\n",
+    "#             print(\"mid_vol=%f\" %mid_vol)\n",
+    "#             print(\"upper_price=%f\" %lower_price)\n",
+    "\n",
+    "        elif option_type == 'p':\n",
+    "            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)\n",
+    "\n",
+    "            if (upper_price - option_price) * (price - option_price) > 0:\n",
+    "                upper_vol = mid_vol \n",
+    "            else:\n",
+    "                lower_vol = mid_vol            \n",
+    "#             print(\"mid_vol=%f\" %mid_vol)\n",
+    "#             print(\"upper_price=%f\" %upper_price)            \n",
+    "            if abs(price - option_price) < precision: break \n",
+    "            if iteration > 50: break\n",
+    "                \n",
+    "    return mid_vol "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[datetime.date(2017, 8, 16),\n",
+       " datetime.date(2017, 8, 18),\n",
+       " datetime.date(2017, 8, 23),\n",
+       " datetime.date(2017, 8, 25),\n",
+       " datetime.date(2017, 8, 30),\n",
+       " datetime.date(2017, 9, 1),\n",
+       " datetime.date(2017, 9, 6),\n",
+       " datetime.date(2017, 9, 8),\n",
+       " datetime.date(2017, 9, 13),\n",
+       " datetime.date(2017, 9, 15),\n",
+       " datetime.date(2017, 9, 22),\n",
+       " datetime.date(2017, 9, 29),\n",
+       " datetime.date(2017, 10, 20),\n",
+       " datetime.date(2017, 11, 17),\n",
+       " datetime.date(2017, 12, 15),\n",
+       " datetime.date(2017, 12, 29),\n",
+       " datetime.date(2018, 1, 19),\n",
+       " datetime.date(2018, 3, 16),\n",
+       " datetime.date(2018, 3, 29),\n",
+       " datetime.date(2018, 6, 15),\n",
+       " datetime.date(2018, 6, 29),\n",
+       " datetime.date(2018, 7, 20),\n",
+       " datetime.date(2018, 9, 21),\n",
+       " datetime.date(2018, 12, 21),\n",
+       " datetime.date(2019, 1, 18),\n",
+       " datetime.date(2019, 3, 15),\n",
+       " datetime.date(2019, 6, 21),\n",
+       " datetime.date(2019, 12, 20),\n",
+       " datetime.date(2020, 7, 17)]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# download option data for all expiry months from Yahoo Finance \n",
+    "# provide a formatted DataFrame with a hierarchical index\n",
+    "opt = Options('spy', 'yahoo')\n",
+    "opt.expiry_dates  # list all the available expiration dates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def IV_plot(opt,option_type,expiry_index):\n",
+    "    expiry = opt.expiry_dates[expiry_index]\n",
+    "    if option_type == 'c':\n",
+    "        data = opt.get_call_data(expiry=expiry)\n",
+    "    elif option_type == 'p':\n",
+    "        data = opt.get_put_data(expiry=expiry)\n",
+    "    r = 0.01 # risk free rate\n",
+    "    d = 0.01 # continuous devidend yield\n",
+    "    s = opt.underlying_price # data_call['Underlying_Price']  undelying price\n",
+    "    expiry = data.index.get_level_values('Expiry')[0] # get the expiry \n",
+    "    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date\n",
+    "    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration\n",
+    "    premium = (data['Ask'] + data['Bid'])/2 # option premium\n",
+    "    strike = list(data.index.get_level_values('Strike')) # get the strike price\n",
+    "    IV = []\n",
+    "    for i in range(len(data)):  \n",
+    "        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))\n",
+    "   \n",
+    "    plt.figure(figsize=(16, 7))\n",
+    "    a = plt.scatter(strike,IV, c='r', label=\"IV by solving BSM\")\n",
+    "    b = plt.scatter(strike,data['IV'],c = 'b', label=\"IV from Yahoo Finance\")\n",
+    "    plt.grid()\n",
+    "    plt.xlabel('strike')\n",
+    "    if option_type == 'c':\n",
+    "        plt.ylabel('Implied Volatility for call option')\n",
+    "        plt.legend((a,b), (\"IV(call) by solving BSM\", \"IV(call) from Yahoo Finance\"))\n",
+    "    elif option_type == 'p':\n",
+    "        plt.ylabel('Implied Volatility for put options')\n",
+    "        plt.legend((a,b), (\"IV(put) by solving BSM\", \"IV(put) from Yahoo Finance\"))\n",
+    "    \n",
+    "    return strike,IV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MXiGEE7PdjzHemuXZKuC/Y4wTurn338AlMcbH2q4fAv4txrigm7p1pEdvGT16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\nZ90nousYkB6hPf7/UPvOtTTyz1SyjMB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wMbuQi9m9klams7edq+KkBbsGHBPmwfb5tl89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\nAQAAAADZoZgFAAAAAGTn/wGMkou6MEyrPwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x134190e90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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WEGOM+xc9OkmSJEmSssiZzMYYF3S9Tx6+cCRJkiRJGlg+58x+O582SZIkSZKG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\nn9CZ2clVoo4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1339dc610>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k_call, IV_call = IV_plot(opt,'c',23)\n",
+    "k_put, IV_put = IV_plot(opt,'p',23)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1325bd5d0>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5MSJgdHQ0UDsAAAAAREEjuxn/VhLzUSMqkUioWCxWbQcAAACAqKoZZs3sw+7+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\nSZIk9Y7JrCRJkiSpd/4GbolVqR9oXvIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x131883d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 7))\n",
+    "e = plt.scatter(k_call,IV_call, c ='red', label=\"IV(call options)\")\n",
+    "f = plt.scatter(k_put,IV_put, c = 'black', label=\"IV(put options)\")\n",
+    "plt.xlabel('strike')\n",
+    "plt.ylabel('Implied Volatility')\n",
+    "plt.legend((e,f), (\"IV (call options)\", \"IV (put options)\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "r = 0.01 # risk free rate\n",
+    "d = 0.01 # continuous devidend yield\n",
+    "expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]\n",
+    "current_date = opt.quote_time.date()  ## get the current date\n",
+    "s = opt.underlying_price # undelying price\n",
+    "num_expiry = len(expiry_dates)\n",
+    "IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]\n",
+    "\n",
+    "for expiry_index in range(num_expiry):\n",
+    "    data = opt.get_put_data(expiry=expiry_dates[expiry_index])\n",
+    "    expiry = expiry_dates[expiry_index] # get the expiry  \n",
+    "    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration    \n",
+    "    premium = (data['Ask'] + data['Bid'])/2.0 # option premium\n",
+    "    strike = data.index.get_level_values('Strike') # get the strike price\n",
+    "    num_strike = len(data)\n",
+    "    for j in range(num_strike):  \n",
+    "        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#  IV = [i for i in IV if i[2]>0.01 and i[2]<3] # delete the implied volatility with 0 value or much higher value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x= [IV[i][0] for i in range(len(IV))]\n",
+    "y= [IV[i][1] for i in range(len(IV))]\n",
+    "z= [IV[i][2] for i in range(len(IV))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11a551f50>"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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XwYOmaVheXsbKygpmZmZw9uxZhEKhnly2DAFJr3enMk0Tz/x3Ck9fTcPlAt5+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GWzddGJadoOECUZXoqfn1lQr/zUcGScf292u+6oUsl6PxqbaETtmNqtZDIQmlJGHquL4gCAUx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ee/47XWOJfaYT1BFEWOHTtGOp1et+RN7WV3zTXX8MTIAnOlKZoDHrJn45guBQtwLVxPpDWi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LW15/Kmsq/WiaRldX10lvF74U/H7/BF97pAefS8YWjpuu1OfKlZJNJHRes9lpnS7htP0+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\n5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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1356245d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(20,12))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "ax.view_init()\n",
+    "ax.scatter(x,y,z)\n",
+    "plt.xlabel(\"Time to Expiration\")\n",
+    "plt.ylabel(\"Strikes\")\n",
+    "plt.title(\"Implied Volatility\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html
new file mode 100755
index 0000000..7a437db
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+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html	
@@ -0,0 +1,209 @@
+<div class="page" title="Page 84">
+<div class="layoutArea">
+<div class="column">
+<h2>Introduction</h2>
+In the last chapter, we discussed two of the volatilities: historical volatility and the implied volatility. This chapter, we will further extend the concept of volatility and introduce the local volatility and the stochastic volatility.
+<h2>Motivation</h2>
+We already knew that volatility is a measure of the fluctuation degree of the underlying assets price series. There are many types of volatilities. As we discussed in the last chapter, historical volatility is the standard deviation of the price series during a certain period. It is a constant and represents the price movement in the past. However, the implied volatility is not based on the historical pricing data of stocks. It is the value of volatility parameter derived from the market quote of options in BSM pricing model. In contrast to historical volatility, implied volatility is forward looking and varies with different options contracts.
+<div class="page" title="Page 82">
+<div class="layoutArea">
+<div class="column">
+
+In the Black–Scholes model, the asset’s price is modeled as a log-normal random variable, which means that the asset’s log-returns are normally distributed. One of the most significant assumptions in BSM model is that the volatility is a constant term over time.
+
+\[\sigma=\sigma_{implied}\]
+
+But in the real world, it could be constant in a small time period but never constant in long term. As we discussed in the last chapter, there is volatility skew for most of the options, which means that the volatility is not constant across strikes.
+
+[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]One way to capture the volatility skew is to assume that that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. But on the other hand, the introduction of additional sources of randomness will increase the complexity of the model. The other way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
+<h2>Local Volatility</h2>
+<h3>1. Definition</h3>
+The constant volatility assumption in BSM model seems not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. That means there exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
+
+[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]The local volatility of the underlying assets is a deterministic function of assets price and the time t.
+
+\[\sigma=\sigma(S_t,t)\]
+
+Therefore under local volatility model, the stochastic process followed by the stock price is
+
+\[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
+
+If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness fro m the stock price: \(W_t\).
+<h3>2. Model Calibration</h3>
+How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.
+
+\[\sigma_{Local}(K,T)=\sqrt{\frac{\frac{\partial C}{\partial T}}{\frac{1}{2}K^2\frac{\partial^2 C}{\partial K^2}}}\]
+
+If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with the different combination of strike prices and expires. For a given date, time(t) and the underlying stock prices(St), a local volatility is derived from the equation that options prices calculated using local volatility equal the market options prices.
+
+But most of the time, we can only get a limited number of contracts with a few strikes and maturities, we can follow the steps below to get the local volatility estimation:
+<ol>
+ 	<li>First, use the available quoted price to calculate the implied volatilities.</li>
+ 	<li>Appy interpolation method to produce a smooth implied volatility surface.</li>
+ 	<li>Plug implied the implied volatilities into BSM model to get all the market prices of European calls.</li>
+ 	<li>Calculate the local volatility according to Dupire formula. In order to avoid taking derivatives, we could use finite differences to approximate the derivatives.</li>
+</ol>
+\[\frac{\partial C}{\partial T}\approx\frac{C(K,T+\Delta T)-C(K,T-\Delta T)}{2\Delta t}\]
+
+\[\frac{\partial^2 C}{\partial K^2}\approx\frac{C(K-\Delta K,T)-2C(K,T)+C(K+\Delta K,T)}{(\Delta K)^2}\]
+
+</div>
+<div class="column">
+<h2>Stochastic Volatility</h2>
+<h3>1. Definition</h3>
+</div>
+</div>
+</div>
+In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock prices are log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is[ref]Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011[/ref]
+
+</div>
+</div>
+</div>
+<div class="page" title="Page 84">
+<div class="layoutArea">
+<div class="column">
+
+\[dS_t = \mu_tS_tdt+\sqrt{v_t}S_tdW_{1,t}\]
+
+\[dv_t=-\lambda(v_t-\overline{v})\ dt+\eta\sqrt{v_t}\ dW_{2,t}\]
+
+\[dW_{1,t},\ dW_{2,t}=\rho \ dt\]
+
+Where the instantaneous variance of the stock price \(v_t\) itself is a stochastic process.
+
+\(\lambda\) is the speed of reversion of \(v_t\) to its long-term mean \(\overline{v})\). We can think of\(\lambda\) as the rate at which the stock price variance reverts back to its long term average value.
+
+\(\eta\) is the volatility of the variance process \(v_t\) (often called the volatility of volatility)
+
+\(W_{1,t}\) and \(W_{2,t}\) are two dependent Wiener processes with correlation coefficient \(\rho\).
+<h3>2. Simulation of the Heston Process</h3>
+We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization[ref]Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>[/ref].
+
+\[v_{t+\Delta t}=\left(\sqrt{v_t}+\frac{1}{2}\eta\sqrt{\Delta t}W_1\right)^2-\lambda(v_t-\overline{v})\Delta t-\frac{\eta^2}{4}\Delta t\]
+
+Then, we take the following steps to simulate Heston process: Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above (Here note that in options pricing, Monte Carlo method use risk neutral result, so here the expected return \(\mu\) should equal the risk free rate r):
+<ul>
+ 	<li>Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above</li>
+ 	<li>We obtain \(S_{t+\Delta t}\) using\[S_{t+\Delta t}=S_t\ \text {exp}\left[(r-\frac{1}{2}v_t)\Delta t+\sqrt{v_t\Delta t}W_2\right]\]</li>
+ 	<li>To generate \(W_1\) and \(W_2\) with correlation \(\rho\), we first generate two independent standard normal variables \(Z_1\) and \(Z_2\), set \(W_1=Z_1\), then \[W_2=\rho Z_1+\sqrt{1-\rho^2}Z_2\]</li>
+</ul>
+<pre class="prettyprint linenums">from numpy import sqrt, exp
+import numpy as np
+
+def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):
+    """
+    option_type:    'p' put option 'c' call option
+    S0:              the spot price of underlying stock
+    K:              the strike price
+    T:              the maturity of options
+    initial_var:    the initial value of variance
+    long_term_var:  the long term average of price variance
+    rate_reversion: the mean reversion rate for the variance
+    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)
+    corr:           the correlation between the standard normal random variables W1 and W2
+    r:              the risk free rate
+    reps:           the number of repeat for monte carlo simulation
+    steps:          the number of steps in each simulation
+    """
+    delta_t = T/float(steps)
+    payoff = 0
+    for i in range(num_reps):
+        vt = initial_var
+        st = S0
+        for j in range(steps):
+            w1 = np.random.normal(0, 1)
+            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)
+            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \
+                 - rate_reversion * (vt - long_term_var) * delta_t \
+                 - 0.25 * vol_of_vol**2 * delta_t
+            st = st * exp((r - 0.5*vt)*delta_t + sqrt(vt*delta_t) * w2)
+        if option_type == 'c':
+                payoff += max(st - K, 0)
+        elif option_type == 'p':
+                payoff += max(K - st, 0)
+
+    return (payoff/float(num_reps)) * (exp(-r*T))
+</pre>
+<h3>3. Calibration of Model Parameters</h3>
+The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from the prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to the paper <em>Parameters recovery via calibration in the Heston model</em>[ref]Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.[/ref] for details of different error measure.
+
+There are five parameters need to be estimated in Heston model:
+<ol>
+ 	<li>\(v_t\) : the initial value of the variance (Bounds of 0 and 1)</li>
+ 	<li>\(\overline{v}\) : the long term average variance of stock price (Bounds of 0 and 1)</li>
+ 	<li>\(\lambda\) : the speed of reversion (non-negativity)</li>
+ 	<li>\(\eta\) : the volatility of the volatility (non-negativity)</li>
+ 	<li>\(\rho\) : the correlation coefficient between two Wiener process (Bounds of -1 and 1)</li>
+</ol>
+Here we use QuantLib[ref]Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>[/ref] Python library to calibrate the parameters.
+
+Let us look at how we can calibrate the Heston model to some market quotes. For example, let's say we are interested in trading SPDR S&amp;P 500 ETF (SPY) options with 4-months maturity. Here we choose all the options contracts written on SPY expire in 4 months. We need the strikes and the market prices of those contracts and the underlying price as the input of our objective function to minimize.
+<pre class="prettyprint linenums">import pandas as pd
+from numpy import sqrt,mean,log,diff
+import QuantLib as ql
+from pandas_datareader.data import Options
+import pandas_datareader.data as web
+import datetime
+opt = Options('spy', 'yahoo')
+expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]
+expiry_index = 14 # choose the contracts expire on 11/17/2017
+data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])
+strikes = list(data.index.get_level_values('Strike'))
+premium = list(data['Last'])
+day_count = ql.Actual365Fixed()
+calendar = ql.UnitedStates()
+calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year) # 08/10/2017
+spot = opt.underlying_price  # spot price is 244.82
+ql.Settings.instance().evaluationDate = calculation_date
+dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))
+risk_free_rate = 0.01
+dividend_rate = 0.0
+flat_ts = ql.YieldTermStructureHandle(
+    ql.FlatForward(calculation_date, risk_free_rate, day_count))
+dividend_ts = ql.YieldTermStructureHandle(
+    ql.FlatForward(calculation_date, dividend_rate, day_count))
+# dummy parameters
+initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;
+# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;
+process = ql.HestonProcess(flat_ts, dividend_ts,
+                           ql.QuoteHandle(ql.SimpleQuote(spot)),
+                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)
+model = ql.HestonModel(process)
+engine = ql.AnalyticHestonEngine(model)
+heston_helpers = []
+date = expiration_dates[expiry_index]
+for j, s in enumerate(strikes):
+    t = (date - calculation_date)
+    p = ql.Period(t, ql.Days)
+    sigma = premium[j]
+    helper = ql.HestonModelHelper(p, calendar, spot, s,
+                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),
+                                  flat_ts,
+                                  dividend_ts)
+    helper.setPricingEngine(engine)
+    heston_helpers.append(helper)
+lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)
+model.calibrate(heston_helpers, lm,
+                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))
+long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()
+print "long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)
+</pre>
+We get the market data at 08/10/2017 and choose the contracts which expire on 11/17/2017. Then we get the following parameters estimation
+<pre class="prettyprint linenums">long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778
+</pre>
+When you get the parameter estimation, you can plug the parameter values into the Heston Monte Carlo options pricing model and get the price estimation with stochastic volatility. But as we already discussed for Heston model, the introduction of randomness of volatility increases the complexity of the estimation. No matter which error measure is chosen, the objective function is highly non-linear and far from being convex and we have 5 parameters in the model. All these drawbacks of Heston models will make the estimated parameter values quite sensitive the initial guess of parameters. Therefore options prices generated by the Heston model are also parameter sensitive. From the above, we can get a sense of how computationally expensive it can be to get accurate values of options in a stochastic volatility model.
+<h2> Summary</h2>
+<div class="page" title="Page 84">
+<div class="layoutArea">
+<div class="column">
+
+In Black–Scholes, that volatility is assumed to be constant, it is not reasonable especially for some exotic options in which the option's payoff is based on the changing volatility. Therefore we introduced the two volatility models to capture the volatility skew.The first approach, local volatility, assumes that the volatility is a deterministic function of time and the underlying asset price. This function must be chosen as to match the observed market option prices. In another stochastic volatility models, the asset price and its volatility are both assumed to be random processes.
+
+The calibration of these models needs the market price of the Vanilla options. When we get the model estimation we can use these models to pricing the corresponding exotic options.
+
+</div>
+</div>
+</div>
+</div>
+</div>
+</div>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.ipynb b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.ipynb
new file mode 100755
index 0000000..3eaa816
--- /dev/null
+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.ipynb	
@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from numpy import sqrt,mean,log,diff\n",
+    "import QuantLib as ql\n",
+    "from pandas_datareader.data import Options\n",
+    "import pandas_datareader.data as web\n",
+    "import datetime "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "opt = Options('spy', 'yahoo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]\n",
+    "expiry_index = 14 # choose the contracts expire in 4 months\n",
+    "data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])\n",
+    "strikes = list(data.index.get_level_values('Strike'))\n",
+    "premium = list(data['Last'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 534,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime.date(2017, 11, 17)"
+      ]
+     },
+     "execution_count": 534,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "opt.expiry_dates[expiry_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "day_count = ql.Actual365Fixed()\n",
+    "calendar = ql.UnitedStates()\n",
+    "calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year)\n",
+    "spot = opt.underlying_price\n",
+    "ql.Settings.instance().evaluationDate = calculation_date\n",
+    "dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))\n",
+    "risk_free_rate = 0.01\n",
+    "dividend_rate = 0.0\n",
+    "flat_ts = ql.YieldTermStructureHandle(\n",
+    "    ql.FlatForward(calculation_date, risk_free_rate, day_count))\n",
+    "dividend_ts = ql.YieldTermStructureHandle(\n",
+    "    ql.FlatForward(calculation_date, dividend_rate, day_count))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 518,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# dummy parameters\n",
+    "initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;\n",
+    "# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;\n",
+    "process = ql.HestonProcess(flat_ts, dividend_ts, \n",
+    "                           ql.QuoteHandle(ql.SimpleQuote(spot)), \n",
+    "                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)\n",
+    "model = ql.HestonModel(process)\n",
+    "engine = ql.AnalyticHestonEngine(model) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 519,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "heston_helpers = []\n",
+    "date = expiration_dates[expiry_index]\n",
+    "for j, s in enumerate(strikes):\n",
+    "    t = (date - calculation_date)\n",
+    "    p = ql.Period(t, ql.Days)\n",
+    "    sigma = premium[j]\n",
+    "    helper = ql.HestonModelHelper(p, calendar, spot, s, \n",
+    "                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),\n",
+    "                                  flat_ts, \n",
+    "                                  dividend_ts)\n",
+    "    helper.setPricingEngine(engine)\n",
+    "    heston_helpers.append(helper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 520,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)\n",
+    "model.calibrate(heston_helpers, lm, \n",
+    "                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))\n",
+    "long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 521,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778\n"
+     ]
+    }
+   ],
+   "source": [
+    "print \"long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f\" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):\n",
+    "    \"\"\"\n",
+    "    option_type:    'p' put option 'c' call option\n",
+    "    S0:              the spot price of underlying stock\n",
+    "    K:              the strike price\n",
+    "    T:              the maturity of options\n",
+    "    initial_var:    the initial value of variance\n",
+    "    long_term_var:  the long term average of price variance\n",
+    "    rate_reversion: the mean reversion rate for the variance\n",
+    "    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)\n",
+    "    corr:           the correlation between the standard normal random variables W1 and W2\n",
+    "    r:              the risk free rate\n",
+    "    reps:           the number of repeat for monte carlo simulation\n",
+    "    steps:          the number of steps in each simulation\n",
+    "    \"\"\"\n",
+    "    delta_t = T/float(steps)\n",
+    "    payoff = 0\n",
+    "    for i in range(num_reps):\n",
+    "        vt = initial_var\n",
+    "        log_st = log(S0)\n",
+    "        for j in range(steps):\n",
+    "            w1 = np.random.normal(0, 1)\n",
+    "            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)\n",
+    "            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \\\n",
+    "                 - rate_reversion * (vt - long_term_var) * delta_t \\\n",
+    "                 - 0.25 * vol_of_vol**2 * delta_t\n",
+    "            if vt < 0: vt = 0.00\n",
+    "            log_st = log_st + (r - 0.5*vt)*delta_t + sqrt(vt)*sqrt(delta_t)*w2\n",
+    "        st = e**(log_st)\n",
+    "        if option_type == 'c':\n",
+    "                payoff += max(st - K, 0)\n",
+    "        elif option_type == 'p':\n",
+    "                payoff += max(K - st, 0)\n",
+    "        \n",
+    "    return (payoff/float(num_reps)) * (exp(-r*T))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 523,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "44.383434958491186"
+      ]
+     },
+     "execution_count": 523,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mc_heston('c',spot,strikes[20],t/365.0,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,0.01,100,1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 524,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36.119999999999997"
+      ]
+     },
+     "execution_count": 524,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "premium[20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 525,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heston = []\n",
+    "\n",
+    "for i in range(len(strikes)):\n",
+    "    heston.append(mc_heston('c',spot,strikes[i],t/365.0,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,0.01,100,100))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 529,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diff = [(heston[i]-premium[i])**2 for i in range(len(premium))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 530,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8885.1013305184661"
+      ]
+     },
+     "execution_count": 530,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(diff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 531,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(110.46562812345368, 127.83),\n",
+       " (110.6988943943971, 112.12),\n",
+       " (55.926776121382609, 55.710000000000001),\n",
+       " (52.981820291517408, 54.030000000000001),\n",
+       " (54.164283840319435, 53.340000000000003),\n",
+       " (54.991136360501223, 52.719999999999999),\n",
+       " (41.216283956629468, 51.090000000000003),\n",
+       " (47.687719919883207, 50.340000000000003),\n",
+       " (44.545781271634148, 49.32),\n",
+       " (47.563439672575591, 48.340000000000003),\n",
+       " (44.635711823762009, 47.340000000000003),\n",
+       " (46.252466035738571, 46.170000000000002),\n",
+       " (47.574492181620364, 45.18),\n",
+       " (41.653315658070859, 43.310000000000002),\n",
+       " (47.604806657885561, 43.229999999999997),\n",
+       " (40.894480554924115, 42.799999999999997),\n",
+       " (38.539091518531059, 42.25),\n",
+       " (43.303139080062273, 40.829999999999998),\n",
+       " (36.234352000924453, 39.880000000000003),\n",
+       " (39.257917188012463, 38.469999999999999),\n",
+       " (44.853854689174561, 36.119999999999997),\n",
+       " (39.640305408524711, 36.310000000000002),\n",
+       " (34.129177338407089, 36.359999999999999),\n",
+       " (36.438197093590269, 35.5),\n",
+       " (39.905750087595855, 34.130000000000003),\n",
+       " (32.778166732302317, 32.649999999999999),\n",
+       " (43.333301445006789, 31.969999999999999),\n",
+       " (36.191610071798877, 30.550000000000001),\n",
+       " (32.439741216413196, 31.300000000000001),\n",
+       " (26.253346286101117, 29.34),\n",
+       " (40.260711758568831, 28.760000000000002),\n",
+       " (30.35683234367302, 27.449999999999999),\n",
+       " (26.437940074957758, 27.579999999999998),\n",
+       " (28.483162465814967, 25.710000000000001),\n",
+       " (25.988006256896163, 25.809999999999999),\n",
+       " (27.515272593610401, 23.98),\n",
+       " (25.183307114602915, 22.760000000000002),\n",
+       " (23.158436475438911, 21.850000000000001),\n",
+       " (33.342453057590774, 21.100000000000001),\n",
+       " (27.131945603771868, 20.82),\n",
+       " (23.601047412852459, 18.010000000000002),\n",
+       " (25.760858340591355, 17.719999999999999),\n",
+       " (23.691135308980339, 16.440000000000001),\n",
+       " (22.185597107961662, 15.16),\n",
+       " (20.277352259807657, 14.58),\n",
+       " (27.451461967610275, 13.720000000000001),\n",
+       " (31.744386996748887, 12.76),\n",
+       " (26.49106582685053, 11.970000000000001),\n",
+       " (21.003755215426228, 11.25),\n",
+       " (19.35608163937891, 10.279999999999999),\n",
+       " (17.351500422342902, 9.5999999999999996),\n",
+       " (22.659139445207355, 8.7899999999999991),\n",
+       " (23.409228489912628, 8.0700000000000003),\n",
+       " (20.961754171171588, 7.5899999999999999),\n",
+       " (16.075588910706614, 6.6600000000000001),\n",
+       " (20.825896797061727, 6.0),\n",
+       " (18.675398030756703, 5.5),\n",
+       " (19.026462754554451, 4.79),\n",
+       " (19.417525830223067, 4.21),\n",
+       " (20.407591273285487, 3.7000000000000002),\n",
+       " (17.174337684195297, 3.1800000000000002),\n",
+       " (17.391313889375883, 2.71),\n",
+       " (16.050599868535851, 2.2000000000000002),\n",
+       " (14.09485225440975, 1.8899999999999999),\n",
+       " (18.208532190234333, 1.48),\n",
+       " (17.350492587503762, 1.1699999999999999),\n",
+       " (15.66130115227647, 0.96999999999999997),\n",
+       " (18.310204566097372, 0.71999999999999997),\n",
+       " (15.244762462507955, 0.55000000000000004),\n",
+       " (12.05896923742357, 0.41999999999999998),\n",
+       " (16.549370172554656, 0.32000000000000001),\n",
+       " (15.127520335730379, 0.25),\n",
+       " (10.679068848118462, 0.19),\n",
+       " (17.091513222601169, 0.16),\n",
+       " (15.164841725875474, 0.13),\n",
+       " (12.162076252506601, 0.12),\n",
+       " (11.179740065544316, 0.10000000000000001),\n",
+       " (15.126628346299601, 0.089999999999999997),\n",
+       " (10.827121868063745, 0.080000000000000002),\n",
+       " (7.955281416676554, 0.080000000000000002),\n",
+       " (11.584285727318449, 0.070000000000000007),\n",
+       " (6.8753197768030745, 0.070000000000000007),\n",
+       " (10.195241428465291, 0.070000000000000007),\n",
+       " (11.098792968645823, 0.080000000000000002),\n",
+       " (10.97397900549743, 0.059999999999999998),\n",
+       " (8.8726648972088906, 0.059999999999999998),\n",
+       " (11.791012392977612, 0.040000000000000001)]"
+      ]
+     },
+     "execution_count": 531,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zip(heston, premium)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/06 Strategy Library/02 Example 1/01 Example 1.html b/06 Strategy Library/02 Example 1/01 Example 1.html
deleted file mode 100644
index c5b2b93..0000000
--- a/06 Strategy Library/02 Example 1/01 Example 1.html	
+++ /dev/null
@@ -1,98 +0,0 @@
-<p>Example 1</p>
-<p>
-    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
-    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
-    term signals.
-    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
-    once. There are three key steps to create and register a consolidator:
-</p>
-<ul>
-    <li>Create the consolidator object.</li>
-    <li>Bind an event handler to handle the new bars.</li>
-    <li>Register it with the subscription manager to start receiving data.</li>
-</ul>
-<div class="section-example-container">
-	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
-{
-	public override void Initialize()
-	{
-		// backtest parameters
-		SetStartDate(2016, 1, 1);
-		SetEndDate(DateTime.Now);
-
-		// cash allocation
-		SetCash(25000);
-
-		//assets or universe selection
-		AddEquity("SPY", Resolution.Minute);
-
-		//create a consolidator object; for tradebars; for a timespan of 30 minutes
-		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
-
-		//bind event handler to data consolidated event.
-		thirtyMinutes.DataConsolidated += OnHalfHour;
-
-		//register the consolidator for data.
-		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
-	}
-
-	//event handler for data!
-	public void OnHalfHour(object sender, TradeBar bar) {
-		Debug(Time.ToString("u") + " " + bar);
-	}
-
-	public override void OnData(Slice data)
-	{ }
-}</pre>
-    <pre class="python">from datetime import datetime, timedelta
-class DataConsolidationAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
-
-        self.SetStartDate(2016,1,1)  #Set Start Date
-        self.SetEndDate(datetime.now())   #Set End Date
-        # Find more symbols here: http://quantconnect.com/data
-        self.AddEquity("SPY", Resolution.Minute)
-
-        # define our 30 minute trade bar consolidator. we can
-        # access the 30 minute bar from the DataConsolidated events
-        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
-
-        # attach our event handler. The event handler is a function that will
-        # be called each time we produce a new consolidated piece of data.
-        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
-
-        # this call adds our 30-minute consolidator to
-        # the manager to receive updates from the engine
-        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
-
-    def ThirtyMinuteBarHandler(self, sender, bar):
-        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
-        self.Debug(str(self.Time) + " " + str(bar))
-    def OnData(self, data):
-        pass
-</pre>
-</div>
-<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
-<div class="section-example-container">
-	<pre class="csharp">// From tick data sources
-var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
-
-//from renko bars
-var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
-</pre>
-    <pre class="python"></pre>
-</div>
-<p>There are two key points to remember:</p>
-<ul>
-    <li>Request a smaller resolution than what you want to produce.</li>
-    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
-        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
-    </li>
-</ul>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
-    Using these building blocks you can combine data together to get any other resolution of data required.</p>
-<div class="video-holder">
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-</div>
diff --git a/06 Strategy Library/03 Example 2/01 Example 2.html b/06 Strategy Library/03 Example 2/01 Example 2.html
deleted file mode 100644
index 54ce442..0000000
--- a/06 Strategy Library/03 Example 2/01 Example 2.html	
+++ /dev/null
@@ -1,98 +0,0 @@
-<p>Example 2</p>
-<p>
-    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
-    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
-    term signals.
-    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
-    once. There are three key steps to create and register a consolidator:
-</p>
-<ul>
-    <li>Create the consolidator object.</li>
-    <li>Bind an event handler to handle the new bars.</li>
-    <li>Register it with the subscription manager to start receiving data.</li>
-</ul>
-<div class="section-example-container">
-	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
-{
-	public override void Initialize()
-	{
-		// backtest parameters
-		SetStartDate(2016, 1, 1);
-		SetEndDate(DateTime.Now);
-
-		// cash allocation
-		SetCash(25000);
-
-		//assets or universe selection
-		AddEquity("SPY", Resolution.Minute);
-
-		//create a consolidator object; for tradebars; for a timespan of 30 minutes
-		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
-
-		//bind event handler to data consolidated event.
-		thirtyMinutes.DataConsolidated += OnHalfHour;
-
-		//register the consolidator for data.
-		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
-	}
-
-	//event handler for data!
-	public void OnHalfHour(object sender, TradeBar bar) {
-		Debug(Time.ToString("u") + " " + bar);
-	}
-
-	public override void OnData(Slice data)
-	{ }
-}</pre>
-    <pre class="python">from datetime import datetime, timedelta
-class DataConsolidationAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
-
-        self.SetStartDate(2016,1,1)  #Set Start Date
-        self.SetEndDate(datetime.now())   #Set End Date
-        # Find more symbols here: http://quantconnect.com/data
-        self.AddEquity("SPY", Resolution.Minute)
-
-        # define our 30 minute trade bar consolidator. we can
-        # access the 30 minute bar from the DataConsolidated events
-        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
-
-        # attach our event handler. The event handler is a function that will
-        # be called each time we produce a new consolidated piece of data.
-        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
-
-        # this call adds our 30-minute consolidator to
-        # the manager to receive updates from the engine
-        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
-
-    def ThirtyMinuteBarHandler(self, sender, bar):
-        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
-        self.Debug(str(self.Time) + " " + str(bar))
-    def OnData(self, data):
-        pass
-</pre>
-</div>
-<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
-<div class="section-example-container">
-	<pre class="csharp">// From tick data sources
-var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
-
-//from renko bars
-var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
-</pre>
-    <pre class="python"></pre>
-</div>
-<p>There are two key points to remember:</p>
-<ul>
-    <li>Request a smaller resolution than what you want to produce.</li>
-    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
-        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
-    </li>
-</ul>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
-    Using these building blocks you can combine data together to get any other resolution of data required.</p>
-<div class="video-holder">
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-</div>
diff --git a/06 Strategy Library/04 Example 3/01 Example 3.html b/06 Strategy Library/04 Example 3/01 Example 3.html
deleted file mode 100644
index 8507a7a..0000000
--- a/06 Strategy Library/04 Example 3/01 Example 3.html	
+++ /dev/null
@@ -1,98 +0,0 @@
-<p>Example 3</p>
-<p>
-    <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be
-    useful for indicators with specific data requirements or to perform long term analysis in conjunction with short
-    term signals.
-    Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized
-    once. There are three key steps to create and register a consolidator:
-</p>
-<ul>
-    <li>Create the consolidator object.</li>
-    <li>Bind an event handler to handle the new bars.</li>
-    <li>Register it with the subscription manager to start receiving data.</li>
-</ul>
-<div class="section-example-container">
-	<pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
-{
-	public override void Initialize()
-	{
-		// backtest parameters
-		SetStartDate(2016, 1, 1);
-		SetEndDate(DateTime.Now);
-
-		// cash allocation
-		SetCash(25000);
-
-		//assets or universe selection
-		AddEquity("SPY", Resolution.Minute);
-
-		//create a consolidator object; for tradebars; for a timespan of 30 minutes
-		var thirtyMinutes = new TradeBarConsolidator(TimeSpan.FromMinutes(30));
-
-		//bind event handler to data consolidated event.
-		thirtyMinutes.DataConsolidated += OnHalfHour;
-
-		//register the consolidator for data.
-		SubscriptionManager.AddConsolidator("SPY", thirtyMinutes);
-	}
-
-	//event handler for data!
-	public void OnHalfHour(object sender, TradeBar bar) {
-		Debug(Time.ToString("u") + " " + bar);
-	}
-
-	public override void OnData(Slice data)
-	{ }
-}</pre>
-    <pre class="python">from datetime import datetime, timedelta
-class DataConsolidationAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
-
-        self.SetStartDate(2016,1,1)  #Set Start Date
-        self.SetEndDate(datetime.now())   #Set End Date
-        # Find more symbols here: http://quantconnect.com/data
-        self.AddEquity("SPY", Resolution.Minute)
-
-        # define our 30 minute trade bar consolidator. we can
-        # access the 30 minute bar from the DataConsolidated events
-        thirtyMinuteConsolidator = TradeBarConsolidator(timedelta(minutes=30))
-
-        # attach our event handler. The event handler is a function that will
-        # be called each time we produce a new consolidated piece of data.
-        thirtyMinuteConsolidator.DataConsolidated += self.ThirtyMinuteBarHandler
-
-        # this call adds our 30-minute consolidator to
-        # the manager to receive updates from the engine
-        self.SubscriptionManager.AddConsolidator("SPY", thirtyMinuteConsolidator)
-
-    def ThirtyMinuteBarHandler(self, sender, bar):
-        '''This is our event handler for our 30-minute trade bar defined above in Initialize(). So each time the consolidator produces a new 30-minute bar, this function will be called automatically. The sender parameter will be the instance of the IDataConsolidator that invoked the event '''
-        self.Debug(str(self.Time) + " " + str(bar))
-    def OnData(self, data):
-        pass
-</pre>
-</div>
-<p>The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.</p>
-<div class="section-example-container">
-	<pre class="csharp">// From tick data sources
-var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
-
-//from renko bars
-var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
-</pre>
-    <pre class="python"></pre>
-</div>
-<p>There are two key points to remember:</p>
-<ul>
-    <li>Request a smaller resolution than what you want to produce.</li>
-    <li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are
-        triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.
-    </li>
-</ul>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars.
-    Using these building blocks you can combine data together to get any other resolution of data required.</p>
-<div class="video-holder">
-    <iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-</div>
diff --git a/07 Applied Options[]/01 Covered Call/01 Covered Call.html b/07 Applied Options[]/01 Covered Call/01 Covered Call.html
new file mode 100755
index 0000000..48e156d
--- /dev/null
+++ b/07 Applied Options[]/01 Covered Call/01 Covered Call.html	
@@ -0,0 +1,86 @@
+<h2>Definition</h2>
+A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
+
+The payoff  is as follows:
+<pre class="prettyprint linenums">import numpy as np
+import matplotlib.pyplot as plt
+%pylab inline
+price = np.arange(110,230,1) # the stock price at expiration date
+strike = 160 # the strike price
+premium = 7.5 # the option premium
+# the payoff of short call position
+payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
+# the payoff of long stock postion
+payoff_long_stock = [i-strike for i in price]
+# the payoff of covered call
+payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
+plt.figure(figsize=(20,11))
+plt.plot(price, payoff_short_call, label = 'short call')
+plt.plot(price, payoff_long_stock, label = 'long stock')
+plt.plot(price, payoff_covered_call, label = 'covered call')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial01-covered-call.png" alt="covered call strategy payoff"/>
+<h2>Implementation</h2>
+<h3>Step 1: Initialize the Algorithm</h3>
+At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
+<pre class="prettyprint linenums">def Initialize(self):
+   self.SetStartDate(2016, 1, 1)
+   self.SetEndDate(2016, 3, 1)
+   self.SetCash(100000)
+   equity = self.AddEquity("IBM", Resolution.Minute)
+   option = self.AddOption("IBM", Resolution.Minute)
+   self.symbol = option.Symbol
+   # set our strike/expiry filter for this option chain
+   option.SetFilter(-3, +3, timedelta(0), timedelta(30))
+   # use the underlying equity as the benchmark
+   self.SetBenchmark(equity.Symbol)
+   self.call = "IBM" # Initialize the call contract
+</pre>
+Second, in the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
+<h3>Step 2: Choose the Call Options Contract</h3>
+First, purchase the underlying stock for 100 shares or <span class="s1">an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.</span>
+
+Second, filter out the call options from candidate contracts.<code>call = [x for x in chain if x.Right == 0]</code> For call options, the right is 0, for put options, the right is 1.
+
+Third, select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
+<pre class="prettyprint linenums">def TradeOptions(self,slice):
+		if slice.OptionChains.Count == 0: return
+    for i in slice.OptionChains:
+        if i.Key != self.symbol: continue
+        chain = i.Value
+        call = [x for x in chain if x.Right == 0] # filter the call options contracts
+        # sorted the contracts according to their expiration dates and choose the ATM options
+        contracts = sorted(sorted(call, key = lambda x: abs(chain.Underlying.Price - x.Strike)),
+                                        key = lambda x: x.Expiry, reverse=True)
+        if len(contracts) == 0: return
+        contract = contracts[0]
+        self.call = contract.Symbol
+        self.Sell(self.call, 1) # short the call options
+        if self.Portfolio["IBM"].Quantity == 0:
+            self.Buy("IBM",100)     # buy 100 the underlying stock
+            self.Log("The stock price at time 0 S(0): {}".format(self.Securities["IBM"].Price))
+</pre>
+Fourth, at the expiration date we print out the price and position information
+<pre class="prettyprint linenums">def OnData(self,slice):
+    if not self.Portfolio[self.call].Invested and self.Time.hour != 0 and self.Time.minute == 1:
+        self.TradeOptions(slice) # sell the call option
+
+    # if the option contract expires, print out the price and position information
+    if slice.Delistings.Count > 0:
+        if [x.Key == self.call for x in slice.Delistings]:
+            self.Log("stock IBM quantity: {0}".format(self.Portfolio["IBM"].Quantity))
+            self.Log("{0} quantity: {1}".format(self.call.Value, self.Portfolio[self.call].Quantity))
+            self.Log("The stock price at Expiry S(T): {}".format(self.Securities["IBM"].Price))
+</pre>
+<h2>Summary</h2>
+From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+<h2>Algorithm</h2>
+Backtest using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9d07e52d5c6199a77597d5f48e68353e'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=31a6d88a35d29c2bbe654a3e5ee5f80e'></script>
diff --git a/07 Applied Options[]/02 Bull Call Spread/02 Bull Call Spread.html b/07 Applied Options[]/02 Bull Call Spread/02 Bull Call Spread.html
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+<h2>Definition</h2>
+Bull Call Spread is an option strategy involved with two call option contracts with the same expiration but different strikes. The strategy buys the call options with a lower strike and sells the same amount of call options with a higher strike price.
+
+This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss from the dropping price of the stock but still create a ceiling to the profit while the price is increasing.
+
+Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
+<pre class="prettyprint linenums">price = np.arange(800,1100,1)
+k_low = 900 # lower strike price for call
+k_high = 1000 # higher strike price for call
+premium_low = 20 # premium of call option with lower strike
+premium_high = 2 # premium of call option with higher strike
+# long call with lower strike
+payoff_long_call = [max(-premium, i-k_low-premium_low ) for i in price]
+# short call with higher strike
+payoff_short_call = [min(premium, -(i-k_high-premium_high)) for i in price]
+payoff = np.sum([payoff_long_call, payoff_short_call], axis=0)
+plt.figure(figsize=(20,11))
+plt.plot(price, payoff_long_call, label = 'long call')
+plt.plot(price, payoff_short_call, label = 'short call')
+plt.plot(price, payoff, label = 'Bull Call Spread')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Bull Call Spread Payoff at Expiration',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-bull-call-spread.png" alt="Tutorial02-bull-call-spread"/>
+From the payoff plot we can see, the maximum profit of the strategy is the difference between call option strike price sold and call option strike price purchased minus the difference between buying lower strike call premium and selling higher strike call premium.
+<h2>Implementation</h2>
+Step 1: First, you need to initialize the algorithm including set the start date, end date and the cash required. Then use <code>option.SetFilter(-6, 6, timedelta(30), timedelta(60))</code> to filter the candidate contracts which expire in 30 days to 60 days from now on. The strike price range involves both ITM and OTM options. Then we get the option chains of GOOG.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2016, 5, 1)
+	self.SetEndDate(2016, 10, 1)
+	self.SetCash(200000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-6, 6, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Choose the contracts with the same expiration date. For demonstration purpose here we sorted the contracts by their expiration dates and choose the options with the furthest expiration date in the option chain.
+<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
+	for i in optionchain:
+		if i.Key != self.symbol: continue
+		chain = i.Value
+		# sorted the optionchain by expiration date and choose the furthest date
+		expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+</pre>
+Step 3: Filter the call options from the contracts which expire on the furthest expiration date in the option chain.
+<pre class="prettyprint linenums">call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+</pre>
+Step 4: Sort the call options with the same expiration date according to their strike price. Then buy the call option with the lowest strike price and sell the call with the highest strike price.
+<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
+	if len(call_contracts) == 0: continue
+	# call option contract with lower strike
+	self.call_low = call_contracts[0]
+	# call option contract with higher strike
+	self.call_high = call_contracts[-1]
+	self.Buy(self.call_low.Symbol, 1)
+	self.Sell(self.call_high.Symbol ,1)
+</pre>
+Note here you need to add the following rules in <code>OnData(self,slice)</code> method because you only need to trade options once and wait until the contracts expire. If you already had securities invested in the portfolio, then you do not need to trade new options.
+<pre class="prettyprint linenums">if not self.Portfolio.Invested:
+	self.TradeOptions(optionchain)
+</pre>
+<h2>Summary</h2>
+This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
+<h2>Algorithm</h2>
+Backtest (SetFilter)
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=06ae7450577c1f0c80edc563c4cf06b8'></script>
+Backtest (OptionChainProvider)
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=db61c966b616e8d1d8f289c4eed5525c'></script>
diff --git a/07 Applied Options[]/03 Long Straddle/03 Long Straddle.html b/07 Applied Options[]/03 Long Straddle/03 Long Straddle.html
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+<h2>Definition</h2>
+Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums">price = np.arange(750,1000,1)
+strike = 900 # strike price for both call and put
+premium_call = 20 # premium of call option
+premium_put = 10 # premium of put option
+# payoff for the long call
+payoff_long_call = [max(-premium_call, i-strike-premium_call) for i in price]
+# payoff for the long put
+payoff_long_put = [max(-premium_put, strike-i-premium_put) for i in price]
+payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_call, label = 'Long Call')
+plt.plot(price, payoff_long_put, label = 'long put')
+plt.plot(price, payoff, label = 'Long Straddle')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Straddle Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-long-straddle.png" alt="long straddle strategy payoff"/>
+Given this plot, if the stock price moves sharply at the expiration either up or down, the Long Straddle will collect positive profit. the potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial, because the stock price can fall to zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be reached if the stock price is exactly equal to the strike price at expiration. Both options will expire worthless.
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 4, 01)
+	self.SetEndDate(2017, 6, 30)
+	self.SetCash(100000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-5, 5, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)</pre>
+Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
+<pre class="prettyprint linenums">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the optionchain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+	# sorted the contracts according to their strike prices
+	call_contracts = sorted(call,key = lambda x: x.Strike)
+	if len(call_contracts) == 0: continue
+	self.call = call_contracts[0]
+</pre>
+Step 3: According to the call option contract, choose the put option with the same strike price and the same expiration date. Then buy the call option and the put option at the same time and wait until expiration.
+<pre class="prettyprint linenums"> for i in chain:
+	if i.Expiry == expiry and i.Right == 1 and i.Strike ==call_contracts[0].Strike:
+	    self.put = i
+self.Buy(self.call.Symbol ,1)
+self.Buy(self.put.Symbol ,1)
+</pre>
+<h2>Summary</h2>
+There are three possible outcomes at expiration for the Long Straddle strategy. If the stock price is at the strike price at expiration, then both the call and the put become worthless and no stock position is created. If the stock price is above the strike price at expiration, the put option expires worthless, the long call is exercised, the stock is purchased at the strike price and a long stock position for is created. If the stock price is below the strike price at expiration, the call expires worthless, the long put is exercised, the stock is sold at the strike price and a short stock position is created. In this algorithm, the undelying asset is GOOG stock. We purchase both the $820 put and the $820 call at time 0. At the expiration, the share price of GOOG rises to 930 then the call option is exercised and the put options become worthless. After expiration, we hold long position for 100 shares of GOOG stock.
+<h2>Algorithm</h2>
+Backtest (Using SetFilter)
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=0ff9a8022311bd57f1fe422eecdd3363'></script>
+Backtest (Using OptionChainProvider)
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75edf3c5d0221d22e8927ffedbf116b2'></script>
diff --git a/07 Applied Options[]/04 Long Strangle/04 Long Strangle.html b/07 Applied Options[]/04 Long Strangle/04 Long Strangle.html
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+<h2>Definition</h2>
+Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums">price = np.arange(700,1000,1)
+# Suppose the undelying price at time 0 is 830
+k_call = 870 # The strike price of OTM call
+k_put = 795 # The strike price of OTM put
+premium_call = 8 # premium of call option
+premium_put = 10 # premium of put option
+# payoff for the long call
+payoff_long_call = [max(-premium_call, i-k_call-premium_call) for i in price]
+# payoff for the long put
+payoff_long_put = [max(-premium_put, k_put-i-premium_put) for i in price]
+payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_call, label = 'Long Call')
+plt.plot(price, payoff_long_put, label = 'long put')
+plt.plot(price, payoff, label = 'Long Strangle')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Strangle Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-long-strangle.png" alt="long strangle strategy payoff"/>
+<div class="play-box">
+
+From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both a put and a call option. The maximum loss occurs when the stock price falls between the strike price of two options. All options are worthless on expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm including setting the start and end date, setting the cash and filtering the options contracts. Note here in SetFilter, the strike price should range from negative to positive because we need to choose out-of-the-money put and call options from candidate contracts. The strike price of OTM call should be greater than ATM options and the strike price of OTM put should be lower than ATM options.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(100000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-15, 15, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Sort the option chain by expiration date and choose an expiration date you want to trade. For demonstration purpose, here we choose options with the furthest expiration date in candidate contracts. Then filter out the call options which expire on that date.
+<pre class="prettyprint linenums">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the option chain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+</pre>
+Step 3: Sort the call options by their expiration date and choose the deep OTM contract which has the largest strike price.
+<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
+if len(call_contracts) == 0: continue
+# choose the deep OTM call option
+self.call = call_contracts[-1]
+</pre>
+Step 4: Select the put options which have the same expiration date with the call option and sort the put options by strike price. Then choose the deep out-of-the-money put which has the minimum strike price among all the available put options.
+<pre class="prettyprint linenums">put_contracts = sorted([i for i in chain if i.Expiry == expiry and i.Right == 1], key = lambda x: x.Strike)
+# choose the deep OTM put option
+self.put = put_contracts[0]
+</pre>
+Step 5: Buy the call and the put options at the same time and wait until expiration.
+<pre class="prettyprint linenums">self.Buy(self.call.Symbol ,1)
+self.Buy(self.put.Symbol ,1)
+</pre>
+<h2>Summary</h2>
+In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16. Therefore we conclude the call option is exercised then we get 100 long stocks position. The put option expires worthless.
+
+You can enter into Long Strangle if you have no clear idea of market direction but forecast there will be a great movement in the underlying asset. As the options you buy are all out of the money, that reduces the cost of this strategy. But because the call and the put options are all out of the money, the stock will need to move even more significantly than long straddle to gain positive profit from this strategy.
+
+</div>
+<h2>Algorithm</h2>
+Backtest using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=89f1f8e5f1ed5e78bd1b4cdf3ab796a1'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e3c07881318d864a9ae25637d6d2d9d3'></script>
diff --git a/07 Applied Options[]/05 Butterfly Spread/05 Butterfly Spread.html b/07 Applied Options[]/05 Butterfly Spread/05 Butterfly Spread.html
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+<h2> Definition</h2>
+Butterfly Spread strategy involves four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Name</th>
+<th> Strategy</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> Long butterfly spread with calls</td>
+<td> Buy 1 ITM call, sell 2 ATM call, buy 1 OTM call</td>
+</tr>
+<tr>
+<td> Long butterfly spread with puts</td>
+<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
+</tr>
+<tr>
+<td> Short butterfly spread with calls</td>
+<td> Sell 1 ITM call, buy 2 ATM call, sell 1 OTM call</td>
+</tr>
+<tr>
+<td> Short butterfly spread with puts</td>
+<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
+</tr>
+</tbody>
+</table>
+Butterfly Spread strategy consists of three legs with a total of four options. In this tutorial, we take Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
+
+The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums">price = np.arange(800,1100,1)
+# Suppose the undelying price at time 0 is 935
+k_itm = 915 # the strike price of ITM call
+k_otm = 955 # the strike price of OTM call
+k_atm = 935 # the strike price of ATM call
+premium_itm = 45 # the premium of ITM call
+premium_otm = 15 # the premium of OTM call
+premium_atm = 25 # the premium of ATM call
+# payoff for the long ITM call position
+payoff_itm_long = [max(-premium_itm, i-k_itm-premium_itm) for i in price]
+# payoff for the long OTM call position
+payoff_otm_long = [max(-premium_otm, i-k_otm-premium_otm) for i in price]
+# payoff for the 2 short ATM call position
+payoff_atm_short = [min(2*premium_atm, -2*(i-k_atm-premium_atm)) for i in price]
+# payoff for Butterfly Spread Strategy
+payoff = np.sum([payoff_itm_long,payoff_otm_long,payoff_atm_short], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_itm_long, label = 'Long ITM Call')
+plt.plot(price, payoff_otm_long, label = 'Long OTM Call')
+plt.plot(price, payoff_atm_short, label = 'Short 2 ATM Call')
+plt.plot(price, payoff, label = 'Long Call Butterfly Spread')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Call Butterfly Spread Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-butterfly-spread.png" alt="butterfly spread strategy payoff"/>
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(150000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-9, 9, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
+<pre class="prettyprint linenums">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the optionchain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+</pre>
+Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
+<pre class="prettyprint linenums"># sorted the contracts according to their strike prices
+call_contracts = sorted(call,key = lambda x: x.Strike)
+if len(call_contracts) == 0: continue
+# choose OTM call
+self.otm_call = call_contracts[-1]
+# choose ITM call
+self.itm_call = call_contracts[0]
+# choose ATM call
+self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+</pre>
+Step 4: Purchase 1 ITM call option and 1 OTM call option, then sell 2 ATM call option.
+<pre class="prettyprint linenums">self.Sell(self.atm_call.Symbol ,2)
+self.Buy(self.itm_call.Symbol ,1)
+self.Buy(self.otm_call.Symbol ,1)
+</pre>
+<h2>Summary</h2>
+From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option strike at $855, 1 ITM call option strike at $810 and sell 2 ATM options strike at 835. At the expiry 05/19/2017, the share price is $930, then the long positions of the ITM option and the OTM option are all exercised, we buy 100 GOOG  shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised. We have to sell 200  GOOG shares to option holder at $835. Then we no longer hold positions after the expiration of this Long Call Butterfly Spread. Since the stock price had a sharp increase during the life of options, we make no profits from this strategy.
+<h2>Algorithm</h2>
+Backtest using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d3c16b8ba4f7df0bebbd766b3dfa7c14'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=dab462cab9b3f5c8b2a23911f3e98843'></script>
diff --git a/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html b/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html
new file mode 100755
index 0000000..d48da3d
--- /dev/null
+++ b/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html	
@@ -0,0 +1,110 @@
+<h2>Definition</h2>
+Iron Condor is an option strategy which involves four option contracts.All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Position</th>
+<th>Strike</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>long 1 OTM put</td>
+<td> A</td>
+</tr>
+<tr>
+<td>short 1 OTM put</td>
+<td> B</td>
+</tr>
+<tr>
+<td>short 1 OTM call</td>
+<td> C</td>
+</tr>
+<tr>
+<td>long 1 OTM call</td>
+<td> D</td>
+</tr>
+</tbody>
+</table>
+The Iron Condor is the combination of a bear put spread and a bull call spread in which the strike price of the long put is lower than the strike price of the long call. If the stock price is between the two short strike prices when the options expire, the strategy will be profitable.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums">price = np.arange(700,950,1)
+k_call_higher = 850 # the strike price of OTM call(Higher k)
+k_call_lower = 840 # the strike price of OTM call(Lower k)
+k_put_higher = 760 # the strike price of OTM put(Higher k)
+k_put_lower = 750 # the strike price of OTM put(Lower k)
+premium_call_higher = 1 # the premium of OTM call(Higher k)
+premium_call_lower = 10 # the premium of OTM call(Lower k)
+premium_put_higher = 11 # the premium of oTM put(Higher k)
+premium_put_lower = 1 # the premium of OTM put(Lower k)
+# payoff for the long put position
+payoff_long_put = [max(premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
+# payoff for the short put position
+payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_call_lower, -(i-k_call_lower+premium_call_lower)) for i in price]
+# payoff for the long call position
+payoff_long_call = [max(premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
+# payoff for Long Iron Condor Strategy
+payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
+plt.plot(price, payoff, label = 'Long Iron Condor',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Iron Condor Strategy Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-iron-condor.png" alt="iron condor strategy payoff"/>
+Here the strike price is A(750), B(760), C(850) and D(860). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 2, 1)
+	self.SetEndDate(2017, 3, 31)
+	self.SetCash(150000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-20, 20, timedelta(0), timedelta(40))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Break the candidate options into two parts: call and put options.
+<pre class="prettyprint linenums">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# filter the call and put options on the contracts
+	call = [i for i in chain if i.Right == 0]
+	put = [i for i in chain if i.Right == 1]
+</pre>
+Step 3: Sort the call and put options by their strike price respectively.
+<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
+put_contracts = sorted(put,key = lambda x: x.Strike)
+</pre>
+Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
+<pre class="prettyprint linenums">if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
+# Buy 1 OTM Put (Lower Strike)
+self.otm_put_lower = put_contracts[0]
+self.Buy(self.otm_put_lower.Symbol ,1)
+# Sell 1 OTM Put
+self.otm_put = put_contracts[15]
+self.Sell(self.otm_put.Symbol ,1)
+# Sell 1 OTM Call
+self.otm_call = call_contracts[-15]
+self.Sell(self.otm_call.Symbol ,1)
+# Buy 1 OTM Call (Higher Strike)
+self.otm_call_higher = call_contracts[-1]
+self.Buy(self.otm_call_higher.Symbol ,1)
+</pre>
+<h2>Summary</h2>
+The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
+<h2>Algorithm</h2>
+Backtesing using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e95a3d8feac3b92b3f39233f00e09ae5'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=66bf622f52dbb2a6cb56ccf992d64765'></script>
diff --git a/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html b/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html
new file mode 100755
index 0000000..2692b90
--- /dev/null
+++ b/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html	
@@ -0,0 +1,112 @@
+<h2>Definition</h2>
+Iron Butterfly is an option strategy which involves four option contracts. All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C.
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Position</th>
+<th>Strike</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Buy 1 OTM put</td>
+<td> A</td>
+</tr>
+<tr>
+<td>Sell 1 ATM put</td>
+<td> B</td>
+</tr>
+<tr>
+<td>Sell 1 ATM call</td>
+<td> B</td>
+</tr>
+<tr>
+<td>Buy 1 OTM call</td>
+<td> C</td>
+</tr>
+</tbody>
+</table>
+Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums">price = np.arange(700,950,1)
+k_atm = 830 # the strike price of ATM call &amp; put
+k_otm_put = 800 # the strike price of OTM put
+k_otm_call = 860 # the strike price of OTM call
+premium_otm_put = 2 # the premium of OTM put
+premium_atm_put = 7 # the premium of ATM put
+premium_atm_call = 8 # the premium of ATM call
+premium_otm_call = 1 # the premium of OTM call
+# payoff for the long put position
+payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
+# payoff for the short put position
+payoff_short_put = [min(premium_atm_put, -(k_atm-i-premium_atm_put)) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_atm_call, -(i-k_atm-premium_atm_call)) for i in price]
+# payoff for the long call position
+payoff_long_call = [max(-premium_otm_call, i-k_otm_call-premium_otm_call) for i in price]
+# payoff for Iron Butterfly Strategy
+payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
+plt.plot(price, payoff, label = 'Iron Butterfly',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Iron Butterfly Strategy Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-iron-butterfly.png" alt="iron butterfly strategy payoff"/>
+From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of ATM options. In this condition, all options expire worthless and you keep all premiums received. We can see the Iron Butterfly has more narrow structures than the Iron Condor. However, the profit can be higher than with the Iron Condor as you receive more premium by selling ATM options than OTM options.
+
+The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two conditions, two puts or two calls are exercised and the other two options expire worthless.
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 2, 1)
+	self.SetEndDate(2017, 3, 31)
+	self.SetCash(300000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-10, 10, timedelta(0), timedelta(30))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Break the candidate contracts into the call and put options.
+<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
+    for i in optionchain:
+        if i.Key != self.symbol: continue
+	chain = i.Value
+	# filter the call and put options from the contracts
+	call = [i for i in chain if i.Right == 0]
+	put = [i for i in chain if i.Right == 1]
+</pre>
+Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
+<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
+put_contracts = sorted(put,key = lambda x: x.Strike)
+if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
+</pre>
+Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
+<pre class="prettyprint linenums"># Sell 1 ATM Put
+self.atm_put = sorted(put_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+self.Sell(self.atm_put.Symbol ,1)
+# Sell 1 ATM Call
+self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+self.Sell(self.atm_call.Symbol ,1)
+# Buy 1 OTM Call
+self.otm_call = call_contracts[-1]
+self.Buy(self.otm_call.Symbol ,1)
+# Buy 1 OTM Put
+self.otm_put = put_contracts[0]
+self.Buy(self.otm_put.Symbol ,1)
+</pre>
+<h2>Summary</h2>
+In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of ATM call, after expiration we hold 100 short positions of GOOG stock.
+<h2>Algorithm</h2>
+Backtest using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=a2fe60a67bc1c78532ba4a1a93211c6c'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d60e69c80aeb5719f06202a0c25b1274'></script>
diff --git a/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html b/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html
new file mode 100755
index 0000000..ba609b0
--- /dev/null
+++ b/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html	
@@ -0,0 +1,85 @@
+<h2>Definition</h2>
+Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
+<h2>Payoff</h2>
+<pre class="prettyprint linenums"># Protective Collar
+price = np.arange(700,950,1)
+# assume at time 0, the price of the undelying stock is 830
+k_otm_put = 800 # the strike price of OTM put
+k_otm_call = 860 # the strike price of OTM call
+premium_otm_put = 6 # the premium of OTM put
+premium_otm_call = 2 # the premium of OTM call
+# payoff for the long put position
+payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_otm_call, -(i-k_otm_call-premium_otm_call)) for i in price]
+# payoff for the underlying stock
+payoff_stock = price - 830
+# payoff for the Protective Collar Strategy
+payoff = np.sum([payoff_long_put,payoff_short_call,payoff_stock], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_stock, label = 'Underlying Stock',linestyle='--')
+plt.plot(price, payoff, label = 'Protective Collar',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Protective Collar Strategy - Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-protective-collar.png" alt="protective collar strategy payoff"/>
+
+According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset add the net credit from the premium. It occurs when the stock price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with limit risk and limit profit.
+<h2>Implementation</h2>
+Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
+<pre class="prettyprint linenums">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(1000000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-10, +10, timedelta(0), timedelta(30))
+	# use the underlying equity as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+Step 2: Choose the expiration date for your options traded and break the options into the call and put contracts. The choice of expiration date depends on the holding period of stocks in your portfolio.
+<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
+    for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# choose the furthest expiration date within 30 days from now on
+	expiry = sorted(chain, key = lambda x: x.Expiry)[-1]
+	# filter the call options contracts
+	call = [x for x in chain if x.Right == 0 and x.Expiry == expiry]
+	# filter the put options contracts
+	put = [x for x in chain if x.Right == 1 and x.Expiry == expiry]
+</pre>
+Step 3: Choose the deep in-the-money call and put options in the list and then sell the call options and buy the put options.
+<pre class="prettyprint linenums">self.otm_call = sorted(call, key = lambda x: x.Strike)[-1]
+self.otm_put = sorted(put, key = lambda x: x.Strike)[0]
+if (self.otm_call is None) or (self.otm_put is None): continue
+self.Sell(self.otm_call.Symbol, 1) # sell the OTM call
+self.Buy(self.otm_put.Symbol, 1) # buy the OTM put
+</pre>
+Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
+<pre>def OnData(self,slice):
+    optionchain = slice.OptionChains
+    for i in slice.OptionChains:
+    if i.Key != self.symbol: continue
+    chains = i.Value
+    contract_list = [x for x in chains]
+    if (slice.OptionChains.Count == 0) or (len(contract_list) == 0): return
+    # if you don't hold options and stocks, buy the stocks and trade the options
+    if not self.Portfolio.Invested:
+	self.Buy("GOOG",100)	 # buy 100 shares of the underlying stock
+	self.TradeOptions(optionchain)   # sell OTM call and buy OTM put
+</pre>
+<h2>Summary</h2>
+In this algorithm, at the beginning  01/04/2016, we purchased 100 GOOG shares. At the same time, we purchased a $715 put at $6 and sells a $772.5 call at $2.45. The share price of GOOG is $739.32, which is between the strike prices of two out-the-money options. At the expiry 01/15/2016, the share price of GOOG drops to $714.32. The call option expire worthless but the put option is exercised. Then we sell 100 GOOG shares at $715. Then we hold neither option positions and stock positions.
+<h2>Algorithm</h2>
+Backtest using SetFilter
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5e793524109c9230ca92970f050f119f'></script>
+Backtest using OptionChainProvider
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=823c79dcfbae11dabb14ee9ea2365c13'></script>

From fab8890d1e401345d3c7b354921f9818d573cf03 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 6 Jun 2018 12:36:04 -0400
Subject: [PATCH 036/753] split covered call tutorial

---
 .../01 Covered Call/01 Covered Call.html      | 107 ++++++++++++------
 1 file changed, 72 insertions(+), 35 deletions(-)

diff --git a/07 Applied Options[]/01 Covered Call/01 Covered Call.html b/07 Applied Options[]/01 Covered Call/01 Covered Call.html
index 48e156d..6d278c9 100755
--- a/07 Applied Options[]/01 Covered Call/01 Covered Call.html	
+++ b/07 Applied Options[]/01 Covered Call/01 Covered Call.html	
@@ -1,34 +1,47 @@
 <h2>Definition</h2>
-A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
-
+<p>
+  A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
+</p>
+<p>
 The payoff  is as follows:
-<pre class="prettyprint linenums">import numpy as np
-import matplotlib.pyplot as plt
-%pylab inline
-price = np.arange(110,230,1) # the stock price at expiration date
-strike = 160 # the strike price
-premium = 7.5 # the option premium
-# the payoff of short call position
-payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
-# the payoff of long stock postion
-payoff_long_stock = [i-strike for i in price]
-# the payoff of covered call
-payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
-plt.figure(figsize=(20,11))
-plt.plot(price, payoff_short_call, label = 'short call')
-plt.plot(price, payoff_long_stock, label = 'long stock')
-plt.plot(price, payoff_covered_call, label = 'covered call')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial01-covered-call.png" alt="covered call strategy payoff"/>
+</p>
+<div class="section-example-container">
+<code class="csharp">
+</code>
+<code class="python">
+  import numpy as np
+  import matplotlib.pyplot as plt
+  %pylab inline
+  price = np.arange(110,230,1) # the stock price at expiration date
+  strike = 160 # the strike price
+  premium = 7.5 # the option premium
+  # the payoff of short call position
+  payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
+  # the payoff of long stock postion
+  payoff_long_stock = [i-strike for i in price]
+  # the payoff of covered call
+  payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
+  plt.figure(figsize=(20,11))
+  plt.plot(price, payoff_short_call, label = 'short call')
+  plt.plot(price, payoff_long_stock, label = 'long stock')
+  plt.plot(price, payoff_covered_call, label = 'covered call')
+  plt.legend(fontsize = 20)
+  plt.xlabel('Stock Price at Expiry',fontsize = 15)
+  plt.ylabel('payoff',fontsize = 15)
+  plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
+  plt.grid(True)
+</code>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial01-covered-call.png" alt="covered call strategy payoff"/>
 <h2>Implementation</h2>
 <h3>Step 1: Initialize the Algorithm</h3>
+<p>
 At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
-<pre class="prettyprint linenums">def Initialize(self):
+</p>
+<div class="section-example-container">
+<code class="csharp">
+</code>
+<code class="python">def Initialize(self):
    self.SetStartDate(2016, 1, 1)
    self.SetEndDate(2016, 3, 1)
    self.SetCash(100000)
@@ -40,15 +53,25 @@ <h3>Step 1: Initialize the Algorithm</h3>
    # use the underlying equity as the benchmark
    self.SetBenchmark(equity.Symbol)
    self.call = "IBM" # Initialize the call contract
-</pre>
+</code>
+</div>
+<p>
 Second, in the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
+</p>
 <h3>Step 2: Choose the Call Options Contract</h3>
-First, purchase the underlying stock for 100 shares or <span class="s1">an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.</span>
-
+<p>
+  First, purchase the underlying stock for 100 shares or an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.
+</p>
+<p>
 Second, filter out the call options from candidate contracts.<code>call = [x for x in chain if x.Right == 0]</code> For call options, the right is 0, for put options, the right is 1.
-
+</p>
+<p>
 Third, select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
-<pre class="prettyprint linenums">def TradeOptions(self,slice):
+</p>
+<div class="section-example-container">
+<code class="csharp">
+</code>
+<code class="python">def TradeOptions(self,slice):
 		if slice.OptionChains.Count == 0: return
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
@@ -64,9 +87,15 @@ <h3>Step 2: Choose the Call Options Contract</h3>
         if self.Portfolio["IBM"].Quantity == 0:
             self.Buy("IBM",100)     # buy 100 the underlying stock
             self.Log("The stock price at time 0 S(0): {}".format(self.Securities["IBM"].Price))
-</pre>
+</code>
+</div>
+<p>
 Fourth, at the expiration date we print out the price and position information
-<pre class="prettyprint linenums">def OnData(self,slice):
+</p>
+<div class="section-example-container">
+<code class="csharp">
+</code>
+<code class="python">def OnData(self,slice):
     if not self.Portfolio[self.call].Invested and self.Time.hour != 0 and self.Time.minute == 1:
         self.TradeOptions(slice) # sell the call option
 
@@ -76,11 +105,19 @@ <h3>Step 2: Choose the Call Options Contract</h3>
             self.Log("stock IBM quantity: {0}".format(self.Portfolio["IBM"].Quantity))
             self.Log("{0} quantity: {1}".format(self.call.Value, self.Portfolio[self.call].Quantity))
             self.Log("The stock price at Expiry S(T): {}".format(self.Securities["IBM"].Price))
-</pre>
+</code>
+</div>
+
 <h2>Summary</h2>
-From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+<p>
+  From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+</p>
 <h2>Algorithm</h2>
+<p>
 Backtest using SetFilter
+</p>
 <script src='https://www.quantconnect.com/terminal/backtest.js?sid=9d07e52d5c6199a77597d5f48e68353e'></script>
+<p>
 Backtest using OptionChainProvider
+</p>
 <script src='https://www.quantconnect.com/terminal/backtest.js?sid=31a6d88a35d29c2bbe654a3e5ee5f80e'></script>

From cd589af3f345151154f0e13877e6c328945a71ff Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 6 Jun 2018 12:50:38 -0400
Subject: [PATCH 037/753] split tutorial

---
 .../01 Covered Call/01 Definition.html        | 35 +++++++++
 ...vered Call.html => 02 Implementation.html} | 73 +++----------------
 .../01 Covered Call/03 Summary.html           |  4 +
 .../01 Covered Call/04 Algorithm.html         |  9 +++
 4 files changed, 60 insertions(+), 61 deletions(-)
 create mode 100755 07 Applied Options[]/01 Covered Call/01 Definition.html
 rename 07 Applied Options[]/01 Covered Call/{01 Covered Call.html => 02 Implementation.html} (55%)
 create mode 100755 07 Applied Options[]/01 Covered Call/03 Summary.html
 create mode 100755 07 Applied Options[]/01 Covered Call/04 Algorithm.html

diff --git a/07 Applied Options[]/01 Covered Call/01 Definition.html b/07 Applied Options[]/01 Covered Call/01 Definition.html
new file mode 100755
index 0000000..7b3078e
--- /dev/null
+++ b/07 Applied Options[]/01 Covered Call/01 Definition.html	
@@ -0,0 +1,35 @@
+<h2>Definition</h2>
+<p>
+  A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
+</p>
+<p>
+The payoff  is as follows:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+  import numpy as np
+  import matplotlib.pyplot as plt
+  %pylab inline
+  price = np.arange(110,230,1) # the stock price at expiration date
+  strike = 160 # the strike price
+  premium = 7.5 # the option premium
+  # the payoff of short call position
+  payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
+  # the payoff of long stock postion
+  payoff_long_stock = [i-strike for i in price]
+  # the payoff of covered call
+  payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
+  plt.figure(figsize=(20,11))
+  plt.plot(price, payoff_short_call, label = 'short call')
+  plt.plot(price, payoff_long_stock, label = 'long stock')
+  plt.plot(price, payoff_covered_call, label = 'covered call')
+  plt.legend(fontsize = 20)
+  plt.xlabel('Stock Price at Expiry',fontsize = 15)
+  plt.ylabel('payoff',fontsize = 15)
+  plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
+  plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial01-covered-call.png" alt="covered call strategy payoff"/>
diff --git a/07 Applied Options[]/01 Covered Call/01 Covered Call.html b/07 Applied Options[]/01 Covered Call/02 Implementation.html
similarity index 55%
rename from 07 Applied Options[]/01 Covered Call/01 Covered Call.html
rename to 07 Applied Options[]/01 Covered Call/02 Implementation.html
index 6d278c9..6895b3b 100755
--- a/07 Applied Options[]/01 Covered Call/01 Covered Call.html	
+++ b/07 Applied Options[]/01 Covered Call/02 Implementation.html	
@@ -1,47 +1,12 @@
-<h2>Definition</h2>
-<p>
-  A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
-</p>
-<p>
-The payoff  is as follows:
-</p>
-<div class="section-example-container">
-<code class="csharp">
-</code>
-<code class="python">
-  import numpy as np
-  import matplotlib.pyplot as plt
-  %pylab inline
-  price = np.arange(110,230,1) # the stock price at expiration date
-  strike = 160 # the strike price
-  premium = 7.5 # the option premium
-  # the payoff of short call position
-  payoff_short_call = [min(premium, -(i - strike-premium)) for i in price]
-  # the payoff of long stock postion
-  payoff_long_stock = [i-strike for i in price]
-  # the payoff of covered call
-  payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
-  plt.figure(figsize=(20,11))
-  plt.plot(price, payoff_short_call, label = 'short call')
-  plt.plot(price, payoff_long_stock, label = 'long stock')
-  plt.plot(price, payoff_covered_call, label = 'covered call')
-  plt.legend(fontsize = 20)
-  plt.xlabel('Stock Price at Expiry',fontsize = 15)
-  plt.ylabel('payoff',fontsize = 15)
-  plt.title('Covered Call Strategy Payoff at Expiration',fontsize = 20)
-  plt.grid(True)
-</code>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial01-covered-call.png" alt="covered call strategy payoff"/>
 <h2>Implementation</h2>
 <h3>Step 1: Initialize the Algorithm</h3>
 <p>
 At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
 </p>
 <div class="section-example-container">
-<code class="csharp">
-</code>
-<code class="python">def Initialize(self):
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
    self.SetStartDate(2016, 1, 1)
    self.SetEndDate(2016, 3, 1)
    self.SetCash(100000)
@@ -53,7 +18,7 @@ <h3>Step 1: Initialize the Algorithm</h3>
    # use the underlying equity as the benchmark
    self.SetBenchmark(equity.Symbol)
    self.call = "IBM" # Initialize the call contract
-</code>
+</pre>
 </div>
 <p>
 Second, in the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
@@ -69,9 +34,9 @@ <h3>Step 2: Choose the Call Options Contract</h3>
 Third, select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
 </p>
 <div class="section-example-container">
-<code class="csharp">
-</code>
-<code class="python">def TradeOptions(self,slice):
+<pre class="csharp">
+</pre>
+<pre class="python">def TradeOptions(self,slice):
 		if slice.OptionChains.Count == 0: return
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
@@ -87,15 +52,15 @@ <h3>Step 2: Choose the Call Options Contract</h3>
         if self.Portfolio["IBM"].Quantity == 0:
             self.Buy("IBM",100)     # buy 100 the underlying stock
             self.Log("The stock price at time 0 S(0): {}".format(self.Securities["IBM"].Price))
-</code>
+</pre>
 </div>
 <p>
 Fourth, at the expiration date we print out the price and position information
 </p>
 <div class="section-example-container">
-<code class="csharp">
-</code>
-<code class="python">def OnData(self,slice):
+<pre class="csharp">
+</pre>
+<pre class="python">def OnData(self,slice):
     if not self.Portfolio[self.call].Invested and self.Time.hour != 0 and self.Time.minute == 1:
         self.TradeOptions(slice) # sell the call option
 
@@ -105,19 +70,5 @@ <h3>Step 2: Choose the Call Options Contract</h3>
             self.Log("stock IBM quantity: {0}".format(self.Portfolio["IBM"].Quantity))
             self.Log("{0} quantity: {1}".format(self.call.Value, self.Portfolio[self.call].Quantity))
             self.Log("The stock price at Expiry S(T): {}".format(self.Securities["IBM"].Price))
-</code>
+</pre>
 </div>
-
-<h2>Summary</h2>
-<p>
-  From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
-</p>
-<h2>Algorithm</h2>
-<p>
-Backtest using SetFilter
-</p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9d07e52d5c6199a77597d5f48e68353e'></script>
-<p>
-Backtest using OptionChainProvider
-</p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=31a6d88a35d29c2bbe654a3e5ee5f80e'></script>
diff --git a/07 Applied Options[]/01 Covered Call/03 Summary.html b/07 Applied Options[]/01 Covered Call/03 Summary.html
new file mode 100755
index 0000000..57b0674
--- /dev/null
+++ b/07 Applied Options[]/01 Covered Call/03 Summary.html	
@@ -0,0 +1,4 @@
+<h2>Summary</h2>
+<p>
+  From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+</p>
diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
new file mode 100755
index 0000000..339c72a
--- /dev/null
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -0,0 +1,9 @@
+<h2>Algorithm</h2>
+<p>
+Backtest using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9d07e52d5c6199a77597d5f48e68353e'></script>
+<p>
+Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=31a6d88a35d29c2bbe654a3e5ee5f80e'></script>

From 1c54a2015738744ee8b6a02eaa7d81053d33e82d Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Wed, 6 Jun 2018 16:54:04 +0000
Subject: [PATCH 038/753] Automated push from server made by Jing Wu

---
 07 Applied Options[]/01 Covered Call/03 Summary.html | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/07 Applied Options[]/01 Covered Call/03 Summary.html b/07 Applied Options[]/01 Covered Call/03 Summary.html
index 57b0674..92e1b92 100755
--- a/07 Applied Options[]/01 Covered Call/03 Summary.html	
+++ b/07 Applied Options[]/01 Covered Call/03 Summary.html	
@@ -1,4 +1,3 @@
-<h2>Summary</h2>
 <p>
-  From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
 </p>

From ef2180a40ccaf96cd0ed912204a1922de5a85ef4 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Wed, 6 Jun 2018 16:54:26 +0000
Subject: [PATCH 039/753] Automated push from server made by Jing Wu

---
 07 Applied Options[]/01 Covered Call/04 Algorithm.html | 1 -
 1 file changed, 1 deletion(-)

diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index 339c72a..b7ae18c 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -1,4 +1,3 @@
-<h2>Algorithm</h2>
 <p>
 Backtest using SetFilter
 </p>

From 4634c9bfdb39fdbd3ef177ba8f3158b5b1404acc Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Wed, 6 Jun 2018 16:55:02 +0000
Subject: [PATCH 040/753] Automated push from server made by Jing Wu

---
 07 Applied Options[]/01 Covered Call/02 Implementation.html | 1 -
 1 file changed, 1 deletion(-)

diff --git a/07 Applied Options[]/01 Covered Call/02 Implementation.html b/07 Applied Options[]/01 Covered Call/02 Implementation.html
index 6895b3b..36f3ac5 100755
--- a/07 Applied Options[]/01 Covered Call/02 Implementation.html	
+++ b/07 Applied Options[]/01 Covered Call/02 Implementation.html	
@@ -1,4 +1,3 @@
-<h2>Implementation</h2>
 <h3>Step 1: Initialize the Algorithm</h3>
 <p>
 At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.

From 27768db43df9de8193b3a61b218719c2e6167600 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Wed, 6 Jun 2018 16:55:49 +0000
Subject: [PATCH 041/753] Automated push from server made by Jing Wu

---
 07 Applied Options[]/01 Covered Call/01 Definition.html | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/07 Applied Options[]/01 Covered Call/01 Definition.html b/07 Applied Options[]/01 Covered Call/01 Definition.html
index 7b3078e..25a802e 100755
--- a/07 Applied Options[]/01 Covered Call/01 Definition.html	
+++ b/07 Applied Options[]/01 Covered Call/01 Definition.html	
@@ -1,6 +1,5 @@
-<h2>Definition</h2>
 <p>
-  A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
+A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
 </p>
 <p>
 The payoff  is as follows:

From c03171f05d0415d9d104aa2ea967872664543d56 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 6 Jun 2018 16:15:14 -0400
Subject: [PATCH 042/753] split tutorial

---
 .../01 General Features of Options.html       | 119 +++++++++---------
 ...-Call Parity and Arbitrage Strategies.html | 118 ++++++++---------
 .../01 Covered Call/02 Implementation.html    |  36 ++----
 .../02 Bull Call Spread/01 Definition.html    |  37 ++++++
 .../02 Bull Call Spread.html                  |  74 -----------
 .../02 Implementation.html                    |  72 +++++++++++
 .../02 Bull Call Spread/03 Summary.html       |   3 +
 .../02 Bull Call Spread/04 Algorithm.html     |   8 ++
 .../03 Long Straddle/01 Definition.html       |  30 +++++
 .../03 Long Straddle/02 Implementation.html   |  51 ++++++++
 .../03 Long Straddle/03 Long Straddle.html    |  64 ----------
 .../03 Long Straddle/03 Summary.html          |   3 +
 .../03 Long Straddle/04 Algorithm.html        |   8 ++
 .../04 Long Strangle/01 Definition.html       |  32 +++++
 .../04 Long Strangle/02 Implementation.html   |  65 ++++++++++
 .../04 Long Strangle/03 Summary.html          |   6 +
 .../04 Long Strangle/04 Algorithm.html        |   8 ++
 .../04 Long Strangle/04 Long Strangle.html    |  77 ------------
 .../05 Butterfly Spread/01 Definition.html    |  67 ++++++++++
 .../02 Implementation.html                    |  61 +++++++++
 .../05 Butterfly Spread/03 Summary.html       |   3 +
 .../05 Butterfly Spread/04 Algorithm.html     |   8 ++
 .../05 Butterfly Spread.html                  | 105 ----------------
 .../06 Iron Condor/01 Definition.html         |  71 +++++++++++
 .../06 Iron Condor/02 Implementation.html     |  63 ++++++++++
 .../06 Iron Condor/03 Summary.html            |   4 +
 .../06 Iron Condor/04 Algorithm.html          |   9 ++
 .../06 Iron Condor/06 Iron Condor.html        | 110 ----------------
 .../07 Iron Butterfly/01 Definition.html      |  73 +++++++++++
 .../07 Iron Butterfly/02 Implementation.html  |  64 ++++++++++
 .../07 Iron Butterfly/03 Summary.html         |   3 +
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diff --git a/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html b/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html
index c946c4a..a3dbb6a 100755
--- a/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html	
@@ -1,18 +1,17 @@
 <h2>Introduction</h2>
-In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options markets are organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
+<p>
+  In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options market is organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
+</p>
 <h2>1. Definition</h2>
-<div class="page" title="Page 1">
-<div class="layoutArea">
-<div class="column">
-
-An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
-
-Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
-
-</div>
-</div>
-</div>
-First, we give the building blocks of an option contract and will discuss them further.
+<p>
+  An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
+</p>
+<p>
+  Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
+</p>
+<p>
+  First, we give the building blocks of an option contract and will discuss them further.
+</p>
 <table class="table qc-table">
 <tbody>
 <tr>
@@ -49,33 +48,39 @@ <h2>1. Definition</h2>
 </tr>
 </tbody>
 </table>
-<h2>2. Options Contracts</h2>
-Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
 
-Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
-
-In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
-<h2 class="p1">3. The Value of Options</h2>
-The option's premium consists of two parts: the intrinsic value and the time value.
+<h2>2. Options Contracts</h2>
+<p>
+  Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
+</p>
+<p>
+  Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
+</p>
+<p>
+  In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
+</p>
 
+<h2>3. The Value of Options</h2>
+<p>
+  The option's premium consists of two parts: the intrinsic value and the time value.
+</p>
+<div class="latex">
 \[Intrinsic Value_{call} = max(Current Underlying Price-Strike Price,0)\]
 \[Intrinsic Value_{put} = max(Strike Price-Current Underlying Price,0)\]
-
-From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
-
+</div>
+<p>
+  From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
+</p>
+<div class="latex">
 \[Time Value= Premium-Intrinsic Value\]
-
-For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contact is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
-<div class="page" title="Page 243">
-<div class="layoutArea">
-<div class="column">
+</div> 
+<p>
+  For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contact is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
+</p>
 <h2>4. Option Moneyness</h2>
-Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
-
-</div>
-<div class="page" title="Page 54">
-<div class="layoutArea">
-<div class="column">
+<p>
+  Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
+</p>
 <table class="table qc-table">
 <tbody>
 <tr>
@@ -94,35 +99,37 @@ <h2>4. Option Moneyness</h2>
 </tr>
 </tbody>
 </table>
-</div>
-<p class="p1"> For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.</p>
-<p class="p1">But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.</p>
-Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+
+<p> 
+  For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.
+</p>
+<p>
+  But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.
+</p>
+<p>
+  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+</p>
+
 <h2>5. Option Exercise and Assignment</h2>
-When you are the buyer of an option you have three ways to deal with your options.
+<p>
+  When you are the buyer of an option you have three ways to deal with your options.
+</p>
 <ol>
  	<li>You can close out the position at any given point prior to expiration (For buyers, write options and for sellers, buy options);</li>
  	<li>Wait until expiration date and out-of-the-money options will become worthless;</li>
  	<li>Exercise the options which are in-the-money, resulting in a trade of the underlying stock (The seller will be assigned the obligation to sell or buy the underlying stocks)</li>
 </ol>
-For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
+<p>
+  For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
+</p>
 <ol>
  	<li><strong>Exercise:</strong> Exercising option means that the option holder executes the right to buy or sell the underlying assets at the strike price.</li>
  	<li><strong> Assignment: </strong>When an option is exercised by the option holder, the option writer will be assigned the obligation to deliver the terms of the options contract. This is called the option assignment.</li>
 </ol>
-<div class="page" title="Page 10">
-<div class="layoutArea">
-<div class="column">
-
-To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
-
-</div>
-</div>
-<h2 class="p1">Summary</h2>
-<p class="p1"><span class="s1">An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.</span></p>
-
-</div>
-</div>
-</div>
-</div>
-</div>
+<p>
+  To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
+</p>
+<h2>Summary</h2>
+<p>
+  An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.
+</p>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html
index 26aa658..51bc85e 100755
--- a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html	
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html	
@@ -1,25 +1,25 @@
 <h2>Introduction</h2>
-In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
+<p>
+  In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
+</p>
 <h3>Option Payoff</h3>
-Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
-
-From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
-
+<p>
+  Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
+</p>
+<p>
+  From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
+</P>
 \[Call_{payoff}=max[0,S_T-K]\]
-<div class="page" title="Page 55">
-<div class="layoutArea">
-<div class="column">
-
-On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
-
+<p>
+  On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
+</p>
 \[Put_{payoff}=max[0,K-S_T]\]
-
-</div>
-</div>
-</div>
+<p>
 Where \(S_T\) is the price of underlying assets at maturity. K is the strike price.
-
-We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
+</p>
+<p>
+  We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
+</p>
 <table class="table qc-table">
 <thead>
 <tr>
@@ -35,7 +35,7 @@ <h3>Option Payoff</h3>
 <td> GOOG170714C00940000</td>
 <td> Call</td>
 <td> 07/14/2017</td>
-<td>$ 940</td>
+<td> $940</td>
 <td> $7.5</td>
 </tr>
 <tr>
@@ -47,8 +47,11 @@ <h3>Option Payoff</h3>
 </tr>
 </tbody>
 </table>
-If you long these two options, the payoff at expire date would be as follows
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
+If you long these two options, the payoff at expiration date would be as follows
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import matplotlib.pyplot as plt
 %pylab inline
 price = np.arange(900,1000,1)
 strike = 940
@@ -69,17 +72,24 @@ <h3>Option Payoff</h3>
 plt.title('Put option Payoff at Expiry')
 plt.grid(True)
 </pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-call-payoff.png" alt="call options payoff" />     <img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-put-payoff.png" alt="put options payoff" />
-
-The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay premuim for purchasing the contracts.
-<h3>2. Put-Call Parity</h3>
-Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next we will demonstrate how to derive the put-call parity according to John Hull's book.
-
-We consider two portfolios as follows,
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-call-payoff.png" alt="call options payoff" />     <img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-put-payoff.png" alt="put options payoff" />
 
-Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
+<p>
+  The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay premuim for purchasing the contracts.
+</p>
 
-Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
+<h3>2. Put-Call Parity</h3>
+<p>
+  Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next we will demonstrate how to derive the put-call parity according to John Hull's book.
+</p>
+<p>We consider two portfolios as follows,</p>
+<p>
+  Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
+</p>
+<p>
+  Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
+</p>
 <table class="table qc-table">
 <tbody>
 <tr>
@@ -99,23 +109,26 @@ <h3>2. Put-Call Parity</h3>
 </tr>
 </tbody>
 </table>
-From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolio must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
-
+<p>
+  From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolio must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
+</p>
 \[Price_{call}+Ke^{-rT}=Price_{put}+S_0\]
-
-If the dividend is paid during the option holding period, the share holder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put–call parity relationship is(D is the present value of dividends):
-
+<p>
+  If the dividend is paid during the option holding period, the share holder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put–call parity relationship is(D is the present value of dividends):
+</p>
 \[Price_{call}+D+Ke^{-rT}=Price_{put}+S_0\]
+
 <h3>3. Synthetic Positions</h3>
 <div class="page" title="Page 57">
 <div class="layoutArea">
 <div class="column">
-
-Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in an underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
-
+<p>
+  Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in an underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
+</p>
 [ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017 <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
-
-There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
+<p>
+  There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
+</p>
 <table class="table qc-table">
 <tbody>
 <tr>
@@ -134,25 +147,18 @@ <h3>3. Synthetic Positions</h3>
 </tr>
 </tbody>
 </table>
-Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
+<p>
+  Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
+</p>
+
 <h3>Algorithm</h3>
 <script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
 <h2>Summary</h2>
-<div class="page" title="Page 264">
-<div class="layoutArea">
-<div class="column">
-
-In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
-
-Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
-
-</div>
-</div>
-</div>
-&nbsp;
-
-&nbsp;
-
-</div>
+<p>
+  In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
+</p>
+<p>
+  Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
+</p>
 </div>
 </div>
diff --git a/07 Applied Options[]/01 Covered Call/02 Implementation.html b/07 Applied Options[]/01 Covered Call/02 Implementation.html
index 36f3ac5..dc972d4 100755
--- a/07 Applied Options[]/01 Covered Call/02 Implementation.html	
+++ b/07 Applied Options[]/01 Covered Call/02 Implementation.html	
@@ -1,6 +1,5 @@
-<h3>Step 1: Initialize the Algorithm</h3>
 <p>
-At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
+Step 1: Initialize the Algorithm： At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
 </p>
 <div class="section-example-container">
 <pre class="csharp">
@@ -20,17 +19,22 @@ <h3>Step 1: Initialize the Algorithm</h3>
 </pre>
 </div>
 <p>
-Second, in the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
+  In the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
 </p>
-<h3>Step 2: Choose the Call Options Contract</h3>
 <p>
-  First, purchase the underlying stock for 100 shares or an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.
+  Step 2: Choose the Call Options Contract: Purchasing the underlying stock for 100 shares or an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.
 </p>
 <p>
-Second, filter out the call options from candidate contracts.<code>call = [x for x in chain if x.Right == 0]</code> For call options, the right is 0, for put options, the right is 1.
+  Step 3: Filter out the call options from candidate contracts.
 </p>
+  <div class="section-example-container">
+  <pre class="csharp">
+  </pre>
+  <pre class="python">call = [x for x in chain if x.Right == OptionRight.Call]
+  </pre>
+  </div>
 <p>
-Third, select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
+  Step 4: Select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
 </p>
 <div class="section-example-container">
 <pre class="csharp">
@@ -53,21 +57,3 @@ <h3>Step 2: Choose the Call Options Contract</h3>
             self.Log("The stock price at time 0 S(0): {}".format(self.Securities["IBM"].Price))
 </pre>
 </div>
-<p>
-Fourth, at the expiration date we print out the price and position information
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def OnData(self,slice):
-    if not self.Portfolio[self.call].Invested and self.Time.hour != 0 and self.Time.minute == 1:
-        self.TradeOptions(slice) # sell the call option
-
-    # if the option contract expires, print out the price and position information
-    if slice.Delistings.Count > 0:
-        if [x.Key == self.call for x in slice.Delistings]:
-            self.Log("stock IBM quantity: {0}".format(self.Portfolio["IBM"].Quantity))
-            self.Log("{0} quantity: {1}".format(self.call.Value, self.Portfolio[self.call].Quantity))
-            self.Log("The stock price at Expiry S(T): {}".format(self.Securities["IBM"].Price))
-</pre>
-</div>
diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html
new file mode 100755
index 0000000..8f36ad9
--- /dev/null
+++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
@@ -0,0 +1,37 @@
+<p>
+ Bull Call Spread is an option strategy involved with two call option contracts with the same expiration but different strikes. The strategy buys the call options with a lower strike and sells the same amount of call options with a higher strike price.
+</p>
+<p>
+ This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss from the dropping price of the stock but still create a ceiling to the profit while the price is increasing.
+</p>
+<p>
+ Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(800,1100,1)
+k_low = 900 # lower strike price for call
+k_high = 1000 # higher strike price for call
+premium_low = 20 # premium of call option with lower strike
+premium_high = 2 # premium of call option with higher strike
+# long call with lower strike
+payoff_long_call = [max(-premium, i-k_low-premium_low ) for i in price]
+# short call with higher strike
+payoff_short_call = [min(premium, -(i-k_high-premium_high)) for i in price]
+payoff = np.sum([payoff_long_call, payoff_short_call], axis=0)
+plt.figure(figsize=(20,11))
+plt.plot(price, payoff_long_call, label = 'long call')
+plt.plot(price, payoff_short_call, label = 'short call')
+plt.plot(price, payoff, label = 'Bull Call Spread')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Bull Call Spread Payoff at Expiration',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-bull-call-spread.png" alt="Tutorial02-bull-call-spread"/>
+<p>
+ From the payoff plot we can see, the maximum profit of the strategy is the difference between call option strike price sold and call option strike price purchased minus the difference between buying lower strike call premium and selling higher strike call premium.
+</p>
diff --git a/07 Applied Options[]/02 Bull Call Spread/02 Bull Call Spread.html b/07 Applied Options[]/02 Bull Call Spread/02 Bull Call Spread.html
deleted file mode 100755
index 80dbca0..0000000
--- a/07 Applied Options[]/02 Bull Call Spread/02 Bull Call Spread.html	
+++ /dev/null
@@ -1,74 +0,0 @@
-<h2>Definition</h2>
-Bull Call Spread is an option strategy involved with two call option contracts with the same expiration but different strikes. The strategy buys the call options with a lower strike and sells the same amount of call options with a higher strike price.
-
-This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss from the dropping price of the stock but still create a ceiling to the profit while the price is increasing.
-
-Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
-<pre class="prettyprint linenums">price = np.arange(800,1100,1)
-k_low = 900 # lower strike price for call
-k_high = 1000 # higher strike price for call
-premium_low = 20 # premium of call option with lower strike
-premium_high = 2 # premium of call option with higher strike
-# long call with lower strike
-payoff_long_call = [max(-premium, i-k_low-premium_low ) for i in price]
-# short call with higher strike
-payoff_short_call = [min(premium, -(i-k_high-premium_high)) for i in price]
-payoff = np.sum([payoff_long_call, payoff_short_call], axis=0)
-plt.figure(figsize=(20,11))
-plt.plot(price, payoff_long_call, label = 'long call')
-plt.plot(price, payoff_short_call, label = 'short call')
-plt.plot(price, payoff, label = 'Bull Call Spread')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Bull Call Spread Payoff at Expiration',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-bull-call-spread.png" alt="Tutorial02-bull-call-spread"/>
-From the payoff plot we can see, the maximum profit of the strategy is the difference between call option strike price sold and call option strike price purchased minus the difference between buying lower strike call premium and selling higher strike call premium.
-<h2>Implementation</h2>
-Step 1: First, you need to initialize the algorithm including set the start date, end date and the cash required. Then use <code>option.SetFilter(-6, 6, timedelta(30), timedelta(60))</code> to filter the candidate contracts which expire in 30 days to 60 days from now on. The strike price range involves both ITM and OTM options. Then we get the option chains of GOOG.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2016, 5, 1)
-	self.SetEndDate(2016, 10, 1)
-	self.SetCash(200000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-6, 6, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Choose the contracts with the same expiration date. For demonstration purpose here we sorted the contracts by their expiration dates and choose the options with the furthest expiration date in the option chain.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-	for i in optionchain:
-		if i.Key != self.symbol: continue
-		chain = i.Value
-		# sorted the optionchain by expiration date and choose the furthest date
-		expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-</pre>
-Step 3: Filter the call options from the contracts which expire on the furthest expiration date in the option chain.
-<pre class="prettyprint linenums">call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 4: Sort the call options with the same expiration date according to their strike price. Then buy the call option with the lowest strike price and sell the call with the highest strike price.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-	if len(call_contracts) == 0: continue
-	# call option contract with lower strike
-	self.call_low = call_contracts[0]
-	# call option contract with higher strike
-	self.call_high = call_contracts[-1]
-	self.Buy(self.call_low.Symbol, 1)
-	self.Sell(self.call_high.Symbol ,1)
-</pre>
-Note here you need to add the following rules in <code>OnData(self,slice)</code> method because you only need to trade options once and wait until the contracts expire. If you already had securities invested in the portfolio, then you do not need to trade new options.
-<pre class="prettyprint linenums">if not self.Portfolio.Invested:
-	self.TradeOptions(optionchain)
-</pre>
-<h2>Summary</h2>
-This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
-<h2>Algorithm</h2>
-Backtest (SetFilter)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=06ae7450577c1f0c80edc563c4cf06b8'></script>
-Backtest (OptionChainProvider)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=db61c966b616e8d1d8f289c4eed5525c'></script>
diff --git a/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html
new file mode 100755
index 0000000..3365258
--- /dev/null
+++ b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html	
@@ -0,0 +1,72 @@
+<p>
+ Step 1: First, you need to initialize the algorithm including set the start date, end date and the cash required. Then use <code>option.SetFilter(-6, 6, timedelta(30), timedelta(60))</code> to filter the candidate contracts which expire in 30 days to 60 days from now on. The strike price range involves both ITM and OTM options. Then we get the option chains of GOOG.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2016, 5, 1)
+	self.SetEndDate(2016, 10, 1)
+	self.SetCash(200000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-6, 6, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Choose the contracts with the same expiration date. For demonstration purpose here we sorted the contracts by their expiration dates and choose the options with the furthest expiration date in the option chain.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def TradeOptions(self,optionchain):
+	for i in optionchain:
+		if i.Key != self.symbol: continue
+		chain = i.Value
+		# sorted the optionchain by expiration date and choose the furthest date
+		expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+</pre>
+</div>
+<p>
+ Step 3: Filter the call options from the contracts which expire on the furthest expiration date in the option chain.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">call = [i for i in chain if i.Expiry == expiry and i.Right == OptionRight.Call]
+</pre>
+</div>
+<p>
+ Step 4: Sort the call options with the same expiration date according to their strike price. Then buy the call option with the lowest strike price and sell the call with the highest strike price.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
+	if len(call_contracts) == 0: continue
+	# call option contract with lower strike
+	self.call_low = call_contracts[0]
+	# call option contract with higher strike
+	self.call_high = call_contracts[-1]
+	self.Buy(self.call_low.Symbol, 1)
+	self.Sell(self.call_high.Symbol ,1)
+</pre>
+</div>
+<p>
+ Note here you need to add the following rules in <code>OnData(self,slice)</code> method because you only need to trade options once and wait until the contracts expire. If you already had securities invested in the portfolio, then you do not need to trade new options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if not self.Portfolio.Invested:
+	self.TradeOptions(optionchain)
+</pre>
+</div>
+<h2>Summary</h2>
+<p>
+This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
+</p>
diff --git a/07 Applied Options[]/02 Bull Call Spread/03 Summary.html b/07 Applied Options[]/02 Bull Call Spread/03 Summary.html
new file mode 100755
index 0000000..22f9601
--- /dev/null
+++ b/07 Applied Options[]/02 Bull Call Spread/03 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
+</p>
diff --git a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html
new file mode 100755
index 0000000..59cd83b
--- /dev/null
+++ b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+Backtest (SetFilter)
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=06ae7450577c1f0c80edc563c4cf06b8'></script>
+<p>
+Backtest (OptionChainProvider)
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=db61c966b616e8d1d8f289c4eed5525c'></script>
diff --git a/07 Applied Options[]/03 Long Straddle/01 Definition.html b/07 Applied Options[]/03 Long Straddle/01 Definition.html
new file mode 100755
index 0000000..dff5980
--- /dev/null
+++ b/07 Applied Options[]/03 Long Straddle/01 Definition.html	
@@ -0,0 +1,30 @@
+<p>
+	Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(750,1000,1)
+strike = 900 # strike price for both call and put
+premium_call = 20 # premium of call option
+premium_put = 10 # premium of put option
+# payoff for the long call
+payoff_long_call = [max(-premium_call, i-strike-premium_call) for i in price]
+# payoff for the long put
+payoff_long_put = [max(-premium_put, strike-i-premium_put) for i in price]
+payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_call, label = 'Long Call')
+plt.plot(price, payoff_long_put, label = 'long put')
+plt.plot(price, payoff, label = 'Long Straddle')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Straddle Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-long-straddle.png" alt="long straddle strategy payoff"/>
+<p>
+ Given this plot, if the stock price moves sharply at the expiration either up or down, the Long Straddle will collect positive profit. the potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial, because the stock price can fall to zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be reached if the stock price is exactly equal to the strike price at expiration. Both options will expire worthless.
+</p>
diff --git a/07 Applied Options[]/03 Long Straddle/02 Implementation.html b/07 Applied Options[]/03 Long Straddle/02 Implementation.html
new file mode 100755
index 0000000..77f07cd
--- /dev/null
+++ b/07 Applied Options[]/03 Long Straddle/02 Implementation.html	
@@ -0,0 +1,51 @@
+<p>
+ Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 4, 01)
+	self.SetEndDate(2017, 6, 30)
+	self.SetCash(100000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-5, 5, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+ Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the optionchain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+	# sorted the contracts according to their strike prices
+	call_contracts = sorted(call,key = lambda x: x.Strike)
+	if len(call_contracts) == 0: continue
+	self.call = call_contracts[0]
+</pre>
+</div>
+<p>
+ Step 3: According to the call option contract, choose the put option with the same strike price and the same expiration date. Then buy the call option and the put option at the same time and wait until expiration.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"> for i in chain:
+	if i.Expiry == expiry and i.Right == 1 and i.Strike ==call_contracts[0].Strike:
+	    self.put = i
+self.Buy(self.call.Symbol ,1)
+self.Buy(self.put.Symbol ,1)
+</pre>
+</div>
diff --git a/07 Applied Options[]/03 Long Straddle/03 Long Straddle.html b/07 Applied Options[]/03 Long Straddle/03 Long Straddle.html
deleted file mode 100755
index 21f342a..0000000
--- a/07 Applied Options[]/03 Long Straddle/03 Long Straddle.html	
+++ /dev/null
@@ -1,64 +0,0 @@
-<h2>Definition</h2>
-Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(750,1000,1)
-strike = 900 # strike price for both call and put
-premium_call = 20 # premium of call option
-premium_put = 10 # premium of put option
-# payoff for the long call
-payoff_long_call = [max(-premium_call, i-strike-premium_call) for i in price]
-# payoff for the long put
-payoff_long_put = [max(-premium_put, strike-i-premium_put) for i in price]
-payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_call, label = 'Long Call')
-plt.plot(price, payoff_long_put, label = 'long put')
-plt.plot(price, payoff, label = 'Long Straddle')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Straddle Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-long-straddle.png" alt="long straddle strategy payoff"/>
-Given this plot, if the stock price moves sharply at the expiration either up or down, the Long Straddle will collect positive profit. the potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial, because the stock price can fall to zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be reached if the stock price is exactly equal to the strike price at expiration. Both options will expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 01)
-	self.SetEndDate(2017, 6, 30)
-	self.SetCash(100000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-5, 5, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)</pre>
-Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the optionchain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-	# sorted the contracts according to their strike prices
-	call_contracts = sorted(call,key = lambda x: x.Strike)
-	if len(call_contracts) == 0: continue
-	self.call = call_contracts[0]
-</pre>
-Step 3: According to the call option contract, choose the put option with the same strike price and the same expiration date. Then buy the call option and the put option at the same time and wait until expiration.
-<pre class="prettyprint linenums"> for i in chain:
-	if i.Expiry == expiry and i.Right == 1 and i.Strike ==call_contracts[0].Strike:
-	    self.put = i
-self.Buy(self.call.Symbol ,1)
-self.Buy(self.put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-There are three possible outcomes at expiration for the Long Straddle strategy. If the stock price is at the strike price at expiration, then both the call and the put become worthless and no stock position is created. If the stock price is above the strike price at expiration, the put option expires worthless, the long call is exercised, the stock is purchased at the strike price and a long stock position for is created. If the stock price is below the strike price at expiration, the call expires worthless, the long put is exercised, the stock is sold at the strike price and a short stock position is created. In this algorithm, the undelying asset is GOOG stock. We purchase both the $820 put and the $820 call at time 0. At the expiration, the share price of GOOG rises to 930 then the call option is exercised and the put options become worthless. After expiration, we hold long position for 100 shares of GOOG stock.
-<h2>Algorithm</h2>
-Backtest (Using SetFilter)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=0ff9a8022311bd57f1fe422eecdd3363'></script>
-Backtest (Using OptionChainProvider)
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75edf3c5d0221d22e8927ffedbf116b2'></script>
diff --git a/07 Applied Options[]/03 Long Straddle/03 Summary.html b/07 Applied Options[]/03 Long Straddle/03 Summary.html
new file mode 100755
index 0000000..95cadda
--- /dev/null
+++ b/07 Applied Options[]/03 Long Straddle/03 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+ There are three possible outcomes at expiration for the Long Straddle strategy. If the stock price is at the strike price at expiration, then both the call and the put become worthless and no stock position is created. If the stock price is above the strike price at expiration, the put option expires worthless, the long call is exercised, the stock is purchased at the strike price and a long stock position for is created. If the stock price is below the strike price at expiration, the call expires worthless, the long put is exercised, the stock is sold at the strike price and a short stock position is created. In this algorithm, the undelying asset is GOOG stock. We purchase both the $820 put and the $820 call at time 0. At the expiration, the share price of GOOG rises to 930 then the call option is exercised and the put options become worthless. After expiration, we hold long position for 100 shares of GOOG stock.
+</p>
diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
new file mode 100755
index 0000000..d393a4c
--- /dev/null
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+ Backtest (Using SetFilter)
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=0ff9a8022311bd57f1fe422eecdd3363'></script>
+<p>
+Backtest (Using OptionChainProvider)
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75edf3c5d0221d22e8927ffedbf116b2'></script>
diff --git a/07 Applied Options[]/04 Long Strangle/01 Definition.html b/07 Applied Options[]/04 Long Strangle/01 Definition.html
new file mode 100755
index 0000000..228e32b
--- /dev/null
+++ b/07 Applied Options[]/04 Long Strangle/01 Definition.html	
@@ -0,0 +1,32 @@
+<p>
+ Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(700,1000,1)
+# Suppose the undelying price at time 0 is 830
+k_call = 870 # The strike price of OTM call
+k_put = 795 # The strike price of OTM put
+premium_call = 8 # premium of call option
+premium_put = 10 # premium of put option
+# payoff for the long call
+payoff_long_call = [max(-premium_call, i-k_call-premium_call) for i in price]
+# payoff for the long put
+payoff_long_put = [max(-premium_put, k_put-i-premium_put) for i in price]
+payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_call, label = 'Long Call')
+plt.plot(price, payoff_long_put, label = 'long put')
+plt.plot(price, payoff, label = 'Long Strangle')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Strangle Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-long-strangle.png" alt="long strangle strategy payoff"/>
+<p>
+ From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both a put and a call option. The maximum loss occurs when the stock price falls between the strike price of two options. All options are worthless on expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
+</p>
diff --git a/07 Applied Options[]/04 Long Strangle/02 Implementation.html b/07 Applied Options[]/04 Long Strangle/02 Implementation.html
new file mode 100755
index 0000000..1ed16be
--- /dev/null
+++ b/07 Applied Options[]/04 Long Strangle/02 Implementation.html	
@@ -0,0 +1,65 @@
+<p>
+	Step 1: Initialize your algorithm including setting the start and end date, setting the cash and filtering the options contracts. Note here in SetFilter, the strike price should range from negative to positive because we need to choose out-of-the-money put and call options from candidate contracts. The strike price of OTM call should be greater than ATM options and the strike price of OTM put should be lower than ATM options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(100000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-15, 15, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Sort the option chain by expiration date and choose an expiration date you want to trade. For demonstration purpose, here we choose options with the furthest expiration date in candidate contracts. Then filter out the call options which expire on that date.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the option chain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+</pre>
+</div>
+<p>
+	Step 3: Sort the call options by their expiration date and choose the deep OTM contract which has the largest strike price.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
+if len(call_contracts) == 0: continue
+# choose the deep OTM call option
+self.call = call_contracts[-1]
+</pre>
+</div>
+<p>
+	Step 4: Select the put options which have the same expiration date with the call option and sort the put options by strike price. Then choose the deep out-of-the-money put which has the minimum strike price among all the available put options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">put_contracts = sorted([i for i in chain if i.Expiry == expiry and i.Right == 1], key = lambda x: x.Strike)
+# choose the deep OTM put option
+self.put = put_contracts[0]
+</pre>
+</div>
+Step 5: Buy the call and the put options at the same time and wait until expiration.
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Buy(self.call.Symbol ,1)
+self.Buy(self.put.Symbol ,1)
+</pre>
+</div>
diff --git a/07 Applied Options[]/04 Long Strangle/03 Summary.html b/07 Applied Options[]/04 Long Strangle/03 Summary.html
new file mode 100755
index 0000000..de22af7
--- /dev/null
+++ b/07 Applied Options[]/04 Long Strangle/03 Summary.html	
@@ -0,0 +1,6 @@
+<p>
+	In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16. Therefore we conclude the call option is exercised then we get 100 long stocks position. The put option expires worthless.
+</p>
+<p>
+	You can enter into Long Strangle if you have no clear idea of market direction but forecast there will be a great movement in the underlying asset. As the options you buy are all out of the money, that reduces the cost of this strategy. But because the call and the put options are all out of the money, the stock will need to move even more significantly than long straddle to gain positive profit from this strategy.
+</p>
diff --git a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html
new file mode 100755
index 0000000..640ac53
--- /dev/null
+++ b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+ Backtest using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=89f1f8e5f1ed5e78bd1b4cdf3ab796a1'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e3c07881318d864a9ae25637d6d2d9d3'></script>
diff --git a/07 Applied Options[]/04 Long Strangle/04 Long Strangle.html b/07 Applied Options[]/04 Long Strangle/04 Long Strangle.html
deleted file mode 100755
index 5a064fd..0000000
--- a/07 Applied Options[]/04 Long Strangle/04 Long Strangle.html	
+++ /dev/null
@@ -1,77 +0,0 @@
-<h2>Definition</h2>
-Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,1000,1)
-# Suppose the undelying price at time 0 is 830
-k_call = 870 # The strike price of OTM call
-k_put = 795 # The strike price of OTM put
-premium_call = 8 # premium of call option
-premium_put = 10 # premium of put option
-# payoff for the long call
-payoff_long_call = [max(-premium_call, i-k_call-premium_call) for i in price]
-# payoff for the long put
-payoff_long_put = [max(-premium_put, k_put-i-premium_put) for i in price]
-payoff = np.sum([payoff_long_call, payoff_long_put], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_call, label = 'Long Call')
-plt.plot(price, payoff_long_put, label = 'long put')
-plt.plot(price, payoff, label = 'Long Strangle')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Strangle Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-long-strangle.png" alt="long strangle strategy payoff"/>
-<div class="play-box">
-
-From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both a put and a call option. The maximum loss occurs when the stock price falls between the strike price of two options. All options are worthless on expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm including setting the start and end date, setting the cash and filtering the options contracts. Note here in SetFilter, the strike price should range from negative to positive because we need to choose out-of-the-money put and call options from candidate contracts. The strike price of OTM call should be greater than ATM options and the strike price of OTM put should be lower than ATM options.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(100000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-15, 15, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Sort the option chain by expiration date and choose an expiration date you want to trade. For demonstration purpose, here we choose options with the furthest expiration date in candidate contracts. Then filter out the call options which expire on that date.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the option chain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 3: Sort the call options by their expiration date and choose the deep OTM contract which has the largest strike price.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-if len(call_contracts) == 0: continue
-# choose the deep OTM call option
-self.call = call_contracts[-1]
-</pre>
-Step 4: Select the put options which have the same expiration date with the call option and sort the put options by strike price. Then choose the deep out-of-the-money put which has the minimum strike price among all the available put options.
-<pre class="prettyprint linenums">put_contracts = sorted([i for i in chain if i.Expiry == expiry and i.Right == 1], key = lambda x: x.Strike)
-# choose the deep OTM put option
-self.put = put_contracts[0]
-</pre>
-Step 5: Buy the call and the put options at the same time and wait until expiration.
-<pre class="prettyprint linenums">self.Buy(self.call.Symbol ,1)
-self.Buy(self.put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16. Therefore we conclude the call option is exercised then we get 100 long stocks position. The put option expires worthless.
-
-You can enter into Long Strangle if you have no clear idea of market direction but forecast there will be a great movement in the underlying asset. As the options you buy are all out of the money, that reduces the cost of this strategy. But because the call and the put options are all out of the money, the stock will need to move even more significantly than long straddle to gain positive profit from this strategy.
-
-</div>
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=89f1f8e5f1ed5e78bd1b4cdf3ab796a1'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e3c07881318d864a9ae25637d6d2d9d3'></script>
diff --git a/07 Applied Options[]/05 Butterfly Spread/01 Definition.html b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html
new file mode 100755
index 0000000..c605044
--- /dev/null
+++ b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html	
@@ -0,0 +1,67 @@
+<p>
+	Butterfly Spread strategy involves four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Name</th>
+<th> Strategy</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> Long butterfly spread with calls</td>
+<td> Buy 1 ITM call, sell 2 ATM call, buy 1 OTM call</td>
+</tr>
+<tr>
+<td> Long butterfly spread with puts</td>
+<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
+</tr>
+<tr>
+<td> Short butterfly spread with calls</td>
+<td> Sell 1 ITM call, buy 2 ATM call, sell 1 OTM call</td>
+</tr>
+<tr>
+<td> Short butterfly spread with puts</td>
+<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
+</tr>
+</tbody>
+</table>
+<p>
+ Butterfly Spread strategy consists of three legs with a total of four options. In this tutorial, we take Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
+</p>
+<p>
+	The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(800,1100,1)
+# Suppose the undelying price at time 0 is 935
+k_itm = 915 # the strike price of ITM call
+k_otm = 955 # the strike price of OTM call
+k_atm = 935 # the strike price of ATM call
+premium_itm = 45 # the premium of ITM call
+premium_otm = 15 # the premium of OTM call
+premium_atm = 25 # the premium of ATM call
+# payoff for the long ITM call position
+payoff_itm_long = [max(-premium_itm, i-k_itm-premium_itm) for i in price]
+# payoff for the long OTM call position
+payoff_otm_long = [max(-premium_otm, i-k_otm-premium_otm) for i in price]
+# payoff for the 2 short ATM call position
+payoff_atm_short = [min(2*premium_atm, -2*(i-k_atm-premium_atm)) for i in price]
+# payoff for Butterfly Spread Strategy
+payoff = np.sum([payoff_itm_long,payoff_otm_long,payoff_atm_short], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_itm_long, label = 'Long ITM Call')
+plt.plot(price, payoff_otm_long, label = 'Long OTM Call')
+plt.plot(price, payoff_atm_short, label = 'Short 2 ATM Call')
+plt.plot(price, payoff, label = 'Long Call Butterfly Spread')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Call Butterfly Spread Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-butterfly-spread.png" alt="butterfly spread strategy payoff"/>
diff --git a/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html
new file mode 100755
index 0000000..c565b12
--- /dev/null
+++ b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html	
@@ -0,0 +1,61 @@
+<p>
+	Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(150000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-9, 9, timedelta(30), timedelta(60))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# sorted the optionchain by expiration date and choose the furthest date
+	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
+	# filter the call options from the contracts expires on that date
+	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
+</pre>
+</div>
+<p>
+	Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># sorted the contracts according to their strike prices
+call_contracts = sorted(call,key = lambda x: x.Strike)
+if len(call_contracts) == 0: continue
+# choose OTM call
+self.otm_call = call_contracts[-1]
+# choose ITM call
+self.itm_call = call_contracts[0]
+# choose ATM call
+self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+</pre>
+</div>
+<p>
+	Step 4: Purchase 1 ITM call option and 1 OTM call option, then sell 2 ATM call option.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Sell(self.atm_call.Symbol ,2)
+self.Buy(self.itm_call.Symbol ,1)
+self.Buy(self.otm_call.Symbol ,1)
+</pre>
+</div>
diff --git a/07 Applied Options[]/05 Butterfly Spread/03 Summary.html b/07 Applied Options[]/05 Butterfly Spread/03 Summary.html
new file mode 100755
index 0000000..facfc5b
--- /dev/null
+++ b/07 Applied Options[]/05 Butterfly Spread/03 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+	From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option strike at $855, 1 ITM call option strike at $810 and sell 2 ATM options strike at 835. At the expiry 05/19/2017, the share price is $930, then the long positions of the ITM option and the OTM option are all exercised, we buy 100 GOOG  shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised. We have to sell 200  GOOG shares to option holder at $835. Then we no longer hold positions after the expiration of this Long Call Butterfly Spread. Since the stock price had a sharp increase during the life of options, we make no profits from this strategy.
+</p>
diff --git a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html
new file mode 100755
index 0000000..175d700
--- /dev/null
+++ b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+	Backtest using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d3c16b8ba4f7df0bebbd766b3dfa7c14'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=dab462cab9b3f5c8b2a23911f3e98843'></script>
diff --git a/07 Applied Options[]/05 Butterfly Spread/05 Butterfly Spread.html b/07 Applied Options[]/05 Butterfly Spread/05 Butterfly Spread.html
deleted file mode 100755
index 797c986..0000000
--- a/07 Applied Options[]/05 Butterfly Spread/05 Butterfly Spread.html	
+++ /dev/null
@@ -1,105 +0,0 @@
-<h2> Definition</h2>
-Butterfly Spread strategy involves four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Name</th>
-<th> Strategy</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> Long butterfly spread with calls</td>
-<td> Buy 1 ITM call, sell 2 ATM call, buy 1 OTM call</td>
-</tr>
-<tr>
-<td> Long butterfly spread with puts</td>
-<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
-</tr>
-<tr>
-<td> Short butterfly spread with calls</td>
-<td> Sell 1 ITM call, buy 2 ATM call, sell 1 OTM call</td>
-</tr>
-<tr>
-<td> Short butterfly spread with puts</td>
-<td> Buy 1 ITM put, sell 2 ATM put, buy 1 OTM put</td>
-</tr>
-</tbody>
-</table>
-Butterfly Spread strategy consists of three legs with a total of four options. In this tutorial, we take Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
-
-The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(800,1100,1)
-# Suppose the undelying price at time 0 is 935
-k_itm = 915 # the strike price of ITM call
-k_otm = 955 # the strike price of OTM call
-k_atm = 935 # the strike price of ATM call
-premium_itm = 45 # the premium of ITM call
-premium_otm = 15 # the premium of OTM call
-premium_atm = 25 # the premium of ATM call
-# payoff for the long ITM call position
-payoff_itm_long = [max(-premium_itm, i-k_itm-premium_itm) for i in price]
-# payoff for the long OTM call position
-payoff_otm_long = [max(-premium_otm, i-k_otm-premium_otm) for i in price]
-# payoff for the 2 short ATM call position
-payoff_atm_short = [min(2*premium_atm, -2*(i-k_atm-premium_atm)) for i in price]
-# payoff for Butterfly Spread Strategy
-payoff = np.sum([payoff_itm_long,payoff_otm_long,payoff_atm_short], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_itm_long, label = 'Long ITM Call')
-plt.plot(price, payoff_otm_long, label = 'Long OTM Call')
-plt.plot(price, payoff_atm_short, label = 'Short 2 ATM Call')
-plt.plot(price, payoff, label = 'Long Call Butterfly Spread')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Call Butterfly Spread Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-butterfly-spread.png" alt="butterfly spread strategy payoff"/>
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(150000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-9, 9, timedelta(30), timedelta(60))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# sorted the optionchain by expiration date and choose the furthest date
-	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
-	# filter the call options from the contracts expires on that date
-	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-</pre>
-Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
-<pre class="prettyprint linenums"># sorted the contracts according to their strike prices
-call_contracts = sorted(call,key = lambda x: x.Strike)
-if len(call_contracts) == 0: continue
-# choose OTM call
-self.otm_call = call_contracts[-1]
-# choose ITM call
-self.itm_call = call_contracts[0]
-# choose ATM call
-self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-</pre>
-Step 4: Purchase 1 ITM call option and 1 OTM call option, then sell 2 ATM call option.
-<pre class="prettyprint linenums">self.Sell(self.atm_call.Symbol ,2)
-self.Buy(self.itm_call.Symbol ,1)
-self.Buy(self.otm_call.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option strike at $855, 1 ITM call option strike at $810 and sell 2 ATM options strike at 835. At the expiry 05/19/2017, the share price is $930, then the long positions of the ITM option and the OTM option are all exercised, we buy 100 GOOG  shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised. We have to sell 200  GOOG shares to option holder at $835. Then we no longer hold positions after the expiration of this Long Call Butterfly Spread. Since the stock price had a sharp increase during the life of options, we make no profits from this strategy.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d3c16b8ba4f7df0bebbd766b3dfa7c14'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=dab462cab9b3f5c8b2a23911f3e98843'></script>
diff --git a/07 Applied Options[]/06 Iron Condor/01 Definition.html b/07 Applied Options[]/06 Iron Condor/01 Definition.html
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+++ b/07 Applied Options[]/06 Iron Condor/01 Definition.html	
@@ -0,0 +1,71 @@
+<p>
+	Iron Condor is an option strategy which involves four option contracts.All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Position</th>
+<th>Strike</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>long 1 OTM put</td>
+<td> A</td>
+</tr>
+<tr>
+<td>short 1 OTM put</td>
+<td> B</td>
+</tr>
+<tr>
+<td>short 1 OTM call</td>
+<td> C</td>
+</tr>
+<tr>
+<td>long 1 OTM call</td>
+<td> D</td>
+</tr>
+</tbody>
+</table>
+<p>
+	The Iron Condor is the combination of a bear put spread and a bull call spread in which the strike price of the long put is lower than the strike price of the long call. If the stock price is between the two short strike prices when the options expire, the strategy will be profitable.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(700,950,1)
+k_call_higher = 850 # the strike price of OTM call(Higher k)
+k_call_lower = 840 # the strike price of OTM call(Lower k)
+k_put_higher = 760 # the strike price of OTM put(Higher k)
+k_put_lower = 750 # the strike price of OTM put(Lower k)
+premium_call_higher = 1 # the premium of OTM call(Higher k)
+premium_call_lower = 10 # the premium of OTM call(Lower k)
+premium_put_higher = 11 # the premium of oTM put(Higher k)
+premium_put_lower = 1 # the premium of OTM put(Lower k)
+# payoff for the long put position
+payoff_long_put = [max(premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
+# payoff for the short put position
+payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_call_lower, -(i-k_call_lower+premium_call_lower)) for i in price]
+# payoff for the long call position
+payoff_long_call = [max(premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
+# payoff for Long Iron Condor Strategy
+payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
+plt.plot(price, payoff, label = 'Long Iron Condor',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Long Iron Condor Strategy Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-iron-condor.png" alt="iron condor strategy payoff"/>
+<p>
+	Here the strike price is A(750), B(760), C(850) and D(860). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
+</p>
diff --git a/07 Applied Options[]/06 Iron Condor/02 Implementation.html b/07 Applied Options[]/06 Iron Condor/02 Implementation.html
new file mode 100755
index 0000000..62d6898
--- /dev/null
+++ b/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
@@ -0,0 +1,63 @@
+<p>
+	Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 2, 1)
+	self.SetEndDate(2017, 3, 31)
+	self.SetCash(150000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-20, 20, timedelta(0), timedelta(40))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Break the candidate options into two parts: call and put options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# filter the call and put options on the contracts
+	call = [i for i in chain if i.Right == 0]
+	put = [i for i in chain if i.Right == 1]
+</pre>
+</div>
+<p>
+	Step 3: Sort the call and put options by their strike price respectively.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
+put_contracts = sorted(put,key = lambda x: x.Strike)
+</pre>
+</div>
+<p>
+	Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
+# Buy 1 OTM Put (Lower Strike)
+self.otm_put_lower = put_contracts[0]
+self.Buy(self.otm_put_lower.Symbol ,1)
+# Sell 1 OTM Put
+self.otm_put = put_contracts[15]
+self.Sell(self.otm_put.Symbol ,1)
+# Sell 1 OTM Call
+self.otm_call = call_contracts[-15]
+self.Sell(self.otm_call.Symbol ,1)
+# Buy 1 OTM Call (Higher Strike)
+self.otm_call_higher = call_contracts[-1]
+self.Buy(self.otm_call_higher.Symbol ,1)
+</pre>
+</div>
diff --git a/07 Applied Options[]/06 Iron Condor/03 Summary.html b/07 Applied Options[]/06 Iron Condor/03 Summary.html
new file mode 100755
index 0000000..0df6c97
--- /dev/null
+++ b/07 Applied Options[]/06 Iron Condor/03 Summary.html	
@@ -0,0 +1,4 @@
+<h2>Summary</h2>
+<p>
+	The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
+</p>
diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
new file mode 100755
index 0000000..127c50d
--- /dev/null
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -0,0 +1,9 @@
+<h2>Algorithm</h2>
+<p>
+	Backtesing using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e95a3d8feac3b92b3f39233f00e09ae5'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=66bf622f52dbb2a6cb56ccf992d64765'></script>
diff --git a/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html b/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html
deleted file mode 100755
index d48da3d..0000000
--- a/07 Applied Options[]/06 Iron Condor/06 Iron Condor.html	
+++ /dev/null
@@ -1,110 +0,0 @@
-<h2>Definition</h2>
-Iron Condor is an option strategy which involves four option contracts.All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Position</th>
-<th>Strike</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>long 1 OTM put</td>
-<td> A</td>
-</tr>
-<tr>
-<td>short 1 OTM put</td>
-<td> B</td>
-</tr>
-<tr>
-<td>short 1 OTM call</td>
-<td> C</td>
-</tr>
-<tr>
-<td>long 1 OTM call</td>
-<td> D</td>
-</tr>
-</tbody>
-</table>
-The Iron Condor is the combination of a bear put spread and a bull call spread in which the strike price of the long put is lower than the strike price of the long call. If the stock price is between the two short strike prices when the options expire, the strategy will be profitable.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,950,1)
-k_call_higher = 850 # the strike price of OTM call(Higher k)
-k_call_lower = 840 # the strike price of OTM call(Lower k)
-k_put_higher = 760 # the strike price of OTM put(Higher k)
-k_put_lower = 750 # the strike price of OTM put(Lower k)
-premium_call_higher = 1 # the premium of OTM call(Higher k)
-premium_call_lower = 10 # the premium of OTM call(Lower k)
-premium_put_higher = 11 # the premium of oTM put(Higher k)
-premium_put_lower = 1 # the premium of OTM put(Lower k)
-# payoff for the long put position
-payoff_long_put = [max(premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
-# payoff for the short put position
-payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_call_lower, -(i-k_call_lower+premium_call_lower)) for i in price]
-# payoff for the long call position
-payoff_long_call = [max(premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
-# payoff for Long Iron Condor Strategy
-payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
-plt.plot(price, payoff, label = 'Long Iron Condor',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Long Iron Condor Strategy Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-iron-condor.png" alt="iron condor strategy payoff"/>
-Here the strike price is A(750), B(760), C(850) and D(860). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 2, 1)
-	self.SetEndDate(2017, 3, 31)
-	self.SetCash(150000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-20, 20, timedelta(0), timedelta(40))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Break the candidate options into two parts: call and put options.
-<pre class="prettyprint linenums">for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# filter the call and put options on the contracts
-	call = [i for i in chain if i.Right == 0]
-	put = [i for i in chain if i.Right == 1]
-</pre>
-Step 3: Sort the call and put options by their strike price respectively.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-put_contracts = sorted(put,key = lambda x: x.Strike)
-</pre>
-Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
-<pre class="prettyprint linenums">if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
-# Buy 1 OTM Put (Lower Strike)
-self.otm_put_lower = put_contracts[0]
-self.Buy(self.otm_put_lower.Symbol ,1)
-# Sell 1 OTM Put
-self.otm_put = put_contracts[15]
-self.Sell(self.otm_put.Symbol ,1)
-# Sell 1 OTM Call
-self.otm_call = call_contracts[-15]
-self.Sell(self.otm_call.Symbol ,1)
-# Buy 1 OTM Call (Higher Strike)
-self.otm_call_higher = call_contracts[-1]
-self.Buy(self.otm_call_higher.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
-<h2>Algorithm</h2>
-Backtesing using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e95a3d8feac3b92b3f39233f00e09ae5'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=66bf622f52dbb2a6cb56ccf992d64765'></script>
diff --git a/07 Applied Options[]/07 Iron Butterfly/01 Definition.html b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html
new file mode 100755
index 0000000..4255a4b
--- /dev/null
+++ b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html	
@@ -0,0 +1,73 @@
+<p>
+	Iron Butterfly is an option strategy which involves four option contracts. All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th> Position</th>
+<th>Strike</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Buy 1 OTM put</td>
+<td> A</td>
+</tr>
+<tr>
+<td>Sell 1 ATM put</td>
+<td> B</td>
+</tr>
+<tr>
+<td>Sell 1 ATM call</td>
+<td> B</td>
+</tr>
+<tr>
+<td>Buy 1 OTM call</td>
+<td> C</td>
+</tr>
+</tbody>
+</table>
+<p>
+	Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = np.arange(700,950,1)
+k_atm = 830 # the strike price of ATM call &amp; put
+k_otm_put = 800 # the strike price of OTM put
+k_otm_call = 860 # the strike price of OTM call
+premium_otm_put = 2 # the premium of OTM put
+premium_atm_put = 7 # the premium of ATM put
+premium_atm_call = 8 # the premium of ATM call
+premium_otm_call = 1 # the premium of OTM call
+# payoff for the long put position
+payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
+# payoff for the short put position
+payoff_short_put = [min(premium_atm_put, -(k_atm-i-premium_atm_put)) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_atm_call, -(i-k_atm-premium_atm_call)) for i in price]
+# payoff for the long call position
+payoff_long_call = [max(-premium_otm_call, i-k_otm_call-premium_otm_call) for i in price]
+# payoff for Iron Butterfly Strategy
+payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
+plt.plot(price, payoff, label = 'Iron Butterfly',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Iron Butterfly Strategy Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-iron-butterfly.png" alt="iron butterfly strategy payoff"/>
+<p>
+	From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of ATM options. In this condition, all options expire worthless and you keep all premiums received. We can see the Iron Butterfly has more narrow structures than the Iron Condor. However, the profit can be higher than with the Iron Condor as you receive more premium by selling ATM options than OTM options.
+</p>
+<p>
+	The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two conditions, two puts or two calls are exercised and the other two options expire worthless.
+</p>
diff --git a/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html
new file mode 100755
index 0000000..0642a41
--- /dev/null
+++ b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html	
@@ -0,0 +1,64 @@
+<p>
+	Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 2, 1)
+	self.SetEndDate(2017, 3, 31)
+	self.SetCash(300000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	option.SetFilter(-10, 10, timedelta(0), timedelta(30))
+	# use the underlying equity GOOG as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Break the candidate contracts into the call and put options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def TradeOptions(self,optionchain):
+    for i in optionchain:
+        if i.Key != self.symbol: continue
+	chain = i.Value
+	# filter the call and put options from the contracts
+	call = [i for i in chain if i.Right == 0]
+	put = [i for i in chain if i.Right == 1]
+</pre>
+</div>
+<p>
+	Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
+put_contracts = sorted(put,key = lambda x: x.Strike)
+if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
+</pre>
+</div>
+<p>
+	Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># Sell 1 ATM Put
+self.atm_put = sorted(put_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+self.Sell(self.atm_put.Symbol ,1)
+# Sell 1 ATM Call
+self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
+self.Sell(self.atm_call.Symbol ,1)
+# Buy 1 OTM Call
+self.otm_call = call_contracts[-1]
+self.Buy(self.otm_call.Symbol ,1)
+# Buy 1 OTM Put
+self.otm_put = put_contracts[0]
+self.Buy(self.otm_put.Symbol ,1)
+</pre>
+</div>
diff --git a/07 Applied Options[]/07 Iron Butterfly/03 Summary.html b/07 Applied Options[]/07 Iron Butterfly/03 Summary.html
new file mode 100755
index 0000000..85595a2
--- /dev/null
+++ b/07 Applied Options[]/07 Iron Butterfly/03 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+	In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of ATM call, after expiration we hold 100 short positions of GOOG stock.
+</p>
diff --git a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html
new file mode 100755
index 0000000..bdaacd8
--- /dev/null
+++ b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+	Backtest using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=a2fe60a67bc1c78532ba4a1a93211c6c'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d60e69c80aeb5719f06202a0c25b1274'></script>
diff --git a/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html b/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html
deleted file mode 100755
index 2692b90..0000000
--- a/07 Applied Options[]/07 Iron Butterfly/07 Iron Butterfly.html	
+++ /dev/null
@@ -1,112 +0,0 @@
-<h2>Definition</h2>
-Iron Butterfly is an option strategy which involves four option contracts. All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C.
-<table class="table qc-table">
-<thead>
-<tr>
-<th> Position</th>
-<th>Strike</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Buy 1 OTM put</td>
-<td> A</td>
-</tr>
-<tr>
-<td>Sell 1 ATM put</td>
-<td> B</td>
-</tr>
-<tr>
-<td>Sell 1 ATM call</td>
-<td> B</td>
-</tr>
-<tr>
-<td>Buy 1 OTM call</td>
-<td> C</td>
-</tr>
-</tbody>
-</table>
-Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums">price = np.arange(700,950,1)
-k_atm = 830 # the strike price of ATM call &amp; put
-k_otm_put = 800 # the strike price of OTM put
-k_otm_call = 860 # the strike price of OTM call
-premium_otm_put = 2 # the premium of OTM put
-premium_atm_put = 7 # the premium of ATM put
-premium_atm_call = 8 # the premium of ATM call
-premium_otm_call = 1 # the premium of OTM call
-# payoff for the long put position
-payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
-# payoff for the short put position
-payoff_short_put = [min(premium_atm_put, -(k_atm-i-premium_atm_put)) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_atm_call, -(i-k_atm-premium_atm_call)) for i in price]
-# payoff for the long call position
-payoff_long_call = [max(-premium_otm_call, i-k_otm_call-premium_otm_call) for i in price]
-# payoff for Iron Butterfly Strategy
-payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_put, label = 'Short Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_long_call, label = 'Long Call',linestyle='--')
-plt.plot(price, payoff, label = 'Iron Butterfly',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Iron Butterfly Strategy Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-iron-butterfly.png" alt="iron butterfly strategy payoff"/>
-From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of ATM options. In this condition, all options expire worthless and you keep all premiums received. We can see the Iron Butterfly has more narrow structures than the Iron Condor. However, the profit can be higher than with the Iron Condor as you receive more premium by selling ATM options than OTM options.
-
-The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two conditions, two puts or two calls are exercised and the other two options expire worthless.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 2, 1)
-	self.SetEndDate(2017, 3, 31)
-	self.SetCash(300000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-10, 10, timedelta(0), timedelta(30))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Break the candidate contracts into the call and put options.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-    for i in optionchain:
-        if i.Key != self.symbol: continue
-	chain = i.Value
-	# filter the call and put options from the contracts
-	call = [i for i in chain if i.Right == 0]
-	put = [i for i in chain if i.Right == 1]
-</pre>
-Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
-<pre class="prettyprint linenums">call_contracts = sorted(call,key = lambda x: x.Strike)
-put_contracts = sorted(put,key = lambda x: x.Strike)
-if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
-</pre>
-Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
-<pre class="prettyprint linenums"># Sell 1 ATM Put
-self.atm_put = sorted(put_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-self.Sell(self.atm_put.Symbol ,1)
-# Sell 1 ATM Call
-self.atm_call = sorted(call_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
-self.Sell(self.atm_call.Symbol ,1)
-# Buy 1 OTM Call
-self.otm_call = call_contracts[-1]
-self.Buy(self.otm_call.Symbol ,1)
-# Buy 1 OTM Put
-self.otm_put = put_contracts[0]
-self.Buy(self.otm_put.Symbol ,1)
-</pre>
-<h2>Summary</h2>
-In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of ATM call, after expiration we hold 100 short positions of GOOG stock.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=a2fe60a67bc1c78532ba4a1a93211c6c'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d60e69c80aeb5719f06202a0c25b1274'></script>
diff --git a/07 Applied Options[]/08 Protective Collar/01 Definition.html b/07 Applied Options[]/08 Protective Collar/01 Definition.html
new file mode 100755
index 0000000..29bbc40
--- /dev/null
+++ b/07 Applied Options[]/08 Protective Collar/01 Definition.html	
@@ -0,0 +1,37 @@
+<p>
+	Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># Protective Collar
+price = np.arange(700,950,1)
+# assume at time 0, the price of the undelying stock is 830
+k_otm_put = 800 # the strike price of OTM put
+k_otm_call = 860 # the strike price of OTM call
+premium_otm_put = 6 # the premium of OTM put
+premium_otm_call = 2 # the premium of OTM call
+# payoff for the long put position
+payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
+# payoff for the short call position
+payoff_short_call = [min(premium_otm_call, -(i-k_otm_call-premium_otm_call)) for i in price]
+# payoff for the underlying stock
+payoff_stock = price - 830
+# payoff for the Protective Collar Strategy
+payoff = np.sum([payoff_long_put,payoff_short_call,payoff_stock], axis=0)
+plt.figure(figsize=(20,15))
+plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
+plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
+plt.plot(price, payoff_stock, label = 'Underlying Stock',linestyle='--')
+plt.plot(price, payoff, label = 'Protective Collar',c='black')
+plt.legend(fontsize = 20)
+plt.xlabel('Stock Price at Expiry',fontsize = 15)
+plt.ylabel('payoff',fontsize = 15)
+plt.title('Protective Collar Strategy - Payoff',fontsize = 20)
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-protective-collar.png" alt="protective collar strategy payoff"/>
+<p>
+	According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset add the net credit from the premium. It occurs when the stock price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with limit risk and limit profit.
+</p>
diff --git a/07 Applied Options[]/08 Protective Collar/02 Implementation.html b/07 Applied Options[]/08 Protective Collar/02 Implementation.html
new file mode 100755
index 0000000..c37dea3
--- /dev/null
+++ b/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
@@ -0,0 +1,69 @@
+<p>
+	Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2017, 4, 1)
+	self.SetEndDate(2017, 5, 30)
+	self.SetCash(1000000)
+	equity = self.AddEquity("GOOG", Resolution.Minute)
+	option = self.AddOption("GOOG", Resolution.Minute)
+	self.symbol = option.Symbol
+	# set our strike/expiry filter for this option chain
+	option.SetFilter(-10, +10, timedelta(0), timedelta(30))
+	# use the underlying equity as the benchmark
+	self.SetBenchmark(equity.Symbol)
+</pre>
+</div>
+<p>
+	Step 2: Choose the expiration date for your options traded and break the options into the call and put contracts. The choice of expiration date depends on the holding period of stocks in your portfolio.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def TradeOptions(self,optionchain):
+    for i in optionchain:
+	if i.Key != self.symbol: continue
+	chain = i.Value
+	# choose the furthest expiration date within 30 days from now on
+	expiry = sorted(chain, key = lambda x: x.Expiry)[-1]
+	# filter the call options contracts
+	call = [x for x in chain if x.Right == 0 and x.Expiry == expiry]
+	# filter the put options contracts
+	put = [x for x in chain if x.Right == 1 and x.Expiry == expiry]
+</pre>
+</div>
+<p>
+	Step 3: Choose the deep in-the-money call and put options in the list and then sell the call options and buy the put options.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.otm_call = sorted(call, key = lambda x: x.Strike)[-1]
+self.otm_put = sorted(put, key = lambda x: x.Strike)[0]
+if (self.otm_call is None) or (self.otm_put is None): continue
+self.Sell(self.otm_call.Symbol, 1) # sell the OTM call
+self.Buy(self.otm_put.Symbol, 1) # buy the OTM put
+</pre>
+</div>
+<p>
+	Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def OnData(self,slice):
+    optionchain = slice.OptionChains
+    for i in slice.OptionChains:
+    if i.Key != self.symbol: continue
+    chains = i.Value
+    contract_list = [x for x in chains]
+    if (slice.OptionChains.Count == 0) or (len(contract_list) == 0): return
+    # if you don't hold options and stocks, buy the stocks and trade the options
+    if not self.Portfolio.Invested:
+	self.Buy("GOOG",100)	 # buy 100 shares of the underlying stock
+	self.TradeOptions(optionchain)   # sell OTM call and buy OTM put
+</pre>
+</div>
diff --git a/07 Applied Options[]/08 Protective Collar/03 Summary.html b/07 Applied Options[]/08 Protective Collar/03 Summary.html
new file mode 100755
index 0000000..08e0894
--- /dev/null
+++ b/07 Applied Options[]/08 Protective Collar/03 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+	In this algorithm, at the beginning  01/04/2016, we purchased 100 GOOG shares. At the same time, we purchased a $715 put at $6 and sells a $772.5 call at $2.45. The share price of GOOG is $739.32, which is between the strike prices of two out-the-money options. At the expiry 01/15/2016, the share price of GOOG drops to $714.32. The call option expire worthless but the put option is exercised. Then we sell 100 GOOG shares at $715. Then we hold neither option positions and stock positions.
+</p>
diff --git a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html
new file mode 100755
index 0000000..e9e3bd1
--- /dev/null
+++ b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+	Backtest using SetFilter
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5e793524109c9230ca92970f050f119f'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=823c79dcfbae11dabb14ee9ea2365c13'></script>
diff --git a/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html b/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html
deleted file mode 100755
index ba609b0..0000000
--- a/07 Applied Options[]/08 Protective Collar/08 Protective Collar.html	
+++ /dev/null
@@ -1,85 +0,0 @@
-<h2>Definition</h2>
-Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
-<h2>Payoff</h2>
-<pre class="prettyprint linenums"># Protective Collar
-price = np.arange(700,950,1)
-# assume at time 0, the price of the undelying stock is 830
-k_otm_put = 800 # the strike price of OTM put
-k_otm_call = 860 # the strike price of OTM call
-premium_otm_put = 6 # the premium of OTM put
-premium_otm_call = 2 # the premium of OTM call
-# payoff for the long put position
-payoff_long_put = [max(-premium_otm_put, k_otm_put-i-premium_otm_put) for i in price]
-# payoff for the short call position
-payoff_short_call = [min(premium_otm_call, -(i-k_otm_call-premium_otm_call)) for i in price]
-# payoff for the underlying stock
-payoff_stock = price - 830
-# payoff for the Protective Collar Strategy
-payoff = np.sum([payoff_long_put,payoff_short_call,payoff_stock], axis=0)
-plt.figure(figsize=(20,15))
-plt.plot(price, payoff_long_put, label = 'Long Put',linestyle='--')
-plt.plot(price, payoff_short_call, label = 'Short Call',linestyle='--')
-plt.plot(price, payoff_stock, label = 'Underlying Stock',linestyle='--')
-plt.plot(price, payoff, label = 'Protective Collar',c='black')
-plt.legend(fontsize = 20)
-plt.xlabel('Stock Price at Expiry',fontsize = 15)
-plt.ylabel('payoff',fontsize = 15)
-plt.title('Protective Collar Strategy - Payoff',fontsize = 20)
-plt.grid(True)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-protective-collar.png" alt="protective collar strategy payoff"/>
-
-According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset add the net credit from the premium. It occurs when the stock price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with limit risk and limit profit.
-<h2>Implementation</h2>
-Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
-<pre class="prettyprint linenums">def Initialize(self):
-	self.SetStartDate(2017, 4, 1)
-	self.SetEndDate(2017, 5, 30)
-	self.SetCash(1000000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	# set our strike/expiry filter for this option chain
-	option.SetFilter(-10, +10, timedelta(0), timedelta(30))
-	# use the underlying equity as the benchmark
-	self.SetBenchmark(equity.Symbol)
-</pre>
-Step 2: Choose the expiration date for your options traded and break the options into the call and put contracts. The choice of expiration date depends on the holding period of stocks in your portfolio.
-<pre class="prettyprint linenums">def TradeOptions(self,optionchain):
-    for i in optionchain:
-	if i.Key != self.symbol: continue
-	chain = i.Value
-	# choose the furthest expiration date within 30 days from now on
-	expiry = sorted(chain, key = lambda x: x.Expiry)[-1]
-	# filter the call options contracts
-	call = [x for x in chain if x.Right == 0 and x.Expiry == expiry]
-	# filter the put options contracts
-	put = [x for x in chain if x.Right == 1 and x.Expiry == expiry]
-</pre>
-Step 3: Choose the deep in-the-money call and put options in the list and then sell the call options and buy the put options.
-<pre class="prettyprint linenums">self.otm_call = sorted(call, key = lambda x: x.Strike)[-1]
-self.otm_put = sorted(put, key = lambda x: x.Strike)[0]
-if (self.otm_call is None) or (self.otm_put is None): continue
-self.Sell(self.otm_call.Symbol, 1) # sell the OTM call
-self.Buy(self.otm_put.Symbol, 1) # buy the OTM put
-</pre>
-Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
-<pre>def OnData(self,slice):
-    optionchain = slice.OptionChains
-    for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    chains = i.Value
-    contract_list = [x for x in chains]
-    if (slice.OptionChains.Count == 0) or (len(contract_list) == 0): return
-    # if you don't hold options and stocks, buy the stocks and trade the options
-    if not self.Portfolio.Invested:
-	self.Buy("GOOG",100)	 # buy 100 shares of the underlying stock
-	self.TradeOptions(optionchain)   # sell OTM call and buy OTM put
-</pre>
-<h2>Summary</h2>
-In this algorithm, at the beginning  01/04/2016, we purchased 100 GOOG shares. At the same time, we purchased a $715 put at $6 and sells a $772.5 call at $2.45. The share price of GOOG is $739.32, which is between the strike prices of two out-the-money options. At the expiry 01/15/2016, the share price of GOOG drops to $714.32. The call option expire worthless but the put option is exercised. Then we sell 100 GOOG shares at $715. Then we hold neither option positions and stock positions.
-<h2>Algorithm</h2>
-Backtest using SetFilter
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5e793524109c9230ca92970f050f119f'></script>
-Backtest using OptionChainProvider
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=823c79dcfbae11dabb14ee9ea2365c13'></script>

From a4fef01bdd4d1ebc3c55a54ca1b73fe34e07e211 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 6 Jun 2018 18:37:24 -0400
Subject: [PATCH 043/753] split tutorial

---
 .../01 General Features of Options.html       | 135 ---------
 .../01 Introduction.html                      |   3 +
 .../02 Definition.html                        |  45 +++
 .../03 Options Contract.html                  |   9 +
 .../04 The Value of Options.html              |  16 +
 .../05 Options Moneyness.html                 |  30 ++
 .../06 Exercise and Assignment.html           |  18 ++
 .../07 Summary.html                           |   3 +
 .../01 Introduction.html                      |   4 +
 .../02 Option Payoff.html                     |  76 +++++
 ...-Call Parity and Arbitrage Strategies.html | 164 -----------
 .../03 Put-Call Parity.html                   |  37 +++
 .../04 Synthetic Positions.html               |  31 ++
 .../05 Algorithm.html                         |   1 +
 .../06 Summary.html                           |   6 +
 .../01 Introduction.html                      |   3 +
 .../02 Brownian motion.html                   | 195 +++++++++++++
 ...stic Processes and Monte Carlo Method.html | 180 ------------
 ...astic process followed by stock price.html |  47 +++
 ...Monte Carlo Simulation of Stock Price.html |  33 +++
 ... Carlo Simulation of European Options.html |  38 +++
 .../06 Summary.html                           |   6 +
 .../01 Introduction.html                      |   3 +
 .../02 Determinants of Option Price.html      |  11 +
 .../03 Factors in BSM model.html              |  52 ++++
 .../04 Model Assumptions.html                 |  12 +
 ...ns Pricing Black Scholes Merton Model.html | 140 ---------
 .../05 Model Equations.html                   |  68 +++++
 .../06 Summary.html                           |   3 +
 .../05 The Greek Letters/01 Introduction.html |   4 +
 .../05 The Greek Letters/02 Delta.html        | 276 ++++++++++++++++++
 .../05 The Greek Letters/03 gamma.html        | 276 ++++++++++++++++++
 .../05 The Greek Letters/04 vega.html         | 276 ++++++++++++++++++
 .../05 The Greek Letters.html                 | 229 ---------------
 .../05 The Greek Letters/05 Theta.html        | 276 ++++++++++++++++++
 .../05 The Greek Letters/06 Rho.html          | 276 ++++++++++++++++++
 .../05 The Greek Letters/07 Summary.html      | 276 ++++++++++++++++++
 .../01 Introduction.html                      |   3 +
 .../02 Historical Volatility.html             |  43 +++
 .../03 Implied Volatility.html                |  72 +++++
 .../04 Volatility Skew.html                   | 101 +++++++
 .../05Volatility Surface.html                 |   8 +
 ...cal Volatility and Implied Volatility.html | 191 ------------
 .../06 The Reason for Volatility Skew.html    |   5 +
 .../07 Summary.html                           |   4 +
 .../04 Long Strangle/02 Implementation.html   |   4 +-
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diff --git a/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html b/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html
deleted file mode 100755
index a3dbb6a..0000000
--- a/06 Introduction to Options[]/01 General Features of Options/01 General Features of Options.html	
+++ /dev/null
@@ -1,135 +0,0 @@
-<h2>Introduction</h2>
-<p>
-  In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options market is organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
-</p>
-<h2>1. Definition</h2>
-<p>
-  An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
-</p>
-<p>
-  Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
-</p>
-<p>
-  First, we give the building blocks of an option contract and will discuss them further.
-</p>
-<table class="table qc-table">
-<tbody>
-<tr>
-<td style="text-align: left;"><strong>Style</strong></td>
-<td style="text-align: left;"><strong>American Option:</strong> the holder of an option has the right to exercise his option at any time before the expiration date
-<strong>European Option:</strong> an option which can only be exercised on its expiration date</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Type</b></td>
-<td style="text-align: left;">Call option, Put option</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Underlying Asset</strong></td>
-<td>The security on which the option is bound. The underlying could be stocks(stock option), stock indices(index option), exchange rate(Foreign exchange option) or even futures(Futures Options).
-Note: The options in the tutorial refer to the stock options.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Premium</strong></td>
-<td style="text-align: left;">The price of the option. (Premiums are quoted on a per share basis).
-The option premium depends on the strike price, volatility of the underlying, as well as the time remaining to expiration.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Strike Price</strong></td>
-<td>The specified price at which the stock can be bought or sold when the option is exercised.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>Expiration Date</strong></td>
-<td>The last day that an options contract is valid. All options expire after a certain period of time. The right to exercise the option will no longer exist once the stock option expires.</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Participants</b></td>
-<td style="text-align: left;"><strong>Holder:</strong> People who buy the options(have long positions)
-<strong>Writer:</strong> People who sell the options(have short positions)</td>
-</tr>
-</tbody>
-</table>
-
-<h2>2. Options Contracts</h2>
-<p>
-  Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
-</p>
-<p>
-  Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
-</p>
-<p>
-  In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
-</p>
-
-<h2>3. The Value of Options</h2>
-<p>
-  The option's premium consists of two parts: the intrinsic value and the time value.
-</p>
-<div class="latex">
-\[Intrinsic Value_{call} = max(Current Underlying Price-Strike Price,0)\]
-\[Intrinsic Value_{put} = max(Strike Price-Current Underlying Price,0)\]
-</div>
-<p>
-  From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
-</p>
-<div class="latex">
-\[Time Value= Premium-Intrinsic Value\]
-</div> 
-<p>
-  For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contact is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
-</p>
-<h2>4. Option Moneyness</h2>
-<p>
-  Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
-</p>
-<table class="table qc-table">
-<tbody>
-<tr>
-<td style="text-align: left;"><b>At the Money(ATM)</b></td>
-<td style="text-align: left;">The strike price is the same as the current price of the underlying asset</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>In the Money(ITM)</b></td>
-<td style="text-align: left;"><strong>Call Option</strong>: the strike price is below the current trading price of the underlying
-<strong>Put option</strong>:  the strike price is above the spot price of the underlying</td>
-</tr>
-<tr>
-<td style="text-align: left;"><b>Out of the Money(OTM)</b></td>
-<td style="text-align: left;"><strong>Call Option: </strong>the strike price is above the current trading price of the underlying
-<strong>Put Option:</strong> the strike price is below the current trading price of the underlying</td>
-</tr>
-</tbody>
-</table>
-
-<p> 
-  For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.
-</p>
-<p>
-  But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.
-</p>
-<p>
-  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
-</p>
-
-<h2>5. Option Exercise and Assignment</h2>
-<p>
-  When you are the buyer of an option you have three ways to deal with your options.
-</p>
-<ol>
- 	<li>You can close out the position at any given point prior to expiration (For buyers, write options and for sellers, buy options);</li>
- 	<li>Wait until expiration date and out-of-the-money options will become worthless;</li>
- 	<li>Exercise the options which are in-the-money, resulting in a trade of the underlying stock (The seller will be assigned the obligation to sell or buy the underlying stocks)</li>
-</ol>
-<p>
-  For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
-</p>
-<ol>
- 	<li><strong>Exercise:</strong> Exercising option means that the option holder executes the right to buy or sell the underlying assets at the strike price.</li>
- 	<li><strong> Assignment: </strong>When an option is exercised by the option holder, the option writer will be assigned the obligation to deliver the terms of the options contract. This is called the option assignment.</li>
-</ol>
-<p>
-  To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
-</p>
-<h2>Summary</h2>
-<p>
-  An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.
-</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/01 Introduction.html b/06 Introduction to Options[]/01 General Features of Options/01 Introduction.html
new file mode 100755
index 0000000..c07ec6b
--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+In this tutorial, we will discuss the basic features of options to help you gain insight on what the option is, how options market is organized and how these contracts are traded. Then we will discuss the settlement rules of option contracts and the moneyness of options.
+</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/02 Definition.html b/06 Introduction to Options[]/01 General Features of Options/02 Definition.html
new file mode 100755
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--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/02 Definition.html	
@@ -0,0 +1,45 @@
+<p>
+  An option is a type of financial derivative. Derivatives are financial contracts and its value is dependent on or derived from underlying assets. The underlying assets could be stocks, indices, commodities, currencies, exchange rates, or the interest rate. When you trade options contracts, it can help you generate profits by betting on the future value or just the moving direction of the underlying assets. So, their value is derived from that of the underlying asset. This is why options are called derivatives.
+</p>
+<p>
+  Puts and calls are two basic classes of options. Call options give the buyer the right to buy the underlying asset at a certain price while put options endow the buyer the rights to sell them. For strict definition, a call/put option is a standardized contract that gives the buyer the right to buy/sell an agreed quantity n of underlying asset S, at a predetermined price K at maturity T. The seller of a call/put option is obliged to sell/buy the underlying assets if the buyer exercises the option.
+</p>
+<p>
+  First, we give the building blocks of an option contract and will discuss them further.
+</p>
+<table class="table qc-table">
+<tbody>
+<tr>
+<td style="text-align: left;"><strong>Style</strong></td>
+<td style="text-align: left;"><strong>American Option:</strong> the holder of an option has the right to exercise his option at any time before the expiration date
+<strong>European Option:</strong> an option which can only be exercised on its expiration date</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Type</b></td>
+<td style="text-align: left;">Call option, Put option</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Underlying Asset</strong></td>
+<td>The security on which the option is bound. The underlying could be stocks(stock option), stock indices(index option), exchange rate(Foreign exchange option) or even futures(Futures Options).
+Note: The options in the tutorial refer to the stock options.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Premium</strong></td>
+<td style="text-align: left;">The price of the option. (Premiums are quoted on a per share basis).
+The option premium depends on the strike price, volatility of the underlying, as well as the time remaining to expiration.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Strike Price</strong></td>
+<td>The specified price at which the stock can be bought or sold when the option is exercised.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Expiration Date</strong></td>
+<td>The last day that an options contract is valid. All options expire after a certain period of time. The right to exercise the option will no longer exist once the stock option expires.</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Participants</b></td>
+<td style="text-align: left;"><strong>Holder:</strong> People who buy the options(have long positions)
+<strong>Writer:</strong> People who sell the options(have short positions)</td>
+</tr>
+</tbody>
+</table>
diff --git a/06 Introduction to Options[]/01 General Features of Options/03 Options Contract.html b/06 Introduction to Options[]/01 General Features of Options/03 Options Contract.html
new file mode 100755
index 0000000..b23a213
--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/03 Options Contract.html	
@@ -0,0 +1,9 @@
+<p>
+  Stock options are traded in units. Each contract entitles the option buyer/owner 100 shares of the underlying stock upon expiration. Thus, if you buy five call option contracts, you are acquiring the right to purchase 500 shares at expiration.  For any given asset at any given time, an option can be bought or sold with multiple expiration dates and strikes. Suppose there are three expiration dates and four strike prices for options on a particular stock, there are a total of 24 different contracts if we consider both put and call options. Currently, all equity options traded on U.S. options exchanges are American-style. But many index options are European-style.
+</p>
+<p>
+  Let us use the example of Apple. You can buy and sell Apple shares (NASDAQ: AAPL), and you can also buy and sell Apple options. But not all stocks have options associated with them. This means that there may be no options available to buy or sell written on a certain stock. You can view the websites of the exchanges to find out which stocks do have options.
+</p>
+<p>
+  In this case, the stock AAPL is known as the underlying asset. The share price of AAPL is the underlying price. And a series of options contracts written on Apple are derivatives. Option contracts have their own symbols. For example, AAPL170728C00143000 is one of the option contracts, it is a call option and the strike price is $143. The contract expires on July 28th. The premium for this contract is $10. If you spend $10 cost on buying one contract, then you get the right to buy 100 shares of AAPL for $143 per share at any time before the expiration date.
+</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html
new file mode 100755
index 0000000..975eb99
--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
@@ -0,0 +1,16 @@
+<p>
+  The option's premium consists of two parts: the intrinsic value and the time value.
+</p>
+<div class="latex">
+\[Intrinsic Value_{call} = max(Current Underlying Price-Strike Price,0)\]
+\[Intrinsic Value_{put} = max(Strike Price-Current Underlying Price,0)\]
+</div>
+<p>
+  From the equations above, only in the money options have intrinsic value.  After we know the intrinsic value, the time value is the difference between the options premium and the intrinsic value.
+</p>
+<div class="latex">
+\[Time Value= Premium-Intrinsic Value\]
+</div>
+<p>
+For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
+</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html
new file mode 100755
index 0000000..5f7a43a
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+++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html	
@@ -0,0 +1,30 @@
+<p>
+  Option moneyness describes the relationship between an option's  strike price and the underlying asset's price. It has important implications for options trading.
+</p>
+<table class="table qc-table">
+<tbody>
+<tr>
+<td style="text-align: left;"><b>At the Money(ATM)</b></td>
+<td style="text-align: left;">The strike price is the same as the current price of the underlying asset</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>In the Money(ITM)</b></td>
+<td style="text-align: left;"><strong>Call Option</strong>: the strike price is below the current trading price of the underlying
+<strong>Put option</strong>:  the strike price is above the spot price of the underlying</td>
+</tr>
+<tr>
+<td style="text-align: left;"><b>Out of the Money(OTM)</b></td>
+<td style="text-align: left;"><strong>Call Option: </strong>the strike price is above the current trading price of the underlying
+<strong>Put Option:</strong> the strike price is below the current trading price of the underlying</td>
+</tr>
+</tbody>
+</table>
+<p>
+  For example, if the price of Apple stock is at $140, then all calls with a strike price below $140 are in the money calls, all puts with a strike price above $140 are in the money puts.  All calls with a strike price above $140 are out of the money calls, all puts with a strike price below $140 are out of the money puts.
+</p>
+<p>
+  But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.
+</p>
+<p>
+  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/06 Exercise and Assignment.html b/06 Introduction to Options[]/01 General Features of Options/06 Exercise and Assignment.html
new file mode 100755
index 0000000..933c12d
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+++ b/06 Introduction to Options[]/01 General Features of Options/06 Exercise and Assignment.html	
@@ -0,0 +1,18 @@
+<p>
+  When you are the buyer of an option you have three ways to deal with your options.
+</p>
+<ul>
+ 	<li>You can close out the position at any given point prior to expiration (For buyers, write options and for sellers, buy options);</li>
+ 	<li>Wait until the expiration date and out-of-the-money options will become worthless;</li>
+ 	<li>Exercise the options which are in-the-money, resulting in a trade of the underlying stock (The seller will be assigned the obligation to sell or buy the underlying stocks)</li>
+</ul>
+<p>
+  For all of those methods, closing out an option in the market is the most frequently used method. At the expiration, you need to deal with the option exercise if you long the options. If you short put or call options, you have to think about the assignment.
+</p>
+<ul>
+ 	<li><strong>Exercise:</strong> Exercising option means that the option holder executes the right to buy or sell the underlying assets at the strike price.</li>
+ 	<li><strong> Assignment: </strong>When an option is exercised by the option holder, the option writer will be assigned the obligation to deliver the terms of the options contract. This is called the option assignment.</li>
+</ul>
+<p>
+  To be concrete, if you write a call option and the option holder decides to exercise, then you must sell the obligated quantity of the underlying security at the strike price to the option holder. Suppose you hold the underlying shares while writing the call option (covered call), those shares would be transferred from you to your counterpart. If you do not hold those shares in your account (naked call), you would then short those shares and deliver them to your counterpart. In another case, if you short a put and the holder decides to exercise, then you must buy those shares. You would now long shares in your account and don't hold options.
+</p>
diff --git a/06 Introduction to Options[]/01 General Features of Options/07 Summary.html b/06 Introduction to Options[]/01 General Features of Options/07 Summary.html
new file mode 100755
index 0000000..4ec6d53
--- /dev/null
+++ b/06 Introduction to Options[]/01 General Features of Options/07 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  An option is a standardized contract between two parties to buy or sell an asset for a certain price. Options are pretty different from equity trading. Here we concerned primarily with the stock options. This chapter we present some introductory material on options market like the basic features of options contracts and the options trading mechanism. In addition, we explain how options markets are organized, how the contracts are traded. Next chapter we will take a close look at how to use QuantConnect API to start your options trading algorithm.
+</p>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html
new file mode 100755
index 0000000..b3a7a65
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html	
@@ -0,0 +1,4 @@
+<h2>Introduction</h2>
+<p>
+  In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put-call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
+</p>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html
new file mode 100755
index 0000000..3325802
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html	
@@ -0,0 +1,76 @@
+<p>
+  Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
+</p>
+<p>
+  From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
+</P>
+\[Call_{payoff}=max[0,S_T-K]\]
+<p>
+  On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
+</p>
+\[Put_{payoff}=max[0,K-S_T]\]
+<p>
+Where \(S_T\) is the price of underlying assets at maturity. K is the strike price.
+</p>
+<p>
+  We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Contract Name</th>
+<th>Type</th>
+<th>Expire Date</th>
+<th>Strike</th>
+<th> Premium</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> GOOG170714C00940000</td>
+<td> Call</td>
+<td> 07/14/2017</td>
+<td> $940</td>
+<td> $7.5</td>
+</tr>
+<tr>
+<td>GOOG170714P00960000</td>
+<td> Put</td>
+<td> 07/14/2017</td>
+<td> $960</td>
+<td> $19.5</td>
+</tr>
+</tbody>
+</table>
+If you long these two options, the payoff at expiration date would be as follows
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import matplotlib.pyplot as plt
+%pylab inline
+price = np.arange(900,1000,1)
+strike = 940
+premium = 7.5
+payoff = [max(-premium, i - strike-premium) for i in price]
+plt.plot(price, payoff)
+plt.xlabel('Price at T S_T ($)')
+plt.ylabel('payoff')
+plt.title('Call option Payoff at Expiry')
+plt.grid(True)
+price = np.arange(900,1000,1)
+strike = 960
+premium = 19.5
+payoff = [max(-premium, strike - i -premium) for i in price]
+plt.plot(price, payoff)
+plt.xlabel('Price at T S_T ($)')
+plt.ylabel('payoff')
+plt.title('Put option Payoff at Expiry')
+plt.grid(True)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-call-payoff.png" alt="call options payoff" />     
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-put-payoff.png" alt="put options payoff" />
+
+<p>
+  The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay the premium for purchasing the contracts.
+</p>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html
deleted file mode 100755
index 51bc85e..0000000
--- a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/02 Put-Call Parity and Arbitrage Strategies.html	
+++ /dev/null
@@ -1,164 +0,0 @@
-<h2>Introduction</h2>
-<p>
-  In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
-</p>
-<h3>Option Payoff</h3>
-<p>
-  Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
-</p>
-<p>
-  From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
-</P>
-\[Call_{payoff}=max[0,S_T-K]\]
-<p>
-  On the other hand, an investor will buy a European put if he is bearish about the market and believes that the underlying stock price will be below the strike at the expiry date. He will make money when the underlying price goes down and lose when it goes up. The European put payoff at T is
-</p>
-\[Put_{payoff}=max[0,K-S_T]\]
-<p>
-Where \(S_T\) is the price of underlying assets at maturity. K is the strike price.
-</p>
-<p>
-  We use the GOOG option contract to show the call and put option payoff. The current share price of GOOG is $945 at 07/12/2017.
-</p>
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Contract Name</th>
-<th>Type</th>
-<th>Expire Date</th>
-<th>Strike</th>
-<th> Premium</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> GOOG170714C00940000</td>
-<td> Call</td>
-<td> 07/14/2017</td>
-<td> $940</td>
-<td> $7.5</td>
-</tr>
-<tr>
-<td>GOOG170714P00960000</td>
-<td> Put</td>
-<td> 07/14/2017</td>
-<td> $960</td>
-<td> $19.5</td>
-</tr>
-</tbody>
-</table>
-If you long these two options, the payoff at expiration date would be as follows
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">import matplotlib.pyplot as plt
-%pylab inline
-price = np.arange(900,1000,1)
-strike = 940
-premium = 7.5
-payoff = [max(-premium, i - strike-premium) for i in price]
-plt.plot(price, payoff)
-plt.xlabel('Price at T S_T ($)')
-plt.ylabel('payoff')
-plt.title('Call option Payoff at Expiry')
-plt.grid(True)
-price = np.arange(900,1000,1)
-strike = 960
-premium = 19.5
-payoff = [max(-premium, strike - i -premium) for i in price]
-plt.plot(price, payoff)
-plt.xlabel('Price at T S_T ($)')
-plt.ylabel('payoff')
-plt.title('Put option Payoff at Expiry')
-plt.grid(True)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-call-payoff.png" alt="call options payoff" />     <img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-put-payoff.png" alt="put options payoff" />
-
-<p>
-  The above payoff diagrams illustrate the cash payoff on an option at the expiration date. For a call option, the net payoff is negative if the price of the underlying asset is less than the strike price(The negative payoff comes from the premium). If the underlying price exceeds the strike price, the gross payoff is the price of the underlying asset minus the strike price and the premium. For a put option, the net payoff is positive if the underlying price is less than the strike price. If the price of the underlying asset exceeds the strike price, the gross payoff is negative because you pay premuim for purchasing the contracts.
-</p>
-
-<h3>2. Put-Call Parity</h3>
-<p>
-  Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next we will demonstrate how to derive the put-call parity according to John Hull's book.
-</p>
-<p>We consider two portfolios as follows,</p>
-<p>
-  Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
-</p>
-<p>
-  Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
-</p>
-<table class="table qc-table">
-<tbody>
-<tr>
-<td> Payoff</td>
-<td> \(S_t &gt; K\)</td>
-<td>\(S_t &lt; K\)</td>
-</tr>
-<tr>
-<td width="33%">Portfolio A</td>
-<td>\(S_t - K+K=S_t\)</td>
-<td>\(0+K=K\)</td>
-</tr>
-<tr>
-<td>Portfolio B</td>
-<td>\(0+S_t =S_t\)</td>
-<td>\(K-S_t+S_t=K\)</td>
-</tr>
-</tbody>
-</table>
-<p>
-  From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolio must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
-</p>
-\[Price_{call}+Ke^{-rT}=Price_{put}+S_0\]
-<p>
-  If the dividend is paid during the option holding period, the share holder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put–call parity relationship is(D is the present value of dividends):
-</p>
-\[Price_{call}+D+Ke^{-rT}=Price_{put}+S_0\]
-
-<h3>3. Synthetic Positions</h3>
-<div class="page" title="Page 57">
-<div class="layoutArea">
-<div class="column">
-<p>
-  Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in an underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
-</p>
-[ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017 <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
-<p>
-  There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
-</p>
-<table class="table qc-table">
-<tbody>
-<tr>
-<td><strong>Strategy</strong></td>
-<td><strong>Content</strong></td>
-</tr>
-<tr>
-<td width="33%">Conversion</td>
-<td>Synthetic Short Position: short call + long put
-The actual stock position: long the underlying stocks</td>
-</tr>
-<tr>
-<td>Reversal</td>
-<td>Synthetic Long Position: long call + short put
-The actual stock position: short the underlying stocks</td>
-</tr>
-</tbody>
-</table>
-<p>
-  Traders use conversions when options are overpriced  relative to the underlying stock and use reversals when options are relatively underpriced.
-</p>
-
-<h3>Algorithm</h3>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
-<h2>Summary</h2>
-<p>
-  In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
-</p>
-<p>
-  Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
-</p>
-</div>
-</div>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/03 Put-Call Parity.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/03 Put-Call Parity.html
new file mode 100755
index 0000000..2f936a2
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/03 Put-Call Parity.html	
@@ -0,0 +1,37 @@
+<p>
+  Put-Call parity describes the relationship between the price of a European put and a call options with the identical strike price K, expiry T and their underlying stock's price. Next, we will demonstrate how to derive the put-call parity according to John Hull's book.
+</p>
+<p>We consider two portfolios as follows,</p>
+<p>
+  Portfolio A: buy one European call option (underlying non-dividend-paying stock S, strike K, expiring at T) plus a zero-coupon bond which pays K at T
+</p>
+<p>
+  Portfolio B: sell one European put option (underlying non-dividend-paying stock S, strike K, expiring at T) plus one share of underlying stock S
+</p>
+<table class="table qc-table">
+<tbody>
+<tr>
+<td> Payoff</td>
+<td> \(S_t &gt; K\)</td>
+<td>\(S_t &lt; K\)</td>
+</tr>
+<tr>
+<td width="33%">Portfolio A</td>
+<td>\(S_t - K+K=S_t\)</td>
+<td>\(0+K=K\)</td>
+</tr>
+<tr>
+<td>Portfolio B</td>
+<td>\(0+S_t =S_t\)</td>
+<td>\(K-S_t+S_t=K\)</td>
+</tr>
+</tbody>
+</table>
+<p>
+  From the above table we can see that in all states, Portfolio A has the same payoff at the options' expiration date as Portfolio B. Thus the present value of two portfolios must be the same. Otherwise, an investor can make risk-free profits by purchasing the undervalued portfolio and selling the overvalued portfolio and holding the position to maturity. Then we have the price equality(suppose the present price of stock S is \(S_0\) )
+</p>
+\[Price_{call}+Ke^{-rT}=Price_{put}+S_0\]
+<p>
+  If the dividend is paid during the option holding period, the shareholder of the stock would receive that additional amount, but the option owner would not. For a dividend-paying stock, the put-call parity relationship is(D is the present value of dividends):
+</p>
+\[Price_{call}+D+Ke^{-rT}=Price_{put}+S_0\]
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html
new file mode 100755
index 0000000..c1e040c
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html	
@@ -0,0 +1,31 @@
+<div class="page" title="Page 57">
+<div class="layoutArea">
+<div class="column">
+<p>
+  Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
+</p>
+[ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017, <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
+<p>
+  There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
+</p>
+<table class="table qc-table">
+<tbody>
+<tr>
+<td><strong>Strategy</strong></td>
+<td><strong>Content</strong></td>
+</tr>
+<tr>
+<td width="33%">Conversion</td>
+<td>Synthetic Short Position: short call + long put
+The actual stock position: long the underlying stocks</td>
+</tr>
+<tr>
+<td>Reversal</td>
+<td>Synthetic Long Position: long call + short put
+The actual stock position: short the underlying stocks</td>
+</tr>
+</tbody>
+</table>
+<p>
+  Traders use conversions when options are overpriced relative to the underlying stock and use reversals when options are relatively underpriced.
+</p>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
new file mode 100755
index 0000000..83a3c24
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/06 Summary.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/06 Summary.html
new file mode 100755
index 0000000..4be79e7
--- /dev/null
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/06 Summary.html	
@@ -0,0 +1,6 @@
+<p>
+  In this chapter, we derived an important relationship between the prices of European put and call options that have the same strike price and time to maturity by constructing two portfolios. Then we described the synthetic positions, a common concept which is always used in arbitrage trading strategies. Finally two simple arbitrage strategies conversion and reversal are demonstrated on QC platform to help you get a better understanding of how to implement algorithms with simple option mechanism.
+</p>
+<p>
+  Next chapter we will dig into the famous option pricing model-Black Sholes Merton Model and discuss how to apply BSM in European options pricing.
+</p>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/01 Introduction.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/01 Introduction.html
new file mode 100755
index 0000000..ae313cc
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Last few chapters we introduced the basic principles and mechanism of options trading. We already knew what an option contract is and the basic relationship between call and put options' price. But how do these contracts traded in the exchange are being priced and where does the option premium come from? In the next few chapters, we will discuss the pricing of options.
+</p>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
new file mode 100755
index 0000000..dd95ff9
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -0,0 +1,195 @@
+<p>
+  In order to value the derivatives like options, the most significant part is to find a model to represent the underlying stock price so that we can price the options based on the underlying price.  We usually use the stochastic process to model the security price.
+</p>
+<p>
+  First, you need to know what the stochastic process is. We say any variable that changes over time in an uncertain way follows a stochastic process. The price of a certain stock at a future time t is unknown at the present so it is a random variable \(S_t\). Then we can think of the movement path of the stock price is a stochastic process since\(S_t\) is a random variable at each time t in the future.
+</p>
+<p>
+  Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
+</p>
+<p>
+  Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables {\(W_t\mid t\geq0} \)} indexed by time t. The one-dimensional Brownian motion is called Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
+</p>
+<ul>
+     <li>\({W_0=0\)</li>
+     <li>For \(t\geq0\) and \(\Delta t\geq0\), the increment \(W_{t+\Delta t}-W_t\) is normally distributed with mean 0 and standard deviation \(\sqrt{\Delta t}\) .</li>
+     <li>For any partitions \(0\leq t_1&lt;t_2&lt;\cdot\cdot\cdot &lt;t_n\), the increments \(W_{t_1}- W_{t_0},W_{t_2}-W_{t_1},\cdot\cdot\cdot, W_{t_n}-W_{t_{n-1}}\) are independent random variables.</li>
+     <li>With probability 1, the function W(t) is continuous at t.</li>
+</ul>
+<p>
+  Intuitively understanding of the definition, Wiener process has independent and normally distributed increments and has continuous sample path.
+</p>
+<p>
+  Next, we simulate the Wiener process and plot the paths attempting to gain an intuitive understanding of a stochastic process. Each path is an independent Wiener process.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import numpy as np
+import matplotlib.pyplot as plt
+%pylab inline
+def wiener_process(T, N):
+    """
+    T: total time
+    N: The total number of steps
+    """
+    W0 = 0
+    dt = T/float(N)
+    # simulate the increments by normal random variable generator
+    increments = np.random.normal(0, 1*np.sqrt(dt), steps)
+    W = [0] + list(np.cumsum(increments))
+    return W
+t = np.linspace(0.0, N*dt, N+1)
+plt.figure(figsize=(15,10))
+for i in range(5):
+    W = wiener_process(10, 1000)
+    plt.plot(t, W)
+    plt.xlabel('time')
+    plt.ylabel('W')
+    plt.grid(True)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-wiener-process.png" alt="wiener process"/>
+
+<p>
+  [ref]Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.[/ref]In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
+</p>
+\[\Delta x=x_{t+\Delta t}-x_t=a\Delta t+b\epsilon\sqrt{\Delta t}\]
+<p>
+  Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with the mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
+</p>
+<h3>2. The stochastic process followed by stock price</h3>
+<p>
+  Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
+</p>
+<p>
+  On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
+</p>
+<p>
+  In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
+</p>
+
+\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
+<p>
+  Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
+</p>
+
+<p>
+  Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
+</p>
+
+<p>
+  The discrete approximation form of (1) is
+</p>
+\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
+<p>
+  We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
+</p>
+\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
+<p>
+  Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln(S) during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
+</p>
+\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+
+\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+<p>
+  Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
+</p>
+\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
+<p>Equivalently</p>
+
+\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
+<p>
+  If we change t to 0 and change \(\Delta t\) to T, we get
+</p>
+\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
+<p>
+  According to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
+</p>
+
+<h2>Monte Carlo Method</h2>
+<p>
+  Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
+</p>
+<h3>1. Monte Carlo Simulation of Stock Price</h3>
+<p>
+  We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import quandl
+quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
+data = quandl.get('WIKI/GOOG')
+close = data['2015-01':'2017-07']['Adj. Close']
+annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
+annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
+mu = annual_return # 0.39644
+sigma = annual_vol # 0.2344
+s0 = close[-1] # 903.5
+T = 3.0/12
+delta_t = 0.001
+num_reps = 500
+steps = T/delta_t
+plt.figure(figsize=(15,10))
+for j in range(num_reps):
+    price_path = [s0]
+    st = s0
+    for i in range(int(steps)):
+        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
+        price_path.append(st)
+    plt.plot(price_path)
+plt.ylabel('stock price',fontsize=15)
+plt.xlabel('steps',fontsize=15)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
+<h3>1. Monte Carlo Simulation of European Options</h3>
+<p>
+  Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
+</p>
+<p>
+  The essence of using Monte Carlo method to price the options is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
+</p>
+<ul>
+     <li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
+     <li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
+     <li>Calculate the payoff of options according to the \(S_T\)</li>
+     <li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
+     <li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
+</ul>
+<p>
+  There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
+    payoff_sum = 0
+    for j in range(num_reps):
+        st = s0
+        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
+        if option_type == 'c':
+            payoff = max(0,st-strike)
+        elif option_type == 'p':
+            payoff = max(0,strike-st)
+        payoff_sum += payoff
+    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
+ return premium
+mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
+</pre>
+</div>
+<p>
+  Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
+</p>
+<h2>Summary</h2>
+<p>
+  In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
+</p>
+<p>
+  In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
+</p>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html
deleted file mode 100755
index 3ebeb9c..0000000
--- a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.html	
+++ /dev/null
@@ -1,180 +0,0 @@
-<h2>Introduction</h2>
-Last few chapters we introduced about the basic principles and mechanism of options trading. We already knew what an option contract is and the basic relationship between call and put options' price. But how do these contracts traded in the exchange are being priced and where does the option premium come from? In the next few chapters, we will discuss the pricing of options.
-<h2>Stochastic Process</h2>
-In order to value the derivatives like options, the most significant part is to find a model to represent the underlying stock price so that we can price the options based on the underlying price.  We usually use the stochastic process to model the security price.
-
-First, you need to know what the stochastic process is. We say any variable that changes over time in an uncertain way follows a stochastic process. The price of a certain stock at a future time t is unknown at the present so it is a random variable \(S_t\). Then we can think of the movement path of the stock price is a stochastic process since\(S_t\) is a random variable at each time t in the future.
-<h3>1. Brownian motion</h3>
-Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
-<div class="page" title="Page 327">
-<div class="layoutArea">
-<div class="column">
-
-Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables {\(W_t\mid t\geq0} \)} indexed by time t. The one-dimensional Brownian motion is called Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
-<ol>
- 	<li>\({W_0=0\)</li>
- 	<li>For \(t\geq0\) and \(\Delta t\geq0\), the increment \(W_{t+\Delta t}-W_t\) is normally distributed with mean 0 and standard deviation \(\sqrt{\Delta t}\) .</li>
- 	<li>For any partitions \(0\leq t_1&lt;t_2&lt;\cdot\cdot\cdot &lt;t_n\), the increments \(W_{t_1}- W_{t_0},W_{t_2}-W_{t_1},\cdot\cdot\cdot, W_{t_n}-W_{t_{n-1}}\) are independent random variables.</li>
- 	<li>With probability 1, the function W(t) is continuous at t.</li>
-</ol>
-Intuitively understanding of the definition, Wiener process has independent and normally distributed increments and has continuous sample path.
-
-Next, we simulate the Wiener process and plot the paths attempting to gain an intuitive understanding of a stochastic process. Each path is an independent Wiener process.
-<pre class="prettyprint linenums">import numpy as np
-import matplotlib.pyplot as plt
-%pylab inline
-def wiener_process(T, N):
-    """
-    T: total time
-    N: The total number of steps
-    """
-    W0 = 0
-    dt = T/float(N)
-    # simulate the increments by normal random variable generator
-    increments = np.random.normal(0, 1*np.sqrt(dt), steps)
-    W = [0] + list(np.cumsum(increments))
-    return W
-t = np.linspace(0.0, N*dt, N+1)
-plt.figure(figsize=(15,10))
-for i in range(5):
-    W = wiener_process(10, 1000)
-    plt.plot(t, W)
-    plt.xlabel('time')
-    plt.ylabel('W')
-    plt.grid(True)
-</pre>
-</div>
-</div>
-</div>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-wiener-process.png" alt="wiener process"/>
-<div class="page" title="Page 328">
-<div class="layoutArea">
-<div class="column">
-
-[ref]Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.[/ref]In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
-
-\[\Delta x=x_{t+\Delta t}-x_t=a\Delta t+b\epsilon\sqrt{\Delta t}\]
-
-Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
-<h3>2. Stochastic process followed by stock price</h3>
-Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
-
-On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
-
-In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
-
-</div>
-</div>
-</div>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
-
-Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
-<div class="page" title="Page 338">
-<div class="layoutArea">
-<div class="column">
-
-Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
-
-</div>
-</div>
-</div>
-The discrete approximation form of (1) is
-
-\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
-
-We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
-
-\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
-
-Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln S during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
-
-\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-
-\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-
-Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
-
-\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
-
-Equivalently
-
-\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
-
-If we change t to 0 and change \(\Delta t\) to T, we get
-
-\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
-
-according to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
-<div class="page" title="Page 333">
-<div class="layoutArea">
-<div class="column">
-<h2>Monte Carlo Method</h2>
-Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
-<h3>1. Monte Carlo Simulation of Stock Price</h3>
-We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
-data = quandl.get('WIKI/GOOG')
-close = data['2015-01':'2017-07']['Adj. Close']
-annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
-annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
-mu = annual_return # 0.39644
-sigma = annual_vol # 0.2344
-s0 = close[-1] # 903.5
-T = 3.0/12
-delta_t = 0.001
-num_reps = 500
-steps = T/delta_t
-plt.figure(figsize=(15,10))
-for j in range(num_reps):
-    price_path = [s0]
-    st = s0
-    for i in range(int(steps)):
-        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
-        price_path.append(st)
-    plt.plot(price_path)
-plt.ylabel('stock price',fontsize=15)
-plt.xlabel('steps',fontsize=15)</pre>
-</div>
-</div>
-</div>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
-<h3>1. Monte Carlo Simulation of European Options</h3>
-Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
-
-The essence of using Monte Carlo method to price the options is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
-<ol>
- 	<li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
- 	<li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
- 	<li>Calculate the payoff of options according to the \(S_T\)</li>
- 	<li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
- 	<li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
-</ol>
-There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
-<pre class="prettyprint linenums">def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
-    payoff_sum = 0
-    for j in range(num_reps):
-        st = s0
-        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
-        if option_type == 'c':
-            payoff = max(0,st-strike)
-        elif option_type == 'p':
-            payoff = max(0,strike-st)
-        payoff_sum += payoff
-    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
- return premium
-mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
-</pre>
-Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
-<h2>Summary</h2>
-In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
-
-In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
-
-</div>
-</div>
-</div>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html
new file mode 100755
index 0000000..1383d72
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html	
@@ -0,0 +1,47 @@
+<p>
+  Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
+</p>
+<p>
+  On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
+</p>
+<p>
+  In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
+</p>
+
+\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
+<p>
+  Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
+</p>
+
+<p>
+  Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
+</p>
+
+<p>
+  The discrete approximation form of (1) is
+</p>
+\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
+<p>
+  We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
+</p>
+\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
+<p>
+  Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln(S) during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
+</p>
+\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+
+\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
+<p>
+  Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
+</p>
+\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
+<p>Equivalently</p>
+
+\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
+<p>
+  If we change t to 0 and change \(\Delta t\) to T, we get
+</p>
+\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
+<p>
+  According to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
+</p>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html
new file mode 100755
index 0000000..9bd14d2
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html	
@@ -0,0 +1,33 @@
+<p>
+  We apply this technique to model stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import quandl
+quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
+data = quandl.get('WIKI/GOOG')
+close = data['2015-01':'2017-07']['Adj. Close']
+annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
+annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
+mu = annual_return # 0.39644
+sigma = annual_vol # 0.2344
+s0 = close[-1] # 903.5
+T = 3.0/12
+delta_t = 0.001
+num_reps = 500
+steps = T/delta_t
+plt.figure(figsize=(15,10))
+for j in range(num_reps):
+    price_path = [s0]
+    st = s0
+    for i in range(int(steps)):
+        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
+        price_path.append(st)
+    plt.plot(price_path)
+plt.ylabel('stock price',fontsize=15)
+plt.xlabel('steps',fontsize=15)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html
new file mode 100755
index 0000000..99a796b
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html	
@@ -0,0 +1,38 @@
+<p>
+  Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff depends on the history price of the underlying asset.
+</p>
+<p>
+  The essence of using Monte Carlo method to price the option is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
+</p>
+<ul>
+     <li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
+     <li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
+     <li>Calculate the payoff of options according to the \(S_T\)</li>
+     <li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
+     <li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
+</ul>
+<p>
+  In option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
+    payoff_sum = 0
+    for j in range(num_reps):
+        st = s0
+        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
+        if option_type == 'c':
+            payoff = max(0,st-strike)
+        elif option_type == 'p':
+            payoff = max(0,strike-st)
+        payoff_sum += payoff
+    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
+ return premium
+mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
+</pre>
+</div>
+<p>
+  Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
+</p>
diff --git a/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/06 Summary.html b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/06 Summary.html
new file mode 100755
index 0000000..f9ac402
--- /dev/null
+++ b/06 Introduction to Options[]/03 Stochastic Processes and Monte Carlo Method/06 Summary.html	
@@ -0,0 +1,6 @@
+<p>
+  In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
+</p>
+<p>
+  In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
+</p>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/01 Introduction.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/01 Introduction.html
new file mode 100755
index 0000000..61306f2
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the last chapter, we modeled the stock price with the Geometric Brownian motion. The logarithm of return \(\text{ln}(S_T/S_0)\) follows the normal distribution \(N\left[(\mu-\sigma^2/2)T,\sigma^2T\right]\). It means the logarithm of stock price\(\text{ln}(S_T)\)follows the normal distribution \(N\left[\text lnS_0+(\mu-\sigma^2/2)T,\sigma^2T\right]\). Based on this basic assumption, in this chapter, we will talk about a famous option pricing model: Black Scholes Merton Model.
+</p>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/02 Determinants of Option Price.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/02 Determinants of Option Price.html
new file mode 100755
index 0000000..5152d68
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/02 Determinants of Option Price.html	
@@ -0,0 +1,11 @@
+<p>
+  In different kinds of asset pricing model like bond pricing, <span class="s1">enterprise valuation, </span>the most commonly used valuation method is to calculate the present value of the expected cash flows of that asset. But options have some characteristics that are different from the common asset. For example, the options value depends on its underlying asset. In addition, the cash flow on options are not constant over periods but depends on the occurrence of specific events which are not predictable. In fact, the value of the option is determined by lots of variables.
+</p>
+<ul>
+     <li><strong>the underlying price:</strong> Change in the value of the underlying asset is the most important factor which affects the options price. For the call option, holders can earn profit from the price rising and put option holders earn profits from the price decline. Therefore, call options become more valuable as the underlying prices increase while the put options will become less valuable.</li>
+     <li><strong>The volatility of the underlying asset:</strong> Volatility is a measure of the degree of fluctuation of the underlying stock price. It is a forward volatility, which means it is a prediction of how much the stock price of the underlying will move in the future over a certain period of time. The higher the predicted volatility is, the higher the probability that the underlying price will move a lot. Thus the greater will the value of the option is both for calls and puts. We will further discuss different types of volatilities in the subsequent tutorials.</li>
+     <li><strong>The strike price of the option:</strong> Call options gain profit when stock prices greater than the strike, it is easy to understand that the higher the strike price is for the call option, the fewer profits the holder can get, the less valuable of the call option. Thus for the put options, options with higher strike price are more valuable.</li>
+     <li><strong>Time to expiration: </strong>If the current date is t, the expiration date of the option contract is T. Then the time to expiration is T-t. For v=both call and put options, the longer time to expiration means there are more changes for the function of stocks prices, and higher probability for the option holders to gain profits from price movement.</li>
+     <li><strong><strong>The risk-free interest rate: </strong></strong>As interest rates increase, the expected return required by investors from the stock tends to increase. On the other hand, the present value of discounted cash flow will decrease. Thus the increase in interest rate will increase the call option price and will decrease the put option price.</li>
+     <li><strong>The dividend yield: </strong>After the dividends paid, the share price of the stock will decrease. During the options holding period, the decline in underlying price is unfavorable for call options holder. Therefore, the increase in dividend yield will decrease the price of call options and increase the price of put options.</li>
+</ul>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html
new file mode 100755
index 0000000..7b75702
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html	
@@ -0,0 +1,52 @@
+<p>
+  After we get an intuition about affecting factors of the options price, we will introduce the BSM option pricing model. The Black-Scholes model for pricing stock options was developed by Fischer Black, Myron Scholes and Robert Merton in the early 1970’s.
+</p>
+<p>
+  First, we introduce the factors in the model. For all the factors listed below, only volatility is not known.  There are many types of volatilities. Then which volatility should be used is a critical question in option pricing model. We will further discuss this part in the next few chapters.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Factors in the model</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td> Stock Price</td>
+<td>S</td>
+</tr>
+<tr>
+<td> Strike Price</td>
+<td> K</td>
+</tr>
+<tr>
+<td> Time to Expiration</td>
+<td> T-t</td>
+</tr>
+<tr>
+<td> Interest Rates</td>
+<td> r</td>
+</tr>
+<tr>
+<td> Future Volatility of the underlying Stock</td>
+<td> σ</td>
+</tr>
+</tbody>
+</table>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">class BsmModel:
+    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):
+        self.s = price # Underlying asset price
+        self.k = strike # Option strike K
+        self.r = interest_rate # Continuous risk fee rate
+        self.q = dividend_yield # Dividend continuous rate
+        self.T = expiry # time to expiry (year)
+        self.sigma = volatility # Underlying volatility
+        self.type = option_type # option type "p" put option "c" call option
+</pre>
+</div>
+<p>
+  There are some details we need to pay attention to about the input of BSM model. Firstly, the model works in continuous time, rather than discrete time. Therefore the risk-free rate r has to be modified to the continuous form. Secondly, the time to expiration should be converted to year. The volatility is the annual volatlity.
+</p>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Model Assumptions.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Model Assumptions.html
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--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Model Assumptions.html	
@@ -0,0 +1,12 @@
+<p>
+  The market assumptions behind the Black–Scholes formula for pricing European options are as follows:
+</p>
+<ul>
+     <li>The volatility of the underlying assets is constant over time</li>
+     <li>The underlying asset price follows the lognormal distribution, this means that the log-returns of stock prices are normally distributed</li>
+     <li>The underlying asset can be traded continuously</li>
+     <li>The underlying stock does not pay dividends during the option's life. But the basic Black-Scholes model was later adjusted for dividends, here we demonstrate the later version with dividend yields.</li>
+     <li>There are no transaction costs or taxes</li>
+     <li>All securities are perfectly divisible, meaning that it is possible to buy any fraction of a share</li>
+     <li>The risk-free rate of interest, r, is constant and the same for all maturities</li>
+</ul>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html
deleted file mode 100755
index 51eabcd..0000000
--- a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/04 Options Pricing Black Scholes Merton Model.html	
+++ /dev/null
@@ -1,140 +0,0 @@
-<h2>Introduction</h2>
-In the last chapter, we modeled the stock price with the Geometric Brownian motion. Then the logarithm of return \(\text{ln}(S_T/S_0)\) follows the normal distribution \(N\left[(\mu-\sigma^2/2)T,\sigma^2T\right]\). It means the logarithm of stock price\(\text{ln}(S_T)\)follows the normal distribution \(N\left[\text lnS_0+(\mu-\sigma^2/2)T,\sigma^2T\right]\). Based on this basic assumption, in this chapter, we will talk about a famous option pricing model: Black Scholes Merton Model.
-<h2>Determinants of  Option Price</h2>
-In different kinds of asset pricing model like bond pricing, <span class="s1">enterprise valuation, </span>the most commonly used valuation method is to calculate the present value of the expected cash flows of that asset. But options have some characteristics that are different from the common asset. For example, the options value depends on its underlying assets. In addition, the cash flows on options are not constant in each time period but depend on the occurrence of specific events which are not predictable. In fact, the value of the option is determined by lots of variables.
-<ol>
- 	<li><strong>the underlying price:</strong> Change in the value of the underlying asset is the most important factor which affects the options price. For the call option, holders can earn profit from the price rising and put option holders earn profits from the price decline. Therefore, call options become more valuable as the underlying prices increase while the put options will become less valuable.</li>
- 	<li><strong>The volatility of the underlying asset:</strong> In tuition, volatility is a measure of the degree of fluctuation of the underlying stock price. It is a forward volatility, which means it is a prediction of how much the stock price of the underlying will move in the future over a certain period of time. The higher the predicted volatility is, the higher the probability that the underlying price will move a lot. Thus the greater will the value of the option is both for calls and puts. We will further discuss the different type of volatilities in the subsequent tutorials.</li>
- 	<li><strong>The strike price of the option:</strong> Call options gain profit when stock prices greater than the strike, it is easy to understand that the higher the strike price is for the call option, the fewer profits the holder can get, the less valuable of the call option. Thus for the put options, options with higher strike price are more valuable.</li>
- 	<li><strong>Time to expiration: </strong>If the current date is t, the expiration date of the option contract is T. Then the time to expiration is T-t. For v=both call and put options, the longer time to expiration means there are more changes for the function of stocks prices, and higher probability for the option holders to gain profits from price movement.</li>
- 	<li><strong><strong>The risk-free interest rate: </strong></strong>As interest rates increase, the expected return required by investors from the stock tends to increase. On the other hand, the present value of discounted cash flow will decrease. Thus the increase in interest rate will increase the call option price and will decrease the put option price.</li>
- 	<li><strong>The dividend yield: </strong>After the dividends paid, the share price of the stock will decrease. During the options holding period, the decline in underlying price is unfavorable for call options holder. Therefore, the increase in dividend yield will decrease the price of call options and increase the price of put options.</li>
-</ol>
-<h2>Factors in the BSM model</h2>
-After we get an intuition about affecting factors of options price. Now we will introduce the BSM option pricing model. The Black-Scholes model for pricing stock options was developed by Fischer Black, Myron Scholes and Robert Merton in the early 1970’s.
-
-First, we introduce the factors in the model. For all the factors listed below, only volatility is not known.  There are many types of volatilities. Then which volatility should be used is a critical question in option pricing model. We will further discuss this part in the next few chapters.
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Factors in the model</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td> Stock Price</td>
-<td>S</td>
-</tr>
-<tr>
-<td> Strike Price</td>
-<td> K</td>
-</tr>
-<tr>
-<td> Time to Expiration</td>
-<td> T-t</td>
-</tr>
-<tr>
-<td> Interest Rates</td>
-<td> r</td>
-</tr>
-<tr>
-<td> Future Volatility of the underlying Stock</td>
-<td> σ</td>
-</tr>
-</tbody>
-</table>
-<div class="page" title="Page 58">
-<div class="layoutArea">
-<div class="column">
-<pre class="prettyprint linenums">class BsmModel:
-    def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):
-        self.s = price # Underlying asset price
-        self.k = strike # Option strike K
-        self.r = interest_rate # Continuous risk fee rate
-        self.q = dividend_yield # Dividend continuous rate
-        self.T = expiry # time to expiry (year)
-        self.sigma = volatility # Underlying volatility
-        self.type = option_type # option type "p" put option "c" call option
-</pre>
-There are some details we need to pay attention to about the input of BSM model. Firstly, the model works in continuous time, rather than discrete time. Therefore the risk-free rate r has to be modified to the continuous form. Secondly, the time to expiration should be converted to year. The volatility is the annual volatlity.
-<h2>Assumptions</h2>
-The market assumptions behind the Black–Scholes formula for pricing European options are as follows:
-<ol>
- 	<li>The volatility of the underlying assets is constant over time</li>
- 	<li>The underlying asset price follows lognormal distribution, this means that the log-returns of stock prices are normally distributed</li>
- 	<li>The underlying asset can be traded continuously</li>
- 	<li>The underlying stock does not pay dividends during the option's life. But the basic Black-Scholes model was later adjusted for dividends, here we demonstrate the later version with dividend yields.</li>
- 	<li>There are no transaction costs or taxes</li>
- 	<li>All securities are perfectly divisible, meaning that it is possible to buy any fraction of a share</li>
- 	<li>The risk-free rate of interest, r, is constant and the same for all maturities</li>
-</ol>
-</div>
-<h2>Equations</h2>
-The derivation of the Black-Scholes model is far too complicated to present here, we only show the formula here. The basic principle is based on the idea of creating a portfolio of the underlying asset and the riskless asset with the same cash flows and hence the same cost as the option being valued. Then we get the Black–Scholes–Merton differential equation. The solutions to the differential equation are the Black-Scholes-Merton formulas for the price of European call and put options
-
-\[c = S_0N(d_1)-Ke^{-rT}N(d_2)\]
-
-\[p =Ke^{-rT}N(-d_2)- S_0N(-d_1)\]
-
-\[d_1=\frac{ln(S_0/K)+(r+\sigma^2/2)T}{\sigma\sqrt{T}}\]
-<div class="column">
-
-\[d_2=\frac{ln(S_0/K)+(r-\sigma^2/2)T}{\sigma\sqrt{T}}=d_1-\sigma\sqrt{T}\]
-<div class="page" title="Page 359">
-<div class="layoutArea">
-<div class="column">
-
-N(x) is the cumulative probability distribution function for a variable with a standard normal distribution. It can be calculated by the integral of the probability density function of standard normal distribution from 0 to x. In Python, you can use the <code>norm.pdf(x)</code> in spicy.stats library. For the following chart, we plot the probability density curve of the standard normal distribution. For example, N(-1) is the area of the left hand of the red line under the curve.
-<pre class="prettyprint linenums">import scipy.stats as sp
-mu = 0
-variance = 1
-x = np.linspace(mu-3*variance,mu+3*variance, 100)
-y = [sp.norm.pdf(i) for i in x]
-plt.plot(x,y)
-d = [-1]
-plt.plot(d*100,np.linspace(0,sp.norm.pdf(d), 100))
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-normal-dist.png" alt="normal distribution"/>
-Then in our BSM model class, we will calculate the European call and put option prices by using BSM formula.
-<pre class="prettyprint linenums">def n(self, d):
-    # cumulative probability distribution function of standard normal distribution
-    return norm.cdf(d)
-
-def dn(self, d):
-    # the first order derivative of n(d)
-    return norm.pdf(d)
-
-def d1(self):
-    d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
-    return d1
-
-def d2(self):
-    d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
-    return d2
-
-def bsm_price(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    if self.type == 'c':
-        price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))
-        return price
-    elif self.type == 'p':
-        price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))
-        return price
-    else:
-        print "option type can only be c or p"
-
-a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)
-a.bsm_price()
-</pre>
-</div>
-For a call option which expires in 90 days and no dividends paid, the underlying price is $42, the strike is $35, the risk-free rate is 0.1, the volatility is 0.2. The price of this option is $6.157.
-
-</div>
-</div>
-</div>
-</div>
-<h2 class="page" title="Page 354"> Summary</h2>
-This tutorial discussed the factors affecting the options price and introduced a famous option pricing model including the input parameters, the assumptions and the formula.
-
-</div>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/05 Model Equations.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/05 Model Equations.html
new file mode 100755
index 0000000..ec7abcb
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/05 Model Equations.html	
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+<p>
+  The derivation of the Black-Scholes model is beyond the scope of this research, we only show the formula here. The basic principle is based on the idea of creating a portfolio of the underlying asset and the riskless asset with the same cash flows and hence the same cost as the option being valued. Then we get the Black–Scholes–Merton differential equation. The solutions to the differential equation are the Black-Scholes-Merton formulas for the price of European call and put options
+</p>
+\[c = S_0N(d_1)-Ke^{-rT}N(d_2)\]
+
+\[p =Ke^{-rT}N(-d_2)- S_0N(-d_1)\]
+
+\[d_1=\frac{ln(S_0/K)+(r+\sigma^2/2)T}{\sigma\sqrt{T}}\]
+
+\[d_2=\frac{ln(S_0/K)+(r-\sigma^2/2)T}{\sigma\sqrt{T}}=d_1-\sigma\sqrt{T}\]
+<p>
+  N(x) is the cumulative probability distribution function for a variable with a standard normal distribution. It can be calculated by the integral of the probability density function of standard normal distribution from 0 to x. In Python, you can use the <code>norm.pdf(x)</code> in spicy.stats library. For the following chart, we plot the probability density curve of the standard normal distribution. For example, N(-1) is the area of the left hand of the red line under the curve.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import scipy.stats as sp
+mu = 0
+variance = 1
+x = np.linspace(mu-3*variance,mu+3*variance, 100)
+y = [sp.norm.pdf(i) for i in x]
+plt.plot(x,y)
+d = [-1]
+plt.plot(d*100,np.linspace(0,sp.norm.pdf(d), 100))
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-normal-dist.png" alt="normal distribution"/>
+<p>
+  Then in our BSM model class, we will calculate the European call and put option prices by using BSM formula.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def n(self, d):
+    # cumulative probability distribution function of standard normal distribution
+    return norm.cdf(d)
+
+def dn(self, d):
+    # the first order derivative of n(d)
+    return norm.pdf(d)
+
+def d1(self):
+    d1 = (log(self.s / self.k) + (self.r - self.q + self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
+    return d1
+
+def d2(self):
+    d2 = (log(self.s / self.k) + (self.r - self.q - self.sigma ** 2 * 0.5) * self.T) / (self.sigma * sqrt(self.T))
+    return d2
+
+def bsm_price(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    if self.type == 'c':
+        price = exp(-self.r*self.T) * (self.s * exp((self.r - self.q)*self.T) * self.n(d1) - self.k * self.n(d2))
+        return price
+    elif self.type == 'p':
+        price = exp(-self.r*self.T) * (self.k * self.n(-d2) - (self.s * exp((self.r - self.q)*self.T) * self.n(-d1)))
+        return price
+    else:
+        print "option type can only be c or p"
+
+a = BsmModel('c', 42, 35, 0.1, 90.0/365, 0.2)
+a.bsm_price()
+</pre>
+</div>
+<p>
+  For a call option which expires in 90 days and no dividends paid, the underlying price is $42, the strike is $35, the risk-free rate is 0.1, the volatility is 0.2. The price of this option is $6.157.
+</p>
diff --git a/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/06 Summary.html b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/06 Summary.html
new file mode 100755
index 0000000..3c9ac3c
--- /dev/null
+++ b/06 Introduction to Options[]/04 Options Pricing Black Scholes Merton Model/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  This tutorial discussed factors affecting the options price and introduced a famous option pricing model including input parameters, assumptions and the formula.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html b/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html
new file mode 100755
index 0000000..dd8f4f1
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html	
@@ -0,0 +1,4 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the exposure of option price or option delta to movement of different factors such as the underlying price, time and volatility. In this tutorial we will discuss various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html b/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html
new file mode 100755
index 0000000..1fe282d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html	
@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html b/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html
new file mode 100755
index 0000000..1fe282d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html	
@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/04 vega.html b/06 Introduction to Options[]/05 The Greek Letters/04 vega.html
new file mode 100755
index 0000000..1fe282d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/04 vega.html	
@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html b/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html
deleted file mode 100755
index 1ee40a4..0000000
--- a/06 Introduction to Options[]/05 The Greek Letters/05 The Greek Letters.html	
+++ /dev/null
@@ -1,229 +0,0 @@
-<div class="page" title="Page 87">
-<div class="layoutArea">
-<div class="column">
-<h2>Introduction</h2>
-Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<div class="page" title="Page 425">
-<div class="layoutArea">
-<div class="column">
-
-Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<pre class="prettyprint linenums">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)</pre>
-<h4>Impact Factors</h4>
-Stock price, days remaining to expiration and implied volatility will impact the Delta.
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-
-In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-<pre class="prettyprint linenums">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-The color of the graph above represents delta value.
-<h3>2. Gamma</h3>
-Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-
-Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<pre class="prettyprint linenums">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-<pre class="prettyprint linenums">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-
-The color of the graph above represents delta value.
-<h3>3. Vega</h3>
-<div class="page" title="Page 438">
-<div class="layoutArea">
-<div class="column">
-
-The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-
-Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<pre class="prettyprint linenums">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-<pre class="prettyprint linenums">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-</div>
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-The color of the graph above represents Vega.
-
-<h3>4. Theta</h3>
-<div class="page" title="Page 432">
-<div class="layoutArea">
-<div class="column">
-
-Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-
-The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<pre class="prettyprint linenums">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-<pre class="prettyprint linenums">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-<h3>5. Rho</h3>
-Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<pre class="prettyprint linenums">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-<pre class="prettyprint linenums">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-<div class="page" title="Page 438">
-<div class="layoutArea">
-<div class="column">
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-
-As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-
-<h2> Summary</h2>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html b/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html
new file mode 100755
index 0000000..1fe282d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html	
@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html b/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html
new file mode 100755
index 0000000..1fe282d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html	
@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html b/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html
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@@ -0,0 +1,276 @@
+<h2>Introduction</h2>
+<p>
+  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
+</p>
+<h3>1. Delta</h3>
+<h4>Definition</h4>
+<p>
+  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
+</p>
+\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
+\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
+def delta(self):
+    d1 = self.d1()
+    if self.type == "c":
+        return exp(-self.q * self.T) * self.n(d1)
+    elif self.type == "p":
+        return exp(-self.q * self.T) * (self.n(d1)-1)
+</pre>
+</div>
+<h4>Impact Factors</h4>
+<p>
+  Stock price, days remaining to expiration and implied volatility will impact the Delta.
+</p>
+<ul>
+ 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
+ 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
+ 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
+</ul>
+<p>
+  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
+</p>
+<p>
+  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
+I = np.ones((shape(s)))
+time = arange(1,12.5,0.5)/12
+T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
+
+contracts = []
+for i in range(shape(s)[0]):
+    for j in range(shape(s)[1]):
+        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
+delta = [x.theta() for x in contracts]
+gamma = [x.gamma() for x in contracts]
+delta = [x.delta() for x in contracts]
+vega = [x.vega() for x in contracts]
+rho = [x.rho() for x in contracts]
+
+gamma = np.array(gamma).reshape(shape(s))
+delta = np.array(delta).reshape(shape(s))
+theta = np.array(theta).reshape(shape(s))
+vega = np.array(vega).reshape(shape(s))
+rho = np.array(rho).reshape(shape(s))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib import animation
+
+z = delta
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(40,290)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('delta')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
+
+<p>
+  The color of the graph above represents delta value.
+</>
+<h3>2. Gamma</h3>
+<p>
+  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
+</p>
+<p>
+  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
+</p>
+\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def gamma(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = gamma
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(12,320)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
+ax.set_zlim3d(0, z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('gamma')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(delta)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
+<p>
+  The color of the graph above represents delta value.
+</p>
+
+<h3>3. Vega</h3>
+<p>
+  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
+</p>
+<p>
+  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
+</p>
+\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def vega(self):
+    d1 = self.d1()
+    dn1 = self.dn(d1)
+    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = vega
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,45)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
+<p>
+  The color of the graph above represents Vega.
+</p>
+
+<h3>4. Theta</h3>
+<p>
+  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
+</p>
+<p>
+  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
+</p>
+\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
+\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def theta(self):
+    d1 = self.d1()
+    d2 = d1 - self.sigma * sqrt(self.T)
+    dn1 = self.dn(d1)
+    if self.type == "c":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
+                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
+                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
+        return theta
+    elif self.type == "p":
+        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
+                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
+                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
+        return theta
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = theta
+# facecolors aren't normalizing as might be expected
+# we need to normalize it to avoid all dark color for value under 0
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(35,300)
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('rho')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
+
+<h3>5. Rho</h3>
+<p>
+  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
+</p>
+\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
+\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rho(self):
+    d2 = self.d2()
+    if self.type == "c":
+        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
+    elif self.type == "p":
+        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
+    return rho
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">z = rho
+norm = matplotlib.colors.Normalize()
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init()
+ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
+ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
+ax.set_zlim3d(z.min(), z.max())
+ax.set_xlabel('stock price')
+ax.set_ylabel('Time to Expiration')
+ax.set_zlabel('vega')
+m = cm.ScalarMappable(cmap=cm.jet)
+m.set_array(z)
+cbar = plt.colorbar(m)
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
+<h2> Summary</h2>
+<p>
+  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+</p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/01 Introduction.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/01 Introduction.html
new file mode 100755
index 0000000..c61ff28
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  The change of volatility can have a significant impact on the performance of options trading. In addition to the Vega we explained in Greeks letter chapter, this part of the volatility tutorial will discuss the concept of volatility, specifically, we discuss realized and implied volatility, their meanings, measurements, uses, and limitations.
+</p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html
new file mode 100755
index 0000000..227e3b1
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html	
@@ -0,0 +1,43 @@
+<h3>1. Definition</h3>
+<p>
+  It is a measurement of how much the price of the asset has changed on average during the certain period of time. In common, volatility is said to be the standard deviation of the return of assets price.
+</p>
+<h3>2. Calculation</h3>
+<p>
+  Next we discuss how to estimate the historical volatility of the option empirically.
+</p>
+\[r_i=ln(\frac{S_i}{S_{i-1}})\quad for\ i=0,1,2,3,...,n\]
+<p>
+  Where (n+1) is the number of observations, \(S_i\) is the stock price at end of it time interval
+</p>
+<p>
+  The  standard deviation of the \(r_i\) is given by
+</p>
+\[std=\sqrt{\frac{1}{n-1}\sum_{i=1}^n(r_i-\overline{r})^2}\]
+<p>
+  where \(\overline{r}\) is the mean of \(r_i\)
+</p>
+<p>
+  If we assume there are n trading days per year. Then the estimate of historical volatility per annum is
+</p>
+\[std\times\sqrt{n}\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import pandas as pd
+from numpy import sqrt,mean,log,diff
+import quandl
+quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
+goog_table = quandl.get('WIKI/GOOG')
+# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016
+close = goog_table['2016-01':'2016-08']['Adj. Close']
+r = diff(log(close))
+r_mean = mean(r)
+diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]
+std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))
+vol = std*sqrt(252)</pre>
+</div>
+<p>
+  An asset has a historical volatility based on its past performance as described above, investors can gain insight on the fluctuations of the underlying price during the past period of time. But it does not tell us anything about the volatility in the market now and in the future. So here we introduce the implied volatility.
+</p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html
new file mode 100755
index 0000000..33b30f7
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
@@ -0,0 +1,72 @@
+<p>
+  In contrast to historical volatility, the implied volatility looks ahead. It is often interpreted as the market’s expectation for the future volatility of a stock and is implied by the price of the stock’s options. Here implied volatility means it is not observable in the market but can be derived from the price of an option.
+</p>
+<h3>1. Definition</h3>
+<p>
+  We use volatility as an input parameter in option pricing model. If we take a look at the BSM pricing, the theoretical price or the fair value of an option is P, where P is a function of historical volatility σ, stock  price S, strike price K, risk-free rate r and the time to expiration T.  That is \(P=f(\sigma,S,K,r,T)\). But the market price of options is not always the same with the theoretical price. Now in contrast, if we are given the market’s prices of calls and puts written on some asset and also the value of S, K, r, T. For each asset we can solve a new volatility that corresponds to the price of each option – the implied volatility. Then the implied volatility is \(\IV=f^{-1}(P,S,K,r,T)\).
+</p>
+
+<h3>2. Calculation</h3>
+<p>
+  Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def bsm_price(option_type, sigma, s, k, r, T, q):
+    # calculate the bsm price of European call and put options
+    sigma = float(sigma)
+    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))
+    d2 = d1 - sigma * np.sqrt(T)
+    if option_type == 'c':
+        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))
+        return price
+    elif option_type == 'p':
+        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))
+        return price
+    else:
+        print('No such option type %s') %option_type
+def implied_vol(option_type, option_price, s, k, r, T, q):
+    # apply bisection method to get the implied volatility by solving the BSM function
+    precision = 0.00001
+    upper_vol = 500.0
+    max_vol = 500.0
+    min_vol = 0.0001
+    lower_vol = 0.0001
+    iteration = 0
+
+    while 1:
+        iteration +=1
+        mid_vol = (upper_vol + lower_vol)/2.0
+        price = bsm_price(option_type, mid_vol, s, k, r, T, q)
+        if option_type == 'c':
+
+            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)
+            if (lower_price - option_price) * (price - option_price) &gt; 0:
+                lower_vol = mid_vol
+            else:
+                upper_vol = mid_vol
+            if abs(price - option_price) &lt; precision: break if mid_vol &gt; max_vol - 5 :
+                mid_vol = 0.000001
+                break
+
+        elif option_type == 'p':
+            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)
+
+            if (upper_price - option_price) * (price - option_price) &gt; 0:
+                upper_vol = mid_vol
+            else:
+                lower_vol = mid_vol
+            if abs(price - option_price) &lt; precision: break if iteration &gt; 50: break
+
+    return mid_vol
+implied_vol('c', 0.3, 3, 3, 0.032, 30.0/365, 0.01)
+</pre>
+</div>
+<p>
+  From the result above, the implied volatility of European call option (with premium c=0.3, S=3, K=3, r=0.032, T =30 days, d=0.01) is 0.87.
+</p>
+<h3>3. Factors Affecting Implied Volatility</h3>
+<p>
+  According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
+</p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/04 Volatility Skew.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/04 Volatility Skew.html
new file mode 100755
index 0000000..0c5f025
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/04 Volatility Skew.html	
@@ -0,0 +1,101 @@
+<p>
+  For European options of the same maturity and the same underlying assets, the implied volatilities vary with the strikes. For a series of put options or call options, if we plot these implied volatilities for a series of options which have the same expiration date and the same underlying with the x-axis being the different strikes, we would get a convex curve. The shape of this curve is like people's smiling, it is being called the volatility. The shape of volatility smile depends on the assets and the market conditions.
+</p>
+<p>
+  Here we give an example how to plot the volatility smile by using the real time options data of SPDR S&amp;P 500 ETF(NYSEARCA: SPY).
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># download option data for all expiry months from Yahoo Finance
+# provide a formatted DataFrame with a hierarchical index
+opt = Options('spy', 'yahoo')
+opt.expiry_dates  # list all the available expiration dates
+def IV_plot(opt,option_type,expiry_index):
+    expiry = opt.expiry_dates[expiry_index]
+    if option_type == 'c':
+        data = opt.get_call_data(expiry=expiry)
+    elif option_type == 'p':
+        data = opt.get_put_data(expiry=expiry)
+    r = 0.01 # risk free rate
+    d = 0.01 # continuous devidend yield
+    s = opt.underlying_price # data_call['Underlying_Price']  undelying price
+    expiry = data.index.get_level_values('Expiry')[0] # get the expiry
+    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date
+    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
+    premium = (data['Ask'] + data['Bid'])/2 # option premium
+    strike = list(data.index.get_level_values('Strike')) # get the strike price
+    IV = []
+    for i in range(len(data)):
+        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))
+
+    plt.figure(figsize=(16, 7))
+    a = plt.scatter(strike,IV, c='r', label="IV by solving BSM")
+    b = plt.scatter(strike,data['IV'],c = 'b', label="IV from Yahoo Finance")
+    plt.grid()
+    plt.xlabel('strike')
+    if option_type == 'c':
+        plt.ylabel('Implied Volatility for call option')
+        plt.legend((a,b), ("IV(call) by solving BSM", "IV(call) from Yahoo Finance"))
+    elif option_type == 'p':
+        plt.ylabel('Implied Volatility for put options')
+        plt.legend((a,b), ("IV(put) by solving BSM", "IV(put) from Yahoo Finance"))
+
+    return strike,IV
+k_call, IV_call = IV_plot(opt,'c',23)
+k_put, IV_put = IV_plot(opt,'p',23)
+plt.figure(figsize=(16, 7))
+e = plt.scatter(k_call,IV_call, c ='red', label="IV(call options)")
+f = plt.scatter(k_put,IV_put, c = 'black', label="IV(put options)")
+plt.xlabel('strike')
+plt.ylabel('Implied Volatility')
+plt.legend((e,f), ("IV (call options)", "IV (put options)"))
+</pre>
+</div>
+<p>
+  The current date is 08/14/2017. We plot the implied volatilities for SPY options which expire on 12/21/2018.
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivcall.png" alt="implied volatility of call options"/>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivput.png" alt="implied volatility of put options"/>
+<p>
+  Plotting these implied volatilities across strikes gives us the implied volatility skew. For the shape of volatility smile, it should be a symmetry convex curve. But from the above chart, the implied volatility curve slopes downward to the right. This is referred to the skew, which means that options with low strikes have higher implied volatilities than those with higher strikes. The smile is not symmetry. The skew of a distribution is a measure of its asymmetry. Although the volatility skew is dynamic, in equity markets it is almost always a decreasing function of the strike. Other asset classes such as FX and commodities have differently shaped skews.
+</p>
+<p>
+  If we plot the call and put options implied volatility smile in the same chart for the same expiration date:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivputcall.png" alt="implied volatility of call and put options"/>
+<p>
+  From the above chart, we can see the implied volatility for put options is higher than call options. Usually, put options trade for a higher price than call options, because traders place more risk in the short put positions, which raises the amount of reward they require to sell the position. Higher option prices signify an increase in risk and are represented by higher implied volatility levels derived from the option pricing model. Then we scattered all the implied volatilities of contracts across all the strikes.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">opt = Options('spy', 'yahoo')
+r = 0.01 # risk free rate
+d = 0.01 # continuous devidend yield
+expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]
+current_date = opt.quote_time.date()  ## get the current date
+s = opt.underlying_price # undelying price
+num_expiry = len(expiry_dates)
+IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]
+
+for expiry_index in range(num_expiry):
+    data = opt.get_put_data(expiry=expiry_dates[expiry_index])
+    expiry = expiry_dates[expiry_index] # get the expiry
+    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
+    premium = (data['Ask'] + data['Bid'])/2.0 # option premium
+    strike = data.index.get_level_values('Strike') # get the strike price
+    num_strike = len(data)
+    for j in range(num_strike):
+        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])
+x= [IV[i][0] for i in range(len(IV))]
+y= [IV[i][1] for i in range(len(IV))]
+z= [IV[i][2] for i in range(len(IV))]
+fig = plt.figure(figsize=(20,11))
+ax = fig.add_subplot(111, projection='3d')
+ax.view_init(20,10)
+ax.scatter(x,y,z)
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-volsurface.png" alt="implied volatility surface"/>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05Volatility Surface.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05Volatility Surface.html
new file mode 100755
index 0000000..057ec70
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05Volatility Surface.html	
@@ -0,0 +1,8 @@
+<p>
+  By fixing the maturity and looking at the implied volatilities of European options on the same underlying but different strikes, we obtain the implied volatility skew or smile. The volatility surface is the three-dimensional surface when we plots the market implied volatilities of European options with different strikes and different maturities.
+</p>
+<p>
+  Through the interpolation method, we can generate the implied volatility surface of SPY options for both put and call options as follows:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-call.png" alt="implied volatility surface for call options"/>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-put.png" alt="implied volatility surface for put options"/>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html
deleted file mode 100755
index 299fc4f..0000000
--- a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.html	
+++ /dev/null
@@ -1,191 +0,0 @@
-<h2>Introduction</h2>
-The change of volatility can have a significant impact on any options trading strategies performance. In addition to the Vega we explained in Greeks letter chapter, this part of the volatility tutorial will discuss the concept of volatility, specifically, we discuss realized and implied volatility, their meanings, measurements, uses, and limitations.
-
-<h2>Historical Volatility</h2>
-
-<h3>1. Definition</h3>
-It is a measurement of how much the price of the asset has changed on average during the certain period of time. In common, volatility is said to be the standard deviation of the return of assets price.
-<h3>2. Calculation</h3>
-Here we discuss how to estimate the historical volatility of the option written on specific underlying asset empirically.
-
-\[r_i=ln(\frac{S_i}{S_{i-1}})\quad for\ i=0,1,2,3,...,n\]
-
-Where (n+1) is the number of observations, \(S_i\) is the stock price at end of it time interval
-
-The  standard deviation of the \(r_i\) is given by
-\[std=\sqrt{\frac{1}{n-1}\sum_{i=1}^n(r_i-\overline{r})^2}\]
-where \(\overline{r}\) is the mean of \(r_i\)
-
-If we assume there are n trading days per year. Then the estimate of historical volatility per annum is
-
-\[std\times\sqrt{n}\]
-<pre class="prettyprint linenums">import pandas as pd
-from numpy import sqrt,mean,log,diff
-import quandl
-quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
-goog_table = quandl.get('WIKI/GOOG')
-# use the daily data of Google(NASDAQ: GOOG) from 01/2016 to 08/2016
-close = goog_table['2016-01':'2016-08']['Adj. Close']
-r = diff(log(close))
-r_mean = mean(r)
-diff_square = [(r[i]-r_mean)**2 for i in range(0,len(r))]
-std = sqrt(sum(diff_square)*(1.0/(len(r)-1)))
-vol = std*sqrt(252)</pre>
-An asset has a historical volatility based on its past performance as described above, investors can gain insight on the fluctuations of the underlying price during the past period of time. But it does not tell us anything about the volatility in the market now and in the future. So here we introduce the implied volatility.
-<h2><strong>Implied Volatility</strong></h2>
-In contrast to historical volatility, the implied volatility looks ahead. It is often interpreted as the market’s expectation for the future volatility of a stock and is implied by the price of the stock’s options. Here implied volatility means it is not observable in the market but can be derived from the price of an option.
-<h3>1. Definition</h3>
-
-We use volatility as an input parameter in option pricing model. If we take a look at the BSM pricing, the theoretical price or the fair value of an option is P, where P is a function of historical volatility σ, stock  price S, strike price K, risk-free rate r and the time to expiration T.  That is \(P=f(\sigma,S,K,r,T)\). But the market price of options is not always the same with the theoretical price. Now in contrast, if we are given the market’s prices of calls and puts written on some asset and also the value of S, K, r, T. For each asset we can solve a new volatility that corresponds to the price of each option – the implied volatility. Then the implied volatility is \(\IV=f^{-1}(P,S,K,r,T)\).
-<h3>2. Calculation</h3>
-Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
-<pre class="prettyprint linenums">def bsm_price(option_type, sigma, s, k, r, T, q):
-    # calculate the bsm price of European call and put options
-    sigma = float(sigma)
-    d1 = (np.log(s / k) + (r - q + sigma ** 2 * 0.5) * T) / (sigma * np.sqrt(T))
-    d2 = d1 - sigma * np.sqrt(T)
-    if option_type == 'c':
-        price = np.exp(-r*T) * (s * np.exp((r - q)*T) * stats.norm.cdf(d1) - k *  stats.norm.cdf(d2))
-        return price
-    elif option_type == 'p':
-        price = np.exp(-r*T) * (k * stats.norm.cdf(-d2) - s * np.exp((r - q)*T) *  stats.norm.cdf(-d1))
-        return price
-    else:
-        print('No such option type %s') %option_type
-def implied_vol(option_type, option_price, s, k, r, T, q):
-    # apply bisection method to get the implied volatility by solving the BSM function
-    precision = 0.00001
-    upper_vol = 500.0
-    max_vol = 500.0
-    min_vol = 0.0001
-    lower_vol = 0.0001
-    iteration = 0
-
-    while 1:
-        iteration +=1
-        mid_vol = (upper_vol + lower_vol)/2.0
-        price = bsm_price(option_type, mid_vol, s, k, r, T, q)
-        if option_type == 'c':
-
-            lower_price = bsm_price(option_type, lower_vol, s, k, r, T, q)
-            if (lower_price - option_price) * (price - option_price) &gt; 0:
-                lower_vol = mid_vol
-            else:
-                upper_vol = mid_vol
-            if abs(price - option_price) &lt; precision: break if mid_vol &gt; max_vol - 5 :
-                mid_vol = 0.000001
-                break
-
-        elif option_type == 'p':
-            upper_price = bsm_price(option_type, upper_vol, s, k, r, T, q)
-
-            if (upper_price - option_price) * (price - option_price) &gt; 0:
-                upper_vol = mid_vol
-            else:
-                lower_vol = mid_vol
-            if abs(price - option_price) &lt; precision: break if iteration &gt; 50: break
-
-    return mid_vol
-implied_vol('c', 0.3, 3, 3, 0.032, 30.0/365, 0.01)
-</pre>
-From the result above, the implied volatility of European call option (with premium c=0.3, S=3, K=3, r=0.032, T =30 days, d=0.01) is 0.87.
-<h3>3. Factors Affecting Implied Volatility</h3>
-According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
-
-<h2>Volatility Skew</h2>
-For European options of the same maturity and the same underlying assets, the implied volatilities vary with the strikes. For a series of put options or call options, if we plot these implied volatilities for a series of options which have the same expiration date and the same underlying with the x axis being the different strikes, we would get a convex curve. The shape of this curve is like people's smiling, it is being called the volatility. The shape of volatility smile depends on the assets and the market conditions.
-
-Here we give an example how to plot the volatility smile by using the real time options data of SPDR S&amp;P 500 ETF(NYSEARCA: SPY).
-<pre class="prettyprint linenums"># download option data for all expiry months from Yahoo Finance
-# provide a formatted DataFrame with a hierarchical index
-opt = Options('spy', 'yahoo')
-opt.expiry_dates  # list all the available expiration dates
-def IV_plot(opt,option_type,expiry_index):
-    expiry = opt.expiry_dates[expiry_index]
-    if option_type == 'c':
-        data = opt.get_call_data(expiry=expiry)
-    elif option_type == 'p':
-        data = opt.get_put_data(expiry=expiry)
-    r = 0.01 # risk free rate
-    d = 0.01 # continuous devidend yield
-    s = opt.underlying_price # data_call['Underlying_Price']  undelying price
-    expiry = data.index.get_level_values('Expiry')[0] # get the expiry
-    current_date = opt.quote_time # current_date = datetime.datetime.now() # get the current date
-    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
-    premium = (data['Ask'] + data['Bid'])/2 # option premium
-    strike = list(data.index.get_level_values('Strike')) # get the strike price
-    IV = []
-    for i in range(len(data)):
-        IV.append(implied_vol(option_type, premium.values[i], s, strike[i], r, time_to_expire, d))
-
-    plt.figure(figsize=(16, 7))
-    a = plt.scatter(strike,IV, c='r', label="IV by solving BSM")
-    b = plt.scatter(strike,data['IV'],c = 'b', label="IV from Yahoo Finance")
-    plt.grid()
-    plt.xlabel('strike')
-    if option_type == 'c':
-        plt.ylabel('Implied Volatility for call option')
-        plt.legend((a,b), ("IV(call) by solving BSM", "IV(call) from Yahoo Finance"))
-    elif option_type == 'p':
-        plt.ylabel('Implied Volatility for put options')
-        plt.legend((a,b), ("IV(put) by solving BSM", "IV(put) from Yahoo Finance"))
-
-    return strike,IV
-k_call, IV_call = IV_plot(opt,'c',23)
-k_put, IV_put = IV_plot(opt,'p',23)
-plt.figure(figsize=(16, 7))
-e = plt.scatter(k_call,IV_call, c ='red', label="IV(call options)")
-f = plt.scatter(k_put,IV_put, c = 'black', label="IV(put options)")
-plt.xlabel('strike')
-plt.ylabel('Implied Volatility')
-plt.legend((e,f), ("IV (call options)", "IV (put options)"))
-</pre>
-The current date is 08/14/2017. We plot the implied volatilities for SPY options which expire on 12/21/2018.
-
-<h2><img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivcall.png" alt="implied volatility of call options"/></h2>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivput.png" alt="implied volatility of put options"/>
-
-Plotting these implied volatilities across strikes gives us the implied volatility skew. For the shape of volatility smile, it should be a symmetry convex curve. But from the above chart, the implied volatility curve slope downward to the right. This is referred to the skew, which means that options with low strikes have higher implied volatilities than those with higher strikes. The smile is not symmetry. The skew of a distribution is a measure of its assymetry. Although the volatility skew is dynamic, in equity markets it is almost always a decreasing function of the strike. Other asset classes such as FX and commodities have differently shaped skews.
-If we plot the call and put options implied volatility smile in the same chart for the same expiration date:
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-ivputcall.png" alt="implied volatility of call and put options"/>
-From the above chart, we can see the implied volatility for put options is higher than call options. Usually, put options trade for a higher price than call options, because traders place more risk in the short put positions, which raises the amount of reward they require to sell the position. Higher option prices signify an increase in risk and are represented by higher implied volatility levels derived from the option pricing model.
-Then we scattered all the implied volatilities of contracts across all the strikes.
-<pre class="prettyprint linenums">opt = Options('spy', 'yahoo')
-r = 0.01 # risk free rate
-d = 0.01 # continuous devidend yield
-expiry_dates = [i for i in opt.expiry_dates if i > opt.quote_time.date()]
-current_date = opt.quote_time.date()  ## get the current date
-s = opt.underlying_price # undelying price
-num_expiry = len(expiry_dates)
-IV = [] # (num_expiry * 3)-dimension list with each row being [time_to_expire, strike, implied_vol]
-
-for expiry_index in range(num_expiry):
-    data = opt.get_put_data(expiry=expiry_dates[expiry_index])
-    expiry = expiry_dates[expiry_index] # get the expiry
-    time_to_expire = float((expiry - current_date).days)/365 # compute time to expiration
-    premium = (data['Ask'] + data['Bid'])/2.0 # option premium
-    strike = data.index.get_level_values('Strike') # get the strike price
-    num_strike = len(data)
-    for j in range(num_strike):
-        IV.append([time_to_expire, strike[j], implied_vol('c', premium.values[j], s, strike[j], r, time_to_expire, d)])
-x= [IV[i][0] for i in range(len(IV))]
-y= [IV[i][1] for i in range(len(IV))]
-z= [IV[i][2] for i in range(len(IV))]
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,10)
-ax.scatter(x,y,z)
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-volsurface.png" alt="implied volatility surface"/>
-<h2>Volatility Surface</h2>
-By fixing a maturity and looking at the implied volatilities of European options on the same underlying but different strikes, we obtain the implied volatility skew or smile. The volatility surface is the three-dimensional surface obtained when we plots the market implied volatilities of European options with different strikes and different maturities.
-By using the interpolation method, we can generate the implied volatility surface of SPY options for both put and call options as follows:
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-call.png" alt="implied volatility surface for call options"/>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-vol-surface-put.png" alt="implied volatility surface for put options"/>
-<h2>The Reason for Volatility Skew</h2>
-The volatility skew shows that for Put options, implied volatility is higher for deep OTM options and then is decreasing as it moves towards ITM options. For call options, the implied volatility is higher for deep ITM options and then is decreasing as it moves towards OTM options. From the demand and supply degree, the skew reflects that investors are more willing to buy deep OTM puts and ITM calls. Why there is volatility skew in the market?
-First, the majority of the equity positions are long. Investors usually have two ways to hedge those long positions risks: Buying downside puts or selling upside calls. The increase in demand create increases in price of downside puts and decreases in price of upside calls. The volatility is a reflection of options price. Therefore the volatility of in-the-money put is higher and the volatility of in-the-money call is lower.
-The second reason for volatility skew is that the market moves down faster than it moves up. The downside market move is riskier than the upside move. Thus the price of OTM puts is higher than OTM calls.
-<h2>Summary</h2>
-In this chapter, we discussed the historical volatility and the implied volatility. The historical volatility of an asset is the statistical measure we know as the standard deviation of the stock return series. The implied volatility of the same asset, on the other hand, is the volatility parameter that we can infer from the prices of traded options written on this asset. In contrast to historical volatility, which looks at fluctuations of asset prices in the past, implied volatility looks ahead. The two volatilities do not necessarily coincide, and although they may be close, they are typically not equal.
-Now we know the constant volatility assumption in Black-Sholes-Merton model is not applicable in the real market because there is volatility skew for most of the options. Then in next chapter we will introduce some volatility models to capture the volatility skew in options pricing.
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 The Reason for Volatility Skew.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 The Reason for Volatility Skew.html
new file mode 100755
index 0000000..3312b4b
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/06 The Reason for Volatility Skew.html	
@@ -0,0 +1,5 @@
+<p>
+  The volatility skew reveals that for put options, implied volatility is higher for deep OTM options and is decreasing as it moves toward ITM options. For call options, the implied volatility is higher for deep ITM options and is decreasing as it moves toward OTM options. From the demand and supply degree, the skew reflects that investors are more willing to buy deep OTM puts and ITM calls. Why there is volatility skew in the market?
+  First, the majority of the equity positions are long. Investors usually have two ways to hedge those long positions risks: Buying downside puts or selling upside calls. The increase in demand create increases in the price of downside puts and decreases in the price of upside calls. The volatility is a reflection of options price. Therefore the volatility of in-the-money put is higher and the volatility of in-the-money call is lower.
+  The second reason for volatility skew is that the market moves down faster than it moves up. The downside market move is riskier than the upside move. Thus the price of OTM puts is higher than OTM calls.
+</p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/07 Summary.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/07 Summary.html
new file mode 100755
index 0000000..e8ac4a6
--- /dev/null
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/07 Summary.html	
@@ -0,0 +1,4 @@
+<p>
+  In this chapter, we discussed the historical volatility and the implied volatility. The historical volatility of an asset is the statistical measure we know as the standard deviation of the stock return series. The implied volatility of the same asset, on the other hand, is the volatility parameter that we can infer from the prices of traded options written on this asset. In contrast to historical volatility, which looks at fluctuations of asset prices in the past, implied volatility looks ahead. The two volatilities do not necessarily coincide, and although they may be close, they are typically not equal.
+  Now we know the constant volatility assumption in Black-Sholes-Merton model is not applicable in the real market because there is volatility skew for most options. In next chapter, we will introduce some volatility models to capture the volatility skew in options pricing.
+</p>
diff --git a/07 Applied Options[]/04 Long Strangle/02 Implementation.html b/07 Applied Options[]/04 Long Strangle/02 Implementation.html
index 1ed16be..5ed0284 100755
--- a/07 Applied Options[]/04 Long Strangle/02 Implementation.html	
+++ b/07 Applied Options[]/04 Long Strangle/02 Implementation.html	
@@ -55,7 +55,9 @@
 self.put = put_contracts[0]
 </pre>
 </div>
-Step 5: Buy the call and the put options at the same time and wait until expiration.
+<p>
+	Step 5: Buy the call and the put options at the same time and wait until expiration.
+</p>
 <div class="section-example-container">
 <pre class="csharp">
 </pre>

From a8f8962f40fc8d3bf7f645bcf09a62d9ddae4996 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 6 Jun 2018 18:48:29 -0400
Subject: [PATCH 044/753] split tutorials

---
 .../01 Introduction.html                      |   1 -
 .../05 The Greek Letters/01 Introduction.html |   1 -
 .../05 The Greek Letters/02 Delta.html        | 193 ------------
 .../05 The Greek Letters/03 gamma.html        | 237 ---------------
 .../05 The Greek Letters/04 vega.html         | 235 ---------------
 .../05 The Greek Letters/05 Theta.html        | 225 --------------
 .../05 The Greek Letters/06 Rho.html          | 237 ---------------
 .../07 Delta, Gamma and Vega Hedging.html     |   7 +
 .../05 The Greek Letters/07 Summary.html      | 274 +-----------------
 .../02 Historical Volatility.html             |   4 +-
 .../03 Implied Volatility.html                |   6 +-
 ...urface.html => 05 Volatility Surface.html} |   0
 12 files changed, 13 insertions(+), 1407 deletions(-)
 create mode 100644 06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html
 rename 06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/{05Volatility Surface.html => 05 Volatility Surface.html} (100%)

diff --git a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html
index b3a7a65..6684954 100755
--- a/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html	
+++ b/06 Introduction to Options[]/02 Put-Call Parity and Arbitrage Strategies/01 Introduction.html	
@@ -1,4 +1,3 @@
-<h2>Introduction</h2>
 <p>
   In this chapter, we will discuss the option's payoff if you long or short the options. Then we discuss the put-call parity which is a relationship between the price of a European call option, the price of a European put option, and the underlying stock price. In addition, an application of put-call parity in arbitrage trading strategies was demonstrated.
 </p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html b/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html
index dd8f4f1..d8fd050 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/01 Introduction.html	
@@ -1,4 +1,3 @@
-<h2>Introduction</h2>
 <p>
   Option Greeks measure the exposure of option price or option delta to movement of different factors such as the underlying price, time and volatility. In this tutorial we will discuss various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
 </p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html b/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html
index 1fe282d..09dde08 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/02 Delta.html	
@@ -1,8 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
 <h4>Definition</h4>
 <p>
   Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
@@ -86,191 +81,3 @@ <h4>Impact Factors</h4>
 <p>
   The color of the graph above represents delta value.
 </>
-<h3>2. Gamma</h3>
-<p>
-  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-</p>
-<p>
-  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-</p>
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-<p>
-  The color of the graph above represents delta value.
-</p>
-
-<h3>3. Vega</h3>
-<p>
-  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-</p>
-<p>
-  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-</p>
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-<p>
-  The color of the graph above represents Vega.
-</p>
-
-<h3>4. Theta</h3>
-<p>
-  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-</p>
-<p>
-  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-</p>
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
-<p>
-  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-</p>
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
-<h2> Summary</h2>
-<p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html b/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html
index 1fe282d..9c74e05 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/03 gamma.html	
@@ -1,92 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<p>
-  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-</p>
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)
-</pre>
-</div>
-<h4>Impact Factors</h4>
-<p>
-  Stock price, days remaining to expiration and implied volatility will impact the Delta.
-</p>
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-<p>
-  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-</p>
-<p>
-  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-<p>
-  The color of the graph above represents delta value.
-</>
-<h3>2. Gamma</h3>
 <p>
   Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
 </p>
@@ -126,151 +37,3 @@ <h3>2. Gamma</h3>
 <p>
   The color of the graph above represents delta value.
 </p>
-
-<h3>3. Vega</h3>
-<p>
-  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-</p>
-<p>
-  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-</p>
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-<p>
-  The color of the graph above represents Vega.
-</p>
-
-<h3>4. Theta</h3>
-<p>
-  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-</p>
-<p>
-  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-</p>
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
-<p>
-  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-</p>
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
-<h2> Summary</h2>
-<p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/04 vega.html b/06 Introduction to Options[]/05 The Greek Letters/04 vega.html
index 1fe282d..e0d5617 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/04 vega.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/04 vega.html	
@@ -1,133 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<p>
-  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-</p>
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)
-</pre>
-</div>
-<h4>Impact Factors</h4>
-<p>
-  Stock price, days remaining to expiration and implied volatility will impact the Delta.
-</p>
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-<p>
-  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-</p>
-<p>
-  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-<p>
-  The color of the graph above represents delta value.
-</>
-<h3>2. Gamma</h3>
-<p>
-  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-</p>
-<p>
-  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-</p>
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-<p>
-  The color of the graph above represents delta value.
-</p>
-
-<h3>3. Vega</h3>
 <p>
   The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
 </p>
@@ -169,108 +39,3 @@ <h3>3. Vega</h3>
 <p>
   The color of the graph above represents Vega.
 </p>
-
-<h3>4. Theta</h3>
-<p>
-  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-</p>
-<p>
-  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-</p>
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
-<p>
-  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-</p>
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
-<h2> Summary</h2>
-<p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html b/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html
index 1fe282d..df30d5d 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/05 Theta.html	
@@ -1,176 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<p>
-  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-</p>
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)
-</pre>
-</div>
-<h4>Impact Factors</h4>
-<p>
-  Stock price, days remaining to expiration and implied volatility will impact the Delta.
-</p>
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-<p>
-  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-</p>
-<p>
-  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-<p>
-  The color of the graph above represents delta value.
-</>
-<h3>2. Gamma</h3>
-<p>
-  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-</p>
-<p>
-  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-</p>
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-<p>
-  The color of the graph above represents delta value.
-</p>
-
-<h3>3. Vega</h3>
-<p>
-  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-</p>
-<p>
-  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-</p>
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-<p>
-  The color of the graph above represents Vega.
-</p>
-
-<h3>4. Theta</h3>
 <p>
   Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
 </p>
@@ -222,55 +49,3 @@ <h3>4. Theta</h3>
 </div>
 
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
-<p>
-  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-</p>
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
-<h2> Summary</h2>
-<p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html b/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html
index 1fe282d..aebfe73 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/06 Rho.html	
@@ -1,229 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<p>
-  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-</p>
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)
-</pre>
-</div>
-<h4>Impact Factors</h4>
-<p>
-  Stock price, days remaining to expiration and implied volatility will impact the Delta.
-</p>
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-<p>
-  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-</p>
-<p>
-  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-<p>
-  The color of the graph above represents delta value.
-</>
-<h3>2. Gamma</h3>
-<p>
-  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-</p>
-<p>
-  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-</p>
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-<p>
-  The color of the graph above represents delta value.
-</p>
-
-<h3>3. Vega</h3>
-<p>
-  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-</p>
-<p>
-  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-</p>
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-<p>
-  The color of the graph above represents Vega.
-</p>
-
-<h3>4. Theta</h3>
-<p>
-  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-</p>
-<p>
-  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-</p>
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
 <p>
   Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
 </p>
@@ -263,14 +37,3 @@ <h3>5. Rho</h3>
 </div>
 
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
-<h2> Summary</h2>
-<p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
-</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html b/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html
new file mode 100644
index 0000000..cc93a2d
--- /dev/null
+++ b/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html	
@@ -0,0 +1,7 @@
+<h2>Delta, Gamma and Vega Hedging</h2>
+<p>
+  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
+</p>
+<p>
+  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+</p>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html b/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html
index 1fe282d..68c105e 100755
--- a/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/07 Summary.html	
@@ -1,276 +1,4 @@
-<h2>Introduction</h2>
-<p>
-  Option Greeks measure the different factors such as the underlying price, time and volatility that affect the price of an option contract. In this tutorial we will discuss the various Greeks, their meanings and their implications on the pricing and how to use them to hedge risks.
-</p>
-<h3>1. Delta</h3>
-<h4>Definition</h4>
-<p>
-  Delta is the rate of change of the option price with respect to the price of the underlying asset. It measures the first-order sensitivity of the price to a movement in stock price S. The option delta is 0.4 means that if the underlying moves by for example 1%, then the value of the option will move by 0.4 × 1%. For European options on an asset that provides a yield at rate q:
-</p>
-\[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
-\[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
-def delta(self):
-    d1 = self.d1()
-    if self.type == "c":
-        return exp(-self.q * self.T) * self.n(d1)
-    elif self.type == "p":
-        return exp(-self.q * self.T) * (self.n(d1)-1)
-</pre>
-</div>
-<h4>Impact Factors</h4>
-<p>
-  Stock price, days remaining to expiration and implied volatility will impact the Delta.
-</p>
-<ul>
- 	<li><strong>Stock Price: </strong>Call option has a positive Delta range from 0 to 1. The Delta is positively correlated to underlying stock price change. While put option has a negative Delta ranges from -1 to 0, its Delta is negatively correlated with the underlying stock price change. At-the-money call options usually have a Delta near .50. At-the-money put options have a Delta near -.50.</li>
- 	<li><strong>Implied Volatility:</strong> Low implied volatility stocks will tend to have higher Delta for the in-the-money options and lower Delta for out-of-the-money options.</li>
- 	<li><strong>Days remaining to expiration:</strong> As expiration nears, in-the-money call Deltas increase toward 1.00, at-the-money call Deltas remain at around 0.5 and out-of-the-money call Deltas fall to 0 provided other inputs remain constant.</li>
-</ul>
-<p>
-  As a general rule, in-the-money options will move more than out-of-the-money options, and short-term options will react more than longer-term options to the same price change in the stock.
-</p>
-<p>
-  In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">s = np.array([range(10,70,1) for i in range(23)])
-I = np.ones((shape(s)))
-time = arange(1,12.5,0.5)/12
-T = np.array([ele for ele in time for i in range(shape(s)[1])]).reshape(shape(s))
-
-contracts = []
-for i in range(shape(s)[0]):
-    for j in range(shape(s)[1]):
-        contracts.append(BsmModel('c',s[i,j],40*I[i,j],0.1*I[i,j], T[i,j],0.5*I[i,j]))
-delta = [x.theta() for x in contracts]
-gamma = [x.gamma() for x in contracts]
-delta = [x.delta() for x in contracts]
-vega = [x.vega() for x in contracts]
-rho = [x.rho() for x in contracts]
-
-gamma = np.array(gamma).reshape(shape(s))
-delta = np.array(delta).reshape(shape(s))
-theta = np.array(theta).reshape(shape(s))
-vega = np.array(vega).reshape(shape(s))
-rho = np.array(rho).reshape(shape(s))
-
-import numpy as np
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import cm
-from matplotlib import animation
-
-z = delta
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(40,290)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('delta')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-delta.png" alt="Greeks letter: delta" />
-
-<p>
-  The color of the graph above represents delta value.
-</>
-<h3>2. Gamma</h3>
-<p>
-  Gamma is the rate of change of the portfolio's delta with respect to the underlying asset's price. It represents the second-order sensitivity of the option to a movement in the underlying asset’s price.
-</p>
-<p>
-  Long options, either calls or puts, always yield positive Gamma. Gamma is higher for options that are at-the-money and closer to expiration because the Delta of the near term options move toward either 0 or 1.00 is imminent. Deeper-in-the-money or farther-out-of-the-money options have lower Gamma as their Deltas already approached 0 or 1.00 (or 0 or -1.00 for puts) and will not change as quickly with movement in the underlying. For European options:
-</p>
-\[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def gamma(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return dn1 * exp(-self.q * self.T) / (self.s * self.sigma * sqrt(self.T))
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = gamma
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(12,320)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(delta),linewidth=0.001, rstride=1, cstride=1, alpha = 0.75)
-ax.set_zlim3d(0, z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('gamma')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(delta)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-gamma.png" alt="The Greeks letters: gamma" />
-<p>
-  The color of the graph above represents delta value.
-</p>
-
-<h3>3. Vega</h3>
-<p>
-  The Vega is the rate of change in the value of the option with respect to the volatility of the underlying asset.
-</p>
-<p>
-  Vega is always positive for long positions and is the same value for both puts and calls. Hence the option price always increases as the volatility increases. Vega for the European options:
-</p>
-\[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def vega(self):
-    d1 = self.d1()
-    dn1 = self.dn(d1)
-    return self.s * sqrt(self.T) * dn1 * exp(-self.q * self.T)
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = vega
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(20,45)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-vega.png" alt="The Greeks letters: vega" />
-<p>
-  The color of the graph above represents Vega.
-</p>
-
-<h3>4. Theta</h3>
-<p>
-  Theta is the rate of change of the value of the option with respect to the passage of time. It is also referred to as the time decay of the portfolio.
-</p>
-<p>
-  The theta of  holding long position of a call or a put option is usually negative. An option that loses 0.1% per day is said to have a Theta of −0.1%. For example, if we buy an OTM call option, the value of this call option decreases as time passes since the option has less time to expiry. If time passes with the price of the underlying asset and its volatility remaining the same, the passing of time will lower the value of the option. Theta for European Options
-</p>
-\[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
-\[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def theta(self):
-    d1 = self.d1()
-    d2 = d1 - self.sigma * sqrt(self.T)
-    dn1 = self.dn(d1)
-    if self.type == "c":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q*self.T) / (2 * sqrt(self.T)) \
-                    + self.q * self.s * self.n(d1) * exp(-self.q*self.T) \
-                    - self.r * self.k * exp(-self.r*self.T) * self.n(d2)
-        return theta
-    elif self.type == "p":
-        theta = -self.s * dn1 * self.sigma * exp(-self.q * self.T) / (2 * sqrt(self.T)) \
-                    - self.q * self.s * self.n(-d1) * exp(-self.q * self.T) \
-                    + self.r * self.k * exp(-self.r * self.T) * self.n(-d2)
-        return theta
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = theta
-# facecolors aren't normalizing as might be expected
-# we need to normalize it to avoid all dark color for value under 0
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init(35,300)
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('rho')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-theta.png" alt="The Greeks letters: theta" />
-
-<h3>5. Rho</h3>
-<p>
-  Rho is the rate of change of the value of a derivative with respect to the interest rate. It is usually small and not a big issue in practice unless the option is deep in-the-money and has a long horizon. The interest rate would matter because we need to discount a larger cash flow over a longer horizon. Rho for the European options:
-</p>
-\[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
-\[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def rho(self):
-    d2 = self.d2()
-    if self.type == "c":
-        rho = self.k * self.T * (exp(-self.r*self.T)) * self.n(d2)
-    elif self.type == "p":
-        rho = -self.k * self.T * (exp(-self.r*self.T)) * self.n(-d2)
-    return rho
-</pre>
-</div>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">z = rho
-norm = matplotlib.colors.Normalize()
-fig = plt.figure(figsize=(20,11))
-ax = fig.add_subplot(111, projection='3d')
-ax.view_init()
-ax.plot_wireframe(s, T, z, rstride=1, cstride=1)
-ax.plot_surface(s, T, z, facecolors=cm.jet(norm(z)),linewidth=0.001, rstride=1, cstride=1, alpha = 0.9)
-ax.set_zlim3d(z.min(), z.max())
-ax.set_xlabel('stock price')
-ax.set_ylabel('Time to Expiration')
-ax.set_zlabel('vega')
-m = cm.ScalarMappable(cmap=cm.jet)
-m.set_array(z)
-cbar = plt.colorbar(m)
-</pre>
-</div>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-rho.png" alt="The Greeks letters: rho"/>
-<h2>Delta, Gamma and Vega Hedging</h2>
-<p>
-  The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
-</p>
-<p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
-</p>
 <h2> Summary</h2>
 <p>
-  In this chapter we discussed the various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
+  In this chapter we discussed various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
 </p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html
index 227e3b1..47c3b7d 100755
--- a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html	
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/02 Historical Volatility.html	
@@ -1,8 +1,8 @@
-<h3>1. Definition</h3>
+<h4>1. Definition</h4>
 <p>
   It is a measurement of how much the price of the asset has changed on average during the certain period of time. In common, volatility is said to be the standard deviation of the return of assets price.
 </p>
-<h3>2. Calculation</h3>
+<h4>2. Calculation</h4>
 <p>
   Next we discuss how to estimate the historical volatility of the option empirically.
 </p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html
index 33b30f7..97ee3ba 100755
--- a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
+++ b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
@@ -1,12 +1,12 @@
 <p>
   In contrast to historical volatility, the implied volatility looks ahead. It is often interpreted as the market’s expectation for the future volatility of a stock and is implied by the price of the stock’s options. Here implied volatility means it is not observable in the market but can be derived from the price of an option.
 </p>
-<h3>1. Definition</h3>
+<h4>1. Definition</h4>
 <p>
   We use volatility as an input parameter in option pricing model. If we take a look at the BSM pricing, the theoretical price or the fair value of an option is P, where P is a function of historical volatility σ, stock  price S, strike price K, risk-free rate r and the time to expiration T.  That is \(P=f(\sigma,S,K,r,T)\). But the market price of options is not always the same with the theoretical price. Now in contrast, if we are given the market’s prices of calls and puts written on some asset and also the value of S, K, r, T. For each asset we can solve a new volatility that corresponds to the price of each option – the implied volatility. Then the implied volatility is \(\IV=f^{-1}(P,S,K,r,T)\).
 </p>
 
-<h3>2. Calculation</h3>
+<h4>2. Calculation</h4>
 <p>
   Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
 </p>
@@ -66,7 +66,7 @@ <h3>2. Calculation</h3>
 <p>
   From the result above, the implied volatility of European call option (with premium c=0.3, S=3, K=3, r=0.032, T =30 days, d=0.01) is 0.87.
 </p>
-<h3>3. Factors Affecting Implied Volatility</h3>
+<h4>3. Factors Affecting Implied Volatility</h4>
 <p>
   According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
 </p>
diff --git a/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05Volatility Surface.html b/06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05 Volatility Surface.html
similarity index 100%
rename from 06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05Volatility Surface.html
rename to 06 Introduction to Options[]/06 Historical Volatility and Implied Volatility/05 Volatility Surface.html

From bd8ed4b758034e4e8800ec607fb64036f57fc9a5 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 7 Jun 2018 17:16:04 -0400
Subject: [PATCH 045/753] update tutorials

---
 .../00 About.html                             |  42 +-
 .../01 What Will I learn %3F.html             |  11 +-
 .../02 Tutorials.html                         | 147 +++--
 .../02 Introduction to Options/00 About.html  |  40 +-
 .../01 What Will I learn %3F.html             |  14 +-
 .../02 Tutorials.html                         | 141 ++--
 .../01 Data Types and Data Structures.html    | 220 -------
 .../01 Introduction.html                      |   3 +
 .../02 Basic Variable Types.html              |  92 +++
 .../03 Basic Math Operations.html             |  21 +
 .../04 Data Collections.html                  | 201 ++++++
 .../05 Common String Operations.html          |  79 +++
 .../06 Summary.html                           |   3 +
 .../01 Introduction.html                      |   3 +
 .../02 Logical Operations and Loops.html      | 194 ------
 .../02 Logical Operations.html                |  65 ++
 .../03 If Statement.html                      |  50 ++
 .../04 Loop Structure.html                    | 113 ++++
 .../05 List Comprehension.html                |  61 ++
 .../06 Summary.html                           |   3 +
 .../01 Introduction.html                      |   3 +
 .../02 Functions.html                         |  28 +
 .../03 Built-in Function.html                 | 124 ++++
 ...ns and Objective-Oriented Programming.html | 194 ------
 .../04 Object-Oriented Programming.html       | 145 +++++
 .../05 Summary.html                           |   7 +
 .../01 Introduction.html                      |   3 +
 .../04 NumPy and Basic Pandas/02 NumPy.html   | 110 ++++
 .../04 NumPy and Basic Pandas/03 Pandas.html  | 266 ++++++++
 .../04 NumPy and Basic Pandas.html            | 229 -------
 .../04 NumPy and Basic Pandas/04 Summary.html |   3 +
 .../01 Introduction.html                      |   3 +
 .../02 Fetching Data.html                     | 104 +++
 .../03 Resampling.html                        | 229 +++++++
 .../04 DataFrame.html                         | 607 ++++++++++++++++++
 ...ing and DataFrame.html => 05 Summary.html} | 546 ++++++++++++----
 .../01 Introduction.html                      |   3 +
 .../02 Rate of Return.html                    | 137 ++++
 .../03 Mean.html                              |  36 ++
 .../04 Variance and Standard Deviation.html   |  29 +
 .../05 Summary.html                           |   3 +
 .../06 Rate of Return, Mean and Variance.html | 122 ----
 .../01 Introduction.html                      |   3 +
 .../02 Random Variables.html                  |   3 +
 .../03 Distributions.html                     | 187 ++++++
 .../04 Normal Distribution.html               |  83 +++
 .../05 Summary.html                           |   3 +
 ...07 Random Variables and Distributions.html | 167 -----
 .../01 Introduction.html                      |   6 +
 .../02 Confidence Interval.html               | 110 ++++
 .../03 Hypothesis testing.html                | 100 +++
 .../04 Summary.html                           | 225 +++++++
 ...dence Interval and Hypothesis Testing.html | 134 ----
 .../01 Introduction.html                      |  79 +++
 .../02 Slope and Intercept.html               |  16 +
 .../03 Python Implementation.html             |  54 ++
 .../04 Parameter Significance.html            |  11 +
 .../05 Model Significance.html                |  14 +
 .../06 Summary.html                           |   3 +
 .../09 Simple Linear Regression.html          | 127 ----
 .../01 Introduction.html                      |  12 +
 .../02 Python Implementation.html             | 290 +++++++++
 .../03 Model Significance Test.html           |  13 +
 .../04 Residual Analysis.html                 |  73 +++
 .../05 Summary.html                           |   3 +
 .../10 Multiple Linear Regression.html        | 320 ---------
 .../11 Linear Algebra/01 Introduction.html    |   3 +
 .../11 Linear Algebra/02 Vectors.html         |  15 +
 .../11 Linear Algebra/03 Matrices.html        |  66 ++
 .../04 Matrix Multiplication.html             |  60 ++
 .../11 Linear Algebra/05 Inverse.html         |  87 +++
 .../06 Linear Equations.html                  |  68 ++
 .../11 Linear Algebra/07 Summary.html         |   3 +
 .../11 Linear Algebra/11 Linear Algebra.html  | 225 -------
 .../01 Introduction.html                      |   4 +
 .../02 Risk Aversion.html                     |  31 +
 .../03 Portfolio.html                         | 102 +++
 .../04 Mean-Variance Analysis.html            |  65 ++
 .../05 Summary.html                           |   3 +
 .../06 Algorithm.html                         |   4 +
 .../12 Modern Portfolio Theory.html           | 155 -----
 .../01 Introduction.html                      |   3 +
 .../02 Capital Asset Pricing Model.html       |  36 ++
 .../03 Computing beta in Practice.html        |  16 +
 .../04 Market-Neutral.html                    |  53 ++
 .../05 Summary.html                           |   3 +
 .../06 Algorithm.html                         |   2 +
 .../13 Capital Asset Pricing Model.html       |  92 ---
 ...icing Model.ipynb => 13 Market Risk.ipynb} |   0
 .../01 Introduction.html                      |   7 +
 .../02 Fama-French Three-Factor Model.html    |  20 +
 .../03 Model Test.html                        |  19 +
 .../04 Factor Returns.html                    |  12 +
 .../05 Other Factors.html                     |  12 +
 .../06 Summary.html                           |   3 +
 .../07 Algorithm.html                         |  10 +
 .../14 Fama-French Multi-Factor Models.html   |  96 ---
 .../07 Delta, Gamma and Vega Hedging.html     |   3 +-
 .../01 Introduction.html                      |   3 +
 .../02 Motivation.html                        |  13 +
 .../03 Local Volatility.html                  |  34 +
 .../04 Stochastic Volatility.html             | 163 +++++
 .../05 Summary.html                           |   6 +
 ... Volatility and Stochastic Volatility.html | 209 ------
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 rename 05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/{05 Pandas-Resampling and DataFrame.html => 05 Summary.html} (57%)
 create mode 100755 05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/01 Introduction.html
 create mode 100755 05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
 create mode 100755 05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html
 create mode 100755 05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html
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 create mode 100755 05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html
 create mode 100755 05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html
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 create mode 100755 05 Introduction to Financial Python[]/09 Simple Linear Regression/04 Parameter Significance.html
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 create mode 100755 05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html
 create mode 100755 05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html
 create mode 100755 05 Introduction to Financial Python[]/11 Linear Algebra/07 Summary.html
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 create mode 100755 05 Introduction to Financial Python[]/12 Modern Portfolio Theory/01 Introduction.html
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 create mode 100755 05 Introduction to Financial Python[]/12 Modern Portfolio Theory/03 Portfolio.html
 create mode 100755 05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html
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 create mode 100755 05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/02 Capital Asset Pricing Model.html
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 create mode 100755 06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/01 Introduction.html
 create mode 100755 06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/02 Motivation.html
 create mode 100755 06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/03 Local Volatility.html
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diff --git a/02 Tutorial Series/01 Introduction to Financial Python/00 About.html b/02 Tutorial Series/01 Introduction to Financial Python/00 About.html
index f389a52..8fec69d 100644
--- a/02 Tutorial Series/01 Introduction to Financial Python/00 About.html	
+++ b/02 Tutorial Series/01 Introduction to Financial Python/00 About.html	
@@ -1,25 +1,21 @@
-<p>
-    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
-    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
-    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
-    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
-    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
-    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
-    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
-    demonstrate the QuantConnect algorithm implemented in Python.
-</p>
+<p>This tutorial series introduces basic Python applied to financial concepts. If you have great investment ideas but don't know how to write them, or if you think you need to learn some basic skills in quantitative finance, then this is a good starting point. The series is broken into four parts: python, math and statistics, basic financial concepts related to investment and financial time series analysis.</p>
+
+<p>We not only introduce the concepts but also show you how to apply the introduced techniques step by step using Python code snippets. We use real financial datasets as examples and after each chapter we design a QuantConnect algorithm applying what we learned.</p>
+
 <div class="row tutorials-page-about">
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-        <p>8 Tutorials</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-        <p>10 Backtests</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-code" aria-hidden="true"></i></p>
-        <p>45 Code Snippets</p>
-    </div>
+	<div class="col-xs-12 col-md-4">
+	&nbsp;
+	<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+	<h3>14 Tutorials</h3>
+	</div>
+	<div class="col-xs-12 col-md-4">
+	&nbsp;
+	<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+	<h3>8 Backtests</h3>
+	</div>
+	<div class="col-xs-12 col-md-4">
+	&nbsp;
+	<p><i class="fa fa-code" aria-hidden="true"></i></p>
+	<h3>167 Code Snippets</h3>
+	</div>
 </div>
-
diff --git a/02 Tutorial Series/01 Introduction to Financial Python/01 What Will I learn %3F.html b/02 Tutorial Series/01 Introduction to Financial Python/01 What Will I learn %3F.html
index 661425d..7593f3e 100644
--- a/02 Tutorial Series/01 Introduction to Financial Python/01 What Will I learn %3F.html	
+++ b/02 Tutorial Series/01 Introduction to Financial Python/01 What Will I learn %3F.html	
@@ -1,6 +1,7 @@
 <div class="row tutorials-learn">
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
-</div>
\ No newline at end of file
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Python</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Statistics</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Linear Algebra</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Modern Portfolio Theory</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Multi-factor Models</div>
+</div>
diff --git a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html
index 6281670..4436f9b 100644
--- a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
+++ b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
@@ -1,60 +1,89 @@
+<h2>Tutorials</h2>
 <table class="table qc-table tutorials-table">
-    <tbody>
-    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
-        <td>1</td>
-        <td>
-            <h2>Covered Call</h2>
-            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
-        <td>2</td>
-        <td>
-            <h2>Bull Call Spread</h2>
-            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
-        <td>3</td>
-        <td>
-            <h2>Long Straddle</h2>
-            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
-        <td>4</td>
-        <td>
-            <h2>Long Strangle</h2>
-            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
-        <td>5</td>
-        <td>
-            <h2>Butterfly Spread</h2>
-            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
-        <td>6</td>
-        <td>
-            <h2>Iron Condor</h2>
-            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
-        <td>7</td>
-        <td>
-            <h2>Iron Butterfly</h2>
-            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
-        <td>8</td>
-        <td>
-            <h2>Protective Collar</h2>
-            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    </tbody>
-</table>
\ No newline at end of file
+<tbody>
+<tr onclick="window.document.location='/tutorials/introduction-python-data-types-data-structures/';"  class="clickable" ><td>1</td>
+<td>
+<h2>Python: Data Types and Data Structures</h2>
+First glimpse of Python.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-logical-operation-loop/';"  class="clickable" ><td>2</td>
+<td>
+<h2>Python: Logical Operations and Loop</h2>
+The essential of programming.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-functions-object-oriented-programming/';"  class="clickable" ><td>3</td>
+<td>
+<h2>Python: Functions and Object-Oriented Programming</h2>
+The Python magic.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-numpy-basic-pandas/';"  class="clickable" ><td>4</td>
+<td>
+<h2>NumPy and Basic Pandas</h2>
+The power scientific calculation package for Python.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-pandas-resampling-dataframe/';"  class="clickable" ><td>5</td>
+<td>
+<h2>Pandas: Resampling and DataFrame</h2>
+The magical Data manipulation tool for Python.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-rate-return-mean-variance/';"  class="clickable" ><td>6</td>
+<td>
+<h2>Rate of Return, Mean and Variance</h2>
+The basic mathematical concepts for quantitative finance.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-random-variable-distributions/';"  class="clickable" ><td>7</td>
+<td>
+<h2>Random Variable and Distributions</h2>
+Point estimation vs interval estimation
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-confidence-interval-hypothesis-testing/';"  class="clickable" ><td>8</td>
+<td>
+<h2>Confidence Interval and Hypothesis Testing</h2>
+Test your ideas rigorously.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-simple-linear-regression/';"  class="clickable" ><td>9</td>
+<td>
+<h2>Simple Linear Regression</h2>
+Find the relationship between two random variables.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-multiple-linear-regression-residual-analysis/';"  class="clickable" ><td>10</td>
+<td>
+<h2>Multiple Linear Regression and residual analysis</h2>
+Explain a random variable using the power of multi-variables.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-linear-algebra/';"  class="clickable" ><td>11</td>
+<td>
+<h2>Linear Algebra</h2>
+Mathematic tool for large scale calculation
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-python-modern-portfolio-theory/';"  class="clickable" ><td>12</td>
+<td>
+<h2>Modern Portfolio Theory</h2>
+Don't put all the eggs in one basket.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/capital-asset-pricing-model/';"  class="clickable" ><td>13</td>
+<td>
+<h2>Market Risk</h2>
+Beta and Alpha.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/fama-french-multi-factor-models/';"  class="clickable" ><td>14</td>
+<td>
+<h2>Fama-French Multi-factor Model</h2>
+The most popular asset pricing model since 1992.
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+</tbody>
+</table>
diff --git a/02 Tutorial Series/02 Introduction to Options/00 About.html b/02 Tutorial Series/02 Introduction to Options/00 About.html
index f389a52..7485821 100644
--- a/02 Tutorial Series/02 Introduction to Options/00 About.html	
+++ b/02 Tutorial Series/02 Introduction to Options/00 About.html	
@@ -1,25 +1,21 @@
-<p>
-    The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
-    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
-    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
-    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
-    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
-    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
-    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
-    demonstrate the QuantConnect algorithm implemented in Python.
-</p>
+<h2>About</h2>
+<p>The goal of this series is to introduce options to those who are option novices and have basic knowledge of applied mathematics, statistics and financial markets. We will primarily talk about the fundamentals of options and cover topics such as what are options, key terms and concepts option traders need to be familiar with(exercise and assignment, The moneyness, Intrinsic and time value of options etc.) After knowing the basics of options, we will teach how to use QuantConnect API to conduct your options research with over 4000 underlying stock symbols.</p>
+<p>The following few options tutorials were created to help you understand exactly how options are used as the investment and risk hedging tools. We will further discuss the pricing method of options like BSM model and Monte Carlo method. And then several metrics to gauge the options risks like the Greek letters, different kinds of volatilities used in options pricing and trading. At the end of some tutorials, we will apply the knowledge in that tutorial to demonstrate some simple algorithms developed with Python on Quantconnect attempting to help you gain an insight into options trading and learn more efficient API tools to better customize your own trading algorithms. </p>
+
 <div class="row tutorials-page-about">
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-file-o" aria-hidden="true"></i></p>
-        <p>8 Tutorials</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
-        <p>10 Backtests</p>
-    </div>
-    <div class="col-xs-12 col-md-4">
-        <p><i class="fa fa-code" aria-hidden="true"></i></p>
-        <p>45 Code Snippets</p>
-    </div>
+<div class="col-xs-12 col-md-4">
+
+<p><i class="fa fa-file-o" aria-hidden="true"></i></p>
+<h3>8 Tutorials</h3>
+</div>
+<div class="col-xs-12 col-md-4">
+
+<p><i class="fa fa-line-chart" aria-hidden="true"></i></p>
+<h3>3 Backtests</h3>
 </div>
+<div class="col-xs-12 col-md-4">
 
+<p><i class="fa fa-code" aria-hidden="true"></i></p>
+<h3>65 Code Snippets</h3>
+</div>
+</div>
diff --git a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html
index 661425d..2e6c07b 100644
--- a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html	
+++ b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html	
@@ -1,6 +1,10 @@
 <div class="row tutorials-learn">
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
-</div>
\ No newline at end of file
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>General Features of Options</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>QuantConnect Options API</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Options Pricing: Black-Sholes-Merton Model</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Stochastic Process</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Monte Carlo Method</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>The Greek Letters</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Historical Volatility and Implied Volatility</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Local Volatility and Stochastic Volatility</div>
+</div>
diff --git a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html
index 6281670..e53a652 100644
--- a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
+++ b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
@@ -1,60 +1,83 @@
 <table class="table qc-table tutorials-table">
-    <tbody>
-    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
-        <td>1</td>
-        <td>
-            <h2>Covered Call</h2>
-            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
-        <td>2</td>
-        <td>
-            <h2>Bull Call Spread</h2>
-            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
-        <td>3</td>
-        <td>
-            <h2>Long Straddle</h2>
-            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
-        <td>4</td>
-        <td>
-            <h2>Long Strangle</h2>
-            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
-        <td>5</td>
-        <td>
-            <h2>Butterfly Spread</h2>
-            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
-        <td>6</td>
-        <td>
-            <h2>Iron Condor</h2>
-            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
-        <td>7</td>
-        <td>
-            <h2>Iron Butterfly</h2>
-            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
-        <td>8</td>
-        <td>
-            <h2>Protective Collar</h2>
-            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    </tbody>
-</table>
\ No newline at end of file
+<tbody>
+<tr onclick="window.document.location='/tutorials/introduction-options-general-features-options';"  class="clickable" >
+<td>1</td>
+<td>
+<h2>General Features of Options</h2>
+Options Contracts
+The Value of Options
+Option Moneyness
+Option Exercise and Assignment
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect';"  class="clickable" >
+<td>2</td>
+<td>
+<h2>QuantConnect Option API</a></h2>
+Option Data Access
+Option Contracts filtration
+Properties of Contracts
+Order Placement
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-put-call-parity-and-arbitrage-strategies';"  class="clickable" >
+<td>3</td>
+<td>
+<h2>Put-Call Parity and Arbitrage Strategies</h2>
+Options Payoff
+Put-Call Parity
+Synthetic Positions
+Arbitrage Strategy
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/stochastic-processes-monte-carlo-method';"  class="clickable" >
+<td>4</td>
+<td>
+<h2>Stochastic Processes and Monte Carlo Method</h2>
+Brownian Motion
+Wiener Process
+Monte Carlo Simulation of Stock Price
+Monte Carlo Simulation of European Options Price
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-black-scholes-merton-model';"  class="clickable" >
+<td>5</td>
+<td>
+<h2>Options Pricing: Black Scholes Merton Model</h2>
+Determinants of Options Price
+Factors of BSM model
+Model Assumptions
+BSM pricing Formulas
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-the-greek-letters';"  class="clickable" >
+<td>6</td>
+<td>
+<h2>The Greek Letters</h2>
+Delta (definition, impact factors, charts)
+Gamma (definition, impact factors, charts)
+Vega (definition, charts)
+Theta (definition, charts)
+Rho (definition, charts)
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-historical-volatility-implied-volatility';"  class="clickable" >
+<td>7</td>
+<td>
+<h2>Historical Volatility and Implied Volatility</h2>
+Historical Volatility (Definition, Calculation)
+Implied Volatility(Definition, Calculation, affect factors)
+Volatility Smile
+Volatility Skew
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+<tr onclick="window.document.location='/tutorials/introduction-options-stochastic-volatility';"  class="clickable" >
+<td>8</td>
+<td>
+<h2>Local Volatility and Stochastic Volatility</h2>
+Local Volatility (Definition, Calculation)
+Stochastic Volatility(Definition, Calculation)
+<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+</tr>
+</tbody>
+</table>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.html
deleted file mode 100755
index 5e777fb..0000000
--- a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Data Types and Data Structures.html	
+++ /dev/null
@@ -1,220 +0,0 @@
-This tutorial provides a basic introduction to the Python programming language. If you are new to Python, you should run the code snippets while reading this tutorial. If you are an advanced Python user, please feel free to skip this chapter.
-
-<h2>Basic Variable Types</h2>
-
-The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
-
-Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
-<pre class="prettyprint linenums">my_string1 = 'Welcome to'
-my_string2 = "QuantConnect"
-print(my_string1 + ' ' + my_string2)
-[out]: Welcome to QuantConnect
-</pre>
-
-An integer is a round number with no values after the decimal point.
-<pre class="prettyprint linenums">my_int = 10
-print(my_int)
-[out]: 10
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-The built-in function int() can convert a string into an integer.
-<pre class="prettyprint linenums">my_string = "100"
-print(type(my_string))
-[out]: type 'str'
-my_int = int(my_string)
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
-<pre class="prettyprint linenums">my_float = 1.0
-print(type(my_float))
-[out]: type 'float'
-my_int = 1
-print(type(my_int))
-[out]: type 'int'
-</pre>
-
-As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
-<pre class="prettyprint linenums">my_string = "100"
-my_float = float(my_string)
-print(type(my_float))
-[out]: type 'float'
-</pre>
-
-A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
-<pre class="prettyprint linenums">my_bool = False
-print(my_bool)
-[out]: False
-print(type(my_bool))
-[out]: type 'bool'
-</pre>
-
-<h2>Basic Math Operations</h2>
-
-The basic math operators in python are demonstrated below:
-<pre class="prettyprint linenums">
-print("Addition ", 1+1)
-print("Subtraction ", 5-2)
-print("Multiplication ", 2*3)
-print("Division", 10/2)
-print("exponent", 2**3)
-[out]:
-Addition  2
-Subtraction  3
-Multiplication  6
-Division  5
-exponent 8
-</pre>
-
-<h2>Data Collections</h2>
-
-<h3>List</h3>
-
-A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
-<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print(my_list)
-[out]: ['Quant', 'Connect', 1, 2, 3]
-</pre>
-
-The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
-<pre class="prettyprint linenums">my_list = ['Quant', 'Connect', 1,2,3]
-print(len(my_list))
-[out]: 5
-print(my_list[0])
-[out]: Quant
-print(my_list[len(my_list) - 1])
-[out]: 3
-</pre>
-
-You can also change the elements in the list by accessing an index and assigning a new value.
-<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
-my_list[2] = 'go'
-print(my_list)
-[out]: ['Quant', 'Connect', 'go', 2, 3]
-</pre>
-
-A list can also be sliced with a colon:
-<pre class="prettyprint linenums">my_list = ['Quant','Connect',1,2,3]
-print(my_list[1:3])
-[out]: ['Connect', 1]
-</pre>
-
-The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
-<pre class="prettyprint linenums">print(my_list[1:])
-[out]: ['Connect', 1, 2, 3]
-</pre>
-
-And all elements up to but excluding index 3:
-<pre class="prettyprint linenums">print(my_list[:3])
-[out]: ['Quant', 'Connect', 1]
-</pre>
-
-If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
-<pre class="prettyprint linenums">my_list = ['Hello', 'Quant']
-my_list.append('Hello')
-print(my_list)
-[out]: ['Hello', 'Quant', 'Hello']
-my_list.remove('Hello')
-print(my_list)
-[out]: ['Quant', 'Hello']
-</pre>
-
-When there are repeated instances of "Hello", the first one is removed.
-
-<h3>Tuple</h3>
-
-A tuple is a data structure type similar to a list. The difference is that a tuple is immutable, which means you can't change the elements in it once it's defined. We create a tuple by putting comma-separated values between parentheses.
-<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
-</pre>
-
-Just like a list, a tuple can be sliced by using index.
-<pre class="prettyprint linenums">my_tuple = ('Welcome','to','QuantConnect')
-print(my_tuple[1:])
-[out]: ('to', 'QuantConnect')
-</pre>
-
-<h3>Set</h3>
-
-A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
-<pre class="prettyprint linenums">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
-stock_set = set(stock_list)
-print(stock_set)
-[out]: set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])
-</pre>
-Set is an easy way to remove duplicate elements from a list.
-
-<h3>Dictionary</h3>
-
-A dictionary is one of the most important data structures in Python. Unlike sequences which are indexed by integers, dictionaries are indexed by keys which can be either strings or floats.
-
-A dictionary is an <strong>unordered</strong> collection of key : value pairs, with the requirement that the keys are unique. We create a dictionary by placing a comma-separated list of key : value pairs within the braces.
-<pre class="prettyprint linenums">my_dic = {'AAPL': 'Apple', 'FB': 'FaceBook', 'GOOG': 'Alphabet'}
-</pre>
-
-After defining a dictionary, we can access any value by indicating its key in brackets.
-<pre class="prettyprint linenums">print(my_dic['GOOG'])
-[out]: Alphabet
-</pre>
-
-We can also change the value associated with a specified key:
-<pre class="prettyprint linenums">my_dic['GOOG'] = 'Alphabet Company'
-print(my_dic['GOOG'])
-[out]: Alphabet Company
-</pre>
-
-The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
-<pre class="prettyprint linenums">print(my_dic.keys())
-[out]: ['GOOG', 'AAPL', 'FB']
-</pre>
-
-<h2>Common String Operations</h2>
-
-A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
-<pre class="prettyprint linenums">my_str = 'Welcome to QuantConnect'
-print(my_str[8:])
-[out]: to QuantConnect
-</pre>
-
-There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
-<pre class="prettyprint linenums">
-print("Counting the number of e's in this sentence".count('e'))
-[out]: 6
-print('The first time e appears in this sentence'.find('e'))
-[out]: 2
-print('all the a in this sentence now becomes e'.replace('a','e'))
-[out]: ell the e in this sentence now becomes e
-</pre>
-
-The most commonly used method for strings is string.split(). This method will split the string by the indicated character and return a list:
-<pre class="prettyprint linenums">Time = '2016-04-01 09:43:00'
-splited_list = Time.split(' ')
-date = splited_list[0]
-time = splited_list[1]
-print(date, time)
-[out]: 2016-04-01 09:43:00
-hour = time.split(':')[0]
-print(hour)
-[out]: 09
-</pre>
-
-We can replace parts of a string by our variable. This is called string formatting.
-<pre class="prettyprint linenums">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
-print(my_time)
-[out]: Hour: 9, Minute: 43
-</pre>
-
-Another way to format a string is to use the % symbol.
-<pre class="prettyprint linenums">print 'pi is %f' % 3.14
-[out]: pi is 3.140000
-print('%s to %s' % ('Welcome', 'QuantConnect'))
-[out]: Welcome to QuantConnect
-</pre>
-
-%s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
-
-<h2>Summary</h2>
-
-We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Introduction.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Introduction.html
new file mode 100755
index 0000000..c1dbfe6
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  This tutorial provides a basic introduction to the Python programming language. If you are new to Python, you should run the code snippets while reading this tutorial. If you are an advanced Python user, please feel free to skip this chapter.
+</p>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html
new file mode 100755
index 0000000..c9e9df0
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html	
@@ -0,0 +1,92 @@
+<p>
+  The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
+</p>
+<p>
+  Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_string1 = 'Welcome to'
+my_string2 = "QuantConnect"
+print(my_string1 + ' ' + my_string2)
+[out]: Welcome to QuantConnect
+</pre>
+</div>
+
+<p>
+  An integer is a round number with no values after the decimal point.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_int = 10
+print(my_int)
+[out]: 10
+print(type(my_int))
+[out]: type 'int'
+</pre>
+</div>
+
+<p>
+  The built-in function int() can convert a string into an integer.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_string = "100"
+print(type(my_string))
+[out]: type 'str'
+my_int = int(my_string)
+print(type(my_int))
+[out]: type 'int'
+</pre>
+</div>
+
+<p>
+  A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_float = 1.0
+print(type(my_float))
+[out]: type 'float'
+my_int = 1
+print(type(my_int))
+[out]: type 'int'
+</pre>
+</div>
+
+<p>
+  As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_string = "100"
+my_float = float(my_string)
+print(type(my_float))
+[out]: type 'float'
+</pre>
+</div>
+
+<p>
+  A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_bool = False
+print(my_bool)
+[out]: False
+print(type(my_bool))
+[out]: type 'bool'
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html
new file mode 100755
index 0000000..c823cc0
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html	
@@ -0,0 +1,21 @@
+<p>
+  The basic math operators in python are demonstrated below:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+print("Addition ", 1+1)
+print("Subtraction ", 5-2)
+print("Multiplication ", 2*3)
+print("Division", 10/2)
+print("exponent", 2**3)
+[out]:
+Addition  2
+Subtraction  3
+Multiplication  6
+Division  5
+exponent 8
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html
new file mode 100755
index 0000000..1dce548
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html	
@@ -0,0 +1,201 @@
+<h4>List</h4>
+
+<p>
+  A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_list = ['Quant', 'Connect', 1,2,3]
+print(my_list)
+[out]: ['Quant', 'Connect', 1, 2, 3]
+</pre>
+</div>
+
+<p>
+  The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_list = ['Quant', 'Connect', 1,2,3]
+print(len(my_list))
+[out]: 5
+print(my_list[0])
+[out]: Quant
+print(my_list[len(my_list) - 1])
+[out]: 3
+</pre>
+</div>
+
+<p>
+  You can also change the elements in the list by accessing an index and assigning a new value.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_list = ['Quant','Connect',1,2,3]
+my_list[2] = 'go'
+print(my_list)
+[out]: ['Quant', 'Connect', 'go', 2, 3]
+</pre>
+</div>
+
+<p>
+  A list can also be sliced with a colon:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_list = ['Quant','Connect',1,2,3]
+print(my_list[1:3])
+[out]: ['Connect', 1]
+</pre>
+</div>
+
+<p>
+  The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print(my_list[1:])
+[out]: ['Connect', 1, 2, 3]
+</pre>
+</div>
+
+<p>
+  And all elements up to but excluding index 3:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print(my_list[:3])
+[out]: ['Quant', 'Connect', 1]
+</pre>
+</div>
+
+<p>
+  If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_list = ['Hello', 'Quant']
+my_list.append('Hello')
+print(my_list)
+[out]: ['Hello', 'Quant', 'Hello']
+my_list.remove('Hello')
+print(my_list)
+[out]: ['Quant', 'Hello']
+</pre>
+</div>
+
+<p>
+  When there are repeated instances of "Hello", the first one is removed.
+</p>
+
+<h4>Tuple</h4>
+
+<p>
+  A tuple is a data structure type similar to a list. The difference is that a tuple is immutable, which means you can't change the elements in it once it's defined. We create a tuple by putting comma-separated values between parentheses.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_tuple = ('Welcome','to','QuantConnect')
+</pre>
+</div>
+
+<p>
+  Just like a list, a tuple can be sliced by using index.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_tuple = ('Welcome','to','QuantConnect')
+print(my_tuple[1:])
+[out]: ('to', 'QuantConnect')
+</pre>
+</div>
+
+<h4>Set</h4>
+
+<p>
+  A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
+stock_set = set(stock_list)
+print(stock_set)
+[out]: set(['GOOG', 'FB', 'AAPL', 'IBM', 'F'])
+</pre>
+</div>
+<p>
+  Set is an easy way to remove duplicate elements from a list.
+</p>
+
+<h4>Dictionary</h4>
+<p>
+  A dictionary is one of the most important data structures in Python. Unlike sequences which are indexed by integers, dictionaries are indexed by keys which can be either strings or floats.
+</p>
+
+<p>
+  A dictionary is an <strong>unordered</strong> collection of key : value pairs, with the requirement that the keys are unique. We create a dictionary by placing a comma-separated list of key : value pairs within the braces.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_dic = {'AAPL': 'Apple', 'FB': 'FaceBook', 'GOOG': 'Alphabet'}
+</pre>
+</div>
+
+<p>
+  After defining a dictionary, we can access any value by indicating its key in brackets.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print(my_dic['GOOG'])
+[out]: Alphabet
+</pre>
+</div>
+
+<p>
+  We can also change the value associated with a specified key:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_dic['GOOG'] = 'Alphabet Company'
+print(my_dic['GOOG'])
+[out]: Alphabet Company
+</pre>
+</div>
+
+<p>
+  The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print(my_dic.keys())
+[out]: ['GOOG', 'AAPL', 'FB']
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html
new file mode 100755
index 0000000..d900c77
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html	
@@ -0,0 +1,79 @@
+<p>
+  A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_str = 'Welcome to QuantConnect'
+print(my_str[8:])
+[out]: to QuantConnect
+</pre>
+</div>
+
+<p>
+  There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+print("Counting the number of e's in this sentence".count('e'))
+[out]: 6
+print('The first time e appears in this sentence'.find('e'))
+[out]: 2
+print('all the a in this sentence now becomes e'.replace('a','e'))
+[out]: ell the e in this sentence now becomes e
+</pre>
+</div>
+
+<p>
+  The most commonly used method for strings is string.split(). This method will split the string by the indicated character and return a list:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">Time = '2016-04-01 09:43:00'
+splited_list = Time.split(' ')
+date = splited_list[0]
+time = splited_list[1]
+print(date, time)
+[out]: 2016-04-01 09:43:00
+hour = time.split(':')[0]
+print(hour)
+[out]: 09
+</pre>
+</div>
+
+<p>
+  We can replace parts of a string by our variable. This is called string formatting.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
+print(my_time)
+[out]: Hour: 9, Minute: 43
+</pre>
+</div>
+
+<p>
+  Another way to format a string is to use the % symbol.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 'pi is %f' % 3.14
+[out]: pi is 3.140000
+print('%s to %s' % ('Welcome', 'QuantConnect'))
+[out]: Welcome to QuantConnect
+</pre>
+</div>
+
+<p>
+  %s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
+</p>
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/06 Summary.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/06 Summary.html
new file mode 100755
index 0000000..3bdfaa1
--- /dev/null
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  We have seen the basic data types and data structures in Python. It's important to keep practicing to become familiar with these data structures. In the next tutorial, we will cover for and while loops and logical operations in Python.
+</p>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/01 Introduction.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/01 Introduction.html
new file mode 100755
index 0000000..e3e2862
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  We discussed the basic data types and data structures in Python in the last tutorial. This chapter covers logical operations and loops in Python, which are very common in programming.
+</p>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html
deleted file mode 100755
index abd7ba1..0000000
--- a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations and Loops.html	
+++ /dev/null
@@ -1,194 +0,0 @@
-We discussed the basic data types and data structures in Python in the last tutorial. This chapter covers logical operations and loops in Python, which are very common in programming.
-
-<h2>Logical Operations</h2>
-
-Like most programming languages, Python has comparison operators:
-<pre class="prettyprint linenums">print 1 == 0    # 1 equals 0
-print 1 == 1    # 1 equals 1
-print 1 != 0    # 1 is not equal to 0
-print 5 &gt;= 5    # 5 is greater than or equal to 5
-print 5 &gt;= 6    # 5 is greater than or equal to 6
-[out]:
-False
-True
-True
-True
-False
-</pre>
-
-Each statement above has a boolean value, which must be either True or False, but not both.
-
-We can combine simple statements P and Q to form complex statements using logical operators:
-<ul>
-    <li>The statement "P and Q" is true if both P and Q are true, otherwise it is false.</li>
-    <li>The statement "P or Q" is false if both P and Q are false, otherwise it is true.</li>
-    <li>The statement "not P" is true if P is false, and vice versa.</li>
-</ul>
-
-<pre class="prettyprint linenums">print 2 &gt; 1 and 3 &gt; 2
-print 2 &gt; 1 and 3 &lt; 2
-print 2 &gt; 1 or  3 &lt; 2
-print 2 &lt; 1 and 3 &lt; 2
-[out]:
-True
-False
-True
-False
-</pre>
-
-When dealing with a very complex logical statement that involves in several statements, we can use brackets to separate and combine them.
-<pre class="prettyprint linenums">print (3 &gt; 2 or 1 &lt; 3) and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
-print 3 &gt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
-[out]:
-False
-True
-</pre>
-
-Comparing the above two statements, we can see that it's wise to use brackets when we make a complex logical statement.
-
-<h2>If Statement</h2>
-
-An if statement executes a segment of code only if its condition is true. A standard if statement consists of 3 segments: if, elif and else.
-<pre class="prettyprint linenums">if condition1:
-    # if condition1 is true, execute the code here
-    # and ignore the rest of this if statement
-elif condition2:
-    # if condition1 is false, and condition2 is true, execute the code here
-    # and ignore the rest of this if statement
-else:
-    # if none of the above conditions is True, execute the code here
-</pre>
-
-An if statement doesn't necessarily has elif and else part. If it's not specified, the indented block of code will be executed when the condition is true, otherwise the whole if statement will be skipped.
-<pre class="prettyprint linenums">i = 0
-if i == 0: print 'i == 0 is True'
-[out]: i==0 is True
-</pre>
-
-As we mentioned above, we can write some complex statements here:
-<pre class="prettyprint linenums">p = 1 &gt; 0
-q = 2 &gt; 3
-if p and q:
-    print 'p and q is true'
-elif p and not q:
-    print 'q is false'
-elif q and not p:
-    print 'p is false'
-else:
-    print 'None of p and q is true'
-[out]: q is false
-</pre>
-
-<h2>Loop Structures</h2>
-
-Loops are an essential part of programming. The "for" and "while" loops run a block of code repeatedly.
-
-<h3>While Loop</h3>
-
-A "while" loop will run repeatedly until a certain condition has been met.
-<pre class="prettyprint linenums">i = 0
-while i &lt; 5:
-    print i
-    i += 1
-[out]:
-0
-1
-2
-3
-4
-</pre>
-
-When making a while loop, we need to ensure that something changes from iteration to iteration so that the while loop will terminate, otherwise it will run forever. Here we used i += 1 (short for i = i + 1) to make i larger after each iteration. This is the most commonly used method to control a while loop.
-
-<h3>For Loop</h3>
-
-A "for" loop will iterate over a sequence of value and terminate when the sequence has ended.
-<pre class="prettyprint linenums">for x in [1,2,3,4,5]: print x
-[out]:
-1
-2
-3
-4
-5
-</pre>
-
-We can also add if statements in a for loop. Here is a real example from our pairs trading algorithm:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-selected = ['AAPL','IBM']
-new_list = []
-for stock in stocks:
-    if stock not in selected:
-        new_list.append(stock)
-print new_list
-[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
-</pre>
-
-Here we iterated all the elements in the list 'stocks'. Later in this chapter we will introduce a smarter way to do this, which is just an one-line code.
-
-<h3>Break and continue</h3>
-
-These are two commonly used commands in a for loop. If "break" is triggered while a loop is executing, the loop will terminate immediately:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-for stock in stocks:
-    print stock
-    if stock == 'FB': break
-[out]:
-AAPL
-GOOG
-IBM
-FB
-</pre>
-
-The "continue" command tells the loop to end this iteration and skip to the next iteration:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-for stock in stocks:
-    if stock == 'FB': continue
-    print stock
-[out]:
-AAPL
-GOOG
-IBM
-F
-V
-G
-GE
-</pre>
-
-<h2>List Comprehension</h2>
-
-List comprehension is a Pythonic way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence. For example, if we want to create a list of squares using for loop:
-<pre class="prettyprint linenums">squares = []
-for i in [1,2,3,4,5]:
-    squares.append(i**2)
-print squares
-[out]: [1, 4, 9, 16, 25]
-</pre>
-
-Using list comprehension:
-<pre class="prettyprint linenums">foo = [1,2,3,4,5]
-squares = [x**2 for x in foo]
-print squares
-[out]: [1, 4, 9, 16, 25]
-</pre>
-
-Recall the example above where we used a for loop to select stocks. Here we use list comprehension:
-<pre class="prettyprint linenums">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-selected = ['AAPL','IBM']
-new_list = [x for x in stocks if x not in selected]
-print new_list
-[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
-</pre>
-
-A list comprehension consists of square brackets containing an expression followed by a "for" clause, and possibly "for" or "if" clauses. For example:
-<pre class="prettyprint linenums">print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
-print [str(x) + ' vs ' + str(y) for x in ['AAPL','GOOG','IBM','FB']
-                                for y in ['F','V','G','GE'] if x != y]
-[out]:
-[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
-['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']
-</pre>
-
-List comprehension is an elegant way to organize one or more for loops when creating a list.
-
-<h2>Summary</h2>
-This chapter has introduced logical operations, loops and list comprehension. In the next chapter we will introduce functions and object-oriented programming, which will enable us to make our codes clean and versatile.
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html
new file mode 100755
index 0000000..b5bc5d1
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html	
@@ -0,0 +1,65 @@
+<p>
+  Like most programming languages, Python has comparison operators:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 1 == 0    # 1 equals 0
+print 1 == 1    # 1 equals 1
+print 1 != 0    # 1 is not equal to 0
+print 5 &gt;= 5    # 5 is greater than or equal to 5
+print 5 &gt;= 6    # 5 is greater than or equal to 6
+[out]:
+False
+True
+True
+True
+False
+</pre>
+</div>
+
+<p>
+  Each statement above has a boolean value, which must be either True or False, but not both.
+</p>
+<p>
+  We can combine simple statements P and Q to form complex statements using logical operators:
+</p>
+<ul>
+    <li>The statement "P and Q" is true if both P and Q are true, otherwise it is false.</li>
+    <li>The statement "P or Q" is false if both P and Q are false, otherwise it is true.</li>
+    <li>The statement "not P" is true if P is false, and vice versa.</li>
+</ul>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 2 &gt; 1 and 3 &gt; 2
+print 2 &gt; 1 and 3 &lt; 2
+print 2 &gt; 1 or  3 &lt; 2
+print 2 &lt; 1 and 3 &lt; 2
+[out]:
+True
+False
+True
+False
+</pre>
+</div>
+
+<p>
+  When dealing with a very complex logical statement that involves in several statements, we can use brackets to separate and combine them.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print (3 &gt; 2 or 1 &lt; 3) and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
+print 3 &gt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
+[out]:
+False
+True
+</pre>
+</div>
+
+<p>
+  Comparing the above two statements, we can see that it's wise to use brackets when we make a complex logical statement.
+</p>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html
new file mode 100755
index 0000000..086ca77
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html	
@@ -0,0 +1,50 @@
+<p>
+  An if statement executes a segment of code only if its condition is true. A standard if statement consists of 3 segments: if, elif and else.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if condition1:
+    # if condition1 is true, execute the code here
+    # and ignore the rest of this if statement
+elif condition2:
+    # if condition1 is false, and condition2 is true, execute the code here
+    # and ignore the rest of this if statement
+else:
+    # if none of the above conditions is True, execute the code here
+</pre>
+
+<p>
+  An if statement doesn't necessarily has elif and else part. If it's not specified, the indented block of code will be executed when the condition is true, otherwise the whole if statement will be skipped.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">i = 0
+if i == 0: print 'i == 0 is True'
+[out]: i==0 is True
+</pre>
+</div>
+
+<p>
+  As we mentioned above, we can write some complex statements here:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">p = 1 &gt; 0
+q = 2 &gt; 3
+if p and q:
+    print 'p and q is true'
+elif p and not q:
+    print 'q is false'
+elif q and not p:
+    print 'p is false'
+else:
+    print 'None of p and q is true'
+[out]: q is false
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/04 Loop Structure.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/04 Loop Structure.html
new file mode 100755
index 0000000..fe5b599
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/04 Loop Structure.html	
@@ -0,0 +1,113 @@
+<p>
+  Loops are an essential part of programming. The "for" and "while" loops run a block of code repeatedly.
+</p>
+
+<h4>While Loop</h4>
+
+<p>
+  A "while" loop will run repeatedly until a certain condition has been met.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">i = 0
+while i &lt; 5:
+    print i
+    i += 1
+[out]:
+0
+1
+2
+3
+4
+</pre>
+</div>
+
+<p>
+  When making a while loop, we need to ensure that something changes from iteration to iteration so that the while loop will terminate, otherwise, it will run forever. Here we used i += 1 (short for i = i + 1) to make i larger after each iteration. This is the most commonly used method to control a while loop.
+</p>
+
+<h4>For Loop</h4>
+
+<p>
+  A "for" loop will iterate over a sequence of value and terminate when the sequence has ended.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for x in [1,2,3,4,5]: print x
+[out]:
+1
+2
+3
+4
+5
+</pre>
+</div>
+
+<p>
+  We can also add if statements in a for loop. Here is a real example from our pairs trading algorithm:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+selected = ['AAPL','IBM']
+new_list = []
+for stock in stocks:
+    if stock not in selected:
+        new_list.append(stock)
+print new_list
+[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
+</pre>
+</div>
+
+<p>
+  Here we iterated all the elements in the list 'stocks'. Later in this chapter, we will introduce a smarter way to do this, which is just a one-line code.
+</p>
+
+<h4>Break and continue</h4>
+
+<p>
+  These are two commonly used commands in a for loop. If "break" is triggered while a loop is executing, the loop will terminate immediately:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+for stock in stocks:
+    print stock
+    if stock == 'FB': break
+[out]:
+AAPL
+GOOG
+IBM
+FB
+</pre>
+</div>
+
+<p>
+  The "continue" command tells the loop to end this iteration and skip to the next iteration:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+for stock in stocks:
+    if stock == 'FB': continue
+    print stock
+[out]:
+AAPL
+GOOG
+IBM
+F
+V
+G
+GE
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html
new file mode 100755
index 0000000..c119a6f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html	
@@ -0,0 +1,61 @@
+<p>
+  List comprehension is a Pythonic way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence. For example, if we want to create a list of squares using for loop:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">squares = []
+for i in [1,2,3,4,5]:
+    squares.append(i**2)
+print squares
+[out]: [1, 4, 9, 16, 25]
+</pre>
+
+<p>
+  Using list comprehension:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">foo = [1,2,3,4,5]
+squares = [x**2 for x in foo]
+print squares
+[out]: [1, 4, 9, 16, 25]
+</pre>
+
+<p>
+  Recall the example above where we used a for loop to select stocks. Here we use list comprehension:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">stocks = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+selected = ['AAPL','IBM']
+new_list = [x for x in stocks if x not in selected]
+print new_list
+[out]: ['GOOG', 'FB', 'F', 'V', 'G', 'GE']
+</pre>
+</div>
+
+<p>
+  A list comprehension consists of square brackets containing an expression followed by a "for" clause, and possibly "for" or "if" clauses. For example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
+print [str(x) + ' vs ' + str(y) for x in ['AAPL','GOOG','IBM','FB']
+                                for y in ['F','V','G','GE'] if x != y]
+[out]:
+[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
+['AAPL vs F', 'AAPL vs V', 'AAPL vs G', 'AAPL vs GE', 'GOOG vs F', 'GOOG vs V', 'GOOG vs G', 'GOOG vs GE', 'IBM vs F', 'IBM vs V', 'IBM vs G', 'IBM vs GE', 'FB vs F', 'FB vs V', 'FB vs G', 'FB vs GE']
+</pre>
+</div>
+
+<p>
+  List comprehension is an elegant way to organize one or more for loops when creating a list.
+</p>
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/06 Summary.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/06 Summary.html
new file mode 100755
index 0000000..cb27d18
--- /dev/null
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  This chapter has introduced logical operations, loops, and list comprehension. In the next chapter, we will introduce functions and object-oriented programming, which will enable us to make our codes clean and versatile.
+</p>
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/01 Introduction.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/01 Introduction.html
new file mode 100755
index 0000000..4bd9a07
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the last tutorial we introduced logical operations, loops and list comprehension. We will introduce functions and object-oriented programming in this chapter, which will enable us to build complex algorithms in more flexible ways.
+</p>
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html
new file mode 100755
index 0000000..518eea0
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html	
@@ -0,0 +1,28 @@
+<p>
+  A function is a reusable block of code. We can use a function to output a value, or do anything else we want. We can easily define our own function by using the keyword "def".
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def product(x,y):
+    return x*y
+print product(2,3)
+[out]: 6
+print product(5,10)
+[out]: 50
+</pre>
+</div>
+<p>
+  The keyword "def" is followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The product() function above has "x" and "y" as its parameters. A function doesn't necessarily have parameters:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def say_hi():
+    print "Welcome to QuantConnect'
+say_hi()
+[out]: Welcome to QuantConnect
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html
new file mode 100755
index 0000000..8f5e07b
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html	
@@ -0,0 +1,124 @@
+<p>
+  <strong>range()</strong> is a function that creates a list containing an arithmetic sequence. It's often used in for loops. The arguments must be integers. If the "step" argument is omitted, it defaults to 1.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+print range(10)
+[out]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+print range(1, 11)
+[out]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+print range(1, 11, 2)
+[out]: [1, 3, 5, 7, 9]
+</pre>
+</div>
+<p>
+  <strong>len()</strong> is another function used together with range() to create a for loop. This function returns the length of an object. The argument must be a sequence or a collection.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
+print "The number of tickers is {}".format(len(tickers))
+for k in range(len(tickers)):
+    print k + 1, tickers[k]
+[out]:
+The number of tickers is 8
+1 AAPL
+2 GOOGL
+3 IBM
+4 FB
+5 F
+6 V
+7 G
+8 GE
+</pre>
+</div>
+<p>
+  Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
+</p>
+
+<strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
+print(list(map(len,tickers)))
+[out]: [4, 5, 3, 2, 1, 1, 1, 2]
+</pre>
+</div>
+<p>
+  The <strong>lambda operator</strong> is a way to create small anonymous functions. These functions are just needed where they have been created. For example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">map(lambda x: x**2, range(10))
+[out]: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+</pre>
+</div>
+
+<p>
+  map() can be applied to more than one list. The lists have to have the same length.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">map(lambda x, y: x+y, [1,2,3,4,5], [5,4,3,2,1])
+[out]: [6, 6, 6, 6, 6]
+</pre>
+</div>
+<strong>sorted()</strong> takes a list or set and returns a new sorted list:
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">sorted([5,2,3,4,1])
+[out]: [1, 2, 3, 4, 5]
+</pre>
+</div>
+<p>
+  We can add a "key" parameter to specify a function to be called on each list element prior to making comparisons. For example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+sorted(price_list, key = lambda x: x[1])
+[out]:
+[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
+</pre>
+</div>
+<p>
+  By default the values are sorted by ascending order. We can change it to descending by adding an optional parameter "reverse'.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+sorted(price_list, key = lambda x: x[1], reverse = True)
+[out]:
+[('GOOGL', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]
+</pre>
+</div>
+<p>
+  Lists also have a function list.sort(). This function takes the same "key" and "reverse" arguments as sorted(), but it doesn't return a new list.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
+price_list.sort(key = lambda x: x[1])
+print price_list
+[out]:
+[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html
deleted file mode 100755
index 560e7f9..0000000
--- a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Functions and Objective-Oriented Programming.html	
+++ /dev/null
@@ -1,194 +0,0 @@
-In the last tutorial we introduced logical operations, loops and list comprehension. We will introduce functions and object-oriented programming in this chapter, which will enable us to build complex algorithms in more flexible ways.
-
-<h2>Functions</h2>
-
-A function is a reusable block of code. We can use a function to output a value, or do anything else we want. We can easily define our own function by using the keyword "def".
-
-<pre class="prettyprint linenums">def product(x,y):
-    return x*y
-print product(2,3)
-[out]: 6
-print product(5,10)
-[out]: 50
-</pre>
-
-The keyword "def" is followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented. The product() function above has "x" and "y" as its parameters. A function doesn't necessarily have parameters:
-
-<pre class="prettyprint linenums">def say_hi():
-    print "Welcome to QuantConnect'
-say_hi()
-[out]: Welcome to QuantConnect
-</pre>
-
-<h2>Built-in Function</h2>
-
-<strong>range()</strong> is a function that creates a list containing an arithmetic sequence. It's often used in for loops. The arguments must be integers. If the "step" argument is omitted, it defaults to 1.
-<pre class="prettyprint linenums">
-print range(10)
-[out]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-print range(1, 11)
-[out]: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-print range(1, 11, 2)
-[out]: [1, 3, 5, 7, 9]
-</pre>
-
-<strong>len()</strong> is another function used together with range() to create a for loop. This function returns the length of an object. The argument must be a sequence or a collection.
-<pre class="prettyprint linenums">
-tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
-print "The number of tickers is {}".format(len(tickers))
-for k in range(len(tickers)):
-    print k + 1, tickers[k]
-[out]:
-The number of tickers is 8
-1 AAPL
-2 GOOGL
-3 IBM
-4 FB
-5 F
-6 V
-7 G
-8 GE
-</pre>
-
-Note: If you want to print only the tickers without those numbers, then simply write "for ticker in tickers: print ticker"
-
-<strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
-<pre class="prettyprint linenums">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
-print(list(map(len,tickers)))
-[out]: [4, 5, 3, 2, 1, 1, 1, 2]
-</pre>
-
-The <strong>lambda operator</strong> is a way to create small anonymous functions. These functions are just needed where they have been created. For example:
-<pre class="prettyprint linenums">map(lambda x: x**2, range(10))
-[out]: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
-</pre>
-
-map() can be applied to more than one list. The lists have to have the same length.
-<pre class="prettyprint linenums">map(lambda x, y: x+y, [1,2,3,4,5], [5,4,3,2,1])
-[out]: [6, 6, 6, 6, 6]
-</pre>
-
-<strong>sorted()</strong> takes a list or set and returns a new sorted list:
-<pre class="prettyprint linenums">sorted([5,2,3,4,1])
-[out]: [1, 2, 3, 4, 5]
-</pre>
-
-We can add a "key" parameter to specify a function to be called on each list element prior to making comparisons. For example:
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-sorted(price_list, key = lambda x: x[1])
-[out]:
-[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
-</pre>
-
-By default the values are sorted by ascending order. We can change it to descending by adding an optional parameter "reverse'.
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-sorted(price_list, key = lambda x: x[1], reverse = True)
-[out]:
-[('GOOGL', 911.71), ('FB', 150), ('AAPL', 144.09), ('WMT', 75.32), ('MSFT', 69)]
-</pre>
-
-Lists also have a function list.sort(). This function takes the same "key" and "reverse" arguments as sorted(), but it doesn't return a new list.
-<pre class="prettyprint linenums">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
-price_list.sort(key = lambda x: x[1])
-print price_list
-[out]:
-[('MSFT', 69), ('WMT', 75.32), ('AAPL', 144.09), ('FB', 150), ('GOOGL', 911.71)]
-</pre>
-
-<h2>Object-Oriented Programming</h2>
-
-Python is an object-oriented programming language. It's important to understand the concept of "objects" because almost every kind of data from QuantConnect API is an object.
-
-<h3>Class</h3>
-A class is a type of data, just like a string, float, or list. When we create an object of that data type, we call it an <strong>instance</strong> of a class.
-
-In Python, everything is an object - everything is an instance of some class. The data stored inside an object are called <strong>attributes</strong>, and the functions which are associated with the object are called <strong>methods</strong>.
-
-For example, as mentioned above, a list is an object of the "list" class, and it has a method list.sort().
-
-We can create our own objects by defining a class. We would do this when it's helpful to group certain functions together. For example, we define a class named "Stock" here:
-<pre class="prettyprint linenums">class Stock:
-    def __init__(self, ticker, open, close, volume):
-        self.ticker = ticker
-        self.open = open
-        self.close = close
-        self.volume = volume
-        self.rate_return = float(close)/open - 1
-
-    def update(self, open, close):
-        self.open = open
-        self.close = close
-        self.rate_return = float(self.close)/self.open - 1
-
-    def print_return(self):
-        print self.rate_return
-</pre>
-
-The "Stock" class has attributes "ticker", "open", "close", "volume" and "rate_return". Inside the class body, the first method is called __init__, which is a special method. When we create a new instance of the class, the __init__ method is immediately executed with all the parameters that we pass to the "Stock" object. The purpose of this method is to set up a new "Stock" object using data we have provided.
-
-Here we create two Stock objects named "apple" and "google".
-<pre class="prettyprint linenums">apple  = Stock('AAPL', 143.69, 144.09, 20109375)
-google = Stock('GOOGL', 898.7, 911.7, 1561616)
-</pre>
-
-Stock objects also have two other methods: update() and print_return(). We can access the attribues of a Stock object and call its methods:
-<pre class="prettyprint linenums">apple.ticker
-[out]: 'AAPL'
-google.print_return()
-[out]: 0.0144653388227
-google.update(912.8,913.4)
-google.print_return()
-[out]: 0.000657318141981
-</pre>
-
-By calling the update() function, we updated the open and close prices of a stock. Please note that when we use the attributes or call the methods <strong>inside a class</strong>, we need to specify them as self.attribute or self.method(), otherwise Python will deem them as global variables and thus raise an error.
-
-We can add an attribute to an object anywhere:
-<pre class="prettyprint linenums">
-apple.ceo = 'Tim Cook'
-apple.ceo
-[out]: 'Tim Cook'
-</pre>
-
-We can check what names (i.e. attributes and methods) are defined on an object using the dir() function:
-<pre class="prettyprint linenums">dir(apple)
-[out]:
-['__doc__',
- '__init__',
- '__module__',
- 'ceo',
- 'close',
- 'open',
- 'print_return',
- 'rate_return',
- 'ticker',
- 'update',
- 'volume']
-</pre>
-
-<h3>Inheritance</h3>
-
-Inheritance is a way of arranging classes in a hierarchy from the most general to the most specific. A "child" class is a more specific type of a "parent" class because a child class will inherit all the attribues and methods of its parent. For example, we define a class named "Child" which inherits "Stock":
-<pre class="prettyprint linenums">class Child(Stock):
-    def __init__(self, name):
-        self.name = name
-</pre>
-
-Then we create an object:
-<pre class="prettyprint linenums">aa = Child('AA')
-print aa.name
-[out]: 'AA'
-aa.update(100, 102)
-print aa.open
-[out]: 100
-print aa.close
-[out]: 102
-print aa.print_return()
-[out]: 0.02</pre>
-As seen above, the new class Child has inherited the methods from Stock.
-
-<h2>Summary</h2>
-
-In this chapter we have introduced functions and classes. When we write a QuantConnect algorithm, we would define our algorithm as a class (QCAlgorithm). This means our algorithm inherited the QC API methods from QCAlgorithm class.
-
-In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html
new file mode 100755
index 0000000..4cba3a9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html	
@@ -0,0 +1,145 @@
+<p>
+  Python is an object-oriented programming language. It's important to understand the concept of "objects" because almost every kind of data from QuantConnect API is an object.
+</p>
+
+<h4>Class</h4>
+<p>
+  A class is a type of data, just like a string, float, or list. When we create an object of that data type, we call it an <strong>instance</strong> of a class.
+</p>
+
+<p>
+  In Python, everything is an object - everything is an instance of some class. The data stored inside an object are called <strong>attributes</strong>, and the functions which are associated with the object are called <strong>methods</strong>.
+</p>
+
+<p>
+  For example, as mentioned above, a list is an object of the "list" class, and it has a method list.sort().
+</p>
+
+<p>
+  We can create our own objects by defining a class. We would do this when it's helpful to group certain functions together. For example, we define a class named "Stock" here:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">class Stock:
+    def __init__(self, ticker, open, close, volume):
+        self.ticker = ticker
+        self.open = open
+        self.close = close
+        self.volume = volume
+        self.rate_return = float(close)/open - 1
+
+    def update(self, open, close):
+        self.open = open
+        self.close = close
+        self.rate_return = float(self.close)/self.open - 1
+
+    def print_return(self):
+        print self.rate_return
+</pre>
+</div>
+
+<p>
+  The "Stock" class has attributes "ticker", "open", "close", "volume" and "rate_return". Inside the class body, the first method is called __init__, which is a special method. When we create a new instance of the class, the __init__ method is immediately executed with all the parameters that we pass to the "Stock" object. The purpose of this method is to set up a new "Stock" object using data we have provided.
+</p>
+
+<p>
+  Here we create two Stock objects named "apple" and "google".
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">apple  = Stock('AAPL', 143.69, 144.09, 20109375)
+google = Stock('GOOGL', 898.7, 911.7, 1561616)
+</pre>
+</div>
+<p>
+  Stock objects also have two other methods: update() and print_return(). We can access the attribues of a Stock object and call its methods:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">apple.ticker
+[out]: 'AAPL'
+google.print_return()
+[out]: 0.0144653388227
+google.update(912.8,913.4)
+google.print_return()
+[out]: 0.000657318141981
+</pre>
+</div>
+
+<p>
+  By calling the update() function, we updated the open and close prices of a stock. Please note that when we use the attributes or call the methods <strong>inside a class</strong>, we need to specify them as self.attribute or self.method(), otherwise Python will deem them as global variables and thus raise an error.
+</p>
+
+<p>
+  We can add an attribute to an object anywhere:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+apple.ceo = 'Tim Cook'
+apple.ceo
+[out]: 'Tim Cook'
+</pre>
+</div>
+<p>
+  We can check what names (i.e. attributes and methods) are defined on an object using the dir() function:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">dir(apple)
+[out]:
+['__doc__',
+ '__init__',
+ '__module__',
+ 'ceo',
+ 'close',
+ 'open',
+ 'print_return',
+ 'rate_return',
+ 'ticker',
+ 'update',
+ 'volume']
+</pre>
+</div>
+
+<h4>Inheritance</h4>
+
+<p>
+  Inheritance is a way of arranging classes in a hierarchy from the most general to the most specific. A "child" class is a more specific type of a "parent" class because a child class will inherit all the attribues and methods of its parent. For example, we define a class named "Child" which inherits "Stock":
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">class Child(Stock):
+    def __init__(self, name):
+        self.name = name
+</pre>
+</div>
+<p>
+  Then we create an object:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aa = Child('AA')
+print aa.name
+[out]: 'AA'
+aa.update(100, 102)
+print aa.open
+[out]: 100
+print aa.close
+[out]: 102
+print aa.print_return()
+[out]: 0.02</pre>
+</div>
+As seen above, the new class Child has inherited the methods from Stock.
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/05 Summary.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/05 Summary.html
new file mode 100755
index 0000000..03fd971
--- /dev/null
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/05 Summary.html	
@@ -0,0 +1,7 @@
+<p>
+  In this chapter we have introduced functions and classes. When we write a QuantConnect algorithm, we would define our algorithm as a class (QCAlgorithm). This means our algorithm inherited the QC API methods from QCAlgorithm class.
+</p>
+
+<p>
+  In the next chapter, we will introduce NumPy and Pandas, which enable us to conduct scientific calculations in Python.
+</p>
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/01 Introduction.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/01 Introduction.html
new file mode 100755
index 0000000..3a2904f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas.
+</p>
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html
new file mode 100755
index 0000000..2b2095a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html	
@@ -0,0 +1,110 @@
+<p>
+  NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data.
+</p>
+<p>
+  Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import numpy as np</pre>
+</div>
+
+<h4>Basic NumPy Arrays</h4>
+
+<p>
+  A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. Here we make an array by passing a list of Apple stock prices:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
+price_array = np.array(price_list)
+print price_array, type(price_array)
+[out]: [ 143.73  145.83  143.68  144.02  143.5   142.62]
+&lt;class 'numpy.ndarray'&gt;</pre>
+</div>
+
+<p>
+  Notice that the type of array is "ndarray" which is a multi-dimensional array. If we pass np.array() a list of lists, it will create a 2-dimensional array.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">Ar = np.array([[1,3], [2,4]])
+print Ar, type(Ar)
+[out]: [[1 3]
+        [2 4]]
+&lt;class 'numpy.ndarray'&gt;</pre>
+</div>
+
+<p>
+  We get the dimensions of an ndarray using the .shape attribute:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print Ar.shape
+[out]: (2, 2)</pre>
+</div>
+
+<p>
+  If we create an 2-dimensional array (i.e. matrix), each row can be accessed by index:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print Ar[0]
+[out]: [1 3]
+print Ar[1]
+[out]: [2 4]</pre>
+</div>
+
+<p>
+  If we want to access the matrix by column instead:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 'First column:', Ar[:,0]
+[out]: First column: [1 2]
+print 'Second column:', Ar[:,1]
+[out]: Second column: [3 4]</pre>
+</div>
+
+<h4>Array Functions</h4>
+
+<p>
+  Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">np.log(price_array)
+[out]: [4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]</pre>
+</div>
+
+<p>
+  Other functions return a single value:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">np.mean(price_array)
+[out]: 143.896666667
+print np.std(price_array)
+[out]: 0.967379047852
+print np.sum(price_array)
+[out]: 863.38
+print np.max(price_array)
+[out]: 145.83
+</pre>
+</div>
+<p>
+  The functions above return the mean, standard deviation, total and maximum value of an array.
+</p>
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html
new file mode 100755
index 0000000..6d6a923
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html	
@@ -0,0 +1,266 @@
+<p>
+  Pandas is one of the most powerful tools for dealing with financial data. First we need to import Pandas:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import pandas as pd</pre>
+</div>
+
+<h4>Series</h4>
+
+<p>
+  Series is a one-dimensional labeled array capable of holding any data type (integers, strings, float, Python object, etc.)
+</p>
+
+<p>
+  We create a Series by calling pd.Series(data), where data can be a dictionary, an array or just a scalar value.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
+s = pd.Series(price)
+print s
+
+0    143.73
+1    145.83
+2    143.68
+3    144.02
+4    143.50
+5    142.62</pre>
+</div>
+
+<p>
+  We can customize the indices of a new Series:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f'])
+print s
+
+a    143.73
+b    145.83
+c    143.68
+d    144.02
+e    143.50
+f    142.62</pre>
+</div>
+
+<p>
+  Or we can change the indices of an existing Series:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s.index = [6,5,4,3,2,1]
+print s
+
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62</pre>
+</div>
+
+<p>
+  Series is like a list since it can be sliced by index:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s[1:]
+print s[:-2]
+
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62
+dtype: float64
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+dtype: float64</pre>
+</div>
+
+<p>
+  Series is also like a dictionary whose values can be set or fetched by index label:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s[4]
+s[4] = 0
+print s
+
+143.68
+6    143.73
+5    145.83
+4      0.00
+3    144.02
+2    143.50
+1    142.62
+dtype: float64</pre>
+</div>
+
+<p>
+  Series can also have a name attribute, which will be used when we make up a Pandas DataFrame using several series.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s = pd.Series(price, name = 'Apple Prices')
+print s
+print s.name
+
+0    143.73
+1    145.83
+2    143.68
+3    144.02
+4    143.50
+5    142.62
+Name: Apple Prices, dtype: float64
+Apple Prices</pre>
+</div>
+
+<p>
+  We can get the statistical summaries of a Series:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s.describe()
+
+count      6.000000
+mean     143.896667
+std        1.059711
+min      142.620000
+25%      143.545000
+50%      143.705000
+75%      143.947500
+max      145.830000</pre>
+</div>
+
+<h4>Time Index</h4>
+
+<p>
+  Pandas has a built-in function specifically for creating date indices: pd.date_range(). We use it to create a new index for our Series:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D')
+print time_index
+s.index = time_index
+print s
+
+DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
+               '2017-01-05', '2017-01-06'],
+              dtype='datetime64[ns]', freq='D')
+2017-01-01    143.73
+2017-01-02    145.83
+2017-01-03    143.68
+2017-01-04    144.02
+2017-01-05    143.50
+2017-01-06    142.62
+Freq: D, Name: Apple Prices, dtype: float64</pre>
+</div>
+
+<p>
+  Series are usually accessed using the iloc[] and loc[] methods. iloc[] is used to access elements by integer index, and loc[] is used to access the index of the series.
+</p>
+
+<p>
+  iloc[] is necessary when the index of a series are integers, take our previous defined series as example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s.index = [6,5,4,3,2,1]
+print s
+print s[1]
+
+6    143.73
+5    145.83
+4    143.68
+3    144.02
+2    143.50
+1    142.62
+Name: Apple Prices, dtype: float64
+142.62</pre>
+</div>
+
+<p>
+  If we intended to take the second element of the series, we would make a mistake here, because the index are integers. In order to access to the element we want, we use iloc[] here:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s.iloc[1]
+[out]: 145.83
+</pre>
+</div>
+
+<p>
+  While working with time series data, we often use time as the index. Pandas provides us with various methods to access the data by time index.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s.index = time_index
+print s['2017-01-03']
+[out]: 143.68</pre>
+</div>
+
+<p>
+  We can even access to a range of dates:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s['2017-01-02':'2017-01-05']
+
+2017-01-02    145.83
+2017-01-03    143.68
+2017-01-04    144.02
+2017-01-05    143.50
+Freq: D, Name: Apple Prices, dtype: float64</pre>
+</div>
+
+<p>
+  Series[] provides us a very flexible way to index data. We can add any condition in the square brackets:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print s[s &lt; np.mean(s)]
+print s[(s &gt; np.mean(s)) &amp; (s &lt; np.mean(s) + 1.64*np.std(s))]
+
+2017-01-01    143.73
+2017-01-03    143.68
+2017-01-05    143.50
+2017-01-06    142.62
+Name: Apple Prices, dtype: float64
+2017-01-04    144.02
+Freq: D, Name: Apple Price List, dtype: float64</pre>
+</div>
+
+<p>
+  As demonstrated, we can use logical operators like &amp; (and), | (or) and ~ (not) to group multiple conditions.
+</p>
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html
deleted file mode 100755
index 31ef442..0000000
--- a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 NumPy and Basic Pandas.html	
+++ /dev/null
@@ -1,229 +0,0 @@
-Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas.
-
-NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data.
-
-Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package:
-<pre class="prettyprint linenums">import numpy as np</pre>
-
-<h2>NumPy</h2>
-
-<h3>Basic NumPy Arrays</h3>
-
-A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. Here we make an array by passing a list of Apple stock prices:
-<pre class="prettyprint linenums">price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
-price_array = np.array(price_list)
-print price_array, type(price_array)
-[out]: [ 143.73  145.83  143.68  144.02  143.5   142.62]
-&lt;class 'numpy.ndarray'&gt;</pre>
-
-Notice that the type of array is "ndarray" which is a multi-dimensional array. If we pass np.array() a list of lists, it will create a 2-dimensional array.
-<pre class="prettyprint linenums">Ar = np.array([[1,3], [2,4]])
-print Ar, type(Ar)
-[out]: [[1 3]
-        [2 4]]
-&lt;class 'numpy.ndarray'&gt;</pre>
-
-We get the dimensions of an ndarray using the .shape attribute:
-<pre class="prettyprint linenums">print Ar.shape
-[out]: (2, 2)</pre>
-
-If we create an 2-dimensional array (i.e. matrix), each row can be accessed by index:
-<pre class="prettyprint linenums">print Ar[0]
-[out]: [1 3]
-print Ar[1]
-[out]: [2 4]</pre>
-
-If we want to access the matrix by column instead:
-<pre class="prettyprint linenums">print 'First column:', Ar[:,0]
-[out]: First column: [1 2]
-print 'Second column:', Ar[:,1]
-[out]: Second column: [3 4]</pre>
-
-<h3>Array Functions</h3>
-
-Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array:
-<pre class="prettyprint linenums">np.log(price_array)
-[out]: [4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]</pre>
-
-Other functions return a single value:
-<pre class="prettyprint linenums">np.mean(price_array)
-[out]: 143.896666667
-print np.std(price_array)
-[out]: 0.967379047852
-print np.sum(price_array)
-[out]: 863.38
-print np.max(price_array)
-[out]: 145.83
-</pre>
-The functions above return the mean, standard deviation, total and maximum value of an array.
-
-<h2>Pandas</h2>
-
-Pandas is one of the most powerful tools for dealing with financial data. First we need to import Pandas:
-<pre class="prettyprint linenums">import pandas as pd</pre>
-
-<h3>Series</h3>
-
-Series is a one-dimensional labeled array capable of holding any data type (integers, strings, float, Python object, etc.)
-
-We create a Series by calling pd.Series(data), where data can be a dictionary, an array or just a scalar value.
-<pre class="prettyprint linenums">price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
-s = pd.Series(price)
-print s
-
-0    143.73
-1    145.83
-2    143.68
-3    144.02
-4    143.50
-5    142.62</pre>
-
-We can customize the indices of a new Series:
-<pre class="prettyprint linenums">s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f'])
-print s
-
-a    143.73
-b    145.83
-c    143.68
-d    144.02
-e    143.50
-f    142.62</pre>
-
-Or we can change the indices of an existing Series:
-<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
-print s
-
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62</pre>
-
-Series is like a list since it can be sliced by index:
-<pre class="prettyprint linenums">print s[1:]
-print s[:-2]
-
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62
-dtype: float64
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-dtype: float64</pre>
-
-Series is also like a dictionary whose values can be set or fetched by index label:
-<pre class="prettyprint linenums">print s[4]
-s[4] = 0
-print s
-
-143.68
-6    143.73
-5    145.83
-4      0.00
-3    144.02
-2    143.50
-1    142.62
-dtype: float64</pre>
-
-Series can also have a name attribute, which will be used when we make up a Pandas DataFrame using several series.
-<pre class="prettyprint linenums">s = pd.Series(price, name = 'Apple Prices')
-print s
-print s.name
-
-0    143.73
-1    145.83
-2    143.68
-3    144.02
-4    143.50
-5    142.62
-Name: Apple Prices, dtype: float64
-Apple Prices</pre>
-
-We can get the statistical summaries of a Series:
-<pre class="prettyprint linenums">print s.describe()
-
-count      6.000000
-mean     143.896667
-std        1.059711
-min      142.620000
-25%      143.545000
-50%      143.705000
-75%      143.947500
-max      145.830000</pre>
-
-<h3>Time Index</h3>
-
-Pandas has a built-in function specifically for creating date indices: pd.date_range(). We use it to create a new index for our Series:
-<pre class="prettyprint linenums">time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D')
-print time_index
-s.index = time_index
-print s
-
-DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
-               '2017-01-05', '2017-01-06'],
-              dtype='datetime64[ns]', freq='D')
-2017-01-01    143.73
-2017-01-02    145.83
-2017-01-03    143.68
-2017-01-04    144.02
-2017-01-05    143.50
-2017-01-06    142.62
-Freq: D, Name: Apple Prices, dtype: float64</pre>
-
-Series are usually accessed using the iloc[] and loc[] methods. iloc[] is used to access elements by integer index, and loc[] is used to access the index of the series.
-
-iloc[] is necessary when the index of a series are integers, take our previous defined series as example:
-<pre class="prettyprint linenums">s.index = [6,5,4,3,2,1]
-print s
-print s[1]
-
-6    143.73
-5    145.83
-4    143.68
-3    144.02
-2    143.50
-1    142.62
-Name: Apple Prices, dtype: float64
-142.62</pre>
-
-If we intended to take the second element of the series, we would make a mistake here, because the index are integers. In order to access to the element we want, we use iloc[] here:
-<pre class="prettyprint linenums">print s.iloc[1]
-[out]: 145.83
-</pre>
-
-While working with time series data, we often use time as the index. Pandas provides us with various methods to access the data by time index.
-<pre class="prettyprint linenums">s.index = time_index
-print s['2017-01-03']
-[out]: 143.68</pre>
-
-We can even access to a range of dates:
-<pre class="prettyprint linenums">print s['2017-01-02':'2017-01-05']
-
-2017-01-02    145.83
-2017-01-03    143.68
-2017-01-04    144.02
-2017-01-05    143.50
-Freq: D, Name: Apple Prices, dtype: float64</pre>
-
-Series[] provides us a very flexible way to index data. We can add any condition in the square brackets:
-<pre class="prettyprint linenums">print s[s &lt; np.mean(s)]
-print s[(s &gt; np.mean(s)) &amp; (s &lt; np.mean(s) + 1.64*np.std(s))]
-
-2017-01-01    143.73
-2017-01-03    143.68
-2017-01-05    143.50
-2017-01-06    142.62
-Name: Apple Prices, dtype: float64
-2017-01-04    144.02
-Freq: D, Name: Apple Price List, dtype: float64</pre>
-
-As demonstrated, we can use logical operators like &amp; (and), | (or) and ~ (not) to group multiple conditions.
-
-<h2>Summary</h2>
-
-Here we have introduced NumPy and Pandas for scientific computing in Python. In the next chapter, we will dive into Pandas to learn resampling and manipulating Pandas DataFrame, which are commonly used in financial data analysis.
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 Summary.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 Summary.html
new file mode 100755
index 0000000..10dcaf9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/04 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  Here we have introduced NumPy and Pandas for scientific computing in Python. In the next chapter, we will dive into Pandas to learn resampling and manipulating Pandas DataFrame, which are commonly used in financial data analysis.
+</p>
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/01 Introduction.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/01 Introduction.html
new file mode 100755
index 0000000..b99780b
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
+</p>
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html
new file mode 100755
index 0000000..f804f38
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
@@ -0,0 +1,104 @@
+<p>
+  Here we use data from the yahoo_finance API.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
+</pre>
+</div>
+<p>
+  We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table['Adj. Close']['2017']
+print aapl
+</pre>
+</div>
+
+<p>
+  Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl['2017-3']
+Date
+2017-03-01    138.657681
+2017-03-02    137.834404
+2017-03-03    138.647762
+2017-03-06    138.211326
+2017-03-07    138.389868
+2017-03-08    137.874080
+2017-03-09    137.556672
+2017-03-10    138.012946
+2017-03-13    138.072460
+2017-03-14    137.864161
+2017-03-15    139.322254
+2017-03-16    139.550391
+2017-03-17    138.856061
+2017-03-20    140.314154
+2017-03-21    138.707276
+2017-03-22    140.274478
+2017-03-23    139.778528
+2017-03-24    139.500796
+2017-03-27    139.738852
+2017-03-28    142.635200
+2017-03-29    142.952608
+2017-03-30    142.764147
+2017-03-31    142.496334
+</pre>
+</div>
+
+<p>
+  Or in several consecutive months:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl['2017-2':'2017-4']
+</pre>
+</div>
+
+<p>
+  .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl.head()
+print aapl.tail(10)
+</pre>
+</div>
+<p>
+  The output:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+Date
+2017-01-03    114.715378
+2017-01-04    114.586983
+2017-01-05    115.169696
+2017-01-06    116.453639
+2017-01-09    117.520300
+Name: Adj. Close, dtype: float64
+Date
+2017-08-08    159.433108
+2017-08-09    160.409148
+2017-08-10    155.270000
+2017-08-11    157.480000
+2017-08-14    159.850000
+2017-08-15    161.600000
+2017-08-16    160.950000
+2017-08-17    157.870000
+2017-08-18    157.500000
+2017-08-21    157.210000
+Name: Adj. Close, dtype: float64
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html
new file mode 100755
index 0000000..6d8492c
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html	
@@ -0,0 +1,229 @@
+<p>
+  <code>series.resample(freq)</code> is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
+</p>
+<p>
+  <code>series.resample.mean()</code> is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">by_month = aapl.resample('M').mean()
+print by_month
+Date
+2017-01-31    118.093136
+2017-02-28    132.456268
+2017-03-31    139.478802
+2017-04-30    141.728436
+2017-05-31    151.386305
+2017-06-30    147.233064
+2017-07-31    147.706190
+2017-08-31    157.444303
+</pre>
+</div>
+
+<p>
+  We can also aggregate the data by week:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">by_week = aapl.resample('W').mean()
+print by_week.head()
+
+Date
+2017-01-31    120.932434
+2017-02-28    136.551200
+2017-03-31    143.532630
+2017-04-30    144.179981
+2017-05-31    156.100000
+2017-06-30    155.450000
+2017-07-31    153.460000
+</pre>
+</div>
+
+<p>
+  We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">three_day = aapl.resample('3D').mean()
+two_week  = aapl.resample('2W').mean()
+two_month = aapl.resample('2M').mean()
+</pre>
+</div>
+
+<p>
+  Besides the mean() method, other methods can also be used with the resampler:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">std = aapl.resample('W').std()    # standard deviation
+max = aapl.resample('W').max()    # maximum value
+min = aapl.resample('W').min()    # minimum value
+</pre>
+</div>
+
+<p>
+  Often we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">last_day = aapl.resample('M').agg(lambda x: x[-1])
+print last_day
+Date
+2017-01-31    119.851150
+2017-02-28    135.880362
+2017-03-31    142.496334
+2017-04-30    142.486415
+2017-05-31    152.142689
+2017-06-30    143.438008
+2017-07-31    148.248489
+2017-08-31    157.210000
+</pre>
+</div>
+
+<p>
+  Or directly calculate the monthly rates of return using the data for the first day and the last day:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
+print monthly_return
+
+Date
+2017-01-31    0.045940
+2017-02-28    0.070409
+2017-03-31    0.033823
+2017-04-30   -0.007736
+2017-05-31    0.039829
+2017-06-30   -0.073528
+2017-07-31    0.033035
+2017-08-31    0.004505
+</pre>
+</div>
+
+<p>
+  Series object also provides us some convenient methods to do some quick calculation.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print monthly_return.mean()
+print monthly_return.std()
+print monthly_return.max()
+[out]: 0.0208974076157
+       0.0476398315185
+       0.0704090212384
+</pre>
+</div>
+
+<p>
+  Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print last_day.diff()
+print last_day.pct_change()
+
+Date
+2017-01-31          NaN
+2017-02-28    16.029211
+2017-03-31     6.615972
+2017-04-30    -0.009919
+2017-05-31     9.656274
+2017-06-30    -8.704681
+2017-07-31     4.810482
+2017-08-31     8.961511
+Freq: M, Name: Adj. Close, dtype: float64
+Date
+2017-01-31         NaN
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449
+</pre>
+</div>
+
+<p>
+  Notice that we induced a NaN value while calculating percentage changes i.e. returns.
+</p>
+
+<p>
+  When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change()
+print daily_return.fillna(0)
+
+Date
+2017-01-31    0.000000
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449
+</pre>
+</div>
+
+<p>
+  Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change()
+print daily_return.fillna(method = 'bfill')
+
+Date
+2017-01-31    0.133743
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.033537
+2017-08-31    0.060449
+</pre>
+</div>
+
+<p>
+  As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
+</p>
+
+<p>
+  We can also simply remove NaN values by <strong>.dropna()</strong>
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change().dropna()
+print daily_return
+
+Date
+2017-02-28    0.133743
+2017-03-31    0.048690
+2017-04-30   -0.000070
+2017-05-31    0.067770
+2017-06-30   -0.057214
+2017-07-31    0.038050
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html
new file mode 100755
index 0000000..f119715
--- /dev/null
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html	
@@ -0,0 +1,607 @@
+<p>
+  The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
+</p>
+<p>
+  More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
+</p>
+<h4>Create DataFrames</h4>
+<p>
+  The most common method of creating a DataFrame is passing a dictionary:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
+        'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
+        'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
+dates = pd.date_range('2017-07-03', periods = 5, freq = 'D')
+df = pd.DataFrame(dict, index = dates)
+print df
+              AAPL    GOOG     IBM
+2017-07-03  143.50  898.70  155.58
+2017-07-04  144.09  911.71  153.67
+2017-07-05  142.73  906.69  152.36
+2017-07-06  144.18  918.59  152.94
+2017-07-07  143.77  926.99  153.49
+</pre>
+</div>
+
+<h4>Manipulating DataFrames</h4>
+
+<p>
+  We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
+</p>
+<p>
+  If a column name contains no spaces, then we can also use df.column_name to fetch a column:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df = aapl_table
+print df.Close.tail(5)
+print df['Adj. Volume'].tail(5)
+Date
+2017-07-24    152.09
+2017-07-25    152.74
+2017-07-26    153.46
+2017-07-27    150.56
+2017-07-28    149.50
+Name: Close, dtype: float64
+Date
+2017-07-24    21122730.0
+2017-07-25    18612649.0
+2017-07-26    15172136.0
+2017-07-27    32175875.0
+2017-07-28    16832947.0
+Name: Adj. Volume, dtype: float64
+</pre>
+</div>
+
+<p>
+  All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_2016 = df['2016']
+aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
+print aapl_month
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+              Open      High     Low   Close      Volume  Ex-Dividend  \
+Date
+2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0
+2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0
+2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0
+2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0
+2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0
+2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0
+2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0
+2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0
+2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0
+2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0
+2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0
+2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0
+
+            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \
+Date
+2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495
+2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856
+2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931
+2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748
+2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913
+2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775
+2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363
+2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460
+2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885
+2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287
+2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807
+2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583
+
+            Adj. Volume
+Date
+2016-01-31   64416504.0
+2016-02-29   35216277.0
+2016-03-31   25888449.0
+2016-04-30   68531478.0
+2016-05-31   42307212.0
+2016-06-30   35836356.0
+2016-07-31   27733688.0
+2016-08-31   29662406.0
+2016-09-30   36379106.0
+2016-10-31   26419398.0
+2016-11-30   36162258.0
+2016-12-31   30586265.0
+</pre>
+</div>
+
+<p>
+  We may select certain columns of a DataFrame using their names:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
+print aapl_bar
+
+              Open      High     Low   Close
+Date
+2016-01-31   94.79   97.3400   94.35   97.34
+2016-02-29   96.86   98.2300   96.65   96.69
+2016-03-31  109.72  109.9000  108.88  108.99
+2016-04-30   93.99   94.7200   92.51   93.74
+2016-05-31   99.60  100.4000   98.82   99.86
+2016-06-30   94.44   95.7700   94.30   95.60
+2016-07-31  104.19  104.5500  103.68  104.21
+2016-08-31  105.66  106.5699  105.64  106.10
+2016-09-30  112.46  113.3700  111.80  113.05
+2016-10-31  113.65  114.2300  113.20  113.54
+2016-11-30  111.56  112.2000  110.27  110.52
+2016-12-31  116.65  117.2000  115.43  115.82
+</pre>
+</div>
+
+<p>
+  We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
+
+              Open    High     Low   Close
+Date
+2016-03-31  109.72  109.90  108.88  108.99
+2016-04-30   93.99   94.72   92.51   93.74
+2016-05-31   99.60  100.40   98.82   99.86
+2016-06-30   94.44   95.77   94.30   95.60
+</pre>
+</div>
+
+<p>
+  The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
+print above
+
+              Open      High     Low   Close
+Date
+2016-03-31  109.72  109.9000  108.88  108.99
+2016-08-31  105.66  106.5699  105.64  106.10
+2016-09-30  112.46  113.3700  111.80  113.05
+2016-10-31  113.65  114.2300  113.20  113.54
+2016-11-30  111.56  112.2000  110.27  110.52
+2016-12-31  116.65  117.2000  115.43  115.82
+</pre>
+</div>
+
+<h4>Data Validation</h4>
+
+<p>
+  As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
+print aapl_bar
+
+              Open      High     Low   Close  rate_return
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+</pre>
+</div>
+
+<p>
+  Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">missing = aapl_bar.isnull()
+print missing
+print '---------------------------------------------'
+print missing.describe()
+
+             Open   High    Low  Close rate_return
+Date
+2016-01-31  False  False  False  False        True
+2016-02-29  False  False  False  False       False
+2016-03-31  False  False  False  False       False
+2016-04-30  False  False  False  False       False
+2016-05-31  False  False  False  False       False
+2016-06-30  False  False  False  False       False
+2016-07-31  False  False  False  False       False
+2016-08-31  False  False  False  False       False
+2016-09-30  False  False  False  False       False
+2016-10-31  False  False  False  False       False
+2016-11-30  False  False  False  False       False
+2016-12-31  False  False  False  False       False
+
+---------------------------------------------
+
+         Open   High    Low  Close rate_return
+count      12     12     12     12          12
+unique      1      1      1      1           2
+top     False  False  False  False       False
+freq       12     12     12     12          11
+</pre>
+</div>
+
+<p>
+  The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
+</p>
+<p>
+  We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
+</p>
+<p>
+  We can also find the rows with missing values easily:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print missing[missing.rate_return == True]
+
+             Open   High    Low  Close rate_return
+Date
+2016-01-31  False  False  False  False        True
+</pre>
+</div>
+
+<p>
+  Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">drop = aapl_bar.dropna()
+print drop
+print '\n--------------------------------------------------\n'
+fill = aapl_bar.fillna(0)
+print fill
+
+              Open      High     Low   Close  rate_return
+Date
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+
+--------------------------------------------------
+
+              Open      High     Low   Close  rate_return
+Date
+2016-01-31   94.79   97.3400   94.35   97.34     0.000000
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+</pre>
+</div>
+
+<h4>DataFrame Concat</h4>
+<p>
+  We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
+</p>
+<p>
+  In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
+s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
+data_frame = pd.concat([s1, s2], axis = 1)
+print data_frame
+
+     AAPL    GOOG
+0  143.50  898.70
+1  144.09  911.71
+2  142.73  906.69
+3  144.18  918.59
+4  143.77  926.99
+</pre>
+</div>
+
+<p>
+  The "axis = 1" parameter will join two DataFrames by columns:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">log_price = np.log(aapl_bar.Close)
+log_price.name = 'log_price'
+print log_price
+print '\n--------------------------------------------\n'
+concat = pd.concat([aapl_bar, log_price], axis = 1)
+print concat
+
+Date
+2016-01-31    4.578210
+2016-02-29    4.571510
+2016-03-31    4.691256
+2016-04-30    4.540525
+2016-05-31    4.603769
+2016-06-30    4.560173
+2016-07-31    4.646408
+2016-08-31    4.664382
+2016-09-30    4.727830
+2016-10-31    4.732155
+2016-11-30    4.705197
+2016-12-31    4.752037
+Freq: M, Name: log_price, dtype: float64
+
+--------------------------------------------
+
+              Open      High     Low   Close  rate_return  log_price
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037
+</pre>
+</div>
+
+<p>
+  We can also join two DataFrames by rows. Consider these two DataFrames:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
+print df_volume
+print '\n-------------------------------------------\n'
+df_2017 = aapl_table.loc['2016-10':'2017-04', ['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
+print df_2017
+
+                Volume  Split Ratio
+Date
+2016-10-31  26419398.0          1.0
+2016-11-30  36162258.0          1.0
+2016-12-31  30586265.0          1.0
+2017-01-31  49200993.0          1.0
+2017-02-28  23482860.0          1.0
+2017-03-31  19661651.0          1.0
+2017-04-30  20247187.0          1.0
+
+-------------------------------------------
+
+              Open     High     Low   Close
+Date
+2016-10-31  113.65  114.230  113.20  113.54
+2016-11-30  111.56  112.200  110.27  110.52
+2016-12-31  116.65  117.200  115.43  115.82
+2017-01-31  121.15  121.390  120.62  121.35
+2017-02-28  137.08  137.435  136.70  136.99
+2017-03-31  143.72  144.270  143.01  143.66
+2017-04-30  144.09  144.300  143.27  143.65
+</pre>
+</div>
+
+<p>
+  Now we merge the DataFrames with our DataFrame 'aapl_bar'
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1)
+print concat
+
+              Open      High     Low   Close  rate_return      Volume  \
+Date
+2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN
+2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN
+2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN
+2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN
+2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN
+2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN
+2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN
+2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN
+2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN
+2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0
+2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0
+2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0
+2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0
+2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0
+2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0
+
+            Split Ratio
+Date
+2016-01-31          NaN
+2016-02-29          NaN
+2016-03-31          NaN
+2016-04-30          NaN
+2016-05-31          NaN
+2016-06-30          NaN
+2016-07-31          NaN
+2016-08-31          NaN
+2016-09-30          NaN
+2016-10-31          1.0
+2016-11-30          1.0
+2016-12-31          1.0
+2017-01-31          1.0
+2017-02-28          1.0
+2017-03-31          1.0
+2017-04-30          1.0
+</pre>
+</div>
+
+<p>
+  By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
+print concat
+
+              Open    High     Low   Close  rate_return      Volume  \
+Date
+2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0
+2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0
+2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0
+
+            Split Ratio
+Date
+2016-10-31          1.0
+2016-11-30          1.0
+2016-12-31          1.0
+
+</pre>
+</div>
+<p>
+  Only the intersection part was left if use 'inner join' method. Now let's try to append a DataFrame to another one:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">append = aapl_bar.append(df_2017)
+print append
+             Close      High     Low    Open  rate_return
+Date
+2016-01-31   97.34   97.3400   94.35   94.79          NaN
+2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.54  114.2300  113.20  113.65          NaN
+2016-11-30  110.52  112.2000  110.27  111.56          NaN
+2016-12-31  115.82  117.2000  115.43  116.65          NaN
+2017-01-31  121.35  121.3900  120.62  121.15          NaN
+2017-02-28  136.99  137.4350  136.70  137.08          NaN
+2017-03-31  143.66  144.2700  143.01  143.72          NaN
+2017-04-30  143.65  144.3000  143.27  144.09          NaN
+
+</pre>
+</div>
+<p>
+  'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_2017], axis = 0)
+print concat
+             Close      High     Low    Open  rate_return
+Date
+2016-01-31   97.34   97.3400   94.35   94.79          NaN
+2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.54  114.2300  113.20  113.65          NaN
+2016-11-30  110.52  112.2000  110.27  111.56          NaN
+2016-12-31  115.82  117.2000  115.43  116.65          NaN
+2017-01-31  121.35  121.3900  120.62  121.15          NaN
+2017-02-28  136.99  137.4350  136.70  137.08          NaN
+2017-03-31  143.66  144.2700  143.01  143.72          NaN
+2017-04-30  143.65  144.3000  143.27  144.09          NaN
+</pre>
+</div>
+<p>
+  Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df_2017.columns = ['Change', 'High', 'Low', 'Close']
+concat = pd.concat([aapl_bar, df_2017], axis = 0)
+print concat
+
+            Change   Close      High     Low    Open  rate_return
+Date
+2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN
+2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678
+2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211
+2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921
+2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287
+2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660
+2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063
+2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136
+2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504
+2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334
+2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599
+2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955
+2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN
+2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN
+2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN
+2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN
+2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN
+2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN
+2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN
+</pre>
+</div>
+<p>
+  Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
+</p>
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html
similarity index 57%
rename from 05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html
rename to 05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html
index 7805251..e217896 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Pandas-Resampling and DataFrame.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
@@ -1,16 +1,35 @@
-In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
-
-Here we use data from the yahoo_finance API.
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'</pre>
-
-We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
-<pre class="prettyprint linenums">aapl_table = quandl.get('WIKI/AAPL')
+<p>
+  In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
+</p>
+<p>
+  Here we use data from the yahoo_finance API.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
+</pre>
+</div>
+<p>
+  We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_table = quandl.get('WIKI/AAPL')
 aapl = aapl_table['Adj. Close']['2017']
-print aapl</pre>
+print aapl
+</pre>
+</div>
 
-Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
-<pre class="prettyprint linenums">print aapl['2017-3']
+<p>
+  Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl['2017-3']
 Date
 2017-03-01    138.657681
 2017-03-02    137.834404
@@ -34,17 +53,37 @@
 2017-03-28    142.635200
 2017-03-29    142.952608
 2017-03-30    142.764147
-2017-03-31    142.496334</pre>
-
-Or in several consecutive months:
-<pre class="prettyprint linenums">aapl['2017-2':'2017-4']</pre>
+2017-03-31    142.496334
+</pre>
+</div>
 
-.head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
-<pre class="prettyprint linenums">print aapl.head()
-print aapl.tail(10)</pre>
+<p>
+  Or in several consecutive months:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl['2017-2':'2017-4']
+</pre>
+</div>
 
-The output:
-<pre class="prettyprint linenums">
+<p>
+  .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl.head()
+print aapl.tail(10)
+</pre>
+</div>
+<p>
+  The output:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
 Date
 2017-01-03    114.715378
 2017-01-04    114.586983
@@ -63,16 +102,22 @@
 2017-08-17    157.870000
 2017-08-18    157.500000
 2017-08-21    157.210000
-Name: Adj. Close, dtype: float64</pre>
+Name: Adj. Close, dtype: float64
+</pre>
+</div>
 
 <h2>Resampling</h2>
-
-series.resample(freq) is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
-
-series.resample.mean() is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
-<pre class="prettyprint linenums">by_month = aapl.resample('M').mean()
+<p>
+  <code>series.resample(freq)</code> is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
+</p>
+<p>
+  <code>series.resample.mean()</code> is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">by_month = aapl.resample('M').mean()
 print by_month
-
 Date
 2017-01-31    118.093136
 2017-02-28    132.456268
@@ -81,10 +126,18 @@ <h2>Resampling</h2>
 2017-05-31    151.386305
 2017-06-30    147.233064
 2017-07-31    147.706190
-2017-08-31    157.444303</pre>
+2017-08-31    157.444303
+</pre>
+</div>
 
-We can also aggregate the data by week:
-<pre class="prettyprint linenums">by_week = aapl.resample('W').mean()
+<p>
+  We can also aggregate the data by week:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">by_week = aapl.resample('W').mean()
 print by_week.head()
 
 Date
@@ -94,22 +147,44 @@ <h2>Resampling</h2>
 2017-04-30    144.179981
 2017-05-31    156.100000
 2017-06-30    155.450000
-2017-07-31    153.460000</pre>
+2017-07-31    153.460000
+</pre>
+</div>
 
-We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
-<pre class="prettyprint linenums">three_day = aapl.resample('3D').mean()
+<p>
+  We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">three_day = aapl.resample('3D').mean()
 two_week  = aapl.resample('2W').mean()
-two_month = aapl.resample('2M').mean()</pre>
+two_month = aapl.resample('2M').mean()
+</pre>
+</div>
+
+<p>
+  Besides the mean() method, other methods can also be used with the resampler:
+</p>
 
-Besides the mean() method, other methods can also be used with the resampler:
-<pre class="prettyprint linenums">std = aapl.resample('W').std()    # standard deviation
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">std = aapl.resample('W').std()    # standard deviation
 max = aapl.resample('W').max()    # maximum value
-min = aapl.resample('W').min()    # minimum value</pre>
+min = aapl.resample('W').min()    # minimum value
+</pre>
+</div>
 
-OFten we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
-<pre class="prettyprint linenums">last_day = aapl.resample('M').agg(lambda x: x[-1])
-print last_day
+<p>
+  Often we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
+</p>
 
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">last_day = aapl.resample('M').agg(lambda x: x[-1])
+print last_day
 Date
 2017-01-31    119.851150
 2017-02-28    135.880362
@@ -118,10 +193,18 @@ <h2>Resampling</h2>
 2017-05-31    152.142689
 2017-06-30    143.438008
 2017-07-31    148.248489
-2017-08-31    157.210000</pre>
+2017-08-31    157.210000
+</pre>
+</div>
+
+<p>
+  Or directly calculate the monthly rates of return using the data for the first day and the last day:
+</p>
 
-Or directly calculate the monthly rates of return using the data for the first day and the last day:
-<pre class="prettyprint linenums">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
 print monthly_return
 
 Date
@@ -132,18 +215,33 @@ <h2>Resampling</h2>
 2017-05-31    0.039829
 2017-06-30   -0.073528
 2017-07-31    0.033035
-2017-08-31    0.004505</pre>
+2017-08-31    0.004505
+</pre>
+</div>
 
-Series object also provides us some convenient methods to do some quick calculation.
-<pre class="prettyprint linenums">print monthly_return.mean()
+<p>
+  Series object also provides us some convenient methods to do some quick calculation.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print monthly_return.mean()
 print monthly_return.std()
 print monthly_return.max()
 [out]: 0.0208974076157
        0.0476398315185
-       0.0704090212384</pre>
+       0.0704090212384
+</pre>
+</div>
 
-Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
-<pre class="prettyprint linenums">print last_day.diff()
+<p>
+  Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print last_day.diff()
 print last_day.pct_change()
 
 Date
@@ -164,12 +262,22 @@ <h2>Resampling</h2>
 2017-05-31    0.067770
 2017-06-30   -0.057214
 2017-07-31    0.033537
-2017-08-31    0.060449</pre>
+2017-08-31    0.060449
+</pre>
+</div>
+
+<p>
+  Notice that we induced a NaN value while calculating percentage changes i.e. returns.
+</p>
 
-Notice that we induced a NaN value while calculating percentage changes i.e. returns.
+<p>
+  When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
+</p>
 
-When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
-<pre class="prettyprint linenums">daily_return = last_day.pct_change()
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(0)
 
 Date
@@ -180,10 +288,17 @@ <h2>Resampling</h2>
 2017-05-31    0.067770
 2017-06-30   -0.057214
 2017-07-31    0.033537
-2017-08-31    0.060449</pre>
+2017-08-31    0.060449
+</pre>
+</div>
 
-Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
-<pre class="prettyprint linenums">daily_return = last_day.pct_change()
+<p>
+  Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(method = 'bfill')
 
 Date
@@ -194,12 +309,22 @@ <h2>Resampling</h2>
 2017-05-31    0.067770
 2017-06-30   -0.057214
 2017-07-31    0.033537
-2017-08-31    0.060449</pre>
+2017-08-31    0.060449
+</pre>
+</div>
+
+<p>
+  As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
+</p>
 
-As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
+<p>
+  We can also simply remove NaN values by <strong>.dropna()</strong>
+</p>
 
-We can also simply remove NaN values by <strong>.dropna()</strong>
-<pre class="prettyprint linenums">daily_return = last_day.pct_change().dropna()
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">daily_return = last_day.pct_change().dropna()
 print daily_return
 
 Date
@@ -208,40 +333,55 @@ <h2>Resampling</h2>
 2017-04-30   -0.000070
 2017-05-31    0.067770
 2017-06-30   -0.057214
-2017-07-31    0.038050</pre>
+2017-07-31    0.038050
+</pre>
+</div>
 
 <h2>DataFrame</h2>
 
-The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
-
-More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
-
+<p>
+  The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
+</p>
+<p>
+  More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
+</p>
 <h3>Create DataFrames</h3>
-
-The most common method of creating a DataFrame is passing a dictionary:
-<pre class="prettyprint linenums">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
+<p>
+  The most common method of creating a DataFrame is passing a dictionary:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
         'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
         'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
 dates = pd.date_range('2017-07-03', periods = 5, freq = 'D')
 df = pd.DataFrame(dict, index = dates)
 print df
-
               AAPL    GOOG     IBM
 2017-07-03  143.50  898.70  155.58
 2017-07-04  144.09  911.71  153.67
 2017-07-05  142.73  906.69  152.36
 2017-07-06  144.18  918.59  152.94
-2017-07-07  143.77  926.99  153.49</pre>
+2017-07-07  143.77  926.99  153.49
+</pre>
+</div>
 
 <h3>Manipulating DataFrames</h3>
 
-We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
+<p>
+  We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
+</p>
+<p>
+  If a column name contains no spaces, then we can also use df.column_name to fetch a column:
+</p>
 
-If a column name contains no spaces, then we can also use df.column_name to fetch a column:
-<pre class="prettyprint linenums">df = aapl_table
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df = aapl_table
 print df.Close.tail(5)
 print df['Adj. Volume'].tail(5)
-
 Date
 2017-07-24    152.09
 2017-07-25    152.74
@@ -255,14 +395,27 @@ <h3>Manipulating DataFrames</h3>
 2017-07-26    15172136.0
 2017-07-27    32175875.0
 2017-07-28    16832947.0
-Name: Adj. Volume, dtype: float64</pre>
+Name: Adj. Volume, dtype: float64
+</pre>
+</div>
+
+<p>
+  All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
+</p>
 
-All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
-<pre class="prettyprint linenums">aapl_2016 = df['2016']
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_2016 = df['2016']
 aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
-print aapl_month</pre>
+print aapl_month
+</pre>
+</div>
 
-<pre class="prettyprint linenums">
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
               Open      High     Low   Close      Volume  Ex-Dividend  \
 Date
 2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0
@@ -306,10 +459,18 @@ <h3>Manipulating DataFrames</h3>
 2016-09-30   36379106.0
 2016-10-31   26419398.0
 2016-11-30   36162258.0
-2016-12-31   30586265.0</pre>
+2016-12-31   30586265.0
+</pre>
+</div>
 
-We may select certain columns of a DataFrame using their names:
-<pre class="prettyprint linenums">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
+<p>
+  We may select certain columns of a DataFrame using their names:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
 print aapl_bar
 
               Open      High     Low   Close
@@ -325,20 +486,36 @@ <h3>Manipulating DataFrames</h3>
 2016-09-30  112.46  113.3700  111.80  113.05
 2016-10-31  113.65  114.2300  113.20  113.54
 2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82</pre>
+2016-12-31  116.65  117.2000  115.43  115.82
+</pre>
+</div>
+
+<p>
+  We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
+</p>
 
-We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
-<pre class="prettyprint linenums">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
 
               Open    High     Low   Close
 Date
 2016-03-31  109.72  109.90  108.88  108.99
 2016-04-30   93.99   94.72   92.51   93.74
 2016-05-31   99.60  100.40   98.82   99.86
-2016-06-30   94.44   95.77   94.30   95.60</pre>
+2016-06-30   94.44   95.77   94.30   95.60
+</pre>
+</div>
 
-The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
-<pre class="prettyprint linenums">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
+<p>
+  The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
 print above
 
               Open      High     Low   Close
@@ -348,12 +525,19 @@ <h3>Manipulating DataFrames</h3>
 2016-09-30  112.46  113.3700  111.80  113.05
 2016-10-31  113.65  114.2300  113.20  113.54
 2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82</pre>
+2016-12-31  116.65  117.2000  115.43  115.82
+</pre>
+</div>
 
 <h3>Data Validation</h3>
 
-As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
-<pre class="prettyprint linenums">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
+<p>
+  As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
 print aapl_bar
 
               Open      High     Low   Close  rate_return
@@ -369,10 +553,18 @@ <h3>Data Validation</h3>
 2016-09-30  112.46  113.3700  111.80  113.05     0.065504
 2016-10-31  113.65  114.2300  113.20  113.54     0.004334
 2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+</pre>
+</div>
+
+<p>
+  Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
+</p>
 
-Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
-<pre class="prettyprint linenums">missing = aapl_bar.isnull()
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">missing = aapl_bar.isnull()
 print missing
 print '---------------------------------------------'
 print missing.describe()
@@ -398,21 +590,39 @@ <h3>Data Validation</h3>
 count      12     12     12     12          12
 unique      1      1      1      1           2
 top     False  False  False  False       False
-freq       12     12     12     12          11</pre>
-
-The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
-
-We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
-
-We can also find the rows with missing values easily:
-<pre class="prettyprint linenums">print missing[missing.rate_return == True]
+freq       12     12     12     12          11
+</pre>
+</div>
+
+<p>
+  The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
+</p>
+<p>
+  We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
+</p>
+<p>
+  We can also find the rows with missing values easily:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print missing[missing.rate_return == True]
 
              Open   High    Low  Close rate_return
 Date
-2016-01-31  False  False  False  False        True</pre>
+2016-01-31  False  False  False  False        True
+</pre>
+</div>
+
+<p>
+  Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
+</p>
 
-Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
-<pre class="prettyprint linenums">drop = aapl_bar.dropna()
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">drop = aapl_bar.dropna()
 print drop
 print '\n--------------------------------------------------\n'
 fill = aapl_bar.fillna(0)
@@ -447,14 +657,22 @@ <h3>Data Validation</h3>
 2016-09-30  112.46  113.3700  111.80  113.05     0.065504
 2016-10-31  113.65  114.2300  113.20  113.54     0.004334
 2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955</pre>
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955
+</pre>
+</div>
 
 <h3>DataFrame Concat</h3>
-
-We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
-
-In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
-<pre class="prettyprint linenums">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
+<p>
+  We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
+</p>
+<p>
+  In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
 s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
 data_frame = pd.concat([s1, s2], axis = 1)
 print data_frame
@@ -464,10 +682,18 @@ <h3>DataFrame Concat</h3>
 1  144.09  911.71
 2  142.73  906.69
 3  144.18  918.59
-4  143.77  926.99</pre>
+4  143.77  926.99
+</pre>
+</div>
+
+<p>
+  The "axis = 1" parameter will join two DataFrames by columns:
+</p>
 
-The "axis = 1" parameter will join two DataFrames by columns:
-<pre class="prettyprint linenums">log_price = np.log(aapl_bar.Close)
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">log_price = np.log(aapl_bar.Close)
 log_price.name = 'log_price'
 print log_price
 print '\n--------------------------------------------\n'
@@ -504,10 +730,18 @@ <h3>DataFrame Concat</h3>
 2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830
 2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155
 2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037</pre>
+2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037
+</pre>
+</div>
 
-We can also join two DataFrames by rows. Consider these two DataFrames:
-<pre class="prettyprint linenums">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
+<p>
+  We can also join two DataFrames by rows. Consider these two DataFrames:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
 print df_volume
 print '\n-------------------------------------------\n'
 df_2017 = aapl_table.loc['2016-10':'2017-04', ['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
@@ -533,10 +767,18 @@ <h3>DataFrame Concat</h3>
 2017-01-31  121.15  121.390  120.62  121.35
 2017-02-28  137.08  137.435  136.70  136.99
 2017-03-31  143.72  144.270  143.01  143.66
-2017-04-30  144.09  144.300  143.27  143.65</pre>
+2017-04-30  144.09  144.300  143.27  143.65
+</pre>
+</div>
+
+<p>
+  Now we merge the DataFrames with our DataFrame 'aapl_bar'
+</p>
 
-Now we merge the DataFrames with our DataFrame 'aapl_bar'
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1)
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1)
 print concat
 
               Open      High     Low   Close  rate_return      Volume  \
@@ -575,10 +817,18 @@ <h3>DataFrame Concat</h3>
 2017-01-31          1.0
 2017-02-28          1.0
 2017-03-31          1.0
-2017-04-30          1.0</pre>
+2017-04-30          1.0
+</pre>
+</div>
+
+<p>
+  By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
+</p>
 
-By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
 print concat
 
               Open    High     Low   Close  rate_return      Volume  \
@@ -592,10 +842,17 @@ <h3>DataFrame Concat</h3>
 2016-10-31          1.0
 2016-11-30          1.0
 2016-12-31          1.0
+
 </pre>
-Only the intersection part was left if use 'inner join' method.
-Now let's try to append a DataFrame to another one:
-<pre class="prettyprint linenums">append = aapl_bar.append(df_2017)
+</div>
+<p>
+  Only the intersection part was left if use 'inner join' method. Now let's try to append a DataFrame to another one:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">append = aapl_bar.append(df_2017)
 print append
              Close      High     Low    Open  rate_return
 Date
@@ -618,9 +875,17 @@ <h3>DataFrame Concat</h3>
 2017-02-28  136.99  137.4350  136.70  137.08          NaN
 2017-03-31  143.66  144.2700  143.01  143.72          NaN
 2017-04-30  143.65  144.3000  143.27  144.09          NaN
+
 </pre>
-'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
-<pre class="prettyprint linenums">concat = pd.concat([aapl_bar, df_2017], axis = 0)
+</div>
+<p>
+  'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
              Close      High     Low    Open  rate_return
 Date
@@ -644,8 +909,14 @@ <h3>DataFrame Concat</h3>
 2017-03-31  143.66  144.2700  143.01  143.72          NaN
 2017-04-30  143.65  144.3000  143.27  144.09          NaN
 </pre>
-Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
-<pre class="prettyprint linenums">df_2017.columns = ['Change', 'High', 'Low', 'Close']
+</div>
+<p>
+  Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">df_2017.columns = ['Change', 'High', 'Low', 'Close']
 concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
 
@@ -669,10 +940,13 @@ <h3>DataFrame Concat</h3>
 2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN
 2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN
 2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN
-2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN</pre>
-
-Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
-
+2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN
+</pre>
+</div>
+<p>
+  Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
+</p>
 <h2>Summary</h2>
-
-Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.
\ No newline at end of file
+<p>
+  Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.
+</p>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/01 Introduction.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/01 Introduction.html
new file mode 100755
index 0000000..b02e12a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we are going to introduce some basic concepts in quantitative finance. We start with rate of return, mean and variance. You may think it's simple to calculate these values, however, there are number of different methods to calculate them. It's important to choose the appropriate calculation methods case by case.
+</p>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
new file mode 100755
index 0000000..2bc4709
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -0,0 +1,137 @@
+<h4>Single-period Return</h4>
+<p>
+  The single-period rate of return can be calculated as following:
+</p>
+\[r = \frac{p_t}{p_0} - 1 = \frac{p_t - p_0}{p_0}\]
+<p>
+  Where \(r\) is the rate of return, \(p_t\) is the asset price at time \(t\), and \(p_0\) is the asset price at time 0.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import numpy as np
+rate_return = 102.0/100 - 1
+print rate_return
+[out]: 0.02
+</pre>
+</div>
+<p>
+  Let's say we bought a stock at $100, and half a year later it will grow to $102. A year later the price will come to $104. How to calculate our total return? Well, we can either deem it as a single-period:
+</p>
+\[r = 104/100 - 1 = 0.04\]
+<p>
+  or as a two-stage period:
+</p>
+\[ r = (1+r_1)*(1+r_2) - 1 = \frac{102}{100} * \frac{104}{102} -1 = 0.04\]
+<p>
+  Here we make calculations twice a year. It's called semi-annual compounding. How about quarterly compounding? Let's assume the stock prices at the end of each quarter are \(p_1, p_2, p_3, p_4\) respectively.
+</p>
+\[r = (1+r_1)*(1+r_2)*(1+r_3)*(1+r_4) -1\]
+<p>
+  The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
+</p>
+<p>
+  Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now!
+</p>
+\[(1+r)^3 = 1+0.65\]
+\[ r = \sqrt[3]{1.65} - 1\ = 0.18167\]
+<p>
+  Strategy A has an higher compounding annual return!
+</p>
+<h4>Logarithm Return</h4>
+<p>
+  We introduced compounding annual return above, which is a kind of <strong>effective rate of return</strong>. You can regard it as a 'hypothetical return'. Strategy B might never have a 18.167% rate of rate annually during the 3-year backtesting period. However, if we assume that the strategy has a 18.167% rate of return every year, it has the same cumulative return over the 3 years. As we mentioned previously, if we assume a strategy is quarterly compounding, the relation between quarterly effective rate of return and annual return is:
+</p>
+\[(1+\frac{r}{4})^4 = 1+r\]
+<p>
+  More generally, if the times of compounding in one year is \(n\) and the annual rate of return is \(r\), the relation is given by:
+</p>
+\[(1+\frac{r}{n})^n = 1+r\]
+<p>
+  Now imagine the stock markets. The prices of your assets is changing every second, or even every millisecond. If the times of compounding, or n, approach to infinite, this is called <strong>continuous compounding</strong>. The calculation formula is given below:
+</p>
+\[\lim_{n \to \infty }(1+\frac{r}{n})^n = e^r\]
+<p>
+  From the above limitation equation, we know that if we assume continuous compounding:
+</p>
+\[e^r = 1 + r = \frac{p_t}{p_0}\]
+<p>
+  Then we take \(ln\) on both side of the equation:
+</p>
+\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
+<p>
+  Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>. This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import quandl
+import numpy as np
+import quandl
+quandl.ApiConfig.api_key = 'zNXvSaz2oX5afVGKjf6o'
+#get quandl data
+aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table.loc['2017-3',['Open','Close']]
+#take log return
+aapl['log_price'] = np.log(aapl.Close)
+aapl['log_return'] = np.log_price.diff()
+print aapl
+</pre>
+</div>
+<p>
+  The output is:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+Date          Open   Close  log_price  log_return
+2017-03-01  137.890  139.79   4.940141         NaN
+2017-03-02  140.000  138.96   4.934186   -0.005955
+2017-03-03  138.780  139.78   4.940070    0.005884
+2017-03-06  139.365  139.34   4.936917   -0.003153
+2017-03-07  139.060  139.52   4.938208    0.001291
+2017-03-08  138.950  139.00   4.934474   -0.003734
+2017-03-09  138.740  138.68   4.932169   -0.002305
+2017-03-10  139.250  139.14   4.935481    0.003311
+2017-03-13  138.850  139.20   4.935912    0.000431
+2017-03-14  139.300  138.99   4.934402   -0.001510
+2017-03-15  139.410  140.46   4.944923    0.010521
+2017-03-16  140.720  140.69   4.946559    0.001636
+2017-03-17  141.000  139.99   4.941571   -0.004988
+2017-03-20  140.400  141.46   4.952017    0.010446
+2017-03-21  142.110  139.84   4.940499   -0.011518
+2017-03-22  139.845  141.42   4.951734    0.011235
+2017-03-23  141.260  140.92   4.948192   -0.003542
+2017-03-24  141.500  140.64   4.946203   -0.001989
+2017-03-27  139.390  140.88   4.947908    0.001705
+2017-03-28  140.910  143.80   4.968423    0.020515
+2017-03-29  143.680  144.12   4.970646    0.002223
+2017-03-30  144.190  143.93   4.969327   -0.001319
+2017-03-31  143.720  143.66   4.967449   -0.001878
+</pre>
+</div>
+
+<p>
+  Here we calculated the daily logarithmic return of Apple stock. Given that we know the daily logarithm return of in this month, we can calculate the monthly return by simply sum all the daily returns up.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">month_return = aapl.log_return.sum()
+print month_return
+[out]: 0.0273081001636
+</pre>
+</div>
+<p>
+  It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
+</p>
+\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
+<p>
+  According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
+</p>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html
new file mode 100755
index 0000000..5fa8705
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html	
@@ -0,0 +1,36 @@
+<h4>Arithmetic Mean</h4>
+<p>
+  Mean is a measure of the central tendency of a data series. It capture the key character of the distribution of the data series. When we talk about mean, by default it refers to <strong>arithmetic mean</strong>. It's defined as the sum of the values divided by the number of observations:
+</p>
+\[\mu = \frac{\sum_{i = 1}^{n}x_i}{n}\]
+<p>
+  Where \((x_1,x_2,x_3.....x_n)\) is our data series.
+</p>
+<p>
+  In python we can use NumPy.mean() to do the calculation:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.mean(aapl.log_price)
+[out]: 4.94597446551
+</pre>
+</div>
+<h4>Geometric Mean</h4>
+<p>
+  The geometric mean is an average that is useful for data series of positive numbers that are better interpreted according to their product, such as growth rate. It's calculated by:
+</p>
+\[\bar{x} = \sqrt[n]{x_1x_2x_3...x_n}\]
+<p>
+  Let's calculate the geometric mean of a series of single-period return:
+</p>
+\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\]
+\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\]
+<p>
+  Now the equation becomes the form which we are familiar with:
+</p>
+\[(1+\bar{r})^n = \frac{p_t}{p_0}\]
+<p>
+  This is why we said it make sense when applied to growth rates.
+</p>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html
new file mode 100755
index 0000000..d933896
--- /dev/null
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html	
@@ -0,0 +1,29 @@
+<h4>Variance</h4>
+<p>
+  <strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by
+</p>
+\[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]
+<p>
+  In python we can use NumPy.var to calculate it:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.var(aapl.log_price)
+</pre>
+</div>
+<h4>Standard Deviation</h4>
+<p>
+  The most commonly used measure of dispersion in finance is <strong>standard deviation</strong>. It's usually represented by \(\sigma\). It's obvious to see the relation between standard deviation and variance:
+</p>
+\[\sigma = \sqrt{\sigma^2} = \sqrt{\frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}}\]
+<p>
+  NumPy also provides us a method to calculate standard deviation.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.std(aapl.log_price)
+[out]: 0.000142032804482
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html
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@@ -0,0 +1,3 @@
+<p>
+  We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those comcepts intensively in our later chapter.
+</p>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/06 Rate of Return, Mean and Variance.html
deleted file mode 100755
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-<h2>Introduction</h2>
-In this chapter we are going to introduce some basic concepts in quantitative finance. We start with rate of return, mean and variance. You may think it's simple to calculate these values, however, there are number of different methods to calculate them. It's important to choose the appropriate calculation methods case by case.
-<h2>Rate of Return</h2>
-<h3>Single-period Return</h3>
-The single-period rate of return can be calculated as following:
-\[r = \frac{p_t}{p_0} - 1 = \frac{p_t - p_0}{p_0}\]
-Where \(r\) is the rate of return, \(p_t\) is the asset price at time \(t\), and \(p_0\) is the asset price at time 0.
-<pre class="prettyprint linenums">
-import numpy as np
-rate_return = 102.0/100 - 1
-print rate_return
-[out]: 0.02
-</pre>
-Let's say we bought a stock at $100, and half a year later it will grow to $102. A year later the price will come to $104. How to calculate our total return?
-Well, we can either deem it as a single-period:
-\[r = 104/100 - 1 = 0.04\]
-or as a two-stage period:
-\[ r = (1+r_1)*(1+r_2) - 1 = \frac{102}{100} * \frac{104}{102} -1 = 0.04\]
-Here we make calculations twice a year. It's called semi-annual compounding. How about quarterly compounding?
-Let's assume the stock prices at the end of each quarter are \(p_1, p_2, p_3, p_4\) respectively.
-\[r = (1+r_1)*(1+r_2)*(1+r_3)*(1+r_4) -1\]
-The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
-Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now!
-\[(1+r)^3 = 1+0.65\]
-\[ r = \sqrt[3]{1.65} - 1\ = 0.18167\]
-Strategy A has an higher compounding annual return!
-<h3>Logarithm Return</h3>
-We introduced compounding annual return above, which is a kind of <strong>effective rate of return</strong>. You can regard it as a 'hypothetical return'. Strategy B might never have a 18.167% rate of rate annually during the 3-year backtesting period. However, if we assume that the strategy has a 18.167% rate of return every year, it has the same cumulative return over the 3 years.
-As we mentioned previously, if we assume a strategy is quarterly compounding, the relation between quarterly effective rate of return and annual return is:
-\[(1+\frac{r}{4})^4 = 1+r\]
-More generally, if the times of compounding in one year is \(n\) and the annual rate of return is \(r\), the relation is given by:
-\[(1+\frac{r}{n})^n = 1+r\]
-Now imagine the stock markets. The prices of your assets is changing every second, or even every millisecond. If the times of compounding, or n, approach to infinite, this is called <strong>continuous compounding</strong>. The calculation formula is given below:
-\[\lim_{n \to \infty }(1+\frac{r}{n})^n = e^r\]
-From the above limitation equation, we know that if we assume continuous compounding:
-\[e^r = 1 + r = \frac{p_t}{p_0}\]
-Then we take \(ln\) on both side of the equation:
-\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
-Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>.
-This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
-<pre class="prettyprint linenums">import quandl
-import numpy as np
-import quandl
-quandl.ApiConfig.api_key = 'zNXvSaz2oX5afVGKjf6o'
-#get quandl data
-aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table.loc['2017-3',['Open','Close']]
-#take log return
-aapl['log_price'] = np.log(aapl.Close)
-aapl['log_return'] = np.log_price.diff()
-print aapl
-</pre>
-The output is:
-<pre class="prettyprint linenums">
-Date          Open   Close  log_price  log_return
-2017-03-01  137.890  139.79   4.940141         NaN
-2017-03-02  140.000  138.96   4.934186   -0.005955
-2017-03-03  138.780  139.78   4.940070    0.005884
-2017-03-06  139.365  139.34   4.936917   -0.003153
-2017-03-07  139.060  139.52   4.938208    0.001291
-2017-03-08  138.950  139.00   4.934474   -0.003734
-2017-03-09  138.740  138.68   4.932169   -0.002305
-2017-03-10  139.250  139.14   4.935481    0.003311
-2017-03-13  138.850  139.20   4.935912    0.000431
-2017-03-14  139.300  138.99   4.934402   -0.001510
-2017-03-15  139.410  140.46   4.944923    0.010521
-2017-03-16  140.720  140.69   4.946559    0.001636
-2017-03-17  141.000  139.99   4.941571   -0.004988
-2017-03-20  140.400  141.46   4.952017    0.010446
-2017-03-21  142.110  139.84   4.940499   -0.011518
-2017-03-22  139.845  141.42   4.951734    0.011235
-2017-03-23  141.260  140.92   4.948192   -0.003542
-2017-03-24  141.500  140.64   4.946203   -0.001989
-2017-03-27  139.390  140.88   4.947908    0.001705
-2017-03-28  140.910  143.80   4.968423    0.020515
-2017-03-29  143.680  144.12   4.970646    0.002223
-2017-03-30  144.190  143.93   4.969327   -0.001319
-2017-03-31  143.720  143.66   4.967449   -0.001878
-</pre>
-
-Here we calculated the daily logarithmic return of Apple stock. Given that we know the daily logarithm return of in this month, we can calculate the monthly return by simply sum all the daily returns up.
-<pre class="prettyprint linenums">month_return = aapl.log_return.sum()
-print month_return
-[out]: 0.0273081001636
-</pre>
-It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
-\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
-According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
-<h2>Mean</h2>
-<h3>Arithmetic Mean</h3>
-Mean is a measure of the central tendency of a data series. It capture the key character of the distribution of the data series. When we talk about mean, by default it refers to <strong>arithmetic mean</strong>. It's defined as the sum of the values divided by the number of observations:
-\[\mu = \frac{\sum_{i = 1}^{n}x_i}{n}\]
-Where \((x_1,x_2,x_3.....x_n)\) is our data series.
-In python we can use NumPy.mean() to do the calculation:
-<pre class="prettyprint linenums">print np.mean(aapl.log_price)
-[out]: 4.94597446551
-</pre>
-<h3>Geometric Mean</h3>
-The geometric mean is an average that is useful for data series of positive numbers that are better interpreted according to their product, such as growth rate. It's calculated by:
-\[\bar{x} = \sqrt[n]{x_1x_2x_3...x_n}\]
-Let's calculate the geometric mean of a series of single-period return:
-\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\]
-\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\]
-Now the equation becomes the form which we are familiar with:
-\[(1+\bar{r})^n = \frac{p_t}{p_0}\]
-This is why we said it make sense when applied to growth rates.
-<h2>Variance and Standard Deviation</h2>
-<h3>Variance</h3>
-<strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by
-\[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]
-In python we can use NumPy.var to calculate it:
-<pre class="prettyprint linenums">print np.var(aapl.log_price)
-</pre>
-<h3>Standard Deviation</h3>
-The most commonly used measure of dispersion in finance is <strong>standard deviation</strong>. It's usually represented by \(\sigma\). It's obvious to see the relation between standard deviation and variance:
-\[\sigma = \sqrt{\sigma^2} = \sqrt{\frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}}\]
-NumPy also provides us a method to calculate standard deviation.
-<pre class="prettyprint linenums">print np.std(aapl.log_price)
-[out]: 0.000142032804482
-</pre>
-<h2>Summary</h2>
-We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those comcepts intensively in our later chapter.
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/01 Introduction.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/01 Introduction.html
new file mode 100755
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--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the last chapter we learned the definition of mean and variance, which are kind of <strong>point estimation</strong>. Point estimation means using sample data to calculate a single value which is to serve as a 'best estimation' of an unknown population. However, this it not enough because point estimations can be deceiving. We need to use more rigorous methods to test our ideas. That's why we consider distribution and hypothesis testing. Random variable distribution is the basis for almost all quantitative finance topics: linear regression, CAPM, Black-Scholes, binomial tree pricing, etc.
+</p>
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html
new file mode 100755
index 0000000..30c1882
--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html	
@@ -0,0 +1,3 @@
+<p>
+  First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}. The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
+</p>
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html
new file mode 100755
index 0000000..24d0692
--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html	
@@ -0,0 +1,187 @@
+<p>
+  Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\). However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We we cover this later. For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It defines as \(P(X&lt;x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
+</p>
+
+<h4>Uniform Distribution</h4>
+<p>
+  Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions. Here we use python to simulate rolling a dice 10000 times.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import random
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+#define a function to simulate rolling a dice
+def dice():
+    number=  [1,2,3,4,5,6]
+    return random.choice(number)
+
+series = np.array([dice() for x in range(10000)])
+print series
+</pre>
+</div>
+
+<p>
+  We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+plt.hist(series,bins = 11,align = 'mid')
+plt.xlabel('Dice Number')
+plt.ylabel('Occurences')
+plt.grid()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot1.png" alt="plot1" />
+<p>
+  Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print len([x for x in series if x &lt;= 3])/float(len(series))
+[out]: 0.4956
+print np.mean(series)
+[out]: 3.5103
+</pre>
+</div>
+<p>
+  \(P(X&lt;3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as
+</p>
+\[\mu = (1+2+3+4+5+6)/6 = 3.5\]
+<p>
+  Or
+</p>
+\[E(x) = 1*\frac{1}{6} + 2*\frac{1}{6}+...+6*\frac{1}{6} = 3.5\]
+<p>
+  More generally, if we a assume the minimum value in a uniform distribution is a and the maximum value is b, the mean can be given by:
+</p>
+\[\bar{u} = \frac{a+b}{2}\]
+<p>
+  Usually we use \(\bar{u}\) to represents the <strong>population mean</strong>, or the 'real mean'. Here we create a sample with 1000 observations, the mean we calculated above is the <strong>sample mean</strong>. Sample mean usually doesn't equal to the theoretical population mean unless the number of observation approaches to infinity. The variance is given by:
+</p>
+\[\sigma^2 = \frac{(b-a)^2}{12}\]
+<p>
+  Deducing the formula is out of our lecture scope. It's useful to realize for a given standard distribution, we can formularize its mean and variance.
+</p>
+<h4>Binomial Distribution</h4>
+<p>
+  A binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent experiments. Let's assume that the market has 50% probability goes up and 50% probability goes down, and we observe it in the next 10 days, what's the distribution of the number of days it goes up? This is a binomial distribution example. In general, if we carry out the experiment n times, and each outcome is independent, with the same probability of success p, the probability of getting exactly k successes is given by the function:
+</p>
+
+\[P(X = K) = C_n^k p^k (1-p)^{n-k}\]
+<p>
+  Where
+</p>
+\[C_n^k = \frac{n!}{(n-k)!k!}\]
+<p>
+  Under such circumstance we say X follows the binomial distribution \(X \sim B(n,p)\). Let's simulate a binomial experiment with success rate p = 0.7 and experiment times n = 10
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def trial():
+    number = [1,2,3,4,5,6,7,8,9,10]
+    a = random.choice(number)
+    if a&lt;= 7:
+        return 1
+    else:
+        return 0
+</pre>
+</div>
+<p>
+  Each time we execute trial(), we did an experiment. If it succeed, it will return 1, otherwise it will return 0. Now we are going to do the experiment 10 times:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">res = [trial() for x in range(10)]
+print sum(res)
+[out]: 7
+</pre>
+</div>
+<p>
+  Now we did the experiment 10 times, and the number of success is sum(res). However, it just means during these 10 experiments we succeed sum(res) times. If we want to see the binomial distribution, we need experiment N times. When n is large enough, our frequency will approach the theoretical probability. Here we simulate each outcome 10000 times:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def binomial(number):
+    l = []
+    for i in range(10000):
+        res = [trial() for x in range(10)]
+        l.append(sum(res))
+    return len([x for x in l if x == number])/float(len(l))
+print binomial(8)
+[out]: 0.2367
+</pre>
+</div>
+<p>
+  The number printed above is the simulated probability that we succeed 8 times if we experiment 10 times. For each possible outcome, we simulate the probability:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">prob = []
+for i in range(1,11):
+    prob.append(binomial(i))
+prob_s = pd.Series(prob,index = range(1,11))
+print prob_s
+[out]: 1     0.0002
+       2     0.0013
+       3     0.0087
+       4     0.0373
+       5     0.1041
+       6     0.2000
+       7     0.2674
+       8     0.2342
+       9     0.1153
+       10    0.0283
+</pre>
+</div>
+<p>
+  Here we got the simulated result of the binomial distribution. Now we are going to check if the simulated frequencies close enough to the theoretical probabilities. Let's take X = 7 and X = 8 as example:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)
+[out]: 0.266827932
+print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)
+[out]: 0.2334744405
+</pre>
+</div>
+<p>
+  As we can see, the simulated results are pretty close to the real probability. we can plot the results as follows:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+plt.bar(range(1,11),prob)
+plt.grid()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot2.png" alt="plot2" />
+<p>
+  Another good property of binomial distribution is that its mean and variance are simple enough:
+</p>
+\[\bar{u} = np\]
+\[\sigma^2 = np(1-P)\]
+<p>
+  We will not introduce the deduction here, but if you are interested in it, we encourage you to do it yourself, based on the probability functions we provided above.
+</p>
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html
new file mode 100755
index 0000000..f0f612a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html	
@@ -0,0 +1,83 @@
+<p>
+  Before looking at normal distribution, let's first talk about continues distribution. As we mentioned above, we use a <strong>probability density function</strong>(PDF) to model the probability that our value is taken our a specific range. We define it as:
+</p>
+\[P(a&lt;X&lt;b) = \int_{a}^{b}f_x(x)dx\]
+<p>
+  Now we can talk about the normal distribution. The <strong>normal distribution</strong> is most commonly used distribution in natural sciences, of course also in financial research. The PDF of normal distribution is given as follows:
+</p>
+\[f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\]
+<p>
+  Where \(\mu\) is the mean of the normal distribution, and \(\sigma\) is the standard deviation.
+</p>
+<p>
+  Generally, if a random variable X follows normal distribution, we represent it by \(X\sim N(\mu, \sigma^2)\). Specifically, if a normal distribution has a 0 mean and 1 standard deviation, we called it <strong>standard normal distribution</strong>. Now let's simulate a standard normal distribution using Python packages to see what it looks like:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+norm.plot.density()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot3.png" alt="plot3" />
+<p>
+  Financial data is highly disordered and is considered to has lots of noise. Most of the time we believe those noise follows normal distribution. It's also widely believed that the return on an asset over a short period of time follows normal distribution. Let's check it with the daily logarithm rates of return on SPY:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import quandl
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+spy_table = quandl.get('BCIW/_SPXT')
+spy = spy_table.loc['2009':'2017',['Open','Close']]
+spy['log_return'] = np.log(spy.Close).diff()
+spy = spy.dropna()
+</pre>
+</div>
+<p>
+  We calculated the logarithm daily return of S&amp;P 500 index from 2009 to present. Let's first have a look at the what the time-series return data looks like:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+spy.log_return.plot()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot4.png" alt="plot4" />
+<p>
+  This is a classic daily return chart. Let's now plot the density chart of the returns:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+spy.log_return.plot.density()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot5.png" alt="plot5" />
+<p>
+  If we observe the x-axis and y-axis carefully, we can see the return of asset is not a standard normal distribution. The peak of the standard normal distribution plot is around 0.4, while it's over 0.6 for this chart. This is because the standard deviation \(\sigma\) of the return is obviously not 1. We can demonstrate the normal distribution with different mean and variance by simulation:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
+de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')
+de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')
+mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')
+df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)
+plt.figure(figsize=(20,10))
+df.plot.density()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot6.png" alt="plot6" />
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/05 Summary.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/05 Summary.html
new file mode 100755
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--- /dev/null
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/05 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we introduced random variable, the difference between discrete random distribution and continuous random distribution, and most importantly, normal distribution. In the next chapter we will introduce how to use these distributions to test our idea or generating trading signals.
+</p>
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html
deleted file mode 100755
index 41e139b..0000000
--- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/07 Random Variables and Distributions.html	
+++ /dev/null
@@ -1,167 +0,0 @@
-<h2>Introduction</h2>
-In the last chapter we learned the definition of mean and variance, which are kind of <strong>point estimation</strong>. Point estimation means using sample data to calculate a single value which is to serve as a 'best estimation' of an unknown population. However, this it not enough because point estimations can be deceiving. We need to use more rigorous methods to test our ideas. That's why we consider distribution and hypothesis testing. Random variable distribution is the basis for almost all quantitative finance topics: linear regression, CAPM, Black-Scholes, binomial tree pricing, etc.
-<h2>Random Variables</h2>
-First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}.
-The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
-<h2>Distributions</h2>
-Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\).
-However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We we cover this later.
-For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It defines as \(P(X&lt;x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
-<h3>Uniform Distribution</h3>
-Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions.
-Here we use python to simulate rolling a dice 10000 times.
-<pre class="prettyprint linenums">import random
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-#define a function to simulate rolling a dice
-def dice():
-    number=  [1,2,3,4,5,6]
-    return random.choice(number)
-
-series = np.array([dice() for x in range(10000)])
-print series
-</pre>
-We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.hist(series,bins = 11,align = 'mid')
-plt.xlabel('Dice Number')
-plt.ylabel('Occurences')
-plt.grid()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot1.png" alt="plot1" />
-Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
-<pre class="prettyprint linenums">print len([x for x in series if x &lt;= 3])/float(len(series))
-[out]: 0.4956
-print np.mean(series)
-[out]: 3.5103
-</pre>
-\(P(X&lt;3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5.
-For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as
-\[\mu = (1+2+3+4+5+6)/6 = 3.5\]
-Or
-\[E(x) = 1*\frac{1}{6} + 2*\frac{1}{6}+...+6*\frac{1}{6} = 3.5\]
-More generally, if we a assume the minimum value in a uniform distribution is a and the maximum value is b, the mean can be given by:
-\[\bar{u} = \frac{a+b}{2}\]
-Usually we use \(\bar{u}\) to represents the <strong>population mean</strong>, or the 'real mean'. Here we create a sample with 1000 observations, the mean we calculated above is the <strong>sample mean</strong>. Sample mean usually doesn't equal to the theoretical population mean unless the number of observation approaches to infinity.
-The variance is given by:
-\[\sigma^2 = \frac{(b-a)^2}{12}\]
-Deducing the formula is out of our lecture scope. It's useful to realize for a given standard distribution, we can formularize its mean and variance.
-<h3>Binomial Distribution</h3>
-A binomial distribution is a discrete probability distribution of the number of successes in a sequence of n independent experiments. Let's assume that the market has 50% probability goes up and 50% probability goes down, and we observe it in the next 10 days, what's the distribution of the number of days it goes up? This is a binomial distribution example.
-In general, if we carry out the experiment n times, and each outcome is independent, with the same probability of success p, the probability of getting exactly k successes is given by the function:
-\[P(X = K) = C_n^k p^k (1-p)^{n-k}\]
-Where
-\[C_n^k = \frac{n!}{(n-k)!k!}\]
-Under such circumstance we say X follows the binomial distribution \(X \sim B(n,p)\).
-Let's simulate a binomial experiment with success rate p = 0.7 and experiment times n = 10
-<pre class="prettyprint linenums">def trial():
-    number = [1,2,3,4,5,6,7,8,9,10]
-    a = random.choice(number)
-    if a&lt;= 7:
-        return 1
-    else:
-        return 0
-</pre>
-Each time we execute trial(), we did an experiment. If it succeed, it will return 1, otherwise it will return 0. Now we are going to do the experiment 10 times:
-<pre class="prettyprint linenums">res = [trial() for x in range(10)]
-print sum(res)
-[out]: 7
-</pre>
-Now we did the experiment 10 times, and the number of success is sum(res). However, it just means during these 10 experiments we succeed sum(res) times. If we want to see the binomial distribution, we need experiment N times. When n is large enough, our frequency will approach the theoretical probability. Here we simulate each outcome 10000 times:
-<pre class="prettyprint linenums">def binomial(number):
-    l = []
-    for i in range(10000):
-        res = [trial() for x in range(10)]
-        l.append(sum(res))
-    return len([x for x in l if x == number])/float(len(l))
-
-print binomial(8)
-[out]: 0.2367
-</pre>
-The number printed above is the simulated probability that we succeed 8 times if we experiment 10 times.
-for each possible outcome, we simulate the probability:
-<pre class="prettyprint linenums">prob = []
-for i in range(1,11):
-    prob.append(binomial(i))
-prob_s = pd.Series(prob,index = range(1,11))
-print prob_s
-[out]: 1     0.0002
-       2     0.0013
-       3     0.0087
-       4     0.0373
-       5     0.1041
-       6     0.2000
-       7     0.2674
-       8     0.2342
-       9     0.1153
-       10    0.0283
-</pre>
-Here we got the simulated result of the binomial distribution. Now we are going to check if the simulated frequencies close enough to the theoretical probabilities. Let's take X = 7 and X = 8 as example:
-<pre class="prettyprint linenums">print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)
-[out]: 0.266827932
-print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)
-[out]: 0.2334744405
-</pre>
-As we can see, the simulated results are pretty close to the real probability!
-we can plot the results as follows:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.bar(range(1,11),prob)
-plt.grid()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot2.png" alt="plot2" />
-Another good property of binomial distribution is that its mean and variance are simple enough:
-\[\bar{u} = np\]
-\[\sigma^2 = np(1-P)\]
-We will not introduce the deduction here, but if you are interested in it, we encourage you to do it yourself, based on the probability functions we provided above.
-<h2>Normal Distribution</h2>
-Before looking at normal distribution, let's first talk about continues distribution. As we mentioned above, we use a <strong>probability density function</strong>(PDF) to model the probability that our value is taken our a specific range. We define it as:
-\[P(a&lt;X&lt;b) = \int_{a}^{b}f_x(x)dx\]
-Now we can talk about the normal distribution. The <strong>normal distribution</strong> is most commonly used distribution in natural sciences, of course also in financial research. The PDF of normal distribution is given as follows:
-\[f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^2}{2\sigma^2}}\]
-Where \(\mu\) is the mean of the normal distribution, and \(\sigma\) is the standard deviation.
-Generally, if a random variable X follows normal distribution, we represent it by \(X\sim N(\mu, \sigma^2)\).
-Specifically, if a normal distribution has a 0 mean and 1 standard deviation, we called it <strong>standard normal distribution</strong>.
-Now let's simulate a standard normal distribution using Python packages to see what it looks like:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-norm.plot.density()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot3.png" alt="plot3" />
-Financial data is highly disordered and is considered to has lots of noise. Most of the time we believe those noise follows normal distribution. It's also widely believed that the return on an asset over a short period of time follows normal distribution. Let's check it with the daily logarithm rates of return on SPY:
-<pre class="prettyprint linenums">import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-spy_table = quandl.get('BCIW/_SPXT')
-spy = spy_table.loc['2009':'2017',['Open','Close']]
-spy['log_return'] = np.log(spy.Close).diff()
-spy = spy.dropna()
-</pre>
-we calculated the logarithm daily return of S&amp;P 500 index from 2009 to present.
-Let's first have a look at the what the time-series return data looks like:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-spy.log_return.plot()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot4.png" alt="plot4" />
-This is a classic daily return chart. Let's now plot the density chart of the returns:
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-spy.log_return.plot.density()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot5.png" alt="plot5" />
-If we observe the x-axis and y-axis carefully, we can see the return of asset is not a standard normal distribution. The peak of the standard normal distribution plot is around 0.4, while it's over 0.6 for this chart. This is because the standard deviation \(\sigma\) of the return is obviously not 1.
-We can demonstrate the normal distribution with different mean and variance by simulation:
-<pre class="prettyprint linenums">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
-de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')
-de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')
-mu_1 = pd.Series(np.random.normal(-2,1,10000),name ='μ = -2, σ = 1')
-df = pd.concat([de_2,de_3,de_0,mu_1],axis = 1)
-plt.figure(figsize=(20,10))
-df.plot.density()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot6.png" alt="plot6" />
-<h2>Summary</h2>
-In this chapter we introduced random variable, the difference between discrete random distribution and continuous random distribution, and most importantly, normal distribution. In the next chapter we will introduce how to use these distributions to test our idea or generating trading signals.
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/01 Introduction.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/01 Introduction.html
new file mode 100755
index 0000000..fd72d46
--- /dev/null
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
+</p>
+<p>
+  As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
+</p>
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
new file mode 100755
index 0000000..272b972
--- /dev/null
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -0,0 +1,110 @@
+<h4>Sample Error</h4>
+<p>
+  Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import quandl
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+#get data from quandl
+spy_table = quandl.get('BCIW/_SPXT')
+spy_total = spy_table[['Open','Close']]
+#calculate log returns
+spy_log_return = np.log(spy_total.Close).diff().dropna()
+print 'Population mean:', np.mean(spy_log_return)
+[out]: Population mean: 0.000443353825615
+print 'Population standard deviation:',np.std(spy_log_return)
+[out]: Population standard deviation: 0.00784267293815
+</pre>
+</div>
+
+<p>
+  Now let's check the recent 10 days sample and recent 1000 days sample:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+[out]: 10 days sample returns: 0.000845189915474
+print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+[out]: 10 days sample standard deviation: 0.00313558001122
+print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+[out]: 1000 days sample returns: 0.000462827047221
+print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+[out]: 1000 days sample standard deviation: 0.00766589174299
+</pre>
+</div>
+<p>
+  As we expected, the two samples has different means and variances.
+</p>
+
+<h4>Confidence Interval</h4>
+<p>
+  In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
+</p>
+\[SE = \frac{\sigma}{\sqrt{n}}\]
+<p>
+  Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
+</p>
+<p>
+  Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
+</p>
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+<p>
+  Where \(\mu\) is the sample mean and SE is the standard error.
+</p>
+<p>
+  This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+#apply the formula above to calculate confidence interval
+bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+#print the outcomes
+print '10 days 95% confidence inverval:', (bottom_1,upper_1)
+[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
+print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
+[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
+</pre>
+</div>
+
+<p>
+  As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
+</p>
+
+<h4>Confidence Interval of Normal Distribution</h4>
+<p>
+  Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
+</p>
+\[\%90 upperabnd = \mu + 1.64*SE\]
+\[\%90 lowerband = \mu + 1.64*SE\]
+<p>
+  The same for other confidence intervals. It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
+</p>
+\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
+\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
+\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
+<p>
+  This can also be remembered by using the chart:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-empirical-rule.png" alt="empirical rule" />
+<h4>Central Limit Theory</h4>
+<p>
+  As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
+</p>
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+<p>
+  Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution? The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed. This is the foundation of population mean confidence interval estimation.
+</p>
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html
new file mode 100755
index 0000000..0cd2597
--- /dev/null
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html	
@@ -0,0 +1,100 @@
+<p>
+  Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample. Let's start from observing our sample:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">mean_1000 = np.mean(spy_log_return.tail(1000))
+std_1000 = np.std(spy_log_return.tail(1000))
+mean_10 = np.mean(spy_log_return.tail(10))
+std_10 = np.std(spy_log_return.tail(10))
+s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
+print s
+
+[out]: mean_10      0.000845
+       std_10       0.003136
+       mean_1000    0.000463
+       std_1000     0.007666
+</pre>
+</div>
+<p>
+  We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
+upper = 0 + 1.64*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00039756352254768874, 0.00039756352254768874)
+</pre>
+</div>
+<p>
+  Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero. Can we claim that with 95% confidence level?
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
+upper = 0 + 1.96*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00047513689280089639, 0.00047513689280089639)
+</pre>
+</div>
+<p>
+  This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level. We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
+</p>
+\[H_0:\bar{\mu} = 0\]
+\[H_0:\bar{\mu} \neq 0\]
+<p>
+  If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis. Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
+</p>
+\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
+<p>
+  Let's calculate the Z score from our sample:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+[out]: 1.90922032428
+</pre>
+</div>
+<p>
+  We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis. One reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import scipy.stats as st
+print (1 - st.norm.cdf(1.9488))
+[out]: 0.025659656888
+</pre>
+</div>
+<p>
+  It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-confidence-interval.jpg" alt="confidence interval" />
+<p>
+  If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
+std_1200 = np.std(spy_log_return.tail(1200))
+z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
+print 'z-score = ',z_score
+[out]: z-score =  2.19793023185
+p_value = (1 - st.norm.cdf(z_score))
+print 'p_value = ',p_value
+[out]: p_value =  0.0139770390655
+</pre>
+</div>
+<p>
+  Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
+</p>
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html
new file mode 100755
index 0000000..5bde740
--- /dev/null
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html	
@@ -0,0 +1,225 @@
+<h2>Introduction</h2>
+<p>
+  In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
+</p>
+<p>
+  As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
+</p>
+
+<h2>Confidence Interval</h2>
+<h3>Sample Error</h3>
+<p>
+  Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import quandl
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+#get data from quandl
+spy_table = quandl.get('BCIW/_SPXT')
+spy_total = spy_table[['Open','Close']]
+#calculate log returns
+spy_log_return = np.log(spy_total.Close).diff().dropna()
+print 'Population mean:', np.mean(spy_log_return)
+[out]: Population mean: 0.000443353825615
+print 'Population standard deviation:',np.std(spy_log_return)
+[out]: Population standard deviation: 0.00784267293815
+</pre>
+</div>
+
+<p>
+  Now let's check the recent 10 days sample and recent 1000 days sample:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+[out]: 10 days sample returns: 0.000845189915474
+print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+[out]: 10 days sample standard deviation: 0.00313558001122
+print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+[out]: 1000 days sample returns: 0.000462827047221
+print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+[out]: 1000 days sample standard deviation: 0.00766589174299
+</pre>
+</div>
+<p>
+  As we expected, the two samples has different means and variances.
+</p>
+
+<h3>Confidence Interval</h3>
+<p>
+  In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
+</p>
+\[SE = \frac{\sigma}{\sqrt{n}}\]
+<p>
+  Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
+</p>
+<p>
+  Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
+</p>
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+<p>
+  Where \(\mu\) is the sample mean and SE is the standard error.
+</p>
+<p>
+  This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+#apply the formula above to calculate confidence interval
+bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
+bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
+#print the outcomes
+print '10 days 95% confidence inverval:', (bottom_1,upper_1)
+[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
+print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
+[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
+</pre>
+</div>
+
+<p>
+  As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
+</p>
+
+<h3>Confidence Interval of Normal Distribution</h3>
+<p>
+  Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
+</p>
+\[\%90 upperabnd = \mu + 1.64*SE\]
+\[\%90 lowerband = \mu + 1.64*SE\]
+<p>
+  The same for other confidence intervals. It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
+</p>
+\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
+\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
+\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
+<p>
+  This can also be remembered by using the chart:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-empirical-rule.png" alt="empirical rule" />
+<h3>Central Limit Theory</h3>
+<p>
+  As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
+</p>
+\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
+<p>
+  Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution? The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed. This is the foundation of population mean confidence interval estimation.
+</p>
+
+<h2>Hypothesis testing</h2>
+<p>
+  Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample. Let's start from observing our sample:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">mean_1000 = np.mean(spy_log_return.tail(1000))
+std_1000 = np.std(spy_log_return.tail(1000))
+mean_10 = np.mean(spy_log_return.tail(10))
+std_10 = np.std(spy_log_return.tail(10))
+s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
+print s
+
+[out]: mean_10      0.000845
+       std_10       0.003136
+       mean_1000    0.000463
+       std_1000     0.007666
+</pre>
+</div>
+<p>
+  We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
+upper = 0 + 1.64*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00039756352254768874, 0.00039756352254768874)
+</pre>
+</div>
+<p>
+  Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero. Can we claim that with 95% confidence level?
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
+upper = 0 + 1.96*std_1000/np.sqrt(1000)
+print (bottom, upper)
+[out]: (-0.00047513689280089639, 0.00047513689280089639)
+</pre>
+</div>
+<p>
+  This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level. We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
+</p>
+\[H_0:\bar{\mu} = 0\]
+\[H_0:\bar{\mu} \neq 0\]
+<p>
+  If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis. Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
+</p>
+\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
+<p>
+  Let's calculate the Z score from our sample:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+[out]: 1.90922032428
+</pre>
+</div>
+<p>
+  We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis. One reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import scipy.stats as st
+print (1 - st.norm.cdf(1.9488))
+[out]: 0.025659656888
+</pre>
+</div>
+<p>
+  It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-confidence-interval.jpg" alt="confidence interval" />
+<p>
+  If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
+std_1200 = np.std(spy_log_return.tail(1200))
+z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
+print 'z-score = ',z_score
+[out]: z-score =  2.19793023185
+p_value = (1 - st.norm.cdf(z_score))
+print 'p_value = ',p_value
+[out]: p_value =  0.0139770390655
+</pre>
+</div>
+<p>
+  Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
+</p>
+<h2>Summary</h2>
+<p>
+  In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.
+</p>
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/08 Confidence Interval and Hypothesis Testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/08 Confidence Interval and Hypothesis Testing.html
deleted file mode 100755
index c9c23cc..0000000
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/08 Confidence Interval and Hypothesis Testing.html	
+++ /dev/null
@@ -1,134 +0,0 @@
-<h2>Introduction</h2>
-In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
-
-As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
-<h2>Confidence Interval</h2>
-<h3>Sample Error</h3>
-Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
-#calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
-[out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
-[out]: Population standard deviation: 0.00784267293815
-</pre>
-Now let's check the recent 10 days sample and recent 1000 days sample:
-<pre class="prettyprint linenums">
-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
-[out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
-[out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
-[out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
-[out]: 1000 days sample standard deviation: 0.00766589174299
-</pre>
-As we expected, the two samples has different means and variances.
-<h3>Confidence Interval</h3>
-In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
-\[SE = \frac{\sigma}{\sqrt{n}}\]
-Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
-Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-Where \(\mu\) is the sample mean and SE is the standard error.
-This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later.
-Let's try to calculate the confidence interval using the samples above:
-<pre class="prettyprint linenums">
-#apply the formula above to calculate confidence interval
-bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-#print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
-[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
-[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
-</pre>
-As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
-<h3>Confidence Interval of Normal Distribution</h3>
-Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
-\[\%90 upperabnd = \mu + 1.64*SE\]
-\[\%90 lowerband = \mu + 1.64*SE\]
-The same for other confidence intervals.
-It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
-\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
-\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
-\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
-This can also be remembered by using the chart:
-<img class="alignnone size-medium" src="https://upload.wikimedia.org/wikipedia/commons/a/a9/Empirical_Rule.PNG" width="769" height="558" />
-<h3>Central Limit Theory</h3>
-As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution?
-The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed.
-This is the foundation of population mean confidence interval estimation.
-<h2>Hypothesis testing</h2>
-Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample.
-Let's start from observing our sample:
-<pre class="prettyprint linenums">mean_1000 = np.mean(spy_log_return.tail(1000))
-std_1000 = np.std(spy_log_return.tail(1000))
-mean_10 = np.mean(spy_log_return.tail(10))
-std_10 = np.std(spy_log_return.tail(10))
-s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
-
-[out]: mean_10      0.000845
-       std_10       0.003136
-       mean_1000    0.000463
-       std_1000     0.007666
-</pre>
-We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
-<pre class="prettyprint linenums">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
-upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00039756352254768874, 0.00039756352254768874)
-</pre>
-Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero.
-Can we claim that with 95% confidence level?
-<pre class="prettyprint linenums">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
-upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00047513689280089639, 0.00047513689280089639)
-</pre>
-This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level.
-We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
-\[H_0:\bar{\mu} = 0\]
-\[H_0:\bar{\mu} \neq 0\]
-If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis.
-Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
-\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
-Let's calculate the Z score from our sample:
-<pre class="prettyprint linenums">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
-[out]: 1.90922032428
-</pre>
-We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis.
-one reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
-<pre class="prettyprint linenums">import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
-[out]: 0.025659656888
-</pre>
-It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
-<img class="alignnone " src="http://napitupulu-jon.appspot.com/galleries/coursera-statistics/3w76.jpg" width="663" height="517" />
-If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level.
-Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
-<pre class="prettyprint linenums">mean_1200 = np.mean(spy_log_return.tail(1200))
-std_1200 = np.std(spy_log_return.tail(1200))
-z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
-[out]: z-score =  2.19793023185
-p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
-[out]: p_value =  0.0139770390655
-</pre>
-Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
-<h2>Summary</h2>
-In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html
new file mode 100755
index 0000000..982ac0e
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html	
@@ -0,0 +1,79 @@
+<p>
+  In finance and economics filed, most of the models are linear ones. We can see linear regression everywhere, from the foundation of the model portfolio theory to the nowadays popular Fama-French asset pricing model. It's very important to understand how linear regression works in order to have a comprehensive understanding of those theories.
+</p>
+<p>
+  If we are holding a stock, we must be curious about the relationship between our stock return and the market return. Let's say we hold Amazon stock on the first day of this year. In order to see the relation directly, we plot the daily return of our stock on the y-axis and plot the S&amp;P 500 index daily return on the x-axis.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import numpy as np
+import pandas as pd
+import quandl
+quandl.ApiConfig.api_key = '_fgkxjSbt5389zGt4crC'
+#get data from quandl
+spy_table = quandl.get('BCIW/_SPXT')
+amzn_table = quandl.get('WIKI/AMZN')
+#fetch data from Jan 2017 to Jun 2017
+spy = spy_table.loc['2017':'2017-6',['Close']]
+amzn = amzn_table.loc['2017':'2017-6',['Close']]
+#calculate log return
+spy_log = np.log(spy.Close).diff().dropna()
+amzn_log = np.log(amzn.Close).diff().dropna()
+df = pd.concat([spy_log,amzn_log],axis = 1).dropna()
+df.columns = ['spx','amzn']
+print df.tail()
+</pre>
+</div>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="center">spy</td>
+        <td align="center">amzn</td>
+    </tr>
+    <tr>
+        <td>2016-12-22</td>
+        <td align="center">-0.004462</td>
+        <td align="center">-0.005543</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.001372</td>
+        <td align="center">-0.007531</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.000928</td>
+        <td align="center">0.000946</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">-0.005671</td>
+        <td align="center">-0.009081</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="center">0.002086</td>
+        <td align="center">-0.020172</td>
+    </tr>
+
+</table>
+<p>
+  We successfully create a DataFrame contains the daily logarithm return of Amazon stock and S&amp;P500. Now let's plot it:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import matplotlib.pyplot as plt
+plt.figure(figsize = (15,10))
+plt.scatter(df.spy,df.amzn)
+plt.show()
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot1.png" alt="plot1" />
+<p>
+  The plot is scattered, but we can see they are approximately correlated: generally the higher SPX's daily return is, the higher Amazon stock's return is. This is called <strong>positively correlated</strong>. We will cover it in the following tutorials.
+</p>
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/02 Slope and Intercept.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/02 Slope and Intercept.html
new file mode 100755
index 0000000..f1d15b8
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/02 Slope and Intercept.html	
@@ -0,0 +1,16 @@
+<p>
+  It's natural that we want to model the relation between these two rates of return. Intuitively we use a straight line to model it, this is called <strong>Linear Regression</strong>. In order to find the best straight line, it's natural to think that the vertical distances between the points of the data set and the fitted line should be minimized. Those vertical distances are called <strong>residual</strong>. Our objective is to make the sum of squared residuals as small as possible. This method is called <strong>ordinary least square</strong>, or <strong>OLS</strong> method. We use x and y to represent the two variable, S&amp;P 500 daily returns and AMZN daily returns. The linear relation is:
+</p>
+
+\[Y = Y = \alpha + \beta*X + \epsilon\]
+<p>
+  Where \(\alpha\) is called <strong>intercept</strong>, \(\beta\) is called <strong>slope</strong>. Generally, if the scatter points can be represented by\(\left\{\right (x_1,y_1),(x_2, y_2),(x_3,y_3)...(x_n,y_n) \left\}\right\), then the intercept and slope are given by:
+</p>
+\[\beta = \frac{\sum_{i=1}^{n}(x-\bar{x})(y-\bar{y})}{\sum_{i=1}^{n}(x-\bar{x})^2}\]
+\[\alpha = \bar{y} - \hat{\beta}\bar{x}\]
+<p>
+  Where \(\bar{x}\) is the mean of X, \(\bar{y}\) is the mean of Y.
+</p>
+<p>
+  In python, we don't need to do the above calculation manually because we have package for it. But it still very important to understand the calculation process of \(\beta\) in order the understand the modern portfolio theory and CAPM, which we will cover in the future.
+</p>
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html
new file mode 100755
index 0000000..b76aeb5
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html	
@@ -0,0 +1,54 @@
+<p>
+  In python, we have a very power package for mathematical models, which is named 'statsmodels'.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import statsmodels.formula.api as sm
+model = sm.ols(formula = 'amzn~spy',data = df).fit()
+print model.summary()
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot2.png" alt="plot2" />
+<p>
+  We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 'pamameters: ',model.params
+[out]: pamameters:  Intercept    0.000012
+                   spy          0.492112
+print 'residual: ', model.resid.tail()
+[out]: residual:  Date
+2016-12-22   -0.003360
+2016-12-23   -0.008219
+2016-12-28    0.000477
+2016-12-29   -0.006303
+2016-12-30   -0.021211
+print 'fitted values: ',model.predict()
+[out]: fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]
+</pre>
+</div>
+
+<p>
+  Now let's have a look at our fitted line:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (15,10))
+plt.scatter(df.spy,df.amzn)
+plt.xlabel('spx_return')
+plt.ylabel('amzn_return')
+plt.plot(df.spy,model.predict(),color = 'red')
+plt.show()
+</pre>
+</div>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-head.png" alt="head" />
+<p>
+  The red line is the fitted linear regression straight line. As we can see there are lot of statistical results in the summary table. Now let's talk about some important statistical parameters.
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-regression-table.png" alt="regression table" />
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/04 Parameter Significance.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/04 Parameter Significance.html
new file mode 100755
index 0000000..5f85774
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/04 Parameter Significance.html	
@@ -0,0 +1,11 @@
+<p>
+  From the summary table we can see 'std err'. This means standard errors of the intercept and slope. The null hypothesis here is \(H_0: \beta = 0\), and the alternative hypothesis is \(H_1: \beta \neq 0\). This hypothesis score is calculated by:
+</p>
+\[t = \frac{\beta - 0}{SE}\]
+<p>
+  Where SE is given by:
+</p>
+\[SE = \sqrt\frac{\frac{1}{n-2}\sum_{i = 1}^{n}\hat{\epsilon}^2}{\sum_{i = i}^{n}(x_i - \bar{x})^2}\]
+<p>
+  The distribution used here is 'Student's t-distribution'. It's different from normal distribution but used in the similar way. The column 't' in this table is the test score, and 'p&gt;|t|' is the p-value. By observing the p-value, we can see that the significance level of spy, or the slope, is very high because the p-value is close to zero. In other words, we have 99.999 confidence to claim that the slope is not 0, and there exists linear relation between X and Y. However, regarding the intercept, the p-value is 0.923, which means we have only 7.7% confidence level that the value of intercept is not 0. We can also see from the plot that the line crosses the origin. The following 2 columns are the lower band and upper band of the parameters at 95% confidence interval. At 95% confidence level, we can claim that the true value of the parameter is within this range.
+</p>
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/05 Model Significance.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/05 Model Significance.html
new file mode 100755
index 0000000..00c8cca
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/05 Model Significance.html	
@@ -0,0 +1,14 @@
+<strong>Sum of Squared Errors, or SSE</strong>, is used to measure the difference between the fitted value and the actual value. It's given by:
+\[SSE = \sum_{i=1}^{n}(y_i - \hat{y_i})^2 = \sum_{i = 1}^{n}\hat{\epsilon_i}^2\]
+<p>
+  If the linear model perfectly fitted the sample, the SSE would be zero. The reason we use squared error here is that the positive and negative errors would offset each other if we simply summed them up. Another measurement of the dispersion of the sample is called <strong>total sum of squares, or SS.</strong>. it's given by:
+</p>
+\[SS = \sum_{i = i}^{n}(y_i - \bar{y}_i)^2\]
+<p>
+  If you are familiar with variance, we can see that SS divided by the number of sample n is the sample variance. From SSE and SS, we can calculate the <strong>Coefficient of Determination</strong>, or <strong>r-square</strong> for short. R-square means the proportion of variation that 'explained' by the linear relationship between X and Y, it's calculated by:
+</p>
+\[r^2 =1 = \frac{SSE}{SS} =1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y_i})^2}{\sum_{i = i}^{n}(y_i - \bar{y}_i)^2}\]
+<p>
+  Let's assume that the model perfectly fitted the sample, which means all of the sample points lie on the straight line. Then the SSE would become zero, and the r-square would become 1. This means perfect fitness. The higher r-square is, the more parts of variation can be explained by the linear relation, the higher significance level the model is.
+  Some other parameters, such as F-statistic, AIC and BIC, are related to multiple linear regression, with would be cover in the next chapter.
+</p>
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/06 Summary.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/06 Summary.html
new file mode 100755
index 0000000..97c4201
--- /dev/null
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we introduced how to implement simple linear in python, and focused on how to read the summary table. In next chapter we will introduced multiple linear regression, which are commonly used to built models in finance and economics.
+</p>
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html
deleted file mode 100755
index 696c08a..0000000
--- a/05 Introduction to Financial Python[]/09 Simple Linear Regression/09 Simple Linear Regression.html	
+++ /dev/null
@@ -1,127 +0,0 @@
-<h2>Introduction</h2>
-In finance and economics filed, most of the models are linear ones. We can see linear regression everywhere, from the foundation of the model portfolio theory to the nowadays popular Fama-French asset pricing model. It's very important to understand how linear regression works in order to have a comprehensive understanding of those theories.
-
-If we are holding a stock, we must be curious about the relationship between our stock return and the market return. Let's say we hold Amazon stock on the first day of this year. In order to see the relation directly, we plot the daily return of our stock on the y-axis and plot the S&amp;P 500 index daily return on the x-axis.
-<pre class="prettyprint linenums">import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = '_fgkxjSbt5389zGt4crC'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-amzn_table = quandl.get('WIKI/AMZN')
-#fetch data from Jan 2017 to Jun 2017
-spy = spy_table.loc['2017':'2017-6',['Close']]
-amzn = amzn_table.loc['2017':'2017-6',['Close']]
-#calculate log return
-spy_log = np.log(spy.Close).diff().dropna()
-amzn_log = np.log(amzn.Close).diff().dropna()
-df = pd.concat([spy_log,amzn_log],axis = 1).dropna()
-df.columns = ['spx','amzn']
-print df.tail()
-</pre>
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="center">spy</td>
-        <td align="center">amzn</td>
-    </tr>
-    <tr>
-        <td>2016-12-22</td>
-        <td align="center">-0.004462</td>
-        <td align="center">-0.005543</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.001372</td>
-        <td align="center">-0.007531</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.000928</td>
-        <td align="center">0.000946</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">-0.005671</td>
-        <td align="center">-0.009081</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="center">0.002086</td>
-        <td align="center">-0.020172</td>
-    </tr>
-
-</table>
-We successfully create a DataFrame contains the daily logarithm return of Amazon stock and S&amp;P500. Now let's plot it:
-<pre class="prettyprint linenums">import matplotlib.pyplot as plt
-plt.figure(figsize = (15,10))
-plt.scatter(df.spy,df.amzn)
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot1.png" alt="plot1" />
-The plot is scattered, but we can see they are approximately correlated: generally the higher SPX's daily return is, the higher Amazon stock's return is. This is called <strong>positively correlated</strong>. We will cover it in the following tutorials.
-<h2>Slope and Intercept</h2>
-It's natural that we want to model the relation between these two rates of return. Intuitively we use a straight line to model it, this is called <strong>Linear Regression</strong>. In order to find the best straight line, it's natural to think that the vertical distances between the points of the data set and the fitted line should be minimized. Those vertical distances are called <strong>residual</strong>. Our objective is to make the sum of squared residuals as small as possible. This method is called <strong>ordinary least square</strong>, or <strong>OLS</strong> method.
-We use x and y to represent the two variable, S&amp;P 500 daily returns and AMZN daily returns. The linear relation is:
-\[Y = Y = \alpha + \beta*X + \epsilon\]
-Where \(\alpha\) is called <strong>intercept</strong>, \(\beta\) is called <strong>slope</strong>.
-Generally, if the scatter points can be represented by\(\left\{\right (x_1,y_1),(x_2, y_2),(x_3,y_3)...(x_n,y_n) \left\}\right\), then the intercept and slope are given by:
-\[\beta = \frac{\sum_{i=1}^{n}(x-\bar{x})(y-\bar{y})}{\sum_{i=1}^{n}(x-\bar{x})^2}\]
-\[\alpha = \bar{y} - \hat{\beta}\bar{x}\]
-Where \(\bar{x}\) is the mean of X, \(\bar{y}\) is the mean of Y.
-In python, we don't need to do the above calculation manually because we have package for it. But it still very important to understand the calculation process of \(\beta\) in order the understand the modern portfolio theory and CAPM, which we will cover in the future.
-<h2>Python Implementation</h2>
-In python, we have a very power package for mathematical models, which is named 'statsmodels'.
-<pre class="prettyprint linenums">import statsmodels.formula.api as sm
-model = sm.ols(formula = 'amzn~spy',data = df).fit()
-print model.summary()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-plot2.png" alt="plot2" />
-We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
-<pre class="prettyprint linenums">print 'pamameters: ',model.params
-[out]: pamameters:  Intercept    0.000012
-                   spy          0.492112
-print 'residual: ', model.resid.tail()
-[out]: residual:  Date
-2016-12-22   -0.003360
-2016-12-23   -0.008219
-2016-12-28    0.000477
-2016-12-29   -0.006303
-2016-12-30   -0.021211
-print 'fitted values: ',model.predict()
-[out]: fitted values:  [-0.00070299 -0.00218348  0.00068734  0.00046907 -0.00277819  0.00103882]
-</pre>
-Now let's have a look at our fitted line:
-<pre class="prettyprint linenums">plt.figure(figsize = (15,10))
-plt.scatter(df.spy,df.amzn)
-plt.xlabel('spx_return')
-plt.ylabel('amzn_return')
-plt.plot(df.spy,model.predict(),color = 'red')
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-head.png" alt="head" />
-The red line is the fitted linear regression straight line.
-As we can see there are lot of statistical results in the summary table. Now let's talk about some important statistical parameters.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial09-regression-table.png" alt="regression table" />
-<h2>Parameter Significance</h2>
-From the summary table we can see 'std err'. This means standard errors of the intercept and slope. The null hypothesis here is \(H_0: \beta = 0\), and the alternative hypothesis is \(H_1: \beta \neq 0\). This hypothesis score is calculated by:
-\[t = \frac{\beta - 0}{SE}\]
-Where SE is given by:
-\[SE = \sqrt\frac{\frac{1}{n-2}\sum_{i = 1}^{n}\hat{\epsilon}^2}{\sum_{i = i}^{n}(x_i - \bar{x})^2}\]
-The distribution used here is 'Student's t-distribution'. It's different from normal distribution but used in the similar way. The column 't' in this table is the test score, and 'p&gt;|t|' is the p-value. By observing the p-value, we can see that the significance level of spy, or the slope, is very high because the p-value is close to zero. In other words, we have 99.999 confidence to claim that the slope is not 0, and there exists linear relation between X and Y. However, regarding the intercept, the p-value is 0.923, which means we have only 7.7% confidence level that the value of intercept is not 0. We can also see from the plot that the line crosses the origin.
-The following 2 columns are the lower band and upper band of the parameters at 95% confidence interval. At 95% confidence level, we can claim that the true value of the parameter is within this range.
-<h2>Model Significance</h2>
-<h3>R-square</h3>
-<strong>Sum of Squared Errors, or SSE</strong>, is used to measure the difference between the fitted value and the actual value. It's given by:
-\[SSE = \sum_{i=1}^{n}(y_i - \hat{y_i})^2 = \sum_{i = 1}^{n}\hat{\epsilon_i}^2\]
-If the linear model perfectly fitted the sample, the SSE would be zero.
-The reason we use squared error here is that the positive and negative errors would offset each other if we simply summed them up.
-Another measurement of the dispersion of the sample is called <strong>total sum of squares, or SS.</strong>. it's given by:
-\[SS = \sum_{i = i}^{n}(y_i - \bar{y}_i)^2\]
-If you are familiar with variance, we can see that SS divided by the number of sample n is the sample variance.
-From SSE and SS, we can calculate the <strong>Coefficient of Determination</strong>, or <strong>r-square</strong> for short. R-square means the proportion of variation that 'explained' by the linear relationship between X and Y, it's calculated by:
-\[r^2 =1 = \frac{SSE}{SS} =1 - \frac{\sum_{i=1}^{n}(y_i - \hat{y_i})^2}{\sum_{i = i}^{n}(y_i - \bar{y}_i)^2}\]
-Let's assume that the model perfectly fitted the sample, which means all of the sample points lie on the straight line. Then the SSE would become zero, and the r-square would become 1. This means perfect fitness. The higher r-square is, the more parts of variation can be explained by the linear relation, the higher significance level the model is.
-Some other parameters, such as F-statistic, AIC and BIC, are related to multiple linear regression, with would be cover in the next chapter.
-<h2>Summary</h2>
-In this chapter we introduced how to implement simple linear in python, and focused on how to read the summary table. In next chapter we will introduced multiple linear regression, which are commonly used to built models in finance and economics.
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/01 Introduction.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/01 Introduction.html
new file mode 100755
index 0000000..db3029b
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/01 Introduction.html	
@@ -0,0 +1,12 @@
+<p>
+  In the last chapter we introduced simple linear regression, which has only one independent variable. In this chapter we will learn about linear regression with multiple independent variables.
+</p>
+<p>
+  A simple linear regression model is written in the following form:
+</p>
+\[ Y = \alpha + \beta X + \epsilon \]
+
+<p>
+  A <strong>multiple linear regression</strong> model with <em>p</em> variables is given by:
+</p>
+\[ Y = \alpha + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_p X_p + \epsilon \]
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html
new file mode 100755
index 0000000..1331c85
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html	
@@ -0,0 +1,290 @@
+<p>
+  In the last chapter we used the S&amp;P 500 index to predict Amazon stock returns. Now we will add more variables to improve our model's predictions. In particular, we shall consider Amazon's competitors.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import numpy as np
+import pandas as pd
+import quandl
+import matplotlib.pyplot as plt
+import statsmodels.formula.api as sm
+
+# Get stock prices
+quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
+spy_table  = quandl.get('BCIW/_SPXT')
+amzn_table = quandl.get('WIKI/AMZN')
+ebay_table = quandl.get('WIKI/EBAY')
+wal_table  = quandl.get('WIKI/WMT')
+aapl_table = quandl.get('WIKI/AAPL')</pre>
+</div>
+
+<p>
+  Then we fetch closing prices starting from 2016:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+spy  = spy_table .loc['2016',['Close']]
+amzn = amzn_table.loc['2016',['Close']]
+ebay = ebay_table.loc['2016',['Close']]
+wal  = wal_table .loc['2016',['Close']]
+aapl = aapl_table.loc['2016',['Close']]
+</pre>
+</div>
+
+<p>
+  After taking log returns of each stock, we concatenate them into a DataFrame, and print out the last 5 rows:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+spy_log  = np.log(spy.Close) .diff().dropna()
+amzn_log = np.log(amzn.Close).diff().dropna()
+ebay_log = np.log(ebay.Close).diff().dropna()
+wal_log  = np.log(wal.Close) .diff().dropna()
+aapl_log = np.log(aapl.Close).diff().dropna()
+df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()
+df.columns = ['SPY', 'AMZN', 'EBAY', 'WAL', 'AAPL']
+df.tail()
+</pre>
+</div>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>Date</td>
+        <td align="right">SPY</td>
+        <td align="right">AMZN</td>
+        <td align="right">EBAY</td>
+        <td align="right">WAL</td>
+        <td align="right">AAPL</td>
+    </tr>
+    <tr>
+        <td>2016-12-23</td>
+        <td align="right">0.001351</td>
+        <td align="right">-0.007531</td>
+        <td align="right">0.008427</td>
+        <td align="right">-0.000719</td>
+        <td align="right">0.001976</td>
+    </tr>
+    <tr>
+        <td>2016-12-27</td>
+        <td align="right">0.002254</td>
+        <td align="right">0.014113</td>
+        <td align="right">0.014993</td>
+        <td align="right">0.002298</td>
+        <td align="right">0.006331</td>
+    </tr>
+    <tr>
+        <td>2016-12-28</td>
+        <td align="right">-0.008218</td>
+        <td align="right">0.000946</td>
+        <td align="right">-0.007635</td>
+        <td align="right">-0.005611</td>
+        <td align="right">-0.004273</td>
+    </tr>
+    <tr>
+        <td>2016-12-29</td>
+        <td align="right">-0.000247</td>
+        <td align="right">-0.009081</td>
+        <td align="right">-0.001000</td>
+        <td align="right">-0.000722</td>
+        <td align="right">-0.000257</td>
+    </tr>
+    <tr>
+        <td>2016-12-30</td>
+        <td align="right">-0.004601</td>
+        <td align="right">-0.020172</td>
+        <td align="right">-0.009720</td>
+        <td align="right">-0.002023</td>
+        <td align="right">-0.007826</td>
+    </tr>
+</table>
+
+<p>
+  As before, we use the 'statsmodels' package to perform simple linear regression:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">simple = sm.ols(formula = 'amzn ~ spy', data = df).fit()
+print simple.summary()
+</pre>
+</div>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="right">coef</td>
+        <td align="right">std err</td>
+        <td align="right">t</td>
+        <td align="right">P>|t|</td>
+        <td align="right">[0.025</td>
+        <td align="right">0.975]</td>
+    </tr>
+    <tr>
+        <td>Intercept</td>
+        <td align="right">9.876e-05</td>
+        <td align="right">0.001</td>
+        <td align="right">0.097</td>
+        <td align="right">0.923</td>
+        <td align="right">-0.002</td>
+        <td align="right">0.002</td>
+    </tr>
+    <tr>
+        <td>spy</td>
+        <td align="right">1.0796</td>
+        <td align="right">0.124</td>
+        <td align="right">8.725</td>
+        <td align="right">0.000</td>
+        <td align="right">0.836</td>
+        <td align="right">1.323</td>
+    </tr>
+</table>
+
+<p>
+  Similarly, we can build a multiple linear regression model:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">model = sm.ols(formula = 'amzn ~ spy + ebay + wal', data = df).fit()
+print model.summary()
+</pre>
+</div>
+
+<table cellspacing="0" cellpadding="3" class="table qc-table">
+    <tr>
+        <td>&nbsp;</td>
+        <td align="right">coef</td>
+        <td align="right">std err</td>
+        <td align="right">t</td>
+        <td align="right">P>|t|</td>
+        <td align="right">[0.025</td>
+        <td align="right">0.975]</td>
+    </tr>
+    <tr>
+        <td>Intercept</td>
+        <td align="right">0.0001</td>
+        <td align="right">0.001</td>
+        <td align="right">0.134</td>
+        <td align="right">0.894</td>
+        <td align="right">-0.002</td>
+        <td align="right">0.002</td>
+    </tr>
+    <tr>
+        <td>spy</td>
+        <td align="right">1.0468</td>
+        <td align="right">0.170</td>
+        <td align="right">6.155</td>
+        <td align="right">0.000</td>
+        <td align="right">0.712</td>
+        <td align="right">1.382</td>
+    </tr>
+    <tr>
+        <td>ebay</td>
+        <td align="right">-0.0795</td>
+        <td align="right">0.058</td>
+        <td align="right">-1.364</td>
+        <td align="right">0.174</td>
+        <td align="right">-0.194</td>
+        <td align="right">0.035</td>
+    </tr>
+    <tr>
+        <td>wal</td>
+        <td align="right">-0.0865</td>
+        <td align="right">0.089</td>
+        <td align="right">-0.976</td>
+        <td align="right">0.330</td>
+        <td align="right">-0.261</td>
+        <td align="right">0.088</td>
+    </tr>
+    <tr>
+        <td>aapl</td>
+        <td align="right">0.1529</td>
+        <td align="right">0.084</td>
+        <td align="right">1.831</td>
+        <td align="right">0.068</td>
+        <td align="right">-0.012</td>
+        <td align="right">0.317</td>
+    </tr>
+</table>
+
+<p>
+  As seen from the summary table, the p-values for Ebay, Walmart and Apple are 0.174, 0.330 and 0.068 respectively, so none of them are significant at a 95% confidence level. The multiple regression model has a higher \( R^2 \) than the simple one: 0.254 vs 0.234. Indeed, \( R^2 \) cannot decrease as the number of variables increases. Why? If an extra variable is added to our regression model, but it cannot account for variations in the response (amzn), then its estimated coefficient will simply be zero. It's as though that variable was never included in the model, so \( R^2 \) will not change. However, it is not always better to add hundreds of variables or we will overfit our model. We'll talk about this in a later chapter.
+</p>
+
+<p>
+  Can we improve our model further? Here we try the <strong>Fama-French 5-factor model</strong>, which is an important model in asset pricing theory. We will cover it in the later tutorials.
+</p>
+
+<p>
+  The data needed are publicly available on French's website.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] We have saved a copy for convenience. The following code fetches the data.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+import urllib2
+from datetime import datetime
+
+url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'
+response   = urllib2.urlopen(url)
+fama_table = pd.read_csv(response)
+
+# Convert time column into index
+fama_table.index = [datetime.strptime(str(x), "%Y%m%d")
+                    for x in fama_table.iloc[:,0]]
+# Remove time column
+fama_table = fama_table.iloc[:,1:]
+</pre>
+</div>
+
+<p>
+  With the data, we can construct a Fama-French factor model:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+fama = fama_table['2016']
+fama = fama.rename(columns = {'Mkt-RF':'MKT'})
+fama = fama.apply(lambda x: x/100)
+fama_df = pd.concat([fama, amzn_log], axis = 1)
+fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA', data = fama_df).fit()
+print fama_model.summary()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-fama.png" alt="fama" />
+<p>
+  The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+result = pd.DataFrame({'simple regression': simple.predict(),
+                       'fama_french': fama_model.predict(),
+                       'sample': df.amzn}, index = df.index)
+
+# Feel free to adjust the chart size
+plt.figure(figsize = (15,7.5))
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])
+plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])
+plt.legend()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-compare.png" alt="compare" />
+<p>
+  Although it's hard to see from the chart above, the predicted return from multiple regression is closer to the actual return. Usually we don't plot the predictions to determine which model is better; we read the summary table.
+</p>
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/03 Model Significance Test.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/03 Model Significance Test.html
new file mode 100755
index 0000000..63be11a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/03 Model Significance Test.html	
@@ -0,0 +1,13 @@
+<p>
+  Instead of using \( R^2 \) to assess whether our regression model is a good fit to the data, we can perform a hypothesis test: the F test.
+  The null and alternative hypotheses of an F test are:
+</p>
+
+\[ H_0: \beta_1 = \beta_2 = \dots = \beta_p = 0  \]
+\[ H_1: \text{At least one coefficient is not 0} \]
+
+<p>
+  We won't explain F test procedure in detail here. You just need to understand the null and alternative hypotheses. In the summary table of an F test, the 'F-statistic' is the F score, while 'prob (F-statistic)' is the p-value. Performing this test on the Fama-French model, we get a p-value of `2.21e-24` so we are almost certain that at least one of the coefficient is not 0.
+  If the p-value is larger than 0.05, you should consider rebuilding your model with other independent variables.
+  In simple linear regression, an F test is equivalent to a t test on the slope, so their p-values will be the same.
+</p>
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html
new file mode 100755
index 0000000..835d65a
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html	
@@ -0,0 +1,73 @@
+<p>
+  Linear regression requires that the predictors and response have a linear relationship. This assumption holds if the residuals are zero on average, <strong>no matter</strong> what values the predictors \( X_1, \dots, X_p \) take.
+  Often it's also assumed that the residuals are independent and normally distributed with the same variance (homoskedasticity), so that we can contruct prediction intervals, for example.
+  To check whether these assumptions hold, we need to analyse the residuals. In statistical arbitrage, residual analysis can also be used to generate signals.
+</p>
+
+<h3>Normality</h3>
+
+<p>
+  The residuals of a linear model usually has a normal distribution. We can plot the residual's density to check for normality:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure()
+#ols.fit().model is a method to access to the residual.
+fama_model.resid.plot.density()
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-residual.png" alt="residual" />
+
+<p>
+  As seen from the plot, the residual is normally distributed. By the way, the residual mean is always zero, up to machine precision:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print 'Residual mean:', np.mean(fama_model.resid)
+[out]: Residual mean: -2.31112163493e-16
+print 'Residual variance:', np.var(fama_model.resid)
+[out]: Residual variance: 0.000205113416293
+</pre>
+</div>
+
+<h3>Homoskedasticity</h3>
+
+<p>
+  This word is difficult to pronounce but not difficult to understand. It means that the residuals have the same variance for all values of X. Otherwise we say that 'heteroskedasticity' is detected.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">plt.figure(figsize = (20,10))
+plt.scatter(df.spy,simple.resid)
+plt.axhline(0.05)
+plt.axhline(-0.05)
+plt.xlabel('x value')
+plt.ylabel('residual')
+plt.show()
+</pre>
+</div>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-variance.png" alt="variance" />
+<p>
+  As seen from the chart, the residuals' variance doesn't increase with X. The three outliers do not change our conclusion. Although we can plot the residuals for simple regression, we can't do this for multiple regression, so we use statsmodels to test for heteroskedasticity:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">from statsmodels.stats import diagnostic as dia
+het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])
+print 'p-value: ', het[-1]
+[out]:p-value of Heteroskedasticity:  0.144075842844
+</pre>
+</div>
+
+<p>
+  No heteroskedasticity is detected at the 95% significance level.
+</p>
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/05 Summary.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/05 Summary.html
new file mode 100755
index 0000000..cff63c1
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/05 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
+</p>
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html
deleted file mode 100755
index 02a930f..0000000
--- a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/10 Multiple Linear Regression.html	
+++ /dev/null
@@ -1,320 +0,0 @@
-In the last chapter we introduced simple linear regression, which has only one independent variable. In this chapter we will learn about linear regression with multiple independent variables.
-
-A simple linear regression model is written in the following form:
-\[ Y = \alpha + \beta X + \epsilon \]
-
-A <strong>multiple linear regression</strong> model with <em>p</em> variables is given by:
-\[ Y = \alpha + \beta_1 X_1 + \beta_2 X_2 + \dots + \beta_p X_p + \epsilon \]
-
-<h2>Python Implementation</h2>
-
-In the last chapter we used the S&amp;P 500 index to predict Amazon stock returns. Now we will add more variables to improve our model's predictions. In particular, we shall consider Amazon's competitors.
-
-<pre class="prettyprint linenums">import numpy as np
-import pandas as pd
-import quandl
-import matplotlib.pyplot as plt
-import statsmodels.formula.api as sm
-
-# Get stock prices
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-spy_table  = quandl.get('BCIW/_SPXT')
-amzn_table = quandl.get('WIKI/AMZN')
-ebay_table = quandl.get('WIKI/EBAY')
-wal_table  = quandl.get('WIKI/WMT')
-aapl_table = quandl.get('WIKI/AAPL')</pre>
-
-Then we fetch closing prices starting from 2016:
-
-<pre class="prettyprint linenums">
-spy  = spy_table .loc['2016',['Close']]
-amzn = amzn_table.loc['2016',['Close']]
-ebay = ebay_table.loc['2016',['Close']]
-wal  = wal_table .loc['2016',['Close']]
-aapl = aapl_table.loc['2016',['Close']]
-</pre>
-
-After taking log returns of each stock, we concatenate them into a DataFrame, and print out the last 5 rows:
-
-<pre class="prettyprint linenums">
-spy_log  = np.log(spy.Close) .diff().dropna()
-amzn_log = np.log(amzn.Close).diff().dropna()
-ebay_log = np.log(ebay.Close).diff().dropna()
-wal_log  = np.log(wal.Close) .diff().dropna()
-aapl_log = np.log(aapl.Close).diff().dropna()
-df = pd.concat([spy_log,amzn_log,ebay_log,wal_log,aapl_log],axis = 1).dropna()
-df.columns = ['SPY', 'AMZN', 'EBAY', 'WAL', 'AAPL']
-df.tail()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>Date</td>
-        <td align="right">SPY</td>
-        <td align="right">AMZN</td>
-        <td align="right">EBAY</td>
-        <td align="right">WAL</td>
-        <td align="right">AAPL</td>
-    </tr>
-    <tr>
-        <td>2016-12-23</td>
-        <td align="right">0.001351</td>
-        <td align="right">-0.007531</td>
-        <td align="right">0.008427</td>
-        <td align="right">-0.000719</td>
-        <td align="right">0.001976</td>
-    </tr>
-    <tr>
-        <td>2016-12-27</td>
-        <td align="right">0.002254</td>
-        <td align="right">0.014113</td>
-        <td align="right">0.014993</td>
-        <td align="right">0.002298</td>
-        <td align="right">0.006331</td>
-    </tr>
-    <tr>
-        <td>2016-12-28</td>
-        <td align="right">-0.008218</td>
-        <td align="right">0.000946</td>
-        <td align="right">-0.007635</td>
-        <td align="right">-0.005611</td>
-        <td align="right">-0.004273</td>
-    </tr>
-    <tr>
-        <td>2016-12-29</td>
-        <td align="right">-0.000247</td>
-        <td align="right">-0.009081</td>
-        <td align="right">-0.001000</td>
-        <td align="right">-0.000722</td>
-        <td align="right">-0.000257</td>
-    </tr>
-    <tr>
-        <td>2016-12-30</td>
-        <td align="right">-0.004601</td>
-        <td align="right">-0.020172</td>
-        <td align="right">-0.009720</td>
-        <td align="right">-0.002023</td>
-        <td align="right">-0.007826</td>
-    </tr>
-</table>
-
-As before, we use the 'statsmodels' package to perform simple linear regression:
-
-<pre class="prettyprint linenums">simple = sm.ols(formula = 'amzn ~ spy', data = df).fit()
-print simple.summary()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="right">coef</td>
-        <td align="right">std err</td>
-        <td align="right">t</td>
-        <td align="right">P>|t|</td>
-        <td align="right">[0.025</td>
-        <td align="right">0.975]</td>
-    </tr>
-    <tr>
-        <td>Intercept</td>
-        <td align="right">9.876e-05</td>
-        <td align="right">0.001</td>
-        <td align="right">0.097</td>
-        <td align="right">0.923</td>
-        <td align="right">-0.002</td>
-        <td align="right">0.002</td>
-    </tr>
-    <tr>
-        <td>spy</td>
-        <td align="right">1.0796</td>
-        <td align="right">0.124</td>
-        <td align="right">8.725</td>
-        <td align="right">0.000</td>
-        <td align="right">0.836</td>
-        <td align="right">1.323</td>
-    </tr>
-</table>
-
-Similarly, we can build a multiple linear regression model:
-
-<pre class="prettyprint linenums">model = sm.ols(formula = 'amzn ~ spy + ebay + wal', data = df).fit()
-print model.summary()
-</pre>
-
-<table cellspacing="0" cellpadding="3" class="table qc-table">
-    <tr>
-        <td>&nbsp;</td>
-        <td align="right">coef</td>
-        <td align="right">std err</td>
-        <td align="right">t</td>
-        <td align="right">P>|t|</td>
-        <td align="right">[0.025</td>
-        <td align="right">0.975]</td>
-    </tr>
-    <tr>
-        <td>Intercept</td>
-        <td align="right">0.0001</td>
-        <td align="right">0.001</td>
-        <td align="right">0.134</td>
-        <td align="right">0.894</td>
-        <td align="right">-0.002</td>
-        <td align="right">0.002</td>
-    </tr>
-    <tr>
-        <td>spy</td>
-        <td align="right">1.0468</td>
-        <td align="right">0.170</td>
-        <td align="right">6.155</td>
-        <td align="right">0.000</td>
-        <td align="right">0.712</td>
-        <td align="right">1.382</td>
-    </tr>
-    <tr>
-        <td>ebay</td>
-        <td align="right">-0.0795</td>
-        <td align="right">0.058</td>
-        <td align="right">-1.364</td>
-        <td align="right">0.174</td>
-        <td align="right">-0.194</td>
-        <td align="right">0.035</td>
-    </tr>
-    <tr>
-        <td>wal</td>
-        <td align="right">-0.0865</td>
-        <td align="right">0.089</td>
-        <td align="right">-0.976</td>
-        <td align="right">0.330</td>
-        <td align="right">-0.261</td>
-        <td align="right">0.088</td>
-    </tr>
-    <tr>
-        <td>aapl</td>
-        <td align="right">0.1529</td>
-        <td align="right">0.084</td>
-        <td align="right">1.831</td>
-        <td align="right">0.068</td>
-        <td align="right">-0.012</td>
-        <td align="right">0.317</td>
-    </tr>
-</table>
-
-As seen from the summary table, the p-values for Ebay, Walmart and Apple are 0.174, 0.330 and 0.068 respectively, so none of them are significant at a 95% confidence level.
-
-The multiple regression model has a higher \( R^2 \) than the simple one: 0.254 vs 0.234. Indeed, \( R^2 \) cannot decrease as the number of variables increases. Why? If an extra variable is added to our regression model, but it cannot account for variations in the response (amzn), then its estimated coefficient will simply be zero. It's as though that variable was never included in the model, so \( R^2 \) will not change.
-
-However, it is not always better to add hundreds of variables or we will overfit our model. We'll talk about this in a later chapter.
-
-Can we improve our model further? Here we try the <strong>Fama-French 5-factor model</strong>, which is an important model in asset pricing theory. We will cover it in the later tutorials.
-
-The data needed are publicly available on French's website.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] We have saved a copy for convenience. The following code fetches the data.
-
-<pre class="prettyprint linenums">
-import urllib2
-from datetime import datetime
-
-url = 'https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/F-F_Research_Data_5_Factors_2x3_daily.csv'
-response   = urllib2.urlopen(url)
-fama_table = pd.read_csv(response)
-
-# Convert time column into index
-fama_table.index = [datetime.strptime(str(x), "%Y%m%d")
-                    for x in fama_table.iloc[:,0]]
-# Remove time column
-fama_table = fama_table.iloc[:,1:]
-</pre>
-
-With the data, we can construct a Fama-French factor model:
-
-<pre class="prettyprint linenums">
-fama = fama_table['2016']
-fama = fama.rename(columns = {'Mkt-RF':'MKT'})
-fama = fama.apply(lambda x: x/100)
-fama_df = pd.concat([fama, amzn_log], axis = 1)
-fama_model = sm.ols(formula = 'Close~MKT+SMB+HML+RMW+CMA', data = fama_df).fit()
-print fama_model.summary()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-fama.png" alt="fama" />
-The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
-
-<pre class="prettyprint linenums">
-result = pd.DataFrame({'simple regression': simple.predict(),
-                       'fama_french': fama_model.predict(),
-                       'sample': df.amzn}, index = df.index)
-
-# Feel free to adjust the chart size
-plt.figure(figsize = (15,7.5))
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','simple regression'])
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','fama_french'])
-plt.plot(result['2016-7':'2016-9'].index,result.loc['2016-7':'2016-9','sample'])
-plt.legend()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-compare.png" alt="compare" />
-Although it's hard to see from the chart above, the predicted return from multiple regression is closer to the actual return. Usually we don't plot the predictions to determine which model is better; we read the summary table.
-
-<h2>Model Significance Test</h2>
-
-Instead of using \( R^2 \) to assess whether our regression model is a good fit to the data, we can perform a hypothesis test: the F test.
-
-The null and alternative hypotheses of an F test are:
-\[ H_0: \beta_1 = \beta_2 = \dots = \beta_p = 0  \]
-\[ H_1: \text{At least one coefficient is not 0} \]
-
-We won't explain F test procedure in detail here. You just need to understand the null and alternative hypotheses. In the summary table of an F test, the 'F-statistic' is the F score, while 'prob (F-statistic)' is the p-value. Performing this test on the Fama-French model, we get a p-value of `2.21e-24` so we are almost certain that at least one of the coefficient is not 0.
-
-If the p-value is larger than 0.05, you should consider rebuilding your model with other independent variables.
-
-In simple linear regression, an F test is equivalent to a t test on the slope, so their p-values will be the same.
-
-<h2>Residual Analysis</h2>
-
-Linear regression requires that the predictors and response have a linear relationship. This assumption holds if the residuals are zero on average, <strong>no matter</strong> what values the predictors \( X_1, \dots, X_p \) take.
-
-Often it's also assumed that the residuals are independent and normally distributed with the same variance (homoskedasticity), so that we can contruct prediction intervals, for example.
-
-To check whether these assumptions hold, we need to analyse the residuals. In statistical arbitrage, residual analysis can also be used to generate signals.
-
-<h3>Normality</h3>
-
-The residuals of a linear model usually has a normal distribution. We can plot the residual's density to check for normality:
-
-<pre class="prettyprint linenums">plt.figure()
-#ols.fit().model is a method to access to the residual.
-fama_model.resid.plot.density()
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-residual.png" alt="residual" />
-
-As seen from the plot, the residual is normally distributed. By the way, the residual mean is always zero, up to machine precision:
-
-<pre class="prettyprint linenums">print 'Residual mean:', np.mean(fama_model.resid)
-[out]: Residual mean: -2.31112163493e-16
-print 'Residual variance:', np.var(fama_model.resid)
-[out]: Residual variance: 0.000205113416293
-</pre>
-
-<h3>Homoskedasticity</h3>
-
-This word is difficult to pronounce but not difficult to understand. It means that the residuals have the same variance for all values of X. Otherwise we say that 'heteroskedasticity' is detected.
-
-<pre class="prettyprint linenums">plt.figure(figsize = (20,10))
-plt.scatter(df.spy,simple.resid)
-plt.axhline(0.05)
-plt.axhline(-0.05)
-plt.xlabel('x value')
-plt.ylabel('residual')
-plt.show()
-</pre>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial10-variance.png" alt="variance" />
-As seen from the chart, the residuals' variance doesn't increase with X. The three outliers do not change our conclusion. Although we can plot the residuals for simple regression, we can't do this for multiple regression, so we use statsmodels to test for heteroskedasticity:
-
-<pre class="prettyprint linenums">from statsmodels.stats import diagnostic as dia
-het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])
-print 'p-value: ', het[-1]
-[out]:p-value of Heteroskedasticity:  0.144075842844
-</pre>
-
-No heteroskedasticity is detected at the 95% significance level.
-
-<h2>Summary</h2>
-
-In this chapter we have introduced multiple linear regression, F test and residual analysis, which are the fundamentals of linear models. In the next chapter we will introduce some linear algebra, which are used in modern portfolio theory and CAPM.
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/01 Introduction.html b/05 Introduction to Financial Python[]/11 Linear Algebra/01 Introduction.html
new file mode 100755
index 0000000..ebb8e05
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Many papers in statistics and quantitative finance make heavy use of linear algebra, so you need to have a working knowledge of it in order to read and apply them to your trading.
+</p>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/02 Vectors.html b/05 Introduction to Financial Python[]/11 Linear Algebra/02 Vectors.html
new file mode 100755
index 0000000..2d75a1e
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/02 Vectors.html	
@@ -0,0 +1,15 @@
+<p>
+  A vector can be thought of as an arrow pointing from the origin to a specific point. Any vector or point can be represented by its coordinates i.e. an array of numbers, such as \((x,y)\) for a 2-dimensional vector, or \((x,y,z)\) for a 3-dimensional one. We usually write a vector as a column:
+</p>
+\[ \mathbf{v} = \begin{pmatrix}
+x \\ y \\ z
+\end{pmatrix} \]
+<a name="scalar"></a>
+<p>
+  The <strong>scalar product</strong> of two vectors \( \mathbf{x} \) and \( \mathbf{y} \) in 2-dimensional space is defined as:
+</p>
+\[ \mathbf{x}^T \mathbf{y} = \begin{pmatrix} x_1 &amp; x_2 \end{pmatrix} \begin{pmatrix} y_1 \\ y_2 \end{pmatrix} = x_1 y_1 + x_2 y_2 \]
+
+<p>
+  This definition can be easily generalized to <em>n</em> dimensional space. Clearly, we cannot take the scalar product of two vectors with different dimensions.
+</p>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html b/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html
new file mode 100755
index 0000000..48e6e97
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html	
@@ -0,0 +1,66 @@
+<p>
+  If we have a few vectors \( \mathbf{v}_1, \mathbf{v}_2, \dots, \mathbf{v}_n \) with the same dimension, then we can put them side-by-side to form a <strong>matrix</strong>. For example, the vectors
+</p>
+\[ v_1 = \begin{pmatrix} 1 \\ 2 \\ 3 \end{pmatrix} \qquad
+   v_2 = \begin{pmatrix} 2 \\ 2 \\ 2 \end{pmatrix} \qquad
+   v_3 = \begin{pmatrix} 3 \\ 1 \\ 1 \end{pmatrix} \]
+
+<p>
+  can be combined to produce a matrix:
+</p>
+\[ m = \begin{pmatrix}
+1 &amp; 2 &amp; 3 \\
+2 &amp; 2 &amp; 2 \\
+3 &amp; 1 &amp; 1
+\end{pmatrix} \]
+
+<p>
+  m is a 3 &times; 3 matrix. We typically describe the dimensions of a matrix as \(m \times n\) where m = number of rows and n = number of columns.
+</p>
+
+<p>
+  A <strong>square</strong> matrix is one with as many rows as columns.
+</p>
+
+<p>
+  Notation: \(x_{ij}\) refers to a specific value in row \(i\) and column \(j\) of a matrix \(X\). For example, \(x_{23}\) is the number in the second row and third column of \(X\).
+</p>
+
+<h3>Python Implementation</h3>
+
+<p>
+  In Python, the NumPy package deals with linear algebra. The array we learned in the NumPy chapter can be deemed as a vector:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import numpy as np
+a = np.array([1,2,3])
+b = np.array([2,2,2])
+c = np.array([3,1,1])
+matrix = np.column_stack((a,b,c))
+print matrix
+print type(matrix)
+[out]:
+[[1 2 3]
+ [2 2 1]
+ [3 2 1]]
+</pre>
+</div>
+
+<p>
+  It is worth noticing that we used column_stack() here to ensure that the vectors are vertical and placed side-by-side to form a matrix. Without the column_stack() function, the vectors will be made horizontal and stacked on top of one another:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">matrix2 = np.array([a,b,c])
+print matrix2
+[out]:
+[[1 2 3]
+ [2 2 2]
+ [3 1 1]]
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html b/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html
new file mode 100755
index 0000000..d99e45f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html	
@@ -0,0 +1,60 @@
+<p>
+  How are two matrices multiplied? Suppose \(X = AB\). Each entry \(x_{ij}\) of matrix \(X\) is the <a href="#scalar">scalar product</a> of row \(i\) from matrix \(A\) with column \(j\) from matrix \(B\). This is best illustrated with an example:
+</p>
+
+\[ AB = \begin{pmatrix}
+a_{11} &amp; a_{12} \\
+a_{21} &amp; a_{22} \\
+a_{31} &amp; a_{32}
+\end{pmatrix}
+\begin{pmatrix}
+b_{11} &amp; b_{12} \\
+b_{21} &amp; b_{22}
+\end{pmatrix} = \begin{pmatrix}
+x_{11} &amp; x_{12} \\
+x_{21} &amp; x_{22} \\
+x_{31} &amp; x_{32}
+\end{pmatrix} \]
+
+\[ x_{\color{red}11} = a_{{\color{red}1} 1} b_{1{\color{red} 1}} + a_{{\color{red}1} 2} b_{2 {\color{red}1}} \]
+\[ x_{\color{red}12} = a_{{\color{red}1} 1} b_{1 {\color{red}2}} + a_{{\color{red}1} 2} b_{2 {\color{red}2}} \]
+\[ x_{\color{red}21} = a_{{\color{red}2} 1} b_{1 {\color{red}1}} + a_{{\color{red}2} 2} b_{2 {\color{red}1}} \]
+\[ x_{\color{red}22} = a_{{\color{red}2} 1} b_{1 {\color{red}2}} + a_{{\color{red}2} 2} b_{2 {\color{red}2}} \]
+\[ \vdots \]
+
+<p>
+  In NumPy, we can multiply matrices with the dot() function:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">A = np.array([[2,3],[4,2],[2,2]])
+B = np.array([[4,2],[4,6]])
+x = np.dot(A,B)
+print x
+[out]:
+[[20 22]
+ [24 20]
+ [16 16]]
+</pre>
+</div>
+
+<p>
+  Since matrix multiplication is defined in terms of scalar products, the matrix product \(AB\) exists only if \(A\) has as many columns as \(B\) has rows. It's useful to remember this shorthand: (m &times; n) &times; (n &times; p) = (m &times; p) which means that an (m &times; n) matrix multiplied by an (n &times; p) matrix yields an (m &times; p) matrix.
+</p>
+
+<p>
+  Reversing the order of multiplication results in an error since B does not have as many columns as A has rows:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">x = np.dot(B,A)
+</pre>
+</div>
+
+<p>
+  A natrual consequence of this fact is that matrix multiplication is <strong>not commutative</strong>. In other words, \(AB \neq BA\) in general.
+</p>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html b/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html
new file mode 100755
index 0000000..145f606
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html	
@@ -0,0 +1,87 @@
+<p>
+  An <strong>identity</strong> matrix is a square matrix with ones on the main diagonal and zeros elsewhere. Here is an \(n \times n\) identity matrix:
+</p>
+
+\[ I_n = \begin{pmatrix}
+   1 &amp; 0 &amp; 0 &amp; ... &amp; 0 \\
+   0 &amp; 1 &amp; 0 &amp; ... &amp; 0 \\
+   0 &amp; 0 &amp; 1 &amp; ... &amp; 0 \\
+   \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
+   0 &amp; 0 &amp; 0 &amp; ... &amp; 1
+\end{pmatrix} \]
+
+<p>
+  Multiplying any matrix by an identity matrix (of the correct shape) is like multiplying a number by 1. Concretely, if \(A\) is an \(m \times n\) matrix, then:
+</p>
+\[ I_mA = AI_n = A \]
+\( A^{-1} \) is the <strong>inverse</strong> matrix of a square matrix \(A\) if:
+\[ AA^{-1} = I = A^{-1}A \]
+
+<p>
+  Some caveats:
+</p>
+<ul>
+    <li>A rectangular matrix will not have an inverse, but it may have a pseudoinverse (not covered in this tutorial).</li>
+    <li>A square matrix may not have an inverse i.e. it may be "singular".</li>
+    <li>If a square matrix has an inverse, then its inverse is unique.</li>
+</ul>
+
+<p>
+  Inverse matrices are computed using the <a href="https://www.mathsisfun.com/algebra/matrix-inverse-row-operations-gauss-jordan.html" target="_blank">Gauss-Jordan method</a>. In NumPy, we use the linalg.inv() function to do it:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print matrix
+print '\n-------------------------\n'
+print np.linalg.inv(matrix)
+[out]:
+[[1 2 3]
+ [2 2 1]
+ [3 2 1]]
+
+-------------------------
+
+[[ 0.   -1.    1.  ]
+ [-0.25  2.   -1.25]
+ [ 0.5  -1.    0.5 ]]
+</pre>
+</div>
+
+<p>
+  Now let's check if the multiplication is \(I\):
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">inverse = np.linalg.inv(matrix)
+print np.dot(matrix, inverse)
+print '\n-------------------------\n'
+print np.dot(inverse,matrix)
+[out]:
+[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]
+ [  0.00000000e+00   1.00000000e+00   1.11022302e-16]
+ [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]
+
+-------------------------
+
+[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]
+ [  6.66133815e-16   1.00000000e+00   0.00000000e+00]
+ [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]
+</pre>
+</div>
+
+<p>
+  Not surprisingly, we ended up with an identity matrix. We can form a non-invertible matrix by making one of its rows a multiple of another:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">singular = np.array([[1,2,3],[1,2,3],[3,3,3]])
+inv = np.linalg.inv(singular)
+[out]: numpy.linalg.linalg.LinAlgError: Singular matrix
+</pre>
+</div>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html b/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html
new file mode 100755
index 0000000..06672e4
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html	
@@ -0,0 +1,68 @@
+<p>
+  A common problem in linear algebra is solving linear equations. Consider the following linear equations:
+</p>
+
+\[  2x + y - z = 8    \]
+\[ -3x - y + 2z = -11 \]
+\[ -2x + y + 2z = -3  \]
+
+<p>
+  If we let:
+</p>
+\[ A = \begin{pmatrix}
+   2  &amp; 1  &amp; -1 \\
+   -3 &amp; -1 &amp; 2  \\
+   -2 &amp; 1  &amp; 2
+\end{pmatrix} \qquad
+\mathbf{x} = \begin{pmatrix} x \\  y  \\ z  \end{pmatrix} \qquad
+\mathbf{b} = \begin{pmatrix} 8 \\ -11 \\ -3 \end{pmatrix} \]
+
+<p>
+  Then the linear equations above can be written as \( A\mathbf{x} = \mathbf{b} \)
+</p>
+
+<p>
+  If A is invertible, then we can multiply \(A^{-1}\) on both sides of the equation to obtain the solution:
+</p>
+\[ A^{-1}A \mathbf{x} = A^{-1}\mathbf{b} \]
+\[ \mathbf{x} = A^{-1}\mathbf{b} \]
+<p>
+  As long as \(A^{-1}\) exists, we can compute it to solve the linear equations:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])
+b = np.array([[8],[-11],[-3]])
+inv_A = np.linalg.inv(A)
+print np.dot(inv_A, b)
+[out]:
+[[ 2.]
+ [ 3.]
+ [-1.]]
+</pre>
+</div>
+
+<p>
+  The solution is <em>x</em> = 2, <em>y</em> = 3, <em>z</em> = &minus;1. However, computing the inverse matrix is not recommended, since it is numerically unstable i.e. small rounding errors can dramatically affect the result.
+</p>
+
+<p>
+  Instead, NumPy solves linear equations by <a href="https://www.youtube.com/watch?v=m3EojSAgIao" target="_blank">LU decomposition</a>:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">print np.linalg.solve(A, b)
+[out]:
+[[ 2.]
+ [ 3.]
+ [-1.]]
+</pre>
+</div>
+
+<p>
+  Of course, we get the same solution. We can check the correctness of the solution by substituting <em>x</em>, <em>y</em> and <em>z</em> into the linear equations.
+</p>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/07 Summary.html b/05 Introduction to Financial Python[]/11 Linear Algebra/07 Summary.html
new file mode 100755
index 0000000..06f1c5e
--- /dev/null
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/07 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we have introduced vectors, matrices, inverse matrices and linear equations. Some applications in finance include: finding arbitrage opportunities by solving linear equations, computing portfolio variance, etc. In the next chapter, we will introduce modern portfolio theory and CAPM.
+</p>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html b/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html
deleted file mode 100755
index e5124ed..0000000
--- a/05 Introduction to Financial Python[]/11 Linear Algebra/11 Linear Algebra.html	
+++ /dev/null
@@ -1,225 +0,0 @@
-Many papers in statistics and quantitative finance make heavy use of linear algebra, so you need to have a working knowledge of it in order to read and apply them to your trading.
-
-<h2>Vectors</h2>
-
-A vector can be thought of as an arrow pointing from the origin to a specific point. Any vector or point can be represented by its coordinates i.e. an array of numbers, such as \((x,y)\) for a 2-dimensional vector, or \((x,y,z)\) for a 3-dimensional one. We usually write a vector as a column:
-\[ \mathbf{v} = \begin{pmatrix}
-x \\ y \\ z
-\end{pmatrix} \]
-<a name="scalar"></a>
-The <strong>scalar product</strong> of two vectors \( \mathbf{x} \) and \( \mathbf{y} \) in 2-dimensional space is defined as:
-\[ \mathbf{x}^T \mathbf{y} = \begin{pmatrix} x_1 &amp; x_2 \end{pmatrix} \begin{pmatrix} y_1 \\ y_2 \end{pmatrix} = x_1 y_1 + x_2 y_2 \]
-
-This definition can be easily generalized to <em>n</em> dimensional space. Clearly, we cannot take the scalar product of two vectors with different dimensions.
-
-<h2>Matrices</h2>
-
-If we have a few vectors \( \mathbf{v}_1, \mathbf{v}_2, \dots, \mathbf{v}_n \) with the same dimension, then we can put them side-by-side to form a <strong>matrix</strong>. For example, the vectors
-\[ v_1 = \begin{pmatrix} 1 \\ 2 \\ 3 \end{pmatrix} \qquad
-   v_2 = \begin{pmatrix} 2 \\ 2 \\ 2 \end{pmatrix} \qquad
-   v_3 = \begin{pmatrix} 3 \\ 1 \\ 1 \end{pmatrix} \]
-
-can be combined to produce a matrix:
-\[ m = \begin{pmatrix}
-1 &amp; 2 &amp; 3 \\
-2 &amp; 2 &amp; 2 \\
-3 &amp; 1 &amp; 1
-\end{pmatrix} \]
-
-m is a 3 &times; 3 matrix. We typically describe the dimensions of a matrix as \(m \times n\) where m = number of rows and n = number of columns.
-
-A <strong>square</strong> matrix is one with as many rows as columns.
-
-Notation: \(x_{ij}\) refers to a specific value in row \(i\) and column \(j\) of a matrix \(X\). For example, \(x_{23}\) is the number in the second row and third column of \(X\).
-
-<h3>Python Implementation</h3>
-
-In Python, the NumPy package deals with linear algebra. The array we learned in the NumPy chapter can be deemed as a vector:
-
-<pre class="prettyprint linenums">import numpy as np
-a = np.array([1,2,3])
-b = np.array([2,2,2])
-c = np.array([3,1,1])
-matrix = np.column_stack((a,b,c))
-print matrix
-print type(matrix)
-[out]:
-[[1 2 3]
- [2 2 1]
- [3 2 1]]
-</pre>
-
-It is worth noticing that we used column_stack() here to ensure that the vectors are vertical and placed side-by-side to form a matrix. Without the column_stack() function, the vectors will be made horizontal and stacked on top of one another:
-
-<pre class="prettyprint linenums">matrix2 = np.array([a,b,c])
-print matrix2
-[out]:
-[[1 2 3]
- [2 2 2]
- [3 1 1]]
-</pre>
-
-<h2>Matrix Multiplication</h2>
-
-How are two matrices multiplied? Suppose \(X = AB\). Each entry \(x_{ij}\) of matrix \(X\) is the <a href="#scalar">scalar product</a> of row \(i\) from matrix \(A\) with column \(j\) from matrix \(B\). This is best illustrated with an example:
-
-\[ AB = \begin{pmatrix}
-a_{11} &amp; a_{12} \\
-a_{21} &amp; a_{22} \\
-a_{31} &amp; a_{32}
-\end{pmatrix}
-\begin{pmatrix}
-b_{11} &amp; b_{12} \\
-b_{21} &amp; b_{22}
-\end{pmatrix} = \begin{pmatrix}
-x_{11} &amp; x_{12} \\
-x_{21} &amp; x_{22} \\
-x_{31} &amp; x_{32}
-\end{pmatrix} \]
-
-Then
-\[ x_{\color{red}11} = a_{{\color{red}1} 1} b_{1{\color{red} 1}} + a_{{\color{red}1} 2} b_{2 {\color{red}1}} \]
-\[ x_{\color{red}12} = a_{{\color{red}1} 1} b_{1 {\color{red}2}} + a_{{\color{red}1} 2} b_{2 {\color{red}2}} \]
-\[ x_{\color{red}21} = a_{{\color{red}2} 1} b_{1 {\color{red}1}} + a_{{\color{red}2} 2} b_{2 {\color{red}1}} \]
-\[ x_{\color{red}22} = a_{{\color{red}2} 1} b_{1 {\color{red}2}} + a_{{\color{red}2} 2} b_{2 {\color{red}2}} \]
-\[ \vdots \]
-
-In NumPy, we can multiply matrices with the dot() function:
-
-<pre class="prettyprint linenums">A = np.array([[2,3],[4,2],[2,2]])
-B = np.array([[4,2],[4,6]])
-x = np.dot(A,B)
-print x
-[out]:
-[[20 22]
- [24 20]
- [16 16]]
-</pre>
-
-Since matrix multiplication is defined in terms of scalar products, the matrix product \(AB\) exists only if \(A\) has as many columns as \(B\) has rows. It's useful to remember this shorthand: (m &times; n) &times; (n &times; p) = (m &times; p) which means that an (m &times; n) matrix multiplied by an (n &times; p) matrix yields an (m &times; p) matrix.
-
-Reversing the order of multiplication results in an error since B does not have as many columns as A has rows:
-
-<pre class="prettyprint linenums">x = np.dot(B,A)
-</pre>
-
-A natrual consequence of this fact is that matrix multiplication is <strong>not commutative</strong>. In other words, \(AB \neq BA\) in general.
-
-<h2>Inverse</h2>
-
-An <strong>identity</strong> matrix is a square matrix with ones on the main diagonal and zeros elsewhere. Here is an \(n \times n\) identity matrix:
-
-\[ I_n = \begin{pmatrix}
-   1 &amp; 0 &amp; 0 &amp; ... &amp; 0 \\
-   0 &amp; 1 &amp; 0 &amp; ... &amp; 0 \\
-   0 &amp; 0 &amp; 1 &amp; ... &amp; 0 \\
-   \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
-   0 &amp; 0 &amp; 0 &amp; ... &amp; 1
-\end{pmatrix} \]
-
-Multiplying any matrix by an identity matrix (of the correct shape) is like multiplying a number by 1. Concretely, if \(A\) is an \(m \times n\) matrix, then:
-\[ I_mA = AI_n = A \]
-
-\( A^{-1} \) is the <strong>inverse</strong> matrix of a square matrix \(A\) if:
-\[ AA^{-1} = I = A^{-1}A \]
-
-Some caveats:
-<ul>
-    <li>A rectangular matrix will not have an inverse, but it may have a pseudoinverse (not covered in this tutorial).</li>
-    <li>A square matrix may not have an inverse i.e. it may be "singular".</li>
-    <li>If a square matrix has an inverse, then its inverse is unique.</li>
-</ul>
-
-Inverse matrices are computed using the <a href="https://www.mathsisfun.com/algebra/matrix-inverse-row-operations-gauss-jordan.html" target="_blank">Gauss-Jordan method</a>. In NumPy, we use the linalg.inv() function to do it:
-
-<pre class="prettyprint linenums">print matrix
-print '\n-------------------------\n'
-print np.linalg.inv(matrix)
-[out]:
-[[1 2 3]
- [2 2 1]
- [3 2 1]]
-
--------------------------
-
-[[ 0.   -1.    1.  ]
- [-0.25  2.   -1.25]
- [ 0.5  -1.    0.5 ]]
-</pre>
-
-Now let's check if the multiplication is \(I\):
-
-<pre class="prettyprint linenums">inverse = np.linalg.inv(matrix)
-print np.dot(matrix, inverse)
-print '\n-------------------------\n'
-print np.dot(inverse,matrix)
-[out]:
-[[  1.00000000e+00  -6.66133815e-16   6.66133815e-16]
- [  0.00000000e+00   1.00000000e+00   1.11022302e-16]
- [  0.00000000e+00  -2.22044605e-16   1.00000000e+00]]
-
--------------------------
-
-[[  1.00000000e+00  -4.44089210e-16  -2.22044605e-16]
- [  6.66133815e-16   1.00000000e+00   0.00000000e+00]
- [  0.00000000e+00   0.00000000e+00   1.00000000e+00]]
-</pre>
-
-Not surprisingly, we ended up with an identity matrix. We can form a non-invertible matrix by making one of its rows a multiple of another:
-
-<pre class="prettyprint linenums">singular = np.array([[1,2,3],[1,2,3],[3,3,3]])
-inv = np.linalg.inv(singular)
-[out]: numpy.linalg.linalg.LinAlgError: Singular matrix
-</pre>
-
-<h2>Linear Equations</h2>
-
-A common problem in linear algebra is solving linear equations. Consider the following linear equations:
-
-\[  2x + y - z = 8    \]
-\[ -3x - y + 2z = -11 \]
-\[ -2x + y + 2z = -3  \]
-
-If we let:
-\[ A = \begin{pmatrix}
-   2  &amp; 1  &amp; -1 \\
-   -3 &amp; -1 &amp; 2  \\
-   -2 &amp; 1  &amp; 2
-\end{pmatrix} \qquad
-\mathbf{x} = \begin{pmatrix} x \\  y  \\ z  \end{pmatrix} \qquad
-\mathbf{b} = \begin{pmatrix} 8 \\ -11 \\ -3 \end{pmatrix} \]
-
-Then the linear equations above can be written as \( A\mathbf{x} = \mathbf{b} \)
-
-If A is invertible, then we can multiply \(A^{-1}\) on both sides of the equation to obtain the solution:
-\[ A^{-1}A \mathbf{x} = A^{-1}\mathbf{b} \]
-Hence
-\[ \mathbf{x} = A^{-1}\mathbf{b} \]
-
-As long as \(A^{-1}\) exists, we can compute it to solve the linear equations:
-
-<pre class="prettyprint linenums">A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])
-b = np.array([[8],[-11],[-3]])
-inv_A = np.linalg.inv(A)
-print np.dot(inv_A, b)
-[out]:
-[[ 2.]
- [ 3.]
- [-1.]]
-</pre>
-
-The solution is <em>x</em> = 2, <em>y</em> = 3, <em>z</em> = &minus;1. However, computing the inverse matrix is not recommended, since it is numerically unstable i.e. small rounding errors can dramatically affect the result.
-
-Instead, NumPy solves linear equations by <a href="https://www.youtube.com/watch?v=m3EojSAgIao" target="_blank">LU decomposition</a>:
-
-<pre class="prettyprint linenums">print np.linalg.solve(A, b)
-[out]:
-[[ 2.]
- [ 3.]
- [-1.]]
-</pre>
-
-Of course, we get the same solution. We can check the correctness of the solution by substituting <em>x</em>, <em>y</em> and <em>z</em> into the linear equations.
-
-<h2>Summary</h2>
-In this chapter we have introduced vectors, matrices, inverse matrices and linear equations. Some applications in finance include: finding arbitrage opportunities by solving linear equations, computing portfolio variance, etc. In the next chapter, we will introduce modern portfolio theory and CAPM.
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/01 Introduction.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/01 Introduction.html
new file mode 100755
index 0000000..080a35d
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  The Modern Portfolio Theory (MPT) suggests how investors should spread their wealth across various assets to minimize risk and maximize return.
+  This chapter is mathematically intense, so don't feel demoralized if you don't understand it on your first reading.
+</p>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html
new file mode 100755
index 0000000..4fa940d
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html	
@@ -0,0 +1,31 @@
+<h2>Risk Aversion</h2>
+
+<p>
+  In portfolio theory, the riskiness of an asset is often measured by the variance (or standard deviation) of its returns. Risk-averse investors do not want their wealth to fluctuate wildly.
+</p>
+
+<p>
+  Risk aversion can be illustrated with a simple example. Which of the following assets do you prefer?
+</p>
+
+<ul>
+    <li>Asset A pays $200 or $0 with 50% probability each.</li>
+    <li>Asset B pays $400 or &minus;$200 (i.e. you lose $200) with 50% probability each.</li>
+</ul>
+
+<p>
+  The expected payouts of A and B are:
+</p>
+
+\[ \mathbb{E}(A) = 0.5 \times 200 + 0.5 \times 0 = 100 \]
+\[ \mathbb{E}(B) = 0.5 \times 400 + 0.5 \times (-200) = 100 \]
+
+<p>
+  The standard deviation of their payouts are:
+</p>
+\[ \sigma_A = \sqrt{0.5(200-100)^2 + 0.5(0-100)^2} = 100 \]
+\[ \sigma_A = \sqrt{0.5(400-100)^2 + 0.5(-200-100)^2} = 300 \]
+
+<p>
+  If you are an risk seeker, you may choose asset B, because you can potentially get a higher payout. MPT assumes that investors prefer asset A since both assets have the same expected payout, but asset A has less risk.
+</p>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/03 Portfolio.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/03 Portfolio.html
new file mode 100755
index 0000000..493ae8b
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/03 Portfolio.html	
@@ -0,0 +1,102 @@
+<p>
+  Suppose we invest some fraction \(w_1, w_2, \dots, w_n\) of our wealth in <em>n</em> risky assets (labelled 1 to <em>n</em>), and the remainder \(w_0\) in a riskless asset such as cash in a bank account.
+</p>
+
+<p>
+  Clearly \(w_0 + w_1 + \dots + w_n = 1\) since our wealth comprises all those assets.
+</p>
+
+<p>
+  Let \(R_0, R_1, \dots, R_n\) be the respective asset returns, then our portfolio return is
+</p>
+
+\[ R_P = w_0 R_0 + w_1 R_1 + \dots + w_n R_n \]
+
+<p>
+  Alternatively, we can eliminate \(w_0\) to get
+</p>
+\[ R_P - R_0 = w_1 (R_1 - R_0) + \dots + w_n (R_n - R_0) \]
+
+<p>
+  Our <strong>expected</strong> portfolio return is
+</p>
+\[ \mathbb{E}(R_P) = w_0 R_0 + w_1 \mathbb{E}(R_1) + \dots + w_n \mathbb{E}(R_n) \]
+
+<p>
+  Note that \( \mathbb{E}(R_0) = R_0 \) since the riskless return is known with certainty, by definition.
+</p>
+<a name="correlation"></a>
+<h4>Correlation</h4>
+
+<p>
+  Before computing portfolio risk, we need to first understand covariance and correlation. They measure the linear relationship between two random variables.
+</p>
+
+<p>
+  The covariance of two random variables X and Y is defined as
+</p>
+\[ \text{Cov}(X, Y) = \mathbb{E} \left[ (X-\mathbb{E}(X)) (Y-\mathbb{E}(Y)) \right] \]
+
+<p>
+  The correlation of X and Y, which is always between &minus;1 and 1, is their covariance after being standardized:
+</p>
+\[ \text{Corr}(X, Y) = \text{Cov} \left( \frac{X-\mathbb{E}(X)}{\sigma_X}, \frac{Y-\mathbb{E}(Y)}{\sigma_Y} \right) = \frac{\text{Cov}(X, Y)}{\sigma_X \sigma_Y} \]
+
+<h4>Risk</h4>
+
+<p>
+  Now we are ready to compute portfolio risk, as measured by the variance of portfolio returns:
+</p>
+\[ \text{Var}(R_P) = \text{Var}(w_0 R_0 + w_1 R_1 + \dots + w_n R_n) \]
+
+<p>
+  Recall that \( \text{Var}(X + c) = \text{Var}(X) \) if <em>c</em> is a known constant, so the term \( w_0 R_0 \) involving the riskless return can be omitted. It will be convenient to use <a href="https://www.mathsisfun.com/algebra/sigma-notation.html" target="_blank">sigma notation</a>:
+</p>
+
+\[ \text{Var}(R_P) = \text{Var} \left( \sum_{k=1}^n w_k R_k \right) \]
+\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k R_k - \mathbb{E} \left( \sum_{k=1}^n w_k R_k \right) \right)^2\right] \]
+\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k \, \left( R_k - \mathbb{E}(R_k) \right) \right)^2\right] \]
+
+<p>
+  So we have a squared sum of <em>n</em> terms. How do we expand it?
+</p>
+\[ \left( \sum_{k=1}^n u_k \right)^2 = (u_1 + \dots + u_n) \, (u_1 + \dots + u_n) \]
+
+<p>
+  If we expand the brackets on the right hand side, every term has the form \( u_i u_j \) where <em>i</em> and <em>j</em> can be 1, 2, ... , or <em>n</em>.
+</p>
+\[ \left( \sum_{k=1}^n u_k \right)^2 = \sum_{i=1}^n \sum_{j=1}^n u_i u_j \]
+
+<p>
+  Therefore
+</p>
+\[ \text{Var}(R_P) = \mathbb{E} \left[ \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, (R_i - \mathbb{E}(R_i)) (R_j - \mathbb{E}(R_j)) \right] \]
+\[ = \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, \text{Cov}(R_i, R_j) \]
+
+<p>
+  The last step arises from the definition of <a href="#correlation">covariance</a>. The only thing left is to express portfolio risk in matrix notation:
+</p>
+
+\[ \text{Var}(R_P) = \mathbf{w}^T \Sigma \mathbf{w} \]
+<p>
+  where
+</p>
+\[ \mathbf{w} = \begin{pmatrix} w_1 \\ \vdots \\ w_n \end{pmatrix} \qquad
+\Sigma = \begin{bmatrix}
+\text{Cov}(R_1, R_1) & \text{Cov}(R_1, R_2) & ... & \text{Cov}(R_1, R_n) \\
+\text{Cov}(R_2, R_1) & \text{Cov}(R_2, R_2) & ... & \text{Cov}(R_2, R_n) \\
+\vdots & \vdots & \ddots & \vdots \\
+\text{Cov}(R_n, R_1) & \text{Cov}(R_n, R_2) & ... & \text{Cov}(R_n, R_n)
+\end{bmatrix} \]
+
+<h4>Intuition</h4>
+
+<p>
+  How can we make sense of portfolio risk? Consider a simple case with a riskless asset and only <em>n</em> = 2 risky assets.
+</p>
+
+\[ \text{Var}(R_P) = w_A^2 \text{Var}(R_A) + w_B^2 \text{Var}(R_B) + 2w_A w_B \text{Cov}(R_A, R_B) \]
+
+<p>
+  Portfolio risk can be reduced by choosing two assets that are negatively correlated. This is the benefit of diversification.
+</p>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html
new file mode 100755
index 0000000..51a1bf9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html	
@@ -0,0 +1,65 @@
+<p>
+  We now try to find a portfolio \( \mathbf{w} = (w_1, ..., w_n) \) that minimizes risk and maximizes return.
+</p>
+
+<p>
+  The chart below has risk (standard deviation of returns) on the horizontal axis and expected return on the vertical axis. The 10 black points represent individual stocks, while each green / blue point is a portfolio of stocks:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial12-portfolio1.png" alt="CAPM portfolio1"/>
+
+<p>
+  Notice that all points (i.e. stocks and portfolios) are enclosed by a hyperbola, known as the <strong>efficient frontier</strong>.
+  All portfolios on the efficient frontier have the maximum expected return for a given level of risk, if we only consider portfolios of risky stocks. Can we achieve higher returns by including a riskless asset? Yes.
+</p>
+
+<h4>Capital Market Line</h4>
+
+<p>
+  The black line on the chart is the Capital Market Line (CML). It is <strong>tangent</strong> to the efficient frontier and cuts the vertical axis at the riskfree return. The point of tangency represents the so-called <strong>market portfolio</strong>.
+  Every point on the CML represents a portfolio comprising the market portfolio and riskless asset in some proportion. Why?
+  Suppose some fraction <em>w</em> of a CML portfolio is the market portfolio, and the remainder (1 &minus; <em>w</em>) is the riskless asset. Then its expected return is
+</p>
+
+\[ \mathbb{E} (R_P) = w \mathbb{E} (R_{\text{market}}) + (1-w) R_0 \]
+
+<p>
+  Since there is only <em>n</em> = 1 risky asset, the variance of the CML portfolio return is
+</p>
+
+\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \]
+
+<p>
+  Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight:
+</p>
+
+\[ \sigma_P = w \sigma_{\text{market}} \]
+
+<p>
+  This equation can be used to eliminate <em>w</em> in the calculation of expected return:
+</p>
+
+\[ \mathbb{E} (R_P) = R_0 + \frac{\mathbb{E} (R_{\text{market}}) - R_0}{\sigma_{\text{market}}} \sigma_P \]
+
+<p>
+  This proves that when \( \mathbb{E} (R_P) \) is plotted against \( \sigma_P \), we will obtain a straight line: the CML.
+</p>
+
+<h4>Portfolio Selection</h4>
+
+<p>
+  Why is the CML significant? For any given level of risk, CML portfolios have a higher return than those on the efficient frontier, so investors should select any of them according to their risk tolerance.
+  Risk-averse investors may give the riskless asset a larger weight in their portfolio. Risk-seeking investors may borrow money (i.e. sell the riskless asset) to invest >100% of their wealth in the market portfolio.
+  Regardless of their risk tolerances, all investors should hold the same stocks in the same proportion in the market portfolio. In other words, they should not pick stocks according to their risk tolerance.
+</p>
+
+<h4>Diversification</h4>
+
+<p>
+  What happens to the efficient frontier and hence the CML if we have only 3 stocks (IBM, GE, and PFE) instead of 10?
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial12-portfolio2.png" alt="CAPM portfolio2"/>
+
+<p>
+  Since we have fewer stocks to choose from, it's not too surprising that our maximum expected return is lower for any level of risk. This demonstrates why diversification is often said to be a "free lunch" in investing.
+</p>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/05 Summary.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/05 Summary.html
new file mode 100755
index 0000000..569da12
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/05 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we have learnt about the modern portfolio theory. It recommends investors to spread their wealth across many asset classes to maximize returns while minimizing risk. In the next chapter, we will introduce the Capital Asset Pricing Model.
+</p>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
new file mode 100755
index 0000000..fd74d48
--- /dev/null
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -0,0 +1,4 @@
+<p>
+  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=03b5af43deef78e3a81c9a8322069567'></script>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html
deleted file mode 100755
index 4b8c325..0000000
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.html	
+++ /dev/null
@@ -1,155 +0,0 @@
-The Modern Portfolio Theory (MPT) suggests how investors should spread their wealth across various assets to minimize risk and maximize return.
-
-This chapter is mathematically intense, so don't feel demoralized if you don't understand it on your first reading.
-
-<h2>Risk Aversion</h2>
-
-In portfolio theory, the riskiness of an asset is often measured by the variance (or standard deviation) of its returns. Risk-averse investors do not want their wealth to fluctuate wildly.
-
-Risk aversion can be illustrated with a simple example. Which of the following assets do you prefer?
-<ul>
-    <li>Asset A pays $200 or $0 with 50% probability each.</li>
-    <li>Asset B pays $400 or &minus;$200 (i.e. you lose $200) with 50% probability each.</li>
-</ul>
-
-The expected payouts of A and B are:
-\[ \mathbb{E}(A) = 0.5 \times 200 + 0.5 \times 0 = 100 \]
-\[ \mathbb{E}(B) = 0.5 \times 400 + 0.5 \times (-200) = 100 \]
-
-The standard deviation of their payouts are:
-\[ \sigma_A = \sqrt{0.5(200-100)^2 + 0.5(0-100)^2} = 100 \]
-\[ \sigma_A = \sqrt{0.5(400-100)^2 + 0.5(-200-100)^2} = 300 \]
-
-If you are an risk seeker, you may choose asset B, because you can potentially get a higher payout. MPT assumes that investors prefer asset A since both assets have the same expected payout, but asset A has less risk.
-
-<h2>Portfolio</h2>
-
-Suppose we invest some fraction \(w_1, w_2, \dots, w_n\) of our wealth in <em>n</em> risky assets (labelled 1 to <em>n</em>), and the remainder \(w_0\) in a riskless asset such as cash in a bank account.
-
-Clearly \(w_0 + w_1 + \dots + w_n = 1\) since our wealth comprises all those assets.
-
-Let \(R_0, R_1, \dots, R_n\) be the respective asset returns, then our portfolio return is
-\[ R_P = w_0 R_0 + w_1 R_1 + \dots + w_n R_n \]
-
-Alternatively, we can eliminate \(w_0\) to get
-\[ R_P - R_0 = w_1 (R_1 - R_0) + \dots + w_n (R_n - R_0) \]
-
-Our <strong>expected</strong> portfolio return is
-\[ \mathbb{E}(R_P) = w_0 R_0 + w_1 \mathbb{E}(R_1) + \dots + w_n \mathbb{E}(R_n) \]
-
-Note that \( \mathbb{E}(R_0) = R_0 \) since the riskless return is known with certainty, by definition.
-<a name="correlation"></a>
-<h3>Correlation</h3>
-
-Before computing portfolio risk, we need to first understand covariance and correlation. They measure the linear relationship between two random variables.
-
-The covariance of two random variables X and Y is defined as
-\[ \text{Cov}(X, Y) = \mathbb{E} \left[ (X-\mathbb{E}(X)) (Y-\mathbb{E}(Y)) \right] \]
-
-The correlation of X and Y, which is always between &minus;1 and 1, is their covariance after being standardized:
-\[ \text{Corr}(X, Y) = \text{Cov} \left( \frac{X-\mathbb{E}(X)}{\sigma_X}, \frac{Y-\mathbb{E}(Y)}{\sigma_Y} \right) = \frac{\text{Cov}(X, Y)}{\sigma_X \sigma_Y} \]
-
-<h3>Risk</h3>
-
-Now we are ready to compute portfolio risk, as measured by the variance of portfolio returns:
-\[ \text{Var}(R_P) = \text{Var}(w_0 R_0 + w_1 R_1 + \dots + w_n R_n) \]
-
-Recall that \( \text{Var}(X + c) = \text{Var}(X) \) if <em>c</em> is a known constant, so the term \( w_0 R_0 \) involving the riskless return can be omitted. It will be convenient to use <a href="https://www.mathsisfun.com/algebra/sigma-notation.html" target="_blank">sigma notation</a>:
-
-\[ \text{Var}(R_P) = \text{Var} \left( \sum_{k=1}^n w_k R_k \right) \]
-\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k R_k - \mathbb{E} \left( \sum_{k=1}^n w_k R_k \right) \right)^2\right] \]
-\[ = \mathbb{E} \left[\left( \sum_{k=1}^n w_k \, \left( R_k - \mathbb{E}(R_k) \right) \right)^2\right] \]
-
-So we have a squared sum of <em>n</em> terms. How do we expand it?
-\[ \left( \sum_{k=1}^n u_k \right)^2 = (u_1 + \dots + u_n) \, (u_1 + \dots + u_n) \]
-
-If we expand the brackets on the right hand side, every term has the form \( u_i u_j \) where <em>i</em> and <em>j</em> can be 1, 2, ... , or <em>n</em>.
-\[ \left( \sum_{k=1}^n u_k \right)^2 = \sum_{i=1}^n \sum_{j=1}^n u_i u_j \]
-
-Therefore
-\[ \text{Var}(R_P) = \mathbb{E} \left[ \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, (R_i - \mathbb{E}(R_i)) (R_j - \mathbb{E}(R_j)) \right] \]
-\[ = \sum_{i=1}^n \sum_{j=1}^n w_i \, w_j \, \text{Cov}(R_i, R_j) \]
-
-The last step arises from the definition of <a href="#correlation">covariance</a>. The only thing left is to express portfolio risk in matrix notation:
-
-\[ \text{Var}(R_P) = \mathbf{w}^T \Sigma \mathbf{w} \]
-where
-\[ \mathbf{w} = \begin{pmatrix} w_1 \\ \vdots \\ w_n \end{pmatrix} \qquad
-\Sigma = \begin{bmatrix}
-\text{Cov}(R_1, R_1) & \text{Cov}(R_1, R_2) & ... & \text{Cov}(R_1, R_n) \\
-\text{Cov}(R_2, R_1) & \text{Cov}(R_2, R_2) & ... & \text{Cov}(R_2, R_n) \\
-\vdots & \vdots & \ddots & \vdots \\
-\text{Cov}(R_n, R_1) & \text{Cov}(R_n, R_2) & ... & \text{Cov}(R_n, R_n)
-\end{bmatrix} \]
-
-<h3>Intuition</h3>
-
-How can we make sense of portfolio risk? Consider a simple case with a riskless asset and only <em>n</em> = 2 risky assets.
-
-\[ \text{Var}(R_P) = w_A^2 \text{Var}(R_A) + w_B^2 \text{Var}(R_B) + 2w_A w_B \text{Cov}(R_A, R_B) \]
-
-Portfolio risk can be reduced by choosing two assets that are negatively correlated. This is the benefit of diversification.
-
-<h2>Mean-Variance Analysis</h2>
-
-We now try to find a portfolio \( \mathbf{w} = (w_1, ..., w_n) \) that minimizes risk and maximizes return.
-
-The chart below has risk (standard deviation of returns) on the horizontal axis and expected return on the vertical axis. The 10 black points represent individual stocks, while each green / blue point is a portfolio of stocks:
-
-<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
-
-Notice that all points (i.e. stocks and portfolios) are enclosed by a hyperbola, known as the <strong>efficient frontier</strong>.
-
-All portfolios on the efficient frontier have the maximum expected return for a given level of risk, if we only consider portfolios of risky stocks. Can we achieve higher returns by including a riskless asset? Yes.
-
-<h3>Capital Market Line</h3>
-
-The black line on the chart is the Capital Market Line (CML). It is <strong>tangent</strong> to the efficient frontier and cuts the vertical axis at the riskfree return. The point of tangency represents the so-called <strong>market portfolio</strong>.
-
-Every point on the CML represents a portfolio comprising the market portfolio and riskless asset in some proportion. Why?
-
-Suppose some fraction <em>w</em> of a CML portfolio is the market portfolio, and the remainder (1 &minus; <em>w</em>) is the riskless asset. Then its expected return is
-
-\[ \mathbb{E} (R_P) = w \mathbb{E} (R_{\text{market}}) + (1-w) R_0 \]
-
-Since there is only <em>n</em> = 1 risky asset, the variance of the CML portfolio return is
-
-\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \]
-
-Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight:
-
-\[ \sigma_P = w \sigma_{\text{market}} \]
-
-This equation can be used to eliminate <em>w</em> in the calculation of expected return:
-
-\[ \mathbb{E} (R_P) = R_0 + \frac{\mathbb{E} (R_{\text{market}}) - R_0}{\sigma_{\text{market}}} \sigma_P \]
-
-This proves that when \( \mathbb{E} (R_P) \) is plotted against \( \sigma_P \), we will obtain a straight line: the CML.
-
-<h3>Portfolio Selection</h3>
-
-Why is the CML significant? For any given level of risk, CML portfolios have a higher return than those on the efficient frontier, so investors should select any of them according to their risk tolerance.
-
-Risk-averse investors may give the riskless asset a larger weight in their portfolio. Risk-seeking investors may borrow money (i.e. sell the riskless asset) to invest >100% of their wealth in the market portfolio.
-
-Regardless of their risk tolerances, all investors should hold the same stocks in the same proportion in the market portfolio. In other words, they should not pick stocks according to their risk tolerance.
-
-<h3>Diversification</h3>
-
-What happens to the efficient frontier and hence the CML if we have only 3 stocks (IBM, GE, and PFE) instead of 10?
-
-<img class="aligncenter size-full wp-image-2155" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown2.png" alt="" width="1184" height="592" />
-
-Since we have fewer stocks to choose from, it's not too surprising that our maximum expected return is lower for any level of risk.
-
-This demonstrates why diversification is often said to be a "free lunch" in investing.
-
-<h2>Summary</h2>
-
-In this chapter we have learnt about the modern portfolio theory. It recommends investors to spread their wealth across many asset classes to maximize returns while minimizing risk. In the next chapter, we will introduce the Capital Asset Pricing Model.
-
-<h2>Algorithm:</h2>
-
-Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=71761bc8ce01e7c81b5ea91f54a82640' ></script>
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/01 Introduction.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/01 Introduction.html
new file mode 100755
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--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the financial literature, you may hear terms like the "beta" or "market risk" of an asset. This chapter will explain where these terms come from and how they can be useful.
+</p>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/02 Capital Asset Pricing Model.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/02 Capital Asset Pricing Model.html
new file mode 100755
index 0000000..ad70283
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/02 Capital Asset Pricing Model.html	
@@ -0,0 +1,36 @@
+<p>
+  As we shall see later, the name "Asset Pricing" is a bit misleading because the CAPM tells us the expected return, rather than the price, of an asset. In the last chapter, we introduced the Capital Market Line (CML) shown in black:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial13-capm1.png" alt="Tutorial13-capm1"/>
+<p>
+  All investors should hold a portfolio on the CML, which is constructed by investing some fraction <em>w</em> of our wealth in the market portfolio and the remainder (1 &minus; <em>w</em>) in the riskless asset. So the return on a CML portfolio is
+</p>
+\[ R = w R_{\text{market}} + (1-w) R_0 \]
+
+<p>
+  If we let &beta; = w, then the equation above becomes
+</p>
+\[ R - R_0 = \beta (R_{\text{market}} - R_0) \]
+
+<p>
+  Notice that &beta; is a measure of how sensitive our CML portfolio return is to the market return. Taking expectation on both sides results in the CAPM:
+</p>
+\[ \mathbb{E}(R) - R_0 = \beta (\mathbb{E} (R_{\text{market}}) - R_0) \]
+
+<p>
+  Taking covariance on both sides instead yields
+</p>
+\[ \text{Cov} (R - R_0, R_{\text{market}}) = \beta \text{Cov} (R_{\text{market}} - R_0, R_{\text{market}}) \]
+
+<p>
+  Now apply two basic facts about covariance:
+</p>
+<ul>
+    <li>\( \text{Cov} (X + c, Y) = \text{Cov} (X, Y) \) where \( c \) is constant</li>
+    <li>\( \text{Cov} (X, X) = \text{Var} (X) \)</li>
+</ul>
+
+<p>
+  Hence we obtain
+</p>
+\[ \beta = \frac {\text{Cov} (R, R_{\text{market}})} {\text{Var} (R_{\text{market}})} \]
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/03 Computing beta in Practice.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/03 Computing beta in Practice.html
new file mode 100755
index 0000000..3bfb5b9
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/03 Computing beta in Practice.html	
@@ -0,0 +1,16 @@
+<p>
+  While the Capital Asset Pricing Model is straightforward, applying it may not be. We first need to choose the timeframe for computing returns: should we use daily, weekly or monthly returns? Then we need to consider the number of data points available for linear regression. For example, if we compute beta using monthly returns in the past 1 year, then we only have 12 data points which is too few.
+  Furthermore, the &beta; of an asset can change over time. The following plot is the daily rolling beta of GE stock with a 6-month rolling windows:
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial13-rolling-beta2.png" alt="Tutorial13-rolling-beta2.png"/>
+
+<p>
+  The &beta; of GE ranged from 0.1 to 0.5 approximately. This is why you need to be careful when using &beta;. It makes no sense to talk about &beta; without a timeframe in mind.
+</p>
+
+<p>
+  The following graph is the rolling p-value of beta. The p-value stays close to zero most of the time. However, during some period it suddenly increased close to 0.1, which corresponds to a 90% confidence interval. This might be caused by some mispricing or market turmoil.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial13-Rolling-p-value3.png" alt="Tutorial13-Rolling-p-value3.png"/>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/04 Market-Neutral.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/04 Market-Neutral.html
new file mode 100755
index 0000000..286f056
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/04 Market-Neutral.html	
@@ -0,0 +1,53 @@
+<p>
+  A portfolio is market-neutral if its <strong>&beta; is zero</strong>. In other words, the portfolio's returns are uncorrelated with market returns. We say that it has no "market risk". Some classical market-neutral strategies are pairs trading, beta-hedged equity portfolio and other derivatives strategies.
+  We have daily returns of Dow 30 stocks from March 2012 to Jan 2015. For each stock, its &beta; on any given day is computed using the past 6 months' returns. As this 6-month window moves forward in time, &beta; will of course change.
+  Once we know how each stock's &beta; has changed over time, we can ask: which stocks' betas are correlated with each other? The table below shows the correlation between each stock's beta.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial13-correlation4.png" alt="Tutorial13-correlation4.png"/>
+<p>
+  How can we construct a market-neutral portfolio? Consider two stocks A and B:
+</p>
+\[ R_A = R_0 + \beta_A (R_{\text{market}} - R_0) \]
+\[ R_B = R_0 + \beta_B (R_{\text{market}} - R_0) \]
+
+<p>
+  Let's allocate <em>w</em> on stock A and (1 &minus; <em>w</em>) on stock B, then market neutrality means
+</p>
+\[ w\beta_A + (1-w) \beta_B = 0 \qquad \Rightarrow \qquad
+   w = \frac{\beta_B}{\beta_B - \beta_A} \]
+
+<p>
+  As mentioned earlier, \(\beta_A\) and \(\beta_B\) will change with time, so will \(w\) in a market-neutral portfolio. However we can achieve market neutrality with constant \(w\) so long as \(\beta_A\) and \(\beta_B\) have a linear relationship:
+</p>
+\[ \beta_A = m\beta_B + c \]
+
+<p>
+  Then eliminate \(\beta_A\) to get
+</p>
+\[ w = \frac{\beta_B}{(1-m) \beta_B - c} \]
+
+<p>
+  If <em>c</em> &approx; 0 then <em>w</em> is roughly constant:
+</p>
+\[ w = \frac{1}{1-m} \]
+
+<p>
+  Note: If <em>c</em> is signficant, then we need 3 stocks to get zero net beta. Usually 2 stocks are sufficient to cancel out most of the market risk.
+</p>
+
+<h3>Example</h3>
+
+<p>
+  The 6-month rolling betas of PG and KO stocks have a correlation of 93%. The linear relationship between their betas is
+</p>
+\[ \beta_{KO} = -0.0097 + 0.969 \beta_{PG} \]
+
+<p>
+  Therefore the market neutral weights are
+</p>
+\[ w_{KO} = \frac{1}{1-0.969} = 32.3 \qquad w_{PG} = 1 - w_{KO} = -31.3 \]
+
+<p>
+  We tested this portfolio both in-sample (March 2012 to Jan 2015) and out-of-sample. It achieved a beta of 0.01 and -0.004 respectively. See the backtest below.
+</p>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/05 Summary.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/05 Summary.html
new file mode 100755
index 0000000..102ee5d
--- /dev/null
+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/05 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  This chapter has explained what market risk means in the context of CAPM, and how market risk can be reduced. In the next chapter we will generalize CAPM to multi-factor models, such as the Fama-French models.
+</p>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/06 Algorithm.html
new file mode 100755
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+++ b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/06 Algorithm.html	
@@ -0,0 +1,2 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8bac244b346cbd9bc3b39dca76192919'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5c75197259238b36a6cea72832c9e709'></script>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html
deleted file mode 100755
index 392b745..0000000
--- a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.html	
+++ /dev/null
@@ -1,92 +0,0 @@
-In the financial literature, you may hear terms like the "beta" or "market risk" of an asset. This chapter will explain where these terms come from and how they can be useful.
-
-<h2>Capital Asset Pricing Model (CAPM)</h2>
-
-As we shall see later, the name "Asset Pricing" is a bit misleading because the CAPM tells us the expected return, rather than the price, of an asset.
-
-In the last chapter, we introduced the Capital Market Line (CML) shown in black:
-
-<img class="aligncenter size-full wp-image-2154" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-2.png" alt="" width="1184" height="592" />
-
-All investors should hold a portfolio on the CML, which is constructed by investing some fraction <em>w</em> of our wealth in the market portfolio and the remainder (1 &minus; <em>w</em>) in the riskless asset. So the return on a CML portfolio is
-\[ R = w R_{\text{market}} + (1-w) R_0 \]
-
-If we let &beta; = w, then the equation above becomes
-\[ R - R_0 = \beta (R_{\text{market}} - R_0) \]
-
-Notice that &beta; is a measure of how sensitive our CML portfolio return is to the market return. Taking expectation on both sides results in the CAPM:
-\[ \mathbb{E}(R) - R_0 = \beta (\mathbb{E} (R_{\text{market}}) - R_0) \]
-
-Taking covariance on both sides instead yields
-\[ \text{Cov} (R - R_0, R_{\text{market}}) = \beta \text{Cov} (R_{\text{market}} - R_0, R_{\text{market}}) \]
-
-Now apply two basic facts about covariance:
-<ul>
-    <li>\( \text{Cov} (X + c, Y) = \text{Cov} (X, Y) \) where \( c \) is constant</li>
-    <li>\( \text{Cov} (X, X) = \text{Var} (X) \)</li>
-</ul>
-
-Hence we obtain
-\[ \beta = \frac {\text{Cov} (R, R_{\text{market}})} {\text{Var} (R_{\text{market}})} \]
-
-<h2>Computing &beta; in Practice</h2>
-
-While the Capital Asset Pricing Model is straightforward, applying it may not be. We first need to choose the timeframe for computing returns: should we use daily, weekly or monthly returns? Then we need to consider the number of data points available for linear regression. For example, if we compute beta using monthly returns in the past 1 year, then we only have 12 data points which is too few.
-
-Furthermore, the &beta; of an asset can change over time. The following plot is the daily rolling beta of GE stock with a 6-month rolling windows:
-
-<img class="aligncenter size-full wp-image-2161" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-3.png" alt="" width="1156" height="558" />
-
-The &beta; of GE ranged from 0.1 to 0.5 approximately. This is why you need to be careful when using &beta;. It makes no sense to talk about &beta; without a timeframe in mind.
-
-The following graph is the rolling p-value of beta. The p-value stays close to zero most of the time. However, during some period it suddenly increased close to 0.1, which corresponds to a 90% confidence interval. This might be caused by some mispricing or market turmoil.
-
-<img class="aligncenter size-full wp-image-2164" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-4.png" alt="" width="1163" height="558" />
-
-<h2>Market-Neutral</h2>
-
-A portfolio is market-neutral if its <strong>&beta; is zero</strong>. In other words, the portfolio's returns are uncorrelated with market returns. We say that it has no "market risk". Some classical market-neutral strategies are pairs trading, beta-hedged equity portfolio and other derivatives strategies.
-
-We have daily returns of Dow 30 stocks from March 2012 to Jan 2015. For each stock, its &beta; on any given day is computed using the past 6 months' returns. As this 6-month window moves forward in time, &beta; will of course change.
-
-Once we know how each stock's &beta; has changed over time, we can ask: which stocks' betas are correlated with each other? The table below shows the correlation between each stock's beta.
-
-<img src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/07/Unknown-5.png" alt="" width="1056" height="578" class="aligncenter size-full wp-image-2174" />
-
-How can we construct a market-neutral portfolio? Consider two stocks A and B:
-\[ R_A = R_0 + \beta_A (R_{\text{market}} - R_0) \]
-\[ R_B = R_0 + \beta_B (R_{\text{market}} - R_0) \]
-
-Let's allocate <em>w</em> on stock A and (1 &minus; <em>w</em>) on stock B, then market neutrality means
-\[ w\beta_A + (1-w) \beta_B = 0 \qquad \Rightarrow \qquad
-   w = \frac{\beta_B}{\beta_B - \beta_A} \]
-
-As mentioned earlier, \(\beta_A\) and \(\beta_B\) will change with time, so will \(w\) in a market-neutral portfolio. However we can achieve market neutrality with constant \(w\) so long as \(\beta_A\) and \(\beta_B\) have a linear relationship:
-\[ \beta_A = m\beta_B + c \]
-
-Then eliminate \(\beta_A\) to get
-\[ w = \frac{\beta_B}{(1-m) \beta_B - c} \]
-
-If <em>c</em> &approx; 0 then <em>w</em> is roughly constant:
-\[ w = \frac{1}{1-m} \]
-
-Note: If <em>c</em> is signficant, then we need 3 stocks to get zero net beta. Usually 2 stocks are sufficient to cancel out most of the market risk.
-
-<h3>Example</h3>
-
-The 6-month rolling betas of PG and KO stocks have a correlation of 93%. The linear relationship between their betas is
-\[ \beta_{KO} = -0.0097 + 0.969 \beta_{PG} \]
-
-Therefore the market neutral weights are
-\[ w_{KO} = \frac{1}{1-0.969} = 32.3 \qquad w_{PG} = 1 - w_{KO} = -31.3 \]
-
-We tested this portfolio both in-sample (March 2012 to Jan 2015) and out-of-sample. It achieved a beta of 0.01 and -0.004 respectively. See the backtest below.
-
-<h2>Summary</h2>
-
-This chapter has explained what market risk means in the context of CAPM, and how market risk can be reduced. In the next chapter we will generalize CAPM to multi-factor models, such as the Fama-French models.
-
-<h2>Algorihtm</h2>
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8bac244b346cbd9bc3b39dca76192919'></script>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5c75197259238b36a6cea72832c9e709'></script>
\ No newline at end of file
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.ipynb b/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Market Risk.ipynb
similarity index 100%
rename from 05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Capital Asset Pricing Model.ipynb
rename to 05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Market Risk.ipynb
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/01 Introduction.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/01 Introduction.html
new file mode 100755
index 0000000..2f41a50
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/01 Introduction.html	
@@ -0,0 +1,7 @@
+<p>
+  In previous chapters, we learnt that the Capital Asset Pricing Model (CAPM) treats the market return as the only factor affecting the return of any asset. This chapter will generalize CAPM to multi-factor models of the following form:
+</p>
+\[ R = \alpha + \beta_1 f_1 + \beta_2 f_2 + \dots + \beta_n f_n \]
+<p>
+  where each \(f_i\) is a factor.
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html
new file mode 100755
index 0000000..30e131f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html	
@@ -0,0 +1,20 @@
+<p>
+  This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns.[ref] <strong>Fama, E F; French, K R</strong> (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref]
+</p>
+<p>
+  The 3-factor model is
+</p>
+\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \]
+
+<p>
+  where
+</p>
+<ul>
+    <li>MKT is the excess return of the market.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
+    <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
+    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
+</ul>
+
+<p>
+  Data on these factors can be downloaded from <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">French's website</a>.
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/03 Model Test.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/03 Model Test.html
new file mode 100755
index 0000000..29d8539
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/03 Model Test.html	
@@ -0,0 +1,19 @@
+<p>
+  To test the 3-factor model, we use it to predict returns on NASDAQ US Small Cap Index and NASDAQ US Large Cap Index. We estimate the model with daily returns in the past 6 years.
+</p>
+
+<p>
+  Results for US Small Cap returns:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial14-regression1.png" alt="Tutorial14-regression1.png"/>
+
+<p>
+  The coefficient of SMB is positive, so when small caps outperform large caps, the Small Cap Index will have higher returns, which is not surprising. By comparing the t statistics of those factors, we know that MKT and SMB are more important factors driving the Small Cap Index.
+</p>
+<p>
+  Results for US Large Cap returns:
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial14-regression2.png" alt="Tutorial14-regression2.png"/>
+<p>
+  As expected, the coefficient of SMB is negative for the Large Cap Index. The coefficient of HML is quite low, which suggests that value and growth stocks take approximately the same weight in the Large Cap Index.
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html
new file mode 100755
index 0000000..5c6b67c
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
@@ -0,0 +1,12 @@
+<p>
+  The Fama-French 5-Factor model comprises two more factors:
+</p>
+<ul>
+    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
+    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
+</ul>
+
+<p>
+  RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
+  <blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote> CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that: <blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote> Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html
new file mode 100755
index 0000000..5c6b67c
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html	
@@ -0,0 +1,12 @@
+<p>
+  The Fama-French 5-Factor model comprises two more factors:
+</p>
+<ul>
+    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
+    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
+</ul>
+
+<p>
+  RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
+  <blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote> CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that: <blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote> Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html
new file mode 100755
index 0000000..549200f
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html
new file mode 100755
index 0000000..b3f9981
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
@@ -0,0 +1,10 @@
+<p>
+  Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.[ref]<strong>AQR (2013)</strong>. A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>[/ref]
+  The paper recommends picking stocks by their value, quality (profitability) and momentum.
+  The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
+  The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
+</P>
+<p>
+  In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d0f89f7876841581a8eee32cf007cd75'></script>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html
deleted file mode 100755
index 9d818b1..0000000
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/14 Fama-French Multi-Factor Models.html	
+++ /dev/null
@@ -1,96 +0,0 @@
-In previous chapters, we learnt that the Capital Asset Pricing Model (CAPM) treats the market return as the only factor affecting the return of any asset. This chapter will generalize CAPM to multi-factor models of the following form:
-\[ R = \alpha + \beta_1 f_1 + \beta_2 f_2 + \dots + \beta_n f_n \]
-where each \(f_i\) is a factor.
-
-<h2>Fama-French Three-Factor Model</h2>
-
-This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns.[ref] <strong>Fama, E F; French, K R</strong> (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref]
-
-The 3-factor model is
-\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \]
-
-where
-<ul>
-    <li>MKT is the excess return of the market.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
-    <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
-    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
-</ul>
-
-Data on these factors can be downloaded from <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">French's website</a>.
-
-<h2>Model test</h2>
-
-To test the 3-factor model, we use it to predict returns on NASDAQ US Small Cap Index and NASDAQ US Large Cap Index. We estimate the model with daily returns in the past 6 years.
-
-Results for US Small Cap returns:
-<img class="aligncenter size-full wp-image-2206" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.20.02-PM.png" alt="" width="1342" height="830" />
-
-The coefficient of SMB is positive, so when small caps outperform large caps, the Small Cap Index will have higher returns, which is not surprising. By comparing the t statistics of those factors, we know that MKT and SMB are more important factors driving the Small Cap Index.
-
-Results for US Large Cap returns:
-<img class="aligncenter size-full wp-image-2205" src="https://www.quantconnect.com/tutorials/wp-content/uploads/2017/08/Screen-Shot-2017-08-01-at-5.18.48-PM.png" alt="" width="1346" height="822" />
-
-As expected, the coefficient of SMB is negative for the Large Cap Index. The coefficient of HML is quite low, which suggests that value and growth stocks take approximately the same weight in the Large Cap Index.
-
-<h2>Factor Returns</h2>
-
-How do we find out the returns on a single factor? We can do so by constructing a <strong>tracking portfolio</strong> and computing its returns. For example, if we want a tracking portfolio of the HML factor, we only need to find 4 stocks to construct a portfolio that has MKT and SMB coefficients of 0 and HML coefficient of 1.
-
-Consider stocks A, B, C, and D whose returns can be explained by the 3-factor model:
-
-\[ R_A = \alpha_1 + \beta_{11} MKT + \beta_{12} SMB + \beta_{13} HML \]
-\[ R_B = \alpha_2 + \beta_{21} MKT + \beta_{22} SMB + \beta_{23} HML \]
-\[ \vdots \]
-
-Let their weights in the tracking portfolio be \(w_A,w_B,w_C,w_D\). We write the coefficients into a matrix:
-\[ \Sigma = \begin{pmatrix}
-   \alpha_1 &amp; \beta_{11} &amp; \beta_{12} &amp; \beta_{13} \\
-   \alpha_2 &amp; \beta_{21} &amp; \beta_{22} &amp; \beta_{23} \\
-   \alpha_3 &amp; \beta_{31} &amp; \beta_{32} &amp; \beta_{33} \\
-   \alpha_4 &amp; \beta_{41} &amp; \beta_{42} &amp; \beta_{43}
-\end{pmatrix} \]
-
-The tracking portfolio is determined by the solution to these linear equations:
-\[ w^T \Sigma = \begin{pmatrix} 0 \\ 0 \\ 0 \\ 1 \end{pmatrix} \]
-
-Economic interpretation: The 3 Fama-French factors represent "systematic risk" which cannot be reduced by diversification. Investors earn those factor returns for taking such risks. Any idiosyncratic risk, or firm-specific risk, can be diversified away and so investors are not paid to take such risks.
-
-<h2>Other Factors</h2>
-
-The Fama-French 5-Factor model comprises two more factors:
-<ul>
-    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
-    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
-</ul>
-
-RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
-
-<blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote>
-
-CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that:
-
-<blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote>
-
-Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
-
-<h2>Summary</h2>
-
-In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
-
-<h2>Algorithm</h2>
-
-Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.[ref]<strong>AQR (2013)</strong>. A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>[/ref]
-
-The paper recommends picking stocks by their value, quality (profitability) and momentum.
-
-The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
-
-The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets.
-
-There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
-
-In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015.
-
-You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=480128b21eebc545b1321e0b8caa26d1'></script>
diff --git a/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html b/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html
index cc93a2d..d087dfa 100644
--- a/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html	
+++ b/06 Introduction to Options[]/05 The Greek Letters/07 Delta, Gamma and Vega Hedging.html	
@@ -1,7 +1,6 @@
-<h2>Delta, Gamma and Vega Hedging</h2>
 <p>
   The delta of the underlying asset is always 1.0, the trader can hedge his positions by buying or selling the number of shares of the underlying asset indicated by the total delta. However, underlying asset positions have Gamma 0 because their Delta is always 1.00 (long) or -1.00 (short) and will not change. In addition, Vega of the underlying asset is also zero because the underlying asset's payoff doesn't vary depending on how its price moves (payoff is not contingent). Therefore, its price is not affected by price volatility.
 </p>
 <p>
-  As a result, in order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
+  In order to adjust Gamma and Vega, it is necessary to take a position in an option or other derivatives. However, if only one other derivative is added, either the Gamma risk or the Vega risk will be canceled out, but not both at the same time. Here we need to use 2 derivatives to make the portfolio delta, gamma, and vega neutral all at once. Note, Vega and Gamma for a portfolio is the sum of the vegas and Gammas of its constituents.
 </p>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/01 Introduction.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/01 Introduction.html
new file mode 100755
index 0000000..b9beab6
--- /dev/null
+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In the last chapter, we discussed two types of volatility: historical volatility and implied volatility. This chapter, we will further extend the concept of volatility and introduce the local volatility and the stochastic volatility.
+</p>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/02 Motivation.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/02 Motivation.html
new file mode 100755
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+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/02 Motivation.html	
@@ -0,0 +1,13 @@
+<p>
+  We already knew that volatility is a measure of the fluctuation degree of the underlying assets price series. As we discussed in the last chapter, historical volatility is the standard deviation of the price series during a certain period. It is a constant and represents the price movement in the past. However, the implied volatility is not based on the historical pricing data of stocks. It is the value of volatility parameter derived from the market quote of options in BSM pricing model. In contrast to historical volatility, implied volatility is forward-looking and varies with different options contracts.
+</p>
+<p>
+  In the Black–Scholes model, the asset’s price is modeled as a log-normal random variable, which means that the asset’s log-returns are normally distributed. One of the most significant assumptions in BSM model is that the volatility is a constant term over time.
+</p>
+\[\sigma=\sigma_{implied}\]
+<p>
+  But in the real world, it could be constant in a small time period but never constant in the long term. There is volatility skew for most options, which means the volatility is not constant across strikes.
+</p>
+<p>
+  [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]One way to capture the volatility skew is to assume that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. On the other hand, the introduction of additional sources of randomness will increase the complexity of the model. Another way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
+<p>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/03 Local Volatility.html
new file mode 100755
index 0000000..e796d68
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+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -0,0 +1,34 @@
+<h4>1. Definition</h4>
+<p>
+  The constant volatility assumption in BSM model is not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. There exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
+</p>
+<p>
+  [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
+</p>
+\[\sigma=\sigma(S_t,t)\]
+
+Therefore with local volatility model, the stochastic process followed by the stock price is
+
+\[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
+
+If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness from the stock price: \(W_t\).
+<h4>2. Model Calibration</h4>
+<p>
+  How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.
+</p>
+\[\sigma_{Local}(K,T)=\sqrt{\frac{\frac{\partial C}{\partial T}}{\frac{1}{2}K^2\frac{\partial^2 C}{\partial K^2}}}\]
+
+<p>
+  If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with different combinations of strike prices and expiration dates. For a given date, time(t) and the underlying stock price(St), a local volatility is derived from the equation that options price calculated with the local volatility equals to the market options price.
+</p>
+<p>
+  Usually, we can only get a limited number of contracts with a few strikes and maturities, we can follow steps below to get the local volatility estimation:
+<ul>
+     <li>First, use the available quoted price to calculate the implied volatilities.</li>
+     <li>Appy interpolation method to produce a smooth implied volatility surface.</li>
+     <li>Plug implied volatilities into BSM model to get all the market prices of European calls.</li>
+     <li>Calculate the local volatility according to Dupire formula. To avoid taking derivatives, we could use finite differences to approximate the derivative.</li>
+</ul>
+\[\frac{\partial C}{\partial T}\approx\frac{C(K,T+\Delta T)-C(K,T-\Delta T)}{2\Delta t}\]
+
+\[\frac{\partial^2 C}{\partial K^2}\approx\frac{C(K-\Delta K,T)-2C(K,T)+C(K+\Delta K,T)}{(\Delta K)^2}\]
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html
new file mode 100755
index 0000000..5874146
--- /dev/null
+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html	
@@ -0,0 +1,163 @@
+<h4>1. Definition</h4>
+
+<p>
+  In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock price is log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is[ref]Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011[/ref]
+</p>
+
+\[dS_t = \mu_tS_tdt+\sqrt{v_t}S_tdW_{1,t}\]
+
+\[dv_t=-\lambda(v_t-\overline{v})\ dt+\eta\sqrt{v_t}\ dW_{2,t}\]
+
+\[dW_{1,t},\ dW_{2,t}=\rho \ dt\]
+
+<p>
+  Where the instantaneous variance of the stock price \(v_t\) itself is a stochastic process.
+</p>
+\(\lambda\) is the speed of reversion of \(v_t\) to its long-term mean \(\overline{v})\). We can think of\(\lambda\) as the rate at which the stock price variance reverts back to its long-term average value.
+
+\(\eta\) is the volatility of the variance process \(v_t\) (often called the volatility of volatility)
+
+\(W_{1,t}\) and \(W_{2,t}\) are two dependent Wiener processes with correlation coefficient \(\rho\).
+<h4>2. Simulation of the Heston Process</h4>
+<p>
+  We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization[ref]Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>[/ref].
+</p>
+\[v_{t+\Delta t}=\left(\sqrt{v_t}+\frac{1}{2}\eta\sqrt{\Delta t}W_1\right)^2-\lambda(v_t-\overline{v})\Delta t-\frac{\eta^2}{4}\Delta t\]
+<p>
+  Then, we take following steps to simulate Heston process: Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above (Here note that in options pricing, Monte Carlo method uses risk-neutral result, so here the expected return \(\mu\) should equal the risk free rate r):
+</p>
+<ul>
+     <li>Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above</li>
+     <li>We obtain \(S_{t+\Delta t}\) using\[S_{t+\Delta t}=S_t\ \text {exp}\left[(r-\frac{1}{2}v_t)\Delta t+\sqrt{v_t\Delta t}W_2\right]\]</li>
+     <li>To generate \(W_1\) and \(W_2\) with correlation \(\rho\), we first generate two independent standard normal variables \(Z_1\) and \(Z_2\), set \(W_1=Z_1\), then \[W_2=\rho Z_1+\sqrt{1-\rho^2}Z_2\]</li>
+</ul>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+from numpy import sqrt, exp
+import numpy as np
+
+def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):
+    """
+    option_type:    'p' put option 'c' call option
+    S0:              the spot price of underlying stock
+    K:              the strike price
+    T:              the maturity of options
+    initial_var:    the initial value of variance
+    long_term_var:  the long term average of price variance
+    rate_reversion: the mean reversion rate for the variance
+    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)
+    corr:           the correlation between the standard normal random variables W1 and W2
+    r:              the risk free rate
+    reps:           the number of repeat for monte carlo simulation
+    steps:          the number of steps in each simulation
+    """
+    delta_t = T/float(steps)
+    payoff = 0
+    for i in range(num_reps):
+        vt = initial_var
+        st = S0
+        for j in range(steps):
+            w1 = np.random.normal(0, 1)
+            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)
+            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \
+                 - rate_reversion * (vt - long_term_var) * delta_t \
+                 - 0.25 * vol_of_vol**2 * delta_t
+            st = st * exp((r - 0.5*vt)*delta_t + sqrt(vt*delta_t) * w2)
+        if option_type == 'c':
+                payoff += max(st - K, 0)
+        elif option_type == 'p':
+                payoff += max(K - st, 0)
+
+    return (payoff/float(num_reps)) * (exp(-r*T))
+  </pre>
+  </div>
+<h4>3. Calibration of Model Parameters</h4>
+<p>
+  The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to this paper <em>Parameters recovery via calibration in the Heston model</em>[ref]Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.[/ref] for details of different error measure.
+</p>
+<p>
+  There are five parameters to be estimated in Heston model:
+</p>
+<ul>
+     <li>\(v_t\) : the initial value of the variance (Bounds of 0 and 1)</li>
+     <li>\(\overline{v}\) : the long term average variance of stock price (Bounds of 0 and 1)</li>
+     <li>\(\lambda\) : the speed of reversion (non-negativity)</li>
+     <li>\(\eta\) : the volatility of the volatility (non-negativity)</li>
+     <li>\(\rho\) : the correlation coefficient between two Wiener process (Bounds of -1 and 1)</li>
+</ul>
+<p>
+  Here we use QuantLib[ref]Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>[/ref] Python library to calibrate the parameters.
+</p>
+<p>
+  Let's look at how we can calibrate the Heston model to some market quotes. For example, let's say we are interested in trading SPDR S&amp;P 500 ETF (SPY) options with 4-months maturity. Here we choose all the options contracts written on SPY expire in 4 months. We need the strikes and the market prices of those contracts and the underlying price as the input of our objective function to minimize.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">import pandas as pd
+from numpy import sqrt,mean,log,diff
+import QuantLib as ql
+from pandas_datareader.data import Options
+import pandas_datareader.data as web
+import datetime
+opt = Options('spy', 'yahoo')
+expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]
+expiry_index = 14 # choose the contracts expire on 11/17/2017
+data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])
+strikes = list(data.index.get_level_values('Strike'))
+premium = list(data['Last'])
+day_count = ql.Actual365Fixed()
+calendar = ql.UnitedStates()
+calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year) # 08/10/2017
+spot = opt.underlying_price  # spot price is 244.82
+ql.Settings.instance().evaluationDate = calculation_date
+dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))
+risk_free_rate = 0.01
+dividend_rate = 0.0
+flat_ts = ql.YieldTermStructureHandle(
+    ql.FlatForward(calculation_date, risk_free_rate, day_count))
+dividend_ts = ql.YieldTermStructureHandle(
+    ql.FlatForward(calculation_date, dividend_rate, day_count))
+# dummy parameters
+initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;
+# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;
+process = ql.HestonProcess(flat_ts, dividend_ts,
+                           ql.QuoteHandle(ql.SimpleQuote(spot)),
+                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)
+model = ql.HestonModel(process)
+engine = ql.AnalyticHestonEngine(model)
+heston_helpers = []
+date = expiration_dates[expiry_index]
+for j, s in enumerate(strikes):
+    t = (date - calculation_date)
+    p = ql.Period(t, ql.Days)
+    sigma = premium[j]
+    helper = ql.HestonModelHelper(p, calendar, spot, s,
+                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),
+                                  flat_ts,
+                                  dividend_ts)
+    helper.setPricingEngine(engine)
+    heston_helpers.append(helper)
+lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)
+model.calibrate(heston_helpers, lm,
+                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))
+long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()
+print "long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)
+</pre>
+</div>
+<p>
+  We get the market data at 08/10/2017 and choose the contracts which expire on 11/17/2017. Then we get the following parameters estimation
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778
+</pre>
+</div>
+<p>
+  When you get the parameter estimation, you can plug the parameter values into the Heston Monte Carlo options pricing model and get the price estimation with stochastic volatility. But as we already discussed for Heston model, the introduction of randomness of volatility increases the complexity of the estimation. No matter which error measure is chosen, the objective function is highly non-linear and far from being convex and we have 5 parameters in the model. All these drawbacks of Heston models will make the estimated parameter values quite sensitive the initial guess of parameters. Therefore options prices generated by the Heston model are also parameter sensitive. From the above, we can get a sense of how computationally expensive it can be to get accurate values of options in a stochastic volatility model.
+</p>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/05 Summary.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/05 Summary.html
new file mode 100755
index 0000000..6f28de7
--- /dev/null
+++ b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/05 Summary.html	
@@ -0,0 +1,6 @@
+<p>
+  In Black–Scholes, that volatility is assumed to be constant, it is not reasonable especially for some exotic options in which the option's payoff is based on the changing volatility. Therefore we introduced the two volatility models to capture the volatility skew. The first approach, local volatility, assumes that the volatility is a deterministic function of time and the underlying asset price. This function must be chosen as to match the observed market option prices. In another stochastic volatility models, the asset price and its volatility are both assumed to be random processes.
+</p>
+<p>
+  The calibration needs the market price of the Vanilla options. When we get the model estimation we can use these models to price exotic options.
+</p>
diff --git a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html b/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html
deleted file mode 100755
index 7a437db..0000000
--- a/06 Introduction to Options[]/07 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.html	
+++ /dev/null
@@ -1,209 +0,0 @@
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-<h2>Introduction</h2>
-In the last chapter, we discussed two of the volatilities: historical volatility and the implied volatility. This chapter, we will further extend the concept of volatility and introduce the local volatility and the stochastic volatility.
-<h2>Motivation</h2>
-We already knew that volatility is a measure of the fluctuation degree of the underlying assets price series. There are many types of volatilities. As we discussed in the last chapter, historical volatility is the standard deviation of the price series during a certain period. It is a constant and represents the price movement in the past. However, the implied volatility is not based on the historical pricing data of stocks. It is the value of volatility parameter derived from the market quote of options in BSM pricing model. In contrast to historical volatility, implied volatility is forward looking and varies with different options contracts.
-<div class="page" title="Page 82">
-<div class="layoutArea">
-<div class="column">
-
-In the Black–Scholes model, the asset’s price is modeled as a log-normal random variable, which means that the asset’s log-returns are normally distributed. One of the most significant assumptions in BSM model is that the volatility is a constant term over time.
-
-\[\sigma=\sigma_{implied}\]
-
-But in the real world, it could be constant in a small time period but never constant in long term. As we discussed in the last chapter, there is volatility skew for most of the options, which means that the volatility is not constant across strikes.
-
-[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]One way to capture the volatility skew is to assume that that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. But on the other hand, the introduction of additional sources of randomness will increase the complexity of the model. The other way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
-<h2>Local Volatility</h2>
-<h3>1. Definition</h3>
-The constant volatility assumption in BSM model seems not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. That means there exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
-
-[ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]The local volatility of the underlying assets is a deterministic function of assets price and the time t.
-
-\[\sigma=\sigma(S_t,t)\]
-
-Therefore under local volatility model, the stochastic process followed by the stock price is
-
-\[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
-
-If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness fro m the stock price: \(W_t\).
-<h3>2. Model Calibration</h3>
-How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.
-
-\[\sigma_{Local}(K,T)=\sqrt{\frac{\frac{\partial C}{\partial T}}{\frac{1}{2}K^2\frac{\partial^2 C}{\partial K^2}}}\]
-
-If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with the different combination of strike prices and expires. For a given date, time(t) and the underlying stock prices(St), a local volatility is derived from the equation that options prices calculated using local volatility equal the market options prices.
-
-But most of the time, we can only get a limited number of contracts with a few strikes and maturities, we can follow the steps below to get the local volatility estimation:
-<ol>
- 	<li>First, use the available quoted price to calculate the implied volatilities.</li>
- 	<li>Appy interpolation method to produce a smooth implied volatility surface.</li>
- 	<li>Plug implied the implied volatilities into BSM model to get all the market prices of European calls.</li>
- 	<li>Calculate the local volatility according to Dupire formula. In order to avoid taking derivatives, we could use finite differences to approximate the derivatives.</li>
-</ol>
-\[\frac{\partial C}{\partial T}\approx\frac{C(K,T+\Delta T)-C(K,T-\Delta T)}{2\Delta t}\]
-
-\[\frac{\partial^2 C}{\partial K^2}\approx\frac{C(K-\Delta K,T)-2C(K,T)+C(K+\Delta K,T)}{(\Delta K)^2}\]
-
-</div>
-<div class="column">
-<h2>Stochastic Volatility</h2>
-<h3>1. Definition</h3>
-</div>
-</div>
-</div>
-In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock prices are log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is[ref]Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011[/ref]
-
-</div>
-</div>
-</div>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-\[dS_t = \mu_tS_tdt+\sqrt{v_t}S_tdW_{1,t}\]
-
-\[dv_t=-\lambda(v_t-\overline{v})\ dt+\eta\sqrt{v_t}\ dW_{2,t}\]
-
-\[dW_{1,t},\ dW_{2,t}=\rho \ dt\]
-
-Where the instantaneous variance of the stock price \(v_t\) itself is a stochastic process.
-
-\(\lambda\) is the speed of reversion of \(v_t\) to its long-term mean \(\overline{v})\). We can think of\(\lambda\) as the rate at which the stock price variance reverts back to its long term average value.
-
-\(\eta\) is the volatility of the variance process \(v_t\) (often called the volatility of volatility)
-
-\(W_{1,t}\) and \(W_{2,t}\) are two dependent Wiener processes with correlation coefficient \(\rho\).
-<h3>2. Simulation of the Heston Process</h3>
-We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization[ref]Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>[/ref].
-
-\[v_{t+\Delta t}=\left(\sqrt{v_t}+\frac{1}{2}\eta\sqrt{\Delta t}W_1\right)^2-\lambda(v_t-\overline{v})\Delta t-\frac{\eta^2}{4}\Delta t\]
-
-Then, we take the following steps to simulate Heston process: Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above (Here note that in options pricing, Monte Carlo method use risk neutral result, so here the expected return \(\mu\) should equal the risk free rate r):
-<ul>
- 	<li>Given the value of \(v_t\) at time t, we first update to \(v_{t+\Delta t}\) using the formula above</li>
- 	<li>We obtain \(S_{t+\Delta t}\) using\[S_{t+\Delta t}=S_t\ \text {exp}\left[(r-\frac{1}{2}v_t)\Delta t+\sqrt{v_t\Delta t}W_2\right]\]</li>
- 	<li>To generate \(W_1\) and \(W_2\) with correlation \(\rho\), we first generate two independent standard normal variables \(Z_1\) and \(Z_2\), set \(W_1=Z_1\), then \[W_2=\rho Z_1+\sqrt{1-\rho^2}Z_2\]</li>
-</ul>
-<pre class="prettyprint linenums">from numpy import sqrt, exp
-import numpy as np
-
-def mc_heston(option_type,S0,K,T,initial_var,long_term_var,rate_reversion,vol_of_vol,corr,r,num_reps,steps):
-    """
-    option_type:    'p' put option 'c' call option
-    S0:              the spot price of underlying stock
-    K:              the strike price
-    T:              the maturity of options
-    initial_var:    the initial value of variance
-    long_term_var:  the long term average of price variance
-    rate_reversion: the mean reversion rate for the variance
-    vol_of_vol:     the volatility of volatility(the variance of the variance of stock price)
-    corr:           the correlation between the standard normal random variables W1 and W2
-    r:              the risk free rate
-    reps:           the number of repeat for monte carlo simulation
-    steps:          the number of steps in each simulation
-    """
-    delta_t = T/float(steps)
-    payoff = 0
-    for i in range(num_reps):
-        vt = initial_var
-        st = S0
-        for j in range(steps):
-            w1 = np.random.normal(0, 1)
-            w2 = corr*w1+sqrt(1-corr**2)*np.random.normal(0, 1)
-            vt = (sqrt(vt) + 0.5 * vol_of_vol * sqrt(delta_t) * w1)**2  \
-                 - rate_reversion * (vt - long_term_var) * delta_t \
-                 - 0.25 * vol_of_vol**2 * delta_t
-            st = st * exp((r - 0.5*vt)*delta_t + sqrt(vt*delta_t) * w2)
-        if option_type == 'c':
-                payoff += max(st - K, 0)
-        elif option_type == 'p':
-                payoff += max(K - st, 0)
-
-    return (payoff/float(num_reps)) * (exp(-r*T))
-</pre>
-<h3>3. Calibration of Model Parameters</h3>
-The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from the prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to the paper <em>Parameters recovery via calibration in the Heston model</em>[ref]Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.[/ref] for details of different error measure.
-
-There are five parameters need to be estimated in Heston model:
-<ol>
- 	<li>\(v_t\) : the initial value of the variance (Bounds of 0 and 1)</li>
- 	<li>\(\overline{v}\) : the long term average variance of stock price (Bounds of 0 and 1)</li>
- 	<li>\(\lambda\) : the speed of reversion (non-negativity)</li>
- 	<li>\(\eta\) : the volatility of the volatility (non-negativity)</li>
- 	<li>\(\rho\) : the correlation coefficient between two Wiener process (Bounds of -1 and 1)</li>
-</ol>
-Here we use QuantLib[ref]Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>[/ref] Python library to calibrate the parameters.
-
-Let us look at how we can calibrate the Heston model to some market quotes. For example, let's say we are interested in trading SPDR S&amp;P 500 ETF (SPY) options with 4-months maturity. Here we choose all the options contracts written on SPY expire in 4 months. We need the strikes and the market prices of those contracts and the underlying price as the input of our objective function to minimize.
-<pre class="prettyprint linenums">import pandas as pd
-from numpy import sqrt,mean,log,diff
-import QuantLib as ql
-from pandas_datareader.data import Options
-import pandas_datareader.data as web
-import datetime
-opt = Options('spy', 'yahoo')
-expiration_dates = [ql.Date(i.day, i.month, i.year) for i in opt.expiry_dates]
-expiry_index = 14 # choose the contracts expire on 11/17/2017
-data = opt.get_call_data(expiry=opt.expiry_dates[expiry_index])
-strikes = list(data.index.get_level_values('Strike'))
-premium = list(data['Last'])
-day_count = ql.Actual365Fixed()
-calendar = ql.UnitedStates()
-calculation_date = ql.Date(opt._quote_time.day,opt._quote_time.month,opt._quote_time.year) # 08/10/2017
-spot = opt.underlying_price  # spot price is 244.82
-ql.Settings.instance().evaluationDate = calculation_date
-dividend_yield = ql.QuoteHandle(ql.SimpleQuote(0.0))
-risk_free_rate = 0.01
-dividend_rate = 0.0
-flat_ts = ql.YieldTermStructureHandle(
-    ql.FlatForward(calculation_date, risk_free_rate, day_count))
-dividend_ts = ql.YieldTermStructureHandle(
-    ql.FlatForward(calculation_date, dividend_rate, day_count))
-# dummy parameters
-initial_var = 0.2; rate_reversion = 0.5; long_term_var = 0.2; corr = -0.75; vol_of_vol = 0.2;
-# initial_var = 0.2; rate_reversion = 0.15; long_term_var = 0.6; corr = -0.75; vol_of_vol = 0.2;
-process = ql.HestonProcess(flat_ts, dividend_ts,
-                           ql.QuoteHandle(ql.SimpleQuote(spot)),
-                           initial_var, rate_reversion, long_term_var, vol_of_vol, corr)
-model = ql.HestonModel(process)
-engine = ql.AnalyticHestonEngine(model)
-heston_helpers = []
-date = expiration_dates[expiry_index]
-for j, s in enumerate(strikes):
-    t = (date - calculation_date)
-    p = ql.Period(t, ql.Days)
-    sigma = premium[j]
-    helper = ql.HestonModelHelper(p, calendar, spot, s,
-                                  ql.QuoteHandle(ql.SimpleQuote(sigma)),
-                                  flat_ts,
-                                  dividend_ts)
-    helper.setPricingEngine(engine)
-    heston_helpers.append(helper)
-lm = ql.LevenbergMarquardt(1e-8, 1e-8, 1e-8)
-model.calibrate(heston_helpers, lm,
-                 ql.EndCriteria(500, 50, 1.0e-8,1.0e-8, 1.0e-8))
-long_term_var, rate_reversion, vol_of_vol, corr, initial_var = model.params()
-print "long_term_var = %f, rate_reversion = %f, vol_of_vol = %f, corr = %f, initial_var = %f" % (long_term_var, rate_reversion, vol_of_vol, corr, initial_var)
-</pre>
-We get the market data at 08/10/2017 and choose the contracts which expire on 11/17/2017. Then we get the following parameters estimation
-<pre class="prettyprint linenums">long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778
-</pre>
-When you get the parameter estimation, you can plug the parameter values into the Heston Monte Carlo options pricing model and get the price estimation with stochastic volatility. But as we already discussed for Heston model, the introduction of randomness of volatility increases the complexity of the estimation. No matter which error measure is chosen, the objective function is highly non-linear and far from being convex and we have 5 parameters in the model. All these drawbacks of Heston models will make the estimated parameter values quite sensitive the initial guess of parameters. Therefore options prices generated by the Heston model are also parameter sensitive. From the above, we can get a sense of how computationally expensive it can be to get accurate values of options in a stochastic volatility model.
-<h2> Summary</h2>
-<div class="page" title="Page 84">
-<div class="layoutArea">
-<div class="column">
-
-In Black–Scholes, that volatility is assumed to be constant, it is not reasonable especially for some exotic options in which the option's payoff is based on the changing volatility. Therefore we introduced the two volatility models to capture the volatility skew.The first approach, local volatility, assumes that the volatility is a deterministic function of time and the underlying asset price. This function must be chosen as to match the observed market option prices. In another stochastic volatility models, the asset price and its volatility are both assumed to be random processes.
-
-The calibration of these models needs the market price of the Vanilla options. When we get the model estimation we can use these models to pricing the corresponding exotic options.
-
-</div>
-</div>
-</div>
-</div>
-</div>
-</div>

From cbe41719422e1db8c2e8d74a3b6b85cc24cb9333 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 7 Jun 2018 18:39:22 -0400
Subject: [PATCH 046/753] update tutorial landing page

---
 .../02 Tutorials.html                         |  55 +++--
 .../02 Introduction to Options/00 About.html  |   1 -
 .../02 Tutorials.html                         |  94 ++++-----
 .../03 Applied Options/02 Tutorials.html      |  18 +-
 .../01 Introduction.html                      |   0
 .../02 Capital Asset Pricing Model.html       |   0
 .../03 Computing beta in Practice.html        |   0
 .../04 Market-Neutral.html                    |   0
 .../05 Summary.html                           |   0
 .../06 Algorithm.html                         |   0
 .../13 Market Risk.ipynb                      |   0
 .../01 Introduction.html                      |   3 +
 .../02 Add Options.html                       |  36 ++++
 .../03 Filter Contracts.html                  |  44 ++++
 .../04 Select Contracts.html                  | 192 ++++++++++++++++++
 .../05 Algorithm.html                         |   4 +
 .../06 Summary.html                           |   4 +
 .../01 Introduction.html                      |   0
 .../02 Option Payoff.html                     |   0
 ...Call Parity and Arbitrage Strategies.ipynb |   0
 .../03 Put-Call Parity.html                   |   0
 .../04 Synthetic Positions.html               |   0
 .../05 Algorithm.html                         |   0
 .../06 Summary.html                           |   0
 .../01 Introduction.html                      |   0
 .../02 Brownian motion.html                   |   0
 ...tic Processes and Monte Carlo Method.ipynb |   0
 ...astic process followed by stock price.html |   0
 ...Monte Carlo Simulation of Stock Price.html |   0
 ... Carlo Simulation of European Options.html |   0
 .../06 Summary.html                           |   0
 .../01 Introduction.html                      |   0
 .../02 Determinants of Option Price.html      |   0
 .../03 Factors in BSM model.html              |   0
 .../04 Model Assumptions.html                 |   0
 ...n Pricing Black Scholes Merton Model.ipynb |   0
 .../05 Model Equations.html                   |   0
 .../06 Summary.html                           |   0
 .../01 Introduction.html                      |   0
 .../02 Delta.html                             |   0
 .../03 gamma.html                             |   0
 .../04 vega.html                              |   0
 .../05 The Greek Letters.ipynb                |   0
 .../05 Theta.html                             |   0
 .../06 Rho.html                               |   0
 .../07 Delta, Gamma and Vega Hedging.html     |   0
 .../07 Summary.html                           |   0
 .../01 Introduction.html                      |   0
 .../02 Historical Volatility.html             |   0
 .../03 Implied Volatility.html                |   0
 .../04 Volatility Skew.html                   |   0
 .../05 Volatility Surface.html                |   0
 ...al Volatility and Implied Volatility.ipynb |   0
 .../06 The Reason for Volatility Skew.html    |   0
 .../07 Summary.html                           |   0
 .../01 Introduction.html                      |   0
 .../02 Motivation.html                        |   0
 .../03 Local Volatility.html                  |   0
 .../04 Stochastic Volatility.html             |   0
 .../05 Summary.html                           |   0
 ...Volatility and Stochastic Volatility.ipynb |   0
 61 files changed, 366 insertions(+), 85 deletions(-)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/01 Introduction.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/02 Capital Asset Pricing Model.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/03 Computing beta in Practice.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/04 Market-Neutral.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/05 Summary.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/06 Algorithm.html (100%)
 rename 05 Introduction to Financial Python[]/{13 Capital Asset Pricing Model => 13 Market Risk}/13 Market Risk.ipynb (100%)
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/01 Introduction.html
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
 create mode 100755 06 Introduction to Options[]/02 QuantConnect Options API/06 Summary.html
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/02 Option Payoff.html (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/02 Put-Call Parity and Arbitrage Strategies.ipynb (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/03 Put-Call Parity.html (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/04 Synthetic Positions.html (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/05 Algorithm.html (100%)
 rename 06 Introduction to Options[]/{02 Put-Call Parity and Arbitrage Strategies => 03 Put-Call Parity and Arbitrage Strategies}/06 Summary.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/02 Brownian motion.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/03 Stochastic Processes and Monte Carlo Method.ipynb (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/03 Stochastic process followed by stock price.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/04 Monte Carlo Simulation of Stock Price.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/05 Monte Carlo Simulation of European Options.html (100%)
 rename 06 Introduction to Options[]/{03 Stochastic Processes and Monte Carlo Method => 04 Stochastic Processes and Monte Carlo Method}/06 Summary.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/02 Determinants of Option Price.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/03 Factors in BSM model.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/04 Model Assumptions.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/04 Option Pricing Black Scholes Merton Model.ipynb (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/05 Model Equations.html (100%)
 rename 06 Introduction to Options[]/{04 Options Pricing Black Scholes Merton Model => 05 Options Pricing Black Scholes Merton Model}/06 Summary.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/02 Delta.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/03 gamma.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/04 vega.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/05 The Greek Letters.ipynb (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/05 Theta.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/06 Rho.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/07 Delta, Gamma and Vega Hedging.html (100%)
 rename 06 Introduction to Options[]/{05 The Greek Letters => 06 The Greek Letters}/07 Summary.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/02 Historical Volatility.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/03 Implied Volatility.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/04 Volatility Skew.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/05 Volatility Surface.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/06 Historical Volatility and Implied Volatility.ipynb (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/06 The Reason for Volatility Skew.html (100%)
 rename 06 Introduction to Options[]/{06 Historical Volatility and Implied Volatility => 07 Historical Volatility and Implied Volatility}/07 Summary.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/01 Introduction.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/02 Motivation.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/03 Local Volatility.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/04 Stochastic Volatility.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/05 Summary.html (100%)
 rename 06 Introduction to Options[]/{07 Local Volatility and Stochastic Volatility => 08 Local Volatility and Stochastic Volatility}/07 Local Volatility and Stochastic Volatility.ipynb (100%)

diff --git a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html
index 4436f9b..d117f5b 100644
--- a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
+++ b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
@@ -1,89 +1,88 @@
-<h2>Tutorials</h2>
 <table class="table qc-table tutorials-table">
 <tbody>
-<tr onclick="window.document.location='/tutorials/introduction-python-data-types-data-structures/';"  class="clickable" ><td>1</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/data-types-and-data-structures';"  class="clickable" ><td>1</td>
 <td>
 <h2>Python: Data Types and Data Structures</h2>
 First glimpse of Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-logical-operation-loop/';"  class="clickable" ><td>2</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/logical-operations-and-loops';"  class="clickable" ><td>2</td>
 <td>
 <h2>Python: Logical Operations and Loop</h2>
 The essential of programming.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-functions-object-oriented-programming/';"  class="clickable" ><td>3</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/functions-and-objective-oriented-programming';"  class="clickable" ><td>3</td>
 <td>
 <h2>Python: Functions and Object-Oriented Programming</h2>
 The Python magic.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-numpy-basic-pandas/';"  class="clickable" ><td>4</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/numpy-and-basic-pandas';"  class="clickable" ><td>4</td>
 <td>
 <h2>NumPy and Basic Pandas</h2>
 The power scientific calculation package for Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-pandas-resampling-dataframe/';"  class="clickable" ><td>5</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/pandas-resampling-and-dataframe';"  class="clickable" ><td>5</td>
 <td>
 <h2>Pandas: Resampling and DataFrame</h2>
 The magical Data manipulation tool for Python.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-rate-return-mean-variance/';"  class="clickable" ><td>6</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/rate-of-return,-mean-and-variance';"  class="clickable" ><td>6</td>
 <td>
 <h2>Rate of Return, Mean and Variance</h2>
 The basic mathematical concepts for quantitative finance.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-random-variable-distributions/';"  class="clickable" ><td>7</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/random-variables-and-distributions';"  class="clickable" ><td>7</td>
 <td>
 <h2>Random Variable and Distributions</h2>
 Point estimation vs interval estimation
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
 <tr onclick="window.document.location='/tutorials/introduction-python-confidence-interval-hypothesis-testing/';"  class="clickable" ><td>8</td>
 <td>
 <h2>Confidence Interval and Hypothesis Testing</h2>
 Test your ideas rigorously.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-simple-linear-regression/';"  class="clickable" ><td>9</td>
+<tr onclick="window.document.location='tutorials/introduction-to-financial-python/simple-linear-regression';"  class="clickable" ><td>9</td>
 <td>
 <h2>Simple Linear Regression</h2>
 Find the relationship between two random variables.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-multiple-linear-regression-residual-analysis/';"  class="clickable" ><td>10</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/multiple-linear-regression';"  class="clickable" ><td>10</td>
 <td>
 <h2>Multiple Linear Regression and residual analysis</h2>
 Explain a random variable using the power of multi-variables.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-linear-algebra/';"  class="clickable" ><td>11</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/linear-algebra';"  class="clickable" ><td>11</td>
 <td>
 <h2>Linear Algebra</h2>
 Mathematic tool for large scale calculation
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-modern-portfolio-theory/';"  class="clickable" ><td>12</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/modern-portfolio-theory';"  class="clickable" ><td>12</td>
 <td>
 <h2>Modern Portfolio Theory</h2>
 Don't put all the eggs in one basket.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/capital-asset-pricing-model/';"  class="clickable" ><td>13</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/market-risk';"  class="clickable" ><td>13</td>
 <td>
 <h2>Market Risk</h2>
 Beta and Alpha.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/fama-french-multi-factor-models/';"  class="clickable" ><td>14</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/fama-french-multi-factor-models';"  class="clickable" ><td>14</td>
 <td>
 <h2>Fama-French Multi-factor Model</h2>
 The most popular asset pricing model since 1992.
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
 </tbody>
 </table>
diff --git a/02 Tutorial Series/02 Introduction to Options/00 About.html b/02 Tutorial Series/02 Introduction to Options/00 About.html
index 7485821..43c78d3 100644
--- a/02 Tutorial Series/02 Introduction to Options/00 About.html	
+++ b/02 Tutorial Series/02 Introduction to Options/00 About.html	
@@ -1,4 +1,3 @@
-<h2>About</h2>
 <p>The goal of this series is to introduce options to those who are option novices and have basic knowledge of applied mathematics, statistics and financial markets. We will primarily talk about the fundamentals of options and cover topics such as what are options, key terms and concepts option traders need to be familiar with(exercise and assignment, The moneyness, Intrinsic and time value of options etc.) After knowing the basics of options, we will teach how to use QuantConnect API to conduct your options research with over 4000 underlying stock symbols.</p>
 <p>The following few options tutorials were created to help you understand exactly how options are used as the investment and risk hedging tools. We will further discuss the pricing method of options like BSM model and Monte Carlo method. And then several metrics to gauge the options risks like the Greek letters, different kinds of volatilities used in options pricing and trading. At the end of some tutorials, we will apply the knowledge in that tutorial to demonstrate some simple algorithms developed with Python on Quantconnect attempting to help you gain an insight into options trading and learn more efficient API tools to better customize your own trading algorithms. </p>
 
diff --git a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html
index e53a652..e793621 100644
--- a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
+++ b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
@@ -1,83 +1,83 @@
 <table class="table qc-table tutorials-table">
 <tbody>
-<tr onclick="window.document.location='/tutorials/introduction-options-general-features-options';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/general-features-of-options';"  class="clickable" >
 <td>1</td>
 <td>
 <h2>General Features of Options</h2>
-Options Contracts
-The Value of Options
-Option Moneyness
-Option Exercise and Assignment
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Options Contracts
+<br>The Value of Options
+<br>Option Moneyness
+<br>Option Exercise and Assignment
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-using-options-in-quantconnect';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/quantconnect-options-api';"  class="clickable" >
 <td>2</td>
 <td>
 <h2>QuantConnect Option API</a></h2>
-Option Data Access
-Option Contracts filtration
-Properties of Contracts
-Order Placement
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Option Data Access
+<br>Option Contracts filtration
+<br>Properties of Contracts
+<br>Order Placement
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-put-call-parity-and-arbitrage-strategies';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/put-call-parity-and-arbitrage-strategies';"  class="clickable" >
 <td>3</td>
 <td>
 <h2>Put-Call Parity and Arbitrage Strategies</h2>
-Options Payoff
-Put-Call Parity
-Synthetic Positions
-Arbitrage Strategy
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Options Payoff
+<br>Put-Call Parity
+<br>Synthetic Positions
+<br>Arbitrage Strategy
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/stochastic-processes-monte-carlo-method';"  class="clickable" >
+<tr onclick="window.document.location='tutorials/introduction-to-options/stochastic-processes-and-monte-carlo-method';"  class="clickable" >
 <td>4</td>
 <td>
 <h2>Stochastic Processes and Monte Carlo Method</h2>
-Brownian Motion
-Wiener Process
-Monte Carlo Simulation of Stock Price
-Monte Carlo Simulation of European Options Price
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Brownian Motion
+<br>Wiener Process
+<br>Monte Carlo Simulation of Stock Price
+<br>Monte Carlo Simulation of European Options Price
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-black-scholes-merton-model';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/options-pricing-black-scholes-merton-model';"  class="clickable" >
 <td>5</td>
 <td>
 <h2>Options Pricing: Black Scholes Merton Model</h2>
-Determinants of Options Price
-Factors of BSM model
-Model Assumptions
-BSM pricing Formulas
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Determinants of Options Price
+<br>Factors of BSM model
+<br>Model Assumptions
+<br>BSM pricing Formulas
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-the-greek-letters';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/the-greek-letters';"  class="clickable" >
 <td>6</td>
 <td>
 <h2>The Greek Letters</h2>
-Delta (definition, impact factors, charts)
-Gamma (definition, impact factors, charts)
-Vega (definition, charts)
-Theta (definition, charts)
-Rho (definition, charts)
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Delta (definition, impact factors, charts)
+<br>Gamma (definition, impact factors, charts)
+<br>Vega (definition, charts)
+<br>Theta (definition, charts)
+<br>Rho (definition, charts)
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-historical-volatility-implied-volatility';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/historical-volatility-and-implied-volatility';"  class="clickable" >
 <td>7</td>
 <td>
 <h2>Historical Volatility and Implied Volatility</h2>
-Historical Volatility (Definition, Calculation)
-Implied Volatility(Definition, Calculation, affect factors)
-Volatility Smile
-Volatility Skew
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Historical Volatility (Definition, Calculation)
+<br>Implied Volatility(Definition, Calculation, affect factors)
+<br>Volatility Smile
+<br>Volatility Skew
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-options-stochastic-volatility';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/local-volatility-and-stochastic-volatility';"  class="clickable" >
 <td>8</td>
 <td>
 <h2>Local Volatility and Stochastic Volatility</h2>
-Local Volatility (Definition, Calculation)
-Stochastic Volatility(Definition, Calculation)
-<a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
+<br>Local Volatility (Definition, Calculation)
+<br>Stochastic Volatility(Definition, Calculation)
+<br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
 </tbody>
 </table>
diff --git a/02 Tutorial Series/03 Applied Options/02 Tutorials.html b/02 Tutorial Series/03 Applied Options/02 Tutorials.html
index 6281670..e78c382 100644
--- a/02 Tutorial Series/03 Applied Options/02 Tutorials.html	
+++ b/02 Tutorial Series/03 Applied Options/02 Tutorials.html	
@@ -1,55 +1,55 @@
 <table class="table qc-table tutorials-table">
     <tbody>
-    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/covered-call';" class="clickable">
         <td>1</td>
         <td>
             <h2>Covered Call</h2>
             A brief introduction to Covered Call Strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/bull-call-spread';" class="clickable">
         <td>2</td>
         <td>
             <h2>Bull Call Spread</h2>
             A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/long-straddle';" class="clickable">
         <td>3</td>
         <td>
             <h2>Long Straddle</h2>
             A brief introduction to Long Straddle strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/long-strangle';" class="clickable">
         <td>4</td>
         <td>
             <h2>Long Strangle</h2>
             A brief introduction to Long Strangle strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/butterfly-spread';" class="clickable">
         <td>5</td>
         <td>
             <h2>Butterfly Spread</h2>
             A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/iron-condor';" class="clickable">
         <td>6</td>
         <td>
             <h2>Iron Condor</h2>
             A brief introduction to Iron Condor strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/iron-butterfly';" class="clickable">
         <td>7</td>
         <td>
             <h2>Iron Butterfly</h2>
             A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
+    <tr onclick="window.document.location='/tutorials/applied-options/protective-collar';" class="clickable">
         <td>8</td>
         <td>
             <h2>Protective Collar</h2>
@@ -57,4 +57,4 @@ <h2>Protective Collar</h2>
             <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
     </tr>
     </tbody>
-</table>
\ No newline at end of file
+</table>
diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/01 Introduction.html b/05 Introduction to Financial Python[]/13 Market Risk/01 Introduction.html
similarity index 100%
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diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/03 Computing beta in Practice.html b/05 Introduction to Financial Python[]/13 Market Risk/03 Computing beta in Practice.html
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diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/05 Summary.html b/05 Introduction to Financial Python[]/13 Market Risk/05 Summary.html
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similarity index 100%
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diff --git a/05 Introduction to Financial Python[]/13 Capital Asset Pricing Model/13 Market Risk.ipynb b/05 Introduction to Financial Python[]/13 Market Risk/13 Market Risk.ipynb
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diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/01 Introduction.html b/06 Introduction to Options[]/02 QuantConnect Options API/01 Introduction.html
new file mode 100755
index 0000000..fd8f150
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  QuantConnect provides US options trade and quotes price data for approximately 4000 symbols, each of which has roughly 10 strikes on average. Data is available starting January 1st, 2008. In this tutorial, we will discuss how to use QuantConnect to start your options trading algorithm.
+</p>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
new file mode 100755
index 0000000..fdc5182
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
@@ -0,0 +1,36 @@
+<p>
+  Before trading options, you need to add options for a given underlying equity and set the resolution in step <code>Initialize</code> with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
+resolution,
+fillDataForward)</td>
+<td><strong>underlying(string):</strong> The underlying equity symbol
+<strong>resolution:</strong> Tick, Second, Minute, Hour, or Daily. Default is minute
+<strong>fillDataForward(bool):</strong> If true, returns the last available data even if none in that time slice. The default value is true.</td>
+</tr>
+</tbody>
+</table>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+    self.SetStartDate(2017, 01, 01)  #Set Start Date
+    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetCash(50000)  #Set Strategy Cash
+    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
+    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
+    self.symbol = option.Symbol
+</pre>
+</div>
+<p>
+  The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
+</p>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html
new file mode 100755
index 0000000..34e182e
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html	
@@ -0,0 +1,44 @@
+<p>
+  After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a>(
+min strike,
+max strike,
+minexpiry,
+maxExpiry)</td>
+<td><strong>min Strike, max Strike:</strong> The min and max strike rank relative to market price
+<b><b>min Expiry, max Expiry: </b></b>The range of time to expiration to include, for example, TimeSpan.FromDays(10) would exclude contracts expiring in less than 10 days</td>
+</tr>
+</tbody>
+</table>
+<p>
+  Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
+</p>
+<p>
+  For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
+</p>
+<p>
+  For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># filter the contracts with strikes between (market price - 10, market price + 10)
+option.SetFilter(-10,10)
+# filter the contracts which expires more than 30 days but no longer than 60 days
+option.SetFilter(TimeSpan.FromDays(30),TimeSpan.FromDays(60))
+# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
+option.SetFilter(-10, +10, timedelta(0), timedelta(180))
+</pre>
+</div>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
new file mode 100755
index 0000000..ca86343
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -0,0 +1,192 @@
+<p>
+  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
+
+<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Properties of Option Contract x</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><code>x.Symbol.Value</code></td>
+<td> Get the string of option contract's symbol</td>
+</tr>
+<tr>
+<td><code>x.AskPrice, x.BidPrice</code></td>
+<td> Get the ask price,  Get the bid price</td>
+</tr>
+<tr>
+<td><code>x.Expiry</code></td>
+<td> Get the expiration date</td>
+</tr>
+<tr>
+<td><code>x.Strike</code></td>
+<td> Get the strike price</td>
+</tr>
+<tr>
+<td><code>x.ImpliedVolatility</code></td>
+<td> Get the implied volatility</td>
+</tr>
+<tr>
+<td><code>x.Greeks</code></td>
+<td>Get the Greeks letter</td>
+</tr>
+<tr>
+<td><code>x.Right</code></td>
+<td>Get the right being purchased
+x.Right ＝ 1  call option[right to buy]
+x.Right ＝ 0  put option[right to sell]</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingLastPrice</code></td>
+<td>Get the last price the underlying security traded at</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingSymbol</code></td>
+<td>Gets the underlying security's symbol</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  We can print out the details of the contract after filtering with Python data frame to show these properties. Assume today is 01/03/2017.  The stock price at 01/03/2017 09:31:00 is $776.01 per share. Here we use  <code>SetFilter(-1, +1, timedelta(0), timedelta(60)) </code>to filter the contracts.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def OnData(self,slice):
+    for i in slice.OptionChains:
+        if i.Key != self.symbol: continue
+	optionchain = i.Value
+	self.Log("underlying price:" + str(optionchain.Underlying.Price))
+	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+			   index=[x.Symbol.Value for x in optionchain],
+			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
+</pre>
+</div>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Symbol</th>
+<th>type(call 0, put 1)</th>
+<th>Strike</th>
+<th>Expiry</th>
+<th>Ask Price</th>
+<th>Bid Price</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>GOOG 170217C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>26.4</td>
+<td>27.9</td>
+</tr>
+<tr>
+<td>GOOG 170120P00782500</td>
+<td>1</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>14.7</td>
+<td>16.3</td>
+</tr>
+<tr>
+<td>GOOG 170120C00782500</td>
+<td>0</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>9.4</td>
+<td>10.2</td>
+</tr>
+<tr>
+<td>GOOG 170120P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>13.4</td>
+<td>15.0</td>
+</tr>
+<tr>
+<td>GOOG 170120C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>10.6</td>
+<td>11.5</td>
+</tr>
+<tr>
+<td>GOOG 170217P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>28.9</td>
+<td>30.8</td>
+</tr>
+<tr>
+<td>GOOG 170120P00777500</td>
+<td>1</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>12.2</td>
+<td>13.7</td>
+</tr>
+<tr>
+<td>GOOG 170120C00777500</td>
+<td>0</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>11.8</td>
+<td>12.9</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in slice.OptionChains:
+    if i.Key != self.symbol: continue
+    chain = i.Value
+# differentiate the call and put options
+call = [x for x in optionchain if chain.Right == 0]
+put = [x for x in optionchain if chain.Right == 1]
+# choose ITM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
+# or choose ATM contracts
+contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+# or choose OTM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
+# sort the contracts by their expiration dates
+contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+</pre>
+</div>
+<p>
+  Finally, we trade the options by using the contract's symbol.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if len(contracts) == 0: continue
+# trade the contracts with the farthest expiration
+symbol = contracts[0].Symbol
+self.MarketOrder(symbol, 1)
+self.MarketOnCloseOrder(symbol, -1)
+</pre>
+</div>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
new file mode 100755
index 0000000..3b2c255
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -0,0 +1,4 @@
+<p>
+  This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=6453fa45f6b98d880a4646a820265a62'></script>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/06 Summary.html b/06 Introduction to Options[]/02 QuantConnect Options API/06 Summary.html
new file mode 100755
index 0000000..10bbff9
--- /dev/null
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/06 Summary.html	
@@ -0,0 +1,4 @@
+<p>
+  After mastering the basic knowledge of options market, this tutorial we take a close at how to use Quantconnect to customize your own options trading. For example, <span lang="EN-GB">how you can access an option chain, </span><span lang="EN-GB">how to view the details of the contract as a Python data frame, and the most important how to trade the specific option contract.
+  Next chapter we will examine some important topics of options like the payoff, Put-Call parity, and the synthetic positions. By learning all those concepts, we will start some brief hedging strategies involving options.
+</p>
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From 7717887a2c53ab939808461bd51f0f9d155cc62a Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 8 Jun 2018 16:19:51 -0400
Subject: [PATCH 047/753] Update tutorials

---
 .../01 Consolidating Data to Build Bars.html  |  47 +-
 .../03 Scheduled Events.html                  |  31 +-
 .../01 Overview.html                          |  43 ++
 .../02 Updating Orders.html                   |  40 ++
 .../03 Full Python Example.html               |  79 ++++
 .../04 Tracking and Managing Orders.html      | 138 ------
 .../01 Introduction.html                      |   3 +
 .../02 Add Options.html                       |  36 ++
 .../03 Filter Contracts.html                  |  44 ++
 .../04 Select Contracts.html                  | 192 ++++++++
 .../05 Algorithm.html                         |   4 +
 .../06 Summary.html                           |   3 +
 .../06 Using Options in QuantConnect.html     | 246 ----------
 .../02 Tutorials.html                         |   4 +-
 .../02 Tutorials.html                         |   2 +-
 .../01 Desktop Charting with LEAN.html        |  30 +-
 ...02 How do I use the API File Provider.html |  27 +-
 ... Ranking Strategy on Dow 30 Companies.html | 126 ------
 .../01 Introduction.html                      |  14 +
 .../02 CAPM Theory.html                       |  45 ++
 .../03 Method.html                            |  99 ++++
 .../04 Summary.html                           |  14 +
 .../05 Algorithm.html                         |   1 +
 .../01 Abstract.html                          |   7 +
 ...eversion and Momentum in Forex Market.html | 195 --------
 .../02 Introduction.html                      |  59 +++
 .../03 Method.html                            | 184 ++++++++
 .../04 Results.html                           |  19 +
 .../05 Summary.html                           |   3 +
 .../06 Algorithm.html                         |   1 +
 .../01 Abstract.html                          |   8 +
 .../02 Framework of Copula.html               |  71 +++
 ...Pairs Trading-Copula vs Cointegration.html | 425 ------------------
 .../03 Part I - Copula Method.html            | 339 ++++++++++++++
 .../04 Part II - Cointegration Method.html    |  48 ++
 .../05 Summary.html                           |  34 ++
 .../06 Algorithm.html                         |   8 +
 .../01 Abstract.html                          |  14 +
 .../02 Method.html                            |  60 +++
 .../03 Conclusion.html                        |  10 +
 .../04 Algorithm.html                         |   8 +
 .../04 The Dynamic Breakout II Strategy.html  |  64 ---
 .../01 Abstract.html                          |  92 ++++
 .../02 Method.html                            |  92 ++++
 .../03 Conclusion.html                        |  92 ++++
 .../04 Algorithm.html                         |  92 ++++
 .../05 Dual Thrust Trading Algorithm.html     |  50 ---
 .../01 Abstract.html                          |  10 +
 .../02 Background.html                        |  15 +
 .../03 Method.html                            | 116 +++++
 .../04 Summary.html                           |   7 +
 .../05 Algorithm.html                         |   5 +
 ... Can Crude Oil Predict Equity Returns.html |  93 ----
 .../01 Abstract.html                          |  11 +
 .../02 Introduction.html                      | 116 +++++
 .../03 Method.html                            | 259 +++++++++++
 .../04 Result.html                            |  22 +
 .../05 Summary.html                           |   6 +
 .../06 Algorithm.html                         |   1 +
 ...orrelation and Cointegration Approach.html | 319 -------------
 .../01 Abstract.html                          |   7 +
 .../02 Introduction.html                      |  56 +++
 .../03 Method.html                            |  87 ++++
 .../04 Summary.html                           |  67 +++
 .../05 Algorithm.html                         |   1 +
 ...w Frequency Component of Forex Market.html | 182 --------
 .../01 Abstract.html                          |   3 +
 ... Factors.html => 02 Factor Selection.html} | 101 +++--
 .../03 Stock Selection.html                   |  16 +
 .../04 Algorithm.html                         |   1 +
 .../01 Abstract.html                          |   8 +
 .../02 Method.html                            | 110 +++++
 .../03 Summary.html                           |   5 +
 .../04 Algorithm.html                         |   1 +
 ...hort-Term Reversal Strategy in Stocks.html |  81 ----
 .../01 Introduction.html                      |  26 ++
 .../02 Implementation.html                    | 106 +++++
 .../03 Summary.html                           |   4 +
 .../04 Algorithm.html                         |   1 +
 ...undamental Factor Long Short Strategy.html | 109 -----
 .../02 Risk Aversion.html                     |   4 +-
 .../04 Factor Returns.html                    |   4 +-
 .../05 Algorithm.html                         |   2 +-
 .../{07 Summary.html => 08 Summary.html}      |   0
 84 files changed, 3084 insertions(+), 2121 deletions(-)
 create mode 100755 01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
 create mode 100755 01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html
 create mode 100755 01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html
 delete mode 100755 01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/01 Introduction.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html
 create mode 100755 01 API Tutorials/04 Using Options in QuantConnect/06 Summary.html
 delete mode 100755 01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html
 delete mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 CAPM Theory.html
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html
 delete mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Combining Mean Reversion and Momentum in Forex Market.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/05 Summary.html
 create mode 100755 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html
 delete mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Pairs Trading-Copula vs Cointegration.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
 create mode 100755 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
 create mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html
 create mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html
 create mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/03 Conclusion.html
 create mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
 delete mode 100755 04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html
 create mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html
 create mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html
 create mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html
 create mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
 delete mode 100755 04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/02 Background.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/04 Summary.html
 create mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
 delete mode 100755 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 Summary.html
 create mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
 delete mode 100755 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/04 Summary.html
 create mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
 delete mode 100755 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html
 create mode 100755 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html
 rename 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/{09 Stock Selection Strategy Based on Fundamental Factors.html => 02 Factor Selection.html} (56%)
 create mode 100755 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html
 create mode 100755 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
 create mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
 create mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
 create mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
 create mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html
 delete mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html
 create mode 100755 04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html
 create mode 100755 04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html
 create mode 100644 04 Strategy Library/11 Fundamental Factor Long Short Strategy/03 Summary.html
 create mode 100755 04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
 delete mode 100755 04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html
 rename 06 Introduction to Options[]/06 The Greek Letters/{07 Summary.html => 08 Summary.html} (100%)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 5d1de5b..22f61df 100755
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -1,13 +1,16 @@
-<code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
+<p>
+  <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
+  Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized once. There are three key steps to create and register a consolidator:
+</p>
 
-Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized once. There are three key steps to create and register a consolidator:
-<ol>
+<ul>
  	<li>Create the consolidator object.</li>
  	<li>Bind an event handler to handle the new bars.</li>
  	<li>Register it with the subscription manager to start receiving data.</li>
-</ol>
-In <b>C#</b> this looks like:
-<pre class="prettyprint linenums">public class ConsolidatorDemoAlgorithm : QCAlgorithm
+</ul>
+
+  <div class="section-example-container">
+  <pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
 {
 	public override void Initialize()
 	{
@@ -39,8 +42,7 @@
 	public override void OnData(Slice data)
 	{ }
 }</pre>
-In <b>Python</b>:
-<pre class="prettyprint linenums">from datetime import datetime, timedelta
+<pre class="python">from datetime import datetime, timedelta
 class DataConsolidationAlgorithm(QCAlgorithm):
 
     def Initialize(self):
@@ -68,23 +70,30 @@
         self.Debug(str(self.Time) + " " + str(bar))
     def OnData(self, data):
         pass
-</pre>
-The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
-
-In <b>C#</b> this looks like:
-<pre class="prettyprint linenums">// From tick data sources
+  </pre>
+  </div>
+<p>
+  The LEAN API also has other consolidator types to handle working with Ticks and RenkoBars.
+</p>
+<div class="section-example-container">
+<pre class="csharp">// From tick data sources
 var tickConsolidator = new TickConsolidator(TimeSpan.FromMinutes(30));
 
 //from renko bars
 var renkoConsolidator = new RenkoConsolidator(TimeSpan.FromMinutes(30));
 </pre>
+<pre class="python">
+</pre>
+</div>
 
-There are two key points to remember:
-<ol>
+<p>
+  There are two key points to remember:
+</p>
+<ul>
  	<li>Request a smaller resolution than what you want to produce.</li>
  	<li>In backtesting we only know the bar is ready on the next data point; so it may appear like daily bars are triggered at odd times. In live trading, they are scanned to be triggered every second at a minimum.</li>
-</ol>
-<p style="text-align: center;">The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.</p>
+</ul>
+<p>
+  The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.
+</p>
 https://www.youtube.com/watch?v=bbZy__qd1aA
-
-&nbsp;
diff --git a/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html b/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html
index 9237c12..66bfb20 100755
--- a/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html	
+++ b/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html	
@@ -1,16 +1,21 @@
-Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
-
-The scheduling is set with two rules: the <code>DateRules</code> and <code>TimeRules</code> classes. The schedule manager takes one of each and executes an action code block.
-In C# this looks like:
-<pre class="prettyprint linenums">Schedule.On(DateRules.On(2013, 10, 7), TimeRules.At(13, 0), () =&gt;
+<p>
+  Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
+</p>
+<p>
+  The scheduling is set with two rules: the <code>DateRules</code> and <code>TimeRules</code> classes. The schedule manager takes one of each and executes an action code block.
+</p>
+<div class="section-example-container">
+<pre class="csharp">Schedule.On(DateRules.On(2013, 10, 7), TimeRules.At(13, 0), () =&gt;
 {
          Debug("Specific Time: Fired at : " + Time);
 });</pre>
-Or in Python:
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.On(2013, 10, 7), self.TimeRules.At(13, 0), Action(self.SpecificTime))
+<pre class="python">self.Schedule.On(self.DateRules.On(2013, 10, 7), self.TimeRules.At(13, 0), Action(self.SpecificTime))
 def SpecificTime(self):
     self.Log("SpecificTime: Fired at : {0}".format(self.Time))</pre>
-The date rules can be specified as below:
+</div>
+<p>
+  The date rules can be specified as below:
+</p>
 <table class="table table-striped qc-table">
 <thead>
 <tr>
@@ -40,7 +45,11 @@
 </tr>
 </tbody>
 </table>
-The time rules trigger specify when on the day the event should be triggered. They can be specified as below:
+
+<p>
+  The time rules trigger specify when on the day the event should be triggered. They can be specified as below:
+</p>
+
 <table class="table table-striped qc-table">
 <thead>
 <tr>
@@ -66,5 +75,7 @@
 </tr>
 </tbody>
 </table>
-For a demonstration see the tutorial video below:
+<p>
+  For a demonstration see the tutorial video below:
+</p>
 https://youtu.be/SUu4MKlm93I
diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
new file mode 100755
index 0000000..eb54cf2
--- /dev/null
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -0,0 +1,43 @@
+<h2>Overview</h2>
+<p>
+  Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
+  When you place a trade you receive an <code>OrderTicket</code> for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
+</p>
+
+<p>
+  You can place several types of orders including:
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Supported Order Types</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="25%">Market Order</td>
+<td><code>MarketOrder("SPY", 100);</code></td>
+</tr>
+<tr>
+<td>Limit Order</td>
+<td><code>var ticket = LimitOrder("SPY", 100, 100.10m);</code></td>
+</tr>
+<tr>
+<td>Stop Market Order</td>
+<td><code>var ticket = StopMarketOrder("SPY", 100, 100.10m);</code></td>
+</tr>
+<tr>
+<td>Stop Limit Order</td>
+<td><code>var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code></td>
+</tr>
+<tr>
+<td>Market On Open Order</td>
+<td><code>var ticket = MarketOnOpen("SPY", 100);</code></td>
+</tr>
+<tr>
+<td>Market On Close Order</td>
+<td><code>var ticket = MarketOnClose("SPY", 100);</code></td>
+</tr>
+</tbody>
+</table>
diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html
new file mode 100755
index 0000000..7c50115
--- /dev/null
+++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
@@ -0,0 +1,40 @@
+<p>
+  Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">var currentStopPrice = _ticket.Get(OrderField.StopPrice);
+</pre>
+<pre class="python"># Retrive the 'StopPrice' for an order from the ticket
+currentStopPrice = _ticket.Get(OrderField.StopPrice)
+</pre>
+<p>
+  Or update the order fields (LimitPrice, StopPrice, Tag or Quantity):
+</p>
+<div class="section-example-container">
+<pre class="csharp">_ticket.Update(new UpdateOrderFields
+{
+    LimitPrice = newLongLimit,
+    Tag = "Update #" + (longOrder.UpdateRequests.Count + 1)
+});
+</pre>
+<pre class="python"># update the order fields (LimitPrice, StopPrice, Tag or Quantity)
+updateOrderFields = UpdateOrderFields()
+updateOrderFields.LimitPrice = newLongLimit
+_ticket.Update(updateOrderFields)
+</pre>
+</div>
+
+<p>
+  You can also cancel your order if required:
+</p>
+<div class="section-example-container">
+<pre class="csharp">_ticket.Cancel();</pre>
+<pre class="python">ticket.Cancel()</pre>
+</div>
+
+
+<p>
+  In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
+https://www.youtube.com/watch?v=HykXfstdNW0
+</p>
diff --git a/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html
new file mode 100755
index 0000000..04388e5
--- /dev/null
+++ b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html	
@@ -0,0 +1,79 @@
+<p>
+  This is a complete port from C# to Python of this Quick Start Lesson.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+```Lesson 6 - Tracking and Managing Orders:
+Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
+```
+import numpy as np
+from decimal import Decimal
+
+class BasicTemplateAlgorithm(QCAlgorithm):
+
+    def Initialize(self):
+        self._limitTicket = None
+        self._stopMarketTicket = None
+        self._stopLimitTicket = None
+        self.SetCash(25000)
+        self.SetStartDate(2009,1,1)
+        self.SetEndDate(2009,6,1)
+        self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
+
+    def OnData(self, slice):
+        if self._limitTicket is None:
+
+            self.MarketOrder(self.spy, 100)
+
+            self._limitTicket = self.LimitOrder(
+                self.spy,
+                100,
+                slice["SPY"].Close * Decimal(0.9),
+                "limit order")
+
+            self._stopMarketTicket = self.StopMarketOrder(
+                self.spy,
+                -100,
+                slice["SPY"].Close * Decimal(0.95),
+                "stop market")
+
+            self._stopLimitTicket = self.StopLimitOrder(
+                self.spy,
+                -100,
+                slice["SPY"].Close * Decimal(0.9),
+                slice["SPY"].Close * Decimal(0.8),
+                "stop market")
+
+
+    def OnEndOfDay(self):
+        ```End of each day, plot our asset & order prices```
+        if self._limitTicket is None:
+            return
+
+        self.Plot("Order Tickets", "SPY", self.Portfolio['SPY'].Price)
+
+        if self._stopMarketTicket.Status != OrderStatus.Filled:
+            self.Plot("Order Tickets", "Stop Price",
+                      self._stopMarketTicket.Get(OrderField.StopPrice))
+
+        if self._stopLimitTicket.Status != OrderStatus.Filled:
+            self.Plot("Order Tickets", "Limit Price",
+                      self._stopLimitTicket.Get(OrderField.LimitPrice))
+
+    def OnOrderEvent(self, OrderEvent):
+        ```Event when the order is filled. Debug log the order fill. :OrderEvent:```
+
+        if OrderEvent.FillQuantity == 0:
+            return
+
+        fetched = self.Transactions.GetOrderById(OrderEvent.OrderId)
+
+        self.Debug("{} was filled. Symbol: {}. Quantity: {}. Direction: {}"
+                   .format(str(fetched.Type),
+                           str(OrderEvent.Symbol),
+                           str(OrderEvent.FillQuantity),
+                           str(OrderEvent.Direction)))
+ </pre>
+ </div>
diff --git a/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html
deleted file mode 100755
index bd19104..0000000
--- a/01 API Tutorials/03 Tracking and Managing Orders/04 Tracking and Managing Orders.html	
+++ /dev/null
@@ -1,138 +0,0 @@
-<h2>Overview</h2>
-Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
-
-When you place a trade you receive an <code>OrderTicket</code> for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
-
-You can place several types of orders including:
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Supported Order Types</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="25%">Market Order</td>
-<td><code>MarketOrder("SPY", 100);</code></td>
-</tr>
-<tr>
-<td>Limit Order</td>
-<td><code>var ticket = LimitOrder("SPY", 100, 100.10m);</code></td>
-</tr>
-<tr>
-<td>Stop Market Order</td>
-<td><code>var ticket = StopMarketOrder("SPY", 100, 100.10m);</code></td>
-</tr>
-<tr>
-<td>Stop Limit Order</td>
-<td><code>var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code></td>
-</tr>
-<tr>
-<td>Market On Open Order</td>
-<td><code>var ticket = MarketOnOpen("SPY", 100);</code></td>
-</tr>
-<tr>
-<td>Market On Close Order</td>
-<td><code>var ticket = MarketOnClose("SPY", 100);</code></td>
-</tr>
-</tbody>
-</table>
-<h2>Updating Orders</h2>
-Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
-<pre class="prettyprint linenums">var currentStopPrice = _ticket.Get(OrderField.StopPrice);</pre>
-Or update the order fields (LimitPrice, StopPrice, Tag or Quantity):
-<pre class="prettyprint linenums">_ticket.Update(new UpdateOrderFields
-{
-    LimitPrice = newLongLimit,
-    Tag = "Update #" + (longOrder.UpdateRequests.Count + 1)
-});</pre>
-You can also cancel your order if required:
-<pre class="prettyprint linenums">_ticket.Cancel();</pre>
-In <b>Python</b>, you can update order like this:
-<pre class="prettyprint linenums"># Retrive the 'StopPrice' for an order from the ticket
-currentStopPrice = _ticket.Get(OrderField.StopPrice)
-# update the order fields (LimitPrice, StopPrice, Tag or Quantity)
-updateOrderFields = UpdateOrderFields()
-updateOrderFields.LimitPrice = newLongLimit
-updateOrderFields.Tag = "Update #{0}".format(len(_ticket.UpdateRequests) + 1)
-_ticket.Update(updateOrderFields)
-# cancel your order if required
-_ticket.Cancel()
-</pre>
-In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
-https://www.youtube.com/watch?v=HykXfstdNW0
-
-<h2>Full Python Example</h2>
-This is a complete port from C# to Python of this Quick Start Lesson.
-<pre class="prettyprint linenums">
-```Lesson 6 - Tracking and Managing Orders: 
-Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
-```
-import numpy as np
-from decimal import Decimal
-
-class BasicTemplateAlgorithm(QCAlgorithm):
-
-    def Initialize(self):
-        self._limitTicket = None
-        self._stopMarketTicket = None
-        self._stopLimitTicket = None
-        self.SetCash(25000)
-        self.SetStartDate(2009,1,1)
-        self.SetEndDate(2009,6,1)
-        self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
-        
-    def OnData(self, slice):
-        if self._limitTicket is None:
-        
-            self.MarketOrder(self.spy, 100)
-            
-            self._limitTicket = self.LimitOrder(
-                self.spy, 
-                100, 
-                slice["SPY"].Close * Decimal(0.9), 
-                "limit order")
-                
-            self._stopMarketTicket = self.StopMarketOrder(
-                self.spy,
-                -100,
-                slice["SPY"].Close * Decimal(0.95),
-                "stop market")
-                
-            self._stopLimitTicket = self.StopLimitOrder(
-                self.spy,
-                -100,
-                slice["SPY"].Close * Decimal(0.9),
-                slice["SPY"].Close * Decimal(0.8),
-                "stop market")
-
-
-    def OnEndOfDay(self):
-        ```End of each day, plot our asset & order prices```
-        if self._limitTicket is None:
-            return
-        
-        self.Plot("Order Tickets", "SPY", self.Portfolio['SPY'].Price)
-        
-        if self._stopMarketTicket.Status != OrderStatus.Filled:
-            self.Plot("Order Tickets", "Stop Price", 
-                      self._stopMarketTicket.Get(OrderField.StopPrice))
-            
-        if self._stopLimitTicket.Status != OrderStatus.Filled:
-            self.Plot("Order Tickets", "Limit Price", 
-                      self._stopLimitTicket.Get(OrderField.LimitPrice))
-
-    def OnOrderEvent(self, OrderEvent):
-        ```Event when the order is filled. Debug log the order fill. :OrderEvent:``` 
-        
-        if OrderEvent.FillQuantity == 0:
-            return
-        
-        fetched = self.Transactions.GetOrderById(OrderEvent.OrderId)
-        
-        self.Debug("{} was filled. Symbol: {}. Quantity: {}. Direction: {}"
-                   .format(str(fetched.Type), 
-                           str(OrderEvent.Symbol), 
-                           str(OrderEvent.FillQuantity), 
-                           str(OrderEvent.Direction)))
-</pre>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/01 Introduction.html b/01 API Tutorials/04 Using Options in QuantConnect/01 Introduction.html
new file mode 100755
index 0000000..fd8f150
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  QuantConnect provides US options trade and quotes price data for approximately 4000 symbols, each of which has roughly 10 strikes on average. Data is available starting January 1st, 2008. In this tutorial, we will discuss how to use QuantConnect to start your options trading algorithm.
+</p>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html b/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html
new file mode 100755
index 0000000..fdc5182
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html	
@@ -0,0 +1,36 @@
+<p>
+  Before trading options, you need to add options for a given underlying equity and set the resolution in step <code>Initialize</code> with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
+</p>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
+resolution,
+fillDataForward)</td>
+<td><strong>underlying(string):</strong> The underlying equity symbol
+<strong>resolution:</strong> Tick, Second, Minute, Hour, or Daily. Default is minute
+<strong>fillDataForward(bool):</strong> If true, returns the last available data even if none in that time slice. The default value is true.</td>
+</tr>
+</tbody>
+</table>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+    self.SetStartDate(2017, 01, 01)  #Set Start Date
+    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetCash(50000)  #Set Strategy Cash
+    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
+    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
+    self.symbol = option.Symbol
+</pre>
+</div>
+<p>
+  The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
+</p>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html
new file mode 100755
index 0000000..34e182e
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html	
@@ -0,0 +1,44 @@
+<p>
+  After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Method</th>
+<th>Parameters</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a>(
+min strike,
+max strike,
+minexpiry,
+maxExpiry)</td>
+<td><strong>min Strike, max Strike:</strong> The min and max strike rank relative to market price
+<b><b>min Expiry, max Expiry: </b></b>The range of time to expiration to include, for example, TimeSpan.FromDays(10) would exclude contracts expiring in less than 10 days</td>
+</tr>
+</tbody>
+</table>
+<p>
+  Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
+</p>
+<p>
+  For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
+</p>
+<p>
+  For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># filter the contracts with strikes between (market price - 10, market price + 10)
+option.SetFilter(-10,10)
+# filter the contracts which expires more than 30 days but no longer than 60 days
+option.SetFilter(TimeSpan.FromDays(30),TimeSpan.FromDays(60))
+# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
+option.SetFilter(-10, +10, timedelta(0), timedelta(180))
+</pre>
+</div>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
new file mode 100755
index 0000000..ca86343
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -0,0 +1,192 @@
+<p>
+  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
+
+<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
+
+<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="2">Properties of Option Contract x</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><code>x.Symbol.Value</code></td>
+<td> Get the string of option contract's symbol</td>
+</tr>
+<tr>
+<td><code>x.AskPrice, x.BidPrice</code></td>
+<td> Get the ask price,  Get the bid price</td>
+</tr>
+<tr>
+<td><code>x.Expiry</code></td>
+<td> Get the expiration date</td>
+</tr>
+<tr>
+<td><code>x.Strike</code></td>
+<td> Get the strike price</td>
+</tr>
+<tr>
+<td><code>x.ImpliedVolatility</code></td>
+<td> Get the implied volatility</td>
+</tr>
+<tr>
+<td><code>x.Greeks</code></td>
+<td>Get the Greeks letter</td>
+</tr>
+<tr>
+<td><code>x.Right</code></td>
+<td>Get the right being purchased
+x.Right ＝ 1  call option[right to buy]
+x.Right ＝ 0  put option[right to sell]</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingLastPrice</code></td>
+<td>Get the last price the underlying security traded at</td>
+</tr>
+<tr>
+<td><code>x.UnderlyingSymbol</code></td>
+<td>Gets the underlying security's symbol</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  We can print out the details of the contract after filtering with Python data frame to show these properties. Assume today is 01/03/2017.  The stock price at 01/03/2017 09:31:00 is $776.01 per share. Here we use  <code>SetFilter(-1, +1, timedelta(0), timedelta(60)) </code>to filter the contracts.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def OnData(self,slice):
+    for i in slice.OptionChains:
+        if i.Key != self.symbol: continue
+	optionchain = i.Value
+	self.Log("underlying price:" + str(optionchain.Underlying.Price))
+	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+			   index=[x.Symbol.Value for x in optionchain],
+			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
+</pre>
+</div>
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Symbol</th>
+<th>type(call 0, put 1)</th>
+<th>Strike</th>
+<th>Expiry</th>
+<th>Ask Price</th>
+<th>Bid Price</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>GOOG 170217C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>26.4</td>
+<td>27.9</td>
+</tr>
+<tr>
+<td>GOOG 170120P00782500</td>
+<td>1</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>14.7</td>
+<td>16.3</td>
+</tr>
+<tr>
+<td>GOOG 170120C00782500</td>
+<td>0</td>
+<td>782.5</td>
+<td>2017-01-20</td>
+<td>9.4</td>
+<td>10.2</td>
+</tr>
+<tr>
+<td>GOOG 170120P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>13.4</td>
+<td>15.0</td>
+</tr>
+<tr>
+<td>GOOG 170120C00780000</td>
+<td>0</td>
+<td>780.0</td>
+<td>2017-01-20</td>
+<td>10.6</td>
+<td>11.5</td>
+</tr>
+<tr>
+<td>GOOG 170217P00780000</td>
+<td>1</td>
+<td>780.0</td>
+<td>2017-02-17</td>
+<td>28.9</td>
+<td>30.8</td>
+</tr>
+<tr>
+<td>GOOG 170120P00777500</td>
+<td>1</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>12.2</td>
+<td>13.7</td>
+</tr>
+<tr>
+<td>GOOG 170120C00777500</td>
+<td>0</td>
+<td>777.5</td>
+<td>2017-01-20</td>
+<td>11.8</td>
+<td>12.9</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in slice.OptionChains:
+    if i.Key != self.symbol: continue
+    chain = i.Value
+# differentiate the call and put options
+call = [x for x in optionchain if chain.Right == 0]
+put = [x for x in optionchain if chain.Right == 1]
+# choose ITM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
+# or choose ATM contracts
+contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+# or choose OTM contracts
+contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
+# sort the contracts by their expiration dates
+contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+</pre>
+</div>
+<p>
+  Finally, we trade the options by using the contract's symbol.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if len(contracts) == 0: continue
+# trade the contracts with the farthest expiration
+symbol = contracts[0].Symbol
+self.MarketOrder(symbol, 1)
+self.MarketOnCloseOrder(symbol, -1)
+</pre>
+</div>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html b/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html
new file mode 100755
index 0000000..3b2c255
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html	
@@ -0,0 +1,4 @@
+<p>
+  This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=6453fa45f6b98d880a4646a820265a62'></script>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/06 Summary.html b/01 API Tutorials/04 Using Options in QuantConnect/06 Summary.html
new file mode 100755
index 0000000..da313e2
--- /dev/null
+++ b/01 API Tutorials/04 Using Options in QuantConnect/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+  After mastering the basic knowledge of options market, this tutorial we take a close at how to use Quantconnect to customize your own options trading. For example, <span lang="EN-GB">how you can access an option chain, </span><span lang="EN-GB">how to view the details of the contract as a Python data frame, and the most important how to trade the specific option contract.
+</p>
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html b/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html
deleted file mode 100755
index 93086b9..0000000
--- a/01 API Tutorials/04 Using Options in QuantConnect/06 Using Options in QuantConnect.html	
+++ /dev/null
@@ -1,246 +0,0 @@
-QuantConnect provides US options trade and quotes price data for approximately 4000 symbols, each of which has roughly 10 strikes on average. Data is available starting January 1st, 2010. In this tutorial, we will discuss how to use QuantConnect to start your options trading algorithm.
-<h2> Step 1: Add Options</h2>
-Before trading options, you need to add options for a given underlying equity and set the resolution in step <code>Initialize</code> with AddOption method. The commonly used parameters will be explained in the method table. Please refer to the link below for details of each method.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Method</th>
-<th>Parameters</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
-resolution,
-fillDataForward)</td>
-<td><strong>underlying(string):</strong> The underlying equity symbol
-<strong>resolution:</strong> Tick, Second, Minute, Hour, or Daily. Default is minute
-<strong>fillDataForward(bool):</strong> If true, returns the last available data even if none in that time slice. The default value is true.</td>
-</tr>
-</tbody>
-</table>
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2017, 01, 01)  #Set Start Date
-    self.SetEndDate(2017, 06, 30)  #Set End Date
-    self.SetCash(50000)  #Set Strategy Cash
-    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
-    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
-    self.symbol = option.Symbol
-</pre>
-The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
-<h2>Step 2: Filter Contracts - Coarse Selection</h2>
-After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Method</th>
-<th>Parameters</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a>(
-min strike,
-max strike,
-minexpiry,
-maxExpiry)</td>
-<td><strong>min Strike, max Strike:</strong> The min and max strike rank relative to market price
-<b><b>min Expiry, max Expiry: </b></b>The range of time to expiration to include, for example, TimeSpan.FromDays(10) would exclude contracts expiring in less than 10 days</td>
-</tr>
-</tbody>
-</table>
-Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
-
-For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
-
-For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.
-<pre class="prettyprint linenums"># filter the contracts with strikes between (market price - 10, market price + 10)
-option.SetFilter(-10,10)
-# filter the contracts which expires more than 30 days but no longer than 60 days
-option.SetFilter(TimeSpan.FromDays(30),TimeSpan.FromDays(60))
-# filter the contracts with strikes between(ATM Strike - 10 * strike space value, market price + 10 * strike space value) and with expiration days less than 180 days
-option.SetFilter(-10, +10, timedelta(0), timedelta(180))</pre>
-<div class="page" title="Page 242">
-<div class="layoutArea">
-<div class="column">
-<h2>Step 3: Choose Contracts - Fine Selection</h2>
-For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
-
-<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
-
-<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
-
-<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="2">Properties of Option Contract x</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><code>x.Symbol.Value</code></td>
-<td> Get the string of option contract's symbol</td>
-</tr>
-<tr>
-<td><code>x.AskPrice, x.BidPrice</code></td>
-<td> Get the ask price,  Get the bid price</td>
-</tr>
-<tr>
-<td><code>x.Expiry</code></td>
-<td> Get the expiration date</td>
-</tr>
-<tr>
-<td><code>x.Strike</code></td>
-<td> Get the strike price</td>
-</tr>
-<tr>
-<td><code>x.ImpliedVolatility</code></td>
-<td> Get the implied volatility</td>
-</tr>
-<tr>
-<td><code>x.Greeks</code></td>
-<td>Get the Greeks letter</td>
-</tr>
-<tr>
-<td><code>x.Right</code></td>
-<td>Get the right being purchased
-x.Right ＝ OptionRight.Call  call option[right to buy]
-x.Right ＝ OptionRight.Put  put option[right to sell]</td>
-</tr>
-<tr>
-<td><code>x.UnderlyingLastPrice</code></td>
-<td>Get the last price the underlying security traded at</td>
-</tr>
-<tr>
-<td><code>x.UnderlyingSymbol</code></td>
-<td>Gets the underlying security's symbol</td>
-</tr>
-</tbody>
-</table>
-We can print out the details of the contract after filtering with Python data frame to show these properties. Assume today is 01/03/2017.  The stock price at 01/03/2017 09:31:00 is $776.01 per share. Here we use  <code>SetFilter(-1, +1, timedelta(0), timedelta(60)) </code>to filter the contracts.
-<pre class="prettyprint linenums">def OnData(self,slice):
-    for i in slice.OptionChains:
-        if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
-</pre>
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Symbol</th>
-<th>type(call 0, put 1)</th>
-<th>Strike</th>
-<th>Expiry</th>
-<th>Ask Price</th>
-<th>Bid Price</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>GOOG 170217C00780000</td>
-<td>0</td>
-<td>780.0</td>
-<td>2017-02-17</td>
-<td>26.4</td>
-<td>27.9</td>
-</tr>
-<tr>
-<td>GOOG 170120P00782500</td>
-<td>1</td>
-<td>782.5</td>
-<td>2017-01-20</td>
-<td>14.7</td>
-<td>16.3</td>
-</tr>
-<tr>
-<td>GOOG 170120C00782500</td>
-<td>0</td>
-<td>782.5</td>
-<td>2017-01-20</td>
-<td>9.4</td>
-<td>10.2</td>
-</tr>
-<tr>
-<td>GOOG 170120P00780000</td>
-<td>1</td>
-<td>780.0</td>
-<td>2017-01-20</td>
-<td>13.4</td>
-<td>15.0</td>
-</tr>
-<tr>
-<td>GOOG 170120C00780000</td>
-<td>0</td>
-<td>780.0</td>
-<td>2017-01-20</td>
-<td>10.6</td>
-<td>11.5</td>
-</tr>
-<tr>
-<td>GOOG 170217P00780000</td>
-<td>1</td>
-<td>780.0</td>
-<td>2017-02-17</td>
-<td>28.9</td>
-<td>30.8</td>
-</tr>
-<tr>
-<td>GOOG 170120P00777500</td>
-<td>1</td>
-<td>777.5</td>
-<td>2017-01-20</td>
-<td>12.2</td>
-<td>13.7</td>
-</tr>
-<tr>
-<td>GOOG 170120C00777500</td>
-<td>0</td>
-<td>777.5</td>
-<td>2017-01-20</td>
-<td>11.8</td>
-<td>12.9</td>
-</tr>
-</tbody>
-</table>
-&nbsp;
-
-Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
-<pre class="prettyprint linenums">for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
-</pre>
-Finally, we trade the options by using the contract's symbol.
-<pre class="prettyprint linenums">if len(contracts) == 0: continue
-# trade the contracts with the farthest expiration
-symbol = contracts[0].Symbol
-self.MarketOrder(symbol, 1)
-self.MarketOnCloseOrder(symbol, -1)
-
-</pre>
-<h2>Algorithm</h2>
-This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c4467dd2b39f215cd685caaac9069ef3'></script>
-<h2>Summary</h2>
-After mastering the basic knowledge of options market, this tutorial we take a close at how to use Quantconnect to customize your own options trading. For example, <span lang="EN-GB">how you can access an option chain, </span><span lang="EN-GB">how to view the details of the contract as a Python data frame, and the most important how to trade the specific option contract.</span>
-
-Next chapter we will examine some important topics of options like the payoff, Put-Call parity, and the synthetic positions. By learning all those concepts, we will start some brief hedging strategies involving options.
-
-</div>
-</div>
-</div>
diff --git a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html
index d117f5b..9ddd2e6 100644
--- a/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
+++ b/02 Tutorial Series/01 Introduction to Financial Python/02 Tutorials.html	
@@ -42,13 +42,13 @@ <h2>Random Variable and Distributions</h2>
 Point estimation vs interval estimation
 <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='/tutorials/introduction-python-confidence-interval-hypothesis-testing/';"  class="clickable" ><td>8</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/confidence-interval-and-hypothesis-testing';"  class="clickable" ><td>8</td>
 <td>
 <h2>Confidence Interval and Hypothesis Testing</h2>
 Test your ideas rigorously.
 <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='tutorials/introduction-to-financial-python/simple-linear-regression';"  class="clickable" ><td>9</td>
+<tr onclick="window.document.location='/tutorials/introduction-to-financial-python/simple-linear-regression';"  class="clickable" ><td>9</td>
 <td>
 <h2>Simple Linear Regression</h2>
 Find the relationship between two random variables.
diff --git a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html
index e793621..3ebc133 100644
--- a/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
+++ b/02 Tutorial Series/02 Introduction to Options/02 Tutorials.html	
@@ -30,7 +30,7 @@ <h2>Put-Call Parity and Arbitrage Strategies</h2>
 <br>Arbitrage Strategy
 <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
 </tr>
-<tr onclick="window.document.location='tutorials/introduction-to-options/stochastic-processes-and-monte-carlo-method';"  class="clickable" >
+<tr onclick="window.document.location='/tutorials/introduction-to-options/stochastic-processes-and-monte-carlo-method';"  class="clickable" >
 <td>4</td>
 <td>
 <h2>Stochastic Processes and Monte Carlo Method</h2>
diff --git a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
index ff69fd4..e107831 100755
--- a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
+++ b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
@@ -1,9 +1,14 @@
-With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
+<p>
+  With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
+</p>
+<p>
+  Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
+</p>
+<p>
+  Two configuration changes are required for desktop charting to work:
+</p>
 
-Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
-
-Two configuration changes are required for desktop charting to work:
-<ol>
+<ul>
  	<li>Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file.
 <pre class="prettyprint">  "environment": "backtesting-desktop",</pre>
 </li>
@@ -12,11 +17,14 @@
   "job-user-id": "....",
   "api-access-token": "...........",</pre>
 </li>
-</ol>
-With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
-
-If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
+</ul>
 
-In the tutorial video below we demonstrate this feature on LEAN:
+<p>
+  With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
+  If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
+</p>
+<p>
+  In the tutorial video below we demonstrate this feature on LEAN:
+</p>
 
-https://www.youtube.com/watch?v=m6llfznP4d4
\ No newline at end of file
+https://www.youtube.com/watch?v=m6llfznP4d4
diff --git a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
index 01a444d..ab0922a 100755
--- a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
+++ b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
@@ -1,16 +1,27 @@
-Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually; but using the API File Provider we provide a way for you to automatically install this into your LEAN project.
+<p>
+  Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually; but using the API File Provider we provide a way for you to automatically install this into your LEAN project.
+</p>
 
-Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.
+<p>
+  Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.
+</p>
 
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-data-library-screen-shot.png" alt="Data library collection for FXCM EURUSD data" />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-data-library-screen-shot.png" alt="Data library collection for FXCM EURUSD data" />
 
-Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.
+<p>
+  Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.
+</p>
 
-This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:
-<ol>
+<p>
+  This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:
+</p>
+
+<ul>
  	<li>Insert your <code>job-user-id</code> and <code>api-access-token</code> into the relevant config fields. You can find these on your <a href="/account">Account</a> page.</li>
  	<li>Update the <code>data-provider</code> configuration to refer to the APIDataProvider class. This is called <code>QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider</code>.</li>
-</ol>
-Check out the video below where we guide you step by step through how to use the API Data Provider.
+</ul>
+<p>
+  Check out the video below where we guide you step by step through how to use the API Data Provider.
+</p>
 
 https://www.youtube.com/watch?v=uyHp_jyeSpA
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html
deleted file mode 100755
index 26f5877..0000000
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 CAPM Alpha Ranking Strategy on Dow 30 Companies.html	
+++ /dev/null
@@ -1,126 +0,0 @@
-<h2>Abstract</h2>
-This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM) [ref]<a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>[/ref], a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
-<ol>
-	<li>How to use historical data</li>
-	<li>Set an event handler</li>
-	<li>Conduct linear regression</li>
-	<li>Build your own functions in the QuantConnect Algorithm Lab</li>
-</ol>
- The implementation of the strategy demonstrates that stocks beat the market last month are likely to beat the market again in the subsequent month. This algorithm performs well when the market is smooth. However when the market volatility increases the model fails to capture alpha and it performs poorly. What we learn from this is that market fluctuations decrease the significance level of the linear regression coefficients, especially when we are using daily returns to fit the model.
-<h2>CAPM Theory</h2>
-The capital asset pricing model (CAPM) describes the relationship between systematic risk and expected return for assets, typically stocks. The formula for calculating the expected return of an asset given its risk is as follows:
-
-\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \]
-
-where:
-
-\[r_f = Risk Free Rate\]
-\[\beta = Beta of the security\]
-
-\[r_m = Expected market return\]
-
-\[\epsilon = Tracking error\]
-
-This formula can be better understood if we refactor the formula as seen below:
-
-\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \]
-
-The left side of the equation gives us the difference between the asset return and risk free rate, the <strong>"excess return"</strong>. If we regress the <strong>market excess return</strong> against the <strong>asset excess return </strong>the slope represents the <strong>"beta"</strong> of the asset. Therefore, beta can also be calculated by the equation:
-
-\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\]
-
-So beta can be described as:
-
-\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\]
-
-The formula above indicates that beta can be explained as<strong> "correlated relative volatility"</strong>. To make this simpler, beta can be calculated by doing a simple linear regression which can be viewed as a factor to explain the return, and the tracking error can represent alpha.
-
-To make this theory more convenient for our algorithm, we change the above formula into the following form:
-
-\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\]
-
-<strong>r*(1-β) </strong>on the right hand side of the equation is a very small item, making it negligible in the context of the Dow 30 companies. If we regress the stocks return with the return of the benchmark, the slope and intercept will be beta and alpha.
-
-<h2>Method</h2>
-Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:
-<ol>
- 	<li>Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.</li>
- 	<li>Conduct simple linear regression on the return of each stock against a benchmark (S&amp;P 500 index, SPY).</li>
- 	<li>Rank the stocks by their intercepts.</li>
- 	<li>Liquidate all our positions and purchase the first 2 stocks in our sorted list.</li>
-</ol>
-Dow Jones components change very infrequently, with the last change being on March 19th, 2015 [ref]<strong> <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a></strong>[/ref]. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
-<h3>Step 1: Setup Event Handler</h3>
-In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))</pre>
-<h3>Step 2: Linear Regression Function</h3>
-In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope.
-<pre class="prettyprint linenums">def regression(self,x,y):
-    x = np.array(x)
-    x = np.diff(x)/x[:-1]
-    y = np.array(y)
-    y = np.diff(y)/y[:-1]
-    A = np.vstack([x, np.ones(len(x))]).T
-    result = np.linalg.lstsq(A, y)[0]
-    beta = result[0]
-    alpha = result[1]
-    return(alpha,beta)</pre>
-<h3>Step 3: History Function</h3>
-Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
-<pre class="prettyprint linenums">def get_regression_data(self,symbol,history):
-    symbol_price = []
-    benchmark_price = []
-    for i in history:
-        bar = i[symbol]
-        benchmark = i[self.benchmark]
-        symbol_price.append(bar.Close)
-        benchmark_price.append(benchmark.Close)
-
-    result = self.regression(symbol_price,benchmark_price)
-    return result</pre>
-<h3>Step 4: Rebalance Function:</h3>
-This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: <a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>.
-<pre class="prettyprint linenums">def rebalance(self):
-    # get historical stock symbols and prices, then put them in tuples
-    history = self.History(self.regression_dates, Resolution.Daily)
-    filter = []
-    for i in self.symbols:
-        filter.append((i,self.get_regression_data(i, history)[0]))
-    # sort the filter by alpha
-    filter.sort(key = lambda x : x[1],reverse = True)
-    sorted_symbols = []
-    for i in range(2):
-    	sorted_symbols.append(filter[i][0])
-    # get the symbols of our holding stocks
-    holding_list = []
-    for i in self.Portfolio:
-    	if i.Value.Invested:
-    		holding_list.append(i.Value.Symbol)
-    # if we have holdings and we are not going to hold them anymore, sell them
-    if holding_list:
-    	for i in holding_list:
-    		if i not in sorted_symbols:
-    			self.Liquidate(i)
-    # Long the 2 stock in our list.
-    for i in sorted_symbols:
-    	self.SetHoldings(i,1)</pre>
-<h2>Conclusion</h2>
-We have demonstrated that during a smooth market, the stocks that beat the market last month are likely to beat the market again in the subsequent month. When there is market fluctuation, the significance level of linear regression will reduce and the model performance will decrease.
-
-We can understand this by looking at the covariance of the asset(x) and the benchmark (y). As the covariance reduces to zero, the beta will decrease.
-
-\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \b{x})^2}\]
-
-As an experiment, we tested the algorithm on market data from 2015. This was a much more volatile period for the market with a fluctuation that returned a mean close to zero and dropped neaerly 10% from Aug 18th to Aug 25th of that year. The algorithm performed quite poorly in this year with a return rate of -11.58%.
-
-The risks associated with this strategy include a high drawdown, lack of hedging and not stop-loss. Since we are using leverage, the risk is increased and it has a margin call in January as a result.
-
-We can improve the performance by applying the following techniques:
-
-<ol>
-	<li>Conduct optimizations: we can implement mean-variance analysis to determine the asset allocation each month and select more stocks to trade. This will lower our risk and manage the portfolio more scientifically.</li>
-	<li>Take beta into consideration: If we want to be more aggressive, we can select targets by a combination of alpha and beta. This means we choose stocks with a high alpha that are more volatile than the market. If we are conservative investors however, we can make the strategy market-neutral, which means the portfolio would not be affected by the market performance. For example, if we long two stocks with beta 1 and -1 respectively at the same position size, our portfolio becomes market-neutral.</li>
-</ol>
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html
new file mode 100755
index 0000000..6b734e5
--- /dev/null
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html	
@@ -0,0 +1,14 @@
+<p>
+	This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM) [ref]<a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>[/ref], a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
+</p>
+
+<ul>
+	<li>How to use historical data</li>
+	<li>Set an event handler</li>
+	<li>Conduct linear regression</li>
+	<li>Build your own functions in the QuantConnect Algorithm Lab</li>
+</ul>
+
+<p>
+	The implementation of the strategy demonstrates that stocks beat the market last month are likely to beat the market again in the subsequent month. This algorithm performs well when the market is smooth. However when the market volatility increases the model fails to capture alpha and it performs poorly. What we learn from this is that market fluctuations decrease the significance level of the linear regression coefficients, especially when we are using daily returns to fit the model.
+</p>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 CAPM Theory.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 CAPM Theory.html
new file mode 100755
index 0000000..da9e6ff
--- /dev/null
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 CAPM Theory.html	
@@ -0,0 +1,45 @@
+<p>
+	The capital asset pricing model (CAPM) describes the relationship between systematic risk and expected return for assets, typically stocks. The formula for calculating the expected return of an asset given its risk is as follows:
+</p>
+
+\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \]
+
+<p>
+	where:
+</p>
+
+\[r_f = Risk Free Rate\]
+\[\beta = Beta of the security\]
+
+\[r_m = Expected market return\]
+
+\[\epsilon = Tracking error\]
+
+<p>
+	This formula can be better understood if we refactor the formula as seen below:
+</p>
+
+\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \]
+
+<p>
+	The left side of the equation gives us the difference between the asset return and risk free rate, the <strong>"excess return"</strong>. If we regress the <strong>market excess return</strong> against the <strong>asset excess return </strong>the slope represents the <strong>"beta"</strong> of the asset. Therefore, beta can also be calculated by the equation:
+</p>
+
+\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\]
+
+<p>
+	So beta can be described as:
+</p>
+
+\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\]
+
+<p>
+	The formula above indicates that beta can be explained as<strong> "correlated relative volatility"</strong>. To make this simpler, beta can be calculated by doing a simple linear regression which can be viewed as a factor to explain the return, and the tracking error can represent alpha.
+	To make this theory more convenient for our algorithm, we change the above formula into the following form:
+</p>
+
+\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\]
+
+<p>
+	<strong>r*(1-β) </strong>on the right hand side of the equation is a very small item, making it negligible in the context of the Dow 30 companies. If we regress the stocks return with the return of the benchmark, the slope and intercept will be beta and alpha.
+</p>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
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+<p>
+	Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:
+</p>
+
+<ul>
+ 	<li>Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.</li>
+ 	<li>Conduct simple linear regression on the return of each stock against a benchmark (S&amp;P 500 index, SPY).</li>
+ 	<li>Rank the stocks by their intercepts.</li>
+ 	<li>Liquidate all our positions and purchase the first 2 stocks in our sorted list.</li>
+</ul>
+<p>
+	Dow Jones components change very infrequently, with the last change being on March 19th, 2015 [ref]<strong> <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a></strong>[/ref]. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
+</p>
+
+<h3>Step 1: Setup Event Handler</h3>
+<p>
+	In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
+	</pre>
+	</div>
+
+<h3>Step 2: Linear Regression Function</h3>
+<p>
+	In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def regression(self,x,y):
+    x = np.array(x)
+    x = np.diff(x)/x[:-1]
+    y = np.array(y)
+    y = np.diff(y)/y[:-1]
+    A = np.vstack([x, np.ones(len(x))]).T
+    result = np.linalg.lstsq(A, y)[0]
+    beta = result[0]
+    alpha = result[1]
+    return(alpha,beta)
+</pre>
+</div>
+<h3>Step 3: History Function</h3>
+<p>
+	Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def get_regression_data(self,symbol,history):
+    symbol_price = []
+    benchmark_price = []
+    for i in history:
+        bar = i[symbol]
+        benchmark = i[self.benchmark]
+        symbol_price.append(bar.Close)
+        benchmark_price.append(benchmark.Close)
+
+    result = self.regression(symbol_price,benchmark_price)
+    return result
+</pre>
+</div>
+<h3>Step 4: Rebalance Function:</h3>
+<p>
+	This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: <a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def rebalance(self):
+    # get historical stock symbols and prices, then put them in tuples
+    history = self.History(self.regression_dates, Resolution.Daily)
+    filter = []
+    for i in self.symbols:
+        filter.append((i,self.get_regression_data(i, history)[0]))
+    # sort the filter by alpha
+    filter.sort(key = lambda x : x[1],reverse = True)
+    sorted_symbols = []
+    for i in range(2):
+    	sorted_symbols.append(filter[i][0])
+    # get the symbols of our holding stocks
+    holding_list = []
+    for i in self.Portfolio:
+    	if i.Value.Invested:
+    		holding_list.append(i.Value.Symbol)
+    # if we have holdings and we are not going to hold them anymore, sell them
+    if holding_list:
+    	for i in holding_list:
+    		if i not in sorted_symbols:
+    			self.Liquidate(i)
+    # Long the 2 stock in our list.
+    for i in sorted_symbols:
+    	self.SetHoldings(i,1)
+		</pre>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html
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+<p>
+	We have demonstrated that during a smooth market, the stocks that beat the market last month are likely to beat the market again in the subsequent month. When there is market fluctuation, the significance level of linear regression will reduce and the model performance will decrease. We can understand this by looking at the covariance of the asset(x) and the benchmark (y). As the covariance reduces to zero, the beta will decrease.
+</p>
+
+\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \b{x})^2}\]
+
+<p>
+	As an experiment, we tested the algorithm on market data from 2015. This was a much more volatile period for the market with a fluctuation that returned a mean close to zero and dropped neaerly 10% from Aug 18th to Aug 25th of that year. The algorithm performed quite poorly in this year with a return rate of -11.58%. The risks associated with this strategy include a high drawdown, lack of hedging and not stop-loss. Since we are using leverage, the risk is increased and it has a margin call in January as a result. We can improve the performance by applying the following techniques:
+</p>
+
+<ul>
+	<li>Conduct optimizations: we can implement mean-variance analysis to determine the asset allocation each month and select more stocks to trade. This will lower our risk and manage the portfolio more scientifically.</li>
+	<li>Take beta into consideration: If we want to be more aggressive, we can select targets by a combination of alpha and beta. This means we choose stocks with a high alpha that are more volatile than the market. If we are conservative investors however, we can make the strategy market-neutral, which means the portfolio would not be affected by the market performance. For example, if we long two stocks with beta 1 and -1 respectively at the same position size, our portfolio becomes market-neutral.</li>
+</ul>
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
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+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html
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+<p>
+  In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research [ref]<strong>Alina F. Serban,</strong> Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>[/ref] which was based on research in the equity market by Ronald J. Balvers and Yangru Wu [ref]<strong>Ronald J. Balvers, Yangru Wu,</strong> Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
+</p>
+
+<p>
+  In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity, UIP [ref]<strong>Investopedia</strong>, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>[/ref]). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang [ref]<strong>Chiang, T., Jiang, C., 1995. </strong>Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
+</p>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Combining Mean Reversion and Momentum in Forex Market.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Combining Mean Reversion and Momentum in Forex Market.html
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-<h2>Abstract</h2>
-In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research [ref]<strong>Alina F. Serban,</strong> Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>[/ref] which was based on research in the equity market by Ronald J. Balvers and Yangru Wu [ref]<strong>Ronald J. Balvers, Yangru Wu,</strong> Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
-
-In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity, UIP [ref]<strong>Investopedia</strong>, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>[/ref]). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang [ref]<strong>Chiang, T., Jiang, C., 1995. </strong>Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref].
-
-We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
-<h2>Introduction</h2>
-The strategy is centered on <strong>uncovered interest parity (UIP)</strong> theory. UIP states that the change in the exchange rate should incorporate any interest rate differentials between the two currencies. By looking for patterns in the deviation from UIP we can potential generate abnormal returns.
-<h3>Interest Parity Conditions</h3>
-UIP states that an investor who borrows money in their home country and lends it in another country with a higher interest rate should expect a zero return due to the changes in exchange rate. In other words:
-
-\[1+r_t = (1+r^{i}_t)(\frac{F^{i}_t}{S^{i}_t})\]
-Where \(r_t\) is the domestic interest rate, \(r^{i}\) is the foreign interest rate, \(S^{i}\) is the spot exchange rate and \(F^{i}\) is the forward rate. We can also replace F forward rate with expected spot rate:
-
-\[1+r_t = (1+r^{i}_t)(\frac{E(S^{i}_{t+1})}{S^{i}_t})\]
-
-Taking logs of the above two equations, we obtain:
-
-\[r_t - r^{i}_t = \ln F^{i}_t - \ln S^{i}_t\]
-
-\[r_t - r^{i}_t =\ln S^{i}_{t+1} - \ln S^{i}_t\]
-
-The deviation from UIP is denoted by y and defined as follows:
-
-\[y^{i}_{t+1} =\ln S^{i}_{t+1} -\ln F^{i}_t\]
-<h3>Model and Parameter Estimation</h3>
-Fama and French and Summers [ref]<strong>Fama, E., 1984. </strong>Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.[/ref] constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
-
-\[x^{i}_t = (1 - \delta ^{i})\mu ^{i} + \delta ^{i}x^{i}_{t-1} + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
-
-Using the equation above Serban adapts it to find the abnormal return in the forex market. The \(\delta \) represents the speed of mean reversion and can differ by country, while the \(\rho \) represents the momentum strength and can vary by country and by lag. The parameter \(\mu \) also varies by country. Accounting for these changes:
-
-\[y^{i}_t = -(1 - \delta ^{i})(x^{i}_{t-1} - \mu ^{i}) + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
-<h3>Trading Strategy</h3>
-The trading strategy from the paper allows \(\mu \) to change by country, while let \(\rho \) and \(\delta \) stay fixed. By applying Ordinary Least Squared(OLS) regression, the model estimates the return y for each currency. We construct the portfolio by taking a long position on the currency with the highest expected return and taking a short position on the currency with the lowest expected return. We hold these positions for one month, and repeat the process each month. There are two exceptions to this strategy: if all expected returns are positive, we take a long position only, and vice versa.
-
-To limit the number of parameters we need to estimate and find a solution easily we only allow \(\mu \) to change by country. According to the paper if we let ρ stay fixed and J =3, we can obtain the highest return for this strategy. If so the equation can be simplified as:
-
-\[y^{i}_t = -(1-\delta )(x^{i}_{t-1} - \mu ^{i}) + \rho (x^{i}_{t-1} - x^{i}_{t-4}) + \epsilon ^{i}_t\]
-
-When applying the above equation, we found that the scale of the mean reversion for each currency are very different, and this difference in scale is large enough to affect the accuracy of our rank. We made an adjustment to standardize the mean-reversion. While calculating \(\mu \) (the mean of the log prices) we also calculated standard deviation σ. In this tutorial we replace \(x-\mu \) with \(\frac{x - \mu}{\sigma } \). This captures the mean reversion factor better than the author's technique.
-<h3>Data Description</h3>
-The paper used monthly exchange rate data for the Canadian Dollar/USD, German Mark/Euro, UK Pound/USD and Japanese Yen/USD, from 1978 to 2008. Due to data availability, we used Euro/USD instead of German Mark/Euro, and the earliest data available starts from 2004. Each time we launch the strategy we use all of the available historical data prior to the start date to build the OLS model and uses that model for the entire backtest. The paper used 1/3 of their data as the training dataset and the rest of the test set. We directly test our model on backtesting, because QuantConnect makes this easier.
-<h2>Method</h2>
-In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution.
-<h3>Step 1: Request Historical Data</h3>
-The first function takes two arguments: symbol and number of daily data points requested. This function requests historical <a href="https://www.quantconnect.com/docs#Consolidating-Data-TradeBars-vs-QuoteBars">QuoteBars</a> and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">DataFrame</a>. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
-<pre class="prettyprint linenums">def get_history(self,symbol, num):
-    data = {}
-    dates = []
-    history = self.History([symbol], num, Resolution.Daily).loc[symbol]['close'] #request the historical data for a single symbol
-    for time in history.index:
-        t = time.to_pydatetime().date()
-        dates.append(t)
-    dates = pd.to_datetime(dates)
-    df = pd.DataFrame(history)
-    df.reset_index(drop=True)
-    df.index = dates
-    df.columns = ['price']
-    return df
-
-def calculate_return(self,df):
-    #calculate the mean for further use
-    mean = np.mean(df.price)
-    # cauculate the standard deviation
-    sd = np.std(df.price)
-    # pandas method to take the last datapoint of each month.
-    df = df.resample('BM',how = lambda x: x[-1])
-    # the following three lines are for further experiment purpose
-    # df['j1'] = df.price.shift(1) - df.price.shift(2)
-    # df['j2'] = df.price.shift(2) - df.price.shift(3)
-    # df['j3'] = df.price.shift(3) - df.price.shift(4)
-    # take the return as depend variable
-    df['log_return'] = df.price - df.price.shift(1)
-    # calculate the reversal factor
-    df['reversal'] = (df.price.shift(1) - mean)/sd
-    # calculate the momentum factor
-    df['mom'] = df.price.shift(1) - df.price.shift(4)
-    df = df.dropna() #remove nan value
-    return (df,mean,sd)</pre>
-<h3>Step 2: Build Predictive Model</h3>
-The concat function requests history and joins the results into a single DataFrame. As \(\mu \) varies by country so we assign the mean and standard deviation to the symbol for each currency for future use. The OLS function takes the resulting DataFrame to conduct an OLS regression. We write it into a function because it's easier to change the formula here if we need.
-<pre class="prettyprint linenums">def concat(self):
-    # we requested as many daily tradebars as we can
-    his = self.get_history(self.quoted[0].Value,20*365)
-    # get the clean DataFrame for linear regression
-    his = self.calculate_return(his)
-    # add property to the symbol object for further use.
-    self.quoted[0].mean = his[1]
-    self.quoted[0].sd = his[2]
-    df = his[0]
-    # repeat the above procedure for each symbols, and concat the dataframes
-    for i in range(1,len(self.quoted)):
-        his = self.get_history(self.quoted[i].Value,20*365)
-        his = self.calculate_return(his)
-        self.quoted[i].mean = his[1]
-        self.quoted[i].sd = his[2]
-        df = pd.concat([df,his[0]])
-    df = df.sort_index()
-    # remove outliers that outside the 99.9% confidence interval
-    df = df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() &lt; 3).all(axis=1)]
-    return df
-
-def OLS(self,df):
-    res = sm.ols(formula = 'return ~ reversal + mom',data = df).fit()
-    return res</pre>
-<h3>Step 3: Apply Predictive Model</h3>
-The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
-<pre class="prettyprint linenums">def predict(self,symbol):
-    # get current month in string
-    month = str(self.Time).split(' ')[0][5:7]
-    # request the data in the last three months
-    res = self.get_history(symbol.Value,33*3)
-    # pandas method to take the last datapoint of each month
-    res = res.resample('BM',how = lambda x: x[-1])
-    # remove the data points in the current month
-    res = res[res.index.month != int(month)]
-    # calculate the variables
-    res = self.calculate_input(res,symbol.mean,symbol.sd)
-    res = res.ix[0]
-    # take the coefficient. The first one will not be used for sum-product because it's the intercept
-    params = self.formula.params[1:]
-    # calculate the expected return
-    re = sum([a*b for a,b in zip(res[1:],params)]) + self.formula.params[0]
-    return re
-
-def calculate_input(self, df, mean, sd):
-    df['reversal'] = (df.price - mean)/sd
-    df['mom'] = df.price - df.price.shift(3)
-    df = df.dropna()
-    return df
-</pre>
-There are a few points of note:
-<ol>
- 	<li>We need historical TradeBars for the last three months. To do this we requested 99 bars and use a pandas DataFrame to extract a data point for the end of each month.</li>
- 	<li>We use event schedule to execute the strategy at the first trading day, however, sometimes the first day of the month could be on the 2nd if the 1st falls on a weekend. To fix this we remove the data from the current month, leaving only the last 3 months of data.</li>
- 	<li>We start from the second element of res (res[1:]) because res and params are different lengths. This was hard to detect because Python would not throw error when running [a*b for a,b in zip(res,params)] even if the length of the two lists are different.</li>
- 	<li>This function also used pandas DataFrame methods extensively. For more information please refer to <a href="http://pandas.pydata.org">pandas</a>.</li>
-</ol>
-<h3>Step 4: Initializing the Model</h3>
-In the Initialize function we prepare the data and conduct a linear regression. The class property 'self.formula' is the result of the OLS regression. We will use this object each time we rebalance the portfolio.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2013,6,1)
-    self.SetEndDate(2016,6,1)
-    self.SetCash(10000)
-    self.syls = ['EURUSD','GBPUSD','USDCAD','USDJPY']
-    self.quoted = []
-    for i in range(len(self.syls)):
-        self.quoted.append(self.AddForex(self.syls[i],Resolution.Daily,Market.Oanda).Symbol)
-    df = self.concat()
-    self.Log(str(df))
-    self.formula = self.OLS(df)
-    self.Log(str(self.formula.summary()))
-    self.Log(str(df))
-    self.Log(str(df.describe()))
-    for i in self.quoted:
-        self.Log(str(i.mean) + '   ' + str(i.sd))
-    self.Schedule.On(self.DateRules.MonthStart(), self.TimeRules.At(9,31), Action(self.action))</pre>
-<h3>Step 5: Performing Monthly Rebalancing</h3>
-Every month we rebalance the portfolio using the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Schedule Event</a> helper method. The predicted returns are added to the rank array and then sorted by return. The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
-<pre class="prettyprint linenums">def action(self):
-    rank = []
-    long_short = []
-    for i in self.quoted:
-        rank.append((i,self.predict(i)))
-# rank the symbols by their expected return
-    rank.sort(key = lambda x: x[1],reverse = True)
-# the first element in long_short is the one with the highest expected return, which we are going to long, and the second one is going to be shorted.
-    long_short.append(rank[0])
-    long_short.append(rank[-1])
-    self.Liquidate()
-
-# the product &lt; 0 means the expected return of the first one is positive and that of the second one is negative--we are going to long and short.
-    if long_short[0][1]*long_short[1][1] &lt; 0:
-        self.SetHoldings(long_short[0][0],1)
-        self.SetHoldings(long_short[1][0],-1)
-        # this means we long only because all of the expected return is positive
-    elif long_short[0][1] &gt; 0 and long_short[1][1] &gt; 0:
-        self.SetHoldings(long_short[0][0],1)
-# short only
-    else:
-        self.SetHoldings(long_short[1][0],-1)</pre>
-<h2>Results</h2>
-The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1” />
-
-From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are -4.074 and 1.417 for the reversal factor and momentum factor respectively. The p-value of the reversal factor is very small which means this factor has a very high significance level.
-<h3>Backtest Sensitivity Results</h3>
-We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2” />
-The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
-<h2>Conclusion</h2>
-The paper demonstrates there are inefficiencies in the UIP which can be exploited with a hybrid momentum and mean reversion strategy. Although the sample size of the paper is much larger than ours the parameter and significance level of the two models are very close. The strategies we discussed above keep \(\rho \) fixed and only allow \(\mu \) to change by country. If we let \(\rho \) change by lag the significance level of the model might increase, but this could potentially make modeling more difficult by introducing multicollinearity. To test this we wrote this implementation in the algorithm and commented out the lines. If you are interested in exploring this extension to the model you can change these lines to test your strategy.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c1eea4df64cf986eba481f5e5e376e8a'></script>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html
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+<p>
+  The strategy is centered on <strong>uncovered interest parity (UIP)</strong> theory. UIP states that the change in the exchange rate should incorporate any interest rate differentials between the two currencies. By looking for patterns in the deviation from UIP we can potential generate abnormal returns.
+</p>
+
+<h4>Interest Parity Conditions</h4>
+<p>
+  UIP states that an investor who borrows money in their home country and lends it in another country with a higher interest rate should expect a zero return due to the changes in exchange rate. In other words:
+</p>
+
+\[1+r_t = (1+r^{i}_t)(\frac{F^{i}_t}{S^{i}_t})\]
+<p>
+  Where \(r_t\) is the domestic interest rate, \(r^{i}\) is the foreign interest rate, \(S^{i}\) is the spot exchange rate and \(F^{i}\) is the forward rate. We can also replace F forward rate with expected spot rate:
+</p>
+
+\[1+r_t = (1+r^{i}_t)(\frac{E(S^{i}_{t+1})}{S^{i}_t})\]
+
+<p>
+  Taking logs of the above two equations, we obtain:
+</p>
+
+\[r_t - r^{i}_t = \ln F^{i}_t - \ln S^{i}_t\]
+
+\[r_t - r^{i}_t =\ln S^{i}_{t+1} - \ln S^{i}_t\]
+
+<p>
+  The deviation from UIP is denoted by y and defined as follows:
+</p>
+
+\[y^{i}_{t+1} =\ln S^{i}_{t+1} -\ln F^{i}_t\]
+
+<h4>Model and Parameter Estimation</h4>
+<p>
+  Fama and French and Summers [ref]<strong>Fama, E., 1984. </strong>Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.[/ref] constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
+</p>
+
+\[x^{i}_t = (1 - \delta ^{i})\mu ^{i} + \delta ^{i}x^{i}_{t-1} + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
+
+<p>
+  Using the equation above Serban adapts it to find the abnormal return in the forex market. The \(\delta \) represents the speed of mean reversion and can differ by country, while the \(\rho \) represents the momentum strength and can vary by country and by lag. The parameter \(\mu \) also varies by country. Accounting for these changes:
+</p>
+
+\[y^{i}_t = -(1 - \delta ^{i})(x^{i}_{t-1} - \mu ^{i}) + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
+
+<h4>Trading Strategy</h4>
+<p>
+  The trading strategy from the paper allows \(\mu \) to change by country, while let \(\rho \) and \(\delta \) stay fixed. By applying Ordinary Least Squared(OLS) regression, the model estimates the return y for each currency. We construct the portfolio by taking a long position on the currency with the highest expected return and taking a short position on the currency with the lowest expected return. We hold these positions for one month, and repeat the process each month. There are two exceptions to this strategy: if all expected returns are positive, we take a long position only, and vice versa.
+  To limit the number of parameters we need to estimate and find a solution easily we only allow \(\mu \) to change by country. According to the paper if we let ρ stay fixed and J =3, we can obtain the highest return for this strategy. If so the equation can be simplified as:
+</p>
+
+\[y^{i}_t = -(1-\delta )(x^{i}_{t-1} - \mu ^{i}) + \rho (x^{i}_{t-1} - x^{i}_{t-4}) + \epsilon ^{i}_t\]
+
+<p>
+  When applying the above equation, we found that the scale of the mean reversion for each currency are very different, and this difference in scale is large enough to affect the accuracy of our rank. We made an adjustment to standardize the mean-reversion. While calculating \(\mu \) (the mean of the log prices) we also calculated standard deviation σ. In this tutorial we replace \(x-\mu \) with \(\frac{x - \mu}{\sigma } \). This captures the mean reversion factor better than the author's technique.
+</p>
+
+<h4>Data Description</h4>
+<p>
+  The paper used monthly exchange rate data for the Canadian Dollar/USD, German Mark/Euro, UK Pound/USD and Japanese Yen/USD, from 1978 to 2008. Due to data availability, we used Euro/USD instead of German Mark/Euro, and the earliest data available starts from 2004. Each time we launch the strategy we use all of the available historical data prior to the start date to build the OLS model and uses that model for the entire backtest. The paper used 1/3 of their data as the training dataset and the rest of the test set. We directly test our model on backtesting, because QuantConnect makes this easier.
+</p>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html
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@@ -0,0 +1,184 @@
+<p>
+  In order to apply the model, we need to first pull history data to build it. The project can be briefly divided into four parts: the historical data request, model training, prediction and execution.
+</p>
+
+<h3>Step 1: Request Historical Data</h3>
+<p>
+  The first function takes two arguments: symbol and number of daily data points requested. This function requests historical <a href="https://www.quantconnect.com/docs#Consolidating-Data-TradeBars-vs-QuoteBars">QuoteBars</a> and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">DataFrame</a>. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def get_history(self,symbol, num):
+    data = {}
+    dates = []
+    history = self.History([symbol], num, Resolution.Daily).loc[symbol]['close'] #request the historical data for a single symbol
+    for time in history.index:
+        t = time.to_pydatetime().date()
+        dates.append(t)
+    dates = pd.to_datetime(dates)
+    df = pd.DataFrame(history)
+    df.reset_index(drop=True)
+    df.index = dates
+    df.columns = ['price']
+    return df
+
+def calculate_return(self,df):
+    #calculate the mean for further use
+    mean = np.mean(df.price)
+    # cauculate the standard deviation
+    sd = np.std(df.price)
+    # pandas method to take the last datapoint of each month.
+    df = df.resample('BM',how = lambda x: x[-1])
+    # the following three lines are for further experiment purpose
+    # df['j1'] = df.price.shift(1) - df.price.shift(2)
+    # df['j2'] = df.price.shift(2) - df.price.shift(3)
+    # df['j3'] = df.price.shift(3) - df.price.shift(4)
+    # take the return as depend variable
+    df['log_return'] = df.price - df.price.shift(1)
+    # calculate the reversal factor
+    df['reversal'] = (df.price.shift(1) - mean)/sd
+    # calculate the momentum factor
+    df['mom'] = df.price.shift(1) - df.price.shift(4)
+    df = df.dropna() #remove nan value
+    return (df,mean,sd)</pre>
+</div>
+
+<h3>Step 2: Build Predictive Model</h3>
+<p>
+  The concat function requests history and joins the results into a single DataFrame. As \(\mu \) varies by country so we assign the mean and standard deviation to the symbol for each currency for future use. The OLS function takes the resulting DataFrame to conduct an OLS regression. We write it into a function because it's easier to change the formula here if we need.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def concat(self):
+    # we requested as many daily tradebars as we can
+    his = self.get_history(self.quoted[0].Value,20*365)
+    # get the clean DataFrame for linear regression
+    his = self.calculate_return(his)
+    # add property to the symbol object for further use.
+    self.quoted[0].mean = his[1]
+    self.quoted[0].sd = his[2]
+    df = his[0]
+    # repeat the above procedure for each symbols, and concat the dataframes
+    for i in range(1,len(self.quoted)):
+        his = self.get_history(self.quoted[i].Value,20*365)
+        his = self.calculate_return(his)
+        self.quoted[i].mean = his[1]
+        self.quoted[i].sd = his[2]
+        df = pd.concat([df,his[0]])
+    df = df.sort_index()
+    # remove outliers that outside the 99.9% confidence interval
+    df = df[df.apply(lambda x: np.abs(x - x.mean()) / x.std() &lt; 3).all(axis=1)]
+    return df
+
+def OLS(self,df):
+    res = sm.ols(formula = 'return ~ reversal + mom',data = df).fit()
+    return res</pre>
+</div>
+
+<h3>Step 3: Apply Predictive Model</h3>
+<p>
+  The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def predict(self,symbol):
+    # get current month in string
+    month = str(self.Time).split(' ')[0][5:7]
+    # request the data in the last three months
+    res = self.get_history(symbol.Value,33*3)
+    # pandas method to take the last datapoint of each month
+    res = res.resample('BM',how = lambda x: x[-1])
+    # remove the data points in the current month
+    res = res[res.index.month != int(month)]
+    # calculate the variables
+    res = self.calculate_input(res,symbol.mean,symbol.sd)
+    res = res.ix[0]
+    # take the coefficient. The first one will not be used for sum-product because it's the intercept
+    params = self.formula.params[1:]
+    # calculate the expected return
+    re = sum([a*b for a,b in zip(res[1:],params)]) + self.formula.params[0]
+    return re
+
+def calculate_input(self, df, mean, sd):
+    df['reversal'] = (df.price - mean)/sd
+    df['mom'] = df.price - df.price.shift(3)
+    df = df.dropna()
+    return df
+  </pre>
+  </div>
+
+<p>
+  There are a few points of note:
+</p>
+
+<ul>
+ 	<li>We need historical TradeBars for the last three months. To do this we requested 99 bars and use a pandas DataFrame to extract a data point for the end of each month.</li>
+ 	<li>We use event schedule to execute the strategy at the first trading day, however, sometimes the first day of the month could be on the 2nd if the 1st falls on a weekend. To fix this we remove the data from the current month, leaving only the last 3 months of data.</li>
+ 	<li>We start from the second element of res (res[1:]) because res and params are different lengths. This was hard to detect because Python would not throw error when running [a*b for a,b in zip(res,params)] even if the length of the two lists are different.</li>
+ 	<li>This function also used pandas DataFrame methods extensively. For more information please refer to <a href="http://pandas.pydata.org">pandas</a>.</li>
+</ul>
+
+<h3>Step 4: Initializing the Model</h3>
+<p>
+  In the Initialize function we prepare the data and conduct a linear regression. The class property 'self.formula' is the result of the OLS regression. We will use this object each time we rebalance the portfolio.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+    self.SetStartDate(2013,6,1)
+    self.SetEndDate(2016,6,1)
+    self.SetCash(10000)
+    self.syls = ['EURUSD','GBPUSD','USDCAD','USDJPY']
+    self.quoted = []
+    for i in range(len(self.syls)):
+        self.quoted.append(self.AddForex(self.syls[i],Resolution.Daily,Market.Oanda).Symbol)
+    df = self.concat()
+    self.Log(str(df))
+    self.formula = self.OLS(df)
+    self.Log(str(self.formula.summary()))
+    self.Log(str(df))
+    self.Log(str(df.describe()))
+    for i in self.quoted:
+        self.Log(str(i.mean) + '   ' + str(i.sd))
+    self.Schedule.On(self.DateRules.MonthStart(), self.TimeRules.At(9,31), Action(self.action))
+  </pre>
+</div>
+
+<h3>Step 5: Performing Monthly Rebalancing</h3>
+<p>
+  Every month we rebalance the portfolio using the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Schedule Event</a> helper method. The predicted returns are added to the rank array and then sorted by return. The first element in the list is the best return paired with the associated symbol. When all the expected returns in the rank array are positive we only go long the pair with the highest expected return. When all returns are negative, we only go short the pair with the lowest expected return.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def action(self):
+    rank = []
+    long_short = []
+    for i in self.quoted:
+        rank.append((i,self.predict(i)))
+# rank the symbols by their expected return
+    rank.sort(key = lambda x: x[1],reverse = True)
+# the first element in long_short is the one with the highest expected return, which we are going to long, and the second one is going to be shorted.
+    long_short.append(rank[0])
+    long_short.append(rank[-1])
+    self.Liquidate()
+
+# the product &lt; 0 means the expected return of the first one is positive and that of the second one is negative--we are going to long and short.
+    if long_short[0][1]*long_short[1][1] &lt; 0:
+        self.SetHoldings(long_short[0][0],1)
+        self.SetHoldings(long_short[1][0],-1)
+        # this means we long only because all of the expected return is positive
+    elif long_short[0][1] &gt; 0 and long_short[1][1] &gt; 0:
+        self.SetHoldings(long_short[0][0],1)
+# short only
+    else:
+        self.SetHoldings(long_short[1][0],-1)
+      </pre>
+</div>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html
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+<p>
+  The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1” />
+
+<p>
+  From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are -4.074 and 1.417 for the reversal factor and momentum factor respectively. The p-value of the reversal factor is very small which means this factor has a very high significance level.
+</p>
+
+<h3>Backtest Sensitivity Results</h3>
+<p>
+  We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2” />
+<p>
+  The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
+</p>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/05 Summary.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/05 Summary.html
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+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/05 Summary.html	
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+<p>
+  The paper demonstrates there are inefficiencies in the UIP which can be exploited with a hybrid momentum and mean reversion strategy. Although the sample size of the paper is much larger than ours the parameter and significance level of the two models are very close. The strategies we discussed above keep \(\rho \) fixed and only allow \(\mu \) to change by country. If we let \(\rho \) change by lag the significance level of the model might increase, but this could potentially make modeling more difficult by introducing multicollinearity. To test this we wrote this implementation in the algorithm and commented out the lines. If you are interested in exploring this extension to the model you can change these lines to test your strategy.
+</p>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
new file mode 100755
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+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c1eea4df64cf986eba481f5e5e376e8a'></script>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html
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@@ -0,0 +1,8 @@
+<h2>Abstract</h2>
+<p>
+  In this research, We investigate two pairs trading methods and compare the result. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
+</p>
+
+<p>
+  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a> [ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref] </em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012)[ref]Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012.[/ref]. The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
+</p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html
new file mode 100755
index 0000000..62595c4
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+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html	
@@ -0,0 +1,71 @@
+<h3>1. Definition</h3>
+<p>
+  Given a random vector \(X_1,X_2,...,X_p\), its marginal cumulative distribution functions (CDFs) are \(F_i(x) = P[X_i \leq x]\). By applying the probability integral transform to each component, the marginal distributions of \((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\) are uniform (from <a href="https://en.wikipedia.org/wiki/Copula_(probability_theory)">Wikipedia</a>). Then the copula of \(X_1,X_2,...,X_p\) is defined as the joint cumulative distribution function of \(U_1,U_2,...,U_p\), for which the marginal distribution of each variable U is uniform as  \(U(0,1)\).
+</p>
+
+\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
+
+<p>
+  Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.[ref]Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>[/ref]
+</p>
+
+<h3>2. Bivariate Copulas</h3>
+<p>
+  Since this research focuses on bivariate copulas (for pairs trading we have 2 random variables) some probabilistic properties are specified.
+  Let X and Y be two random variables with cumulative probability function \(F_1(X)\) and \(F_2(Y)\). \(U=F_1(X), V=F_2(Y)\) which are uniformly distributed.  Then the copula function is \(C(u,v)=P(U\leq u,V\leq v)\). Taking the partial derivative of the copula function over U and V would give the conditional distribution function as follows:
+</p>
+
+\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\]
+\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\]
+
+<h3>3. Archimedean Copulas</h3>
+<p>
+  There are many copula functions that enable us to describe dependence structures between variables, other than the Gaussian assumption. Here we will focus three of these; the <em>Clayton</em>, <em>Gumbel</em> and <em>Frank</em> copula formulas from the Archimedean class.
+  Archimedean copulas[ref]Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>[/ref] are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\) [ref]LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.[/ref].
+</p>
+
+\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
+
+<p>
+  The probability density function is:
+</p>
+
+\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\]
+
+<p>
+  Where \(\phi_{(2)}^{-1}\) is the inverse of the second derivative of the generator function.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: left;">Copula</th>
+<th style="text-align: center;">Copula function C(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton Copula</td>
+<td style="text-align: left;">\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Gumbel Copula</td>
+<td style="text-align: left;">\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank Copula</td>
+<td>\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]</td>
+</tr>
+</tbody>
+</table>
+
+
+<p>
+  Genest and MacKay (1986) [ref]Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283[/ref] proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
+</p>
+
+\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
+
+<p>
+  So we can easily estimate the parameter in Archimedean copulas if we know Kendall’s tau rank measure and the generator function. Please refer to step 3 to see the formulas.
+</p>
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-<h2>Abstract</h2>
-We investigate two pairs trading methods and compare the results. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
-
-In this tutorial, we will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a> [ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref] </em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012)[ref]Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012.[/ref]. The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
-<h2>Framework of Copula</h2>
-<h3>1. Definition</h3>
-Given a random vector \(X_1,X_2,...,X_p\), its marginal cumulative distribution functions (CDFs) are \(F_i(x) = P[X_i \leq x]\). By applying the probability integral transform to each component, the marginal distributions of \((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\) are uniform (from <a href="https://en.wikipedia.org/wiki/Copula_(probability_theory)">Wikipedia</a>).
-
-Then the copula of \(X_1,X_2,...,X_p\) is defined as the joint cumulative distribution function of \(U_1,U_2,...,U_p\), for which the marginal distribution of each variable U is uniform as  \(U(0,1)\).
-
-\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
-
-Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.[ref]Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>[/ref]
-<h3>2. Bivariate Copulas</h3>
-Since this research focuses on bivariate copulas (for pairs trading we have 2 random variables) some probabilistic properties are specified.
-
-Let X and Y be two random variables with cumulative probability function \(F_1(X)\) and \(F_2(Y)\). \(U=F_1(X), V=F_2(Y)\) which are uniformly distributed.  Then the copula function is \(C(u,v)=P(U\leq u,V\leq v)\). Taking the partial derivative of the copula function over U and V would give the conditional distribution function as follows:
-
-\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\]
-
-\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\]
-<h3>3. Archimedean Copulas</h3>
-There are many copula functions that enable us to describe dependence structures between variables, other than the Gaussian assumption. Here we will focus three of these; the <em>Clayton</em>, <em>Gumbel</em> and <em>Frank</em> copula formulas from the Archimedean class.
-
-Archimedean copulas[ref]Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>[/ref] are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\) [ref]LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.[/ref].
-
-\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
-
-The probability density function is:
-
-\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\]
-
-Where \(\phi_{(2)}^{-1}\) is the inverse of the second derivative of the generator function.
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: left;">Copula</th>
-<th style="text-align: center;">Copula function C(u,v;θ)</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: left;">Clayton Copula</td>
-<td style="text-align: left;">\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Gumbel Copula</td>
-<td style="text-align: left;">\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Frank Copula</td>
-<td>\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]</td>
-</tr>
-</tbody>
-</table>
-&nbsp;
-
-Genest and MacKay (1986) [ref]Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283[/ref] proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
-
-\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
-
-So we can easily estimate the parameter in Archimedean copulas if we know Kendall’s tau rank measure and the generator function. Please refer to step 3 to see the formulas.
-<h2>Part I: Copula Method</h2>
-ETFs have many different stock sectors and asset classes which provide us a wide range of pairs trading candidates. Our data set consists of daily data of the ETFs traded on the NASDAQ or the NYSE.
-
-We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
-<h3>Step 1: Selecting the Paired Stocks</h3>
-The general method of pair selection is based on both fundamental and statistical analysis.[ref]<strong>Jean Folger</strong>. Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>[/ref]
-<h4><strong>1) Assemble a list of potentially related pairs</strong></h4>
-Any random pairs could be correlated. It is possible that those variables are not causally related to each other, but because of a spurious relationship due to either coincidence or the presence of a certain third, unseen factor. Thus, it is important for us to start with a list of securities that have something in common. For this demonstration, we choose some of the most liquid ETFs traded on the Nasdaq or the NYSE.  The relationship for those potentially related pairs could be due to an index, sector or asset class overlap. e.g. QQQ and XLK are two ETFs which track the market leading indices.
-<h4><strong>2) Filter the trading pair with statistical correlation</strong></h4>
-<div class="page" title="Page 12">
-<div class="section">
-<div class="layoutArea">
-<div class="column">
-
-To determine which stock pairs to include in the analysis, correlations between the pre-selected ETF pairs are analyzed. Below are three types of correlation measures we usually use in statistics:
-<table class="table qc-table">
-<thead>
-<tr>
-<th colspan="3">Correlation Measurement Techniques</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td width="33%">Pearson correlation</td>
-<td>\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]</td>
-</tr>
-<tr>
-<td>Kendall rank correlation</td>
-<td>\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]</td>
-</tr>
-<tr>
-<td>Spearman rank correlation</td>
-<td>\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]</td>
-</tr>
-<tr>
-<td colspan="2"> \(n\) = number of value in each data set
-\(n_c\) = number of concordant
-\(n_d\) = number of discordant
-\(d_i\) = the difference between the ranks of corresponding values \(x_i\) and \(y_i\)</td>
-</tr>
-</tbody>
-</table>
-</div>
-We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading.
-
-We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
-
-\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n where n is the number of price data
-<pre class="prettyprint linenums">def _pair_selection(self):
-
-    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
-                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
-    logreturn={}
-    for i in range(2):
-        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
-        history = self.History(syl, self.lookbackdays,Resolution.Daily)
-        # generate the log return series of paired stocks
-        close = history['close'].unstack(level=0)
-        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-        for j in tick_syl[i]:
-            logreturn[j] = df_logreturn[j]
-    # estimate coefficients of different correlation measures
-    tau_coef,pr_coef,sr_coef= [],[],[]
-    for i in range(len(tick_syl[i])):
-        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
-        tau_coef.append(kendalltau(tik_x, tik_y)[0])
-        pr_coef.append(pearsonr(tik_x, tik_y)[0])
-        sr_coef.append(spearmanr(tik_x, tik_y)[0])
-    index_max = tau_coef.index(max(tau_coef))
-    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
-</pre>
-<h3>Step 2: Estimating Marginal Distributions of log-return</h3>
-In order to construct the copula, we need to transform the log-return series \(R_x\) and \(R_y\) to two uniformly distributed values u and v. This can be done by estimating the marginal distribution functions of \(R_x\) and \(R_y\) and plugging the return values into a distribution function. As we make no assumptions about the distribution of the two log-return series, here we use the empirical distribution function to approach the marginal distribution \(F_1(R_x)\) and \(F_2(R_y)\). The Python ECDF function from the statsmodel library gives us the Empirical CDF as a step function.
-<h3>Step 3: Estimating Copula Parameters</h3>
-As discussed above, we estimate the copula parameter theta by the relationship between the copula and the dependence measure Kendall’s tau, for each of the Archimedean copulas.
-<table class="table qc-table">
-<thead>
-<tr>
-<th>Copula</th>
-<th style="text-align: center;">Kendall's tau</th>
-<th style="text-align: center;">parameter θ</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Clayton Copula</td>
-<td>\[\frac{\theta}{\theta +2}\]</td>
-<td>\[\theta=2\tau(1-\tau)^{-1}\]</td>
-</tr>
-<tr>
-<td>Gumbel Copula</td>
-<td>\[1-\theta^{-1}\]</td>
-<td>\[\theta=(1-\tau)^{-1}\]</td>
-</tr>
-<tr>
-<td>Frank Copula</td>
-<td>\[1+4[D_1(\theta)-1]/\theta\]</td>
-<td>\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]</td>
-</tr>
-<tr>
-<td colspan="3">\[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]</td>
-</tr>
-</tbody>
-</table>
-</div>
-<pre class="prettyprint linenums">def _parameter(self, family, tau):
-    if  family == 'clayton':
-        return 2*tau/(1-tau)
-    elif family == 'frank':
-        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
-    	# frank_fun is the squared difference
-    	# Minimize the frank_fun would give the parameter theta for the frank copula
-      integrand = lambda t: t/(np.exp(t)-1)
-    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
-      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
-    elif family == 'gumbel':
-        return 1/(1-tau)</pre>
-</div>
-</div>
-<h3>Step 4: Selecting the Best Fitting Copula</h3>
-Once we get the parameter estimation for the copula functions, we use the AIC criteria to select the copula that provides the best fit in algorithm initialization.
-
-\[AIC=-2L(\theta)+2k\]
-
-where \(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\) is the log-likelihood function and k is the number of parameters, here k=1.
-
-The density functions of each copula function are as follows:
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;">Copula</th>
-<th style="text-align: center;">Density function c(u,v;θ)</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: left;">Clayton Copula</td>
-<td style="text-align: left;">\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]</td>
-</tr>
-<tr>
-<td>Gumbel Copula</td>
-<td>\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]</td>
-</tr>
-<tr>
-<td style="text-align: left;">Frank Copula</td>
-<td style="text-align: left;">\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]</td>
-</tr>
-<tr>
-<td colspan="2">\[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]</td>
-</tr>
-</tbody>
-</table>
-<div class="page" title="Page 23">
-<div class="layoutArea">
-<div class="column">
-<pre class="prettyprint linenums">def _lpdf_copula(self, family, theta, u, v):
-    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
-    if  family == 'clayton':
-        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
-    elif family == 'frank':
-        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
-        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
-        pdf = num/denom
-    elif family == 'gumbel':
-        A = (-np.log(u))**theta + (-np.log(v))**theta
-        c = np.exp(-A**(1/theta))
-        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
-    return np.log(pdf)
-</pre>
-The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" &amp; "XLK".
-<pre class="prettyprint linenums">self.family = ['clayton', 'frank', 'gumbel']
-tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
-AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
-for i in self.family:
-    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
-    # Replace nan with zero and inf with finite numbers in lpdf list
-    lpdf = np.nan_to_num(lpdf)
-    loglikelihood = sum(lpdf)
-    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
-    # choose the copula with the minimum AIC
-    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
-</pre>
-&nbsp;
-<h3>Step 5: Generating the Trading Signals</h3>
-The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I(2013)[ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref], the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
-
-Given current returns \(R_x, R_y\) of stock X and stock Y, we define the "mis-pricing indexes" are:
-
-\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\]
-
-\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
-
-For further <span class="s1">mathematical proof</span>, please refer to Xie W, Wu Y(2013)[ref]Xie W, Wu Y. Copula-based pairs trading strategy[C]//Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.[/ref]
-
-The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
-
-Gumbel Copula
-
-\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\]
-
-\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]
-
-</div>
-</div>
-</div>
-Clayton Copula
-
-\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
-
-\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
-
-Frank Copula
-
-\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \]
-
-\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]
-
-After selection of trading pairs and the best-fitted copulas, we take the following steps for trading. Please note we implement the Steps 1, 2, 3 and 4 on the first day of each month using the daily data for the last 12 months, which means our empirical distribution functions and copula parameters theta estimation are updated once a month. In summary each month:
-<ol>
- 	<li>During the 12 months' rolling formation period, daily close prices are used to calculate the daily log returns for the pair of ETFs and then compute Kendall's rank correlation.</li>
- 	<li>Estimate the marginal distribution functions of log returns of X and Y, which are ecdf_x and ecdf_y separately.</li>
- 	<li>Plug Kendall's tau into copula parameter estimation functions to get the value of theta.</li>
- 	<li>Run linear regression over the two price series. The coefficient is used to determine how many shares of stock X and Y to buy and sell. For example, if the coefficient is 2, for every X share that is bought or sold, 2 units of Y are sold or bought.</li>
-</ol>
-<pre class="prettyprint linenums">def _set_signal(self):
-    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
-    # generate the log return series of paired stocks
-    close = history['close'].unstack(level=0)
-    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
-    # estimate Kendall'rank correlation each trading day
-    tau = kendalltau(x, y)[0]
-    # etstimate the copula parameter: theta
-    self.theta = self._parameter(self.copula, tau)
-    # simulate the empirical distribution function for returns of two paired stocks
-    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
-    # run linear regression over the two history return series
-    self.coef = stats.linregress(x,y).slope
-</pre>
-Finally during the trading period, each day we convert today's returns to u and v by using empirical distribution functions ecdf_x and ecdf_y. After that, two mispricing indexes are calculated every trading day by using the estimated copula C.  The algorithm constructs short positions in X and long positions in Y on the days that \(MI_{Y|X}&lt;0.05\) and \(MI_{X|Y}&gt;0.95\). It constructs short position in Y and long positions in X on the days that \(MI_{Y|X}&gt;0.95\) and \(MI_{X|Y}&lt;0.05\).
-<pre class="prettyprint linenums">def OnData(self,data):
-    for i in self.syl:
-        self.price_list[i].append(self.Portfolio[i].Price)
-    # compute today's log return of 2 stocks
-    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
-    else:
-        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
-        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
-    # Convert the two returns to uniform values u and v using the empirical distribution functions
-    u_value = self.ecdf_x(return_x)
-    v_value = self.ecdf_y(return_y)
-    # Compute the mispricing indices for u and v by using estimated copula
-    self._misprice_index(self.copula, self.theta, u_value, v_value)
-    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
-        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-        else:
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
-        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
-        else:
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)</pre>
-<h2 class="page" title="Page 5">Part II: Cointegration Method</h2>
-For the cointegration pairs trading method, we choose the same ETF pair "GLD" &amp; "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
-<h3 class="page" title="Page 5">Step 1: Generate the Spread Series</h3>
-At the start of each month, we generate the log price series of two ETFs with the daily close. Then the spread series is estimated using regression analysis based on log price series data.
-
-For equities X and Y, we run linear regression over the log price series and get the coefficient β.
-
-\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
-<h3>Step 2: Compute the Threshold</h3>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
-
-</div>
-<pre class="prettyprint linenums">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
-                     index = [i.Time for i in self.symbols[0].hist_window])
-
-price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
-                     index = [i.Time for i in self.symbols[1].hist_window])
-if len(price_x) < 250: return
-spread = self.regr(np.log(price_x), np.log(price_y))
-mean = np.mean(spread)
-std = np.std(spread)
-ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
-if spread[-1] > mean + self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
-        self.Sell(self.symbols[1], 100)
-        self.Buy(self.symbols[0],  ratio * 100)
-
-elif spread[-1] < mean - self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
-        self.Sell(self.symbols[0], 100)
-        self.Buy(self.symbols[1], ratio * 100)
-else:
-    self.Liquidate()
-</pre>
-</div>
-</div>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-<h3>Step 3: Set up the Trading Signals</h3>
-On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread&gt;mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its <span class="s1">equilibrium (defined as less than half a standard deviation from zero spread).</span>
-
-The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
-
-</div>
-</div>
-</div>
-<h2>Conclusion</h2>
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;">method</th>
-<th style="text-align: center;">Transactions</th>
-<th style="text-align: center;">Profit</th>
-<th style="text-align: center;">Sharpe Ratio</th>
-<th style="text-align: center;">Drawdown</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: center;">Copula</td>
-<td style="text-align: center;">346</td>
-<td style="text-align: center;">274.293%</td>
-<td style="text-align: center;">1.022</td>
-<td style="text-align: center;">19.4%</td>
-</tr>
-<tr>
-<td style="text-align: center;">Cointegration</td>
-<td style="text-align: center;">91</td>
-<td style="text-align: center;">26.358%</td>
-<td style="text-align: center;">0.298</td>
-<td style="text-align: center;">23.7%</td>
-</tr>
-</tbody>
-</table>
-Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
-
-Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method.
-
-It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
-<h2>Backtest</h2>
-Backtest for copula method
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=343dc596a484a20a579ae77c79a12495'></script>
-Backtest for cointegration method
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d4443ef5bd0d45da3d003fe8e49a966e'></script>
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+<p>
+  ETFs have many different stock sectors and asset classes which provide us a wide range of pairs trading candidates. Our data set consists of daily data of the ETFs traded on the NASDAQ or the NYSE.
+</p>
+
+<p>
+  We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
+</p>
+
+<h3>Step 1: Selecting the Paired Stocks</h3>
+<p>
+  The general method of pair selection is based on both fundamental and statistical analysis.[ref]<strong>Jean Folger</strong>. Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>[/ref]
+</p>
+
+<h4><strong>1) Assemble a list of potentially related pairs</strong></h4>
+<p>
+  Any random pairs could be correlated. It is possible that those variables are not causally related to each other, but because of a spurious relationship due to either coincidence or the presence of a certain third, unseen factor. Thus, it is important for us to start with a list of securities that have something in common. For this demonstration, we choose some of the most liquid ETFs traded on the Nasdaq or the NYSE.  The relationship for those potentially related pairs could be due to an index, sector or asset class overlap. e.g. QQQ and XLK are two ETFs which track the market leading indices.
+</p>
+
+<h4><strong>2) Filter the trading pair with statistical correlation</strong></h4>
+
+<p>
+  To determine which stock pairs to include in the analysis, correlations between the pre-selected ETF pairs are analyzed. Below are three types of correlation measures we usually use in statistics:
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="3">Correlation Measurement Techniques</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="33%">Pearson correlation</td>
+<td>\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]</td>
+</tr>
+<tr>
+<td>Kendall rank correlation</td>
+<td>\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]</td>
+</tr>
+<tr>
+<td>Spearman rank correlation</td>
+<td>\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]</td>
+</tr>
+<tr>
+<td colspan="2"> \(n\) = number of value in each data set
+\(n_c\) = number of concordant
+\(n_d\) = number of discordant
+\(d_i\) = the difference between the ranks of corresponding values \(x_i\) and \(y_i\)</td>
+</tr>
+</tbody>
+</table>
+</div>
+
+<p>
+  We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading. We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
+</p>
+
+\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n where n is the number of price data
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def _pair_selection(self):
+
+    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
+                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
+    logreturn={}
+    for i in range(2):
+        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
+        history = self.History(syl, self.lookbackdays,Resolution.Daily)
+        # generate the log return series of paired stocks
+        close = history['close'].unstack(level=0)
+        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+        for j in tick_syl[i]:
+            logreturn[j] = df_logreturn[j]
+    # estimate coefficients of different correlation measures
+    tau_coef,pr_coef,sr_coef= [],[],[]
+    for i in range(len(tick_syl[i])):
+        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
+        tau_coef.append(kendalltau(tik_x, tik_y)[0])
+        pr_coef.append(pearsonr(tik_x, tik_y)[0])
+        sr_coef.append(spearmanr(tik_x, tik_y)[0])
+    index_max = tau_coef.index(max(tau_coef))
+    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+</pre>
+</div>
+
+<h3>Step 2: Estimating Marginal Distributions of log-return</h3>
+<p>
+  In order to construct the copula, we need to transform the log-return series \(R_x\) and \(R_y\) to two uniformly distributed values u and v. This can be done by estimating the marginal distribution functions of \(R_x\) and \(R_y\) and plugging the return values into a distribution function. As we make no assumptions about the distribution of the two log-return series, here we use the empirical distribution function to approach the marginal distribution \(F_1(R_x)\) and \(F_2(R_y)\). The Python ECDF function from the statsmodel library gives us the Empirical CDF as a step function.
+</p>
+<h3>Step 3: Estimating Copula Parameters</h3>
+<p>
+  As discussed above, we estimate the copula parameter theta by the relationship between the copula and the dependence measure Kendall’s tau, for each of the Archimedean copulas.
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th>Copula</th>
+<th style="text-align: center;">Kendall's tau</th>
+<th style="text-align: center;">parameter θ</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Clayton Copula</td>
+<td>\[\frac{\theta}{\theta +2}\]</td>
+<td>\[\theta=2\tau(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel Copula</td>
+<td>\[1-\theta^{-1}\]</td>
+<td>\[\theta=(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Frank Copula</td>
+<td>\[1+4[D_1(\theta)-1]/\theta\]</td>
+<td>\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]</td>
+</tr>
+<tr>
+<td colspan="3">\[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]</td>
+</tr>
+</tbody>
+</table>
+</div>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def _parameter(self, family, tau):
+    if  family == 'clayton':
+        return 2*tau/(1-tau)
+    elif family == 'frank':
+        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
+    	# frank_fun is the squared difference
+    	# Minimize the frank_fun would give the parameter theta for the frank copula
+      integrand = lambda t: t/(np.exp(t)-1)
+    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
+      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
+    elif family == 'gumbel':
+        return 1/(1-tau)</pre>
+</div>
+
+<h3>Step 4: Selecting the Best Fitting Copula</h3>
+<p>
+  Once we get the parameter estimation for the copula functions, we use the AIC criteria to select the copula that provides the best fit in algorithm initialization.
+</p>
+
+\[AIC=-2L(\theta)+2k\]
+
+<p>
+  where \(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\) is the log-likelihood function and k is the number of parameters, here k=1.
+</p>
+
+<p>
+  The density functions of each copula function are as follows:
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">Copula</th>
+<th style="text-align: center;">Density function c(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton Copula</td>
+<td style="text-align: left;">\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel Copula</td>
+<td>\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank Copula</td>
+<td style="text-align: left;">\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]</td>
+</tr>
+<tr>
+<td colspan="2">\[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]</td>
+</tr>
+</tbody>
+</table>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def _lpdf_copula(self, family, theta, u, v):
+    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
+    if  family == 'clayton':
+        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
+    elif family == 'frank':
+        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
+        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
+        pdf = num/denom
+    elif family == 'gumbel':
+        A = (-np.log(u))**theta + (-np.log(v))**theta
+        c = np.exp(-A**(1/theta))
+        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
+    return np.log(pdf)
+</pre>
+</div>
+
+<p>
+  The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" &amp; "XLK".
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.family = ['clayton', 'frank', 'gumbel']
+tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
+AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
+for i in self.family:
+    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
+    # Replace nan with zero and inf with finite numbers in lpdf list
+    lpdf = np.nan_to_num(lpdf)
+    loglikelihood = sum(lpdf)
+    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
+    # choose the copula with the minimum AIC
+    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+</pre>
+</div>
+
+
+<h3>Step 5: Generating the Trading Signals</h3>
+<p>
+  The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I(2013)[ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref], the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
+</p>
+
+<p>
+  Given current returns \(R_x, R_y\) of stock X and stock Y, we define the "mis-pricing indexes" are:
+</p>
+
+\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\]
+
+\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
+
+<p>
+  For further mathematical proof, please refer to Xie W, Wu Y(2013)[ref]Xie W, Wu Y. Copula-based pairs trading strategy[C]//Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.[/ref] The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
+</p>
+<p>
+Gumbel Copula
+</p>
+\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\]
+
+\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]
+
+<p>
+  Clayton Copula
+</p>
+
+\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+<p>
+  Frank Copula
+</p>
+
+\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \]
+
+\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]
+
+<p>
+  After selection of trading pairs and the best-fitted copulas, we take the following steps for trading. Please note we implement the Steps 1, 2, 3 and 4 on the first day of each month using the daily data for the last 12 months, which means our empirical distribution functions and copula parameters theta estimation are updated once a month. In summary each month:
+</p>
+
+<ul>
+ 	<li>During the 12 months' rolling formation period, daily close prices are used to calculate the daily log returns for the pair of ETFs and then compute Kendall's rank correlation.</li>
+ 	<li>Estimate the marginal distribution functions of log returns of X and Y, which are ecdf_x and ecdf_y separately.</li>
+ 	<li>Plug Kendall's tau into copula parameter estimation functions to get the value of theta.</li>
+ 	<li>Run linear regression over the two price series. The coefficient is used to determine how many shares of stock X and Y to buy and sell. For example, if the coefficient is 2, for every X share that is bought or sold, 2 units of Y are sold or bought.</li>
+</ul>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def _set_signal(self):
+    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
+    # generate the log return series of paired stocks
+    close = history['close'].unstack(level=0)
+    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
+    # estimate Kendall'rank correlation each trading day
+    tau = kendalltau(x, y)[0]
+    # etstimate the copula parameter: theta
+    self.theta = self._parameter(self.copula, tau)
+    # simulate the empirical distribution function for returns of two paired stocks
+    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
+    # run linear regression over the two history return series
+    self.coef = stats.linregress(x,y).slope
+</pre>
+</div>
+
+<p>
+  Finally during the trading period, each day we convert today's returns to u and v by using empirical distribution functions ecdf_x and ecdf_y. After that, two mispricing indexes are calculated every trading day by using the estimated copula C.  The algorithm constructs short positions in X and long positions in Y on the days that \(MI_{Y|X}&lt;0.05\) and \(MI_{X|Y}&gt;0.95\). It constructs short position in Y and long positions in X on the days that \(MI_{Y|X}&gt;0.95\) and \(MI_{X|Y}&lt;0.05\).
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def OnData(self,data):
+    for i in self.syl:
+        self.price_list[i].append(self.Portfolio[i].Price)
+    # compute today's log return of 2 stocks
+    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
+    else:
+        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
+        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
+    # Convert the two returns to uniform values u and v using the empirical distribution functions
+    u_value = self.ecdf_x(return_x)
+    v_value = self.ecdf_y(return_y)
+    # Compute the mispricing indices for u and v by using estimated copula
+    self._misprice_index(self.copula, self.theta, u_value, v_value)
+    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
+        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+        else:
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
+        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+        else:
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+</pre>
+</div>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
new file mode 100755
index 0000000..775afe1
--- /dev/null
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -0,0 +1,48 @@
+<p>
+  For the cointegration pairs trading method, we choose the same ETF pair "GLD" &amp; "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
+</p>
+
+<h3>Step 1: Generate the Spread Series</h3>
+<p>
+  At the start of each month, we generate the log price series of two ETFs with the daily close. Then the spread series is estimated using regression analysis based on log price series data. For equities X and Y, we run linear regression over the log price series and get the coefficient β.
+</p>
+
+\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
+
+<h3>Step 2: Compute the Threshold</h3>
+
+<p>
+  Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
+                     index = [i.Time for i in self.symbols[0].hist_window])
+
+price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
+                     index = [i.Time for i in self.symbols[1].hist_window])
+if len(price_x) < 250: return
+spread = self.regr(np.log(price_x), np.log(price_y))
+mean = np.mean(spread)
+std = np.std(spread)
+ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
+if spread[-1] > mean + self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
+        self.Sell(self.symbols[1], 100)
+        self.Buy(self.symbols[0],  ratio * 100)
+
+elif spread[-1] < mean - self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
+        self.Sell(self.symbols[0], 100)
+        self.Buy(self.symbols[1], ratio * 100)
+else:
+    self.Liquidate()
+</pre>
+</div>
+
+<h3>Step 3: Set up the Trading Signals</h3>
+<p>
+  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread&gt;mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
+</p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
new file mode 100755
index 0000000..d156309
--- /dev/null
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -0,0 +1,34 @@
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">method</th>
+<th style="text-align: center;">Transactions</th>
+<th style="text-align: center;">Profit</th>
+<th style="text-align: center;">Sharpe Ratio</th>
+<th style="text-align: center;">Drawdown</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: center;">Copula</td>
+<td style="text-align: center;">346</td>
+<td style="text-align: center;">274.293%</td>
+<td style="text-align: center;">1.022</td>
+<td style="text-align: center;">19.4%</td>
+</tr>
+<tr>
+<td style="text-align: center;">Cointegration</td>
+<td style="text-align: center;">91</td>
+<td style="text-align: center;">26.358%</td>
+<td style="text-align: center;">0.298</td>
+<td style="text-align: center;">23.7%</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
+</p>
+<p>
+  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
+</p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
new file mode 100755
index 0000000..1b95dbc
--- /dev/null
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+  Backtest for copula method
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=343dc596a484a20a579ae77c79a12495'></script>
+<p>
+  Backtest for cointegration method
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d4443ef5bd0d45da3d003fe8e49a966e'></script>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html
new file mode 100755
index 0000000..d0bf5d3
--- /dev/null
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html	
@@ -0,0 +1,14 @@
+<h2>Abstract</h2>
+<p>
+  In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
+</p>
+<p>
+  First we decide the look-back period based on the change rate of volatility, then we make trading decisions based on the highest high and lowest low from the look back period as well as a Bollinger Bands indicator. It is an auto adaptive trading system that can adjust its buy and sell rules depending on the performance of these rules in the past. In addition to Forex markets it is widely used in future and equity markets. You can refer to <a href="https://www.youtube.com/watch?v=MxGVqSvLqDQ">this video</a> to learn more about dynamic break out II.
+</p>
+
+<p>
+  The original Dynamic Break Out system was developed by George Pruitt for Futures Magazine in 1996. The logic behind the dynamic breakout system is that the volatility component changes the lookback period, then the enter and exit points are decided by the highest high and lowest low price over the lookback period.  The newer version of the Dynamic Break Out is just like the original, except we introduce the Bollinger Band and adjust the number of look back days using the market volatility, so different market conditions perform better with different parameters. In addition, the stop loss signal is fixed in version one, but in version two the liquidate point is based on the moving average indicator and the length of moving average is dynamically changed with the look-back period.
+</p>
+<p>
+  We backtested the strategy on EURUSD and GBPUSD over 6 years period.  The result suggests a drawdown of 20% and the strategy caught the market turning points. It is especially profitable in a trending market.
+</p>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html
new file mode 100755
index 0000000..be7cda6
--- /dev/null
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html	
@@ -0,0 +1,60 @@
+<h3>Step 1: Determine the look back periods</h3>
+
+<p>
+  The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.[ref]<strong>Robert C. Miner(October 20, 2008)</strong>. High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>[/ref]   <b> </b>
+  To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">close = self.History(self.syl, 31, Resolution.Daily)['close']
+   todayvol = np.std(close[1:self.numdays+1])
+   yesterdayvol = np.std(close[0:self.numdays])
+   deltavol = (todayvol - yesterdayvol) / todayvol
+   self.numdays = round(self.numdays * (1 + deltavol)) # the number of days must be integer
+ </pre>
+ </div>
+
+<p>
+  Though the look back days are dynamic, it needs to be restricted within an acceptable range of 20 to 60.
+</p>
+
+<h3>Step 2: Choose the algorithm buy/sell point</h3>
+
+<p>
+  For a buy setup, the close price of the previous day must be above the upper Bollinger Band. In addition the ask price must be above the highest high of the most recent N days. Where N is the look back days from Step-1.
+  For a sell setup, the close price of previous day must be below the lower Bollinger Band and the ask price must be below the lowest low of the most recent N days. The length of the Bollinger Band calculation is the same number of look back days that is generated by Step-1.
+</p>
+<p>
+  Bollinger Band is a popular technical indicator. k is a constant. Here we choose k=2.
+</p>
+\[ Upper Band = moving\ average + k\times standard\  deviation \]
+
+\[ Lower Band = moving\ average - k\times standard\  deviation \]
+<p>
+  QuantConnect provides more than 100 technical indicators for you to use in your algorithm. These are provided as class objects in Python. A full list of the indicators and their properties can be found in the reference table of <a href="https://www.quantconnect.com/docs#Indicators">the documentation page</a>.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.bolband = self.BB(self.syl,self.numdays,decimal.Decimal(2),MovingAverageType.Exponential,Resolution.Daily)
+     self.upband = self.bolband.UpperBand
+     self.lowband = self.bolband.LowerBand
+</pre>
+</div>
+
+<h3>Step 3: Choose the algorithm liquidation point</h3>
+<p>
+  The exit signal for an existing holding is determined by calculating a simple moving average of closing prices for the past look back days. That is to say, we liquidate a long position if the current price is lower than the moving average of the close price over the look back period, and vice versa for selling a short position.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.buypoint = max(self.high)
+self.sellpoint = min(self.low)
+historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
+self.longLiqPoint = np.mean(historyclose)
+self.shortLiqPoint = np.mean(historyclose)
+self.yesterdayclose = historyclose.iloc[-1]
+</pre>
+</div>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/03 Conclusion.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/03 Conclusion.html
new file mode 100755
index 0000000..ca4ed1c
--- /dev/null
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/03 Conclusion.html	
@@ -0,0 +1,10 @@
+<p>
+  For six years backtesting of EURUSD, the overall statistics show an annual rate of return of 2.3% and with a Sharpe Ratio of 0.31. EURUSD has a significant uptrend from 2010 to 2012. This momentum strategy outperforms the market and seems to be profitable from 2010 to 2014.  The maximum drawdown occurs in May 2015 to December 2015 and is roughly 14%. From our results we find the strategy works best in an trending forex market.
+</p>
+<p>
+  In contrast, GBPUSD is pretty volatile during the tested period from 2010 to 2016. Our testing demonstrated a negative annual rate of return with a drawdown of approximately 19%.
+  When the volatility decreases, the price tends to continue following the current trend. Volatility causes the the look back days to decrease when computing the bollinger bands, making it easier to enter a trade.  If the market volatility increases we increase the look back days in order to filter the fake signals, making it harder to enter a trade.
+</p>
+<p>
+  Here we use the standard deviation of price as a measure of market volatility. To improve the model we could choose other measures of volatility like standard deviation of logarithm return series or other stochastic volatility measures.
+</p>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
new file mode 100755
index 0000000..32760d0
--- /dev/null
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
@@ -0,0 +1,8 @@
+<p>
+  Backtest for EURUSD
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=87d3047e8c358ea9b4779e0b5d238a74'></script>
+<p>
+  Backtest for GBPUSD
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4bacf930111e3502993b7e7c9b315be0'></script>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html
deleted file mode 100755
index 6f79c26..0000000
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 The Dynamic Breakout II Strategy.html	
+++ /dev/null
@@ -1,64 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
-
-First we decide the look-back period based on the change rate of volatility, then we make trading decisions based on the highest high and lowest low from the look back period as well as a Bollinger Bands indicator. It is an auto adaptive trading system that can adjust its buy and sell rules depending on the performance of these rules in the past. In addition to Forex markets it is widely used in future and equity markets. You can refer to <a href="https://www.youtube.com/watch?v=MxGVqSvLqDQ">this video</a> to learn more about dynamic break out II.
-
-The original Dynamic Break Out system was developed by George Pruitt for Futures Magazine in 1996. The logic behind the dynamic breakout system is that the volatility component changes the lookback period, then the enter and exit points are decided by the highest high and lowest low price over the lookback period.  The newer version of the Dynamic Break Out is just like the original, except we introduce the Bollinger Band and adjust the number of look back days using the market volatility, so different market conditions perform better with different parameters. In addition, the stop loss signal is fixed in version one, but in version two the liquidate point is based on the moving average indicator and the length of moving average is dynamically changed with the look-back period.
-
-We backtested the strategy on EURUSD and GBPUSD over 6 years period.  The result suggests a drawdown of 20% and the strategy caught the market turning points. It is especially profitable in a trending market.
-<h2>Method</h2>
-<h3>Step 1: Determine the look back periods</h3>
-
-The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.[ref]<strong>Robert C. Miner(October 20, 2008)</strong>. High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>[/ref]   <b> </b>
-
-To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
-<pre class="prettyprint linenums">close = self.History(self.syl, 31, Resolution.Daily)['close']
-   todayvol = np.std(close[1:self.numdays+1])
-   yesterdayvol = np.std(close[0:self.numdays])
-   deltavol = (todayvol - yesterdayvol) / todayvol
-   self.numdays = round(self.numdays * (1 + deltavol)) # the number of days must be integer
-</pre>
-
-Though the look back days are dynamic, it needs to be restricted within an acceptable range of 20 to 60.
-
-<h3>Step 2: Choose the algorithm buy/sell point</h3>
-
-For a buy setup, the close price of the previous day must be above the upper Bollinger Band. In addition the ask price must be above the highest high of the most recent N days. Where N is the look back days from Step-1.
-
-For a sell setup, the close price of previous day must be below the lower Bollinger Band and the ask price must be below the lowest low of the most recent N days. The length of the Bollinger Band calculation is the same number of look back days that is generated by Step-1.
-
-Bollinger Band is a popular technical indicator. k is a constant. Here we choose k=2.
-\[ Upper Band = moving\ average + k\times standard\  deviation \]
-
-\[ Lower Band = moving\ average - k\times standard\  deviation \]
-
-&nbsp;
-
-QuantConnect provides more than 100 technical indicators for you to use in your algorithm. These are provided as class objects in Python. A full list of the indicators and their properties can be found in the reference table of <a href="https://www.quantconnect.com/docs#Indicators">the documentation page</a>.
-<pre class="prettyprint linenums">self.bolband = self.BB(self.syl,self.numdays,decimal.Decimal(2),MovingAverageType.Exponential,Resolution.Daily)
-     self.upband = self.bolband.UpperBand
-     self.lowband = self.bolband.LowerBand
-</pre>
-<h3>Step 3: Choose the algorithm liquidation point</h3>
-The exit signal for an existing holding is determined by calculating a simple moving average of closing prices for the past look back days. That is to say, we liquidate a long position if the current price is lower than the moving average of the close price over the look back period, and vice versa for selling a short position.
-<pre class="prettyprint linenums">self.buypoint = max(self.high)
-self.sellpoint = min(self.low)
-historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
-self.longLiqPoint = np.mean(historyclose)
-self.shortLiqPoint = np.mean(historyclose)
-self.yesterdayclose = historyclose.iloc[-1]
-</pre>
-<h2>Conclusion</h2>
-For six years backtesting of EURUSD, the overall statistics show an annual rate of return of 2.3% and with a Sharpe Ratio of 0.31. EURUSD has a significant uptrend from 2010 to 2012. This momentum strategy outperforms the market and seems to be profitable from 2010 to 2014.  The maximum drawdown occurs in May 2015 to December 2015 and is roughly 14%. From our results we find the strategy works best in an trending forex market.
-
-In contrast, GBPUSD is pretty volatile during the tested period from 2010 to 2016. Our testing demonstrated a negative annual rate of return with a drawdown of approximately 19%.
-
-When the volatility decreases, the price tends to continue following the current trend. Volatility causes the the look back days to decrease when computing the bollinger bands, making it easier to enter a trade.  If the market volatility increases we increase the look back days in order to filter the fake signals, making it harder to enter a trade.
-
-Here we use the standard deviation of price as a measure of market volatility. To improve the model we could choose other measures of volatility like standard deviation of logarithm return series or other stochastic volatility measures.
-
-<h2>Algorithm</h2>
-Backtest for EURUSD
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=87d3047e8c358ea9b4779e0b5d238a74'></script>
-Backtest for GBPUSD
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4bacf930111e3502993b7e7c9b315be0'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html
new file mode 100755
index 0000000..57c0667
--- /dev/null
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html	
@@ -0,0 +1,92 @@
+<h2>Abstract</h2>
+<p>
+  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+</p>
+<p>
+  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
+</p>
+<h2>Method</h2>
+<h3>Step 1 : Initialization of algorithm</h3>
+<p>
+  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+  self.SetStartDate(2004, 1, 1)
+  self.SetEndDate(2017, 8, 30)
+  self.SetCash(100000)
+  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
+  self.syl = equity.Symbol
+</pre>
+</div>
+<p>
+  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
+</pre>
+</div>
+<p>
+  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
+</p>
+
+<h3>Step 2:  Implementation of algorithm</h3>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+
+<p>
+  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
+  self.high = history.loc[self.syl.Value]['high']
+  self.low = history.loc[self.syl.Value]['low']
+  self.close = history.loc[self.syl.Value]['close']
+</pre>
+</div>
+
+<h3>Step 3: Trading Implementation</h3>
+<p>
+  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+
+<p>
+  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
+        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
+            if holdings &gt;= 0:
+                self.SetHoldings(self.syl, 1)
+            else:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, 1)
+        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, -1)
+            else:
+                self.SetHoldings(self.syl, -1)</pre>
+</div>
+<h2>Conclusion</h2>
+<p>
+  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
+  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+</p>
+
+<h2>Algorithm</h2>
+<p>
+  Backtest result for ETF: SPY from 2004 to 2017
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html
new file mode 100755
index 0000000..57c0667
--- /dev/null
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
@@ -0,0 +1,92 @@
+<h2>Abstract</h2>
+<p>
+  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+</p>
+<p>
+  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
+</p>
+<h2>Method</h2>
+<h3>Step 1 : Initialization of algorithm</h3>
+<p>
+  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+  self.SetStartDate(2004, 1, 1)
+  self.SetEndDate(2017, 8, 30)
+  self.SetCash(100000)
+  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
+  self.syl = equity.Symbol
+</pre>
+</div>
+<p>
+  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
+</pre>
+</div>
+<p>
+  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
+</p>
+
+<h3>Step 2:  Implementation of algorithm</h3>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+
+<p>
+  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
+  self.high = history.loc[self.syl.Value]['high']
+  self.low = history.loc[self.syl.Value]['low']
+  self.close = history.loc[self.syl.Value]['close']
+</pre>
+</div>
+
+<h3>Step 3: Trading Implementation</h3>
+<p>
+  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+
+<p>
+  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
+        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
+            if holdings &gt;= 0:
+                self.SetHoldings(self.syl, 1)
+            else:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, 1)
+        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, -1)
+            else:
+                self.SetHoldings(self.syl, -1)</pre>
+</div>
+<h2>Conclusion</h2>
+<p>
+  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
+  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+</p>
+
+<h2>Algorithm</h2>
+<p>
+  Backtest result for ETF: SPY from 2004 to 2017
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html
new file mode 100755
index 0000000..57c0667
--- /dev/null
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html	
@@ -0,0 +1,92 @@
+<h2>Abstract</h2>
+<p>
+  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+</p>
+<p>
+  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
+</p>
+<h2>Method</h2>
+<h3>Step 1 : Initialization of algorithm</h3>
+<p>
+  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+  self.SetStartDate(2004, 1, 1)
+  self.SetEndDate(2017, 8, 30)
+  self.SetCash(100000)
+  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
+  self.syl = equity.Symbol
+</pre>
+</div>
+<p>
+  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
+</pre>
+</div>
+<p>
+  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
+</p>
+
+<h3>Step 2:  Implementation of algorithm</h3>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+
+<p>
+  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
+  self.high = history.loc[self.syl.Value]['high']
+  self.low = history.loc[self.syl.Value]['low']
+  self.close = history.loc[self.syl.Value]['close']
+</pre>
+</div>
+
+<h3>Step 3: Trading Implementation</h3>
+<p>
+  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+
+<p>
+  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
+        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
+            if holdings &gt;= 0:
+                self.SetHoldings(self.syl, 1)
+            else:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, 1)
+        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, -1)
+            else:
+                self.SetHoldings(self.syl, -1)</pre>
+</div>
+<h2>Conclusion</h2>
+<p>
+  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
+  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+</p>
+
+<h2>Algorithm</h2>
+<p>
+  Backtest result for ETF: SPY from 2004 to 2017
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
new file mode 100755
index 0000000..57c0667
--- /dev/null
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -0,0 +1,92 @@
+<h2>Abstract</h2>
+<p>
+  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+</p>
+<p>
+  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
+</p>
+<h2>Method</h2>
+<h3>Step 1 : Initialization of algorithm</h3>
+<p>
+  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+  self.SetStartDate(2004, 1, 1)
+  self.SetEndDate(2017, 8, 30)
+  self.SetCash(100000)
+  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
+  self.syl = equity.Symbol
+</pre>
+</div>
+<p>
+  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
+</pre>
+</div>
+<p>
+  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
+</p>
+
+<h3>Step 2:  Implementation of algorithm</h3>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+
+<p>
+  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
+  self.high = history.loc[self.syl.Value]['high']
+  self.low = history.loc[self.syl.Value]['low']
+  self.close = history.loc[self.syl.Value]['close']
+</pre>
+</div>
+
+<h3>Step 3: Trading Implementation</h3>
+<p>
+  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+
+<p>
+  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
+        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
+            if holdings &gt;= 0:
+                self.SetHoldings(self.syl, 1)
+            else:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, 1)
+        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
+                self.Liquidate(self.syl)
+                self.SetHoldings(self.syl, -1)
+            else:
+                self.SetHoldings(self.syl, -1)</pre>
+</div>
+<h2>Conclusion</h2>
+<p>
+  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
+  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+</p>
+
+<h2>Algorithm</h2>
+<p>
+  Backtest result for ETF: SPY from 2004 to 2017
+</p>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html
deleted file mode 100755
index 1eb79a9..0000000
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 Dual Thrust Trading Algorithm.html	
+++ /dev/null
@@ -1,50 +0,0 @@
-<h2>Abstract</h2>
-The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
-
-In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
-<h2>Method</h2>
-<h3>Step 1 : Initialization of algorithm</h3>
-Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
-<pre class="prettyprint linenums">def Initialize(self):
-  self.SetStartDate(2004, 1, 1)
-  self.SetEndDate(2017, 8, 30)
-  self.SetCash(100000)
-  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
-  self.syl = equity.Symbol
-</pre>
-Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
-</pre>
-Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
-<h3>Step 2:  Implementation of algorithm</h3>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
-
-In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
-<pre class="prettyprint linenums">history = self.History([self.syl.Value], 4, Resolution.Daily)
-  self.high = history.loc[self.syl.Value]['high']
-  self.low = history.loc[self.syl.Value]['low']
-  self.close = history.loc[self.syl.Value]['close']
-</pre>
-<h3>Step 3: Trading Implementation</h3>
-The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
-
-This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
-<pre class="prettyprint linenums"> holdings = self.Portfolio[self.syl].Quantity
-        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
-            if holdings &gt;= 0:
-                self.SetHoldings(self.syl, 1)
-            else:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, 1)
-        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, -1)
-            else:
-                self.SetHoldings(self.syl, -1)</pre>
-<h2>Conclusion</h2>
-We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
-<h2>Algorithm</h2>
-Backtest result for ETF: SPY from 2004 to 2017
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html
new file mode 100755
index 0000000..bb9ab9c
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html	
@@ -0,0 +1,10 @@
+<p>
+  In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". [ref]<strong>Gerben, Driesprong (2007)</strong>. Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>[/ref] by?by Gerben, Ben and Benjamin (2007).
+  If the predicted return is larger than the risk-free rate, the portfolio is fully invested in stock; if the predicted return is lower than the risk-free rate, the portfolio is invested in short-term U.S T-bills. The backtesting period starts in 1980 and is divided into an in-sample period where regression analysis is made and an out?of sample period where the regression result is embedded "statically" into the strategy.
+</p>
+<p>
+  In our implementation of the strategy we adapt the method of the original paper to make it more applicable to the current market. We have set our backtesting period to be from 2010 to 2017 and we refresh our regression analysis each month to form a rolling dynamic projection. This is because?empirical evidence shows?us the correlation between oil and stocks is not as strong as in the 1980's. We use?the price of S&amp;P GSCI? Crude Oil Total Return Index ETNs to represent spot oil price, and import T-bill data from Quandl by defining a custom class.?We use the "Schedule" API to trigger an event every month automatically?and the "History" function to retrieve data for regression analysis.
+</p>
+<p>
+  Our analysis shows this strategy under performs the market in recent years. In the 9 year analysis period the algorithm was mostly long the S&amp;P500 index and only 9 trades were performed as the markets were strongly bullish. The trades could potentially simply be due to the weakening of the relationship between stocks and oil.
+</p>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/02 Background.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/02 Background.html
new file mode 100755
index 0000000..368ea3b
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/02 Background.html	
@@ -0,0 +1,15 @@
+<p>
+  We assume the predicted return of the stock is proportional to the return of oil. This can be represented by the regression equation:
+</p>
+
+\[r^{stock}_t=a_0+a_1r^{oil}_{t-1}+e_t\]
+
+<p>
+  with
+</p>
+
+\[e_t=r^{stock}_t-E_{t-1}[r^{stock}_t]\].
+
+<p>
+  The independent variable is the return of the oil and the dependent variable is the return of the stock. We use the monthly returns over a regression period of 2 years, giving us 22 observations to regress.?Every month regression analysis is conducted, and we use the estimated coefficient from the regression to compute the expected stock return with the given return of oil.
+</p>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html
new file mode 100755
index 0000000..5fddb28
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html	
@@ -0,0 +1,116 @@
+<p>
+  The algorithm implementation consists of mainly three parts: Defining the custom imported data, initialization of the strategy parameters, and monthly re-balancing of the portfolio.
+</p>
+
+<h3>Step 1: Defining Custom Imported Data</h3>
+<p>
+  We import T-Bill data from Quandl - a marketplace for financial, economic and alternative data. This requires defining a small class that tells QuantConnect how to interpret?the Quandl data.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">class TBill(PythonData):
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/USTREASURY/BILLRATES.csv?api_key=XXXXXXXXX&amp;order=asc", SubscriptionTransportMedium.RemoteFile)
+    def Reader(self, config, line, date, isLiveMode):
+        tbill = TBill()
+        tbill.Symbol = config.Symbol
+        # Example Line Format:
+        # Date      4 Wk Bank Discount Rate
+        # 2017-06-01 		0.8
+        if not (line.strip() and line[0].isdigit()): return None
+        try:
+            data = line.split(',')
+            value = float(data[1])*0.01
+            value = decimal.Decimal(value)
+            if value == 0: return None
+            tbill.Time = datetime.strptime(data[0], "%Y-%m-%d")
+            tbill.Value = value
+            tbill["Close"] = float(value)
+            return tbill;
+        except ValueError:
+            return None
+</pre>
+</div>
+<p>
+  We first provide the source of the data as a URL to Quandl's API in the GetSource method. We need to make sure the data is?organized in ascending order which is done with the?"order=asc" parameter.?You need to substitute the API key in the URL for your personal Quandl API token.
+  The Reader method parses a line of the data file. When using custom data you need to minimally set?the Time property?and Value property. In this example we find the value property from the headings in the spreadsheet is the second column and we reference it with data[1]. We set the Close property to the same value.
+</p>
+
+<p>
+  In our "Initialize" function we use the following commands to add the custom data into our portfolio.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.AddData(TBill, "tbill")
+self.tbill = self.Securities["tbill"].Symbol
+</pre>
+</div>
+<h3>Step 2: Initialization of the Strategy Parameters</h3>
+<p>
+  In our?"Initialize" function we set the cash amount, start-end date as well as other parameters that are specific to this strategy.?We set two parameters for the regression analysis period:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.regPeriod = 24
+self.daysInMonth = 21
+</pre>
+</div>
+<p>
+  The variable "regPeriod" indicates how many months we are going to take into consideration in our regression analysis. We assume 21 days per month and request a historical period of approximately 2 years. We jump back in steps of 21 days and assume it is roughly 1 month of return.
+  We need to set up "Schedule" function in "Initialize" so as to trigger the monthly re-balancing function every month.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy),Action(self.MonthlyReg))
+</pre>
+</div>
+<h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
+<p>
+  Every month we reconstruct the regression analysis to determine whether to be 100% long stocks or T-Bill contracts. We perform this re-balancing in the MonthlyReg function at the start of each month.?We use the History function to retrieve historical data for oil and stocks?and then divide the T-Bill rate by 12 to make it comparable to the monthly expected return of stocks.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">hist = self.History([self.oil, self.spy], self.regPeriod*self.daysInMonth, Resolution.Daily)
+oilSeries = hist.loc[str(self.oil)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
+spySeries = hist.loc[str(self.spy)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
+rf = float(self.Securities[self.tbill].Price)/12.0
+</pre>
+</div>
+<p>
+  Then we make an OLS regression by using "numpy" to make the prediction on next month's stock return.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">x = np.array(oilSeries)
+x = (np.diff(x)/x[:-1])
+y = np.array(spySeries)
+y = (np.diff(y)/y[:-1])
+A = np.vstack([x[:-1],np.ones(len(x[:-1]))]).T
+beta, alpha = np.linalg.lstsq(A,y[1:])[0]
+yPred = alpha + x[-1]*beta
+</pre>
+</div>
+<p>
+  Finally, we compare the expected return of stocks with risk-free rate. If the former is larger than the latter, we invest fully in stocks; otherwise we liquidate our holdings. Because we cannot purchase T-Bill contracts the performance is likely slightly underestimated.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if yPred &gt; rf:
+	self.SetHoldings(self.spy, 1)
+else:
+	self.Liquidate(self.spy)</pre>
+</div>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/04 Summary.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/04 Summary.html
new file mode 100755
index 0000000..5751224
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/04 Summary.html	
@@ -0,0 +1,7 @@
+<p>
+  We backtested this strategy over the period beginning in 2010 and ending in 2017. It has a sharpe ratio of 0.72 beating the benchmark's 0.6 over a similar period.
+  Although the annual return closely matches that of the paper,?it is largely a coincidence of the strong bull market in recent years.?If we look at the monthly regression results, we could find that in most cases, the p-value is not small enough to reject the null hypothesis that there is no correlation between oil and stocks. So the investment decisions based on the insignificant statistical results are almost meaningless. The performance of this strategy cannot effectively beat the benchmark, mostly?due to?the weakened correlation between oil and stocks.
+</p>
+<p>
+  Further research and backtesting could be conducted on assets other than oil that have a stronger relationship with stocks.
+</p>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
new file mode 100755
index 0000000..f597f06
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
@@ -0,0 +1,5 @@
+<p>
+  Strategy code, as well as backtesting result, is attached below. We also put other choices of implementation in the comments.
+</p>
+
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8242236c21a102fbff14d8c3eb30f841'></script>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html
deleted file mode 100755
index c2d4a8f..0000000
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 Can Crude Oil Predict Equity Returns.html	
+++ /dev/null
@@ -1,93 +0,0 @@
-<h2>Abstract</h2>
-In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". [ref]<strong>Gerben, Driesprong (2007)</strong>. Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>[/ref] by?by Gerben, Ben and Benjamin (2007).
-
-If the predicted return is larger than the risk-free rate, the portfolio is fully invested in stock; if the predicted return is lower than the risk-free rate, the portfolio is invested in short-term U.S T-bills. The backtesting period starts in 1980 and is divided into an in-sample period where regression analysis is made and an out?of sample period where the regression result is embedded "statically" into the strategy.
-
-In our implementation of the strategy we adapt the method of the original paper to make it more applicable to the current market. We have set our backtesting period to be from 2010 to 2017 and we refresh our regression analysis each month to form a rolling dynamic projection. This is because?empirical evidence shows?us the correlation between oil and stocks is not as strong as in the 1980's. We use?the price of S&amp;P GSCI? Crude Oil Total Return Index ETNs to represent spot oil price, and import T-bill data from Quandl by defining a custom class.?We use the "Schedule" API to trigger an event every month automatically?and the "History" function to retrieve data for regression analysis.
-
-Our analysis shows this strategy under performs the market in recent years. In the 9 year analysis period the algorithm was mostly long the S&amp;P500 index and only 9 trades were performed as the markets were strongly bullish. The trades could potentially simply be due to the weakening of the relationship between stocks and oil.
-<h2>Background</h2>
-We assume the predicted return of the stock is proportional to the return of oil. This can be represented by the regression equation:
-
-\[r^{stock}_t=a_0+a_1r^{oil}_{t-1}+e_t\]
-
-with
-
-\[e_t=r^{stock}_t-E_{t-1}[r^{stock}_t]\].
-
-The independent variable is the return of the oil and the dependent variable is the return of the stock. We use the monthly returns over a regression period of 2 years, giving us 22 observations to regress.?Every month regression analysis is conducted, and we use the estimated coefficient from the regression to compute the expected stock return with the given return of oil.
-<h2>Method</h2>
-The algorithm implementation consists of mainly three parts: Defining the custom imported data, initialization of the strategy parameters, and monthly re-balancing of the portfolio.
-<h3>Step 1: Defining Custom Imported Data</h3>
-We import T-Bill data from Quandl - a marketplace for financial, economic and alternative data. This requires defining a small class that tells QuantConnect how to interpret?the Quandl data.
-<pre class="prettyprint linenums">class TBill(PythonData):
-    def GetSource(self, config, date, isLiveMode):
-        return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/USTREASURY/BILLRATES.csv?api_key=XXXXXXXXX&amp;order=asc", SubscriptionTransportMedium.RemoteFile)
-    def Reader(self, config, line, date, isLiveMode):
-        tbill = TBill()
-        tbill.Symbol = config.Symbol
-        # Example Line Format:
-        # Date      4 Wk Bank Discount Rate
-        # 2017-06-01 		0.8
-        if not (line.strip() and line[0].isdigit()): return None
-        try:
-            data = line.split(',')
-            value = float(data[1])*0.01
-            value = decimal.Decimal(value)
-            if value == 0: return None
-            tbill.Time = datetime.strptime(data[0], "%Y-%m-%d")
-            tbill.Value = value
-            tbill["Close"] = float(value)
-            return tbill;
-        except ValueError:
-            return None
-</pre>
-We first provide the source of the data as a URL to Quandl's API in the GetSource method. We need to make sure the data is?organized in ascending order which is done with the?"order=asc" parameter.?You need to substitute the API key in the URL for your personal Quandl API token.
-
-The Reader method parses a line of the data file. When using custom data you need to minimally set?the Time property?and Value property. In this example we find the value property from the headings in the spreadsheet is the second column and we reference it with data[1]. We set the Close property to the same value.
-
-In our "Initialize" function we use the following commands to add the custom data into our portfolio.
-<pre class="prettyprint linenums">self.AddData(TBill, "tbill")
-self.tbill = self.Securities["tbill"].Symbol
-</pre>
-<h3>Step 2: Initialization of the Strategy Parameters</h3>
-In our?"Initialize" function we set the cash amount, start-end date as well as other parameters that are specific to this strategy.?We set two parameters for the regression analysis period:
-<pre class="prettyprint linenums">self.regPeriod = 24
-self.daysInMonth = 21
-</pre>
-The variable "regPeriod" indicates how many months we are going to take into consideration in our regression analysis. We assume 21 days per month and request a historical period of approximately 2 years. We jump back in steps of 21 days and assume it is roughly 1 month of return.
-
-We need to set up "Schedule" function in "Initialize" so as to trigger the monthly re-balancing function every month.
-<pre class="prettyprint linenums">self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy),Action(self.MonthlyReg))
-</pre>
-<h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
-Every month we reconstruct the regression analysis to determine whether to be 100% long stocks or T-Bill contracts. We perform this re-balancing in the MonthlyReg function at the start of each month.?We use the History function to retrieve historical data for oil and stocks?and then divide the T-Bill rate by 12 to make it comparable to the monthly expected return of stocks.
-<pre class="prettyprint linenums">hist = self.History([self.oil, self.spy], self.regPeriod*self.daysInMonth, Resolution.Daily)
-oilSeries = hist.loc[str(self.oil)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
-spySeries = hist.loc[str(self.spy)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
-rf = float(self.Securities[self.tbill].Price)/12.0
-</pre>
-Then we make an OLS regression by using "numpy" to make the prediction on next month's stock return.
-<pre class="prettyprint linenums">x = np.array(oilSeries)
-x = (np.diff(x)/x[:-1])
-y = np.array(spySeries)
-y = (np.diff(y)/y[:-1])
-A = np.vstack([x[:-1],np.ones(len(x[:-1]))]).T
-beta, alpha = np.linalg.lstsq(A,y[1:])[0]
-yPred = alpha + x[-1]*beta
-</pre>
-Finally, we compare the expected return of stocks with risk-free rate. If the former is larger than the latter, we invest fully in stocks; otherwise we liquidate our holdings. Because we cannot purchase T-Bill contracts the performance is likely slightly underestimated.
-<pre class="prettyprint linenums">if yPred &gt; rf:
-	self.SetHoldings(self.spy, 1)
-else:
-	self.Liquidate(self.spy)</pre>
-<h2>Conclusion</h2>
-We backtested this strategy over the period beginning in 2010 and ending in 2017. It has a sharpe ratio of 0.72 beating the benchmark's 0.6 over a similar period.
-
-Although the annual return closely matches that of the paper,?it is largely a coincidence of the strong bull market in recent years.?If we look at the monthly regression results, we could find that in most cases, the p-value is not small enough to reject the null hypothesis that there is no correlation between oil and stocks. So the investment decisions based on the insignificant statistical results are almost meaningless. The performance of this strategy cannot effectively beat the benchmark, mostly?due to?the weakened correlation between oil and stocks.
-
-Further research and backtesting could be conducted on assets other than oil that have a stronger relationship with stocks.
-<h2>Algorithm</h2>
-Strategy code, as well as backtesting result, is attached below. We also put other choices of implementation in the comments.
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8242236c21a102fbff14d8c3eb30f841'></script>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html
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+<p>
+  In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work.[ref]<strong>George J. Miao </strong>High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>[/ref]We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
+</p>
+
+<p>
+  This strategy is especially profitable when the market is performing poorly. The profit is resulted from mispricing, and mispricings are likely to happen when the market goes down or volatility increases.
+</p>
+
+<p>
+  To explore this strategy further, we design this strategy to be flexible. We can change the data resolution into 5 minutes, 10 minutes or even 30 minutes by simply changing a parameter. It's also essential to choose optimized entering, closing and stop loss threshold. Everyone can has his/her own version of this strategy.
+</p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html
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+<p>
+  High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012[ref]<strong>Cartea &amp; Penalva, 2012 </strong>Where is the value in high frequency trading?<strong><a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a></strong>[/ref]).
+  Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006[ref]<strong>Gatev, Goetzmann, &amp; Rouwenhorst, 2006 </strong>Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>[/ref]) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
+</p>
+
+<h3>Data Description</h3>
+<p>
+  In order to have more pairs with high correlation, we select stocks in a specific industry. Economically, we prefer traditional sectors because the companies in these sector are more likely to be close substitutes. If we selected N stocks, the number of pairs can be calculated by \(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\). In the demonstrated strategy we used 80 stocks, so we have 3160 pairs in total. We used minute data and aggregate them into lower resolution, thus 1 minute is the highest resolution for this strategy.
+</p>
+
+<h3>Correlation Approach</h3>
+<p>
+  Correlations measure the relationship between two stocks that have price trends. They tend to move together, and thus are correlated. Correlation filter is the first step to screen the candidate pairs.
+  Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
+</p>
+
+\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}}\]
+
+<p>
+  Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
+</p>
+
+<p>
+  However, the pairs trading based on a correlation approach alone would have a disadvantage of instabilities over time. Correlation coefficients do not necessarily imply  mean-reversion between the prices of the two stock pairs. In order to overcome the above issue, a cointegration approach was further used as the second-step of the selection process for the pairs.
+</p>
+
+<h3>Cointegration Approach</h3>
+<p>
+  The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger[ref]<strong>Engle and Granger</strong> Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236">Online Copy</a></strong>[/ref]Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
+</p>
+
+<p>
+  The definition of cointegration is the following: assume that \(x_t\) and \(y_t\) are two time series that were non-stationary. If there exists a parameter \(\gamma\) such that the following equation:
+</p>
+
+\[z_t = y_t - \gamma x_t\]
+
+<p>
+  It is a stationary process, then \(x_t\) and \(y_t\) would be cointegrated. This process is a powerful tool for investigating common asset trends in multivariate time series.
+</p>
+<p>
+  In our case, Let \(p_t^A\) and \(p_t^B\) be the prices of two stocks A and B respectively. If it is assumed that {\({p_t^A, p_t^B}\)} is individually non-stationary, there exists the parameter \(\gamma\) such that the following equation was a stationary process
+</p>
+
+\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]
+
+<p>
+  where \(\mu\) is a mean of the cointegration model. \(\epsilon_t\) is a stationary, mean-revering process and was referred to as a <em>cointegration residual</em>. The parameter \(\gamma\) is known as a <em>cointegration coefficient. </em>The equation above represents a model of cointegrated pair for stocks A and B.
+</p>
+
+<p>
+  It's essential to understand how the conitegration residual together with the cointegration coefficient determines our trading direction. If \(\epsilon\) is positive, in a given confidence interval, this is a signal that stock A is relatively overpriced and stock B is relatively underpriced, and we are going to long B and short A; If If \(\epsilon\) is negative, we are going to long A and short B.
+</p>
+
+<h3>Cointegration Verification(optional reading part)</h3>
+<p>
+  In the Engle-Granger method(Engle &amp; Granger, 1987), we first set up a cointegration regression between stock A and stock B as stated in the equation above, and then estimate the regression parameters \(\mu\) and \(\gamma\) using an ordinary least squares(OLS). Subsequently, we tested the regression residual \(epsilon_t\) to determine whether or not it was stationary.
+</p>
+
+<p>
+  The most popular stationary test in the area of cointegration, the Augmented Dickey Fuller (ADF) test, was used on the regression residual \(\epsilon\) to determine whether it had a unit root.
+</p>
+<p>
+  Testing for the presence of the unit root in the regression residual using the ADF test was given by
+</p>
+
+\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \DeltaZ_{t-i} + \mu_t\]
+
+<p>
+  where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
+</p>
+
+<p>
+  The number of lag order p in the equation is usually unknown and therefore had to be estimated. To determine the number of lag p, the information criteria for lag order selection was used. Here we choose Bayesian Information Criterion(BIC)
+</p>
+
+\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]
+
+<p>
+  Where T is the sample size.
+</p>
+<p>
+  The unit root test for the regression residual \(\epsilon\) using the ADF test was then carried out under the null hypothesis \(H_0 : \gamma = 0\) versus the alternative hypothesis \(H_1 : \gamma &lt; 0\). A statistical value of the ADF test was obtained by
+</p>
+
+\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]
+
+<p>
+  The test result in the equation above is compared with the critical value of the ADF test. If the test result is less than the critical value, then the null hypothesis is rejected. This means the regression residual \(\epsilon\) is stationary. Thus, the two stock prices {\({p_t^A, p_t^B}\)} are cointegrated.
+</p>
+<h3>Pairs Trading Strategy</h3>
+<p>
+  The pairs trading strategy uses trading signals based on the regression residual \(\epsilon\) and were modeled as a mean-reverting process.
+</p>
+
+<p>
+  In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
+</p>
+  The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
+</p>
+
+<p>
+  In the training period, each of the training data contained a 3-month period, which is a dynamic rolling window size. Immediately after the training period, we begin our one-month trading period, and the dynamic rolling window automatically shift ahead to record the new prices of the stocks in each pair. After the first trading period, we use the updated stock prices to select our pairs for trading again, and begin another trading period.
+</p>
+
+<h3>Parameter Adjustment</h3>
+<p>
+  The performance of the strategy is sensitive to the parameters. There are  mainly four parameter to adjust: Opening Threshold, Closing Threshold, Stop-loss Threshold and data resolution.
+</p>
+<p>
+  Opening threshold represents by how many times the residual \(\epsilon\) exceed the standard deviation, which is calculated by \(\frac{\epsilon - \bar{\epsilon}}{\sigma}\). By default we set it to 2.32 and -2.32, which is the critical value for 99% confidence interval if we assume the residual follows normal distribution.
+  Closing threshold is calculated in the same way as opening threshold, we set it to 0.5 by default to close early to prevent further divergence.
+</p>
+<p>
+  Stop-loss Threshold is set to 4.5. This depends on the level of mispricing we can bear. The higher degree our tolerance to risk is, the higher we can set this parameter. However, if we set this number too low, we may have too many pairs closed before reversion to stop loss.
+</p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html
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+<p>
+  In this trading strategy we would define a class named 'pairs'. We manage pairs instead of stocks directly to make it's more convenient for us to calculate correlation and cointegration, update stock prices in the pair and trade on the selected pairs.
+</p>
+
+<h3>Step 1: Pairs Class Definition</h3>
+<p>
+  The pairs is made up of two stocks, stock A and stock B. This class has several properties. The basic properties include symbols of stock A and stock B, the pandas DataFrame that contains time and prices of the two stocks, the current error, the error of the last datapoint, and the lists to record stock prices for update purpose. Instead of updating the DataFrame every 5 minutes, we record the prices in lists to update the DataFrame monthly. This would speed up the algorithm at least 10 times because manipulating DataFrame is very time consuming. The cor_update method is used every month to update the correlation between the two stocks in this pair. The cointegration_test method is also used monthly to do OLS regression, conduct ADF test, and calculate the mean and standard deviation of the residual. The method also assign these calculated values as properties to the pair object.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">class pairs(object):
+    def __init__(self, a, b):
+        self.a = a
+        self.b = b
+        self.name = str(a) + ':' + str(b)
+        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
+    # The number of bars in the rolling window would be determined by the resolution, so we get this
+      information from the shape of the DataFrame here.
+        self.num_bar = self.df.shape[0]
+        self.cor = self.df.corr().ix[0][1]
+    # Set the initial signals to be 0
+        self.error = 0
+        self.last_error = 0
+        self.a_price = []
+        self.a_date = []
+        self.b_price = []
+        self.b_date = []
+
+    def cor_update(self):
+        self.cor = self.df.corr().ix[0][1]
+
+    def cointegration_test(self):
+        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
+    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
+      the tuple is the test value.
+        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
+        self.mean_error = np.mean(self.model.resid)
+        self.sd = np.std(self.model.resid)
+
+    def price_record(self,data_a,data_b):
+        self.a_price.append(float(data_a.Close))
+        self.a_date.append(data_a.EndTime)
+        self.b_price.append(float(data_b.Close))
+        self.b_date.append(data_b.EndTime)
+
+    def df_update(self):
+        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
+                 [self.a_date]).dropna()
+        self.df = pd.concat([self.df,new_df])
+        self.df = self.df.tail(self.num_bar)
+    # after updating the DataFrame, we empty the lists for the incoming data
+        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
+            list = []
+</pre>
+</div>
+<h3>Step 2: Generate and Clean Pairs</h3>
+<p>
+  The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
+  The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def generate_pairs(self):
+    for i in range(len(self.symbols)):
+        for j in range(i+1,len(self.symbols)):
+            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
+
+    self.pair_list = [x for x in self.pair_list if x.cor &gt; self.pair_threshold]
+
+    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
+
+    if len(self.pair_list) &gt; self.pair_num:
+        	self.pair_list = self.pair_list[:self.pair_num]
+
+def pair_clean(self,list):
+    l = []
+    l.append(list[0])
+    for i in list:
+        symbols = [x.a for x in l] + [x.b for x in l]
+        if i.a not in symbols and i.b not in symbols:
+            l.append(i)
+        else:
+            pass
+    return l
+</pre>
+</div>
+
+<h3>Step 3: Warming up Period</h3>
+<p>
+  This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if len(self.symbols[0].prices) &lt; self.num_bar:
+    for symbol in self.symbols:
+        if data.ContainsKey(i) is True:
+    	    symbol.prices.append(float(data[symbol].Close))
+            symbol.dates.append(data[symbol].EndTime)
+        else:
+            self.Log('%s is missing'%str(symbol))
+            self.symbols.remove(symbol)
+    self.data_count = 0
+    return</pre>
+</div>
+<h3>Step 4: Pairs Selection</h3>
+<p>
+  This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
+    if self.generate_count == 0:
+        for symbol in self.symbols:
+        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
+
+        self.generate_pairs()
+        self.generate_count +=1
+        self.Log('pair list length:'+str(len(self.pair_list)))
+
+        for pair in self.pair_list:
+            pair.cor_update()
+    # Update the DataFrame and correlation selection
+    if len(self.pair_list[0].a_price) != 0:
+        for pair in self.pair_list:
+    	    pair.df_update()
+            pair.cor_update()
+
+    self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
+    # Cointegration test
+    for pair in self.selected_pair:
+        pair.cointegration_test()
+
+    self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
+    self.selected_pair.sort(key = lambda x: x.adf)
+    # If no pair passed the two-stage test, return.
+    if len(self.selected_pair) == 0:
+        self.Log('no selected pair')
+        self.count += 1
+        return
+    # clean the pair to avoid overlapping stocks.
+    self.selected_pair = self.pair_clean(self.selected_pair)
+    # assign a property to the selected pair, this is a signal that would be used for trading.
+    for pair in self.selected_pair:
+        pair.touch = 0
+        self.Log(str(pair.adf) + pair.name)
+    # limit the number of selected pairs.
+    if len(self.selected_pair) &gt; self.selected_num:
+        self.selected_pair = self.selected_pair[:self.selected_num]
+
+    self.count +=1
+    self.data_count = 0
+    return
+</pre>
+</div>
+
+<h3>Step 5: Trade Period</h3>
+<p>
+  It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
+</p>
+
+<h4>Updating Pairs</h4>
+<p>
+  This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">num_select = len(self.selected_pair)
+for pair in self.pair_list:
+    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
+        i.price_record(data[i.a],data[i.b])
+    else:
+        self.Log('%s has no data'%str(pair.name))
+        self.pair_list.remove(pair)
+
+for pair in self.selected_pair:
+    pair.last_error = pair.error
+
+for pair in self.trading_pairs:
+    pair.last_error = pair.error</pre>
+</div>
+<h4>Opening Pairs Trading</h4>
+<p>
+  This is most complex part. For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
+  When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in self.selected_pair:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
+    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
+    self.trading_pairs:
+        if i.touch == 0:
+            if i.error &lt; i.mean_error - self.open_size*i.sd and i.last_error &gt; i.mean_error -
+            self.open_size*i.sd:
+                i.touch += -1
+            elif i.error &gt; i.mean_error + self.open_size*i.sd and i.last_error &lt; i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error &gt; i.mean_error - self.open_size*i.sd and i.last_error &lt; i.mean_error -
+            self.open_size*i.sd:
+                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
+                i.record_model = i.model
+                i.record_mean_error = i.mean_error
+                i.record_sd = i.sd
+                self.trading_pairs.append(i)
+                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
+                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
+                i.touch = 0
+         elif i.touch == 1:
+             if i.error &lt; i.mean_error + self.open_size*i.sd and i.last_error &gt; i.mean_error +
+             self.open_size*i.sd:
+             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
+             i.record_model = i.model
+             i.record_mean_error = i.mean_error
+             i.record_sd = i.sd
+             self.trading_pairs.append(i)
+             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
+             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
+             i.touch = 0
+         else:
+             pass
+    else:
+        pass
+</pre>
+</div>
+
+<h4>Closing Pairs Trading</h4>
+<p>
+  This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for i in self.trading_pairs:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
+    if ((i.error &lt; i.record_mean_error + self.close_size*i.record_sd and i.last_error &gt;i.record_mean_error + self.close_size*i.record_sd) or (i.error &gt; i.record_mean_error -
+    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
+    self.stop_loss*i.record_sd:
+        self.Log('close %s to stop loss'%str(i.name))
+        self.Liquidate(i.a)
+        self.Liquidate(i.b)
+        self.trading_pairs.remove(i)
+    else:
+        pass</pre>
+</div>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html
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+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html	
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+<p>
+  We used 10-minute resolution data to backtest the strategy from Jan 2013 to Dec 2016. To demonstrate the in sample training results, we randomly selected a training period that from 2016-09-07 to 2013-11-30.
+</p>
+
+<h3>Training Result</h3>
+<p>
+  The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1” />
+
+<p>
+  The upper part of the following chart plots the stock prices of pair ING vs TCB. The lower part plots by how many times standard deviation the residual deviate from its mean. There are 5 trading opportunities if we set the opening threshold to be 2.32.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2” />
+
+<p>
+  The following chart is the density plot of the residual error. From the shape we can see the error is approximately normal distributed.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3” />
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 Summary.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 Summary.html
new file mode 100755
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+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 Summary.html	
@@ -0,0 +1,6 @@
+<p>
+  The strategy is considered to be market neutral strategy because it a long/short strategy betting on price convergence. Out backtested beta is -0.112, which is within our expectation.
+  Theoretically, the higher resolution we use, the higher win rate is because on one hand the higher resolution would increase the number of datapoint in our training period, which would make it's harder to past the two-stage test; on the other hand the higher resolution data would let us capture minor profit more accurately. However, there is a trade off between performance and backtesting time. The higher resolution will lead backtesting time to increase drastically.
+  The number of stocks in the initialize step would also affect our performance. Theoretically, the more stock we have, we better pairs we are likely to pick. But too many stocks would also be time consuming.
+  what's worth mentioning is that the optimized parameters are different for each sector. It depends on the features of the price patterns in the specific industry. Plotting the pairs prices and the residual to observe is good option to adjust the thresholds.
+</p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
new file mode 100755
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+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5d0576da03c50667c3af8aefa86f4f44'></script>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach.html
deleted file mode 100755
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-<h2>Abstract</h2>
-In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work.[ref]<strong>George J. Miao </strong>High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>[/ref]We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
-
-This strategy is especially profitable when the market is performing poorly. The profit is resulted from mispricing, and mispricings are likely to happen when the market goes down or volatility increases.
-
-To explore this strategy further, we design this strategy to be flexible. We can change the data resolution into 5 minutes, 10 minutes or even 30 minutes by simply changing a parameter. It's also essential to choose optimized entering, closing and stop loss threshold. Everyone can has his/her own version of this strategy.
-<h2>Introduction</h2>
-High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012[ref]<strong>Cartea &amp; Penalva, 2012 </strong>Where is the value in high frequency trading?<strong><a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a></strong>[/ref]).
-<div class="page" title="Page 14">
-<div class="layoutArea">
-<div class="column">
-
-Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006[ref]<strong>Gatev, Goetzmann, &amp; Rouwenhorst, 2006 </strong>Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>[/ref]) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
-
-</div>
-<h3>Data Description</h3>
-In order to have more pairs with high correlation, we select stocks in a specific industry. Economically, we prefer traditional sectors because the companies in these sector are more likely to be close substitutes. If we selected N stocks, the number of pairs can be calculated by \(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\). In the demonstrated strategy we used 80 stocks, so we have 3160 pairs in total. We used minute data and aggregate them into lower resolution, thus 1 minute is the highest resolution for this strategy.
-<h3>Correlation Approach</h3>
-Correlations measure the relationship between two stocks that have price trends. They tend to move together, and thus are correlated. Correlation filter is the first step to screen the candidate pairs.
-
-Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
-
-\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}}\]
-
-Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
-
-However, the pairs trading based on a correlation approach alone would have a disadvantage of instabilities over time. Correlation coefficients do not necessarily imply  mean-reversion between the prices of the two stock pairs. In order to overcome the above issue, a cointegration approach was further used as the second-step of the selection process for the pairs.
-<h3>Cointegration Approach</h3>
-The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger[ref]<strong>Engle and Granger</strong> Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236">Online Copy</a></strong>[/ref]Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
-
-The definition of cointegration is the following: assume that \(x_t\) and \(y_t\) are two time series that were non-stationary. If there exists a parameter \(\gamma\) such that the following equation:
-
-\[z_t = y_t - \gamma x_t\]
-
-was a stationary process, then \(x_t\) and \(y_t\) would be cointegrated. This process is a powerful tool for investigating common asset trends in multivariate time series.
-
-In our case, Let \(p_t^A\) and \(p_t^B\) be the prices of two stocks A and B respectively. If it is assumed that {\({p_t^A, p_t^B}\)} is individually non-stationary, there exists the parameter \(\gamma\) such that the following equation was a stationary process
-
-\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]
-
-where \(\mu\) is a mean of the cointegration model. \(\epsilon_t\) is a stationary, mean-revering process and was referred to as a <em>cointegration residual</em>. The parameter \(\gamma\) is known as a <em>cointegration coefficient. </em>The equation above represents a model of cointegrated pair for stocks A and B.
-
-It's essential to understand how the conitegration residual together with the cointegration coefficient determines our trading direction. If \(\epsilon\) is positive, in a given confidence interval, this is a signal that stock A is relatively overpriced and stock B is relatively underpriced, and we are going to long B and short A; If If \(\epsilon\) is negative, we are going to long A and short B.
-<h3>Cointegration Verification(optional reading part)</h3>
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-In the Engle-Granger method(Engle &amp; Granger, 1987), we first set up a cointegration regression between stock A and stock B as stated in the equation above, and then estimate the regression parameters \(\mu\) and \(\gamma\) using an ordinary least squares(OLS). Subsequently, we tested the regression residual \(epsilon_t\) to determine whether or not it was stationary.
-
-The most popular stationary test in the area of cointegration, the Augmented Dickey Fuller (ADF) test, was used on the regression residual \(\epsilon\) to determine whether it had a unit root.
-<div class="page" title="Page 5">
-<div class="layoutArea">
-<div class="column">
-
-Testing for the presence of the unit root in the regression residual using the ADF test was given by
-
-\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \DeltaZ_{t-i} + \mu_t\]
-
-where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
-
-The number of lag order p in the equation is usually unknown and therefore had to be estimated. To determine the number of lag p, the information criteria for lag order selection was used. Here we choose Bayesian Information Criterion(BIC)
-
-\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]
-
-Where T is the sample size.
-
-The unit root test for the regression residual \(\epsilon\) using the ADF test was then carried out under the null hypothesis \(H_0 : \gamma = 0\) versus the alternative hypothesis \(H_1 : \gamma &lt; 0\). A statistical value of the ADF test was obtained by
-
-\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]
-
-The test result in the equation above is compared with the critical value of the ADF test. If the test result is less than the critical value, then the null hypothesis is rejected. This means the regression residual \(\epsilon\) is stationary. Thus, the two stock prices {\({p_t^A, p_t^B}\)} are cointegrated.
-<h3>Pairs Trading Strategy</h3>
-The pairs trading strategy uses trading signals based on the regression residual \(\epsilon\) and were modeled as a mean-reverting process.
-
-In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
-
-The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
-
-In the training period, each of the training data contained a 3-month period, which is a dynamic rolling window size. Immediately after the training period, we begin our one-month trading period, and the dynamic rolling window automatically shift ahead to record the new prices of the stocks in each pair. After the first trading period, we use the updated stock prices to select our pairs for trading again, and begin another trading period.
-<h3>Parameter Adjustment</h3>
-The performance of the strategy is sensitive to the parameters. There are  mainly four parameter to adjust: Opening Threshold, Closing Threshold, Stop-loss Threshold and data resolution.
-
-Opening threshold represents by how many times the residual \(\epsilon\) exceed the standard deviation, which is calculated by \(\frac{\epsilon - \bar{\epsilon}}{\sigma}\). By default we set it to 2.32 and -2.32, which is the critical value for 99% confidence interval if we assume the residual follows normal distribution.
-
-Closing threshold is calculated in the same way as opening threshold, we set it to 0.5 by default to close early to prevent further divergence.
-
-Stop-loss Threshold is set to 4.5. This depends on the level of mispricing we can bear. The higher degree our tolerance to risk is, the higher we can set this parameter. However, if we set this number too low, we may have too many pairs closed before reversion to stop loss.
-
-</div>
-&nbsp;
-<h2>Method</h2>
-In this trading strategy we would define a class named 'pairs'. We manage pairs instead of stocks directly to make it's more convenient for us to calculate correlation and cointegration, update stock prices in the pair and trade on the selected pairs.
-<h3>Step 1: Pairs Class Definition</h3>
-The pairs is made up of two stocks, stock A and stock B. This class has several properties. The basic properties include symbols of stock A and stock B, the pandas DataFrame that contains time and prices of the two stocks, the current error, the error of the last datapoint, and the lists to record stock prices for update purpose. Instead of updating the DataFrame every 5 minutes, we record the prices in lists to update the DataFrame monthly. This would speed up the algorithm at least 10 times because manipulating DataFrame is very time consuming.
-The cor_update method is used every month to update the correlation between the two stocks in this pair. The cointegration_test method is also used monthly to do OLS regression, conduct ADF test, and calculate the mean and standard deviation of the residual. The method also assign these calculated values as properties to the pair object.
-<pre class="prettyprint linenums">class pairs(object):
-    def __init__(self, a, b):
-        self.a = a
-        self.b = b
-        self.name = str(a) + ':' + str(b)
-        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
-    # The number of bars in the rolling window would be determined by the resolution, so we get this
-      information from the shape of the DataFrame here.
-        self.num_bar = self.df.shape[0]
-        self.cor = self.df.corr().ix[0][1]
-    # Set the initial signals to be 0
-        self.error = 0
-        self.last_error = 0
-        self.a_price = []
-        self.a_date = []
-        self.b_price = []
-        self.b_date = []
-
-    def cor_update(self):
-        self.cor = self.df.corr().ix[0][1]
-
-    def cointegration_test(self):
-        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
-    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
-      the tuple is the test value.
-        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
-        self.mean_error = np.mean(self.model.resid)
-        self.sd = np.std(self.model.resid)
-
-    def price_record(self,data_a,data_b):
-        self.a_price.append(float(data_a.Close))
-        self.a_date.append(data_a.EndTime)
-        self.b_price.append(float(data_b.Close))
-        self.b_date.append(data_b.EndTime)
-
-    def df_update(self):
-        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
-                 [self.a_date]).dropna()
-        self.df = pd.concat([self.df,new_df])
-        self.df = self.df.tail(self.num_bar)
-    # after updating the DataFrame, we empty the lists for the incoming data
-        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
-            list = []
-</pre>
-<h3>Step 2: Generate and Clean Pairs</h3>
-The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
-The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
-<pre class="prettyprint linenums">def generate_pairs(self):
-    for i in range(len(self.symbols)):
-        for j in range(i+1,len(self.symbols)):
-            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
-
-    self.pair_list = [x for x in self.pair_list if x.cor &gt; self.pair_threshold]
-
-    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
-
-    if len(self.pair_list) &gt; self.pair_num:
-        	self.pair_list = self.pair_list[:self.pair_num]
-
-def pair_clean(self,list):
-    l = []
-    l.append(list[0])
-    for i in list:
-        symbols = [x.a for x in l] + [x.b for x in l]
-        if i.a not in symbols and i.b not in symbols:
-            l.append(i)
-        else:
-            pass
-    return l
-</pre>
-<h3>Step 3: Warming up Period</h3>
-This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
-<pre class="prettyprint linenums">if len(self.symbols[0].prices) &lt; self.num_bar:
-    for symbol in self.symbols:
-        if data.ContainsKey(i) is True:
-    	    symbol.prices.append(float(data[symbol].Close))
-            symbol.dates.append(data[symbol].EndTime)
-        else:
-            self.Log('%s is missing'%str(symbol))
-            self.symbols.remove(symbol)
-    self.data_count = 0
-    return</pre>
-<h3>Step 4: Pairs Selection</h3>
-This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
-<pre class="prettyprint linenums">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
-    if self.generate_count == 0:
-        for symbol in self.symbols:
-        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
-
-        self.generate_pairs()
-        self.generate_count +=1
-        self.Log('pair list length:'+str(len(self.pair_list)))
-
-        for pair in self.pair_list:
-            pair.cor_update()
-    # Update the DataFrame and correlation selection
-    if len(self.pair_list[0].a_price) != 0:
-        for pair in self.pair_list:
-    	    pair.df_update()
-            pair.cor_update()
-
-    self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
-    # Cointegration test
-    for pair in self.selected_pair:
-        pair.cointegration_test()
-
-    self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
-    self.selected_pair.sort(key = lambda x: x.adf)
-    # If no pair passed the two-stage test, return.
-    if len(self.selected_pair) == 0:
-        self.Log('no selected pair')
-        self.count += 1
-        return
-    # clean the pair to avoid overlapping stocks.
-    self.selected_pair = self.pair_clean(self.selected_pair)
-    # assign a property to the selected pair, this is a signal that would be used for trading.
-    for pair in self.selected_pair:
-        pair.touch = 0
-        self.Log(str(pair.adf) + pair.name)
-    # limit the number of selected pairs.
-    if len(self.selected_pair) &gt; self.selected_num:
-        self.selected_pair = self.selected_pair[:self.selected_num]
-
-    self.count +=1
-    self.data_count = 0
-    return
-</pre>
-<h3>Step 5: Trade Period</h3>
-It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
-<h4>Updating Pairs</h4>
-This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
-<pre class="prettyprint linenums">num_select = len(self.selected_pair)
-for pair in self.pair_list:
-    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
-        i.price_record(data[i.a],data[i.b])
-    else:
-        self.Log('%s has no data'%str(pair.name))
-        self.pair_list.remove(pair)
-
-for pair in self.selected_pair:
-    pair.last_error = pair.error
-
-for pair in self.trading_pairs:
-    pair.last_error = pair.error</pre>
-<h4>Opening Pairs Trading</h4>
-This is most complex part. For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
-When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
-<pre class="prettyprint linenums">for i in self.selected_pair:
-    price_a = float(data[i.a].Close)
-    price_b = float(data[i.b].Close)
-    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
-    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
-    self.trading_pairs:
-        if i.touch == 0:
-            if i.error &lt; i.mean_error - self.open_size*i.sd and i.last_error &gt; i.mean_error -
-            self.open_size*i.sd:
-                i.touch += -1
-            elif i.error &gt; i.mean_error + self.open_size*i.sd and i.last_error &lt; i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error &gt; i.mean_error - self.open_size*i.sd and i.last_error &lt; i.mean_error -
-            self.open_size*i.sd:
-                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
-                i.record_model = i.model
-                i.record_mean_error = i.mean_error
-                i.record_sd = i.sd
-                self.trading_pairs.append(i)
-                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
-                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
-                i.touch = 0
-         elif i.touch == 1:
-             if i.error &lt; i.mean_error + self.open_size*i.sd and i.last_error &gt; i.mean_error +
-             self.open_size*i.sd:
-             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
-             i.record_model = i.model
-             i.record_mean_error = i.mean_error
-             i.record_sd = i.sd
-             self.trading_pairs.append(i)
-             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
-             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
-             i.touch = 0
-         else:
-             pass
-    else:
-        pass
-</pre>
-<h4>Closing Pairs Trading</h4>
-This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
-<pre class="prettyprint linenums">for i in self.trading_pairs:
-    price_a = float(data[i.a].Close)
-    price_b = float(data[i.b].Close)
-    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
-    if ((i.error &lt; i.record_mean_error + self.close_size*i.record_sd and i.last_error &gt;i.record_mean_error + self.close_size*i.record_sd) or (i.error &gt; i.record_mean_error -
-    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
-    self.stop_loss*i.record_sd:
-        self.Log('close %s to stop loss'%str(i.name))
-        self.Liquidate(i.a)
-        self.Liquidate(i.b)
-        self.trading_pairs.remove(i)
-    else:
-        pass</pre>
-<h2>Result</h2>
-We used 10-minute resolution data to backtest the strategy from Jan 2013 to Dec 2016. To demonstrate the in sample training results, we randomly selected a training period that from 2016-09-07 to 2013-11-30.
-<h3>Training Result</h3>
-The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1” />
-
-The upper part of the following chart plots the stock prices of pair ING vs TCB. The lower part plots by how many times standard deviation the residual deviate from its mean. There are 5 trading opportunities if we set the opening threshold to be 2.32.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2” />
-
-The following chart is the density plot of the residual error. From the shape we can see the error is approximately normal distributed.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3” />
-
-<strong>Backtesting Result</strong>
-
-The strategy is considered to be market neutral strategy because it a long/short strategy betting on price convergence. Out backtested beta is -0.112, which is within our expectation.
-
-Theoretically, the higher resolution we use, the higher win rate is because on one hand the higher resolution would increase the number of datapoint in our training period, which would make it's harder to past the two-stage test; on the other hand the higher resolution data would let us capture minor profit more accurately. However, there is a trade off between performance and backtesting time. The higher resolution will lead backtesting time to increase drastically.
-
-The number of stocks in the initialize step would also affect our performance. Theoretically, the more stock we have, we better pairs we are likely to pick. But too many stocks would also be time consuming.
-
-what's worth mentioning is that the optimized parameters are different for each sector. It depends on the features of the price patterns in the specific industry. Plotting the pairs prices and the residual to observe is good option to adjust the thresholds.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5d0576da03c50667c3af8aefa86f4f44'></script>
-</div>
-</div>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html
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+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html	
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+<p>
+  Trend estimation is a family of methods to detect and predict tendencies and trends in price series just using the history information. Moving average is a commonly used trend following trading tool. Lots of momentum trading strategies in the Forex market are based on the moving average rule, in which signals are triggered if the close is above or below the moving average. But MA has the time lag, therefore can't be used to predict the turning points of market price changes.
+</p>
+<p>
+  In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009) [ref]Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>[/ref].
+  This strategy exploits short-term momentum in the non-linear trend component of the exchange rate which is generated by Hodrick-Prescott Filter (rather than the exchange rate itself) and uses the MA(1, 2) rule to measure this momentum. The strategy was tested on seven kinds of exchange rates and the results shows less robustness and the performance is sensitive to the change of model parameters.
+</p>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html
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+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
@@ -0,0 +1,56 @@
+<p>
+  Hodrick-Prescott Filter decomposes a time series\(y_t\) into two components: the cyclical part(which is short-term) and the trend part(which is long term).
+</p>
+
+\[y_t=\x _t +c_t\]
+
+<p>
+  The filter is the solution to the following optimization problem for \(x_t\)
+</p>
+
+\[\min _{x_t}\left[\sum_{t=1}^n(y_t-x_t)^2+\lambda\sum_{t=2}^{n-1}[(x_{t+1}-x_t)-(x_{t}-x_{t-1})^2] \right]\]
+
+[ref]Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>[/ref]The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
+
+\[\min_{\bf x}{\parallel {\bf{y}}-{\bf{x}}\parallel}_2^2+\lambda {\parallel D\bf x\parallel}_2^2\]
+
+<p>
+  where \({\bf y}=(y_1,y_2,...,y_n),{\bf x}=(x_1,x_2,...,x_n)\in {\rm I\!R}^n\),\(\parallel\cdot\parallel_2\) is the Euclidean norm. D is the (n-2)*n matrix:
+</p>
+
+\[
+
+\left[
+\begin{matrix}
+1 &amp; -2 &amp; 1 &amp; \\
+&amp; 1 &amp; -2 &amp; 1 \\
+&amp; &amp; &amp; \ddots &amp;\\
+&amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
+&amp; &amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
+\end{matrix}
+\right]
+
+\]
+
+<p>
+  The solution of this optimization problem is given by solving the following linear system:
+</p>
+
+\[y=(I+2\lambda D^TD)^{-1}x\]
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def hpfilter(self,X, lamb=1600):
+    X = np.asarray(X, float)
+    if X.ndim &gt; 1:
+    X = X.squeeze()
+    nobs = len(X)
+    I = sparse.eye(nobs,nobs)
+    offsets = np.array([0,1,2])
+    data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1)
+    K = sparse.dia_matrix((data, offsets), shape=(nobs-2,nobs))
+    use_umfpack = True
+    self.trend = spsolve(I+lamb*K.T.dot(K), X,use_umfpack=use_umfpack)
+    cycle = X - self.trend
+</pre>
+</div>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html
new file mode 100755
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+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
@@ -0,0 +1,87 @@
+<p>
+  This low-frequency momentum trading strategies are applied to daily data on seven kinds of exchange rates. We use five years history data before January 2011 for initial estimation of the trend model. Daily exchange rates for the period January 2011 to May 2017 is used for out of sample trading.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+    self.SetStartDate(2011,1,1)
+    self.SetEndDate(2017,5,30)
+    self.SetCash(100000)
+    self.numdays = 360*5  # set the length of training period
+    self.syl = self.AddSecurity(SecurityType.Forex, "EURUSD", Resolution.Daily).Symbol
+    self.n,self.m = 2, 1
+    self.trend = None
+    self.SetBenchmark(self.syl)
+    self.MA_rules = None
+    history = self.History(self.numdays,Resolution.Daily)
+    self.close = [slice[self.syl].Close for slice in history]
+</pre>
+</div>
+
+<h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
+<p>
+  The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(\1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more” />
+
+<p>
+  If we just plot the curve for the first 100 days, we find that the smaller the λ, the more apparent the trend. The curve does not change too much as the λ  smaller than 100. Thus here we choose  λ=100 to extract the trend of daily price data. This trend is our low-frequency component.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-less.jpg" alt="low-frequency-component-less”/>
+
+<p style="text-align: center;">Out-of-sample EUR/USD Trend Estimation</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation” />
+<h3>Step 2: Setting up the Moving Average Rule</h3>
+
+<p>
+  Moving average (MA) rules are very commonly used to generate buy and sell signals from data on the spot exchange rate. The MA rule compares a short-run moving average of the current and lagged exchange rate with a long-run moving average.
+</p>
+
+\[MA(m,n)=\frac{1}{m}\sum_{i=0}^{m-1}S_{t-i}-\frac{1}{n}\sum_{i=0}^{n-1}S_{t-i}\]
+
+
+<p>
+  For HP filter, the non-linear trend is estimated recursively as the paper did. The initial estimation was undertaken using 3 years history data before 2011. The estimation period is then rolled forward each day through the trading period from January 2011 to May 2017.
+</p>
+
+<h3>Step 3: Generating the Trading Signals</h3>
+<p>
+  We generate buy and sell signals by applying an MA(1, 2) rule to the estimated low-frequency component. For MA(m,n), m must be 1 which denotes the current value of low-frequency component. n should be small since large n would generate large time lag, the judgment of turning points is not accurate.
+</p>
+
+<p>
+  A buy signal is generated when the current day’s low-frequency trend is higher than the last day’s low-frequency trend and a sell signal is generated when it is lower.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+def OnData(self,data):
+    self.close.append(self.Portfolio[self.syl].Price)
+    self.hpfilter(self.close[-self.numdays:len(self.close)+1], 100)
+    self.MA_rules_today = (np.mean(self.trend[-self.m : len(self.trend)]) - np.mean(self.trend[-self.n : len(self.trend)]))
+    self.MA_rules_yesterday = (np.mean(self.trend[-self.m-1: len(self.trend)-1]) - np.mean(self.trend[-self.n-1 : len(self.trend)-1]))
+    holdings = self.Portfolio[self.syl].Quantity
+
+    if self.MA_rules_today &gt; 0 and self.MA_rules_yesterday &lt; 0:
+       self.SetHoldings(self.syl, 1)
+    elif self.MA_rules_today &lt; 0 and self.MA_rules_yesterday &gt; 0:
+       self.SetHoldings(self.syl, -1)
+</pre>
+</div>
+<p style="text-align: center;">Trading Signals when λ=1600</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-1600.jpg" alt="trading-signal 1600.jpg” />
+
+<p style="text-align: center;">Trading Signals when λ=100</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-100.jpg" alt="trading-signal-100.jpg" />
+
+<p>
+  The above charts are the in-sample trading signals after applying MA rules on the low-frequency component. The trend curve is more smooth with larger λ. Thus when we applied MA rules, a less smooth trend will trigger more trading opportunities.
+</p>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/04 Summary.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/04 Summary.html
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+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;" colspan="8">Strategy Performance for different currencies in Forex</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Currency</td>
+<td>USDCAD</td>
+<td>EURUSD</td>
+<td> USDCHF</td>
+<td> EURGBP</td>
+<td>CADUSD</td>
+<td>USDNOK</td>
+<td>USDZAR</td>
+</tr>
+<tr>
+<td>Sharp Ratio</td>
+<td>0.361</td>
+<td>0.375</td>
+<td> 0.131</td>
+<td> 0.337</td>
+<td> 0.052</td>
+<td> 0.054</td>
+<td> 0.195</td>
+</tr>
+<tr>
+<td> Total Trades</td>
+<td> 14</td>
+<td> 11</td>
+<td> 12</td>
+<td> 11</td>
+<td> 6</td>
+<td> 16</td>
+<td> 9</td>
+</tr>
+<tr>
+<td> Annual Return</td>
+<td> 3.128%</td>
+<td> 3.272%</td>
+<td> 1.162%</td>
+<td> 2.689%</td>
+<td> 0.043%</td>
+<td> 0.093%</td>
+<td> 2.201%</td>
+</tr>
+<tr>
+<td> Max Drawdown</td>
+<td> 10.3%</td>
+<td> 8.3%</td>
+<td> 24.8%</td>
+<td> 17.6%</td>
+<td> 22.5%</td>
+<td> 30.3%</td>
+<td> 23%</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  The table reports the strategy performance statistics during six and a half years backtesting period. From the table we can see, most of them have the higher maximum drawdown. The number of total trades is small because we applied MA rules on the smoothed trend component.  As the author indicated in the paper, we still find that the performance of this strategy is very sensitive to the choice of lag parameters in MA rules and in a non-monotonic way.
+</p>
+
+<p>
+  The strategy does not generate more stable profits in Forex market generally. That might because that the HP filter technique was designed to be viewed as a trend curve through the entire set of data. When we applied it in trading strategy, the entry of new data into the filter model can cause the trend line to change the trend through past data, makes it harder to identify the trend accurately.
+</p>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
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@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5188517de3244425b56af3e717c90cb9'></script>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market.html
deleted file mode 100755
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+++ /dev/null
@@ -1,182 +0,0 @@
-<h2>Abstract</h2>
-Trend estimation is a family of methods to detect and predict tendencies and trends in price series just using the history information. Moving average is a commonly used trend following trading tool. Lots of momentum trading strategies in the Forex market are based on the moving average rule, in which signals are triggered if the close is above or below the moving average. But MA has the time lag, therefore can't be used to predict the turning points of market price changes.
-
-In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009) [ref]Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>[/ref].
-
-This strategy exploits short-term momentum in the non-linear trend component of the exchange rate which is generated by Hodrick-Prescott Filter (rather than the exchange rate itself) and uses the MA(1, 2) rule to measure this momentum. The strategy was tested on seven kinds of exchange rates and the results shows less robustness and the performance is sensitive to the change of model parameters.
-
-<h2>Introduction</h2>
-Hodrick-Prescott Filter decomposes a time series\(y_t\) into two components: the cyclical part(which is short-term) and the trend part(which is long term).
-
-\[y_t=\x _t +c_t\]
-
-The filter is the solution to the following optimization problem for \(x_t\)
-
-\[\min _{x_t}\left[\sum_{t=1}^n(y_t-x_t)^2+\lambda\sum_{t=2}^{n-1}[(x_{t+1}-x_t)-(x_{t}-x_{t-1})^2] \right]\]
-
-[ref]Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>[/ref]The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
-
-\[\min_{\bf x}{\parallel {\bf{y}}-{\bf{x}}\parallel}_2^2+\lambda {\parallel D\bf x\parallel}_2^2\]
-
-where \({\bf y}=(y_1,y_2,...,y_n),{\bf x}=(x_1,x_2,...,x_n)\in {\rm I\!R}^n\),\(\parallel\cdot\parallel_2\) is the Euclidean norm. D is the (n-2)*n matrix:
-
-\[
-
-\left[
-\begin{matrix}
-1 &amp; -2 &amp; 1 &amp; \\
-&amp; 1 &amp; -2 &amp; 1 \\
-&amp; &amp; &amp; \ddots &amp;\\
-&amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
-&amp; &amp; &amp; &amp; 1 &amp; -2 &amp; 1 \\
-\end{matrix}
-\right]
-
-\]
-
-The solution of this optimization problem is given by solving the following linear system:
-
-\[y=(I+2\lambda D^TD)^{-1}x\]
-<pre class="prettyprint linenums">def hpfilter(self,X, lamb=1600):
-    X = np.asarray(X, float)
-    if X.ndim &gt; 1:
-    X = X.squeeze()
-    nobs = len(X)
-    I = sparse.eye(nobs,nobs)
-    offsets = np.array([0,1,2])
-    data = np.repeat([[1.],[-2.],[1.]], nobs, axis=1)
-    K = sparse.dia_matrix((data, offsets), shape=(nobs-2,nobs))
-    use_umfpack = True
-    self.trend = spsolve(I+lamb*K.T.dot(K), X,use_umfpack=use_umfpack)
-    cycle = X - self.trend
-</pre>
-
-<h2>Method</h2>
-
-This low-frequency momentum trading strategies are applied to daily data on seven kinds of exchange rates. We use five years history data before January 2011 for initial estimation of the trend model. Daily exchange rates for the period January 2011 to May 2017 is used for out of sample trading.
-<pre class="prettyprint linenums">def Initialize(self):
-    self.SetStartDate(2011,1,1)
-    self.SetEndDate(2017,5,30)
-    self.SetCash(100000)
-    self.numdays = 360*5  # set the length of training period
-    self.syl = self.AddSecurity(SecurityType.Forex, "EURUSD", Resolution.Daily).Symbol
-    self.n,self.m = 2, 1
-    self.trend = None
-    self.SetBenchmark(self.syl)
-    self.MA_rules = None
-    history = self.History(self.numdays,Resolution.Daily)
-    self.close = [slice[self.syl].Close for slice in history]</pre>
-
-<h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
-The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(\1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more” />
-
-If we just plot the curve for the first 100 days, we find that the smaller the λ, the more apparent the trend. The curve does not change too much as the λ  smaller than 100. Thus here we choose  λ=100 to extract the trend of daily price data. This trend is our low-frequency component.
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-less.jpg" alt="low-frequency-component-less”/>
-
-<p style="text-align: center;">Out-of-sample EUR/USD Trend Estimation</p>
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation” />
-<h3>Step 2: Setting up the Moving Average Rule</h3>
-
-Moving average (MA) rules are very commonly used to generate buy and sell signals from data on the spot exchange rate. The MA rule compares a short-run moving average of the current and lagged exchange rate with a long-run moving average.
-
-\[MA(m,n)=\frac{1}{m}\sum_{i=0}^{m-1}S_{t-i}-\frac{1}{n}\sum_{i=0}^{n-1}S_{t-i}\]
-
-
-For HP filter, the non-linear trend is estimated recursively as the paper did. The initial estimation was undertaken using 3 years history data before 2011. The estimation period is then rolled forward each day through the trading period from January 2011 to May 2017.
-
-<h3>Step 3: Generating the Trading Signals</h3>
-We generate buy and sell signals by applying an MA(1, 2) rule to the estimated low-frequency component. For MA(m,n), m must be 1 which denotes the current value of low-frequency component. n should be small since large n would generate large time lag, the judgment of turning points is not accurate.
-
-A buy signal is generated when the current day’s low-frequency trend is higher than the last day’s low-frequency trend and a sell signal is generated when it is lower.
-<pre class="prettyprint linenums">
-def OnData(self,data):
-    self.close.append(self.Portfolio[self.syl].Price)
-    self.hpfilter(self.close[-self.numdays:len(self.close)+1], 100)
-    self.MA_rules_today = (np.mean(self.trend[-self.m : len(self.trend)]) - np.mean(self.trend[-self.n : len(self.trend)]))
-    self.MA_rules_yesterday = (np.mean(self.trend[-self.m-1: len(self.trend)-1]) - np.mean(self.trend[-self.n-1 : len(self.trend)-1]))
-    holdings = self.Portfolio[self.syl].Quantity
-
-    if self.MA_rules_today &gt; 0 and self.MA_rules_yesterday &lt; 0:
-       self.SetHoldings(self.syl, 1)
-    elif self.MA_rules_today &lt; 0 and self.MA_rules_yesterday &gt; 0:
-       self.SetHoldings(self.syl, -1)
-</pre>
-<p style="text-align: center;">Trading Signals when λ=1600</p>
-
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-1600.jpg" alt="trading-signal 1600.jpg” />
-
-<p style="text-align: center;">Trading Signals when λ=100</p>
-<img src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-100.jpg" alt="trading-signal-100.jpg" />
-
-The above charts are the in-sample trading signals after applying MA rules on the low-frequency component. The trend curve is more smooth with larger λ. Thus when we applied MA rules, a less smooth trend will trigger more trading opportunities.
-<h2>Conclusion</h2>
-<table class="table qc-table">
-<thead>
-<tr>
-<th style="text-align: center;" colspan="8">Strategy Performance for different currencies in Forex</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Currency</td>
-<td>USDCAD</td>
-<td>EURUSD</td>
-<td> USDCHF</td>
-<td> EURGBP</td>
-<td>CADUSD</td>
-<td>USDNOK</td>
-<td>USDZAR</td>
-</tr>
-<tr>
-<td>Sharp Ratio</td>
-<td>0.361</td>
-<td>0.375</td>
-<td> 0.131</td>
-<td> 0.337</td>
-<td> 0.052</td>
-<td> 0.054</td>
-<td> 0.195</td>
-</tr>
-<tr>
-<td> Total Trades</td>
-<td> 14</td>
-<td> 11</td>
-<td> 12</td>
-<td> 11</td>
-<td> 6</td>
-<td> 16</td>
-<td> 9</td>
-</tr>
-<tr>
-<td> Annual Return</td>
-<td> 3.128%</td>
-<td> 3.272%</td>
-<td> 1.162%</td>
-<td> 2.689%</td>
-<td> 0.043%</td>
-<td> 0.093%</td>
-<td> 2.201%</td>
-</tr>
-<tr>
-<td> Max Drawdown</td>
-<td> 10.3%</td>
-<td> 8.3%</td>
-<td> 24.8%</td>
-<td> 17.6%</td>
-<td> 22.5%</td>
-<td> 30.3%</td>
-<td> 23%</td>
-</tr>
-</tbody>
-</table>
-
-The table reports the strategy performance statistics during six and a half years backtesting period. From the table we can see, most of them have the higher maximum drawdown. The number of total trades is small because we applied MA rules on the smoothed trend component.  As the author indicated in the paper, we still find that the performance of this strategy is very sensitive to the choice of lag parameters in MA rules and in a non-monotonic way.
-
-The strategy does not generate more stable profits in Forex market generally. That might because that the HP filter technique was designed to be viewed as a trend curve through the entire set of data. When we applied it in trading strategy, the entry of new data into the filter model can cause the trend line to change the trend through past data, makes it harder to identify the trend accurately.
-
-We also tried the kernel regression method in the paper. However, because the distribution parameter in the Python kernel regression package can not be adjusted. The appropriate trend component is not being extracted well from kernel regression. The smoothing effect of the regression is not good as expected. Thus here we choose not to move on to the trading part of kernel regression method.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5188517de3244425b56af3e717c90cb9'></script>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html
new file mode 100755
index 0000000..e273a9b
--- /dev/null
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+	In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em>Ayhan Yüksel[ref]<em>Factor </em>Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>[/ref]
+</p>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/09 Stock Selection Strategy Based on Fundamental Factors.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html
similarity index 56%
rename from 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/09 Stock Selection Strategy Based on Fundamental Factors.html
rename to 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html
index 43a412a..f40573f 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/09 Stock Selection Strategy Based on Fundamental Factors.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
@@ -1,10 +1,14 @@
-<h2>Abstract</h2>
-In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em>Ayhan Yüksel[ref]<em>Factor </em>Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>[/ref]
-<h2>Part 1. Factor Selection</h2>
-QuantConnect provides Morningstar fundamentals data for US Equities. Valuation Ratios is daily data. For others like operation ratios and financial statements data are available for multiple periods depending on the property. Please refer to <a href="https://www.quantconnect.com/data#fundamentals/usa/morningstar">Data Library</a> for detailed factors available.
+<p>
+	QuantConnect provides Morningstar fundamentals data for US Equities. Valuation Ratios is daily data. For others like operation ratios and financial statements data are available for multiple periods depending on the property. Please refer to <a href="https://www.quantconnect.com/data#fundamentals/usa/morningstar">Data Library</a> for detailed factors available.
+</p>
+<p>
+	The algorithm is designed to test the significance of one factor each time.
+</p>
 
-The algorithm is designed to test the significance of one factor each time.
-<pre class="prettyprint linenums">def Initialize(self):
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
 	self.SetStartDate(2005,01,01)  #Set Start Date
 	self.SetEndDate(2012,03,01)    #Set End Date
 	self.SetCash(50000)            #Set Strategy Cash
@@ -20,12 +24,20 @@ <h2>Part 1. Factor Selection</h2>
         # store the monthly returns of different portfolios in a dataframe
 	self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1))
         # schedule an event to fire at the first trading day of SPY
-	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))</pre>
+	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))
+</pre>
+</div>
 <h3>Step 1: Ranking the stocks by factor values</h3>
-1. First, we sort the stocks by daily dollar volume and take the top stocks with the highest dollar volumes as our candidates. There is a convenient way using our universe selection API. Universes are refreshed every day by default, but can be refreshed as often as required. This is controlled by the variable <code>UniverseSettings.Resolution</code>. You can refer to the <a href="https://www.quantconnect.com/docs#Universes">documentation</a> for more details.  Here we use <a href="https://www.quantconnect.com/docs#Scheduled-Events">Scheduled events API</a> to trigger code to run at the first trading day each month and use three flag variables to control the rebalancing of <code>CoarseSelection</code>, <code>FineSelection</code> and <code>Ondata</code> functions.
-
-Coarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property <code>x.HasFundamentalData</code> to exclude them from our candidate stocks pool.
-<pre class="prettyprint linenums"># sort the data by daily dollar volume and take the top entries
+<p>
+	1. First, we sort the stocks by daily dollar volume and take the top stocks with the highest dollar volumes as our candidates. There is a convenient way using our universe selection API. Universes are refreshed every day by default, but can be refreshed as often as required. This is controlled by the variable <code>UniverseSettings.Resolution</code>. You can refer to the <a href="https://www.quantconnect.com/docs#Universes">documentation</a> for more details.  Here we use <a href="https://www.quantconnect.com/docs#Scheduled-Events">Scheduled events API</a> to trigger code to run at the first trading day each month and use three flag variables to control the rebalancing of <code>CoarseSelection</code>, <code>FineSelection</code> and <code>Ondata</code> functions.
+</p>
+<p>
+	Coarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property <code>x.HasFundamentalData</code> to exclude them from our candidate stocks pool.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python"># sort the data by daily dollar volume and take the top entries
 def CoarseSelectionFunction(self, coarse):
     if self.flag1:
         CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
@@ -35,8 +47,13 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
     else:
         return []
 </pre>
+</div>
+
 2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
-<pre class="prettyprint linenums">def FineSelectionFunction(self, fine):
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def FineSelectionFunction(self, fine):
 	if self.flag1:
 		self.flag1 = 0
 		self.flag2 = 1
@@ -51,9 +68,16 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 	else:
 		return []
 </pre>
+</div>
 <h3>Step 2: Compute the monthly return of portfolios</h3>
-1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
-<pre class="prettyprint linenums">sorted_symbol = self.symbol
+<p>
+	1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">sorted_symbol = self.symbol
 self.AddEquity("SPY") # add benchmark
 for x in sorted_symbol:
 	self.AddEquity(x)
@@ -74,12 +98,17 @@ <h3>Step 2: Compute the monthly return of portfolios</h3>
 # the length of monthly_return list should be divisible by the number of portfolios
 monthly_return = monthly_return[:int(math.floor(len(monthly_return) / self.numOfPortfolio) * self.numOfPortfolio)]
 </pre>
-<div class="page" title="Page 15">
-<div class="layoutArea">
-<div class="column">
+</div>
 
-2. We divide the stocks into 5 portfolios and compute the average monthly returns of each portfolio. Then we add the monthly return of benchmark "SPY" at the last line of the data frame <code>df_return</code>.
-<pre class="prettyprint linenums">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
+
+<p>
+	2. We divide the stocks into 5 portfolios and compute the average monthly returns of each portfolio. Then we add the monthly return of benchmark "SPY" at the last line of the data frame <code>df_return</code>.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
 # calculate the average return of different portfolios
 port_avg_return = np.mean(reshape_return,axis=1).tolist()
 # add return of "SPY" as the benchmark  to the end of the return list
@@ -90,14 +119,22 @@ <h3>Step 2: Compute the monthly return of portfolios</h3>
 port_avg_return.append(benchmark_monthly_return)
 self.df_return[str(self.flag3)] = port_avg_return
 </pre>
+</div>
 <h3>Step 3: Generate the metrics to test the factor significance</h3>
-After getting the monthly returns of portfolios and the benchmark, we compute the average annual return and excess return over benchmark of each portfolio across the whole backtesting period. Then We generate three metrics to judge the significance of each factor.
-<ol>
+<p>
+	After getting the monthly returns of portfolios and the benchmark, we compute the average annual return and excess return over benchmark of each portfolio across the whole backtesting period. Then We generate three metrics to judge the significance of each factor.
+</p>
+
+<ul>
  	<li>The first metrics is the correlation between the portfolio' returns and their rank. The absolute value of the correlation coefficient should larger than 0.8.</li>
  	<li>If the return of the rank first portfolio larger than the portfolio at the bottom of the return rankings, we define it the win portfolio and the loss portfolio and vice versa. The win probability is the probability that the win portfolio return outperform the benchmark return. The loss probability is the probability that the loss portfolio return underperform the benchmark.  If the factor is significant, both loss and win probability should greater than 0.4.</li>
  	<li>The excess return of win portfolio should be greater than 0.25, while the excess return of loss portfolio should be lower than 0.05.</li>
-</ol>
-<pre class="prettyprint linenums">def calculate_criteria(self,df_port_return):
+</ul>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def calculate_criteria(self,df_port_return):
 	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
 	annual_return = (total_return+1)**(1./6)-1
 	excess_return = annual_return - np.array(annual_return)[-1]
@@ -125,6 +162,7 @@ <h3>Step 3: Generate the metrics to test the factor significance</h3>
 
 	return test_result</pre>
 </div>
+
 <table class="table qc-table">
 <thead>
 <tr>
@@ -196,17 +234,6 @@ <h3>Step 3: Generate the metrics to test the factor significance</h3>
 </table>
 </div>
 </div>
-We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.
-<h2>Part 2. Stock Selection</h2>
-<h3>Step 1: Rank the stocks by factor values</h3>
-First, we remove the  stocks without fundamental data or have zero factor value. For each pre-selected factor, we rank the stocks by those factor values. The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive.
-<h3>Step 2: Calculate equally weighted composite factor scores</h3>
-The second step is using different selected factor variables to calculate an equally weighted composite factor score for each stock.
-<ul>
- 	<li>First, according to the factor order, we place our universe stocks into 5 distinct quintile portfolios, named P1, P2, P3, P4 and P5. The ranking of portfolios sets out the preference of the factor model, i.e. the first portfolio (P1) corresponds to the “most preferred” stocks, while the fifth (P5) corresponds to the “least preferred” stocks.Suppose there are n stocks in total. Then the stocks fall into the first rank portfolio will have score p, the stocks fall into the second rank portfolio will get score p-1 and so on. Then we can get a score for every stock. We did the same calculation for each factor.</li>
- 	<li>Second, we calculate a “Composite Factor Score” by combining the six-factor scores and using an equal weighting scheme. Then we get composite factor score for each stock.</li>
- 	<li> Third, we then rank the stocks in our universe according to their Composite Factor Scores and choose the highest ranked 20 stocks to construct our portfolios at the beginning of each month.</li>
- 	<li>At the end of each month, we repeat the above steps to construct the new portfolio and adjust the holding stocks.</li>
-</ul>
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4f8946295bb7358518b35ecbd3d46f01'></script>
+<p>
+	We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.
+</p>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html
new file mode 100755
index 0000000..96ff5a9
--- /dev/null
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html	
@@ -0,0 +1,16 @@
+<h3>Step 1: Rank the stocks by factor values</h3>
+<p>
+	First, we remove the  stocks without fundamental data or have zero factor value. For each pre-selected factor, we rank the stocks by those factor values. The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive.
+</p>
+
+<h3>Step 2: Calculate equally weighted composite factor scores</h3>
+<p>
+	The second step is using different selected factor variables to calculate an equally weighted composite factor score for each stock.
+</p>
+
+<ul>
+ 	<li>First, according to the factor order, we place our universe stocks into 5 distinct quintile portfolios, named P1, P2, P3, P4 and P5. The ranking of portfolios sets out the preference of the factor model, i.e. the first portfolio (P1) corresponds to the “most preferred” stocks, while the fifth (P5) corresponds to the “least preferred” stocks.Suppose there are n stocks in total. Then the stocks fall into the first rank portfolio will have score p, the stocks fall into the second rank portfolio will get score p-1 and so on. Then we can get a score for every stock. We did the same calculation for each factor.</li>
+ 	<li>Second, we calculate a “Composite Factor Score” by combining the six-factor scores and using an equal weighting scheme. Then we get composite factor score for each stock.</li>
+ 	<li> Third, we then rank the stocks in our universe according to their Composite Factor Scores and choose the highest ranked 20 stocks to construct our portfolios at the beginning of each month.</li>
+ 	<li>At the end of each month, we repeat the above steps to construct the new portfolio and adjust the holding stocks.</li>
+</ul>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
new file mode 100755
index 0000000..906fafd
--- /dev/null
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4f8946295bb7358518b35ecbd3d46f01'></script>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
new file mode 100755
index 0000000..26e2357
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -0,0 +1,8 @@
+<p>
+  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". [ref]<strong>Groot, Wilma (2011)</strong>. Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>[/ref] The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
+</p>
+<p>
+  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
+  In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
+  In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
+</p>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
new file mode 100755
index 0000000..3a07010
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -0,0 +1,110 @@
+<p>
+  The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
+</p>
+
+<h3>Step 1: Initialization</h3>
+<p>
+  In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use <em>self._numOfWeeks</em> to count?the number of weeks that have passed since the start date, and <em>self.</em>_LastDay to indicate whether it is a new week. <em>self._ifWarmUp</em> is true when the <em>self._numOfWeeks</em> is 3, which means as long as next week's data come, we can make our investment decisions.??<em>self._stocks</em> is a list containing all the symbols of the 100 stocks that are taken into consideration. <em>self._values</em> is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def Initialize(self):
+        self.SetStartDate(2002, 1, 3)
+        self.SetEndDate(2016, 12, 1)
+        self.SetCash(1000000)
+
+        self.UniverseSettings.Resolution = Resolution.Daily
+        self.AddUniverse(self.CoarseSelectionFunction)
+        self._numberOfSymbols = 100
+        self._numberOfTradings = 0.1 * self._numberOfSymbols
+
+        self._numOfWeeks = 0
+        self._LastDay = -1
+        self._ifWarmUp = False
+
+        self._stocks = []
+        self._values = {}</pre>
+</div>
+<p>
+  Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">def CoarseSelectionFunction(self, coarse):
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+        top100 = sortedByDollarVolume[:self._numberOfSymbols]
+        list = List[Symbol]()
+        for x in top100:
+                list.Add(x.Symbol)
+        return list
+</pre>
+</div>
+<h3>Step 2:?Warm Up</h3>
+<p>
+  Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
+</p>
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">self._stocks = []
+self.uni_symbol = None
+symbols = self.UniverseManager.Keys
+for i in symbols:
+        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
+                self.uni_symbol = i
+        for i in self.UniverseManager[self.uni_symbol].Members:
+                self._stocks.append(i.Value.Symbol)
+                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
+
+</pre>
+</div>
+<p>
+  We get all the symbols of qualified stocks from UniverseManager and keep them in <em>self._stocks</em> which is a list. Then we create for each key in the dictionary <em>self._values</em> a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">for stock in self._stocks:
+        self._values[stock].append(self.Securities[stock].Price)</pre>
+</div>
+<h3>Step 3:?Weekly Rebalancing</h3>
+<p>
+  After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">returns = {}
+for stock in self._stocks:
+        newPrice = self.Securities[stock].Price
+        oldPrice = self._values[stock].pop(0)
+        self._values[stock].append(newPrice)
+        returns[stock] = newPrice/oldPrice
+</pre>
+</div>
+<p>
+  Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">newArr = [(v,k) for k,v in returns.items()]
+newArr.sort()
+for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
+        self.SetHoldings(stock, 0)
+for ret, stock in newArr[0:self._numberOfTradings]:
+        self.SetHoldings(stock, 0.5/self._numberOfTradings)
+for ret, stock in newArr[-self._numberOfTradings:]:
+        self.SetHoldings(stock, -0.5/self._numberOfTradings)
+</pre>
+</div>
+<p>
+  Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
+</p>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
new file mode 100755
index 0000000..0c2b46e
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html	
@@ -0,0 +1,5 @@
+<p>
+  In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead.
+  If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&amp;P 500 benchmark.
+  Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc.
+</p>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html
new file mode 100755
index 0000000..c381329
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75595e3ad41ca49ebcc6ab95aeaa92b8'></script>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html
deleted file mode 100755
index d09982f..0000000
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/10 Short-Term Reversal Strategy in Stocks.html	
+++ /dev/null
@@ -1,81 +0,0 @@
-<h2>Abstract</h2>
-This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". [ref]<strong>Groot, Wilma (2011)</strong>. Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>[/ref] The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
-
-One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
-
-In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
-
-In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
-<h2>Method</h2>
-The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
-<h3>Step 1:?Initialization</h3>
-In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use <em>self._numOfWeeks</em> to count?the number of weeks that have passed since the start date, and <em>self.</em>_LastDay to indicate whether it is a new week. <em>self._ifWarmUp</em> is true when the <em>self._numOfWeeks</em> is 3, which means as long as next week's data come, we can make our investment decisions.??<em>self._stocks</em> is a list containing all the symbols of the 100 stocks that are taken into consideration. <em>self._values</em> is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
-<pre class="prettyprint linenums">def Initialize(self):
-        self.SetStartDate(2002, 1, 3)
-        self.SetEndDate(2016, 12, 1)
-        self.SetCash(1000000)
-
-        self.UniverseSettings.Resolution = Resolution.Daily
-        self.AddUniverse(self.CoarseSelectionFunction)
-        self._numberOfSymbols = 100
-        self._numberOfTradings = 0.1 * self._numberOfSymbols
-
-        self._numOfWeeks = 0
-        self._LastDay = -1
-        self._ifWarmUp = False
-
-        self._stocks = []
-        self._values = {}</pre>
-Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
-<pre class="prettyprint linenums">def CoarseSelectionFunction(self, coarse):
-        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-        top100 = sortedByDollarVolume[:self._numberOfSymbols]
-        list = List[Symbol]()
-        for x in top100:
-                list.Add(x.Symbol)
-        return list
-</pre>
-<h3>Step 2:?Warm Up</h3>
-Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
-<pre class="prettyprint linenums">self._stocks = []
-self.uni_symbol = None
-symbols = self.UniverseManager.Keys
-for i in symbols:
-        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
-                self.uni_symbol = i
-        for i in self.UniverseManager[self.uni_symbol].Members:
-                self._stocks.append(i.Value.Symbol)
-                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
-
-</pre>
-We get all the symbols of qualified stocks from UniverseManager and keep them in <em>self._stocks</em> which is a list. Then we create for each key in the dictionary <em>self._values</em> a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
-<pre class="prettyprint linenums">for stock in self._stocks:
-        self._values[stock].append(self.Securities[stock].Price)</pre>
-<h3>Step 3:?Weekly Rebalancing</h3>
-After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
-<pre class="prettyprint linenums">returns = {}
-for stock in self._stocks:
-        newPrice = self.Securities[stock].Price
-        oldPrice = self._values[stock].pop(0)
-        self._values[stock].append(newPrice)
-        returns[stock] = newPrice/oldPrice
-</pre>
-Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
-<pre class="prettyprint linenums">newArr = [(v,k) for k,v in returns.items()]
-newArr.sort()
-for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
-        self.SetHoldings(stock, 0)
-for ret, stock in newArr[0:self._numberOfTradings]:
-        self.SetHoldings(stock, 0.5/self._numberOfTradings)
-for ret, stock in newArr[-self._numberOfTradings:]:
-        self.SetHoldings(stock, -0.5/self._numberOfTradings)
-</pre>
-Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
-<h2>Conclusion</h2>
-In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead.
-
-If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&amp;P 500 benchmark.
-
-Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc.
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75595e3ad41ca49ebcc6ab95aeaa92b8'></script>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html
new file mode 100755
index 0000000..0a28fcf
--- /dev/null
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html	
@@ -0,0 +1,26 @@
+<p>
+  In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm[ref]A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>[/ref]. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
+</p>
+<h3>Factors</h3>
+<p>
+  The paper strategy used three factors together to rank stocks: value, quality and momentum.
+</p>
+<p>
+  Value: The most commonly used measure for value is P/B ratio(price-to-book value). Intuitively, the stocks with high P/B ratio are likely to be overpriced, and those stocks are labeled as growth stock. On the other hand, the stocks with low P/B value are considered to be value stocks. Following this logic, we use book value per share as a measure for value in our algorithm: the stocks with high book value per share rank high.
+</p>
+<p>
+  Quality: Quality is a comprehensive factor. The paper used total profits over asset, gross margins, and free cash flow over assets. For simplicity, we used only operation margin as our quality factor. Here we assume that the companies with high operation margin are profitable, and their stocks are the quality ones.
+</p>
+<p>
+  Momentum: The paper strategy is quarterly rebalanced, so it used recent one-year return, three-month and returns around earning events as measures for momentum. While our algorithm is monthly rebalanced, we simply use recent monthly return as our momentum factor.
+</p>
+
+<h3>Ranking</h3>
+<p>
+  Ranking is the core process for stock selection. We first rank all the stocks according to each factor, then assign weights to each factor to get the final rank.
+  Specifically, in our algorithm we have 250 stocks in total. We rank them according to their book values per share, operation margins and one-month returns by descending order. For each stock, its index in each sorted list is its score on each factor. e.g. If stock A ranks 1st by value, 10th by quality and 30th by momentum, its scores on the value, quality and momentum are 1, 10 and 30 respectively.
+</p>
+
+<p>
+  The last step is to calculate the final score of each stocks. We use the same weight as the paper does: 40% on value, 40% on quality and 20% on momentum. In this way, stock A's final score is 1*0.4 + 10*0.4 + 30*0.2 = 10.4. Finally, we can rank all the stocks by their scores by <strong>ascending</strong> order. It worth attention that the stock with the <strong>lowest</strong> score is the best and it ranks 1st, and the one with the highest score is the worst.
+</p>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html
new file mode 100755
index 0000000..2778e5f
--- /dev/null
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html	
@@ -0,0 +1,106 @@
+<p>
+  In this implementation, the FineSelectionFunction would be the core part because we have to rank the stocks in this process. We also need a ScheduledEvent handler to rebalance the portfolio every month. We would introduce the process step by step.
+</p>
+
+<h3>CoarseSelectionFunction</h3>
+<p>
+  This function is a filter for the whole asset universe(around 8000 stocks). We select the top 250 with highest dollar volume to ensure liquidity. We also filter out the stocks without fundamental information or with a too low price(less than $5).
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    # if the rebalance flag is not 1, return null list to save time.
+    if self.reb != 1:
+        return return self.long + self.short
+
+    # make universe selection once a month
+    # drop stocks which have no fundamental data or have too low prices
+    selected = [x for x in coarse if (x.HasFundamentalData)
+                and (float(x.Price) > 5)]
+
+    sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
+    top = sortedByDollarVolume[:self.num_coarse]
+    return [i.Symbol for i in top]
+</pre>
+</div>
+
+<h3>FineSelectionFunction</h3>
+<p>
+  Here is the core function. The process is that we make three sorted list to store the stocks, and then use a dictionary to store the score information. For the dictionary, the keys are Symbols and the values are their scores. Finally we sort the dictionary to get the final rank. we store the top 20 stocks to long in the list self.long and the bottom 20 stocks to short in the list self.short.
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+  def FineSelectionFunction(self, fine):
+      # return the same symbol list if it's not time to rebalance
+      if self.reb != 1:
+          return self.long+self.short
+      self.reb = 0
+
+  # drop stocks which don't have the information we need.
+  # you can try replacing those factor with your own factors here
+
+      filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
+                                      and x.ValuationRatios.PriceChange1M
+                                      and x.ValuationRatios.BookValuePerShare]
+
+      self.Log('remained to select %d'%(len(filtered_fine)))
+
+      # rank stocks by three factor.
+      sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+      sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
+      sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+
+      stock_dict = {}
+
+      # assign a score to each stock, you can also change the rule of scoring here.
+      for i,ele in enumerate(sortedByfactor1):
+          rank1 = i
+          rank2 = sortedByfactor2.index(ele)
+          rank3 = sortedByfactor3.index(ele)
+          score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
+          stock_dict[ele] = score
+
+      # sort the stocks by their scores
+      self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
+      sorted_symbol = [x[0] for x in self.sorted_stock]
+
+      # sotre the top stocks into the long_list and the bottom ones into the short_list
+      self.long = [x.Symbol for x in sorted_symbol[:self.num_fine]]
+      self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]]
+
+      return self.long+self.short</pre>
+</div>
+<h3>Rebalance</h3>
+<p>
+  Our portfolio is rebalanced monthly, so first of all we should write a ScheduledEvent handler in the Initialize function:
+</p>
+
+<div class="section-example-container">
+<pre class="csharp">
+</pre>
+<pre class="python">
+def Initialize(self):
+# Use SPY as a benchmark for market open
+    self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))
+Our rebalanced method is straightforward: We first liquidate the stocks that are no longer in the long/short list, and then assign equal weight to the stocks we are going to long or short.
+def rebalance(self):
+# if this month the stock are not going to be long/short, liquidate it.
+    long_short_list = self.long + self.short
+    for i in self.Portfolio.Values:
+        if (i.Invested) and (i.Symbol not in long_short_list):
+            self.Liquidate(i.Symbol)
+
+    # Assign each stock equally. Always hold 10% cash to avoid margin call
+    for i in self.long:
+        self.SetHoldings(i,0.9/self.num_fine)
+
+    for i in self.short:
+        self.SetHoldings(i,-0.9/self.num_fine)
+</pre>
+</div>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/03 Summary.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/03 Summary.html
new file mode 100644
index 0000000..7fa12d8
--- /dev/null
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/03 Summary.html	
@@ -0,0 +1,4 @@
+<p>
+  The strategy was backtest at a 60-year timespan. It's persistent, systematic and intuitive. Although our version is very different from the paper one, the logic and intuition behind are the same. It has a better performance than the paper strategy because we added short positions.
+  For further development, we can try to rank a large number of stocks, and do some portfolio optimization instead of holding stocks equally.
+</p>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
new file mode 100755
index 0000000..0da6e08
--- /dev/null
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
@@ -0,0 +1 @@
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>
diff --git a/04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html b/04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html
deleted file mode 100755
index c2cb0be..0000000
--- a/04 Strategy Library/11 Fundamental Factor Long:Short Strategy/11 Fundamental Factor Long Short Strategy.html	
+++ /dev/null
@@ -1,109 +0,0 @@
-<h2>Introduction</h2>
-In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm[ref]A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>[/ref]. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
-
-<h3>Factors</h3>
-The paper strategy used three factors together to rank stocks: value, quality and momentum.
-
-Value: The most commonly used measure for value is P/B ratio(price-to-book value). Intuitively, the stocks with high P/B ratio are likely to be overpriced, and those stocks are labeled as growth stock. On the other hand, the stocks with low P/B value are considered to be value stocks. Following this logic, we use book value per share as a measure for value in our algorithm: the stocks with high book value per share rank high.
-
-Quality: Quality is a comprehensive factor. The paper used total profits over asset, gross margins, and free cash flow over assets. For simplicity, we used only operation margin as our quality factor. Here we assume that the companies with high operation margin are profitable, and their stocks are the quality ones.
-
-Momentum: The paper strategy is quarterly rebalanced, so it used recent one-year return, three-month and returns around earning events as measures for momentum. While our algorithm is monthly rebalanced, we simply use recent monthly return as our momentum factor.
-
-<h3>Ranking</h3>
-Ranking is the core process for stock selection. We first rank all the stocks according to each factor, then assign weights to each factor to get the final rank.
-
-Specifically, in our algorithm we have 250 stocks in total. We rank them according to their book values per share, operation margins and one-month returns by descending order. For each stock, its index in each sorted list is its score on each factor. e.g. If stock A ranks 1st by value, 10th by quality and 30th by momentum, its scores on the value, quality and momentum are 1, 10 and 30 respectively.
-
-The last step is to calculate the final score of each stocks. We use the same weight as the paper does: 40% on value, 40% on quality and 20% on momentum. In this way, stock A's final score is 1*0.4 + 10*0.4 + 30*0.2 = 10.4. Finally, we can rank all the stocks by their scores by <strong>ascending</strong> order. It worth attention that the stock with the <strong>lowest</strong> score is the best and it ranks 1st, and the one with the highest score is the worst.
-
-<h2>Implementation</h2>
-In this implementation, the FineSelectionFunction would be the core part because we have to rank the stocks in this process. We also need a ScheduledEvent handler to rebalance the portfolio every month. We would introduce the process step by step.
-
-<h3>CoarseSelectionFunction</h3>
-This function is a filter for the whole asset universe(around 8000 stocks). We select the top 250 with highest dollar volume to ensure liquidity. We also filter out the stocks without fundamental information or with a too low price(less than $5).
-<pre class="prettyprint linenums">
-def CoarseSelectionFunction(self, coarse):
-    # if the rebalance flag is not 1, return null list to save time.
-    if self.reb != 1:
-        return return self.long + self.short
-
-    # make universe selection once a month
-    # drop stocks which have no fundamental data or have too low prices
-    selected = [x for x in coarse if (x.HasFundamentalData)
-                and (float(x.Price) > 5)]
-
-    sortedByDollarVolume = sorted(selected, key=lambda x: x.DollarVolume, reverse=True)
-    top = sortedByDollarVolume[:self.num_coarse]
-    return [i.Symbol for i in top]
-</pre>
-
-<h3>FineSelectionFunction</h3>
-Here is the core function. The process is that we make three sorted list to store the stocks, and then use a dictionary to store the score information. For the dictionary, the keys are Symbols and the values are their scores. Finally we sort the dictionary to get the final rank. we store the top 20 stocks to long in the list self.long and the bottom 20 stocks to short in the list self.short.
-<pre class="prettyprint linenums">
-  def FineSelectionFunction(self, fine):
-      # return the same symbol list if it's not time to rebalance
-      if self.reb != 1:
-          return self.long+self.short
-      self.reb = 0
-
-  # drop stocks which don't have the information we need.
-  # you can try replacing those factor with your own factors here
-
-      filtered_fine = [x for x in fine if x.OperationRatios.OperationMargin.Value
-                                      and x.ValuationRatios.PriceChange1M
-                                      and x.ValuationRatios.BookValuePerShare]
-
-      self.Log('remained to select %d'%(len(filtered_fine)))
-
-      # rank stocks by three factor.
-      sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-      sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=True)
-      sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-
-      stock_dict = {}
-
-      # assign a score to each stock, you can also change the rule of scoring here.
-      for i,ele in enumerate(sortedByfactor1):
-          rank1 = i
-          rank2 = sortedByfactor2.index(ele)
-          rank3 = sortedByfactor3.index(ele)
-          score = sum([rank1*0.2,rank2*0.4,rank3*0.4])
-          stock_dict[ele] = score
-
-      # sort the stocks by their scores
-      self.sorted_stock = sorted(stock_dict.items(), key=lambda d:d[1],reverse=False)
-      sorted_symbol = [x[0] for x in self.sorted_stock]
-
-      # sotre the top stocks into the long_list and the bottom ones into the short_list
-      self.long = [x.Symbol for x in sorted_symbol[:self.num_fine]]
-      self.short = [x.Symbol for x in sorted_symbol[-self.num_fine:]]
-
-      return self.long+self.short</pre>
-<h3>Rebalance</h3>
-Our portfolio is rebalanced monthly, so first of all we should write a ScheduledEvent handler in the Initialize function:
-<pre class="prettyprint linenums">
-def Initialize(self):
-# Use SPY as a benchmark for market open
-    self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy,5), Action(self.rebalance))
-Our rebalanced method is straightforward: We first liquidate the stocks that are no longer in the long/short list, and then assign equal weight to the stocks we are going to long or short.
-def rebalance(self):
-# if this month the stock are not going to be long/short, liquidate it.
-    long_short_list = self.long + self.short
-    for i in self.Portfolio.Values:
-        if (i.Invested) and (i.Symbol not in long_short_list):
-            self.Liquidate(i.Symbol)
-
-    # Assign each stock equally. Always hold 10% cash to avoid margin call
-    for i in self.long:
-        self.SetHoldings(i,0.9/self.num_fine)
-
-    for i in self.short:
-        self.SetHoldings(i,-0.9/self.num_fine)
-</pre>
-<h2>Summary</h2>
-The strategy was backtest at a 60-year timespan. It's persistent, systematic and intuitive. Although our version is very different from the paper one, the logic and intuition behind are the same. It has a better performance than the paper strategy because we added short positions.
-For further development, we can try to rank a large number of stocks, and do some portfolio optimization instead of holding stocks equally.
-
-<h2>Algorithm</h2>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html
index 4fa940d..1fb2a12 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/02 Risk Aversion.html	
@@ -1,6 +1,4 @@
-<h2>Risk Aversion</h2>
-
-<p>
+<p>
   In portfolio theory, the riskiness of an asset is often measured by the variance (or standard deviation) of its returns. Risk-averse investors do not want their wealth to fluctuate wildly.
 </p>
 
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html
index 5c6b67c..4b98610 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
@@ -8,5 +8,7 @@
 
 <p>
   RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
-  <blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote> CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that: <blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote> Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+  "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>.
+  A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref]
+  who pointed out that: "A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns." Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
 </p>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index 83a3c24..f87661f 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1 +1 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8310800c78c7179b80062b374ad116c6'></script>
+<script src='https://www.quantconnect.com/terminal/backtest.js?sid=b986091b5e1822b3d74f1231bf44eb2c'></script>
diff --git a/06 Introduction to Options[]/06 The Greek Letters/07 Summary.html b/06 Introduction to Options[]/06 The Greek Letters/08 Summary.html
similarity index 100%
rename from 06 Introduction to Options[]/06 The Greek Letters/07 Summary.html
rename to 06 Introduction to Options[]/06 The Greek Letters/08 Summary.html

From 3b4917b11e9f4e02229f0ddd692d873a9841bf50 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 8 Jun 2018 17:01:17 -0400
Subject: [PATCH 048/753] Updated tutorials

---
 .../01 Overview.html                          |  1 -
 .../01 Strategy Library.php                   |  2 +-
 .../01 Abstract.html                          |  1 -
 .../04 Part II - Cointegration Method.html    |  4 +-
 .../05 Summary.html                           |  7 +-
 .../01 Abstract.html                          |  1 -
 .../01 Abstract.html                          | 86 ------------------
 .../02 Method.html                            | 19 ----
 .../03 Conclusion.html                        | 88 -------------------
 .../04 Algorithm.html                         | 88 -------------------
 .../02 Introduction.html                      |  5 +-
 .../03 Method.html                            |  2 +-
 .../04 Result.html                            |  2 -
 .../02 Introduction.html                      |  2 +-
 .../03 Method.html                            |  2 +-
 .../03 Stock Selection.html                   |  1 +
 .../02 Method.html                            |  4 +-
 17 files changed, 16 insertions(+), 299 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
index eb54cf2..ca80763 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -1,4 +1,3 @@
-<h2>Overview</h2>
 <p>
   Tracking and managing orders is an important part of an algorithmic trading strategy. Intelligent order management encourages discipline and a deep understanding of your algorithm. Through the QuantConnect API you can get order fields, update their values and cancel pending orders. This can be useful for lowering trading costs and improving order fills.
   When you place a trade you receive an <code>OrderTicket</code> for you to access the order. This allows you to safely (asynchronously) update and cancel the order while in live trading. In live trading you cannot assume order updates are processed successfully as the brokerage may have already filled the trade.
diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6c36a5b..2ec333f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -58,7 +58,7 @@
         'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action'
     ],
     [
-        'name' => 'Fundamental Factor Long/Short Strategy',
+        'name' => 'Fundamental Factor Long Short Strategy',
         'link' => 'strategy-library/fundamental-factor-long-short-strategy',
         'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors'
     ],
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html
index c862647..619bd8b 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
@@ -1,4 +1,3 @@
-<h2>Abstract</h2>
 <p>
   In this research, We investigate two pairs trading methods and compare the result. Pairs trading involves in investigating the dependence structure between two highly correlated assets. With the assumption that mean reversion will occur, long or short positions are entered in the opposite direction when there is a price divergence. Typically the asset price distribution is modeled by a  Gaussian distribution of return series but the joint normal distribution may fail to catch some key features of the dependence of stock pairs' price like tail dependence. We investigate using copula theory to identify these trading opportunities.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 775afe1..eba7754 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -1,5 +1,5 @@
 <p>
-  For the cointegration pairs trading method, we choose the same ETF pair "GLD" &amp; "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
+  For the cointegration pairs trading method, we choose the same ETF pair "GLD" and "DGL".  There is no need to choose a copula function so there is only a 12 month rolling formation period. The trading period is 5 years from January 2011 to  May 2017.
 </p>
 
 <h3>Step 1: Generate the Spread Series</h3>
@@ -44,5 +44,5 @@ <h3>Step 2: Compute the Threshold</h3>
 
 <h3>Step 3: Set up the Trading Signals</h3>
 <p>
-  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread&gt;mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
+  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
index d156309..08033bf 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -1,3 +1,7 @@
+<p>
+  Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
+</p>
+
 <table class="table qc-table">
 <thead>
 <tr>
@@ -26,9 +30,6 @@
 </tbody>
 </table>
 
-<p>
-  Ultimately pairs trading intends to capture the price divergence of two correlated assets through mean reversion. Our results demonstrate that the copula approach for pairs trading is superior to the conventional cointegration method because it is based on the probability of the dependence structure, vs cointegration which relies on simple linear regression variance from normal pricing. We found through testing the performance of the copula method less sensitive to the starting parameters. Because the cointegration method relies on standard distribution and the ETF pairs had low volatility there were few trading opportunities.
-</p>
 <p>
   Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
 </p>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html
index d0bf5d3..2340509 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html	
@@ -1,4 +1,3 @@
-<h2>Abstract</h2>
 <p>
   In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
 </p>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html
index 57c0667..6de45fe 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html	
@@ -1,92 +1,6 @@
-<h2>Abstract</h2>
 <p>
   The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
 </p>
 <p>
   In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
 </p>
-<h2>Method</h2>
-<h3>Step 1 : Initialization of algorithm</h3>
-<p>
-  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def Initialize(self):
-  self.SetStartDate(2004, 1, 1)
-  self.SetEndDate(2017, 8, 30)
-  self.SetCash(100000)
-  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
-  self.syl = equity.Symbol
-</pre>
-</div>
-<p>
-  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
-</pre>
-</div>
-<p>
-  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
-</p>
-
-<h3>Step 2:  Implementation of algorithm</h3>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
-
-<p>
-  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
-  self.high = history.loc[self.syl.Value]['high']
-  self.low = history.loc[self.syl.Value]['low']
-  self.close = history.loc[self.syl.Value]['close']
-</pre>
-</div>
-
-<h3>Step 3: Trading Implementation</h3>
-<p>
-  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
-</p>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
-
-<p>
-  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
-        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
-            if holdings &gt;= 0:
-                self.SetHoldings(self.syl, 1)
-            else:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, 1)
-        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, -1)
-            else:
-                self.SetHoldings(self.syl, -1)</pre>
-</div>
-<h2>Conclusion</h2>
-<p>
-  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
-</p>
-
-<h2>Algorithm</h2>
-<p>
-  Backtest result for ETF: SPY from 2004 to 2017
-</p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html
index 57c0667..83e84e2 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
@@ -1,11 +1,3 @@
-<h2>Abstract</h2>
-<p>
-  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
-</p>
-<p>
-  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
-</p>
-<h2>Method</h2>
 <h3>Step 1 : Initialization of algorithm</h3>
 <p>
   Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
@@ -79,14 +71,3 @@ <h3>Step 3: Trading Implementation</h3>
             else:
                 self.SetHoldings(self.syl, -1)</pre>
 </div>
-<h2>Conclusion</h2>
-<p>
-  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
-</p>
-
-<h2>Algorithm</h2>
-<p>
-  Backtest result for ETF: SPY from 2004 to 2017
-</p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html
index 57c0667..b50deaa 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html	
@@ -1,92 +1,4 @@
-<h2>Abstract</h2>
-<p>
-  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
-</p>
-<p>
-  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
-</p>
-<h2>Method</h2>
-<h3>Step 1 : Initialization of algorithm</h3>
-<p>
-  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def Initialize(self):
-  self.SetStartDate(2004, 1, 1)
-  self.SetEndDate(2017, 8, 30)
-  self.SetCash(100000)
-  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
-  self.syl = equity.Symbol
-</pre>
-</div>
-<p>
-  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
-</pre>
-</div>
-<p>
-  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
-</p>
-
-<h3>Step 2:  Implementation of algorithm</h3>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
-
-<p>
-  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
-  self.high = history.loc[self.syl.Value]['high']
-  self.low = history.loc[self.syl.Value]['low']
-  self.close = history.loc[self.syl.Value]['close']
-</pre>
-</div>
-
-<h3>Step 3: Trading Implementation</h3>
-<p>
-  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
-</p>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
-
-<p>
-  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
-        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
-            if holdings &gt;= 0:
-                self.SetHoldings(self.syl, 1)
-            else:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, 1)
-        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, -1)
-            else:
-                self.SetHoldings(self.syl, -1)</pre>
-</div>
-<h2>Conclusion</h2>
 <p>
   We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
   This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
 </p>
-
-<h2>Algorithm</h2>
-<p>
-  Backtest result for ETF: SPY from 2004 to 2017
-</p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index 57c0667..e5e22ef 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -1,91 +1,3 @@
-<h2>Abstract</h2>
-<p>
-  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
-</p>
-<p>
-  In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
-</p>
-<h2>Method</h2>
-<h3>Step 1 : Initialization of algorithm</h3>
-<p>
-  Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
-</p>
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">def Initialize(self):
-  self.SetStartDate(2004, 1, 1)
-  self.SetEndDate(2017, 8, 30)
-  self.SetCash(100000)
-  equity = self.AddSecurity(SecurityType.Equity, "SPY", Resolution.Hour)
-  self.syl = equity.Symbol
-</pre>
-</div>
-<p>
-  Although this is an intraday strategy, we still want to test the strategy within a long period not just within one day. The scheduling methods sets up an event to fire at a specific date or time and will execute the scheduled event function.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
-</pre>
-</div>
-<p>
-  Here Schedule.On(DateRules, TimeRules, Action()) will trigger every trading day for our stock, at market open. Events are scheduled using date and time rules. Date rules specify on what dates and event will fire. Time rules specify at what time on those dates the event will fire. Inside of the scheduled function, we can pull the recent historical data and current open price each trading day.
-</p>
-
-<h3>Step 2:  Implementation of algorithm</h3>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
-
-<p>
-  In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
-  self.high = history.loc[self.syl.Value]['high']
-  self.low = history.loc[self.syl.Value]['low']
-  self.close = history.loc[self.syl.Value]['close']
-</pre>
-</div>
-
-<h3>Step 3: Trading Implementation</h3>
-<p>
-  The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
-</p>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
-
-<p>
-  This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
-</p>
-
-<div class="section-example-container">
-<pre class="csharp">
-</pre>
-<pre class="python"> holdings = self.Portfolio[self.syl].Quantity
-        if self.Portfolio[self.syl].Price &gt;= self.selltrig:
-            if holdings &gt;= 0:
-                self.SetHoldings(self.syl, 1)
-            else:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, 1)
-        elif self.Portfolio[self.syl].Price &lt; self.selltrig: if holdings &gt;= 0:
-                self.Liquidate(self.syl)
-                self.SetHoldings(self.syl, -1)
-            else:
-                self.SetHoldings(self.syl, -1)</pre>
-</div>
-<h2>Conclusion</h2>
-<p>
-  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
-</p>
-
-<h2>Algorithm</h2>
 <p>
   Backtest result for ETF: SPY from 2004 to 2017
 </p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html
index 478f092..9d79c03 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html	
@@ -14,7 +14,7 @@ <h3>Correlation Approach</h3>
   Consider two stocks A and B, a correlation coefficient between the stocks was a statistic that provide a measure of how the two stocks A and B were associated. The correlation coefficient \(\rho\) of stock A and stock B was obtained by
 </p>
 
-\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}}\]
+\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}\]
 
 <p>
   Where \(\bar{A}\) and \(\bar{B}\) are the mean prices of stock A and stock B respectively, N denoted a trading data range. \(\rho\) is in the range of [-1,1]. The more positive \(\rho\) is, the more positive the association of stock A and stock B is.
@@ -64,7 +64,7 @@ <h3>Cointegration Verification(optional reading part)</h3>
   Testing for the presence of the unit root in the regression residual using the ADF test was given by
 </p>
 
-\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \DeltaZ_{t-i} + \mu_t\]
+\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \Delta Z_{t-i} + \mu_t\]
 
 <p>
   where \(\alpha\) is a constant, \(\beta\) is the coefficient on a time trend, p is the lag of order of the autoregressive process, \(\mu_t\) is an error term and serially uncorrelated.
@@ -96,6 +96,7 @@ <h3>Pairs Trading Strategy</h3>
 <p>
   In order to select potential stocks for pairs trading, the two-stage correlation and cointegration approach was used. The first step is to identify potential stock pairs from the same sector, where the stock pairs are selected with correlation coefficient of at least 0.9 using the correlation approach. The second step is to check the the cointegration of the pairs passed the correlation test. If the test value of cointegration is equal or less than -3.34, which is the critical value at a 95% confidence lever, the null hypothesis \(H_0 : \gamma = 0\) is rejected, thus the residual \(\epsilon\) is stationary, and the pair passed the cointegration test. The third step is to rank all of the stock pairs that passed the two-stage test according to their cointegration test values. The smaller the cointegration test value is, the higher rank the stock pair is assigned to. Financial selection of the stock pairs from the top rank is used for pairs trading.
 </p>
+<p>
   The final step of the strategy is to define trading rules. To open a pairs trading, the regression residual \(\epsilon_t\) must cross over and down the positive \(\sigma\) standard deviation above the mean or cross down and over the negative \(\sigma\) standard deviation below the mean. If the residual is positive, we short stock B and long stock A; if the residual is negative, we short Stock A and long Stock B. When the regression residual (\epsilon_t\) returned to a certain level, the pairs trading is closed. Further more, in order to prevent the loss of too much on a single pairs trading, a stop-loss is used to close the pairs when the residual hit \(4\epsilon\) positive or negative standard deviation.
 </p>
 
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html
index 9c41f6d..58aed95 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html	
@@ -190,7 +190,7 @@ <h4>Updating Pairs</h4>
 </div>
 <h4>Opening Pairs Trading</h4>
 <p>
-  This is most complex part. For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
+  For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
   When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
 </p>
 
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html
index 502ebd0..170c057 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html	
@@ -1,8 +1,6 @@
 <p>
   We used 10-minute resolution data to backtest the strategy from Jan 2013 to Dec 2016. To demonstrate the in sample training results, we randomly selected a training period that from 2016-09-07 to 2013-11-30.
 </p>
-
-<h3>Training Result</h3>
 <p>
   The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
 </p>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html
index 0257fd3..c4046ea 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
@@ -2,7 +2,7 @@
   Hodrick-Prescott Filter decomposes a time series\(y_t\) into two components: the cyclical part(which is short-term) and the trend part(which is long term).
 </p>
 
-\[y_t=\x _t +c_t\]
+\[y_t=x _t +c_t\]
 
 <p>
   The filter is the solution to the following optimization problem for \(x_t\)
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html
index c222398..6c20aa5 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
@@ -22,7 +22,7 @@
 
 <h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
 <p>
-  The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(\1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
+  The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
 </p>
 
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more” />
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html
index 96ff5a9..62400c9 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 Stock Selection.html	
@@ -1,3 +1,4 @@
+Next we will select the stocks.
 <h3>Step 1: Rank the stocks by factor values</h3>
 <p>
 	First, we remove the  stocks without fundamental data or have zero factor value. For each pre-selected factor, we rank the stocks by those factor values. The order is descending if the factor correlation is negative, it is ascending if the factor correlation is positive.
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index 3a07010..3fdccdc 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -43,7 +43,7 @@ <h3>Step 1: Initialization</h3>
         return list
 </pre>
 </div>
-<h3>Step 2:?Warm Up</h3>
+<h3>Step 2:Warm Up</h3>
 <p>
   Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
 </p>
@@ -72,7 +72,7 @@ <h3>Step 2:?Warm Up</h3>
 <pre class="python">for stock in self._stocks:
         self._values[stock].append(self.Securities[stock].Price)</pre>
 </div>
-<h3>Step 3:?Weekly Rebalancing</h3>
+<h3>Step 3:Weekly Rebalancing</h3>
 <p>
   After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
 </p>

From 620f362161c23a7341e096c4731f19676df719ff Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 8 Jun 2018 18:45:50 -0400
Subject: [PATCH 049/753] update tutorials

---
 .../01 Consolidating Data to Build Bars.html              | 2 +-
 ...{03 Scheduled Events.html => 02 Scheduled Events.html} | 2 +-
 .../01 Desktop Charting with LEAN.html                    | 2 +-
 .../02 How do I use the API File Provider.html            | 2 +-
 .../04 Results.html                                       | 4 ++--
 .../03 Part I - Copula Method.html                        | 3 +--
 .../05 Dual Thrust Trading Algorithm/02 Method.html       | 8 +++++---
 .../04 Result.html                                        | 6 +++---
 .../03 Method.html                                        | 8 ++++----
 9 files changed, 19 insertions(+), 18 deletions(-)
 rename 01 API Tutorials/02 Scheduled Events/{03 Scheduled Events.html => 02 Scheduled Events.html} (93%)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 22f61df..3850405 100755
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -96,4 +96,4 @@
 <p>
   The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.
 </p>
-https://www.youtube.com/watch?v=bbZy__qd1aA
+<iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
diff --git a/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html b/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html
similarity index 93%
rename from 01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html
rename to 01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html
index 66bfb20..8c909a9 100755
--- a/01 API Tutorials/02 Scheduled Events/03 Scheduled Events.html	
+++ b/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html	
@@ -78,4 +78,4 @@
 <p>
   For a demonstration see the tutorial video below:
 </p>
-https://youtu.be/SUu4MKlm93I
+<iframe width="560" height="315" src="https://www.youtube.com/embed/SUu4MKlm93I" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
diff --git a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
index e107831..40ca098 100755
--- a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
+++ b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
@@ -27,4 +27,4 @@
   In the tutorial video below we demonstrate this feature on LEAN:
 </p>
 
-https://www.youtube.com/watch?v=m6llfznP4d4
+<iframe width="560" height="315" src="https://www.youtube.com/embed/m6llfznP4d4" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
diff --git a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
index ab0922a..9176435 100755
--- a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
+++ b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
@@ -24,4 +24,4 @@
   Check out the video below where we guide you step by step through how to use the API Data Provider.
 </p>
 
-https://www.youtube.com/watch?v=uyHp_jyeSpA
+<iframe width="560" height="315" src="https://www.youtube.com/embed/uyHp_jyeSpA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html
index bf0363c..0ec705b 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html	
@@ -2,7 +2,7 @@
   The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1" />
 
 <p>
   From these results we can say the limited sample size does not impair the feasibility of this model. The t-stats of the coefficients are -4.074 and 1.417 for the reversal factor and momentum factor respectively. The p-value of the reversal factor is very small which means this factor has a very high significance level.
@@ -13,7 +13,7 @@ <h3>Backtest Sensitivity Results</h3>
   We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2" />
 <p>
   The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
index 7a5b1c7..88ec2f4 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
@@ -49,7 +49,7 @@ <h4><strong>2) Filter the trading pair with statistical correlation</strong></h4
 </tr>
 </tbody>
 </table>
-</div>
+
 
 <p>
   We can get these coefficients in Python using functions from the stats library in SciPy. The correlations have been calculated using daily log stock price returns during the training formation period. We found the 3 correlation techniques give the paired ETFs the same correlation coefficient ranking. The Pearson correlation assumes that both variables should be normally distributed. Thus here we use Kendall rank as the correlation measure and choose the pairs with the highest Kendall rank correlation to implement the pairs trading. We get the daily historical closing price of our ETFs pair by using the History function and converting the prices to a log return series. Let \(P_x\) and \(P_y\) denote the historical stock price series for stock x and stock y. The log returns for the ETFs pair are given by:
@@ -123,7 +123,6 @@ <h3>Step 3: Estimating Copula Parameters</h3>
 </tr>
 </tbody>
 </table>
-</div>
 
 <div class="section-example-container">
 <pre class="csharp">
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html
index 83e84e2..db77e12 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
@@ -2,6 +2,7 @@ <h3>Step 1 : Initialization of algorithm</h3>
 <p>
   Firstly we need to initialize the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must be initialized.
 </p>
+
 <div class="section-example-container">
 <pre class="csharp">
 </pre>
@@ -28,7 +29,7 @@ <h3>Step 1 : Initialization of algorithm</h3>
 </p>
 
 <h3>Step 2:  Implementation of algorithm</h3>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-price-range.png" alt="Tutorial05-dual-thrust-price-range" />
 
 <p>
   In order to calculate the range, each trading day we need the close, high and low price data over the most recent N days. In addition, the open price of the current day is required in order to generate the signals. QuantConnect provides History(Symbol, TimeSpan, Resolution) method to get the history data like open, close, high and low for all configured securities over the requested N-days time span. Then the range is calculated by \[range = max(HH-LC, HC-LL)\]. In this implementation we choose N=4. It is less than one week and the range would reflect the recent price change.
@@ -49,7 +50,7 @@ <h3>Step 3: Trading Implementation</h3>
   The long signal is calculated by \[cap = open + K_1 \times Range\]. The short signal is calculated by \[floor = open – K_2 \times  Range\] where K1 and K2 are the parameters. When K1 is greater than K2, it is much easier to trigger the long signal and vice versa. For demonstration, here we choose K1 = K2 = 0.5. In live trading, we can still use historical data to optimize those parameters or adjust the parameters according to the market trend. K1 should be small than k2 if you are bullish on the market and k1 should be much bigger if you are bearish on the market.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial05-dual-thrust-trading.png" alt="Tutorial05-dual-thrust-trading" />
 
 <p>
   This system is a reversal system, so if the investor holds a short position when the price breaks the cap line, the short margin should be liquidated first before opening a long position. If the investor holds a long position when the price breaks the floor line, the long margin should be liquidated first before opening a new short position.
@@ -69,5 +70,6 @@ <h3>Step 3: Trading Implementation</h3>
                 self.Liquidate(self.syl)
                 self.SetHoldings(self.syl, -1)
             else:
-                self.SetHoldings(self.syl, -1)</pre>
+                self.SetHoldings(self.syl, -1)
+</pre>
 </div>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html
index 170c057..b94900f 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 Result.html	
@@ -5,16 +5,16 @@
   The following table demonstrates the top 10 selected pairs in the training period mentioned above. We can see that the pairs with the highest correlation coefficient doesn't not necessarily has the best ADF test value. We made the rank by ADF test value because it's more robust.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1" />
 
 <p>
   The upper part of the following chart plots the stock prices of pair ING vs TCB. The lower part plots by how many times standard deviation the residual deviate from its mean. There are 5 trading opportunities if we set the opening threshold to be 2.32.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2" />
 
 <p>
   The following chart is the density plot of the residual error. From the shape we can see the error is approximately normal distributed.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3" />
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html
index 6c20aa5..5678468 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
@@ -25,17 +25,17 @@ <h3>Step 1: Calibrating the Filter Smoothing Parameter λ</h3>
   The Ravn–Uhlig rule is commonly used to set the smoothing parameter λ in HP filter and must be greater than 0. It is adjusted by the changing frequency of observations and must be greater than 0. Hodrick and Prescott (1997) recommended setting λ to 1,600 for quarterly data. The Ravn–Uhlig rule sets  \(\lambda = 1600p^4\) , where p is the number of periods per quarter. As for our daily exchange rate data, we should have set λ to be \(1600\times (30 \times 4)^4\). But when we use this value as the λ, the curve is almost a straight line since the trend becomes smoother as λ → ∞. In order to avoid excessive smoothing, we gradually decrease the λ and draw the smoothing curve. Below is the chart of EURUSD daily price from the year 2010 to 2011. t100 denotes the trend component after filter with the parameter λ=100.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-more.jpg" alt="low-frequency-component-more" />
 
 <p>
   If we just plot the curve for the first 100 days, we find that the smaller the λ, the more apparent the trend. The curve does not change too much as the λ  smaller than 100. Thus here we choose  λ=100 to extract the trend of daily price data. This trend is our low-frequency component.
 </p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-less.jpg" alt="low-frequency-component-less”/>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-low-frequency-component-less.jpg" alt="low-frequency-component-less"/>
 
 <p style="text-align: center;">Out-of-sample EUR/USD Trend Estimation</p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-out-of-sample-trend-estimation.jpg" alt="out-of-sample-trend-estimation" />
 <h3>Step 2: Setting up the Moving Average Rule</h3>
 
 <p>
@@ -77,7 +77,7 @@ <h3>Step 3: Generating the Trading Signals</h3>
 </div>
 <p style="text-align: center;">Trading Signals when λ=1600</p>
 
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-1600.jpg" alt="trading-signal 1600.jpg” />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-1600.jpg" alt="trading-signal 1600.jpg" />
 
 <p style="text-align: center;">Trading Signals when λ=100</p>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-trading-signal-100.jpg" alt="trading-signal-100.jpg" />

From 35cf6b0ad2d4370ca29dc0f2d5800016ee082cba Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 11 Jun 2018 16:19:44 -0400
Subject: [PATCH 050/753] add references

---
 .../06 References.html                        |  8 +++++
 .../01 Abstract.html                          |  4 +--
 .../02 Introduction.html                      |  2 +-
 .../07 References.html                        | 18 +++++++++++
 .../01 Abstract.html                          |  2 +-
 .../02 Framework of Copula.html               |  6 ++--
 .../03 Part I - Copula Method.html            |  6 ++--
 .../05 Summary.html                           |  2 +-
 .../07 References.html                        | 32 +++++++++++++++++++
 .../01 Abstract.html                          |  2 +-
 .../02 Method.html                            |  2 +-
 .../05 References.html                        |  9 ++++++
 .../01 Abstract.html                          |  2 +-
 .../05 References.html                        |  5 +++
 .../01 Abstract.html                          |  2 +-
 .../06 References.html                        |  5 +++
 .../01 Abstract.html                          |  2 +-
 .../02 Introduction.html                      |  6 ++--
 .../07 References.html                        | 16 ++++++++++
 .../01 Abstract.html                          |  2 +-
 .../02 Introduction.html                      |  5 +--
 .../06 References.html                        |  8 +++++
 .../01 Abstract.html                          |  2 +-
 .../05 References.html                        |  5 +++
 .../01 Abstract.html                          |  2 +-
 .../05 References.html                        |  5 +++
 .../01 Introduction.html                      |  2 +-
 .../05 References.html                        |  5 +++
 .../02 Python Implementation.html             |  2 +-
 .../06 References.html                        |  5 +++
 .../02 Fama-French Three-Factor Model.html    |  2 +-
 .../04 Factor Returns.html                    |  2 +-
 .../05 Other Factors.html                     |  4 +--
 .../07 Algorithm.html                         |  2 +-
 .../08 References.html                        | 17 ++++++++++
 .../02 Option Payoff.html                     |  2 +-
 .../04 Synthetic Positions.html               |  2 +-
 .../07 References.html                        |  9 ++++++
 .../02 Brownian motion.html                   |  2 +-
 ...ic Processes and Monte Carlo Method.ipynb} |  0
 .../07 References.html                        |  5 +++
 ... Pricing Black Scholes Merton Model.ipynb} |  0
 ...tters.ipynb => 06 The Greek Letters.ipynb} |  0
 .../06 The Greek Letters/08 Summary.html      |  1 -
 .../03 Implied Volatility.html                |  2 +-
 ...l Volatility and Implied Volatility.ipynb} |  0
 .../08 References.html                        |  5 +++
 .../02 Motivation.html                        |  2 +-
 .../03 Local Volatility.html                  |  2 +-
 .../04 Stochastic Volatility.html             |  9 +++---
 .../06 References.html                        | 17 ++++++++++
 ...olatility and Stochastic Volatility.ipynb} |  0
 52 files changed, 216 insertions(+), 43 deletions(-)
 create mode 100755 04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 References.html
 create mode 100644 04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/07 References.html
 create mode 100644 04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 References.html
 create mode 100644 04 Strategy Library/04 The Dynamic Breakout II Strategy/05 References.html
 create mode 100644 04 Strategy Library/05 Dual Thrust Trading Algorithm/05 References.html
 create mode 100644 04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 References.html
 create mode 100644 04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 References.html
 create mode 100644 04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/06 References.html
 create mode 100644 04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
 create mode 100644 04 Strategy Library/11 Fundamental Factor Long Short Strategy/05 References.html
 create mode 100644 05 Introduction to Financial Python[]/10 Multiple Linear Regression/06 References.html
 create mode 100644 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html
 create mode 100644 06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/07 References.html
 rename 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/{03 Stochastic Processes and Monte Carlo Method.ipynb => 04 Stochastic Processes and Monte Carlo Method.ipynb} (100%)
 create mode 100644 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/07 References.html
 rename 06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/{04 Option Pricing Black Scholes Merton Model.ipynb => 05 Option Pricing Black Scholes Merton Model.ipynb} (100%)
 rename 06 Introduction to Options[]/06 The Greek Letters/{05 The Greek Letters.ipynb => 06 The Greek Letters.ipynb} (100%)
 rename 06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/{06 Historical Volatility and Implied Volatility.ipynb => 07 Historical Volatility and Implied Volatility.ipynb} (100%)
 create mode 100644 06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/08 References.html
 create mode 100644 06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/06 References.html
 rename 06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/{07 Local Volatility and Stochastic Volatility.ipynb => 08 Local Volatility and Stochastic Volatility.ipynb} (100%)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 References.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 References.html
new file mode 100755
index 0000000..f6f75d0
--- /dev/null
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a>
+  </li>
+  <li>
+    <a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>
+  </li>
+</ol>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html
index 33b5a0a..7850891 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/01 Abstract.html	
@@ -1,7 +1,7 @@
 <p>
-  In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research [ref]<strong>Alina F. Serban,</strong> Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>[/ref] which was based on research in the equity market by Ronald J. Balvers and Yangru Wu [ref]<strong>Ronald J. Balvers, Yangru Wu,</strong> Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
+  In this tutorial we build a strategy combining momentum and mean reversion for the foreign exchange markets from Alina F. Serban's research which was based on research in the equity market by Ronald J. Balvers and Yangru Wu. Serban creates a momentum factor using returns of the last 3 months, and a mean reversion factor as a deviation from the mean price. Using these factors we use regression to predict the returns of the coming month. We apply the strategy from Serban's paper and update the mean reversion factor for to improve its significance level.
 </p>
 
 <p>
-  In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity, UIP [ref]<strong>Investopedia</strong>, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>[/ref]). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang [ref]<strong>Chiang, T., Jiang, C., 1995. </strong>Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>[/ref]. We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
+  In theory when trading foreign exchange the expected return accrued in each currency should be the same when adjusted for exchange rates (uncovered interest parity). This suggests the markets should predominately be mean reverting, however in practice we see short term momentum trends and long term mean reversion. This was phenomenon was first noticed by Chiang and Jiang. We tested the theory on EURUSD, GBPUSD, USDCAD and USDJPY and re-balanced monthly. The model significance level and coefficients are close to those in paper, but the returns and Sharpe Ratios obtained are not as good as what the paper claimed. The algorithm achieved a fairly stable annual return of 11%, 0.8 Sharpe Ratio and 11% drawdown.
 </p>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html
index 145740e..4a19969 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/02 Introduction.html	
@@ -30,7 +30,7 @@ <h4>Interest Parity Conditions</h4>
 
 <h4>Model and Parameter Estimation</h4>
 <p>
-  Fama and French and Summers [ref]<strong>Fama, E., 1984. </strong>Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.[/ref] constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
+  Fama and French and Summers constructed a simple model for stock price that is the sum of a random walk and a stationary component - they represent the natural log of the stock price with <strong>x</strong>. The stationary component represents the temporary swings in stock price (characterized by coefficient \(\delta \)), the parameter \(\mu \) captures the random walk drift component and a coefficient accounts for the momentum effect \(\rho \). Balvers and Wu construct the log of stock prices as:
 </p>
 
 \[x^{i}_t = (1 - \delta ^{i})\mu ^{i} + \delta ^{i}x^{i}_{t-1} + \sum_{j=1}^{J}\rho ^{i}(x^{i}_{t-1} - x^{i}_{t-j-1}) + \epsilon ^{u}_t\]
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/07 References.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/07 References.html
new file mode 100644
index 0000000..d1f2696
--- /dev/null
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/07 References.html	
@@ -0,0 +1,18 @@
+<ol>
+  <li>
+    Alina F. Serban, Combining mean reversion and momentum trading strategies in foreign exchange markets <a href="http://isiarticles.com/bundles/Article/pre/pdf/14874.pdf">Online Copy</a>
+  </li>
+  <li>
+  <li>
+    Investopedia, Uncovered Interest Rate Parity. <a href="http://www.investopedia.com/terms/u/uncoveredinterestrateparity.asp">Online Copy</a>
+  </li>
+  <li>
+    Ronald J. Balvers, Yangru Wu, Momentum and mean reversion across national equity markets <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>
+  </li>
+  <li>
+    Chiang, T., Jiang, C., 1995. Foreign exchange returns over short and long horizons. International Review of Economics and Finance 4, 267–282. <a href="https://pdfs.semanticscholar.org/c98c/533334b6962d06b44ae7a796b5fcffbe2fe4.pdf">Online Copy</a>
+  </li>
+  <li>
+    Fama, E., 1984. Forward and spot exchange rates. Journal of Monetary Economics <a href="http://eml.berkeley.edu/~craine/EconH195/Fall_13/webpage/Fama_Forward%20Discount.pdf">Online Copy</a> 14, 319–338.
+  </li>  
+<ol>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html
index 619bd8b..4937cd0 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
@@ -3,5 +3,5 @@
 </p>
 
 <p>
-  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a> [ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref] </em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012)[ref]Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012.[/ref]. The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
+  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a></em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012). The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html
index 62595c4..27a28d6 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 Framework of Copula.html	
@@ -6,7 +6,7 @@ <h3>1. Definition</h3>
 \[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
 
 <p>
-  Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.[ref]Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>[/ref]
+  Copulas function contains all the dependency characteristics of the marginal distributions and will better describe the linear and non-linear relationship between variables, using probability. They allow the marginal distributions to be modeled independently from each other, and no assumption on the joint behavior of the marginals is required.
 </p>
 
 <h3>2. Bivariate Copulas</h3>
@@ -21,7 +21,7 @@ <h3>2. Bivariate Copulas</h3>
 <h3>3. Archimedean Copulas</h3>
 <p>
   There are many copula functions that enable us to describe dependence structures between variables, other than the Gaussian assumption. Here we will focus three of these; the <em>Clayton</em>, <em>Gumbel</em> and <em>Frank</em> copula formulas from the Archimedean class.
-  Archimedean copulas[ref]Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>[/ref] are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\) [ref]LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.[/ref].
+  Archimedean copulas are based on the Laplace transforms φ of univariate distribution functions. They are constructed by a particular generator function \(\phi\).
 </p>
 
 \[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
@@ -61,7 +61,7 @@ <h3>3. Archimedean Copulas</h3>
 
 
 <p>
-  Genest and MacKay (1986) [ref]Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283[/ref] proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
+  Genest and MacKay proved that the relation between the copula generator function and Kendall rank correlation tau in the bivariate case can be given by:
 </p>
 
 \[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
index 88ec2f4..290bb7f 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
@@ -8,7 +8,7 @@
 
 <h3>Step 1: Selecting the Paired Stocks</h3>
 <p>
-  The general method of pair selection is based on both fundamental and statistical analysis.[ref]<strong>Jean Folger</strong>. Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>[/ref]
+  The general method of pair selection is based on both fundamental and statistical analysis.
 </p>
 
 <h4><strong>1) Assemble a list of potentially related pairs</strong></h4>
@@ -225,7 +225,7 @@ <h3>Step 4: Selecting the Best Fitting Copula</h3>
 
 <h3>Step 5: Generating the Trading Signals</h3>
 <p>
-  The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I(2013)[ref]Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a> [/ref], the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
+  The copula functions include all the information about the dependence structures of two return series. According to Stander Y, Marais D, Botha I. in Trading strategies with copulas, the fitted copula is used to derive the confidence bands for the conditional marginal distribution function of \(C(v\mid u)\) and \(C(u\mid v)\), that is the mispricing indexes. When the market observations fall outside the confidence band, it is an indication that pairs trading opportunity is available. Here we choose 95%  as the upper confidence band, 5% as the lower confidence band as indicated in the paper. The confidence level was selected based on a back-test analysis in the paper that shows using 95% seems to lead to appropriate trading opportunities to be identified.
 </p>
 
 <p>
@@ -237,7 +237,7 @@ <h3>Step 5: Generating the Trading Signals</h3>
 \[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
 
 <p>
-  For further mathematical proof, please refer to Xie W, Wu Y(2013)[ref]Xie W, Wu Y. Copula-based pairs trading strategy[C]//Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.[/ref] The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
+  For further mathematical proof, please refer to Xie W, Wu Y. Copula-based pairs trading strategy. The conditional probability formulas of bivariate copulas can be derived by taking partial derivatives of copula functions shown in Table 1. The results are as follows:
 </p>
 <p>
 Gumbel Copula
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
index 08033bf..cd9ff46 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -31,5 +31,5 @@
 </table>
 
 <p>
-  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions[ref]Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.[/ref].
+  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 References.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 References.html
new file mode 100644
index 0000000..1d7ae89
--- /dev/null
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 References.html	
@@ -0,0 +1,32 @@
+<ol>
+  <li>
+    Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a>
+  </li>
+  <li>
+    Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012"> Online Copy</a>
+  </li>
+  <li>
+    Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>
+  </li>
+  <li>
+    Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>
+  </li>
+  <li>
+    Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>
+  </li>
+  <li>
+    LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.
+  </li>
+  <li>
+    Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283
+  </li>
+  <li>
+    Jean Folger, Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>
+  </li>
+  <li>
+    Xie W, Wu Y. Copula-based pairs trading strategy[C] Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.
+  </li>
+  <li>
+    Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.
+  </li>
+ </ol>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html
index 2340509..516dc8f 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/01 Abstract.html	
@@ -1,5 +1,5 @@
 <p>
-  In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a> [ref]<strong>George Pruitt, John R. Hill, Michael Russak (September 2012)</strong>. Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>[/ref].
+  In this tutorial we will take a close look at the Dynamic Breakout II strategy based on the book <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html">Building Winning Trading Systems</a>.
 </p>
 <p>
   First we decide the look-back period based on the change rate of volatility, then we make trading decisions based on the highest high and lowest low from the look back period as well as a Bollinger Bands indicator. It is an auto adaptive trading system that can adjust its buy and sell rules depending on the performance of these rules in the past. In addition to Forex markets it is widely used in future and equity markets. You can refer to <a href="https://www.youtube.com/watch?v=MxGVqSvLqDQ">this video</a> to learn more about dynamic break out II.
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html
index be7cda6..9db7090 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html	
@@ -1,7 +1,7 @@
 <h3>Step 1: Determine the look back periods</h3>
 
 <p>
-  The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.[ref]<strong>Robert C. Miner(October 20, 2008)</strong>. High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>[/ref]   <b> </b>
+  The lookback period is the number of bars back from the most recent bar that the price or indicator looks at to make the momentum calculations.
   To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
 </p>
 <div class="section-example-container">
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/05 References.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/05 References.html
new file mode 100644
index 0000000..789d29f
--- /dev/null
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/05 References.html	
@@ -0,0 +1,9 @@
+<ol>
+  <li>
+    George Pruitt, John R. Hill, Michael Russak (September 2012). Building Winning Trading Systems, page 126, <a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118168275.html"> Online Copy</a>
+  </li>
+  <li>
+    Robert C. Miner(October 20, 2008). High Probability Trading Strategies: Entry to Exit Tactics for the Forex, Futures, and Stock Markets, page 37, <a href="https://www.amazon.com/High-Probability-Trading-Strategies-Tactics/dp/0470181664"> Online Copy</a>
+  </li>
+  <li>
+</ol>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html
index 6de45fe..70f3f5b 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/01 Abstract.html	
@@ -1,5 +1,5 @@
 <p>
-  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a>  [ref]<strong>Gang Wei(May 2012)</strong>. Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>[/ref] is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
+  The <a href="http://www.finance-directory.org/investing/site/6395">Dual Thrust trading algorithm</a> is a famous strategy developed by Michael Chalek. It has been commonly used in futures, forex and equity markets. The idea of Dual Thrust is similar to a typical breakout system, however dual thrust uses the historical price to construct update the look back period - theoretically making it more stable in any given period.
 </p>
 <p>
   In this tutorial we give a brief introduction to the strategy and show how to implement this algorithm on QuantConnect. After pulling in the historical price of the chosen stock, the range is calculated based on the close, high and low over the most recent N-days.  A position is opened when the market moves a certain range from the opening price. We tested the strategy on individual stocks under two market states: a trending market and range bound market.  The results suggest this momentum trading system works better in trending market but will trigger some fake buy and sell signals in much more volatile market. Under the range bound market, we can adjust the parameters to get better return. As a comparison of individual stocks, we also implemented the strategy on SPY. The result suggested that the strategy beat the market.
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 References.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 References.html
new file mode 100644
index 0000000..ef5c6ab
--- /dev/null
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/05 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Gang Wei(May 2012). Dual Thrust Intraday Strategy, <a href="http://www.xcf.cn/zjfxs/yjzs/jrgc/201210/P020121004348795101761.pdfl"> Online Copy</a>
+  </li>
+</ol>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html
index bb9ab9c..1a715ed 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/01 Abstract.html	
@@ -1,5 +1,5 @@
 <p>
-  In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". [ref]<strong>Gerben, Driesprong (2007)</strong>. Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>[/ref] by?by Gerben, Ben and Benjamin (2007).
+  In this tutorial we use regression to predict the return from the stock market and compare it to the short-term U.S. T-bill rate. It is based on the paper?"<em><strong>Striking Oil: Another Puzzle?</strong></em>". by Gerben, Ben and Benjamin (2007).
   If the predicted return is larger than the risk-free rate, the portfolio is fully invested in stock; if the predicted return is lower than the risk-free rate, the portfolio is invested in short-term U.S T-bills. The backtesting period starts in 1980 and is divided into an in-sample period where regression analysis is made and an out?of sample period where the regression result is embedded "statically" into the strategy.
 </p>
 <p>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 References.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 References.html
new file mode 100644
index 0000000..2ce7aae
--- /dev/null
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/06 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Gerben, Driesprong (2007). Striking Oil: Another Puzzle? page 1, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract-id=460500"> Online Copy</a>
+  </li>
+</ol>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html
index db56f65..83bdc3c 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 Abstract.html	
@@ -1,5 +1,5 @@
 <p>
-  In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work.[ref]<strong>George J. Miao </strong>High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>[/ref]We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
+  In this tutorial we implement a high frequency and dynamic pairs trading strategy based on market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. This strategy is based on George J. Miao's work. We applied this trading strategy to the U.S. bank sector stocks, backtested this strategy with 10-minute stock data from 2012 to 2013. Our trading strategy yields a compounding annual return up to 29.4% and a 0.968 sharpe ratio.
 </p>
 
 <p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html
index 9d79c03..85d9f93 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 Introduction.html	
@@ -1,6 +1,6 @@
 <p>
-  High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012[ref]<strong>Cartea &amp; Penalva, 2012 </strong>Where is the value in high frequency trading?<strong><a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a></strong>[/ref]).
-  Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006[ref]<strong>Gatev, Goetzmann, &amp; Rouwenhorst, 2006 </strong>Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>[/ref]) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
+  High Frequency Trading(HFT) is a type of quantitative trading characterized by short holding period and the use of sophisticated computer method to trade securities rapidly. It aims to capture small profit on every short-term trade.(Cartea &amp; Penalva, 2012).
+  Statistical arbitrage is a situation where there is a statistical mispricing of one or more assets based on the expected values of these assets. When a profit situation takes place from pricing inefficiencies between securities, traders can identify the statistical arbitrage situation through mathematical models. Statistical arbitrage depends heavily on the ability of market prices to return to a historical or predicted mean. <strong>The Law of One Price(LOP)</strong> lays the foundation for this assumption. LOP states that two stocks with the same payoff in every state of nature must have the same current value (Gatev, Goetzmann, &amp; Rouwenhorst, 2006) Thus, two stock prices spread between close substitute assets should have a stable, long-term equilibrium price over time.
 </p>
 
 <h3>Data Description</h3>
@@ -26,7 +26,7 @@ <h3>Correlation Approach</h3>
 
 <h3>Cointegration Approach</h3>
 <p>
-  The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger[ref]<strong>Engle and Granger</strong> Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236">Online Copy</a></strong>[/ref]Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
+  The Cointegration concept, an innovative mathematical model in economics developed by Nobel laureates Engle and Granger. Cointegration states that, in some instances, despite two given non-stationary time series, a specific linear combination of the two time series is actually stationary. In other word, the two time series move together in a lockstep pattern.
 </p>
 
 <p>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 References.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 References.html
new file mode 100644
index 0000000..b50463a
--- /dev/null
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 References.html	
@@ -0,0 +1,16 @@
+<ol>
+  
+  <li>
+    George J. Miao High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>
+  </li>
+  <li>
+    Cartea &amp; Penalva, 2012, Where is the value in high frequency trading? <a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a>
+  </li>
+  <li>
+    Gatev, Goetzmann, &amp; Rouwenhorst, 2006, Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>
+  </li>
+  <li>
+    Engle and Granger, Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236"><strong>Online Copy</strong></a>
+  </li>
+
+</ol>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html
index 355fc18..e664e2f 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/01 Abstract.html	
@@ -2,6 +2,6 @@
   Trend estimation is a family of methods to detect and predict tendencies and trends in price series just using the history information. Moving average is a commonly used trend following trading tool. Lots of momentum trading strategies in the Forex market are based on the moving average rule, in which signals are triggered if the close is above or below the moving average. But MA has the time lag, therefore can't be used to predict the turning points of market price changes.
 </p>
 <p>
-  In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009) [ref]Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>[/ref].
+  In this tutorial, I developed a trend following strategy which is proposed in the paper Harris R D F, Yilmaz F(2009).
   This strategy exploits short-term momentum in the non-linear trend component of the exchange rate which is generated by Hodrick-Prescott Filter (rather than the exchange rate itself) and uses the MA(1, 2) rule to measure this momentum. The strategy was tested on seven kinds of exchange rates and the results shows less robustness and the performance is sensitive to the change of model parameters.
 </p>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html
index c4046ea..807b390 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
@@ -9,8 +9,9 @@
 </p>
 
 \[\min _{x_t}\left[\sum_{t=1}^n(y_t-x_t)^2+\lambda\sum_{t=2}^{n-1}[(x_{t+1}-x_t)-(x_{t}-x_{t-1})^2] \right]\]
-
-[ref]Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>[/ref]The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
+<p>
+  The second term is the discrete derivative of the trend xt which characterizes the smoothness of the curve. We can rewrite the above formula in vector form:
+</p>
 
 \[\min_{\bf x}{\parallel {\bf{y}}-{\bf{x}}\parallel}_2^2+\lambda {\parallel D\bf x\parallel}_2^2\]
 
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/06 References.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/06 References.html
new file mode 100644
index 0000000..3574dd3
--- /dev/null
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/06 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    Harris R D F, Yilmaz F. A momentum trading strategy based on the low-frequency component of the exchange rate[J]. Journal of Banking &amp; Finance, 2009, 33(9): 1575-1585. <a href="http://www.sciencedirect.com/science/article/pii/S0378426609000521">online copy</a>
+  </li>
+  <li>
+    Dao T L. Momentum Strategies with L1 Filter[J]. Browser Download This Paper, 2014. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2780280">online copy</a>
+  </li>  
+</ol>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html
index e273a9b..371a2b4 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-	In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em>Ayhan Yüksel[ref]<em>Factor </em>Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>[/ref]
+	In recent years, factor investing gained significant popularity among global institutional investors. In this tutorial, we first developed a factor selection model to test if factors have the ability to differentiate potential winners and losers in the stock market. Then we use those  preselected factors to implement the factor ranking stock selection algorithm based on <em>Factor Based Stock Selection Model for Turkish Equities, 2015, </em> Ayhan Yüksel.
 </p>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html
new file mode 100644
index 0000000..145c7bf
--- /dev/null
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>
+  </li>
+</ol>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
index 26e2357..19ef597 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -1,5 +1,5 @@
 <p>
-  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". [ref]<strong>Groot, Wilma (2011)</strong>. Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>[/ref] The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
+  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
 </p>
 <p>
   One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
new file mode 100644
index 0000000..6c1e761
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Groot, Wilma (2011). Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>
+  </li>
+</ol>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html
index 0a28fcf..f3424da 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/01 Introduction.html	
@@ -1,5 +1,5 @@
 <p>
-  In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm[ref]A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>[/ref]. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
+  In this tutorial we implemented a long/short equity strategy based on fundamental factors. The idea comes from AQR white book: A New Core Equity Paradigm. The original version is a long only strategy. We developed it into a long/short version. The paper strategy used some fundamental data as measures of value, quality and momentum, and then ranked all the stocks in the universe according to the factors. The strategy only long the stocks ranking at the top, but our algorithm would at the same time short the stocks ranking at the bottom. This strategy consistently beats the market and has solid economic intuition.
 </p>
 <h3>Factors</h3>
 <p>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/05 References.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/05 References.html
new file mode 100644
index 0000000..e199f94
--- /dev/null
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/05 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    A New Core Equity Paradigm<a href="https://www.aqr.com/library/aqr-publications/a-new-core-equity-paradigm"><strong>Online Copy</strong></a>
+  </li>
+</ol>
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html
index 1331c85..bc4556e 100755
--- a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html	
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html	
@@ -226,7 +226,7 @@
 </p>
 
 <p>
-  The data needed are publicly available on French's website.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] We have saved a copy for convenience. The following code fetches the data.
+  The data needed are publicly available on French's website. We have saved a copy for convenience. The following code fetches the data.
 </p>
 
 <div class="section-example-container">
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/06 References.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/06 References.html
new file mode 100644
index 0000000..7ac3bff
--- /dev/null
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/06 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>
+  </li>
+</ol>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html
index 30e131f..21d0568 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html	
@@ -10,7 +10,7 @@
   where
 </p>
 <ul>
-    <li>MKT is the excess return of the market.[ref]<a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>[/ref] It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
+    <li>MKT is the excess return of the market. It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
     <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
     <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
 </ul>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html
index 4b98610..9aa59ac 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
@@ -7,7 +7,7 @@
 </ul>
 
 <p>
-  RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
+  RMW was proposed by Novy-Marx (2013) who wrote that:
   "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>.
   A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref]
   who pointed out that: "A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns." Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html
index 5c6b67c..510c436 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html	
@@ -7,6 +7,6 @@
 </ul>
 
 <p>
-  RMW was proposed by Novy-Marx (2013)[ref]<strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>[/ref] who wrote that:
-  <blockquote>"Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies."</blockquote> CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>. A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref] who pointed out that: <blockquote>"A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns."</blockquote> Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+  RMW was proposed by Novy-Marx (2013) who wrote that:
+  "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014) who pointed out that: A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns. Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
 </p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html
index b3f9981..5246b7d 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
@@ -1,5 +1,5 @@
 <p>
-  Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.[ref]<strong>AQR (2013)</strong>. A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>[/ref]
+  Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.
   The paper recommends picking stocks by their value, quality (profitability) and momentum.
   The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
   The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html
new file mode 100644
index 0000000..b915797
--- /dev/null
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html	
@@ -0,0 +1,17 @@
+<ol>
+  <li>
+    <a href="http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html" target="_blank">mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/f-f_factors.html</a>
+  </li>
+  <li>
+    AQR (2013). A New Core Equity Paradigm: Using Value, Momentum, and Quality to Outperform Markets. Retrieved from <a href="https://www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf" target="_blank">www.aqr.com/~/media/files/papers/aqr-a-new-core-equity-paradigm.pdf</a>
+  </li>
+  <li>
+    Robert Novy-Marx (2013). The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>
+  </li>
+  <li>
+    Fama, E F; French, K R (2015). A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>
+  </li>
+  <li>
+    <strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>
+  </li> 
+</ol>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html
index 3325802..ab58f5f 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html	
@@ -2,7 +2,7 @@
   Consider Google(NASDAQ: GOOG) is trading at $910 now and you are bullish on this stock and expect it to rise over the next three months. The minimum capital requirement to buy 100 shares of GOOG is $90000 but you only have limited capital for stock investors. You could buy 1 call option contract written on GOOG which expires after three months with a $900 strike price. If GOOG rises to $950 in three months, you can get  ($950 - $900) *100 = $5000 by paying just a small amount of premium instead of a full cost of shares. Even if the share price of GOOG falls below $900, you lose only the premium. This is one of the biggest benefits of trading options and is also called the financial leverage. Without borrowing the capital, you can control a larger number of shares by investing in options other than purchasing the shares themselves.
 </p>
 <p>
-  From the above example, [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref] The investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
+  From the above example, the investor will buy a European call if he has a bullish view on the market and believes the underlying stock price will be above the strike price at the expiry date. He will make money when the underlying price goes up and lose when it goes down. The payoff of a call option at T is
 </P>
 \[Call_{payoff}=max[0,S_T-K]\]
 <p>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html
index c1e040c..85ce65c 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/04 Synthetic Positions.html	
@@ -4,7 +4,7 @@
 <p>
   Through the put-call parity, we can find that there is a synthetic equivalent for all of the basic positions in underlying assets and its corresponding options. In other words, the risk profile(the possible profit or loss) of any position can be exactly duplicated with a complex combination of the other basic positions. These equivalents are synthetic underlying and synthetic options.
 </p>
-[ref]Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017, <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>[/ref]The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
+The rule for creating synthetics is that the strike price and expiration date, of the calls and puts, must be identical. For creating synthetics, with both the underlying stock and its options, the number of shares of stock must equal the number of shares represented by the options.
 <p>
   There are many arbitrage strategies based on the idea of the synthetic position. Here we demonstrate two of the most common strategies: the conversion and the reversal.
 </p>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/07 References.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/07 References.html
new file mode 100644
index 0000000..76391e6
--- /dev/null
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/07 References.html	
@@ -0,0 +1,9 @@
+<ol>
+  <li>
+    Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.
+  </li>
+  <li>
+    Put-Call Parity and Arbitrage Opportunity, Jim Graham, February 6, 2017, <a href="http://www.investopedia.com/articles/optioninvestor/05/011905.asp">Online Copy</a>
+  </li>
+<ol>
+  
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index dd95ff9..36ca9d9 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -54,7 +54,7 @@
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-wiener-process.png" alt="wiener process"/>
 
 <p>
-  [ref]Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.[/ref]In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
+  In ordinary calculus,\(\text{d}x=a\ \text{d}t\) is used to indicate that\(\Delta x=a\Delta t\) as \( \Delta t\rightarrow0\). We use the similar notation here. The mean change per unit time for a stochastic process is known as the drift rate and the variance per unit time is known as the variance rate. We can write the derivative of Wiener process over time t in this form: \(dW_t\).A standard Wiener process has a drift rate (i.e. average change per unit time) of 0 and a variance rate of 1 per unit time. If we extend the concept of Wiener process to a generalized Wiener process in the form: \(\text d\ x_t=a\ \text d t+b\ \text dW_t\). The drift rate and the variance rate can be set equal to any chosen constant. If we write it as approximate discrete form as
 </p>
 \[\Delta x=x_{t+\Delta t}-x_t=a\Delta t+b\epsilon\sqrt{\Delta t}\]
 <p>
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.ipynb b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Stochastic Processes and Monte Carlo Method.ipynb
similarity index 100%
rename from 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic Processes and Monte Carlo Method.ipynb
rename to 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Stochastic Processes and Monte Carlo Method.ipynb
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/07 References.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/07 References.html
new file mode 100644
index 0000000..55387f6
--- /dev/null
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/07 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Hull J C. Options, futures, and other derivatives[M]. Pearson Education India, 2006.
+  </li>
+</ol>
diff --git a/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/04 Option Pricing Black Scholes Merton Model.ipynb b/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Option Pricing Black Scholes Merton Model.ipynb
similarity index 100%
rename from 06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/04 Option Pricing Black Scholes Merton Model.ipynb
rename to 06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Option Pricing Black Scholes Merton Model.ipynb
diff --git a/06 Introduction to Options[]/06 The Greek Letters/05 The Greek Letters.ipynb b/06 Introduction to Options[]/06 The Greek Letters/06 The Greek Letters.ipynb
similarity index 100%
rename from 06 Introduction to Options[]/06 The Greek Letters/05 The Greek Letters.ipynb
rename to 06 Introduction to Options[]/06 The Greek Letters/06 The Greek Letters.ipynb
diff --git a/06 Introduction to Options[]/06 The Greek Letters/08 Summary.html b/06 Introduction to Options[]/06 The Greek Letters/08 Summary.html
index 68c105e..928b210 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/08 Summary.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/08 Summary.html	
@@ -1,4 +1,3 @@
-<h2> Summary</h2>
 <p>
   In this chapter we discussed various Greeks, their meanings and their implications on the pricing and hedging of derivatives. We also presented some useful formulas for calculating the Greeks value of European options and generated the plots to show how they changed with strikes and time to expirations. The Greeks letter is the sensitivity measure of options price to the market variables. Next chapter we will discuss another major risk measures: volatility.
 </p>
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html
index 97ee3ba..0e25054 100755
--- a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
+++ b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
@@ -68,5 +68,5 @@ <h4>2. Calculation</h4>
 </p>
 <h4>3. Factors Affecting Implied Volatility</h4>
 <p>
-  According to the time value description in the first tutorial, [ref]Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>[/ref]in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
+  According to the time value description in the first tutorial, in general, the more time to expiration, the greater the time value of the option. Investors are willing to pay extra money for zero intrinsic value options which have more time to expiration because more time increases the likelihood of price movement and fluctuations, it is the options will become profitable. Implied volatility tends to be an increasing function of maturity. A short-dated option often has a low implied volatility, whereas a long-dated option tends to have a high implied volatility.
 </p>
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.ipynb b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/07 Historical Volatility and Implied Volatility.ipynb
similarity index 100%
rename from 06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/06 Historical Volatility and Implied Volatility.ipynb
rename to 06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/07 Historical Volatility and Implied Volatility.ipynb
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/08 References.html b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/08 References.html
new file mode 100644
index 0000000..34ceca1
--- /dev/null
+++ b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/08 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Options Pricing: Intrinsic Value And Time Value, Jean Folger, <a href="http://www.investopedia.com/university/options-pricing/intrinsic-value-time-value.asp?lgl=rira-baseline-vertical">Online Copy</a>
+  </li>
+</ol>  
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/02 Motivation.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/02 Motivation.html
index c6fdf46..6546510 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/02 Motivation.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/02 Motivation.html	
@@ -9,5 +9,5 @@
   But in the real world, it could be constant in a small time period but never constant in the long term. There is volatility skew for most options, which means the volatility is not constant across strikes.
 </p>
 <p>
-  [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]One way to capture the volatility skew is to assume that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. On the other hand, the introduction of additional sources of randomness will increase the complexity of the model. Another way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
+  One way to capture the volatility skew is to assume that the volatility itself is a random variable, this is the stochastic volatility model we will discuss next. On the other hand, the introduction of additional sources of randomness will increase the complexity of the model. Another way to capture the volatility skew but without introducing the additional source of randomness is the local volatility.
 <p>
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index e796d68..68bc274 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -3,7 +3,7 @@ <h4>1. Definition</h4>
   The constant volatility assumption in BSM model is not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. There exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
 </p>
 <p>
-  [ref]Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
+  The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
 </p>
 \[\sigma=\sigma(S_t,t)\]
 
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html
index 5874146..286f179 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html	
@@ -1,9 +1,8 @@
 <h4>1. Definition</h4>
 
 <p>
-  In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock price is log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is[ref]Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011[/ref]
+  In stochastic volatility models, the asset price and its volatility are both assumed to be random processes and can change over time. There are many stochastic volatility models. Here we will present the most well-known and popular one: the Heston Model. In Heston model, the stock price is log-normal distributed, the volatility process is a positive increasing function of a mean-reversion process. That is
 </p>
-
 \[dS_t = \mu_tS_tdt+\sqrt{v_t}S_tdW_{1,t}\]
 
 \[dv_t=-\lambda(v_t-\overline{v})\ dt+\eta\sqrt{v_t}\ dW_{2,t}\]
@@ -20,7 +19,7 @@ <h4>1. Definition</h4>
 \(W_{1,t}\) and \(W_{2,t}\) are two dependent Wiener processes with correlation coefficient \(\rho\).
 <h4>2. Simulation of the Heston Process</h4>
 <p>
-  We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization[ref]Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>[/ref].
+  We already discuss how to simulate the stock price process with Monte Carlo method in the introduction to stochastic process tutorial. In order to simulate the variance process, we need to write it into discrete form. The derivation can be found in paper Rouah F D. Euler and Milstein discretization.
 </p>
 \[v_{t+\Delta t}=\left(\sqrt{v_t}+\frac{1}{2}\eta\sqrt{\Delta t}W_1\right)^2-\lambda(v_t-\overline{v})\Delta t-\frac{\eta^2}{4}\Delta t\]
 <p>
@@ -75,7 +74,7 @@ <h4>2. Simulation of the Heston Process</h4>
   </div>
 <h4>3. Calibration of Model Parameters</h4>
 <p>
-  The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to this paper <em>Parameters recovery via calibration in the Heston model</em>[ref]Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.[/ref] for details of different error measure.
+  The calibration of a model is the process of seeking the model parameters for which the model result best matches the market option data. This data usually consists of market quoted prices for European plain vanilla call options or of Black-Scholes implied volatilities derived from prices. Calibrating the Heston model is equivalent to solving the non-linear constrained optimization problem: Minimize the error between the market quotes of options and the options price estimated by Heston model. The object function could be the absolute value of the error or the absolute squared error. You can refer to this paper <em>Parameters recovery via calibration in the Heston model</em> for details of different error measure.
 </p>
 <p>
   There are five parameters to be estimated in Heston model:
@@ -88,7 +87,7 @@ <h4>3. Calibration of Model Parameters</h4>
      <li>\(\rho\) : the correlation coefficient between two Wiener process (Bounds of -1 and 1)</li>
 </ul>
 <p>
-  Here we use QuantLib[ref]Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>[/ref] Python library to calibrate the parameters.
+  Here we use QuantLib Python library to calibrate the parameters.
 </p>
 <p>
   Let's look at how we can calibrate the Heston model to some market quotes. For example, let's say we are interested in trading SPDR S&amp;P 500 ETF (SPY) options with 4-months maturity. Here we choose all the options contracts written on SPY expire in 4 months. We need the strikes and the market prices of those contracts and the underlying price as the input of our objective function to minimize.
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/06 References.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/06 References.html
new file mode 100644
index 0000000..750b8af
--- /dev/null
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/06 References.html	
@@ -0,0 +1,17 @@
+<ol>
+  <li>
+    Bouzoubaa M, Osseiran A. Exotic options and hybrids: A guide to structuring, pricing and trading[M]. John Wiley &amp; Sons, 2010.[/ref]
+  </li>
+  <li>
+    Gatheral J. The volatility surface: a practitioner's guide[M]. John Wiley &amp; Sons, 2011
+  </li>
+  <li>
+    Rouah F D. Euler and Milstein discretization[J]. Documento de Trabajo, Sapient Global Markets, Estados Unidos, 2011. <a href="http://www.frouah.com/finance%20notes/Euler%20and%20Milstein%20Discretization.pdf">Online Copy</a>
+  </li>
+  <li>
+    Escobar, Marcos, and Christoph Gschnaidtner. "Parameters recovery via calibration in the Heston model: A comprehensive review." <i>Wilmott</i> 2016.86 (2016): 60-81.
+  </li>
+  <li>
+    Modeling Volatility Smile and Heston Model Calibration Using QuantLib Python, Goutham Balaraman, <a href="http://gouthamanbalaraman.com/blog/volatility-smile-heston-model-calibration-quantlib-python.html">online copy</a>
+  </li>
+</ol>
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.ipynb b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/08 Local Volatility and Stochastic Volatility.ipynb
similarity index 100%
rename from 06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/07 Local Volatility and Stochastic Volatility.ipynb
rename to 06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/08 Local Volatility and Stochastic Volatility.ipynb

From 116980288e8f245a62818ce70d0c78929d9b1ccb Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 11 Jun 2018 16:41:25 -0400
Subject: [PATCH 051/753] remove reference link

---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html
index 6b734e5..2894372 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 Introduction.html	
@@ -1,5 +1,5 @@
 <p>
-	This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM) [ref]<a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>[/ref], a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
+	This tutorial performs a simple linear regression to build the Capital Asset Pricing Model(CAPM), a classical model developed by William F. Sharpe and Harry Markowitz. This model yields alpha and beta for each asset and is traded by going long on the stocks ranked with the highest alpha. This tutorial will demonstrate the following:
 </p>
 
 <ul>

From 474630bf78c2325e734eca4107ad98474b294582 Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 11 Jun 2018 16:42:18 -0400
Subject: [PATCH 052/753] Remove reference link

---
 .../03 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index 5e40239..c7becd2 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -9,7 +9,7 @@
  	<li>Liquidate all our positions and purchase the first 2 stocks in our sorted list.</li>
 </ul>
 <p>
-	Dow Jones components change very infrequently, with the last change being on March 19th, 2015 [ref]<strong> <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a></strong>[/ref]. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
+	Dow Jones components change very infrequently, with the last change being on March 19th, 2015. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
 </p>
 
 <h3>Step 1: Setup Event Handler</h3>

From 36b98b52587a146349235107d672714e944756a2 Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 11 Jun 2018 16:45:46 -0400
Subject: [PATCH 053/753] Update 04 Factor Returns.html

---
 .../04 Factor Returns.html                                   | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html
index 9aa59ac..5da096f 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/04 Factor Returns.html	
@@ -8,7 +8,6 @@
 
 <p>
   RMW was proposed by Novy-Marx (2013) who wrote that:
-  "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014)[ref]<strong>Fama, E F; French, K R (2015)</strong>.
-  A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>[/ref]
-  who pointed out that: "A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns." Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
+  "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014).
+  The article pointed out that: "A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns." Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
 </p>

From 98ded2eedf4981ac0491707544c082762314c36f Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 11 Jun 2018 16:49:02 -0400
Subject: [PATCH 054/753] Update 04 Summary.html

---
 .../04 Summary.html                                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html
index e92d8df..53a6e91 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 Summary.html	
@@ -2,7 +2,7 @@
 	We have demonstrated that during a smooth market, the stocks that beat the market last month are likely to beat the market again in the subsequent month. When there is market fluctuation, the significance level of linear regression will reduce and the model performance will decrease. We can understand this by looking at the covariance of the asset(x) and the benchmark (y). As the covariance reduces to zero, the beta will decrease.
 </p>
 
-\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \b{x})^2}\]
+\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \beta{x})^2}\]
 
 <p>
 	As an experiment, we tested the algorithm on market data from 2015. This was a much more volatile period for the market with a fluctuation that returned a mean close to zero and dropped neaerly 10% from Aug 18th to Aug 25th of that year. The algorithm performed quite poorly in this year with a return rate of -11.58%. The risks associated with this strategy include a high drawdown, lack of hedging and not stop-loss. Since we are using leverage, the risk is increased and it has a margin call in January as a result. We can improve the performance by applying the following techniques:

From 2f5640da9b8cf13a2cb206205e7ab84c55fcaafb Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 11 Jun 2018 16:50:42 -0400
Subject: [PATCH 055/753] Update 08 References.html

---
 .../14 Fama-French Multi-Factor Models/08 References.html       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html
index b915797..79b3bcb 100644
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html	
@@ -12,6 +12,6 @@
     Fama, E F; French, K R (2015). A Five-Factor Asset Pricing Model. 116: 1–22. doi:10.1016/j.jfineco.2014.10.010. Retrieved from <a href="https://www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf" target="_blank">www8.gsb.columbia.edu/programs/sites/programs/files/finance/Finance%20Seminar/spring%202014/ken%20french.pdf</a>
   </li>
   <li>
-    <strong>Robert Novy-Marx (2013)</strong> The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>
+    Robert Novy-Marx (2013). The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>
   </li> 
 </ol>

From 69ef9e0567a312639b1bf59af5a92273a6555232 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Tue, 12 Jun 2018 17:15:10 -0400
Subject: [PATCH 056/753] update url page generator

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2ec333f..94b70ab 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -83,7 +83,7 @@
     <?php foreach ($strategyMap as $strategy) { ?>
         <tr>
             <td>
-                <a class="docs-internal-link" href="/tutorials2/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
+                <a class="docs-internal-link" href="/tutorials/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
                 <p><?= $strategy['description'] ?></p>
             </td>
         </tr>

From bf7268f17b8ac8a295200768098af391983a5111 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Wed, 13 Jun 2018 12:49:31 -0400
Subject: [PATCH 057/753] Update 05 Algorithm.html

---
 .../04 Using Options in QuantConnect/05 Algorithm.html    | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html b/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html
index 3b2c255..cf59940 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/05 Algorithm.html	
@@ -1,4 +1,10 @@
 <p>
   This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=6453fa45f6b98d880a4646a820265a62'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+  <div class="qc-embed-dummy" style="padding-top: 56.25%;">
+  </div>
+  <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+    <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0px; margin: 0px;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&amp;request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+  </div>
+</div>

From 8acf1c7730232221cdc33ce013d35aa04dc007f7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 13 Jun 2018 16:53:10 +0000
Subject: [PATCH 058/753] Automated push from server made by Jared Broad

---
 .../02 Basic Variable Types.html              | 49 +++++--------------
 1 file changed, 13 insertions(+), 36 deletions(-)

diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html
index c9e9df0..1f3b20b 100755
--- a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html	
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/02 Basic Variable Types.html	
@@ -1,42 +1,31 @@
-<p>
-  The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
+<p>
+The basic types of variables in Python are: strings, integers, floating point numbers and booleans.
 </p>
 <p>
   Strings in python are identified as a contiguous set of characters represented in either single quotes (' ') or double quotes (" ").
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_string1 = 'Welcome to'
 my_string2 = "QuantConnect"
 print(my_string1 + ' ' + my_string2)
 [out]: Welcome to QuantConnect
 </pre>
-</div>
-
+</div>
 <p>
   An integer is a round number with no values after the decimal point.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_int = 10
 print(my_int)
 [out]: 10
 print(type(my_int))
 [out]: type 'int'
 </pre>
-</div>
-
+</div>
 <p>
   The built-in function int() can convert a string into an integer.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_string = "100"
 print(type(my_string))
 [out]: type 'str'
@@ -44,15 +33,11 @@
 print(type(my_int))
 [out]: type 'int'
 </pre>
-</div>
-
+</div>
 <p>
   A floating point number, or a float, is a real number in mathematics. In Python we need to include a value after a decimal point to define it as a float.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_float = 1.0
 print(type(my_float))
 [out]: type 'float'
@@ -60,29 +45,21 @@
 print(type(my_int))
 [out]: type 'int'
 </pre>
-</div>
-
+</div>
 <p>
   As you can see above, if we don't include a decimal value, the variable would be defined as an integer. The built-in function float() can convert a string or an integer into a float.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_string = "100"
 my_float = float(my_string)
 print(type(my_float))
 [out]: type 'float'
 </pre>
-</div>
-
+</div>
 <p>
   A boolean, or bool, is a binary variable. Its value can only be True or False. It is useful when we do some logic operations, which would be covered in our next chapter.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_bool = False
 print(my_bool)
 [out]: False

From dccc9d98bc8bdbe862d0dc599cd91ca8fea64894 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 13 Jun 2018 16:54:08 +0000
Subject: [PATCH 059/753] Automated push from server made by Jared Broad

---
 .../05 Common String Operations.html          | 44 +++++--------------
 1 file changed, 12 insertions(+), 32 deletions(-)

diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html
index d900c77..0c25160 100755
--- a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html	
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/05 Common String Operations.html	
@@ -1,23 +1,16 @@
-<p>
-  A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
-</p>
-
+<p>
+A string is an immutable sequence of characters. It can be sliced by index just like a tuple:
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_str = 'Welcome to QuantConnect'
 print(my_str[8:])
 [out]: to QuantConnect
 </pre>
-</div>
-
+</div>
 <p>
   There are many methods associated with strings. We can use string.count() to count the occurrences of a character in a string, use string.find() to return the index of a specific character, and use string.replace() to replace characters.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">
 print("Counting the number of e's in this sentence".count('e'))
 [out]: 6
@@ -26,15 +19,11 @@
 print('all the a in this sentence now becomes e'.replace('a','e'))
 [out]: ell the e in this sentence now becomes e
 </pre>
-</div>
-
+</div>
 <p>
   The most commonly used method for strings is string.split(). This method will split the string by the indicated character and return a list:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">Time = '2016-04-01 09:43:00'
 splited_list = Time.split(' ')
 date = splited_list[0]
@@ -45,35 +34,26 @@
 print(hour)
 [out]: 09
 </pre>
-</div>
-
+</div>
 <p>
   We can replace parts of a string by our variable. This is called string formatting.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_time = 'Hour: {}, Minute: {}'.format(9, 43)
 print(my_time)
 [out]: Hour: 9, Minute: 43
 </pre>
-</div>
-
+</div>
 <p>
   Another way to format a string is to use the % symbol.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">print 'pi is %f' % 3.14
 [out]: pi is 3.140000
 print('%s to %s' % ('Welcome', 'QuantConnect'))
 [out]: Welcome to QuantConnect
 </pre>
-</div>
-
+</div>
 <p>
   %s is a placeholder that takes in a string. Similarly %f takes a float and %d takes an integer.
 </p>

From c9bd11e9ce77246866984ef1b4f3ca992a80b4a7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 13 Jun 2018 16:56:43 +0000
Subject: [PATCH 060/753] Automated push from server made by Jared Broad

---
 .../04 Data Collections.html                  | 128 +++++-------------
 1 file changed, 34 insertions(+), 94 deletions(-)

diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html
index 1dce548..12a2744 100755
--- a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html	
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/04 Data Collections.html	
@@ -1,25 +1,17 @@
-<h4>List</h4>
-
-<p>
-  A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
-</p>
-
+<h4>List</h4>
+<p>
+A list is an <strong>ordered</strong> collection of values. A list is mutable, which means you can change a list's value without changing the list itself. Creating a list is simply putting different comma-separated values between square brackets.
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_list = ['Quant', 'Connect', 1,2,3]
 print(my_list)
 [out]: ['Quant', 'Connect', 1, 2, 3]
 </pre>
-</div>
-
+</div>
 <p>
   The values in a list are called "elements". We can access list elements by indexing. Python index starts from 0. So if you have a list of length <em>n</em>, the index of the first element will be 0, and that of the last element will be <em>n</em> &minus; 1. By the way, the length of a list can be obtained by the built-in function len().
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_list = ['Quant', 'Connect', 1,2,3]
 print(len(my_list))
 [out]: 5
@@ -28,66 +20,46 @@
 print(my_list[len(my_list) - 1])
 [out]: 3
 </pre>
-</div>
-
+</div>
 <p>
   You can also change the elements in the list by accessing an index and assigning a new value.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_list = ['Quant','Connect',1,2,3]
 my_list[2] = 'go'
 print(my_list)
 [out]: ['Quant', 'Connect', 'go', 2, 3]
 </pre>
-</div>
-
+</div>
 <p>
   A list can also be sliced with a colon:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_list = ['Quant','Connect',1,2,3]
 print(my_list[1:3])
 [out]: ['Connect', 1]
 </pre>
-</div>
-
+</div>
 <p>
   The slice starts from the first element indicated, but excludes the last element indicated. Here we select all elements starting from index 1, which refers to the second element:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">print(my_list[1:])
 [out]: ['Connect', 1, 2, 3]
 </pre>
-</div>
-
+</div>
 <p>
   And all elements up to but excluding index 3:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">print(my_list[:3])
 [out]: ['Quant', 'Connect', 1]
 </pre>
-</div>
-
+</div>
 <p>
   If you wish to add or remove an element from a list, you can use the append() and remove() methods for lists as follows:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_list = ['Hello', 'Quant']
 my_list.append('Hello')
 print(my_list)
@@ -96,47 +68,32 @@
 print(my_list)
 [out]: ['Quant', 'Hello']
 </pre>
-</div>
-
+</div>
 <p>
   When there are repeated instances of "Hello", the first one is removed.
-</p>
-
-<h4>Tuple</h4>
-
+</p>
+<h4>Tuple</h4>
 <p>
   A tuple is a data structure type similar to a list. The difference is that a tuple is immutable, which means you can't change the elements in it once it's defined. We create a tuple by putting comma-separated values between parentheses.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_tuple = ('Welcome','to','QuantConnect')
 </pre>
-</div>
-
+</div>
 <p>
   Just like a list, a tuple can be sliced by using index.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_tuple = ('Welcome','to','QuantConnect')
 print(my_tuple[1:])
 [out]: ('to', 'QuantConnect')
 </pre>
-</div>
-
-<h4>Set</h4>
-
+</div>
+<h4>Set</h4>
 <p>
   A set is an <strong>unordered</strong> collection with <strong>no duplicate</strong> elements. The built-in function set() can be used to create sets.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">stock_list = ['AAPL','GOOG','IBM','AAPL','IBM','FB','F','GOOG']
 stock_set = set(stock_list)
 print(stock_set)
@@ -145,56 +102,39 @@ <h4>Set</h4>
 </div>
 <p>
   Set is an easy way to remove duplicate elements from a list.
-</p>
-
+</p>
 <h4>Dictionary</h4>
 <p>
   A dictionary is one of the most important data structures in Python. Unlike sequences which are indexed by integers, dictionaries are indexed by keys which can be either strings or floats.
-</p>
-
+</p>
 <p>
   A dictionary is an <strong>unordered</strong> collection of key : value pairs, with the requirement that the keys are unique. We create a dictionary by placing a comma-separated list of key : value pairs within the braces.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_dic = {'AAPL': 'Apple', 'FB': 'FaceBook', 'GOOG': 'Alphabet'}
 </pre>
-</div>
-
+</div>
 <p>
   After defining a dictionary, we can access any value by indicating its key in brackets.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">print(my_dic['GOOG'])
 [out]: Alphabet
 </pre>
-</div>
-
+</div>
 <p>
   We can also change the value associated with a specified key:
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">my_dic['GOOG'] = 'Alphabet Company'
 print(my_dic['GOOG'])
 [out]: Alphabet Company
 </pre>
-</div>
-
+</div>
 <p>
   The built-in method of the dictionary object dict.keys() returns a list of all the keys used in the dictionary.
-</p>
-
+</p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">print(my_dic.keys())
 [out]: ['GOOG', 'AAPL', 'FB']
 </pre>

From 39337aa529f854e209d54ed76d76fd3d1e62eaf7 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 13 Jun 2018 14:55:34 -0400
Subject: [PATCH 061/753] Update tutorials

---
 .../03 Full Python Example.html               |   3 +-
 .../02 Add Options.html                       |   3 +-
 .../03 Filter Contracts.html                  |   3 +-
 .../04 Select Contracts.html                  |   9 +-
 .../03 Method.html                            |  12 +-
 .../03 Method.html                            |  15 +--
 .../03 Part I - Copula Method.html            |  18 +--
 .../04 Part II - Cointegration Method.html    |   3 +-
 .../02 Method.html                            |   9 +-
 .../02 Method.html                            |  12 +-
 .../03 Method.html                            |  21 ++--
 .../03 Method.html                            |  21 ++--
 .../02 Introduction.html                      |   3 +-
 .../03 Method.html                            |   6 +-
 .../02 Factor Selection.html                  |  18 +--
 .../02 Method.html                            |  18 +--
 .../02 Implementation.html                    |   9 +-
 .../03 Basic Math Operations.html             |   2 -
 .../02 Logical Operations.html                |   9 +-
 .../02 Functions.html                         |   3 +-
 .../03 Built-in Function.html                 |  27 ++---
 .../04 Object-Oriented Programming.html       |  21 ++--
 .../04 NumPy and Basic Pandas/02 NumPy.html   |  24 ++--
 .../04 NumPy and Basic Pandas/03 Pandas.html  |  42 +++----
 .../02 Fetching Data.html                     |  18 +--
 .../03 Resampling.html                        |  33 ++----
 .../04 DataFrame.html                         |  57 +++------
 .../05 Summary.html                           | 108 ++++++------------
 .../02 Rate of Return.html                    |  12 +-
 .../03 Mean.html                              |   3 +-
 .../04 Variance and Standard Deviation.html   |   6 +-
 .../03 Distributions.html                     |  27 ++---
 .../04 Normal Distribution.html               |  15 +--
 .../02 Confidence Interval.html               |   9 +-
 .../03 Hypothesis testing.html                |  18 +--
 .../04 Summary.html                           |  27 ++---
 .../01 Introduction.html                      |   6 +-
 .../03 Python Implementation.html             |   9 +-
 .../02 Python Implementation.html             |  24 ++--
 .../04 Residual Analysis.html                 |  12 +-
 .../11 Linear Algebra/03 Matrices.html        |   6 +-
 .../04 Matrix Multiplication.html             |   6 +-
 .../11 Linear Algebra/05 Inverse.html         |   9 +-
 .../06 Linear Equations.html                  |   6 +-
 .../02 Add Options.html                       |   3 +-
 .../03 Filter Contracts.html                  |   3 +-
 .../04 Select Contracts.html                  |   9 +-
 .../02 Option Payoff.html                     |   3 +-
 .../02 Brownian motion.html                   |   9 +-
 ...Monte Carlo Simulation of Stock Price.html |   3 +-
 ... Carlo Simulation of European Options.html |   3 +-
 .../03 Factors in BSM model.html              |   3 +-
 .../05 Model Equations.html                   |   6 +-
 .../06 The Greek Letters/02 Delta.html        |   6 +-
 .../06 The Greek Letters/03 gamma.html        |   6 +-
 .../06 The Greek Letters/04 vega.html         |   6 +-
 .../06 The Greek Letters/05 Theta.html        |   6 +-
 .../06 The Greek Letters/06 Rho.html          |   6 +-
 .../02 Historical Volatility.html             |   3 +-
 .../03 Implied Volatility.html                |   3 +-
 .../04 Volatility Skew.html                   |   6 +-
 .../04 Stochastic Volatility.html             |   9 +-
 .../01 Covered Call/01 Definition.html        |   3 +-
 .../01 Covered Call/02 Implementation.html    |   6 +-
 .../02 Bull Call Spread/01 Definition.html    |   3 +-
 .../02 Implementation.html                    |  15 +--
 .../03 Long Straddle/01 Definition.html       |   3 +-
 .../03 Long Straddle/02 Implementation.html   |   9 +-
 .../04 Long Strangle/01 Definition.html       |   3 +-
 .../04 Long Strangle/02 Implementation.html   |  15 +--
 .../05 Butterfly Spread/01 Definition.html    |   3 +-
 .../02 Implementation.html                    |  12 +-
 .../06 Iron Condor/01 Definition.html         |   3 +-
 .../06 Iron Condor/02 Implementation.html     |  12 +-
 .../07 Iron Butterfly/01 Definition.html      |   3 +-
 .../07 Iron Butterfly/02 Implementation.html  |  12 +-
 .../08 Protective Collar/01 Definition.html   |   3 +-
 .../02 Implementation.html                    |  12 +-
 78 files changed, 310 insertions(+), 622 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html
index 04388e5..20d1cf2 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html	
@@ -2,8 +2,7 @@
   This is a complete port from C# to Python of this Quick Start Lesson.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 ```Lesson 6 - Tracking and Managing Orders:
 Immediately place 4 orders. Plot end of day price of each order in 'Order Tickets' plot. Debug log filled orders.
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html b/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html
index fdc5182..0ab569f 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/02 Add Options.html	
@@ -20,8 +20,7 @@
 </tbody>
 </table>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
     self.SetStartDate(2017, 01, 01)  #Set Start Date
     self.SetEndDate(2017, 06, 30)  #Set End Date
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html
index 34e182e..02b6927 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/03 Filter Contracts.html	
@@ -32,8 +32,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># filter the contracts with strikes between (market price - 10, market price + 10)
 option.SetFilter(-10,10)
 # filter the contracts which expires more than 30 days but no longer than 60 days
diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index ca86343..9b62ccc 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -61,8 +61,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
@@ -158,8 +157,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in slice.OptionChains:
     if i.Key != self.symbol: continue
     chain = i.Value
@@ -181,8 +179,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if len(contracts) == 0: continue
 # trade the contracts with the farthest expiration
 symbol = contracts[0].Symbol
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index c7becd2..b6d9f96 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -17,8 +17,7 @@ <h3>Step 1: Setup Event Handler</h3>
 	In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
     self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
 	</pre>
@@ -30,8 +29,7 @@ <h3>Step 2: Linear Regression Function</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def regression(self,x,y):
     x = np.array(x)
     x = np.diff(x)/x[:-1]
@@ -49,8 +47,7 @@ <h3>Step 3: History Function</h3>
 	Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def get_regression_data(self,symbol,history):
     symbol_price = []
     benchmark_price = []
@@ -70,8 +67,7 @@ <h3>Step 4: Rebalance Function:</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def rebalance(self):
     # get historical stock symbols and prices, then put them in tuples
     history = self.History(self.regression_dates, Resolution.Daily)
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html
index 8ba444f..e74071f 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/03 Method.html	
@@ -7,8 +7,7 @@ <h3>Step 1: Request Historical Data</h3>
   The first function takes two arguments: symbol and number of daily data points requested. This function requests historical <a href="https://www.quantconnect.com/docs#Consolidating-Data-TradeBars-vs-QuoteBars">QuoteBars</a> and builds it into a pandas DataFrame. For more information about pandas DataFrame, please refer to the help documentation <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">DataFrame</a>. The calculate_return function takes a DataFrame as an argument to calculate the mean and standard deviation of the log prices, and create new columns for the DataFrame (return, reversal factor and momentum) - it prepares the DataFrame for multiple linear regression.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def get_history(self,symbol, num):
     data = {}
     dates = []
@@ -50,8 +49,7 @@ <h3>Step 2: Build Predictive Model</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def concat(self):
     # we requested as many daily tradebars as we can
     his = self.get_history(self.quoted[0].Value,20*365)
@@ -83,8 +81,7 @@ <h3>Step 3: Apply Predictive Model</h3>
   The predict function uses the history for the last 3 months, merges it into a DataFrame and then calculates the updated factors. Using these updated factors (together with the model we built) we calculate the expected return.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def predict(self,symbol):
     # get current month in string
     month = str(self.Time).split(' ')[0][5:7]
@@ -128,8 +125,7 @@ <h3>Step 4: Initializing the Model</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
     self.SetStartDate(2013,6,1)
     self.SetEndDate(2016,6,1)
@@ -156,8 +152,7 @@ <h3>Step 5: Performing Monthly Rebalancing</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def action(self):
     rank = []
     long_short = []
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
index 290bb7f..9c26993 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
@@ -58,8 +58,7 @@ <h4><strong>2) Filter the trading pair with statistical correlation</strong></h4
 \[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n where n is the number of price data
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def _pair_selection(self):
 
     tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
@@ -125,8 +124,7 @@ <h3>Step 3: Estimating Copula Parameters</h3>
 </table>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def _parameter(self, family, tau):
     if  family == 'clayton':
         return 2*tau/(1-tau)
@@ -183,8 +181,7 @@ <h3>Step 4: Selecting the Best Fitting Copula</h3>
 </table>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def _lpdf_copula(self, family, theta, u, v):
     ''' estimate the log probability density function of three kinds of Archimedean copulas '''
     if  family == 'clayton':
@@ -206,8 +203,7 @@ <h3>Step 4: Selecting the Best Fitting Copula</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.family = ['clayton', 'frank', 'gumbel']
 tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
 AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
@@ -274,8 +270,7 @@ <h3>Step 5: Generating the Trading Signals</h3>
 </ul>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def _set_signal(self):
     history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
     # generate the log return series of paired stocks
@@ -298,8 +293,7 @@ <h3>Step 5: Generating the Trading Signals</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def OnData(self,data):
     for i in self.syl:
         self.price_list[i].append(self.Portfolio[i].Price)
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index eba7754..7011faf 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -16,8 +16,7 @@ <h3>Step 2: Compute the Threshold</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
                      index = [i.Time for i in self.symbols[0].hist_window])
 
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html
index 9db7090..9c029fb 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/02 Method.html	
@@ -5,8 +5,7 @@ <h3>Step 1: Determine the look back periods</h3>
   To start the look back period is set to 20 days to determine its buy and sell levels. We change the number of look back days in proportion to changes in market volatility. Through this method the number of look back days changes on a daily basis. At the end of each day, the current market volatility is calculated by the standard deviation of the past 30 day's closing prices.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">close = self.History(self.syl, 31, Resolution.Daily)['close']
    todayvol = np.std(close[1:self.numdays+1])
    yesterdayvol = np.std(close[0:self.numdays])
@@ -35,8 +34,7 @@ <h3>Step 2: Choose the algorithm buy/sell point</h3>
   QuantConnect provides more than 100 technical indicators for you to use in your algorithm. These are provided as class objects in Python. A full list of the indicators and their properties can be found in the reference table of <a href="https://www.quantconnect.com/docs#Indicators">the documentation page</a>.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.bolband = self.BB(self.syl,self.numdays,decimal.Decimal(2),MovingAverageType.Exponential,Resolution.Daily)
      self.upband = self.bolband.UpperBand
      self.lowband = self.bolband.LowerBand
@@ -48,8 +46,7 @@ <h3>Step 3: Choose the algorithm liquidation point</h3>
   The exit signal for an existing holding is determined by calculating a simple moving average of closing prices for the past look back days. That is to say, we liquidate a long position if the current price is lower than the moving average of the close price over the look back period, and vice versa for selling a short position.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.buypoint = max(self.high)
 self.sellpoint = min(self.low)
 historyclose = self.History(self.syl, self.numdays, Resolution.Daily)['close']
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html
index db77e12..53cb527 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/02 Method.html	
@@ -4,8 +4,7 @@ <h3>Step 1 : Initialization of algorithm</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
   self.SetStartDate(2004, 1, 1)
   self.SetEndDate(2017, 8, 30)
@@ -19,8 +18,7 @@ <h3>Step 1 : Initialization of algorithm</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.Schedule.On(self.DateRules.EveryDay(self.syl),self.TimeRules.AfterMarketOpen(self.syl,0),Action(self.SetSignal))
 </pre>
 </div>
@@ -36,8 +34,7 @@ <h3>Step 2:  Implementation of algorithm</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">history = self.History([self.syl.Value], 4, Resolution.Daily)
   self.high = history.loc[self.syl.Value]['high']
   self.low = history.loc[self.syl.Value]['low']
@@ -57,8 +54,7 @@ <h3>Step 3: Trading Implementation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"> holdings = self.Portfolio[self.syl].Quantity
         if self.Portfolio[self.syl].Price &gt;= self.selltrig:
             if holdings &gt;= 0:
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html
index 5fddb28..46a7747 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/03 Method.html	
@@ -8,8 +8,7 @@ <h3>Step 1: Defining Custom Imported Data</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">class TBill(PythonData):
     def GetSource(self, config, date, isLiveMode):
         return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/USTREASURY/BILLRATES.csv?api_key=XXXXXXXXX&amp;order=asc", SubscriptionTransportMedium.RemoteFile)
@@ -43,8 +42,7 @@ <h3>Step 1: Defining Custom Imported Data</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.AddData(TBill, "tbill")
 self.tbill = self.Securities["tbill"].Symbol
 </pre>
@@ -55,8 +53,7 @@ <h3>Step 2: Initialization of the Strategy Parameters</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.regPeriod = 24
 self.daysInMonth = 21
 </pre>
@@ -67,8 +64,7 @@ <h3>Step 2: Initialization of the Strategy Parameters</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.Schedule.On(self.DateRules.MonthStart(self.spy), self.TimeRules.AfterMarketOpen(self.spy),Action(self.MonthlyReg))
 </pre>
 </div>
@@ -78,8 +74,7 @@ <h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">hist = self.History([self.oil, self.spy], self.regPeriod*self.daysInMonth, Resolution.Daily)
 oilSeries = hist.loc[str(self.oil)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
 spySeries = hist.loc[str(self.spy)]['close'][self.daysInMonth-1:self.regPeriod*self.daysInMonth:self.daysInMonth]
@@ -91,8 +86,7 @@ <h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">x = np.array(oilSeries)
 x = (np.diff(x)/x[:-1])
 y = np.array(spySeries)
@@ -107,8 +101,7 @@ <h3>Step 3: Monthly Re-balancing of the Portfolio</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if yPred &gt; rf:
 	self.SetHoldings(self.spy, 1)
 else:
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html
index 58aed95..0b619ee 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 Method.html	
@@ -8,8 +8,7 @@ <h3>Step 1: Pairs Class Definition</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">class pairs(object):
     def __init__(self, a, b):
         self.a = a
@@ -62,8 +61,7 @@ <h3>Step 2: Generate and Clean Pairs</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def generate_pairs(self):
     for i in range(len(self.symbols)):
         for j in range(i+1,len(self.symbols)):
@@ -95,8 +93,7 @@ <h3>Step 3: Warming up Period</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if len(self.symbols[0].prices) &lt; self.num_bar:
     for symbol in self.symbols:
         if data.ContainsKey(i) is True:
@@ -114,8 +111,7 @@ <h3>Step 4: Pairs Selection</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
     if self.generate_count == 0:
         for symbol in self.symbols:
@@ -172,8 +168,7 @@ <h4>Updating Pairs</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">num_select = len(self.selected_pair)
 for pair in self.pair_list:
     if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
@@ -195,8 +190,7 @@ <h4>Opening Pairs Trading</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in self.selected_pair:
     price_a = float(data[i.a].Close)
     price_b = float(data[i.b].Close)
@@ -241,8 +235,7 @@ <h4>Closing Pairs Trading</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in self.trading_pairs:
     price_a = float(data[i.a].Close)
     price_b = float(data[i.b].Close)
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html
index 807b390..0ff3a93 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/02 Introduction.html	
@@ -39,8 +39,7 @@
 
 \[y=(I+2\lambda D^TD)^{-1}x\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def hpfilter(self,X, lamb=1600):
     X = np.asarray(X, float)
     if X.ndim &gt; 1:
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html
index 5678468..d23890a 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/03 Method.html	
@@ -3,8 +3,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
     self.SetStartDate(2011,1,1)
     self.SetEndDate(2017,5,30)
@@ -59,8 +58,7 @@ <h3>Step 3: Generating the Trading Signals</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 def OnData(self,data):
     self.close.append(self.Portfolio[self.syl].Price)
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html
index f40573f..d1a76f4 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
@@ -6,8 +6,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2005,01,01)  #Set Start Date
 	self.SetEndDate(2012,03,01)    #Set End Date
@@ -35,8 +34,7 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 	Coarse Universe selection is the built-in universe data provided by QuantConnect which allows you to filter the universe of over 16000 symbols to perform rough filtering before your algorithm. Because coarse selection function takes all the equities including ETFs which have no fundamental data into account, we need the property <code>x.HasFundamentalData</code> to exclude them from our candidate stocks pool.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># sort the data by daily dollar volume and take the top entries
 def CoarseSelectionFunction(self, coarse):
     if self.flag1:
@@ -51,8 +49,7 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 
 2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def FineSelectionFunction(self, fine):
 	if self.flag1:
 		self.flag1 = 0
@@ -75,8 +72,7 @@ <h3>Step 2: Compute the monthly return of portfolios</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">sorted_symbol = self.symbol
 self.AddEquity("SPY") # add benchmark
 for x in sorted_symbol:
@@ -106,8 +102,7 @@ <h3>Step 2: Compute the monthly return of portfolios</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
 # calculate the average return of different portfolios
 port_avg_return = np.mean(reshape_return,axis=1).tolist()
@@ -132,8 +127,7 @@ <h3>Step 3: Generate the metrics to test the factor significance</h3>
 </ul>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def calculate_criteria(self,df_port_return):
 	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
 	annual_return = (total_return+1)**(1./6)-1
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index 3fdccdc..63e4b63 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -8,8 +8,7 @@ <h3>Step 1: Initialization</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
         self.SetStartDate(2002, 1, 3)
         self.SetEndDate(2016, 12, 1)
@@ -32,8 +31,7 @@ <h3>Step 1: Initialization</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def CoarseSelectionFunction(self, coarse):
         sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
         top100 = sortedByDollarVolume[:self._numberOfSymbols]
@@ -48,8 +46,7 @@ <h3>Step 2:Warm Up</h3>
   Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self._stocks = []
 self.uni_symbol = None
 symbols = self.UniverseManager.Keys
@@ -67,8 +64,7 @@ <h3>Step 2:Warm Up</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for stock in self._stocks:
         self._values[stock].append(self.Securities[stock].Price)</pre>
 </div>
@@ -78,8 +74,7 @@ <h3>Step 3:Weekly Rebalancing</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">returns = {}
 for stock in self._stocks:
         newPrice = self.Securities[stock].Price
@@ -93,8 +88,7 @@ <h3>Step 3:Weekly Rebalancing</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">newArr = [(v,k) for k,v in returns.items()]
 newArr.sort()
 for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html
index 2778e5f..834dad1 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/02 Implementation.html	
@@ -8,8 +8,7 @@ <h3>CoarseSelectionFunction</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 def CoarseSelectionFunction(self, coarse):
     # if the rebalance flag is not 1, return null list to save time.
@@ -33,8 +32,7 @@ <h3>FineSelectionFunction</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
   def FineSelectionFunction(self, fine):
       # return the same symbol list if it's not time to rebalance
@@ -82,8 +80,7 @@ <h3>Rebalance</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 def Initialize(self):
 # Use SPY as a benchmark for market open
diff --git a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html
index c823cc0..997b322 100755
--- a/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html	
+++ b/05 Introduction to Financial Python[]/01 Data Types and Data Structures/03 Basic Math Operations.html	
@@ -3,8 +3,6 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
 <pre class="python">
 print("Addition ", 1+1)
 print("Subtraction ", 5-2)
diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html
index b5bc5d1..983c12f 100755
--- a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html	
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/02 Logical Operations.html	
@@ -2,8 +2,7 @@
   Like most programming languages, Python has comparison operators:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print 1 == 0    # 1 equals 0
 print 1 == 1    # 1 equals 1
 print 1 != 0    # 1 is not equal to 0
@@ -31,8 +30,7 @@
 </ul>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print 2 &gt; 1 and 3 &gt; 2
 print 2 &gt; 1 and 3 &lt; 2
 print 2 &gt; 1 or  3 &lt; 2
@@ -50,8 +48,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print (3 &gt; 2 or 1 &lt; 3) and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
 print 3 &gt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3) and not ( 3 &lt; 2 or 1 &lt; 3 and (1!=3 and 4&gt;3))
 [out]:
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html
index 518eea0..dc8cd02 100755
--- a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html	
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/02 Functions.html	
@@ -18,8 +18,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def say_hi():
     print "Welcome to QuantConnect'
 say_hi()
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html
index 8f5e07b..69758bf 100755
--- a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html	
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/03 Built-in Function.html	
@@ -3,8 +3,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 print range(10)
 [out]: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
@@ -19,8 +18,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 tickers = ['AAPL', 'GOOGL', 'IBM', 'FB', 'F', 'V', 'G', 'GE']
 print "The number of tickers is {}".format(len(tickers))
@@ -44,8 +42,7 @@
 
 <strong>map()</strong> is a function that applies a specific function to every item of a sequence or collection, and returns a list of the results.
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">tickers = ['AAPL','GOOG','IBM','FB','F','V', 'G', 'GE']
 print(list(map(len,tickers)))
 [out]: [4, 5, 3, 2, 1, 1, 1, 2]
@@ -56,8 +53,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">map(lambda x: x**2, range(10))
 [out]: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
 </pre>
@@ -68,16 +64,14 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">map(lambda x, y: x+y, [1,2,3,4,5], [5,4,3,2,1])
 [out]: [6, 6, 6, 6, 6]
 </pre>
 </div>
 <strong>sorted()</strong> takes a list or set and returns a new sorted list:
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">sorted([5,2,3,4,1])
 [out]: [1, 2, 3, 4, 5]
 </pre>
@@ -87,8 +81,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
 sorted(price_list, key = lambda x: x[1])
 [out]:
@@ -100,8 +93,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
 sorted(price_list, key = lambda x: x[1], reverse = True)
 [out]:
@@ -113,8 +105,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price_list = [('AAPL', 144.09), ('GOOGL', 911.71), ('MSFT', 69), ('FB', 150), ('WMT', 75.32)]
 price_list.sort(key = lambda x: x[1])
 print price_list
diff --git a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html
index 4cba3a9..b8b85ac 100755
--- a/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html	
+++ b/05 Introduction to Financial Python[]/03 Functions and Objective-Oriented Programming/04 Object-Oriented Programming.html	
@@ -20,8 +20,7 @@ <h4>Class</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">class Stock:
     def __init__(self, ticker, open, close, volume):
         self.ticker = ticker
@@ -49,8 +48,7 @@ <h4>Class</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">apple  = Stock('AAPL', 143.69, 144.09, 20109375)
 google = Stock('GOOGL', 898.7, 911.7, 1561616)
 </pre>
@@ -60,8 +58,7 @@ <h4>Class</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">apple.ticker
 [out]: 'AAPL'
 google.print_return()
@@ -81,8 +78,7 @@ <h4>Class</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 apple.ceo = 'Tim Cook'
 apple.ceo
@@ -93,8 +89,7 @@ <h4>Class</h4>
   We can check what names (i.e. attributes and methods) are defined on an object using the dir() function:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">dir(apple)
 [out]:
 ['__doc__',
@@ -118,8 +113,7 @@ <h4>Inheritance</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">class Child(Stock):
     def __init__(self, name):
         self.name = name
@@ -129,8 +123,7 @@ <h4>Inheritance</h4>
   Then we create an object:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aa = Child('AA')
 print aa.name
 [out]: 'AA'
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html
index 2b2095a..3388671 100755
--- a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html	
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/02 NumPy.html	
@@ -5,8 +5,7 @@
   Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import numpy as np</pre>
 </div>
 
@@ -17,8 +16,7 @@ <h4>Basic NumPy Arrays</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
 price_array = np.array(price_list)
 print price_array, type(price_array)
@@ -31,8 +29,7 @@ <h4>Basic NumPy Arrays</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">Ar = np.array([[1,3], [2,4]])
 print Ar, type(Ar)
 [out]: [[1 3]
@@ -45,8 +42,7 @@ <h4>Basic NumPy Arrays</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print Ar.shape
 [out]: (2, 2)</pre>
 </div>
@@ -56,8 +52,7 @@ <h4>Basic NumPy Arrays</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print Ar[0]
 [out]: [1 3]
 print Ar[1]
@@ -68,8 +63,7 @@ <h4>Basic NumPy Arrays</h4>
   If we want to access the matrix by column instead:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print 'First column:', Ar[:,0]
 [out]: First column: [1 2]
 print 'Second column:', Ar[:,1]
@@ -82,8 +76,7 @@ <h4>Array Functions</h4>
   Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">np.log(price_array)
 [out]: [4.96793654  4.98244156  4.9675886   4.96995218  4.96633504  4.96018375]</pre>
 </div>
@@ -93,8 +86,7 @@ <h4>Array Functions</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">np.mean(price_array)
 [out]: 143.896666667
 print np.std(price_array)
diff --git a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html
index 6d6a923..e14f201 100755
--- a/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html	
+++ b/05 Introduction to Financial Python[]/04 NumPy and Basic Pandas/03 Pandas.html	
@@ -3,8 +3,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import pandas as pd</pre>
 </div>
 
@@ -19,8 +18,7 @@ <h4>Series</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62]
 s = pd.Series(price)
 print s
@@ -38,8 +36,7 @@ <h4>Series</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f'])
 print s
 
@@ -55,8 +52,7 @@ <h4>Series</h4>
   Or we can change the indices of an existing Series:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s.index = [6,5,4,3,2,1]
 print s
 
@@ -72,8 +68,7 @@ <h4>Series</h4>
   Series is like a list since it can be sliced by index:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s[1:]
 print s[:-2]
 
@@ -95,8 +90,7 @@ <h4>Series</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s[4]
 s[4] = 0
 print s
@@ -116,8 +110,7 @@ <h4>Series</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s = pd.Series(price, name = 'Apple Prices')
 print s
 print s.name
@@ -137,8 +130,7 @@ <h4>Series</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s.describe()
 
 count      6.000000
@@ -158,8 +150,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D')
 print time_index
 s.index = time_index
@@ -186,8 +177,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s.index = [6,5,4,3,2,1]
 print s
 print s[1]
@@ -207,8 +197,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s.iloc[1]
 [out]: 145.83
 </pre>
@@ -219,8 +208,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s.index = time_index
 print s['2017-01-03']
 [out]: 143.68</pre>
@@ -231,8 +219,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s['2017-01-02':'2017-01-05']
 
 2017-01-02    145.83
@@ -247,8 +234,7 @@ <h4>Time Index</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print s[s &lt; np.mean(s)]
 print s[(s &gt; np.mean(s)) &amp; (s &lt; np.mean(s) + 1.64*np.std(s))]
 
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html
index f804f38..d859d77 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
@@ -2,8 +2,7 @@
   Here we use data from the yahoo_finance API.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import quandl
 quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
 </pre>
@@ -12,8 +11,7 @@
   We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_table = quandl.get('WIKI/AAPL')
 aapl = aapl_table['Adj. Close']['2017']
 print aapl
@@ -24,8 +22,7 @@
   Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl['2017-3']
 Date
 2017-03-01    138.657681
@@ -58,8 +55,7 @@
   Or in several consecutive months:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl['2017-2':'2017-4']
 </pre>
 </div>
@@ -68,8 +64,7 @@
   .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl.head()
 print aapl.tail(10)
 </pre>
@@ -78,8 +73,7 @@
   The output:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 Date
 2017-01-03    114.715378
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html
index 6d8492c..dc555b3 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/03 Resampling.html	
@@ -5,8 +5,7 @@
   <code>series.resample.mean()</code> is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">by_month = aapl.resample('M').mean()
 print by_month
 Date
@@ -26,8 +25,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">by_week = aapl.resample('W').mean()
 print by_week.head()
 
@@ -46,8 +44,7 @@
   We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">three_day = aapl.resample('3D').mean()
 two_week  = aapl.resample('2W').mean()
 two_month = aapl.resample('2M').mean()
@@ -59,8 +56,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">std = aapl.resample('W').std()    # standard deviation
 max = aapl.resample('W').max()    # maximum value
 min = aapl.resample('W').min()    # minimum value
@@ -72,8 +68,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">last_day = aapl.resample('M').agg(lambda x: x[-1])
 print last_day
 Date
@@ -93,8 +88,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
 print monthly_return
 
@@ -115,8 +109,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print monthly_return.mean()
 print monthly_return.std()
 print monthly_return.max()
@@ -130,8 +123,7 @@
   Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print last_day.diff()
 print last_day.pct_change()
 
@@ -166,8 +158,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(0)
 
@@ -187,8 +178,7 @@
   Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(method = 'bfill')
 
@@ -213,8 +203,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change().dropna()
 print daily_return
 
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html
index f119715..1a716b7 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/04 DataFrame.html	
@@ -9,8 +9,7 @@ <h4>Create DataFrames</h4>
   The most common method of creating a DataFrame is passing a dictionary:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
         'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
         'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
@@ -36,8 +35,7 @@ <h4>Manipulating DataFrames</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df = aapl_table
 print df.Close.tail(5)
 print df['Adj. Volume'].tail(5)
@@ -63,8 +61,7 @@ <h4>Manipulating DataFrames</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_2016 = df['2016']
 aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
 print aapl_month
@@ -72,8 +69,7 @@ <h4>Manipulating DataFrames</h4>
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
               Open      High     Low   Close      Volume  Ex-Dividend  \
 Date
@@ -127,8 +123,7 @@ <h4>Manipulating DataFrames</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
 print aapl_bar
 
@@ -154,8 +149,7 @@ <h4>Manipulating DataFrames</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
 
               Open    High     Low   Close
@@ -172,8 +166,7 @@ <h4>Manipulating DataFrames</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
 print above
 
@@ -194,8 +187,7 @@ <h4>Data Validation</h4>
   As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
 print aapl_bar
 
@@ -221,8 +213,7 @@ <h4>Data Validation</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">missing = aapl_bar.isnull()
 print missing
 print '---------------------------------------------'
@@ -264,8 +255,7 @@ <h4>Data Validation</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print missing[missing.rate_return == True]
 
              Open   High    Low  Close rate_return
@@ -279,8 +269,7 @@ <h4>Data Validation</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">drop = aapl_bar.dropna()
 print drop
 print '\n--------------------------------------------------\n'
@@ -329,8 +318,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
 s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
 data_frame = pd.concat([s1, s2], axis = 1)
@@ -350,8 +338,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">log_price = np.log(aapl_bar.Close)
 log_price.name = 'log_price'
 print log_price
@@ -398,8 +385,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
 print df_volume
 print '\n-------------------------------------------\n'
@@ -435,8 +421,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1)
 print concat
 
@@ -485,8 +470,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
 print concat
 
@@ -509,8 +493,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">append = aapl_bar.append(df_2017)
 print append
              Close      High     Low    Open  rate_return
@@ -542,8 +525,7 @@ <h4>DataFrame Concat</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
              Close      High     Low    Open  rate_return
@@ -573,8 +555,7 @@ <h4>DataFrame Concat</h4>
   Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df_2017.columns = ['Change', 'High', 'Low', 'Close']
 concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html
index e217896..6ab6e8a 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
@@ -5,8 +5,7 @@
   Here we use data from the yahoo_finance API.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import quandl
 quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
 </pre>
@@ -15,8 +14,7 @@
   We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_table = quandl.get('WIKI/AAPL')
 aapl = aapl_table['Adj. Close']['2017']
 print aapl
@@ -27,8 +25,7 @@
   Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl['2017-3']
 Date
 2017-03-01    138.657681
@@ -61,8 +58,7 @@
   Or in several consecutive months:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl['2017-2':'2017-4']
 </pre>
 </div>
@@ -71,8 +67,7 @@
   .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl.head()
 print aapl.tail(10)
 </pre>
@@ -81,8 +76,7 @@
   The output:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 Date
 2017-01-03    114.715378
@@ -114,8 +108,7 @@ <h2>Resampling</h2>
   <code>series.resample.mean()</code> is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">by_month = aapl.resample('M').mean()
 print by_month
 Date
@@ -135,8 +128,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">by_week = aapl.resample('W').mean()
 print by_week.head()
 
@@ -155,8 +147,7 @@ <h2>Resampling</h2>
   We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">three_day = aapl.resample('3D').mean()
 two_week  = aapl.resample('2W').mean()
 two_month = aapl.resample('2M').mean()
@@ -168,8 +159,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">std = aapl.resample('W').std()    # standard deviation
 max = aapl.resample('W').max()    # maximum value
 min = aapl.resample('W').min()    # minimum value
@@ -181,8 +171,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">last_day = aapl.resample('M').agg(lambda x: x[-1])
 print last_day
 Date
@@ -202,8 +191,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
 print monthly_return
 
@@ -224,8 +212,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print monthly_return.mean()
 print monthly_return.std()
 print monthly_return.max()
@@ -239,8 +226,7 @@ <h2>Resampling</h2>
   Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print last_day.diff()
 print last_day.pct_change()
 
@@ -275,8 +261,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(0)
 
@@ -296,8 +281,7 @@ <h2>Resampling</h2>
   Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change()
 print daily_return.fillna(method = 'bfill')
 
@@ -322,8 +306,7 @@ <h2>Resampling</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">daily_return = last_day.pct_change().dropna()
 print daily_return
 
@@ -350,8 +333,7 @@ <h3>Create DataFrames</h3>
   The most common method of creating a DataFrame is passing a dictionary:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
         'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
         'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
@@ -377,8 +359,7 @@ <h3>Manipulating DataFrames</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df = aapl_table
 print df.Close.tail(5)
 print df['Adj. Volume'].tail(5)
@@ -404,8 +385,7 @@ <h3>Manipulating DataFrames</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_2016 = df['2016']
 aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
 print aapl_month
@@ -413,8 +393,7 @@ <h3>Manipulating DataFrames</h3>
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
               Open      High     Low   Close      Volume  Ex-Dividend  \
 Date
@@ -468,8 +447,7 @@ <h3>Manipulating DataFrames</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
 print aapl_bar
 
@@ -495,8 +473,7 @@ <h3>Manipulating DataFrames</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
 
               Open    High     Low   Close
@@ -513,8 +490,7 @@ <h3>Manipulating DataFrames</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
 print above
 
@@ -535,8 +511,7 @@ <h3>Data Validation</h3>
   As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
 print aapl_bar
 
@@ -562,8 +537,7 @@ <h3>Data Validation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">missing = aapl_bar.isnull()
 print missing
 print '---------------------------------------------'
@@ -605,8 +579,7 @@ <h3>Data Validation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print missing[missing.rate_return == True]
 
              Open   High    Low  Close rate_return
@@ -620,8 +593,7 @@ <h3>Data Validation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">drop = aapl_bar.dropna()
 print drop
 print '\n--------------------------------------------------\n'
@@ -670,8 +642,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
 s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
 data_frame = pd.concat([s1, s2], axis = 1)
@@ -691,8 +662,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">log_price = np.log(aapl_bar.Close)
 log_price.name = 'log_price'
 print log_price
@@ -739,8 +709,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
 print df_volume
 print '\n-------------------------------------------\n'
@@ -776,8 +745,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1)
 print concat
 
@@ -826,8 +794,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
 print concat
 
@@ -850,8 +817,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">append = aapl_bar.append(df_2017)
 print append
              Close      High     Low    Open  rate_return
@@ -883,8 +849,7 @@ <h3>DataFrame Concat</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
              Close      High     Low    Open  rate_return
@@ -914,8 +879,7 @@ <h3>DataFrame Concat</h3>
   Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">df_2017.columns = ['Change', 'High', 'Low', 'Close']
 concat = pd.concat([aapl_bar, df_2017], axis = 0)
 print concat
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 2bc4709..c9641b3 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -8,8 +8,7 @@ <h4>Single-period Return</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import numpy as np
 rate_return = 102.0/100 - 1
@@ -66,8 +65,7 @@ <h4>Logarithm Return</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import quandl
 import numpy as np
 import quandl
@@ -86,8 +84,7 @@ <h4>Logarithm Return</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 Date          Open   Close  log_price  log_return
 2017-03-01  137.890  139.79   4.940141         NaN
@@ -121,8 +118,7 @@ <h4>Logarithm Return</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">month_return = aapl.log_return.sum()
 print month_return
 [out]: 0.0273081001636
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html
index 5fa8705..9d51b9c 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html	
@@ -11,8 +11,7 @@ <h4>Arithmetic Mean</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.mean(aapl.log_price)
 [out]: 4.94597446551
 </pre>
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html
index d933896..5161451 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html	
@@ -7,8 +7,7 @@ <h4>Variance</h4>
   In python we can use NumPy.var to calculate it:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.var(aapl.log_price)
 </pre>
 </div>
@@ -21,8 +20,7 @@ <h4>Standard Deviation</h4>
   NumPy also provides us a method to calculate standard deviation.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.std(aapl.log_price)
 [out]: 0.000142032804482
 </pre>
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html
index 24d0692..d80c6dc 100755
--- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html	
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html	
@@ -8,8 +8,7 @@ <h4>Uniform Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import random
 import matplotlib.pyplot as plt
 import numpy as np
@@ -29,8 +28,7 @@ <h4>Uniform Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 plt.hist(series,bins = 11,align = 'mid')
 plt.xlabel('Dice Number')
@@ -44,8 +42,7 @@ <h4>Uniform Distribution</h4>
   Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print len([x for x in series if x &lt;= 3])/float(len(series))
 [out]: 0.4956
 print np.mean(series)
@@ -86,8 +83,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def trial():
     number = [1,2,3,4,5,6,7,8,9,10]
     a = random.choice(number)
@@ -102,8 +98,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">res = [trial() for x in range(10)]
 print sum(res)
 [out]: 7
@@ -114,8 +109,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def binomial(number):
     l = []
     for i in range(10000):
@@ -131,8 +125,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">prob = []
 for i in range(1,11):
     prob.append(binomial(i))
@@ -155,8 +148,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print (float(factorial(10))/(factorial(7)*factorial(10-7)))*(0.7**7)*(0.3**3)
 [out]: 0.266827932
 print (float(factorial(10))/(factorial(8)*factorial(10-8)))*(0.7**8)*(0.3**2)
@@ -168,8 +160,7 @@ <h4>Binomial Distribution</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 plt.bar(range(1,11),prob)
 plt.grid()
diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html
index f0f612a..6ab172b 100755
--- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html	
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/04 Normal Distribution.html	
@@ -14,8 +14,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 norm.plot.density()
 plt.show()
@@ -27,8 +26,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import quandl
 quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
 spy_table = quandl.get('BCIW/_SPXT')
@@ -42,8 +40,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 spy.log_return.plot()
 plt.show()
@@ -55,8 +52,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 spy.log_return.plot.density()
 plt.show()
@@ -68,8 +64,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">de_2 = pd.Series(np.random.normal(0,2,10000),name = 'μ = 0, σ = 2')
 de_3 = pd.Series(np.random.normal(0,3,10000),name = 'μ = 0, σ = 3')
 de_0 = pd.Series(np.random.normal(0,0.5,10000), name ='μ = 0, σ = 0.5')
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
index 272b972..82a7170 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -4,8 +4,7 @@ <h4>Sample Error</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -28,8 +27,7 @@ <h4>Sample Error</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 print '10 days sample returns:', np.mean(spy_log_return.tail(10))
 [out]: 10 days sample returns: 0.000845189915474
@@ -64,8 +62,7 @@ <h4>Confidence Interval</h4>
   This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 #apply the formula above to calculate confidence interval
 bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html
index 0cd2597..acd6912 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html	
@@ -3,8 +3,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">mean_1000 = np.mean(spy_log_return.tail(1000))
 std_1000 = np.std(spy_log_return.tail(1000))
 mean_10 = np.mean(spy_log_return.tail(10))
@@ -23,8 +22,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
 upper = 0 + 1.64*std_1000/np.sqrt(1000)
 print (bottom, upper)
@@ -36,8 +34,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
 upper = 0 + 1.96*std_1000/np.sqrt(1000)
 print (bottom, upper)
@@ -57,8 +54,7 @@
   Let's calculate the Z score from our sample:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
 [out]: 1.90922032428
 </pre>
@@ -68,8 +64,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import scipy.stats as st
 print (1 - st.norm.cdf(1.9488))
 [out]: 0.025659656888
@@ -83,8 +78,7 @@
   If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
 std_1200 = np.std(spy_log_return.tail(1200))
 z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html
index 5bde740..3cc20c3 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html	
@@ -13,8 +13,7 @@ <h3>Sample Error</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -37,8 +36,7 @@ <h3>Sample Error</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 print '10 days sample returns:', np.mean(spy_log_return.tail(10))
 [out]: 10 days sample returns: 0.000845189915474
@@ -73,8 +71,7 @@ <h3>Confidence Interval</h3>
   This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 #apply the formula above to calculate confidence interval
 bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
@@ -124,8 +121,7 @@ <h2>Hypothesis testing</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">mean_1000 = np.mean(spy_log_return.tail(1000))
 std_1000 = np.std(spy_log_return.tail(1000))
 mean_10 = np.mean(spy_log_return.tail(10))
@@ -144,8 +140,7 @@ <h2>Hypothesis testing</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
 upper = 0 + 1.64*std_1000/np.sqrt(1000)
 print (bottom, upper)
@@ -157,8 +152,7 @@ <h2>Hypothesis testing</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
 upper = 0 + 1.96*std_1000/np.sqrt(1000)
 print (bottom, upper)
@@ -178,8 +172,7 @@ <h2>Hypothesis testing</h2>
   Let's calculate the Z score from our sample:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
 [out]: 1.90922032428
 </pre>
@@ -189,8 +182,7 @@ <h2>Hypothesis testing</h2>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import scipy.stats as st
 print (1 - st.norm.cdf(1.9488))
 [out]: 0.025659656888
@@ -204,8 +196,7 @@ <h2>Hypothesis testing</h2>
   If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
 std_1200 = np.std(spy_log_return.tail(1200))
 z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html
index 982ac0e..142730d 100755
--- a/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html	
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/01 Introduction.html	
@@ -5,8 +5,7 @@
   If we are holding a stock, we must be curious about the relationship between our stock return and the market return. Let's say we hold Amazon stock on the first day of this year. In order to see the relation directly, we plot the daily return of our stock on the y-axis and plot the S&amp;P 500 index daily return on the x-axis.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import numpy as np
 import pandas as pd
 import quandl
@@ -64,8 +63,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import matplotlib.pyplot as plt
 plt.figure(figsize = (15,10))
 plt.scatter(df.spy,df.amzn)
diff --git a/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html b/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html
index b76aeb5..0e0f7d9 100755
--- a/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html	
+++ b/05 Introduction to Financial Python[]/09 Simple Linear Regression/03 Python Implementation.html	
@@ -2,8 +2,7 @@
   In python, we have a very power package for mathematical models, which is named 'statsmodels'.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import statsmodels.formula.api as sm
 model = sm.ols(formula = 'amzn~spy',data = df).fit()
 print model.summary()
@@ -15,8 +14,7 @@
   We built a simple linear regression model above by using the ols() function in statsmodels. The 'model' instance has lots of properties. The most commonly used one is parameters, or slope and intercept. We can access to them by:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print 'pamameters: ',model.params
 [out]: pamameters:  Intercept    0.000012
                    spy          0.492112
@@ -36,8 +34,7 @@
   Now let's have a look at our fitted line:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (15,10))
 plt.scatter(df.spy,df.amzn)
 plt.xlabel('spx_return')
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html
index bc4556e..6b532d0 100755
--- a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html	
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/02 Python Implementation.html	
@@ -3,8 +3,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import numpy as np
 import pandas as pd
 import quandl
@@ -25,8 +24,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 spy  = spy_table .loc['2016',['Close']]
 amzn = amzn_table.loc['2016',['Close']]
@@ -41,8 +39,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 spy_log  = np.log(spy.Close) .diff().dropna()
 amzn_log = np.log(amzn.Close).diff().dropna()
@@ -111,8 +108,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">simple = sm.ols(formula = 'amzn ~ spy', data = df).fit()
 print simple.summary()
 </pre>
@@ -153,8 +149,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">model = sm.ols(formula = 'amzn ~ spy + ebay + wal', data = df).fit()
 print model.summary()
 </pre>
@@ -230,8 +225,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import urllib2
 from datetime import datetime
@@ -252,8 +246,7 @@
   With the data, we can construct a Fama-French factor model:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 fama = fama_table['2016']
 fama = fama.rename(columns = {'Mkt-RF':'MKT'})
@@ -268,8 +261,7 @@
   The Fama-French 5-factor model has a higher \( R^2 \) of 0.387. We can compare the predictions from simple linear regression and Fama-French multiple regression by plotting them together on one chart:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 result = pd.DataFrame({'simple regression': simple.predict(),
                        'fama_french': fama_model.predict(),
diff --git a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html
index 835d65a..5089062 100755
--- a/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html	
+++ b/05 Introduction to Financial Python[]/10 Multiple Linear Regression/04 Residual Analysis.html	
@@ -11,8 +11,7 @@ <h3>Normality</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure()
 #ols.fit().model is a method to access to the residual.
 fama_model.resid.plot.density()
@@ -26,8 +25,7 @@ <h3>Normality</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print 'Residual mean:', np.mean(fama_model.resid)
 [out]: Residual mean: -2.31112163493e-16
 print 'Residual variance:', np.var(fama_model.resid)
@@ -42,8 +40,7 @@ <h3>Homoskedasticity</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">plt.figure(figsize = (20,10))
 plt.scatter(df.spy,simple.resid)
 plt.axhline(0.05)
@@ -59,8 +56,7 @@ <h3>Homoskedasticity</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">from statsmodels.stats import diagnostic as dia
 het = dia.het_breuschpagan(fama_model.resid,fama_df[['MKT','SMB','HML','RMW','CMA']][1:])
 print 'p-value: ', het[-1]
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html b/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html
index 48e6e97..5d230de 100755
--- a/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html	
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/03 Matrices.html	
@@ -33,8 +33,7 @@ <h3>Python Implementation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import numpy as np
 a = np.array([1,2,3])
 b = np.array([2,2,2])
@@ -54,8 +53,7 @@ <h3>Python Implementation</h3>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">matrix2 = np.array([a,b,c])
 print matrix2
 [out]:
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html b/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html
index d99e45f..65073a3 100755
--- a/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html	
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/04 Matrix Multiplication.html	
@@ -27,8 +27,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">A = np.array([[2,3],[4,2],[2,2]])
 B = np.array([[4,2],[4,6]])
 x = np.dot(A,B)
@@ -49,8 +48,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">x = np.dot(B,A)
 </pre>
 </div>
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html b/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html
index 145f606..ddaa975 100755
--- a/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html	
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/05 Inverse.html	
@@ -31,8 +31,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print matrix
 print '\n-------------------------\n'
 print np.linalg.inv(matrix)
@@ -54,8 +53,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">inverse = np.linalg.inv(matrix)
 print np.dot(matrix, inverse)
 print '\n-------------------------\n'
@@ -78,8 +76,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">singular = np.array([[1,2,3],[1,2,3],[3,3,3]])
 inv = np.linalg.inv(singular)
 [out]: numpy.linalg.linalg.LinAlgError: Singular matrix
diff --git a/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html b/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html
index 06672e4..4b36d00 100755
--- a/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html	
+++ b/05 Introduction to Financial Python[]/11 Linear Algebra/06 Linear Equations.html	
@@ -31,8 +31,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">A = np.array([[2,1,-1],[-3,-1,2],[-2,1,2]])
 b = np.array([[8],[-11],[-3]])
 inv_A = np.linalg.inv(A)
@@ -53,8 +52,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">print np.linalg.solve(A, b)
 [out]:
 [[ 2.]
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
index fdc5182..0ab569f 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
@@ -20,8 +20,7 @@
 </tbody>
 </table>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
     self.SetStartDate(2017, 01, 01)  #Set Start Date
     self.SetEndDate(2017, 06, 30)  #Set End Date
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html
index 34e182e..02b6927 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html	
@@ -32,8 +32,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># filter the contracts with strikes between (market price - 10, market price + 10)
 option.SetFilter(-10,10)
 # filter the contracts which expires more than 30 days but no longer than 60 days
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index ca86343..9b62ccc 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -61,8 +61,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
@@ -158,8 +157,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in slice.OptionChains:
     if i.Key != self.symbol: continue
     chain = i.Value
@@ -181,8 +179,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if len(contracts) == 0: continue
 # trade the contracts with the farthest expiration
 symbol = contracts[0].Symbol
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html
index ab58f5f..7f3f36a 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/02 Option Payoff.html	
@@ -44,8 +44,7 @@
 </table>
 If you long these two options, the payoff at expiration date would be as follows
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import matplotlib.pyplot as plt
 %pylab inline
 price = np.arange(900,1000,1)
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index 36ca9d9..29780ed 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -23,8 +23,7 @@
   Next, we simulate the Wiener process and plot the paths attempting to gain an intuitive understanding of a stochastic process. Each path is an independent Wiener process.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import numpy as np
 import matplotlib.pyplot as plt
@@ -118,8 +117,7 @@ <h3>1. Monte Carlo Simulation of Stock Price</h3>
   We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import quandl
 quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
@@ -165,8 +163,7 @@ <h3>1. Monte Carlo Simulation of European Options</h3>
   There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
     payoff_sum = 0
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html
index 9bd14d2..5037557 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html	
@@ -2,8 +2,7 @@
   We apply this technique to model stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import quandl
 quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html
index 99a796b..97012b7 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html	
@@ -15,8 +15,7 @@
   In option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
     payoff_sum = 0
diff --git a/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html b/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html
index 7b75702..3e90225 100755
--- a/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html	
+++ b/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/03 Factors in BSM model.html	
@@ -34,8 +34,7 @@
 </tbody>
 </table>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">class BsmModel:
     def __init__(self, option_type, price, strike, interest_rate, expiry, volatility, dividend_yield=0):
         self.s = price # Underlying asset price
diff --git a/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Model Equations.html b/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Model Equations.html
index ec7abcb..d389eac 100755
--- a/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Model Equations.html	
+++ b/06 Introduction to Options[]/05 Options Pricing Black Scholes Merton Model/05 Model Equations.html	
@@ -12,8 +12,7 @@
   N(x) is the cumulative probability distribution function for a variable with a standard normal distribution. It can be calculated by the integral of the probability density function of standard normal distribution from 0 to x. In Python, you can use the <code>norm.pdf(x)</code> in spicy.stats library. For the following chart, we plot the probability density curve of the standard normal distribution. For example, N(-1) is the area of the left hand of the red line under the curve.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import scipy.stats as sp
 mu = 0
 variance = 1
@@ -29,8 +28,7 @@
   Then in our BSM model class, we will calculate the European call and put option prices by using BSM formula.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def n(self, d):
     # cumulative probability distribution function of standard normal distribution
     return norm.cdf(d)
diff --git a/06 Introduction to Options[]/06 The Greek Letters/02 Delta.html b/06 Introduction to Options[]/06 The Greek Letters/02 Delta.html
index 09dde08..dce03fa 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/02 Delta.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/02 Delta.html	
@@ -5,8 +5,7 @@ <h4>Definition</h4>
 \[delta(call)=\frac{\partial c}{\partial S}=e^{-q(T-t)}N(d1)\]
 \[delta(put)=\frac{\partial p}{\partial S}=e^{-q(T-t)}(N(d1)-1)\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">''' Greek letters for European options on an asset that provides a yield at rate q '''
 def delta(self):
     d1 = self.d1()
@@ -32,8 +31,7 @@ <h4>Impact Factors</h4>
   In order to demonstrate how those Greeks values change with the time to expiration and the underlying price, we choose 60*23 call options contracts with the stock price ranging from 10 to 70, time to expiration ranging from 0 to 1 year. The strikes of all the contracts are 40,  the interest rates are 0.1, the volatilities are all 0.5. We construct the contracts data as a 23*60 matrix.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">s = np.array([range(10,70,1) for i in range(23)])
 I = np.ones((shape(s)))
 time = arange(1,12.5,0.5)/12
diff --git a/06 Introduction to Options[]/06 The Greek Letters/03 gamma.html b/06 Introduction to Options[]/06 The Greek Letters/03 gamma.html
index 9c74e05..5a71535 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/03 gamma.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/03 gamma.html	
@@ -6,8 +6,7 @@
 </p>
 \[gamma(call)=gamma(put)=\frac{N^{'}(d_1)e^{-q(T-t)}}{S\sigma\sqrt{(T-t)}}\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def gamma(self):
     d1 = self.d1()
     dn1 = self.dn(d1)
@@ -16,8 +15,7 @@
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">z = gamma
 fig = plt.figure(figsize=(20,11))
 ax = fig.add_subplot(111, projection='3d')
diff --git a/06 Introduction to Options[]/06 The Greek Letters/04 vega.html b/06 Introduction to Options[]/06 The Greek Letters/04 vega.html
index e0d5617..c7d3dc7 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/04 vega.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/04 vega.html	
@@ -6,8 +6,7 @@
 </p>
 \[vega(call)=vega(put)=S\sqrt{(T-t)}N^{'}(d_1)e^{-q(T-t)}\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def vega(self):
     d1 = self.d1()
     dn1 = self.dn(d1)
@@ -16,8 +15,7 @@
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">z = vega
 norm = matplotlib.colors.Normalize()
 fig = plt.figure(figsize=(20,11))
diff --git a/06 Introduction to Options[]/06 The Greek Letters/05 Theta.html b/06 Introduction to Options[]/06 The Greek Letters/05 Theta.html
index df30d5d..ecc937f 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/05 Theta.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/05 Theta.html	
@@ -7,8 +7,7 @@
 \[Theta(call)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})+qSN(d_1)e^{(-q(T-t))}-rKe^{-r(T-t)}N(d_2)\]
 \[Theta(put)=-SN^{'}(d_1)\sigma e^{-q(T-t)}/(2\sqrt{T-t})-qSN(-d_1)e^{(-q(T-t))}+rKe^{-r(T-t)}N(-d_2)\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def theta(self):
     d1 = self.d1()
     d2 = d1 - self.sigma * sqrt(self.T)
@@ -27,8 +26,7 @@
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">z = theta
 # facecolors aren't normalizing as might be expected
 # we need to normalize it to avoid all dark color for value under 0
diff --git a/06 Introduction to Options[]/06 The Greek Letters/06 Rho.html b/06 Introduction to Options[]/06 The Greek Letters/06 Rho.html
index aebfe73..85c4d45 100755
--- a/06 Introduction to Options[]/06 The Greek Letters/06 Rho.html	
+++ b/06 Introduction to Options[]/06 The Greek Letters/06 Rho.html	
@@ -4,8 +4,7 @@
 \[Rho(call)=K(T-t)e^{-r(T-t)}N(d_2)\]
 \[Rho(put)=-K(T-t)e^{-r(T-t)}N(-d_2)\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def rho(self):
     d2 = self.d2()
     if self.type == "c":
@@ -17,8 +16,7 @@
 </div>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">z = rho
 norm = matplotlib.colors.Normalize()
 fig = plt.figure(figsize=(20,11))
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/02 Historical Volatility.html b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/02 Historical Volatility.html
index 47c3b7d..2646bcc 100755
--- a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/02 Historical Volatility.html	
+++ b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/02 Historical Volatility.html	
@@ -22,8 +22,7 @@ <h4>2. Calculation</h4>
 </p>
 \[std\times\sqrt{n}\]
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 import pandas as pd
 from numpy import sqrt,mean,log,diff
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html
index 0e25054..55fe947 100755
--- a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
+++ b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/03 Implied Volatility.html	
@@ -11,8 +11,7 @@ <h4>2. Calculation</h4>
   Here we use the bisection method to solve the BSM pricing equation and find the root which is the implied volatility. We use Yahoo Finance Python API to get the real time option data.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def bsm_price(option_type, sigma, s, k, r, T, q):
     # calculate the bsm price of European call and put options
     sigma = float(sigma)
diff --git a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/04 Volatility Skew.html b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/04 Volatility Skew.html
index 0c5f025..fba17fd 100755
--- a/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/04 Volatility Skew.html	
+++ b/06 Introduction to Options[]/07 Historical Volatility and Implied Volatility/04 Volatility Skew.html	
@@ -5,8 +5,7 @@
   Here we give an example how to plot the volatility smile by using the real time options data of SPDR S&amp;P 500 ETF(NYSEARCA: SPY).
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># download option data for all expiry months from Yahoo Finance
 # provide a formatted DataFrame with a hierarchical index
 opt = Options('spy', 'yahoo')
@@ -69,8 +68,7 @@
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">opt = Options('spy', 'yahoo')
 r = 0.01 # risk free rate
 d = 0.01 # continuous devidend yield
diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html
index 286f179..ba298ef 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/04 Stochastic Volatility.html	
@@ -31,8 +31,7 @@ <h4>2. Simulation of the Heston Process</h4>
      <li>To generate \(W_1\) and \(W_2\) with correlation \(\rho\), we first generate two independent standard normal variables \(Z_1\) and \(Z_2\), set \(W_1=Z_1\), then \[W_2=\rho Z_1+\sqrt{1-\rho^2}Z_2\]</li>
 </ul>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
 from numpy import sqrt, exp
 import numpy as np
@@ -94,8 +93,7 @@ <h4>3. Calibration of Model Parameters</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">import pandas as pd
 from numpy import sqrt,mean,log,diff
 import QuantLib as ql
@@ -152,8 +150,7 @@ <h4>3. Calibration of Model Parameters</h4>
 </p>
 
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">long_term_var = 0.191762, rate_reversion = 0.000001, vol_of_vol = 0.215442, corr = -0.817388, initial_var = 0.198778
 </pre>
 </div>
diff --git a/07 Applied Options[]/01 Covered Call/01 Definition.html b/07 Applied Options[]/01 Covered Call/01 Definition.html
index 25a802e..882627b 100755
--- a/07 Applied Options[]/01 Covered Call/01 Definition.html	
+++ b/07 Applied Options[]/01 Covered Call/01 Definition.html	
@@ -5,8 +5,7 @@
 The payoff  is as follows:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">
   import numpy as np
   import matplotlib.pyplot as plt
diff --git a/07 Applied Options[]/01 Covered Call/02 Implementation.html b/07 Applied Options[]/01 Covered Call/02 Implementation.html
index dc972d4..2583e0d 100755
--- a/07 Applied Options[]/01 Covered Call/02 Implementation.html	
+++ b/07 Applied Options[]/01 Covered Call/02 Implementation.html	
@@ -2,8 +2,7 @@
 Step 1: Initialize the Algorithm： At the beginning of your algorithm, you need to set the start date, the end date and the cash required for the algorithm. For options algorithm, you need to add the equity and the options written on this equity.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
    self.SetStartDate(2016, 1, 1)
    self.SetEndDate(2016, 3, 1)
@@ -37,8 +36,7 @@
   Step 4: Select the most ATM contract with the furthest expiration date from call options contracts. Sell this call contract for each 100 shares of stock you own. Here you can also choose ITM or OTM contract based on your expected level of profit and the level of risk you can take.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def TradeOptions(self,slice):
 		if slice.OptionChains.Count == 0: return
     for i in slice.OptionChains:
diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html
index 8f36ad9..9b8de43 100755
--- a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
@@ -8,8 +8,7 @@
  Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(800,1100,1)
 k_low = 900 # lower strike price for call
 k_high = 1000 # higher strike price for call
diff --git a/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html
index 3365258..974201f 100755
--- a/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html	
@@ -2,8 +2,7 @@
  Step 1: First, you need to initialize the algorithm including set the start date, end date and the cash required. Then use <code>option.SetFilter(-6, 6, timedelta(30), timedelta(60))</code> to filter the candidate contracts which expire in 30 days to 60 days from now on. The strike price range involves both ITM and OTM options. Then we get the option chains of GOOG.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2016, 5, 1)
 	self.SetEndDate(2016, 10, 1)
@@ -21,8 +20,7 @@
 	Step 2: Choose the contracts with the same expiration date. For demonstration purpose here we sorted the contracts by their expiration dates and choose the options with the furthest expiration date in the option chain.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def TradeOptions(self,optionchain):
 	for i in optionchain:
 		if i.Key != self.symbol: continue
@@ -35,8 +33,7 @@
  Step 3: Filter the call options from the contracts which expire on the furthest expiration date in the option chain.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">call = [i for i in chain if i.Expiry == expiry and i.Right == OptionRight.Call]
 </pre>
 </div>
@@ -44,8 +41,7 @@
  Step 4: Sort the call options with the same expiration date according to their strike price. Then buy the call option with the lowest strike price and sell the call with the highest strike price.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
 	if len(call_contracts) == 0: continue
 	# call option contract with lower strike
@@ -60,8 +56,7 @@
  Note here you need to add the following rules in <code>OnData(self,slice)</code> method because you only need to trade options once and wait until the contracts expire. If you already had securities invested in the portfolio, then you do not need to trade new options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if not self.Portfolio.Invested:
 	self.TradeOptions(optionchain)
 </pre>
diff --git a/07 Applied Options[]/03 Long Straddle/01 Definition.html b/07 Applied Options[]/03 Long Straddle/01 Definition.html
index dff5980..9f8525e 100755
--- a/07 Applied Options[]/03 Long Straddle/01 Definition.html	
+++ b/07 Applied Options[]/03 Long Straddle/01 Definition.html	
@@ -2,8 +2,7 @@
 	Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(750,1000,1)
 strike = 900 # strike price for both call and put
 premium_call = 20 # premium of call option
diff --git a/07 Applied Options[]/03 Long Straddle/02 Implementation.html b/07 Applied Options[]/03 Long Straddle/02 Implementation.html
index 77f07cd..afa998a 100755
--- a/07 Applied Options[]/03 Long Straddle/02 Implementation.html	
+++ b/07 Applied Options[]/03 Long Straddle/02 Implementation.html	
@@ -2,8 +2,7 @@
  Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 4, 01)
 	self.SetEndDate(2017, 6, 30)
@@ -21,8 +20,7 @@
  Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in optionchain:
 	if i.Key != self.symbol: continue
 	chain = i.Value
@@ -40,8 +38,7 @@
  Step 3: According to the call option contract, choose the put option with the same strike price and the same expiration date. Then buy the call option and the put option at the same time and wait until expiration.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"> for i in chain:
 	if i.Expiry == expiry and i.Right == 1 and i.Strike ==call_contracts[0].Strike:
 	    self.put = i
diff --git a/07 Applied Options[]/04 Long Strangle/01 Definition.html b/07 Applied Options[]/04 Long Strangle/01 Definition.html
index 228e32b..e5691db 100755
--- a/07 Applied Options[]/04 Long Strangle/01 Definition.html	
+++ b/07 Applied Options[]/04 Long Strangle/01 Definition.html	
@@ -2,8 +2,7 @@
  Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(700,1000,1)
 # Suppose the undelying price at time 0 is 830
 k_call = 870 # The strike price of OTM call
diff --git a/07 Applied Options[]/04 Long Strangle/02 Implementation.html b/07 Applied Options[]/04 Long Strangle/02 Implementation.html
index 5ed0284..71cc2bf 100755
--- a/07 Applied Options[]/04 Long Strangle/02 Implementation.html	
+++ b/07 Applied Options[]/04 Long Strangle/02 Implementation.html	
@@ -2,8 +2,7 @@
 	Step 1: Initialize your algorithm including setting the start and end date, setting the cash and filtering the options contracts. Note here in SetFilter, the strike price should range from negative to positive because we need to choose out-of-the-money put and call options from candidate contracts. The strike price of OTM call should be greater than ATM options and the strike price of OTM put should be lower than ATM options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 4, 1)
 	self.SetEndDate(2017, 5, 30)
@@ -21,8 +20,7 @@
 	Step 2: Sort the option chain by expiration date and choose an expiration date you want to trade. For demonstration purpose, here we choose options with the furthest expiration date in candidate contracts. Then filter out the call options which expire on that date.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in optionchain:
 	if i.Key != self.symbol: continue
 	chain = i.Value
@@ -36,8 +34,7 @@
 	Step 3: Sort the call options by their expiration date and choose the deep OTM contract which has the largest strike price.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
 if len(call_contracts) == 0: continue
 # choose the deep OTM call option
@@ -48,8 +45,7 @@
 	Step 4: Select the put options which have the same expiration date with the call option and sort the put options by strike price. Then choose the deep out-of-the-money put which has the minimum strike price among all the available put options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">put_contracts = sorted([i for i in chain if i.Expiry == expiry and i.Right == 1], key = lambda x: x.Strike)
 # choose the deep OTM put option
 self.put = put_contracts[0]
@@ -59,8 +55,7 @@
 	Step 5: Buy the call and the put options at the same time and wait until expiration.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.Buy(self.call.Symbol ,1)
 self.Buy(self.put.Symbol ,1)
 </pre>
diff --git a/07 Applied Options[]/05 Butterfly Spread/01 Definition.html b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html
index c605044..905e999 100755
--- a/07 Applied Options[]/05 Butterfly Spread/01 Definition.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html	
@@ -34,8 +34,7 @@
 	The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(800,1100,1)
 # Suppose the undelying price at time 0 is 935
 k_itm = 915 # the strike price of ITM call
diff --git a/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html
index c565b12..2508d10 100755
--- a/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html	
@@ -2,8 +2,7 @@
 	Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 4, 1)
 	self.SetEndDate(2017, 5, 30)
@@ -20,8 +19,7 @@
 	Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in optionchain:
 	if i.Key != self.symbol: continue
 	chain = i.Value
@@ -35,8 +33,7 @@
 	Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># sorted the contracts according to their strike prices
 call_contracts = sorted(call,key = lambda x: x.Strike)
 if len(call_contracts) == 0: continue
@@ -52,8 +49,7 @@
 	Step 4: Purchase 1 ITM call option and 1 OTM call option, then sell 2 ATM call option.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.Sell(self.atm_call.Symbol ,2)
 self.Buy(self.itm_call.Symbol ,1)
 self.Buy(self.otm_call.Symbol ,1)
diff --git a/07 Applied Options[]/06 Iron Condor/01 Definition.html b/07 Applied Options[]/06 Iron Condor/01 Definition.html
index 7b9b695..0722b91 100755
--- a/07 Applied Options[]/06 Iron Condor/01 Definition.html	
+++ b/07 Applied Options[]/06 Iron Condor/01 Definition.html	
@@ -31,8 +31,7 @@
 	The Iron Condor is the combination of a bear put spread and a bull call spread in which the strike price of the long put is lower than the strike price of the long call. If the stock price is between the two short strike prices when the options expire, the strategy will be profitable.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(700,950,1)
 k_call_higher = 850 # the strike price of OTM call(Higher k)
 k_call_lower = 840 # the strike price of OTM call(Lower k)
diff --git a/07 Applied Options[]/06 Iron Condor/02 Implementation.html b/07 Applied Options[]/06 Iron Condor/02 Implementation.html
index 62d6898..37ffc44 100755
--- a/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
+++ b/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
@@ -2,8 +2,7 @@
 	Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 2, 1)
 	self.SetEndDate(2017, 3, 31)
@@ -20,8 +19,7 @@
 	Step 2: Break the candidate options into two parts: call and put options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">for i in optionchain:
 	if i.Key != self.symbol: continue
 	chain = i.Value
@@ -34,8 +32,7 @@
 	Step 3: Sort the call and put options by their strike price respectively.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
 put_contracts = sorted(put,key = lambda x: x.Strike)
 </pre>
@@ -44,8 +41,7 @@
 	Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
 # Buy 1 OTM Put (Lower Strike)
 self.otm_put_lower = put_contracts[0]
diff --git a/07 Applied Options[]/07 Iron Butterfly/01 Definition.html b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html
index 4255a4b..73c4d69 100755
--- a/07 Applied Options[]/07 Iron Butterfly/01 Definition.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html	
@@ -31,8 +31,7 @@
 	Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">price = np.arange(700,950,1)
 k_atm = 830 # the strike price of ATM call &amp; put
 k_otm_put = 800 # the strike price of OTM put
diff --git a/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html
index 0642a41..3631f5f 100755
--- a/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html	
@@ -2,8 +2,7 @@
 	Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 2, 1)
 	self.SetEndDate(2017, 3, 31)
@@ -20,8 +19,7 @@
 	Step 2: Break the candidate contracts into the call and put options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def TradeOptions(self,optionchain):
     for i in optionchain:
         if i.Key != self.symbol: continue
@@ -35,8 +33,7 @@
 	Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">call_contracts = sorted(call,key = lambda x: x.Strike)
 put_contracts = sorted(put,key = lambda x: x.Strike)
 if len(call_contracts) == 0 or len(put_contracts) == 0 : continue
@@ -46,8 +43,7 @@
 	Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># Sell 1 ATM Put
 self.atm_put = sorted(put_contracts,key = lambda x: abs(chain.Underlying.Price - x.Strike))[0]
 self.Sell(self.atm_put.Symbol ,1)
diff --git a/07 Applied Options[]/08 Protective Collar/01 Definition.html b/07 Applied Options[]/08 Protective Collar/01 Definition.html
index 29bbc40..68eb983 100755
--- a/07 Applied Options[]/08 Protective Collar/01 Definition.html	
+++ b/07 Applied Options[]/08 Protective Collar/01 Definition.html	
@@ -2,8 +2,7 @@
 	Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python"># Protective Collar
 price = np.arange(700,950,1)
 # assume at time 0, the price of the undelying stock is 830
diff --git a/07 Applied Options[]/08 Protective Collar/02 Implementation.html b/07 Applied Options[]/08 Protective Collar/02 Implementation.html
index c37dea3..57e2c1a 100755
--- a/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
+++ b/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
@@ -2,8 +2,7 @@
 	Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def Initialize(self):
 	self.SetStartDate(2017, 4, 1)
 	self.SetEndDate(2017, 5, 30)
@@ -21,8 +20,7 @@
 	Step 2: Choose the expiration date for your options traded and break the options into the call and put contracts. The choice of expiration date depends on the holding period of stocks in your portfolio.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def TradeOptions(self,optionchain):
     for i in optionchain:
 	if i.Key != self.symbol: continue
@@ -39,8 +37,7 @@
 	Step 3: Choose the deep in-the-money call and put options in the list and then sell the call options and buy the put options.
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">self.otm_call = sorted(call, key = lambda x: x.Strike)[-1]
 self.otm_put = sorted(put, key = lambda x: x.Strike)[0]
 if (self.otm_call is None) or (self.otm_put is None): continue
@@ -52,8 +49,7 @@
 	Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
 </p>
 <div class="section-example-container">
-<pre class="csharp">
-</pre>
+
 <pre class="python">def OnData(self,slice):
     optionchain = slice.OptionChains
     for i in slice.OptionChains:

From 66277707ae63b23aa03a9fc68b6de6730480537a Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Wed, 13 Jun 2018 15:11:59 -0400
Subject: [PATCH 062/753] Update 05 Algorithm.html

---
 .../05 Algorithm.html                                         | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index 1e8cd4b..295b5dd 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1 +1,3 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
+<div>
+  <script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
+</div>

From 2334acd22023bc4252870c77e11c25d9c3e38733 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Wed, 13 Jun 2018 15:12:40 -0400
Subject: [PATCH 063/753] Update 05 Algorithm.html


From 518a72f5c5f179523ce95434d4ea4439adb241ab Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 13 Jun 2018 18:34:40 -0400
Subject: [PATCH 064/753] Updated tutorials

---
 .../05 Algorithm.html                             | 10 ++++++++--
 .../06 Algorithm.html                             |  7 ++++++-
 .../06 Algorithm.html                             | 15 +++++++++++++--
 .../04 Algorithm.html                             | 14 ++++++++++++--
 .../04 Algorithm.html                             |  7 ++++++-
 .../05 Algorithm.html                             |  8 ++++++--
 .../06 Algorithm.html                             |  7 ++++++-
 .../05 Algorithm.html                             |  7 ++++++-
 .../04 Algorithm.html                             |  7 ++++++-
 .../04 Algorithm.html                             |  7 ++++++-
 .../04 Algorithm.html                             |  7 ++++++-
 .../12 Modern Portfolio Theory/06 Algorithm.html  |  7 ++++++-
 .../13 Market Risk/06 Algorithm.html              | 15 +++++++++++++--
 .../07 Algorithm.html                             |  7 ++++++-
 .../02 QuantConnect Options API/05 Algorithm.html |  7 ++++++-
 .../05 Algorithm.html                             |  7 ++++++-
 .../01 Covered Call/04 Algorithm.html             | 14 ++++++++++++--
 .../02 Bull Call Spread/04 Algorithm.html         | 14 ++++++++++++--
 .../03 Long Straddle/04 Algorithm.html            | 14 ++++++++++++--
 .../04 Long Strangle/04 Algorithm.html            | 15 +++++++++++++--
 .../05 Butterfly Spread/04 Algorithm.html         | 15 +++++++++++++--
 .../06 Iron Condor/04 Algorithm.html              | 15 +++++++++++++--
 .../07 Iron Butterfly/04 Algorithm.html           | 15 +++++++++++++--
 .../08 Protective Collar/04 Algorithm.html        | 15 +++++++++++++--
 24 files changed, 219 insertions(+), 37 deletions(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index 295b5dd..e0b1d5a 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1,3 +1,9 @@
-<div>
-  <script src='https://www.quantconnect.com/terminal/backtest.js?sid=2f525825fa6fb72ee4b23b9e63ecf025'></script>
+<p>
+	Backtest using OptionChainProvider
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2f525825fa6fb72ee4b23b9e63ecf025.html"></iframe>
+    </div>
 </div>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
index 28c0672..78a2422 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=c1eea4df64cf986eba481f5e5e376e8a'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c1eea4df64cf986eba481f5e5e376e8a.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 1b95dbc..0f2a79f 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -1,8 +1,19 @@
 <p>
   Backtest for copula method
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=343dc596a484a20a579ae77c79a12495'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_343dc596a484a20a579ae77c79a12495.html"></iframe>
+    </div>
+</div>
+
 <p>
   Backtest for cointegration method
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d4443ef5bd0d45da3d003fe8e49a966e'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d4443ef5bd0d45da3d003fe8e49a966e.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
index 32760d0..bebadac 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
@@ -1,8 +1,18 @@
 <p>
   Backtest for EURUSD
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=87d3047e8c358ea9b4779e0b5d238a74'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_87d3047e8c358ea9b4779e0b5d238a74.html"></iframe>
+    </div>
+</div>
 <p>
   Backtest for GBPUSD
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4bacf930111e3502993b7e7c9b315be0'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4bacf930111e3502993b7e7c9b315be0.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index e5e22ef..906b055 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -1,4 +1,9 @@
 <p>
   Backtest result for ETF: SPY from 2004 to 2017
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=ffe3fbed009f9502f145e96b81638575'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ffe3fbed009f9502f145e96b81638575.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
index f597f06..987eca8 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
@@ -1,5 +1,9 @@
 <p>
   Strategy code, as well as backtesting result, is attached below. We also put other choices of implementation in the comments.
 </p>
-
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8242236c21a102fbff14d8c3eb30f841'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8242236c21a102fbff14d8c3eb30f841.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
index f81320e..3376611 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5d0576da03c50667c3af8aefa86f4f44'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5d0576da03c50667c3af8aefa86f4f44.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
index 73cb42b..5a31153 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5188517de3244425b56af3e717c90cb9'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5188517de3244425b56af3e717c90cb9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index 906fafd..4f29ddb 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=4f8946295bb7358518b35ecbd3d46f01'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4f8946295bb7358518b35ecbd3d46f01.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html
index c381329..058cc18 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75595e3ad41ca49ebcc6ab95aeaa92b8'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75595e3ad41ca49ebcc6ab95aeaa92b8.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
index 0da6e08..d1731e4 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=56878e349b15cf73edc52a34d49c4973'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_56878e349b15cf73edc52a34d49c4973.html"></iframe>
+    </div>
+</div>
diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
index fd74d48..2fc9305 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -1,4 +1,9 @@
 <p>
   Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=03b5af43deef78e3a81c9a8322069567'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_03b5af43deef78e3a81c9a8322069567.html"></iframe>
+    </div>
+</div>
diff --git a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html
index 2363341..5b8cd66 100755
--- a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
@@ -1,2 +1,13 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=8bac244b346cbd9bc3b39dca76192919'></script>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5c75197259238b36a6cea72832c9e709'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8bac244b346cbd9bc3b39dca76192919.html"></iframe>
+    </div>
+</div>
+
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5c75197259238b36a6cea72832c9e709.html"></iframe>
+    </div>
+</div>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html
index 5246b7d..5db089b 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
@@ -7,4 +7,9 @@
 <p>
   In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d0f89f7876841581a8eee32cf007cd75'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d0f89f7876841581a8eee32cf007cd75.html"></iframe>
+    </div>
+</div>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 3b2c255..43fce30 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -1,4 +1,9 @@
 <p>
   This simple example demonstrates how you can inspect the option chain to pick a specific option contract to trade.
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=6453fa45f6b98d880a4646a820265a62'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+    </div>
+</div>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index f87661f..4b0d63d 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1 +1,6 @@
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=b986091b5e1822b3d74f1231bf44eb2c'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_b986091b5e1822b3d74f1231bf44eb2c.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index b7ae18c..66d9131 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -1,8 +1,18 @@
 <p>
 Backtest using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=9d07e52d5c6199a77597d5f48e68353e'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9d07e52d5c6199a77597d5f48e68353e.html"></iframe>
+    </div>
+</div>
 <p>
 Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=31a6d88a35d29c2bbe654a3e5ee5f80e'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_31a6d88a35d29c2bbe654a3e5ee5f80e.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html
index 59cd83b..2bc623d 100755
--- a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
@@ -1,8 +1,18 @@
 <p>
 Backtest (SetFilter)
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=06ae7450577c1f0c80edc563c4cf06b8'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_06ae7450577c1f0c80edc563c4cf06b8.html"></iframe>
+    </div>
+</div>
 <p>
 Backtest (OptionChainProvider)
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=db61c966b616e8d1d8f289c4eed5525c'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_db61c966b616e8d1d8f289c4eed5525c.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
index d393a4c..0aa161d 100755
--- a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -1,8 +1,18 @@
 <p>
  Backtest (Using SetFilter)
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=0ff9a8022311bd57f1fe422eecdd3363'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_0ff9a8022311bd57f1fe422eecdd3363.html"></iframe>
+    </div>
+</div>
 <p>
 Backtest (Using OptionChainProvider)
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=75edf3c5d0221d22e8927ffedbf116b2'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75edf3c5d0221d22e8927ffedbf116b2.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html
index 640ac53..b4df8b4 100755
--- a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
+++ b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
@@ -1,8 +1,19 @@
 <p>
  Backtest using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=89f1f8e5f1ed5e78bd1b4cdf3ab796a1'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_89f1f8e5f1ed5e78bd1b4cdf3ab796a1.html"></iframe>
+    </div>
+</div>
+
 <p>
 	Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e3c07881318d864a9ae25637d6d2d9d3'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e3c07881318d864a9ae25637d6d2d9d3.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html
index 175d700..2c2b9e4 100755
--- a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
@@ -1,8 +1,19 @@
 <p>
 	Backtest using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d3c16b8ba4f7df0bebbd766b3dfa7c14'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d3c16b8ba4f7df0bebbd766b3dfa7c14.html"></iframe>
+    </div>
+</div>
+
 <p>
 	Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=dab462cab9b3f5c8b2a23911f3e98843'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_dab462cab9b3f5c8b2a23911f3e98843.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
index 127c50d..55d9629 100755
--- a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -2,8 +2,19 @@ <h2>Algorithm</h2>
 <p>
 	Backtesing using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=e95a3d8feac3b92b3f39233f00e09ae5'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e95a3d8feac3b92b3f39233f00e09ae5.html"></iframe>
+    </div>
+</div>
 <p>
 	Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=66bf622f52dbb2a6cb56ccf992d64765'></script>
+
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_66bf622f52dbb2a6cb56ccf992d64765.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html
index bdaacd8..968a1fb 100755
--- a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
@@ -1,8 +1,19 @@
 <p>
 	Backtest using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=a2fe60a67bc1c78532ba4a1a93211c6c'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2fe60a67bc1c78532ba4a1a93211c6c.html"></iframe>
+    </div>
+</div>
+
 <p>
 	Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=d60e69c80aeb5719f06202a0c25b1274'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d60e69c80aeb5719f06202a0c25b1274.html"></iframe>
+    </div>
+</div>
diff --git a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html
index e9e3bd1..16f8299 100755
--- a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
+++ b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
@@ -1,8 +1,19 @@
 <p>
 	Backtest using SetFilter
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=5e793524109c9230ca92970f050f119f'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5e793524109c9230ca92970f050f119f.html"></iframe>
+    </div>
+</div>
+
 <p>
 	Backtest using OptionChainProvider
 </p>
-<script src='https://www.quantconnect.com/terminal/backtest.js?sid=823c79dcfbae11dabb14ee9ea2365c13'></script>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_823c79dcfbae11dabb14ee9ea2365c13.html"></iframe>
+    </div>
+</div>

From 7607d0cc767bc3f6def7bd07f8c49db3da951fcb Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Thu, 14 Jun 2018 11:52:00 -0400
Subject: [PATCH 065/753] Add missing closing div.

---
 .../02 Logical Operations and Loops/03 If Statement.html         | 1 +
 1 file changed, 1 insertion(+)

diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html
index 086ca77..98c8f4e 100755
--- a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html	
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/03 If Statement.html	
@@ -14,6 +14,7 @@
 else:
     # if none of the above conditions is True, execute the code here
 </pre>
+</div>
 
 <p>
   An if statement doesn't necessarily has elif and else part. If it's not specified, the indented block of code will be executed when the condition is true, otherwise the whole if statement will be skipped.

From 22c0cdccbea4c4c23acf427019ede2fcda015509 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Thu, 14 Jun 2018 11:56:53 -0400
Subject: [PATCH 066/753] Add missing closing divs

---
 .../05 List Comprehension.html                                | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html
index c119a6f..72537b0 100755
--- a/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html	
+++ b/05 Introduction to Financial Python[]/02 Logical Operations and Loops/05 List Comprehension.html	
@@ -11,7 +11,8 @@
 print squares
 [out]: [1, 4, 9, 16, 25]
 </pre>
-
+</div>
+  
 <p>
   Using list comprehension:
 </p>
@@ -24,6 +25,7 @@
 print squares
 [out]: [1, 4, 9, 16, 25]
 </pre>
+</div>
 
 <p>
   Recall the example above where we used a for loop to select stocks. Here we use list comprehension:

From f028e8061bd3525b61f2609c9333bec9b741027b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 21 Jun 2018 11:49:08 -0400
Subject: [PATCH 067/753] Add strategy tutorials

---
 .../12 Asset Class Trend Following/01 Introduction.html    | 4 ++++
 .../12 Asset Class Trend Following/02 Method.html          | 5 +++++
 .../12 Asset Class Trend Following/03 Algorithm.html       | 6 ++++++
 .../13 Asset Class Momentum/01 Introduction.html           | 4 ++++
 04 Strategy Library/13 Asset Class Momentum/02 Method.html | 3 +++
 .../13 Asset Class Momentum/03 Algorithm.html              | 6 ++++++
 .../14 Sector Momentum/01 Introduction.html                | 3 +++
 04 Strategy Library/14 Sector Momentum/02 Method.html      | 5 +++++
 04 Strategy Library/14 Sector Momentum/03 Algorithm.html   | 6 ++++++
 .../15 Short Term Reversal/01 Introduction.html            | 3 +++
 04 Strategy Library/15 Short Term Reversal/02 Method.html  | 6 ++++++
 .../15 Short Term Reversal/03 Algorithm.html               | 6 ++++++
 .../16 Overnight Anomaly/01 Introduction.html              | 3 +++
 04 Strategy Library/16 Overnight Anomaly/02 Method.html    | 7 +++++++
 04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html | 6 ++++++
 04 Strategy Library/17 Forex Momentum/01 Introduction.html | 3 +++
 04 Strategy Library/17 Forex Momentum/02 Method.html       | 5 +++++
 04 Strategy Library/17 Forex Momentum/03 Algorithm.html    | 6 ++++++
 18 files changed, 87 insertions(+)
 create mode 100644 04 Strategy Library/12 Asset Class Trend Following/01 Introduction.html
 create mode 100644 04 Strategy Library/12 Asset Class Trend Following/02 Method.html
 create mode 100644 04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html
 create mode 100644 04 Strategy Library/13 Asset Class Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/13 Asset Class Momentum/02 Method.html
 create mode 100644 04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html
 create mode 100644 04 Strategy Library/14 Sector Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/14 Sector Momentum/02 Method.html
 create mode 100644 04 Strategy Library/14 Sector Momentum/03 Algorithm.html
 create mode 100644 04 Strategy Library/15 Short Term Reversal/01 Introduction.html
 create mode 100644 04 Strategy Library/15 Short Term Reversal/02 Method.html
 create mode 100644 04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
 create mode 100644 04 Strategy Library/16 Overnight Anomaly/01 Introduction.html
 create mode 100644 04 Strategy Library/16 Overnight Anomaly/02 Method.html
 create mode 100644 04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html
 create mode 100644 04 Strategy Library/17 Forex Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/17 Forex Momentum/02 Method.html
 create mode 100644 04 Strategy Library/17 Forex Momentum/03 Algorithm.html

diff --git a/04 Strategy Library/12 Asset Class Trend Following/01 Introduction.html b/04 Strategy Library/12 Asset Class Trend Following/01 Introduction.html
new file mode 100644
index 0000000..df8b8b8
--- /dev/null
+++ b/04 Strategy Library/12 Asset Class Trend Following/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Asset class trend following is a strategy that tries to exploit a momentum anomaly between various assets. It uses various moving averages or momentum filters to gain an exposure to an asset class only at the time when there is a higher probability for outperformance with less risk.
+  The basic logic behind the trend following is finding a method to detect the trend of price movement and buy an asset when its price trend goes up, and sell when its trend goes down.
+</p>
diff --git a/04 Strategy Library/12 Asset Class Trend Following/02 Method.html b/04 Strategy Library/12 Asset Class Trend Following/02 Method.html
new file mode 100644
index 0000000..cf3215f
--- /dev/null
+++ b/04 Strategy Library/12 Asset Class Trend Following/02 Method.html	
@@ -0,0 +1,5 @@
+<p>
+  This algorithm applies to trend following ideas to 5 ETFs in different asset classes like stocks, bonds, and commodities. The simple moving average is used to detect the trend. When the closing price is over its ten-month simple moving average,
+  we give equal allocation to those ETFs, otherwise stay in cash. 
+  SMA(symbol, period, resolution) is used to generate the moving average value In LEAN implementation. A warm-up period of ten months is set to prime the data and initialize the indicator so the SMA is ready to use when the algorithm starts.
+</p>
diff --git a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html
new file mode 100644
index 0000000..d97266e
--- /dev/null
+++ b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6caef9a7716468be0d790b63b7f463f6.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html b/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html
new file mode 100644
index 0000000..43d89b5
--- /dev/null
+++ b/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+Unlike the first trending following algorithm, this algorithm finds the entry point with the momentum effect. The momentum anomaly says that what was strongly going up in the past will probably continue to go up in the near future. 
+In the calculation, it refers to the rate of change in price movements for a particular asset.
+</p>
diff --git a/04 Strategy Library/13 Asset Class Momentum/02 Method.html b/04 Strategy Library/13 Asset Class Momentum/02 Method.html
new file mode 100644
index 0000000..0869d5c
--- /dev/null
+++ b/04 Strategy Library/13 Asset Class Momentum/02 Method.html	
@@ -0,0 +1,3 @@
+<p>
+  The portfolio of this algorithm contains 5 ETFs in different asset classes. LEAN has the momentum indicator MOM(symbol, period). The period is 12 months. After obtaining the most recent momentum value, we pick 3 ETFs with the strongest 12-month momentum into the portfolio and weight them equally. Hold for 1 month and then rebalance the portfolio with new momentum. Unlike asset class trend following strategy which combines asset classes into one portfolio, this rotational momentum system compares the performance of asset classes and picks only the best-performing assets from investment universe into investor's portfolio. The portfolio is rebalanced every month and portfolio's holdings are rotated so that only the best-performing assets are held.
+</p>
diff --git a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html
new file mode 100644
index 0000000..41c9021
--- /dev/null
+++ b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_1cb38e96cec6120fc85152da661605c1.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/14 Sector Momentum/01 Introduction.html b/04 Strategy Library/14 Sector Momentum/01 Introduction.html
new file mode 100644
index 0000000..8de7af5
--- /dev/null
+++ b/04 Strategy Library/14 Sector Momentum/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+Sector rotation is a popular strategy with which capital is actively reallocated from one sector to another based upon changing market conditions.
+</p>
diff --git a/04 Strategy Library/14 Sector Momentum/02 Method.html b/04 Strategy Library/14 Sector Momentum/02 Method.html
new file mode 100644
index 0000000..74709a4
--- /dev/null
+++ b/04 Strategy Library/14 Sector Momentum/02 Method.html	
@@ -0,0 +1,5 @@
+<p>
+This algorithm is an adaptation of asset class momentum. Instead of rotating ETFs in different asset classes,
+the sector momentum algorithm picks 10 sector ETFs and pick 3 ETFs with the strongest 12-month momentum into
+the portfolio and weight them equally. The portfolio is rebalanced at the start of each month.
+</p>
diff --git a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html
new file mode 100644
index 0000000..f9e9dee
--- /dev/null
+++ b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f7b25a5e872436195cb27a815b5f7e1.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/15 Short Term Reversal/01 Introduction.html b/04 Strategy Library/15 Short Term Reversal/01 Introduction.html
new file mode 100644
index 0000000..ebad458
--- /dev/null
+++ b/04 Strategy Library/15 Short Term Reversal/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+The short-term reversal is the phenomenon that stocks with relatively low returns over the past month or week earn positive abnormal returns in the following month or week, and stocks with high returns earn negative abnormal returns. 
+</p>
diff --git a/04 Strategy Library/15 Short Term Reversal/02 Method.html b/04 Strategy Library/15 Short Term Reversal/02 Method.html
new file mode 100644
index 0000000..593091d
--- /dev/null
+++ b/04 Strategy Library/15 Short Term Reversal/02 Method.html	
@@ -0,0 +1,6 @@
+<p>
+  To apply short-term reversal in stocks market, first, we use the universe selection API to pick the stocks with the price higher than 4 and rank those stocks by dollar volume and
+  choose the top 100 stocks as our asset pool. In fine universe selection, the prescreened stocks are sorted by market cap we choose the top 20. To detect the reversal effect,
+  the return is the most straightforward measure of the stock history performance. the indicator RateOfReturn() is used to calculate the monthly return. We go long on the 10 stocks
+  with the lowest performance in the previous month and go short on the 10 stocks with the greatest performance from the previous month.
+</p>
diff --git a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
new file mode 100644
index 0000000..81ac714
--- /dev/null
+++ b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8ce50fdd465d8629d4c73135938477d9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html b/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html
new file mode 100644
index 0000000..fea63b4
--- /dev/null
+++ b/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+Instead of collecting profit from intraday trading, this algorithm is trying to view the overnight returns.
+</p>
diff --git a/04 Strategy Library/16 Overnight Anomaly/02 Method.html b/04 Strategy Library/16 Overnight Anomaly/02 Method.html
new file mode 100644
index 0000000..6a5e21a
--- /dev/null
+++ b/04 Strategy Library/16 Overnight Anomaly/02 Method.html	
@@ -0,0 +1,7 @@
+<p>
+  The strategy buys SPY ETF at its closing price and sells it at the opening each day.
+  The strategy makes a lot of trades, therefore, the whole strategy is very sensitive
+  to slippage costs and fees. Those returns are canceled out once transaction costs
+  are taken into account. With the InteractiveBrokers transaction model, fees for 20
+  years backtest is almost 25% of the initial cash.
+</p>
diff --git a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html
new file mode 100644
index 0000000..ee01eb0
--- /dev/null
+++ b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d31f2d20f4a060418985dd8c0c606c13.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/17 Forex Momentum/01 Introduction.html b/04 Strategy Library/17 Forex Momentum/01 Introduction.html
new file mode 100644
index 0000000..418c3bf
--- /dev/null
+++ b/04 Strategy Library/17 Forex Momentum/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Momentum is a trend following strategy, where the strategy buys the assets which have performed well in the past and sells the assets which have performed bad.
+</p>
diff --git a/04 Strategy Library/17 Forex Momentum/02 Method.html b/04 Strategy Library/17 Forex Momentum/02 Method.html
new file mode 100644
index 0000000..6347296
--- /dev/null
+++ b/04 Strategy Library/17 Forex Momentum/02 Method.html	
@@ -0,0 +1,5 @@
+<p>
+  This algorithm applies momentum to the forex market. Our universe consists of 15 forex pairs
+  and covers period from 2006 to 2018. The algorithm goes long 3 currencies with strongest 12-month
+  momentum against USD and goes short 3 currencies with lowest 12-month momentum against USD. 
+</p>
diff --git a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html
new file mode 100644
index 0000000..c3c3112
--- /dev/null
+++ b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_336bbb8e9e4a55a6adfa00c0d9f904bb.html"></iframe>
+    </div>
+</div>

From 22db3063111245fc43b71e14b5e3dcd48ea8ece4 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 22 Jun 2018 18:26:27 -0400
Subject: [PATCH 068/753] Add strategy tutorial 18

---
 .../01 Introduction.html                        |  5 +++++
 .../02 Method.html                              | 17 +++++++++++++++++
 .../03 Algorithm.html                           |  6 ++++++
 3 files changed, 28 insertions(+)
 create mode 100644 04 Strategy Library/18 Volatility Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/18 Volatility Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html

diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/01 Introduction.html b/04 Strategy Library/18 Volatility Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..20edd72
--- /dev/null
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The low volatility effect in equities refers to that stocks which previously
+  exhibited lower volatility will earn higher risk-adjusted returns than those with higher volatility.
+  This algorithm extends the study of the low volatility effect to U.S stocks with higher market capital.
+</p>
diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/02 Method.html b/04 Strategy Library/18 Volatility Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..8f3cdc8
--- /dev/null
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/02 Method.html	
@@ -0,0 +1,17 @@
+<h4>Universe Selection</h4>
+<p>
+  To construct the investment universe which consists of US large cap stocks, first in coarse universe selection,
+  we exclude stocks without fundamental data and the price is below 5. A universe of 100 stocks is selected based on the dollar volume. In fine universe selection, we pick 50 stocks from the coarse universe with the highest market cap.
+</p>
+<h4>Calculate the Volatility</h4>
+<p>
+  We create <code>SymbolData</code> class and use <code>RollingWindow</code> to store the price data for symbols returned by fine universe.
+  The lookback period is 252 trading days. First, we request history data to initialize the RollingWindow for the added symbols and update it's value with the closing price every day in OnData().
+</p>
+<p>
+  The standard deviation is the typical statistic used to measure volatility. It is defined as the square root of the average variance of the data from its mean. We use the closing price series in RollingWindow to calculate the volatility.
+</p>
+<h4>Trading stocks</h4>
+<p>
+  The trading logic is we go long 5 stocks with the lowest volatility and liquidate stocks in the portfolio which does not in the lowest volatility list. The portfolio is rebalanced at the first trading day each month.
+</p>
diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..720e924
--- /dev/null
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_34e8ae2763e7e2122634d4091120d320.html"></iframe>
+    </div>
+</div>

From c5f414e76d188df0944c8eab99c680a8de4421b7 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Tue, 26 Jun 2018 12:26:38 -0400
Subject: [PATCH 069/753] Fix broken html tags

---
 .../02 Factor Selection.html                        | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html
index d1a76f4..eb8ac07 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
@@ -47,7 +47,14 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 </pre>
 </div>
 
-2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending order of their factor values. Here we use 12-months' total risk-based capital data <code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.  It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.</span>
+<p>
+	2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending 
+	order of their factor values. Here we use 12-months' total risk-based capital data 
+	<code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.
+	It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> 
+	can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.
+</p>
+
 <div class="section-example-container">
 
 <pre class="python">def FineSelectionFunction(self, fine):
@@ -66,6 +73,7 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 		return []
 </pre>
 </div>
+
 <h3>Step 2: Compute the monthly return of portfolios</h3>
 <p>
 	1. At the end of each month, we extract the one-month history close prices of each stock and compute the monthly returns.
@@ -226,8 +234,7 @@ <h3>Step 3: Generate the metrics to test the factor significance</h3>
 </tr>
 </tbody>
 </table>
-</div>
-</div>
+
 <p>
 	We choose 4 factors: FCFYield, PriceChange1M, BookValuePerShare and RevenueGrowth.
 </p>

From 8474bdcf366adb5b6a8b24aca5ed2d64d034a876 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 10:10:36 -0400
Subject: [PATCH 070/753] Update 01 Introduction.html

---
 .../13 Asset Class Momentum/01 Introduction.html              | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html b/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html
index 43d89b5..15ad281 100644
--- a/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html	
+++ b/04 Strategy Library/13 Asset Class Momentum/01 Introduction.html	
@@ -1,4 +1,4 @@
 <p>
-Unlike the first trending following algorithm, this algorithm finds the entry point with the momentum effect. The momentum anomaly says that what was strongly going up in the past will probably continue to go up in the near future. 
-In the calculation, it refers to the rate of change in price movements for a particular asset.
+This trend following algorithm finds its entry points using the momentum effect. The momentum anomaly says that what was strongly going up in the past will probably continue to go up in the near future. 
+The calculation performed uses the rate of change in price movements for a particular asset.
 </p>

From 8b1edd928d3644baa6e0376e468d6f28d66ef6c7 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:15:39 -0400
Subject: [PATCH 071/753] Update 02 Method.html

---
 04 Strategy Library/15 Short Term Reversal/02 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/15 Short Term Reversal/02 Method.html b/04 Strategy Library/15 Short Term Reversal/02 Method.html
index 593091d..2393a78 100644
--- a/04 Strategy Library/15 Short Term Reversal/02 Method.html	
+++ b/04 Strategy Library/15 Short Term Reversal/02 Method.html	
@@ -1,6 +1,6 @@
 <p>
   To apply short-term reversal in stocks market, first, we use the universe selection API to pick the stocks with the price higher than 4 and rank those stocks by dollar volume and
   choose the top 100 stocks as our asset pool. In fine universe selection, the prescreened stocks are sorted by market cap we choose the top 20. To detect the reversal effect,
-  the return is the most straightforward measure of the stock history performance. the indicator RateOfReturn() is used to calculate the monthly return. We go long on the 10 stocks
+  the return is the most straightforward measure of the stock history performance. The RateOfReturn indicator is used to calculate the monthly return. We go long on the 10 stocks
   with the lowest performance in the previous month and go short on the 10 stocks with the greatest performance from the previous month.
 </p>

From 4351f249cf679df23a58817e0db6be93723516f6 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:22:04 -0400
Subject: [PATCH 072/753] Update 03 Algorithm.html

---
 .../18 Volatility Effect in Stocks/03 Algorithm.html            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html
index 720e924..0a0907d 100644
--- a/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_34e8ae2763e7e2122634d4091120d320.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_34e8ae2763e7e2122634d4091120d320.html"></iframe>
     </div>
 </div>

From 57dbab60944aa481b76e20558887cf2ab6712107 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:22:16 -0400
Subject: [PATCH 073/753] Update 03 Algorithm.html

---
 04 Strategy Library/17 Forex Momentum/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html
index c3c3112..b7fd60e 100644
--- a/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/17 Forex Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_336bbb8e9e4a55a6adfa00c0d9f904bb.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_336bbb8e9e4a55a6adfa00c0d9f904bb.html"></iframe>
     </div>
 </div>

From d5fea28a50c0f874a73ea5a2c9c9ba1e84e88196 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:22:33 -0400
Subject: [PATCH 074/753] Update 03 Algorithm.html

---
 04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html
index ee01eb0..e4a3020 100644
--- a/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/16 Overnight Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d31f2d20f4a060418985dd8c0c606c13.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d31f2d20f4a060418985dd8c0c606c13.html"></iframe>
     </div>
 </div>

From 03c6c2f2d76470701b7facfd896fc0ac51366468 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:22:44 -0400
Subject: [PATCH 075/753] Update 03 Algorithm.html

---
 04 Strategy Library/15 Short Term Reversal/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
index 81ac714..648b728 100644
--- a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
+++ b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8ce50fdd465d8629d4c73135938477d9.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8ce50fdd465d8629d4c73135938477d9.html"></iframe>
     </div>
 </div>

From cba178de8474a06d62844c74310069f4f9d76553 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:22:58 -0400
Subject: [PATCH 076/753] Update 03 Algorithm.html

---
 04 Strategy Library/14 Sector Momentum/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html
index f9e9dee..6095e1b 100644
--- a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f7b25a5e872436195cb27a815b5f7e1.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f7b25a5e872436195cb27a815b5f7e1.html"></iframe>
     </div>
 </div>

From 42e2bb3463f54b13a433876047d438f9bd5f8cee Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:23:10 -0400
Subject: [PATCH 077/753] Update 03 Algorithm.html

---
 .../12 Asset Class Trend Following/03 Algorithm.html            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html
index d97266e..ca04542 100644
--- a/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
+++ b/04 Strategy Library/12 Asset Class Trend Following/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6caef9a7716468be0d790b63b7f463f6.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6caef9a7716468be0d790b63b7f463f6.html"></iframe>
     </div>
 </div>

From 4a5298c0d9bd157f32b8343fdebc4261da9fddaf Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 11:23:22 -0400
Subject: [PATCH 078/753] Update 03 Algorithm.html

---
 04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html
index 41c9021..0e5258c 100644
--- a/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/13 Asset Class Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_1cb38e96cec6120fc85152da661605c1.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_1cb38e96cec6120fc85152da661605c1.html"></iframe>
     </div>
 </div>

From f1dfb073a62bf29f1d5754d9e246debe070f936c Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 27 Jun 2018 12:16:56 -0400
Subject: [PATCH 079/753] Updates tutorials

---
 .../01 Strategy Library.php                   | 64 +++++++++++++++----
 .../05 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  4 +-
 .../04 Algorithm.html                         |  4 +-
 .../04 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../13 Market Risk/06 Algorithm.html          |  4 +-
 .../07 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../01 Covered Call/04 Algorithm.html         |  4 +-
 .../02 Bull Call Spread/04 Algorithm.html     |  4 +-
 .../03 Long Straddle/04 Algorithm.html        |  4 +-
 .../05 Butterfly Spread/04 Algorithm.html     |  4 +-
 .../06 Iron Condor/03 Summary.html            |  1 -
 .../06 Iron Condor/04 Algorithm.html          |  5 +-
 .../07 Iron Butterfly/04 Algorithm.html       |  4 +-
 .../08 Protective Collar/04 Algorithm.html    |  4 +-
 24 files changed, 85 insertions(+), 45 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 94b70ab..87aeba6 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -10,57 +10,99 @@
     [
         'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
         'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
-        'description' => 'Applying CAPM model to rank Dow Jones 30 companies'
+        'description' => 'Applys CAPM model to rank Dow Jones 30 companies.'
     ],
     [
         'name' => 'Combining Mean Reversion and Momentum in Forex Market',
         'link' => 'strategy-library/combining-mean-reversion-and-momentum-in-forex-market',
-        'description' => 'Combining momentum and mean reversion techniques in the forex markets'
+        'description' => 'Combines momentum and mean reversion techniques in the forex markets.'
     ],
     [
         'name' => 'Pairs Trading-Copula vs Cointegration',
         'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
-        'description' => 'Applying Copula and Cointergration method to pairs trading'
+        'description' => 'Applys Copula and Cointergration method to pairs trading.'
     ],
     [
         'name' => 'The Dynamic Breakout II Strategy',
         'link' => 'strategy-library/the-dynamic-breakout-ii-strategy',
-        'description' => 'A demonstration of dynamic breakout II strategy'
+        'description' => 'A demonstration of dynamic breakout II strategy.'
     ],
     [
         'name' => 'Dual Thrust Trading Algorithm',
         'link' => 'strategy-library/dual-thrust-trading-algorithm',
-        'description' => 'A demontration of Dual Thrust Intraday strategy'
+        'description' => 'A demontration of Dual Thrust Intraday strategy.'
     ],
     [
         'name' => 'Can Crude Oil Predict Equity Returns',
         'link' => 'strategy-library/can-crude-oil-predict-equity-returns',
-        'description' => 'Applying regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate'
+        'description' => 'Applys regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.'
     ],
     [
         'name' => 'Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach',
         'link' => 'strategy-library/intraday-dynamic-pairs-trading-using-correlation-and-cointegration-approach',
-        'description' => 'A high frequency pairs trading algorithm based on cointegration'
+        'description' => 'A high frequency pairs trading algorithm based on cointegration.'
     ],
     [
         'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
         'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
-        'description' => 'Applying high frequency filter to the momentum strategy'
+        'description' => 'Applys high frequency filter to the momentum strategy.'
     ],
     [
         'name' => 'Stock Selection Strategy Based on Fundamental Factors',
         'link' => 'strategy-library/stock-selection-strategy-based-on-fundamental-factors',
-        'description' => 'MorningStar Fundamental factors universe selection algorithm'
+        'description' => 'MorningStar Fundamental factors universe selection algorithm.'
     ],
     [
         'name' => 'Short-Term Reversal Strategy in Stocks',
         'link' => 'strategy-library/short-term-reversal-strategy-in-stocks',
-        'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action'
+        'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action.'
     ],
     [
         'name' => 'Fundamental Factor Long Short Strategy',
         'link' => 'strategy-library/fundamental-factor-long-short-strategy',
-        'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors'
+        'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors.'
+    ],
+    [
+        'name' => 'Asset Class Trend Following',
+        'link' => 'strategy-library/asset-class-trend-following',
+        'quantpedia' => '1',
+        'description' => 'Selects ETFs over ten-month moving average and assigns an equally weighted allocation.'
+    ],
+    [
+        'name' => 'Asset Class Momentum',
+        'link' => 'strategy-library/asset-class-momentum',
+        'quantpedia' => '2',
+        'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.'
+    ],
+    [
+        'name' => 'Sector Momentum',
+        'link' => 'strategy-library/sector-momentum',
+        'quantpedia' => '3',
+        'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.'
+    ],
+    [
+        'name' => 'Overnight Anomaly',
+        'link' => 'strategy-library/overnight-anomaly',
+        'quantpedia' => '4',
+        'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.'
+    ],
+    [
+        'name' => 'Volatility Effect in Stocks',
+        'link' => 'strategy-library/volatility-effect-in-stocks',
+        'quantpedia' => '7',
+        'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility on the past one year.'
+    ],
+    [
+        'name' => 'Forex Momentum',
+        'link' => 'strategy-library/forex-momentum',
+        'quantpedia' => '8',
+        'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.'
+    ],
+    [
+        'name' => 'Short Term Reversal',
+        'link' => 'strategy-library/short-term-reversal',
+        'quantpedia' => '13',
+        'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.'
     ],
 ];
 
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index e0b1d5a..3d63cc8 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2f525825fa6fb72ee4b23b9e63ecf025.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2f525825fa6fb72ee4b23b9e63ecf025.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
index 78a2422..3c5b8f8 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c1eea4df64cf986eba481f5e5e376e8a.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c1eea4df64cf986eba481f5e5e376e8a.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 0f2a79f..f9b1045 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_343dc596a484a20a579ae77c79a12495.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_343dc596a484a20a579ae77c79a12495.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d4443ef5bd0d45da3d003fe8e49a966e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d4443ef5bd0d45da3d003fe8e49a966e.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html
index bebadac..58173b8 100755
--- a/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/04 The Dynamic Breakout II Strategy/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_87d3047e8c358ea9b4779e0b5d238a74.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_87d3047e8c358ea9b4779e0b5d238a74.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4bacf930111e3502993b7e7c9b315be0.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index 906b055..bb58a27 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -4,6 +4,6 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ffe3fbed009f9502f145e96b81638575.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ffe3fbed009f9502f145e96b81638575.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html
index 987eca8..01b99d6 100755
--- a/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
+++ b/04 Strategy Library/06 Can Crude Oil Predict Equity Returns/05 Algorithm.html	
@@ -4,6 +4,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8242236c21a102fbff14d8c3eb30f841.html"></iframe>
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diff --git a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html
index 3376611..69b6365 100755
--- a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
+++ b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5d0576da03c50667c3af8aefa86f4f44.html"></iframe>
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diff --git a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html
index 5a31153..03b3ba2 100755
--- a/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
+++ b/04 Strategy Library/08 The Momentum Strategy Based on the Low Frequency Component of Forex Market/05 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5188517de3244425b56af3e717c90cb9.html"></iframe>
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diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index 4f29ddb..bcf0bc2 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4f8946295bb7358518b35ecbd3d46f01.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html
index 058cc18..009e778 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75595e3ad41ca49ebcc6ab95aeaa92b8.html"></iframe>
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diff --git a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html
index d1731e4..53170f5 100755
--- a/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
+++ b/04 Strategy Library/11 Fundamental Factor Long Short Strategy/04 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_56878e349b15cf73edc52a34d49c4973.html"></iframe>
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diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
index 2fc9305..8d6082c 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -4,6 +4,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_03b5af43deef78e3a81c9a8322069567.html"></iframe>
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diff --git a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html
index 5b8cd66..3069322 100755
--- a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
@@ -1,13 +1,13 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8bac244b346cbd9bc3b39dca76192919.html"></iframe>
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5c75197259238b36a6cea72832c9e709.html"></iframe>
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diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html
index 5db089b..e3c69c8 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
@@ -10,6 +10,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d0f89f7876841581a8eee32cf007cd75.html"></iframe>
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diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index 4b0d63d..12d6ce9 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1,6 +1,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_b986091b5e1822b3d74f1231bf44eb2c.html"></iframe>
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diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index 66d9131..9fa4e5e 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -4,7 +4,7 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9d07e52d5c6199a77597d5f48e68353e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9d07e52d5c6199a77597d5f48e68353e.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
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+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_31a6d88a35d29c2bbe654a3e5ee5f80e.html"></iframe>
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diff --git a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html
index 2bc623d..0e1c81b 100755
--- a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_06ae7450577c1f0c80edc563c4cf06b8.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_06ae7450577c1f0c80edc563c4cf06b8.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_db61c966b616e8d1d8f289c4eed5525c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_db61c966b616e8d1d8f289c4eed5525c.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
index 0aa161d..61a4361 100755
--- a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_0ff9a8022311bd57f1fe422eecdd3363.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_0ff9a8022311bd57f1fe422eecdd3363.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
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     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75edf3c5d0221d22e8927ffedbf116b2.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75edf3c5d0221d22e8927ffedbf116b2.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html
index 2c2b9e4..134f5b1 100755
--- a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d3c16b8ba4f7df0bebbd766b3dfa7c14.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d3c16b8ba4f7df0bebbd766b3dfa7c14.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
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     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_dab462cab9b3f5c8b2a23911f3e98843.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_dab462cab9b3f5c8b2a23911f3e98843.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/06 Iron Condor/03 Summary.html b/07 Applied Options[]/06 Iron Condor/03 Summary.html
index 0df6c97..c8b3b02 100755
--- a/07 Applied Options[]/06 Iron Condor/03 Summary.html	
+++ b/07 Applied Options[]/06 Iron Condor/03 Summary.html	
@@ -1,4 +1,3 @@
-<h2>Summary</h2>
 <p>
 	The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
 </p>
diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
index 55d9629..7fdc852 100755
--- a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -1,11 +1,10 @@
-<h2>Algorithm</h2>
 <p>
 	Backtesing using SetFilter
 </p>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e95a3d8feac3b92b3f39233f00e09ae5.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e95a3d8feac3b92b3f39233f00e09ae5.html"></iframe>
     </div>
 </div>
 <p>
@@ -15,6 +14,6 @@ <h2>Algorithm</h2>
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     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_66bf622f52dbb2a6cb56ccf992d64765.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_66bf622f52dbb2a6cb56ccf992d64765.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html
index 968a1fb..cd42f7d 100755
--- a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2fe60a67bc1c78532ba4a1a93211c6c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2fe60a67bc1c78532ba4a1a93211c6c.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
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     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d60e69c80aeb5719f06202a0c25b1274.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d60e69c80aeb5719f06202a0c25b1274.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html
index 16f8299..245885d 100755
--- a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
+++ b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5e793524109c9230ca92970f050f119f.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5e793524109c9230ca92970f050f119f.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_823c79dcfbae11dabb14ee9ea2365c13.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_823c79dcfbae11dabb14ee9ea2365c13.html"></iframe>
     </div>
 </div>

From 4cc747514fce7abe9eef028f81a204c79c566bec Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 27 Jun 2018 12:42:37 -0400
Subject: [PATCH 080/753] Fixes minor errors

---
 .../00 Strategy Library/01 Strategy Library.php           | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 87aeba6..82547ac 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -10,7 +10,7 @@
     [
         'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
         'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
-        'description' => 'Applys CAPM model to rank Dow Jones 30 companies.'
+        'description' => 'Applies CAPM model to rank Dow Jones 30 companies.'
     ],
     [
         'name' => 'Combining Mean Reversion and Momentum in Forex Market',
@@ -20,7 +20,7 @@
     [
         'name' => 'Pairs Trading-Copula vs Cointegration',
         'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
-        'description' => 'Applys Copula and Cointergration method to pairs trading.'
+        'description' => 'Applies Copula and Cointergration method to pairs trading.'
     ],
     [
         'name' => 'The Dynamic Breakout II Strategy',
@@ -35,7 +35,7 @@
     [
         'name' => 'Can Crude Oil Predict Equity Returns',
         'link' => 'strategy-library/can-crude-oil-predict-equity-returns',
-        'description' => 'Applys regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.'
+        'description' => 'Applies regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.'
     ],
     [
         'name' => 'Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach',
@@ -45,7 +45,7 @@
     [
         'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
         'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
-        'description' => 'Applys high frequency filter to the momentum strategy.'
+        'description' => 'Applies high frequency filter to the momentum strategy.'
     ],
     [
         'name' => 'Stock Selection Strategy Based on Fundamental Factors',

From 7fe1ce45590f3a1b0e52a36641a33a1668ec91c8 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 27 Jun 2018 12:47:31 -0400
Subject: [PATCH 081/753] Update 01 Strategy Library.php

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 82547ac..8e32174 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -90,7 +90,7 @@
         'name' => 'Volatility Effect in Stocks',
         'link' => 'strategy-library/volatility-effect-in-stocks',
         'quantpedia' => '7',
-        'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility on the past one year.'
+        'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility in the past one year.'
     ],
     [
         'name' => 'Forex Momentum',

From 3402727b44449fb1e289d01ac7c25410acb5eb29 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 28 Jun 2018 17:32:30 -0400
Subject: [PATCH 082/753] Add pairs trading strategy

---
 .../00 Strategy Library/01 Strategy Library.php    |  6 ++++++
 .../01 Introduction.html                           |  6 ++++++
 .../19 Pairs Trading with Stocks/02 Method.html    | 14 ++++++++++++++
 .../19 Pairs Trading with Stocks/03 Algorithm.html |  6 ++++++
 4 files changed, 32 insertions(+)
 create mode 100644 04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/19 Pairs Trading with Stocks/02 Method.html
 create mode 100644 04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 8e32174..b150122 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -98,6 +98,12 @@
         'quantpedia' => '8',
         'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.'
     ],
+    [
+        'name' => 'Pairs Trading with Stocks',
+        'link' => 'strategy-library/pairs-trading-with-stocks',
+        'quantpedia' => '12',
+        'description' => 'Looks for the security that minimizes the sum of squared deviations and long-short position is opened when pair prices have diverged by multiple of standard deviations.'
+    ],
     [
         'name' => 'Short Term Reversal',
         'link' => 'strategy-library/short-term-reversal',
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html b/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html
new file mode 100644
index 0000000..c2d7a05
--- /dev/null
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  The pairs trading algorithm aims to find two stocks which have prices that moved historically together.
+  If price series diverges, long and short positions are opened in the opposite direction. With the assumption
+  of mean reversion, the algorithm expects to make profits from the abnormal fluctuation of prices.
+  The crucial part of pairs trading is how to find the paired stocks and how to define the price divergence.
+</p>
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/02 Method.html b/04 Strategy Library/19 Pairs Trading with Stocks/02 Method.html
new file mode 100644
index 0000000..0a9dd73
--- /dev/null
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/02 Method.html	
@@ -0,0 +1,14 @@
+<h4> Pairs Formation </h4>
+<p>
+  The first step of this algorithm is to select stock pairs from a universe of stocks. We use the history request to get the history closing price for the last one year. This is called the formation period.
+  The matching partner for each stock is found by looking for the security that minimizes the sum of squared deviations between two normalized price series. Assume there are two stocks A and B with the price series X and Y. For price normalization, the starting price during formation period is set to $1.
+  The formula of distance measure is
+  \[\sum_{i=1}^n{(\frac{x_i}{x_1}-\frac{y_i}{y_1}})^2\]
+  Top 4 pairs with the smallest historical distance measure are then traded. The trading pairs are selected
+  every half year. We use the schedule event method to fire the rebalance function.
+</p>
+<h4> Trading Pairs </h4>
+<p>
+  As prices in a pair of stocks were closely cointegrated in past, there is high probability that those two securities share common sources of fundamental return correlations. A temporary shock could move one stock out of the common price band which presents statistical arbitrage opportunity. Given the trading pairs, the trading period is the next six months. We calculate the price spread series of the last one year. When pair prices have diverged by two standard deviations,
+  which means the spread is 2 times standard deviation away from its long-term mean, the algorithm will go short the stock which price is diverging up and go long the stock which price is diverging down. The position is closed when prices revert back.
+</p>
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html
new file mode 100644
index 0000000..17a97c9
--- /dev/null
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_72532ccafeaa844c81aa75c5696b4b24.html"></iframe>
+    </div>
+</div>

From e9eb8fec5e4e8b581b2e681dc032615ae55bd9b0 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 28 Jun 2018 18:02:44 -0400
Subject: [PATCH 083/753] Update 01 Introduction.html

---
 .../19 Pairs Trading with Stocks/01 Introduction.html           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html b/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html
index c2d7a05..d22f310 100644
--- a/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html	
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/01 Introduction.html	
@@ -2,5 +2,5 @@
   The pairs trading algorithm aims to find two stocks which have prices that moved historically together.
   If price series diverges, long and short positions are opened in the opposite direction. With the assumption
   of mean reversion, the algorithm expects to make profits from the abnormal fluctuation of prices.
-  The crucial part of pairs trading is how to find the paired stocks and how to define the price divergence.
+  The crucial part of pairs trading is determining which stocks are correlated and how to define a price divergence.
 </p>

From 243b3d8d155277c8a1f328569726e77256204e50 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 29 Jun 2018 17:48:49 -0400
Subject: [PATCH 084/753] Add forex carry trade algorithm

---
 .../01 Strategy Library.php                   |  6 +++++
 .../20 Forex Carry Trade/01 Introduction.html |  5 +++++
 .../20 Forex Carry Trade/02 Method.html       | 22 +++++++++++++++++++
 .../20 Forex Carry Trade/03 Algorithm.html    |  6 +++++
 4 files changed, 39 insertions(+)
 create mode 100644 04 Strategy Library/20 Forex Carry Trade/01 Introduction.html
 create mode 100644 04 Strategy Library/20 Forex Carry Trade/02 Method.html
 create mode 100644 04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index b150122..6893889 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -86,6 +86,12 @@
         'quantpedia' => '4',
         'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.'
     ],
+    [
+        'name' => 'Forex Carry Trade',
+        'link' => 'strategy-library/forex-carry-trade',
+        'quantpedia' => '5',
+        'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.'
+    ],
     [
         'name' => 'Volatility Effect in Stocks',
         'link' => 'strategy-library/volatility-effect-in-stocks',
diff --git a/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html
new file mode 100644
index 0000000..7ef3b97
--- /dev/null
+++ b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The fundamental reason is important behind interest rate changes. Carry trade is one of
+  the most popular trading strategies among currency traders. It systematically sells low
+  interest rate currencies and buys high interest rates currencies trying to capture the spread between the rates.   
+</p>
diff --git a/04 Strategy Library/20 Forex Carry Trade/02 Method.html b/04 Strategy Library/20 Forex Carry Trade/02 Method.html
new file mode 100644
index 0000000..673aa83
--- /dev/null
+++ b/04 Strategy Library/20 Forex Carry Trade/02 Method.html	
@@ -0,0 +1,22 @@
+<h4> Importing Custom Data </h4>
+<p>
+  The central bank interest rate data is from Quandl. For the trading universe, we choose 9 currencies
+  whose central bank interest rate data is available in Quandl. The method to import the custom data is
+  <code>AddData(type, symbol, resoltuion, timeZone, fillDataForward)</code>. As the custom file has it's unique
+  colume name, we need to create a class to specify the colume name of interest rate.
+</p>
+<div class="section-example-container">
+<pre class="python">
+from QuantConnect.Python import PythonQuandl
+class QuandlRate(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = 'Value'
+</pre>
+</div>
+<p>
+  We save the interest rate symbol and the correspondent forex asset symbol into a dictionary.
+</p>
+<h4> Monthly Rebalance Trading </h4>
+<p>
+  Next step we sort the forex symbol by the value of interest rate. The algorithm goes long the currency with the highest interest rates and goes short the currency with the lowest interest rate. The strategy is rebalanced monthly. The schedule event method is used to fire the rebalance event at the first trading day each month.
+</p>
diff --git a/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html
new file mode 100644
index 0000000..1bb774b
--- /dev/null
+++ b/04 Strategy Library/20 Forex Carry Trade/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_798687a2edab75475f0c9095ecd71c1f.html"></iframe>
+    </div>
+</div>

From d85d9152545d37a3541a0ed9598aa329dc5858e5 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 29 Jun 2018 18:21:40 -0400
Subject: [PATCH 085/753] Modified the introduction

---
 .../20 Forex Carry Trade/01 Introduction.html             | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html
index 7ef3b97..2277a06 100644
--- a/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html	
+++ b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html	
@@ -1,5 +1,7 @@
 <p>
-  The fundamental reason is important behind interest rate changes. Carry trade is one of
-  the most popular trading strategies among currency traders. It systematically sells low
-  interest rate currencies and buys high interest rates currencies trying to capture the spread between the rates.   
+  Carry trade is very common in the foreign exchange market.
+  The strategy systematically sells low interest rate currencies and buys high interest rates currencies. The “carry” of a asset is the
+  opportunity cost of holding that asset. Carry trade strategy holds one currency relative to another in order to capture
+  the spread between the rates. We can think of this strategy as borrowing money in one country with a lower interest rate and investing it in another
+  country with a higher interest rate.  
 </p>

From 65847cb140e2b1b4bb1714b42a76930297c6484a Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 29 Jun 2018 18:41:49 -0400
Subject: [PATCH 086/753] Modified introduction

---
 .../20 Forex Carry Trade/01 Introduction.html                | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html
index 2277a06..b0ab324 100644
--- a/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html	
+++ b/04 Strategy Library/20 Forex Carry Trade/01 Introduction.html	
@@ -1,7 +1,4 @@
 <p>
   Carry trade is very common in the foreign exchange market.
-  The strategy systematically sells low interest rate currencies and buys high interest rates currencies. The “carry” of a asset is the
-  opportunity cost of holding that asset. Carry trade strategy holds one currency relative to another in order to capture
-  the spread between the rates. We can think of this strategy as borrowing money in one country with a lower interest rate and investing it in another
-  country with a higher interest rate.  
+  The strategy systematically sells low-interest rate currencies and buys high-interest rates currencies. The “carry” of an asset is the opportunity cost of holding that asset. Carry trade strategy holds one currency relative to another in order to capture the spread between the rates. We can think of this strategy as borrowing money from one country with a lower interest rate and investing it in another country with a higher interest rate.  
 </p>

From dfa4d849a134e520787c83c4a410cfe5d730905b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 2 Jul 2018 16:24:51 -0400
Subject: [PATCH 087/753] Add strategy Momentum Effect in Stocks

---
 .../01 Strategy Library.php                   |  6 +++
 .../01 Introduction.html                      |  4 ++
 .../02 Method.html                            | 37 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++
 4 files changed, 53 insertions(+)
 create mode 100644 04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6893889..75cb382 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -116,6 +116,12 @@
         'quantpedia' => '13',
         'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.'
     ],
+    [
+        'name' => 'Momentum Effect in Stocks',
+        'link' => 'strategy-library/momentum-effect-in-stocks',
+        'quantpedia' => '14',
+        'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html b/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..dfa7cae
--- /dev/null
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  The momentum anomaly says that what was strongly going up in the past will probably continue to go up shortly. Stocks which outperform peers on 3-12 month period tend to perform well also in the future.
+  This algorithm will explore the momentum effect on large-cap stocks. 
+</p>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..8029888
--- /dev/null
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
@@ -0,0 +1,37 @@
+<h4> Universe Selection </h4>
+<p>
+  We use the universe selection API to create the momentum portfolio. The coarse universe selection eliminates stocks with the price lower than 5 and ETFs which does not have fundamental data. The fine universe selection chooses 50 biggest companies by market capitalization.
+</p>
+<h4> Momentum Calculation</h4>
+<p>
+  Momentum is the absolute difference in stocks.
+  \[Momentum = Close_{today}-Close_{N-days-ago}\]
+  LEAN has the Momentum indicator. We create a class to save the momentum value and warm up the indictor for each symbol.
+  <div class="section-example-container">
+  <pre class="python">
+class SymbolData:
+    def __init__(self, symbol, lookback):
+        self.symbol = symbol
+        self.MOM = Momentum(lookback)
+
+    def WarmUpIndicator(self, history):
+        # warm up the Momentum indicator with the history request
+        for tuple in history.itertuples():
+            item = IndicatorDataPoint(self.symbol, tuple.Index, float(tuple.close))
+            self.MOM.Update(item)
+  </pre>
+  </div>
+<p>
+  Dictionary <code>self.symbolDataDict</code> is used to save the momentum class instance <code>SymbolData</code> for each symbol.
+  In <code>OnSecuritiesChanged</code> event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary. Each day in <code>OnData</code>,
+  Momentum indicator for all symbols in the dictionary will be updated with the latest closing price.
+</p>
+<p>
+  We choose the period of the momentum to be 12 months. Stocks with the best 12-month momentum (12-month performance) are then added to our portfolio and are weighted equally.
+</p>
+
+<h4> Monthly Rebalance</h4>
+<p>
+  The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the bool variable
+  <code>self.monthly_rebalance</code> to manage the universe selection. At the start of each month, the universe selection will filter new stocks. In other days, it will return the same symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol. You might not be able to update the indicator value in OnData().
+</p>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..a8ca0da
--- /dev/null
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ec30318cc245fe200ca1261b7f26f17b.html"></iframe>
+    </div>
+</div>

From 5e17d98b3460708bdcb33a613596cc8035e4f968 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 2 Jul 2018 16:36:38 -0400
Subject: [PATCH 088/753] Update 01 Introduction.html

---
 .../21 Momentum Effect in Stocks/01 Introduction.html          | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html b/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html
index dfa7cae..6baa7a8 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/01 Introduction.html	
@@ -1,4 +1,3 @@
 <p>
-  The momentum anomaly says that what was strongly going up in the past will probably continue to go up shortly. Stocks which outperform peers on 3-12 month period tend to perform well also in the future.
-  This algorithm will explore the momentum effect on large-cap stocks. 
+The momentum anomaly says that what was strongly going up in the near past will probably continue to go up shortly. Stocks which outperform peers on 3-12 month period tend to perform well also in the future. This algorithm will explore the momentum effect on large-cap stocks. 
 </p>

From 842796bf6a9515e25847b3f8c18d912b5d1920ad Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 2 Jul 2018 16:41:26 -0400
Subject: [PATCH 089/753] Update 02 Method.html

---
 .../21 Momentum Effect in Stocks/02 Method.html        | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html
index 8029888..2f6518a 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
@@ -1,8 +1,8 @@
 <h4> Universe Selection </h4>
 <p>
-  We use the universe selection API to create the momentum portfolio. The coarse universe selection eliminates stocks with the price lower than 5 and ETFs which does not have fundamental data. The fine universe selection chooses 50 biggest companies by market capitalization.
+   We use the universe selection API to create a momentum portfolio. Our coarse-universe selection eliminates stocks with a price lower than $5 and ETFs which do not have fundamental data. Fine-universe selection chooses the 50 largest companies ranked by market capitalization.
 </p>
-<h4> Momentum Calculation</h4>
+<h4>Momentum Calculation</h4>
 <p>
   Momentum is the absolute difference in stocks.
   \[Momentum = Close_{today}-Close_{N-days-ago}\]
@@ -23,15 +23,15 @@ <h4> Momentum Calculation</h4>
   </div>
 <p>
   Dictionary <code>self.symbolDataDict</code> is used to save the momentum class instance <code>SymbolData</code> for each symbol.
-  In <code>OnSecuritiesChanged</code> event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary. Each day in <code>OnData</code>,
+  In <code>OnSecuritiesChanged</code> event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary. Each day in <code>OnData</code>, the 
   Momentum indicator for all symbols in the dictionary will be updated with the latest closing price.
 </p>
 <p>
-  We choose the period of the momentum to be 12 months. Stocks with the best 12-month momentum (12-month performance) are then added to our portfolio and are weighted equally.
+  We choose a period of 12 months for the momentum indicator. Stocks with the best 12-month momentum (12-month performance) are then added to our portfolio and are weighted equally.
 </p>
 
 <h4> Monthly Rebalance</h4>
 <p>
   The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the bool variable
-  <code>self.monthly_rebalance</code> to manage the universe selection. At the start of each month, the universe selection will filter new stocks. In other days, it will return the same symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol. You might not be able to update the indicator value in OnData().
+  <code>self.monthly_rebalance</code> to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator.
 </p>

From a1aae29766a8dccecee6d2bbe287eb955dc62e1c Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Mon, 2 Jul 2018 18:20:56 -0400
Subject: [PATCH 090/753] Add sources to the list model

---
 .../01 Strategy Library.php                   | 55 +++++++++++++++----
 1 file changed, 44 insertions(+), 11 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 75cb382..1b24342 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -65,61 +65,81 @@
     [
         'name' => 'Asset Class Trend Following',
         'link' => 'strategy-library/asset-class-trend-following',
-        'quantpedia' => '1',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/1'
+        ],
         'description' => 'Selects ETFs over ten-month moving average and assigns an equally weighted allocation.'
     ],
     [
         'name' => 'Asset Class Momentum',
         'link' => 'strategy-library/asset-class-momentum',
-        'quantpedia' => '2',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/2'
+        ],
         'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.'
     ],
     [
         'name' => 'Sector Momentum',
         'link' => 'strategy-library/sector-momentum',
-        'quantpedia' => '3',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/3'
+        ],
         'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.'
     ],
     [
         'name' => 'Overnight Anomaly',
         'link' => 'strategy-library/overnight-anomaly',
-        'quantpedia' => '4',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/4'
+        ],
         'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.'
     ],
     [
         'name' => 'Forex Carry Trade',
         'link' => 'strategy-library/forex-carry-trade',
-        'quantpedia' => '5',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/5'
+        ],
         'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.'
     ],
     [
         'name' => 'Volatility Effect in Stocks',
         'link' => 'strategy-library/volatility-effect-in-stocks',
-        'quantpedia' => '7',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/7'
+        ],
         'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility in the past one year.'
     ],
     [
         'name' => 'Forex Momentum',
         'link' => 'strategy-library/forex-momentum',
-        'quantpedia' => '8',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/8'
+        ],
         'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.'
     ],
     [
         'name' => 'Pairs Trading with Stocks',
         'link' => 'strategy-library/pairs-trading-with-stocks',
-        'quantpedia' => '12',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/12'
+        ],
         'description' => 'Looks for the security that minimizes the sum of squared deviations and long-short position is opened when pair prices have diverged by multiple of standard deviations.'
     ],
     [
         'name' => 'Short Term Reversal',
         'link' => 'strategy-library/short-term-reversal',
-        'quantpedia' => '13',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/13'
+        ],
         'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.'
     ],
     [
         'name' => 'Momentum Effect in Stocks',
         'link' => 'strategy-library/momentum-effect-in-stocks',
-        'quantpedia' => '14',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/14'
+        ],
         'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe'
     ],
 ];
@@ -140,11 +160,24 @@
     </tr>
     </thead>
     <tbody>
-    <?php foreach ($strategyMap as $strategy) { ?>
+    <?php foreach ($strategyMap as $strategy) {
+        $sources = '';
+        if (array_key_exists('sources', $strategy)) {
+            $sources .= '<p class="sources">';
+            $sources .= (count($strategy['sources']) > 1 ? 'Sources:' : 'Source:');
+            foreach ($strategy['sources'] as $key => $source) {
+                $sources .= " <a href=\"{$source}\" rel=\"nofollow\" >{$key}</a>,";
+            }
+
+            $sources = trim($sources, ',');
+            $sources .= '</p>';
+        }
+        ?>
         <tr>
             <td>
                 <a class="docs-internal-link" href="/tutorials/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
                 <p><?= $strategy['description'] ?></p>
+                <?= $sources ?>
             </td>
         </tr>
     <?php } ?>

From 96ab9a05c4fc6bcfb9943e0f979c4e423ec211c5 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 3 Jul 2018 11:30:30 -0400
Subject: [PATCH 091/753] Create 00 Introduction.html

---
 01 API Tutorials/00 Introduction/00 Introduction.html | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 01 API Tutorials/00 Introduction/00 Introduction.html

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
new file mode 100644
index 0000000..108e61b
--- /dev/null
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  API tutorials seek to give you an introduction to building an algorithm using the QuantConnect API.
+</p>

From 493fada1dd80519c6e7bba248be05f2df705d542 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 3 Jul 2018 11:30:58 -0400
Subject: [PATCH 092/753] Create Introduction.html

---
 02 Tutorial Series/00 Introduction/Introduction.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 02 Tutorial Series/00 Introduction/Introduction.html

diff --git a/02 Tutorial Series/00 Introduction/Introduction.html b/02 Tutorial Series/00 Introduction/Introduction.html
new file mode 100644
index 0000000..4c361d8
--- /dev/null
+++ b/02 Tutorial Series/00 Introduction/Introduction.html	
@@ -0,0 +1 @@
+<p>Tutorial Series</p>

From deafec0cd28f796e3e8907beae73aa0acc603052 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 3 Jul 2018 11:31:17 -0400
Subject: [PATCH 093/753] Create Introduction.html

---
 03 Open Source/00 Introduction/Introduction.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/00 Introduction/Introduction.html

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
new file mode 100644
index 0000000..7b9b4c6
--- /dev/null
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -0,0 +1 @@
+Open Source Tutorials

From b54cc200157444a291f6cae66e946b7b19dc8818 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 3 Jul 2018 11:37:15 -0400
Subject: [PATCH 094/753] Update 01 Home.html

---
 00 Home/01 Home/01 Home.html | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index 845cb8b..170ca8b 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -9,20 +9,16 @@ <h2 style="margin-top: 8px;"><span style="font-size: 22px;">Learn to use QuantCo
 </div>
 <div>
 	<div class="home-buttons-backdrop"></div>
-	<div class="row home-buttons" style="padding-top: 18px;"><a class="docs-internal-link" href="/docs/key-concepts/developing-in-the-ide"><img src="https://cdn.quantconnect.com/web/i/docs/docs_using_qc.rev0.png">
-            Using QuantConnect
-        </a><a class="docs-internal-link" href="/docs/algorithm-reference/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_ref_rev0.png">
-            Algorithm Reference
-        </a><a class="docs-internal-link" href="/docs/algorithm-framework/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_algo_framework_rev0.png">
-            Algorithm Framework
+	<div class="row home-buttons" style="padding-top: 18px;"><a class="docs-internal-link" href="/tutorials/api-tutorials/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/api_tutorials_2x.png">
+            API Tutorials
+        </a><a class="docs-internal-link" href="/tutorials/tutorial-series/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/tutorial_series_2x.png">
+            Tutorial Series
         </a>
 	</div>
-	<div class="row home-buttons" style="padding-bottom: 18px;"><a class="docs-internal-link" href="/docs/research/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_research_rev0.png">
-            Research
-        </a><a class="docs-internal-link" href="/docs/tutorials/tutorial-series"><img src="https://cdn.quantconnect.com/web/i/docs/docs_tutorials_rev0.png">
-            Tutorials
-        </a><a class="docs-internal-link" href="/docs/live-trading/overview"><img src="https://cdn.quantconnect.com/web/i/docs/docs_live_trading_rev0.png">
-            Live Trading
+	<div class="row home-buttons" style="padding-bottom: 18px;"><a class="docs-internal-link" href="/tutorials/open-source/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/open_source_2x.png">
+            Open Source
+        </a><a class="docs-internal-link" href="/tutorials/strategy-library/strategy-library"><img src="https://cdn.quantconnect.com/tutorials/i/strategy_library_2x.png">
+            Strategy Library
         </a>
 	</div>
 </div>

From 73591cc47c4fb5d7d6c3a6ac63a3aaa119783edf Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 3 Jul 2018 18:33:26 -0400
Subject: [PATCH 095/753] Add tutorial Momentum Effect in Country Equity
 Indexes

---
 .../00 Strategy Library/01 Strategy Library.php    | 10 +++++++++-
 .../01 Introduction.html                           |  3 +++
 .../02 Method.html                                 | 11 +++++++++++
 .../03 Conclusion.html                             |  4 ++++
 .../04 Algorithm.html                              | 14 ++++++++++++++
 5 files changed, 41 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 Introduction.html
 create mode 100644 04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html
 create mode 100644 04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 Conclusion.html
 create mode 100644 04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1b24342..4939fb0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -140,7 +140,15 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/14'
         ],
-        'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe'
+        'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe.'
+    ],
+    [
+        'name' => 'Momentum Effect in Country Equity Indexes',
+        'link' => 'strategy-library/momentum-effect-in-country-equity-indexes',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/15'
+        ],
+        'description' => 'Goes long stocks with the best 12-month momentum in the country equity indexes ETFs.'
     ],
 ];
 
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 Introduction.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 Introduction.html
new file mode 100644
index 0000000..37a614e
--- /dev/null
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  This algorithm examines the momentum effect in country indexes exchange-traded funds.
+</p>
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html
new file mode 100644
index 0000000..4715425
--- /dev/null
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html	
@@ -0,0 +1,11 @@
+<p>
+  The algorithm 35 counrty indices ETFs as the trading universe. As the universe doesn't change over time. We use the momentum indicator helper method
+  <code>self.MOM(symbol, period, resolution)</code>. This helper method creates a new momentum indicator and computes the absolute n-period change in
+  the security. In contracst to the indicator constructor <code>Momentum(period)</code>, the helper method indocator will be automatically updated on
+  the given resolution.
+</p>
+<p>
+  In <code>Initialize()</code>, we set the warm up period to the momentum period and create the dictionary <code>self.data</code> to save the indicator of
+  each symbol. Each month, the top five indices ETFs with the best 6-month momentum will be selected to open long position. ETFs which is no longer in
+  this top list will be liquidated. This shceduled event API is used to schedule the portfolio rebalance at the start of each month. 
+</p>
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 Conclusion.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 Conclusion.html
new file mode 100644
index 0000000..5b5cc52
--- /dev/null
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 Conclusion.html	
@@ -0,0 +1,4 @@
+<p>
+  The algorithm result shows that momentum effects do exist in the country indices.
+  The strategy of holding for one month, a portfolio of the five best performing country indices ETFs over the previous six months, was found to out-perform the equal-weighted portfolio by around 40% per annum over the 16-year period from 2002 to 2018.
+</p>
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html
new file mode 100644
index 0000000..4292434
--- /dev/null
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
@@ -0,0 +1,14 @@
+<h4>The momentum Effect</h4>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_bd83aa417032f8407382a1c065aa7511.html"></iframe>
+    </div>
+</div>
+<h4>Equal Weighted Benchmark</h4>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_32bbadd991642fc989e1857afb12003d.html"></iframe>
+    </div>
+</div>

From 83a2deeb9079ed34641edaa4cac9098c5e9b59bd Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 5 Jul 2018 09:36:53 -0400
Subject: [PATCH 096/753] Fix spelling error

---
 .../02 Method.html                                    | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html
index 4715425..6b6f900 100644
--- a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html	
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 Method.html	
@@ -1,11 +1,8 @@
 <p>
-  The algorithm 35 counrty indices ETFs as the trading universe. As the universe doesn't change over time. We use the momentum indicator helper method
-  <code>self.MOM(symbol, period, resolution)</code>. This helper method creates a new momentum indicator and computes the absolute n-period change in
-  the security. In contracst to the indicator constructor <code>Momentum(period)</code>, the helper method indocator will be automatically updated on
-  the given resolution.
+The algorithm picks 35 country indexes ETFs as the trading universe. As the symbols in the universe don't change over time, we use the momentum indicator helper method
+  <code>self.MOM(symbol, period, resolution)</code>. This helper method creates a new momentum indicator and computes the absolute n-period change in the security. In contrast to the indicator constructor <code>Momentum(period)</code>, the helper method indicator will be automatically updated on the given resolution.
 </p>
 <p>
-  In <code>Initialize()</code>, we set the warm up period to the momentum period and create the dictionary <code>self.data</code> to save the indicator of
-  each symbol. Each month, the top five indices ETFs with the best 6-month momentum will be selected to open long position. ETFs which is no longer in
-  this top list will be liquidated. This shceduled event API is used to schedule the portfolio rebalance at the start of each month. 
+  In <code>Initialize()</code>, we set the warm-up period to the momentum period and create the dictionary <code>self.data</code> to save the indicator of
+  each symbol. On each month, the top five indexes ETFs with the best 6-month momentum will be selected to open long position. ETFs which are no longer in this top list will be liquidated. The scheduled event API is used to schedule the portfolio to rebalance at the start of each month.
 </p>

From 180f116d165b581fff64fdd12cdcadbe4566a8d4 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 5 Jul 2018 12:28:10 -0400
Subject: [PATCH 097/753] Update 04 Algorithm.html

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html
index 4292434..37631d8 100644
--- a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 Algorithm.html	
@@ -1,4 +1,4 @@
-<h4>The momentum Effect</h4>
+<h4>The Momentum Effect</h4>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">

From 1dada8c91c7e9e9de8b265c68d9915c8b4aca268 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 18:20:29 +0000
Subject: [PATCH 098/753] Automated push from server made by Jared Broad

---
 01 API Tutorials/00 Introduction/00 Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
index 108e61b..2eca825 100644
--- a/01 API Tutorials/00 Introduction/00 Introduction.html	
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
-  API tutorials seek to give you an introduction to building an algorithm using the QuantConnect API.
+Our API Tutorials Series covers common challenges the community faces building an algorithm with the QuantConnect/LEAN API. Check out the following posts to get started!
 </p>

From 35efeb7d9f9663db9e4147ab485cc99ebe9253ba Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 18:31:07 +0000
Subject: [PATCH 099/753] Automated push from server made by Jared Broad

---
 01 API Tutorials/00 Introduction/00 Introduction.html | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
index 2eca825..404dd57 100644
--- a/01 API Tutorials/00 Introduction/00 Introduction.html	
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -1,3 +1,4 @@
 <p>
-Our API Tutorials Series covers common challenges the community faces building an algorithm with the QuantConnect/LEAN API. Check out the following posts to get started!
+API tutorials seek to give you an introduction to building an algorithm using the QuantConnect API.
 </p>
+<style>   .tutorial-box { border: 1px solid #cccccc; border-radius: 5px; min-height: 50px; line-height: 50px; padding: 15px }   a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; }   a:hover .tutorial-box { text-decoration: none; } </style> <a href="/tutorials/api-tutorials/consolidating-data-to-build-bars"><div class="tutorial-box">    <h4><i class="fa fa-book"></i> Consolidating Data to Build Bars</h4>    <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p></div></a>.
\ No newline at end of file

From d0ef43e364d0d0f7c4e8a12202c5801f38988cd7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 18:49:05 +0000
Subject: [PATCH 100/753] Automated push from server made by Jared Broad

---
 01 API Tutorials/00 Introduction/00 Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
index 404dd57..af10abe 100644
--- a/01 API Tutorials/00 Introduction/00 Introduction.html	
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -1,4 +1,4 @@
 <p>
 API tutorials seek to give you an introduction to building an algorithm using the QuantConnect API.
 </p>
-<style>   .tutorial-box { border: 1px solid #cccccc; border-radius: 5px; min-height: 50px; line-height: 50px; padding: 15px }   a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; }   a:hover .tutorial-box { text-decoration: none; } </style> <a href="/tutorials/api-tutorials/consolidating-data-to-build-bars"><div class="tutorial-box">    <h4><i class="fa fa-book"></i> Consolidating Data to Build Bars</h4>    <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p></div></a>.
\ No newline at end of file
+<style>   .tutorial-box { border: 1px solid #cccccc; border-radius: 5px; padding: 15px }   a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}a.tutorial-link:hover  { text-decoration: none; }i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } </style><a class="tutorial-link" href="/tutorials/api-tutorials/consolidating-data-to-build-bars">  <div class="tutorial-box">        <h4><i class="fa fa-bar-chart-o"></i> Consolidating Data to Build Bars</h4>        <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/scheduled-events">  <div class="tutorial-box">        <h4><i class="fa fa-calendar"></i> Using Scheduled Events</h4>        <p>Trigger algorithm functions at specific times of the day or month.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/tracking-and-managing-orders">  <div class="tutorial-box">        <h4><i class="fa fa-exchange"></i> Trading and Order Management</h4>        <p>Learn how to use QuantConnect order tickets system to properly manage your algorithm trades.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/using-options-in-quantconnect">  <div class="tutorial-box">        <h4><i class="fa fa-file-text-o"></i> Using Options in QuantConnect</h4>        <p>Learn how to filter and selections for your backtesting.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a>
\ No newline at end of file

From f859ac0b9f93bdfa90559e85468ce32327e355cb Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 18:51:56 +0000
Subject: [PATCH 101/753] Automated push from server made by Jared Broad

---
 .../00 Introduction/00 Introduction.html      | 35 ++++++++++++++++++-
 1 file changed, 34 insertions(+), 1 deletion(-)

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
index af10abe..9de84d4 100644
--- a/01 API Tutorials/00 Introduction/00 Introduction.html	
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -1,4 +1,37 @@
 <p>
 API tutorials seek to give you an introduction to building an algorithm using the QuantConnect API.
 </p>
-<style>   .tutorial-box { border: 1px solid #cccccc; border-radius: 5px; padding: 15px }   a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}a.tutorial-link:hover  { text-decoration: none; }i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } </style><a class="tutorial-link" href="/tutorials/api-tutorials/consolidating-data-to-build-bars">  <div class="tutorial-box">        <h4><i class="fa fa-bar-chart-o"></i> Consolidating Data to Build Bars</h4>        <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/scheduled-events">  <div class="tutorial-box">        <h4><i class="fa fa-calendar"></i> Using Scheduled Events</h4>        <p>Trigger algorithm functions at specific times of the day or month.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/tracking-and-managing-orders">  <div class="tutorial-box">        <h4><i class="fa fa-exchange"></i> Trading and Order Management</h4>        <p>Learn how to use QuantConnect order tickets system to properly manage your algorithm trades.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a><a class="tutorial-link" href="/tutorials/api-tutorials/using-options-in-quantconnect">  <div class="tutorial-box">        <h4><i class="fa fa-file-text-o"></i> Using Options in QuantConnect</h4>        <p>Learn how to filter and selections for your backtesting.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>  </div></a>
\ No newline at end of file
+<style>   
+.tutorial-box { border: 1px solid #cccccc; border-radius: 5px; padding: 15px }   
+a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}
+a.tutorial-link:hover  { text-decoration: none; }
+i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
+</style>
+<a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/consolidating-data-to-build-bars">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-bar-chart-o"></i> Consolidating Data to Build Bars</h4>    
+    <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+<a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/scheduled-events">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-calendar"></i> Using Scheduled Events</h4>    
+    <p>Trigger algorithm functions at specific times of the day or month.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+<a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/tracking-and-managing-orders">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-exchange"></i> Trading and Order Management</h4>    
+    <p>Learn how to use QuantConnect order tickets system to properly manage your algorithm trades.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+<a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/using-options-in-quantconnect">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-file-text-o"></i> Using Options in QuantConnect</h4>    
+    <p>Learn how to filter and select Options for your backtesting.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
\ No newline at end of file

From 2413329d964adf8d1a5a6d5e67fbc2aeec4b9690 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 6 Jul 2018 14:54:59 -0400
Subject: [PATCH 102/753] Delete 00 About.html

---
 .../00 About.html                             | 31 -------------------
 1 file changed, 31 deletions(-)
 delete mode 100644 02 Tutorial Series/04 Introduction to Machine Learning/00 About.html

diff --git a/02 Tutorial Series/04 Introduction to Machine Learning/00 About.html b/02 Tutorial Series/04 Introduction to Machine Learning/00 About.html
deleted file mode 100644
index 032955e..0000000
--- a/02 Tutorial Series/04 Introduction to Machine Learning/00 About.html	
+++ /dev/null
@@ -1,31 +0,0 @@
-<p>
-Testing saving function from server, full test from production <b style="color:red">v2</b>
-</p>
-<p>
-The goal of this series is to introduce the common options strategies to those who already have basic knowledge of
-    options markets and most importantly, we will teach users how to start your simple options trading algorithm on
-    QuantConnect. These strategies will demonstrate a few of the ways in which options can be used to produce an
-    interesting relationship between profit and stock price. We will primarily talk about the strategies like the
-    Covered Call which involves a single option and the undelying stock, the spreads which involve either taking a
-    position in two or more calls or puts, the conbinations like straddle or strangle which involve taking a position in
-    both calls and puts on the same stocks. For each strategy, except for the description of strategy itself, we will
-    demonstrate the QuantConnect algorithm implemented in Python.
-</p>
-<div class="row tutorials-page-about">
-	<div class="col-xs-12 col-md-4">
-		<p><i class="fa fa-file-o" aria-hidden="true"></i>
-		</p>
-		<p>8 Tutorials</p>
-	</div>
-	<div class="col-xs-12 col-md-4">
-		<p><i class="fa fa-line-chart" aria-hidden="true"></i>
-		</p>
-		<p>10 Backtests</p>
-	</div>
-	<div class="col-xs-12 col-md-4">
-		<p><i class="fa fa-code" aria-hidden="true"></i>
-		</p>
-		<p>45 Code Snippets</p>
-	</div>
-</div>
-

From afbae58c3cd138f41cf12f7dccba3343a726578d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 6 Jul 2018 14:55:10 -0400
Subject: [PATCH 103/753] Delete 01 What Will I learn %3F.html

---
 .../01 What Will I learn %3F.html                           | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 02 Tutorial Series/04 Introduction to Machine Learning/01 What Will I learn %3F.html

diff --git a/02 Tutorial Series/04 Introduction to Machine Learning/01 What Will I learn %3F.html b/02 Tutorial Series/04 Introduction to Machine Learning/01 What Will I learn %3F.html
deleted file mode 100644
index 661425d..0000000
--- a/02 Tutorial Series/04 Introduction to Machine Learning/01 What Will I learn %3F.html	
+++ /dev/null
@@ -1,6 +0,0 @@
-<div class="row tutorials-learn">
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Common Options Strategies</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Using Options API</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Payoff Analysis</div>
-    <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Risk Profile of Options Trading</div>
-</div>
\ No newline at end of file

From dc418128604d7d17a12ff30fdc367d72c6fbdc11 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 6 Jul 2018 14:55:19 -0400
Subject: [PATCH 104/753] Delete 02 Tutorials.html

---
 .../02 Tutorials.html                         | 60 -------------------
 1 file changed, 60 deletions(-)
 delete mode 100644 02 Tutorial Series/04 Introduction to Machine Learning/02 Tutorials.html

diff --git a/02 Tutorial Series/04 Introduction to Machine Learning/02 Tutorials.html b/02 Tutorial Series/04 Introduction to Machine Learning/02 Tutorials.html
deleted file mode 100644
index 6281670..0000000
--- a/02 Tutorial Series/04 Introduction to Machine Learning/02 Tutorials.html	
+++ /dev/null
@@ -1,60 +0,0 @@
-<table class="table qc-table tutorials-table">
-    <tbody>
-    <tr onclick="window.document.location='/tutorials/applied-options-covered-call/';" class="clickable">
-        <td>1</td>
-        <td>
-            <h2>Covered Call</h2>
-            A brief introduction to Covered Call Strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-bull-call-spread';" class="clickable">
-        <td>2</td>
-        <td>
-            <h2>Bull Call Spread</h2>
-            A brief introduction to Bull Call Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-straddle';" class="clickable">
-        <td>3</td>
-        <td>
-            <h2>Long Straddle</h2>
-            A brief introduction to Long Straddle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-long-strangle';" class="clickable">
-        <td>4</td>
-        <td>
-            <h2>Long Strangle</h2>
-            A brief introduction to Long Strangle strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-butterfly-spread';" class="clickable">
-        <td>5</td>
-        <td>
-            <h2>Butterfly Spread</h2>
-            A brief introduction to Butterfly Spread strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-condor';" class="clickable">
-        <td>6</td>
-        <td>
-            <h2>Iron Condor</h2>
-            A brief introduction to Iron Condor strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-iron-butterfly';" class="clickable">
-        <td>7</td>
-        <td>
-            <h2>Iron Butterfly</h2>
-            A brief introduction to Iron Butterfly strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    <tr onclick="window.document.location='/tutorials/applied-options-protective-collar';" class="clickable">
-        <td>8</td>
-        <td>
-            <h2>Protective Collar</h2>
-            A brief introduction to Protective Collar strategy and the algorithm implemented in python.
-            <br><a><i class="fa fa-chevron-right"></i> Read Tutorial</a></td>
-    </tr>
-    </tbody>
-</table>
\ No newline at end of file

From d5977e7cdfe7ef563787400f3fa45be55241e60f Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 6 Jul 2018 15:02:21 -0400
Subject: [PATCH 105/753] Fix iron condor code/chart

---
 .../06 Iron Condor/01 Definition.html         | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/07 Applied Options[]/06 Iron Condor/01 Definition.html b/07 Applied Options[]/06 Iron Condor/01 Definition.html
index 0722b91..1cc2aa6 100755
--- a/07 Applied Options[]/06 Iron Condor/01 Definition.html	
+++ b/07 Applied Options[]/06 Iron Condor/01 Definition.html	
@@ -32,23 +32,23 @@
 </p>
 <div class="section-example-container">
 
-<pre class="python">price = np.arange(700,950,1)
+<pre class="python">price = np.arange(700,920,1)
 k_call_higher = 850 # the strike price of OTM call(Higher k)
-k_call_lower = 840 # the strike price of OTM call(Lower k)
-k_put_higher = 760 # the strike price of OTM put(Higher k)
+k_call_lower = 820 # the strike price of OTM call(Lower k)
+k_put_higher = 780 # the strike price of OTM put(Higher k)
 k_put_lower = 750 # the strike price of OTM put(Lower k)
-premium_call_higher = 1 # the premium of OTM call(Higher k)
+premium_call_higher = 2 # the premium of OTM call(Higher k)
 premium_call_lower = 10 # the premium of OTM call(Lower k)
-premium_put_higher = 11 # the premium of oTM put(Higher k)
-premium_put_lower = 1 # the premium of OTM put(Lower k)
+premium_put_higher = 10 # the premium of oTM put(Higher k)
+premium_put_lower = 2   # the premium of OTM put(Lower k)
 # payoff for the long put position
-payoff_long_put = [max(premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
+payoff_long_put = [max(-premium_put_lower, k_put_lower-i-premium_put_lower) for i in price]
 # payoff for the short put position
-payoff_short_put = [min(premium_put_higher, -(k_put_higher-i+premium_put_higher)) for i in price]
+payoff_short_put = [min(premium_put_higher, -(k_put_higher-i-premium_put_higher)) for i in price]
 # payoff for the short call position
-payoff_short_call = [min(premium_call_lower, -(i-k_call_lower+premium_call_lower)) for i in price]
+payoff_short_call = [min(premium_call_lower, -(i-k_call_lower-premium_call_lower)) for i in price]
 # payoff for the long call position
-payoff_long_call = [max(premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
+payoff_long_call = [max(-premium_call_higher, i-k_call_higher-premium_call_higher) for i in price]
 # payoff for Long Iron Condor Strategy
 payoff = np.sum([payoff_long_put,payoff_short_put,payoff_short_call,payoff_long_call], axis=0)
 plt.figure(figsize=(20,15))
@@ -66,5 +66,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-iron-condor.png" alt="iron condor strategy payoff"/>
 <p>
-	Here the strike price is A(750), B(760), C(850) and D(860). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
+	Here the strike price is A(750), B(780), C(820) and D(850). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
 </p>

From 6adc86df7a68e6d3465a0c864453e8163126a498 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 19:03:24 +0000
Subject: [PATCH 106/753] Automated push from server made by Jared Broad

---
 .../00 Introduction/Introduction.html         | 26 ++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

diff --git a/02 Tutorial Series/00 Introduction/Introduction.html b/02 Tutorial Series/00 Introduction/Introduction.html
index 4c361d8..3b4dbb8 100644
--- a/02 Tutorial Series/00 Introduction/Introduction.html	
+++ b/02 Tutorial Series/00 Introduction/Introduction.html	
@@ -1 +1,25 @@
-<p>Tutorial Series</p>
+<p>
+QuantConnect maintains collections of related tutorials we call a <b>Tutorial Series</b>. We have tutorial series covering the topic below - each with a set of 8-10 child tutorials beneath them.</p>
+<style>   
+.tutorial-box { border: 1px solid #cccccc; border-radius: 5px; padding: 15px }   
+a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}
+a.tutorial-link:hover  { text-decoration: none; }
+i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
+</style>
+<a class="tutorial-link docs-internal-link" href="/tutorials/tutorial-series/introduction-to-financial-python">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-list-alt"></i> 100 Introduction to Financial Python</h4>    
+    <p>Learn how to use Numpy and Pandas for performing basic financial mathematics.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+<a class="tutorial-link docs-internal-link" href="/tutorials/tutorial-series/introduction-to-options">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-file-text-o"></i> 101 Introduction to Options</h4>    
+    <p>Learn how to use options in QuantConnect, along with some core mathematical concepts.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a><a class="tutorial-link docs-internal-link" href="/tutorials/tutorial-series/applied-options">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-file-text-o"></i> 102 Options Applied</h4>    
+    <p>Apply your option knowledge with a series of real strategies implemented in QuantConnect.</p>    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
\ No newline at end of file

From b9981aa4f67b4f82d45a0cd98cd4eb65b2bd73db Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 19:04:51 +0000
Subject: [PATCH 107/753] Automated push from server made by Jared Broad

---
 01 API Tutorials/00 Introduction/00 Introduction.html | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/01 API Tutorials/00 Introduction/00 Introduction.html b/01 API Tutorials/00 Introduction/00 Introduction.html
index 9de84d4..7f208e0 100644
--- a/01 API Tutorials/00 Introduction/00 Introduction.html	
+++ b/01 API Tutorials/00 Introduction/00 Introduction.html	
@@ -9,28 +9,28 @@
 </style>
 <a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/consolidating-data-to-build-bars">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-bar-chart-o"></i> Consolidating Data to Build Bars</h4>    
+    <h4><i class="fa fa-bar-chart-o"></i> 101 Consolidating Data to Build Bars</h4>    
     <p>Learn how to create bars of any size for your algorithm and apply them to your indicators.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>
 <a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/scheduled-events">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-calendar"></i> Using Scheduled Events</h4>    
+    <h4><i class="fa fa-calendar"></i> 102 Using Scheduled Events</h4>    
     <p>Trigger algorithm functions at specific times of the day or month.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>
 <a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/tracking-and-managing-orders">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-exchange"></i> Trading and Order Management</h4>    
+    <h4><i class="fa fa-exchange"></i> 103 Trading and Order Management</h4>    
     <p>Learn how to use QuantConnect order tickets system to properly manage your algorithm trades.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>
 <a class="tutorial-link docs-internal-link" href="/tutorials/api-tutorials/using-options-in-quantconnect">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-file-text-o"></i> Using Options in QuantConnect</h4>    
+    <h4><i class="fa fa-file-text-o"></i> 104 Using Options in QuantConnect</h4>    
     <p>Learn how to filter and select Options for your backtesting.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>

From e3622e060f786425c61c835348092ffb7610f5b0 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:25:33 +0000
Subject: [PATCH 108/753] Automated push from server made by Jared Broad

---
 .../00 Introduction/Introduction.html         | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 7b9b4c6..56f5876 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -1 +1,19 @@
-Open Source Tutorials
+<p>
+QuantConnect has a large open source community driving innovation in the LEAN Algorithmic Trading Engine. We have collected relevant tutorials here for community members learn from and use with their local installations of LEAN.</p><style>   
+.tutorial-box { border: 1px solid #cccccc; border-radius: 5px; padding: 15px }   
+a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}
+a.tutorial-link:hover  { text-decoration: none; }
+i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
+</style><a class="tutorial-link docs-internal-link" href="/tutorials/open-source/desktop-charting-with-lean">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-list-alt"></i> 100 Desktop Charting with LEAN</h4>    
+    <p>Guide to using the desktop charting environment that comes with LEAN (UX v1.0).</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a><a class="tutorial-link docs-internal-link" href="/tutorials/open-source/how-do-i-use-the-api-file-provider">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-list-alt"></i> 101 Using the API File Provider</h4>    
+    <p>Configuring your installation to pull financial data from the QuantConnect website repository.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>

From 18524d8504ab9a60cb17b757acfacd967b2304d1 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:27:46 +0000
Subject: [PATCH 109/753] Automated push from server made by Jared Broad

---
 ...02 How do I use the API File Provider.html | 33 +++++--------------
 1 file changed, 8 insertions(+), 25 deletions(-)

diff --git a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
index 9176435..79485b6 100755
--- a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
+++ b/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html	
@@ -1,27 +1,10 @@
 <p>
-  Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually; but using the API File Provider we provide a way for you to automatically install this into your LEAN project.
-</p>
-
-<p>
-  Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.
-</p>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-data-library-screen-shot.png" alt="Data library collection for FXCM EURUSD data" />
-
-<p>
-  Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.
-</p>
-
-<p>
-  This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:
-</p>
-
-<ul>
- 	<li>Insert your <code>job-user-id</code> and <code>api-access-token</code> into the relevant config fields. You can find these on your <a href="/account">Account</a> page.</li>
- 	<li>Update the <code>data-provider</code> configuration to refer to the APIDataProvider class. This is called <code>QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider</code>.</li>
+Ensuring a high data quality is one of the hardest parts of setting reliable backtesting. There are many challenges to ensuring your data is in the right format, free of errors or omissions and historically accurate. We've tried to address this for you by opening the LEAN Data Library and letting you download our data. LEAN data is organized into millions of tiny files which can be difficult to put into place manually, but using the API File Provider we provide a way for you to automatically install this into your LEAN project.</p>
+<p>Currently we provide forex and cfd data for each of the major vendors we support in tick, second, minute, hour and daily resolution. To request data; go to your <a href="/data">data library</a> and specify what you'd like to download.</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-data-library-screen-shot.png" alt="Data library collection for FXCM EURUSD data">
+<p>Each data file has a unique URL to download the data for the day which will get a LEAN formatted CSV zip. By using the API File Downloader we check if you have the file in your data-folder; and if not we attempt to download it from the API. This assumes you have already added the data to your repository through the web interface.</p>
+<p>This requires 2 key changes to the config.json file in the QuantConnect.Lean.Launcher Project:</p>
+<ul>	<li>Insert your <code>job-user-id</code> and <code>api-access-token</code> into the relevant config fields. You can find these on your <a href="/account">Account</a> page.</li>	<li>Update the <code>data-provider</code> configuration to refer to the APIDataProvider class. This is called <code>QuantConnect.Lean.Engine.DataFeeds.ApiDataProvider</code>.</li>
 </ul>
-<p>
-  Check out the video below where we guide you step by step through how to use the API Data Provider.
-</p>
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/uyHp_jyeSpA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<p>Check out the video below where we guide you step by step through how to use the API Data Provider.</p>
+<iframe width="100%" height="425" src="https://www.youtube.com/embed/uyHp_jyeSpA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe>

From 5fdddd59b3c385c064ef9e894ac84df6cd0e9498 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:28:06 +0000
Subject: [PATCH 110/753] Automated push from server made by Jared Broad

---
 .../01 Desktop Charting with LEAN.html                     | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
index 40ca098..91f56e4 100755
--- a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
+++ b/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
@@ -1,5 +1,5 @@
 <p>
-  With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
+With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
 </p>
 <p>
   Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
@@ -7,7 +7,6 @@
 <p>
   Two configuration changes are required for desktop charting to work:
 </p>
-
 <ul>
  	<li>Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file.
 <pre class="prettyprint">  "environment": "backtesting-desktop",</pre>
@@ -18,7 +17,6 @@
   "api-access-token": "...........",</pre>
 </li>
 </ul>
-
 <p>
   With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
   If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
@@ -26,5 +24,4 @@
 <p>
   In the tutorial video below we demonstrate this feature on LEAN:
 </p>
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/m6llfznP4d4" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<iframe width="100%" height="425" src="https://www.youtube.com/embed/m6llfznP4d4" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From fe0f5e92fd97beb0ac7f5e047ed8e84d919bf140 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:28:27 +0000
Subject: [PATCH 111/753] Automated push from server made by Jared Broad

---
 .../01 Consolidating Data to Build Bars.html             | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html
index 3850405..65121c5 100755
--- a/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
+++ b/01 API Tutorials/01 Consolidating Data to Build Bars/01 Consolidating Data to Build Bars.html	
@@ -1,15 +1,13 @@
 <p>
-  <code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
+<code>Consolidators</code> are used to combine data together from finer resolutions into larger ones. This can be useful for indicators with specific data requirements or to perform long term analysis in conjunction with short term signals.
   Consolidators should be constructed and setup in your Initialize() method; this ensures they are only initialized once. There are three key steps to create and register a consolidator:
 </p>
-
 <ul>
  	<li>Create the consolidator object.</li>
  	<li>Bind an event handler to handle the new bars.</li>
  	<li>Register it with the subscription manager to start receiving data.</li>
 </ul>
-
-  <div class="section-example-container">
+<div class="section-example-container">
   <pre class="csharp">public class ConsolidatorDemoAlgorithm : QCAlgorithm
 {
 	public override void Initialize()
@@ -85,7 +83,6 @@
 <pre class="python">
 </pre>
 </div>
-
 <p>
   There are two key points to remember:
 </p>
@@ -96,4 +93,4 @@
 <p>
   The raw data of QuantConnect is provided in Tick, Minute, Second, Hour or Daily bars. Using these building blocks you can combine data together to get any other resolution of data required.
 </p>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<iframe width="100%" height="425" src="https://www.youtube.com/embed/bbZy__qd1aA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From 4bba773a288d68c4bc01de952f5657cdeb2cdb42 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:28:45 +0000
Subject: [PATCH 112/753] Automated push from server made by Jared Broad

---
 .../02 Scheduled Events/02 Scheduled Events.html            | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html b/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html
index 8c909a9..eac686e 100755
--- a/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html	
+++ b/01 API Tutorials/02 Scheduled Events/02 Scheduled Events.html	
@@ -1,5 +1,5 @@
 <p>
-  Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
+Scheduled events allow you to trigger code blocks for execution at specific times according to rules you set. This feature helps coordinate your algorithm activities and perform analysis at regular intervals; while letting the trading engine take care of market holidays.
 </p>
 <p>
   The scheduling is set with two rules: the <code>DateRules</code> and <code>TimeRules</code> classes. The schedule manager takes one of each and executes an action code block.
@@ -45,11 +45,9 @@
 </tr>
 </tbody>
 </table>
-
 <p>
   The time rules trigger specify when on the day the event should be triggered. They can be specified as below:
 </p>
-
 <table class="table table-striped qc-table">
 <thead>
 <tr>
@@ -78,4 +76,4 @@
 <p>
   For a demonstration see the tutorial video below:
 </p>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/SUu4MKlm93I" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+<iframe width="100%" height="425" src="https://www.youtube.com/embed/SUu4MKlm93I" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From 8b8f00557f7069074c8005e239daa7104d2bfba4 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 6 Jul 2018 20:31:04 +0000
Subject: [PATCH 113/753] Automated push from server made by Jared Broad

---
 .../02 Updating Orders.html                              | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html
index 7c50115..88d0f7e 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
@@ -1,7 +1,6 @@
 <p>
-  Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
+Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
 </p>
-
 <div class="section-example-container">
 <pre class="csharp">var currentStopPrice = _ticket.Get(OrderField.StopPrice);
 </pre>
@@ -24,7 +23,6 @@
 _ticket.Update(updateOrderFields)
 </pre>
 </div>
-
 <p>
   You can also cancel your order if required:
 </p>
@@ -32,9 +30,6 @@
 <pre class="csharp">_ticket.Cancel();</pre>
 <pre class="python">ticket.Cancel()</pre>
 </div>
-
-
 <p>
-  In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.
-https://www.youtube.com/watch?v=HykXfstdNW0
+  In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.<iframe width="100%" height="425" src="https://www.youtube.com/watch?v=HykXfstdNW0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
 </p>

From 604fbc4d0c5210520388f573fc5374e1544ba24b Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 6 Jul 2018 16:34:13 -0400
Subject: [PATCH 114/753] Update 02 Updating Orders.html

---
 .../03 Tracking and Managing Orders/02 Updating Orders.html   | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html
index 88d0f7e..a923569 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
@@ -31,5 +31,7 @@
 <pre class="python">ticket.Cancel()</pre>
 </div>
 <p>
-  In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit target as time passes.<iframe width="100%" height="425" src="https://www.youtube.com/watch?v=HykXfstdNW0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+  In the video below we demonstrate putting it all together to create a moving take-profit order which gradually decreases its profit 
+  target as time passes.
 </p>
+  <iframe width="100%" height="425" src="https://www.youtube.com/embed/HykXfstdNW0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From 558edebd53fdc044ab0fbba4a1b26db3bddd6785 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 6 Jul 2018 17:41:24 -0400
Subject: [PATCH 115/753] Add strategy Mean Reversion Effect in Country Equity
 Indexes

---
 .../00 Strategy Library/01 Strategy Library.php           | 8 ++++++++
 .../01 Introduction.html                                  | 5 +++++
 .../02 Method.html                                        | 8 ++++++++
 .../04 Algorithm.html                                     | 6 ++++++
 4 files changed, 27 insertions(+)
 create mode 100644 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html
 create mode 100644 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html
 create mode 100644 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4939fb0..b687e68 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -150,6 +150,14 @@
         ],
         'description' => 'Goes long stocks with the best 12-month momentum in the country equity indexes ETFs.'
     ],
+    [
+        'name' => 'Mean Reversion Effect in Country Equity Indexes',
+        'link' => 'strategy-library/mean-reversion-effect-in-country-equity-indexes',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/16'
+        ],
+        'description' => 'Goes long country equity indexes ETFs with the worst 36-month return and short ETFs with the best 36-month return.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html
new file mode 100644
index 0000000..bd06f7b
--- /dev/null
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  Mean reversion in stock price is the assumption that the price will tend to move back to the average price over time.
+  Mean reversion trading often refers to counter-trend or reversal trading. The strategy manage to catch the turning of a price trend.
+  This algorithm will explore the mean reversion effect in country equity indexes.
+</p>
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html
new file mode 100644
index 0000000..c217215
--- /dev/null
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html	
@@ -0,0 +1,8 @@
+<p>
+  The investment universe consists of 19 ETFs which invest in individual country equity indexes.
+  The strategy is going to long on the bottom four countries with the worst 36-month return and short on the top 4 countries with the best 36-month return. The helper indicator method <code>self.ROC(symbol, period, resolution)<code>
+  is used to calculate 36-month return where the resolution is daily and the period is 36*21.
+</p>
+<p>
+  The portfolio is reweighted every three years. We schedule the event to fire every month. <code>self.months<code> is the variable to save the number of months, and the algorithm jumps the rebalance execution if the amount of passed months does not equate to 36.
+</p>
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html
new file mode 100644
index 0000000..547579a
--- /dev/null
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_afd2c9d67fd51d602bf8eac2ef28d712.html"></iframe>
+    </div>
+</div>

From 5217cd74d85d821bd214637b055af304f89c7d55 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 6 Jul 2018 19:45:55 -0400
Subject: [PATCH 116/753] Update 01 Introduction.html

---
 .../01 Introduction.html                                       | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html
index bd06f7b..1d69fc6 100644
--- a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html	
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/01 Introduction.html	
@@ -1,5 +1,4 @@
 <p>
   Mean reversion in stock price is the assumption that the price will tend to move back to the average price over time.
-  Mean reversion trading often refers to counter-trend or reversal trading. The strategy manage to catch the turning of a price trend.
-  This algorithm will explore the mean reversion effect in country equity indexes.
+  Mean reversion trading often refers to counter-trend or reversal trading. This algorithm will explore the mean reversion effect in country equity indexes.
 </p>

From db65d56a1a0bc7ea5dd37763a22286a7e34749db Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 12:39:18 -0400
Subject: [PATCH 117/753] Creating a brokeragee

---
 .../02 How do I use the API File Provider.html                    | 0
 03 Open Source/03 Creating a Brokerage/01 Introduction.html       | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/{02 How do I use the API File Provider => 02 Using the API File Provider}/02 How do I use the API File Provider.html (100%)
 mode change 100755 => 100644
 create mode 100644 03 Open Source/03 Creating a Brokerage/01 Introduction.html

diff --git a/03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/02 Using the API File Provider/02 How do I use the API File Provider.html
old mode 100755
new mode 100644
similarity index 100%
rename from 03 Open Source/02 How do I use the API File Provider/02 How do I use the API File Provider.html
rename to 03 Open Source/02 Using the API File Provider/02 How do I use the API File Provider.html
diff --git a/03 Open Source/03 Creating a Brokerage/01 Introduction.html b/03 Open Source/03 Creating a Brokerage/01 Introduction.html
new file mode 100644
index 0000000..e69de29

From f53d14ac7ef875c4d1a48856d9777f9af9e47ee7 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 12:44:03 -0400
Subject: [PATCH 118/753] Brokerage guide

---
 .../01 Introduction.html                                          | 0
 .../03 Brokerage Development Guide/02 Guide Contents.html         | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/{03 Creating a Brokerage => 03 Brokerage Development Guide}/01 Introduction.html (100%)
 create mode 100644 03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html

diff --git a/03 Open Source/03 Creating a Brokerage/01 Introduction.html b/03 Open Source/03 Brokerage Development Guide/01 Introduction.html
similarity index 100%
rename from 03 Open Source/03 Creating a Brokerage/01 Introduction.html
rename to 03 Open Source/03 Brokerage Development Guide/01 Introduction.html
diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
new file mode 100644
index 0000000..e69de29

From 6b85a23684bc13d69265b940c8e8ae145a542fe5 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 12:46:51 -0400
Subject: [PATCH 119/753] Update 01 Introduction.html

---
 .../03 Brokerage Development Guide/01 Introduction.html        | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/03 Open Source/03 Brokerage Development Guide/01 Introduction.html b/03 Open Source/03 Brokerage Development Guide/01 Introduction.html
index e69de29..ff51a8f 100644
--- a/03 Open Source/03 Brokerage Development Guide/01 Introduction.html	
+++ b/03 Open Source/03 Brokerage Development Guide/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+Welcome to the brokerage development guide.
+</p>

From 080466e68db9bbef2eec5c0bb2caffb5f2ade707 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 12:47:07 -0400
Subject: [PATCH 120/753] Update 02 Guide Contents.html

---
 .../03 Brokerage Development Guide/02 Guide Contents.html        | 1 +
 1 file changed, 1 insertion(+)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index e69de29..f021d30 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -0,0 +1 @@
+<p>guide contents.</p>

From b14a8d34cec2a9bc1975cd553a314b51b3176a2a Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 12:47:50 -0400
Subject: [PATCH 121/753] Create 00.html

---
 03 Open Source/03 Brokerage Development Guide/00.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/03 Brokerage Development Guide/00.html

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
new file mode 100644
index 0000000..48cdce8
--- /dev/null
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -0,0 +1 @@
+placeholder

From 9af603e97c1b96b6bfe64ac5a7734d562da67217 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 16:52:44 +0000
Subject: [PATCH 122/753] Automated push from server made by Jared Broad

---
 03 Open Source/03 Brokerage Development Guide/00.html | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 48cdce8..45b1891 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1 +1,2 @@
-placeholder
+<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
+<div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From 439d1491a8ed6ef67dff5c79e8d97ac618e5dc08 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 16:56:05 +0000
Subject: [PATCH 123/753] Automated push from server made by Gustavo Avils

---
 03 Open Source/03 Brokerage Development Guide/00.html | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 45b1891..943ce0f 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,2 +1 @@
-<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
-<div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file
+<style> .work-in-progress { width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style><div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From f24d54ae6fe76221039f6053dc41a3242e9e0288 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 16:58:12 +0000
Subject: [PATCH 124/753] Automated push from server made by Gustavo Avils

---
 03 Open Source/03 Brokerage Development Guide/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 943ce0f..347b8fd 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1 +1 @@
-<style> .work-in-progress { width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style><div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file
+<style> .work-in-progress {    width: 100%;    border: 1px solid orange;     border-radius: 5px;    padding: 15px;    color: orange; }</style><div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From add469ff1ce5259337e21fb9de0e7906e4740d67 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 17:01:00 +0000
Subject: [PATCH 125/753] Automated push from server made by Gustavo Avils

---
 03 Open Source/03 Brokerage Development Guide/00.html | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 347b8fd..45b1891 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1 +1,2 @@
-<style> .work-in-progress {    width: 100%;    border: 1px solid orange;     border-radius: 5px;    padding: 15px;    color: orange; }</style><div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file
+<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
+<div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From e0fea2ccebcb840e97a0f065764711c34bbeb445 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 17:02:05 +0000
Subject: [PATCH 126/753] Automated push from server made by Gustavo Avils

---
 03 Open Source/03 Brokerage Development Guide/00.html | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 45b1891..55e6566 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,2 +1,5 @@
-<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
+<style> .work-in-progress {
+
+
+    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
 <div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From ce16358f8b60acf4be28acb3816fafce11e34453 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 17:06:27 +0000
Subject: [PATCH 127/753] Automated push from server made by Gustavo Avils

---
 03 Open Source/03 Brokerage Development Guide/00.html | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 55e6566..45b1891 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,5 +1,2 @@
-<style> .work-in-progress {
-
-
-    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
+<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
 <div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file

From 20f2508f640d93cd08ff5bdc0e2c9fb006d524a7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 17:11:54 +0000
Subject: [PATCH 128/753] Automated push from server made by Gustavo Avils

---
 .../03 Brokerage Development Guide/00.html         | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 45b1891..788b1b7 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,2 +1,12 @@
-<style> .work-in-progress {    width: 100%;    border: 1px solid orange;    border-radius: 5px;    padding: 15px;    color: orange; }</style>
-<div class="work-in-progress"> <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.</div>
\ No newline at end of file
+<style>
+.work-in-progress {
+    width: 100%;
+    border: 1px solid orange;
+    border-radius: 5px;
+    padding: 15px;
+    color: orange;
+}
+</style>
+<div class="work-in-progress"> 
+    <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.
+</div>
\ No newline at end of file

From 8f7cf5b5a39b2ad3a1b9e07b0f16713eda77db5d Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 18:35:16 +0000
Subject: [PATCH 129/753] Automated push from server made by Jared Broad

---
 03 Open Source/03 Brokerage Development Guide/00.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 788b1b7..0f72c0d 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,10 +1,10 @@
 <style>
 .work-in-progress {
     width: 100%;
-    border: 1px solid orange;
+    border: 1px solid #F0AD4E;
     border-radius: 5px;
     padding: 15px;
-    color: orange;
+    color: #F0AD4E;
 }
 </style>
 <div class="work-in-progress"> 

From 6abc272dc347c5092b539bfd0828ff81a4c559eb Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 20:47:34 +0000
Subject: [PATCH 130/753] Automated push from server made by Jared Broad

---
 .../01 Introduction.html                             | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/01 Introduction.html b/03 Open Source/03 Brokerage Development Guide/01 Introduction.html
index ff51a8f..a239d1d 100644
--- a/03 Open Source/03 Brokerage Development Guide/01 Introduction.html	
+++ b/03 Open Source/03 Brokerage Development Guide/01 Introduction.html	
@@ -1,3 +1,9 @@
-<p>
-Welcome to the brokerage development guide.
-</p>
+<p>
+Creating a fully supported brokerage is a challenging endeavor. LEAN requires a number of individual pieces which together form a complete brokerage implementation. This guide aims to describe in as much detail as possible what you need to do for each module. There are <b>nine</b> key components to implement - these are described individually in the sections below.
+</p>
+
+<p>
+Each submitted brokerage pull-request must pass all tests before being merged. Partially completed brokerage implementations are acceptable if merged to a branch. It's easy to fall behind master so be sure to keep your branch updated with the master branch. Please make sure to read LEAN's <a href="https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md#code-style-and-testing">coding style guidelines</a> and comply with the code commenting and design standards.
+</p>
+
+

From 8f9e8dbed3ef4b7f1f2b2700d25016c6d1d24dd7 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 10 Jul 2018 17:06:10 -0400
Subject: [PATCH 131/753] Add algorithm to explore the liquidity effect in
 stocks

---
 .../00 Strategy Library/01 Strategy Library.php   |  8 ++++++++
 .../01 Introduction.html                          |  4 ++++
 .../24 Liquidity Effect in Stocks/02 Method.html  | 15 +++++++++++++++
 .../04 Algorithm.html                             |  6 ++++++
 4 files changed, 33 insertions(+)
 create mode 100644 04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/24 Liquidity Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index b687e68..f62e4f8 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -158,6 +158,14 @@
         ],
         'description' => 'Goes long country equity indexes ETFs with the worst 36-month return and short ETFs with the best 36-month return.'
     ],
+    [
+        'name' => 'Liquidity Effect in Stocks',
+        'link' => 'strategy-library/liquidity-effect-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/18'
+        ],
+        'description' => 'Goes long stocks with the lowest turnover and short on stocks with the highest turnover from the lowest market-cap quartile.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html b/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..e4b39e4
--- /dev/null
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Liquidity has a powerful impact on price and the valuation of equities. Stocks with little liquidity are used to earning higher returns than stocks with high liquidity. In this algorithm,
+  We present the effect of liquidity on returns for the lowest capitalization quartile from the largest 1500 stocks.
+</p>
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/02 Method.html b/04 Strategy Library/24 Liquidity Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..6fd2878
--- /dev/null
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/02 Method.html	
@@ -0,0 +1,15 @@
+<p>
+  In coarse universe selection, we filter stocks whose price is higher than $5. ADRs, ETFs and closed-end funds are all excluded with the property of coarse fundamental object <code>HasFundamentalData</code>.
+</p>
+<p>
+  In fine universe selection, in the first step, we exclude stocks with the market cap less than ten million.
+  To evaluate the liquidity of stocks, we choose the annual turnover which is the number of shares traded divided by the stock’s outstanding shares.
+  The main advantage of turnover against volume is its market capitalization-neutrality, as either small-cap or large-cap stocks can have low or high turnover rates.
+  Although turnover is capitalization neutral, the liquidity effect is the strongest among small-cap stocks. Therefore, stocks are then divided into quartiles based on their market capitalization. Stocks from the lowest market-cap quartile are again divided into 5% and 95% quantiles based on their turnover. To calculate the turnover,
+  we request the historical daily volume for the last one year and compute the mean volume. The turnover is the average annual volume divided by <code>BasicAverageShares</code> in <code>EarningReports</code>.
+  Stocks in the 5% quantile and in the top 95% percentile are saved in <code>self.long</code> and <code>self.short</code> lists respectively.
+</p>
+<p>
+  In OnData(), the algorithm goes long on stocks in the lowest turnover list and short on stocks in the highest turnover list. Stocks not in those two lists are liquidated.
+  The portfolio is rebalanced once a year and stocks are weighted equally.
+</p>
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html
new file mode 100644
index 0000000..5041f10
--- /dev/null
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ba1bb35b26896e2e710a510f62230a24.html"></iframe>
+    </div>
+</div>

From f4ce8ce2296fa7b95627b76e97e099ce724ac0fe Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 21:07:28 +0000
Subject: [PATCH 132/753] Automated push from server made by Jared Broad

---
 .../02 Guide Contents.html                    | 36 ++++++++++++++++++-
 1 file changed, 35 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index f021d30..77211a7 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -1 +1,35 @@
-<p>guide contents.</p>
+<table class="table qc-table">
+   <thead>
+      <tr><th colspan="2">Implementation Steps</th></tr>
+   </thead>
+   <tbody>
+       <tr>
+           <td>1.</td><td width="95%"><a href="#">Laying a Foundation, (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
+       </tr>
+       <tr>
+           <td>2.</td><td width="95%"><a href="#">Creating The Brokerage (IBrokerage)</a> <br/>Installing key brokerage application logic, where possible using a brokerage SDK.</td>           
+       </tr>
+       <tr>
+           <td>3.</td><td width="95%"><a href="#">Translating Symbol Conventions (ISymbolMapper)</a> <br/>Translate brokerage specific tickers to LEAN format for a uniform algorithm design experience.</td>           
+       </tr>
+       <tr>
+           <td>4.</td><td width="95%"><a href="#">Describe Broker Limitations (IBrokerageModel)</a> <br/>Describe brokerage support of orders and set transaction models.</td>
+       </tr>
+       <tr>
+           <td>5.</td><td width="95%"><a href="#">Enable Live Data Streaming (IDataQueueHandler)</a> <br/>Live streaming data service from brokerage supplied source.</td>           
+       </tr>
+       <tr>
+           <td>6.</td><td width="95%"><a href="#">Enable Serving Historical Data (IHistoryProvider)</a> <br/>Tap into the brokerage historical data API to serve history for live algorithms.</td>           
+       </tr>
+       <tr>
+           <td>7.</td><td width="95%"><a href="#">Download Data (IDataDownloader)</a> <br/> Save data from the brokerage to disk in LEAN format.</td>           
+       </tr>
+       <tr>
+           <td>8.</td><td width="95%"><a href="#">Describe Brokerage Fee Structures (IFeeModel)</a> <br/>Enable accurate backtesting with specific fee structures of the brokerage.</td>           
+       </tr>
+       <tr>
+           <td>9.</td><td width="95%"><a href="#">Update Algorithm API for Easy Setup of Brokerage Models (ISecurityTransactionModel)</a> <br/>Combine the various models together to form a brokerage set.</td>           
+       </tr>
+   </tbody>
+   
+</table>
\ No newline at end of file

From ce97b56c03646ce457e11ac98ee666a6a882ee0c Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 21:15:20 +0000
Subject: [PATCH 133/753] Automated push from server made by Jared Broad

---
 03 Open Source/03 Brokerage Development Guide/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 0f72c0d..60478aa 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -8,5 +8,5 @@
 }
 </style>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> July 10th, 2018: The Brokerage Development Guide is an active work in progress.
+    <i class="fa fa-warning"></i> Updated July 10th, 2018: The Brokerage Development Guide is an active work in progress.
 </div>
\ No newline at end of file

From 0026d6c4cf2467b58e9f6f73cfb3b164301d1c55 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 10 Jul 2018 17:21:37 -0400
Subject: [PATCH 134/753] Update 01 Introduction.html

---
 .../24 Liquidity Effect in Stocks/01 Introduction.html         | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html b/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html
index e4b39e4..abeb595 100644
--- a/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/01 Introduction.html	
@@ -1,4 +1,3 @@
 <p>
-  Liquidity has a powerful impact on price and the valuation of equities. Stocks with little liquidity are used to earning higher returns than stocks with high liquidity. In this algorithm,
-  We present the effect of liquidity on returns for the lowest capitalization quartile from the largest 1500 stocks.
+  Liquidity has a powerful impact on price and the valuation of equities. Stocks with little liquidity are used to earning higher returns than stocks with high liquidity. In this algorithm, we present the effect of liquidity on returns for the lowest capitalization quartile from the largest 1500 stocks.
 </p>

From a03e642a2d67769cb21c458b5c54ac3f717ee749 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 10 Jul 2018 17:44:52 -0400
Subject: [PATCH 135/753] Update the option algortihm using SetFilter

---
 07 Applied Options[]/03 Long Straddle/04 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
index 61a4361..3e98c40 100755
--- a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_0ff9a8022311bd57f1fe422eecdd3363.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f07db5f247cf314e82292b375e1cb77.html"></iframe>
     </div>
 </div>
 <p>

From bc76992a9cd4f3071b78c76809a6eb60724962a3 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 22:04:35 +0000
Subject: [PATCH 136/753] Automated push from server made by Jared Broad

---
 .../03 Brokerage Development Guide/02 Guide Contents.html     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index 77211a7..79c5a2d 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -32,4 +32,6 @@
        </tr>
    </tbody>
    
-</table>
\ No newline at end of file
+</table>
+
+<p>The root of the brokerage system is the algorithm job packets. These hold configuration information about how to run LEAN. The program logic is a little convoluted; it moves from <i>config.json > create job packet > create brokerage factory matching name > set job packet brokerage data > factory creates brokerage instance</i>. Because of this we'll start creating a brokerage at the root -- the configuration and brokerage factory...</p>
\ No newline at end of file

From 95ec2db72fea329a06feb67cfbfd05a703a03eaa Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 22:04:59 +0000
Subject: [PATCH 137/753] Automated push from server made by Jared Broad

---
 03 Open Source/03 Brokerage Development Guide/00.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index 60478aa..b10f6dd 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -1,10 +1,10 @@
 <style>
 .work-in-progress {
     width: 100%;
-    border: 1px solid #F0AD4E;
+    border: 1px solid #f5ae29;
     border-radius: 5px;
     padding: 15px;
-    color: #F0AD4E;
+    color: #f5ae29;
 }
 </style>
 <div class="work-in-progress"> 

From 5bcf49a6b0cbd99f294c524926831949971d68f9 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 10 Jul 2018 22:06:31 +0000
Subject: [PATCH 138/753] Automated push from server made by Jared Broad

---
 .../03 Brokerage Development Guide/02 Guide Contents.html   | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index 79c5a2d..8dcb796 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -4,7 +4,7 @@
    </thead>
    <tbody>
        <tr>
-           <td>1.</td><td width="95%"><a href="#">Laying a Foundation, (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
+           <td>1.</td><td width="95%"><a href="#">Laying a Foundation (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
        </tr>
        <tr>
            <td>2.</td><td width="95%"><a href="#">Creating The Brokerage (IBrokerage)</a> <br/>Installing key brokerage application logic, where possible using a brokerage SDK.</td>           
@@ -32,6 +32,4 @@
        </tr>
    </tbody>
    
-</table>
-
-<p>The root of the brokerage system is the algorithm job packets. These hold configuration information about how to run LEAN. The program logic is a little convoluted; it moves from <i>config.json > create job packet > create brokerage factory matching name > set job packet brokerage data > factory creates brokerage instance</i>. Because of this we'll start creating a brokerage at the root -- the configuration and brokerage factory...</p>
\ No newline at end of file
+</table>
\ No newline at end of file

From f96c4b41dfe4a188eec55ba1880f634cf06f7b28 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 07:51:02 -0400
Subject: [PATCH 139/753] Added placeholder titles

---
 .../03 Brokerage Development Guide/03 Laying a Foundation.html    | 0
 .../03 Brokerage Development Guide/04 Creating the Brokerage.html | 0
 .../05 Translating Symbol Conventions.html                        | 0
 .../06 Describe Brokerage Limitations.html                        | 0
 .../07 Enable Live Data Streaming.html                            | 0
 .../08 Enable Serving Historical Data.html                        | 0
 .../03 Brokerage Development Guide/09 Downloading Data.html       | 0
 .../10 Brokerage Fee Structures.html                              | 0
 .../03 Brokerage Development Guide/11 Updating Algorithm API.html | 0
 9 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/04 Creating the Brokerage.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/05 Translating Symbol Conventions.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/06 Describe Brokerage Limitations.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/07 Enable Live Data Streaming.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/08 Enable Serving Historical Data.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/09 Downloading Data.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/10 Brokerage Fee Structures.html
 create mode 100644 03 Open Source/03 Brokerage Development Guide/11 Updating Algorithm API.html

diff --git a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html b/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/04 Creating the Brokerage.html b/03 Open Source/03 Brokerage Development Guide/04 Creating the Brokerage.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/05 Translating Symbol Conventions.html b/03 Open Source/03 Brokerage Development Guide/05 Translating Symbol Conventions.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/06 Describe Brokerage Limitations.html b/03 Open Source/03 Brokerage Development Guide/06 Describe Brokerage Limitations.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/07 Enable Live Data Streaming.html b/03 Open Source/03 Brokerage Development Guide/07 Enable Live Data Streaming.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/08 Enable Serving Historical Data.html b/03 Open Source/03 Brokerage Development Guide/08 Enable Serving Historical Data.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/09 Downloading Data.html b/03 Open Source/03 Brokerage Development Guide/09 Downloading Data.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/10 Brokerage Fee Structures.html b/03 Open Source/03 Brokerage Development Guide/10 Brokerage Fee Structures.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/03 Brokerage Development Guide/11 Updating Algorithm API.html b/03 Open Source/03 Brokerage Development Guide/11 Updating Algorithm API.html
new file mode 100644
index 0000000..e69de29

From edf6ff3365cefca3f3b64e2213915b3ff2146a22 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 11:52:46 +0000
Subject: [PATCH 140/753] Automated push from server made by Jared Broad

---
 .../03 Brokerage Development Guide/02 Guide Contents.html     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index 8dcb796..303f539 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -32,4 +32,6 @@
        </tr>
    </tbody>
    
-</table>
\ No newline at end of file
+</table>
+
+<p>The root of the brokerage system is the algorithm job packets. These hold configuration information about how to run LEAN. The program logic is a little convoluted; it moves from <i>config.json > create job packet > create brokerage factory matching name > set job packet brokerage data > factory creates brokerage instance</i>. Because of this we'll start creating a brokerage at the root -- the configuration and brokerage factory...</p>
\ No newline at end of file

From 80e1b9c180419051a6998f506366a5e3fb48101e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 12:46:32 +0000
Subject: [PATCH 141/753] Automated push from server made by Jared Broad

---
 .../03 Laying a Foundation.html                | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html b/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html
index e69de29..820427b 100644
--- a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html	
+++ b/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html	
@@ -0,0 +1,18 @@
+<p>
+The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.
+</p>
+
+<h4>Create Authentication Configuration</h4>
+<p>
+In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.
+</p>
+
+<h4>Create Brokerage Data</h4>
+<p>
+By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.
+</p>
+
+<h4>Create Brokerage Model</h4>
+<p>
+Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>.  Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.
+</p>
\ No newline at end of file

From 4b09e9b999384a27f6b55f64b0781b34a08f96d7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 12:59:10 +0000
Subject: [PATCH 142/753] Automated push from server made by Jared Broad

---
 .../03 Laying a Foundation.html                           | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html b/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html
index 820427b..aa46b2a 100644
--- a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html	
+++ b/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html	
@@ -15,4 +15,12 @@ <h4>Create Brokerage Data</h4>
 <h4>Create Brokerage Model</h4>
 <p>
 Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>.  Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.
+</p>
+
+<h4>Create Brokerage Instance in Factory</h4>
+<p>
+The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.
+</p>
+<p>
+Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start up process. 
 </p>
\ No newline at end of file

From 1eea129fef8ead7a050ff19cf12bbc99d4bb4426 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 13:42:35 +0000
Subject: [PATCH 143/753] Automated push from server made by Jared Broad

---
 03 Open Source/03 Brokerage Development Guide/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/03 Brokerage Development Guide/00.html
index b10f6dd..fcee4ea 100644
--- a/03 Open Source/03 Brokerage Development Guide/00.html	
+++ b/03 Open Source/03 Brokerage Development Guide/00.html	
@@ -8,5 +8,5 @@
 }
 </style>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Updated July 10th, 2018: The Brokerage Development Guide is an active work in progress.
+    <i class="fa fa-warning"></i> Updated July 11th, 2018: The Brokerage Development Guide is an active work in progress.
 </div>
\ No newline at end of file

From a4c263f81df714b617d8849fc1f734aebad50b4c Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 15:14:40 +0000
Subject: [PATCH 144/753] Automated push from server made by Jared Broad

---
 .../03 Brokerage Development Guide/02 Guide Contents.html       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
index 303f539..9db0ad6 100644
--- a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html	
@@ -4,7 +4,7 @@
    </thead>
    <tbody>
        <tr>
-           <td>1.</td><td width="95%"><a href="#">Laying a Foundation (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
+           <td>1.</td><td width="95%"><a href="#Brokerage-Development-Guide-Laying-a-Foundation">Laying a Foundation (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
        </tr>
        <tr>
            <td>2.</td><td width="95%"><a href="#">Creating The Brokerage (IBrokerage)</a> <br/>Installing key brokerage application logic, where possible using a brokerage SDK.</td>           

From 8ec0793a4bd704034e8377c6d985dd3d69c43182 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 12:16:42 -0400
Subject: [PATCH 145/753] Added step titles

---
 ...ng a Foundation.html => 03 Step 1%3A Laying a Foundation.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/03 Brokerage Development Guide/{03 Laying a Foundation.html => 03 Step 1%3A Laying a Foundation.html} (100%)

diff --git a/03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html b/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/03 Laying a Foundation.html
rename to 03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html

From 4492bf9368ef29306185a43297e5c5b6be165e56 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 12:19:13 -0400
Subject: [PATCH 146/753] Added step titles

---
 ...he Brokerage.html => 04 Step 2%3A Creating the Brokerage.html} | 0
 ...ions.html => 05 Step 3%3A Translating Symbol Conventions.html} | 0
 ...ions.html => 06 Step 4%3A Describe Brokerage Limitations.html} | 0
 ...treaming.html => 07 Step 5%3A Enable Live Data Streaming.html} | 0
 ...Data.html => 08 Step 6%3A Enable Serving Historical Data.html} | 0
 ...9 Downloading Data.html => 09 Step 7%3A Downloading Data.html} | 0
 ...Structures.html => 10 Step 8%3A Brokerage Fee Structures.html} | 0
 ...lgorithm API.html => 11 Step 9%3A Updating Algorithm API.html} | 0
 8 files changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/03 Brokerage Development Guide/{04 Creating the Brokerage.html => 04 Step 2%3A Creating the Brokerage.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{05 Translating Symbol Conventions.html => 05 Step 3%3A Translating Symbol Conventions.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{06 Describe Brokerage Limitations.html => 06 Step 4%3A Describe Brokerage Limitations.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{07 Enable Live Data Streaming.html => 07 Step 5%3A Enable Live Data Streaming.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{08 Enable Serving Historical Data.html => 08 Step 6%3A Enable Serving Historical Data.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{09 Downloading Data.html => 09 Step 7%3A Downloading Data.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{10 Brokerage Fee Structures.html => 10 Step 8%3A Brokerage Fee Structures.html} (100%)
 rename 03 Open Source/03 Brokerage Development Guide/{11 Updating Algorithm API.html => 11 Step 9%3A Updating Algorithm API.html} (100%)

diff --git a/03 Open Source/03 Brokerage Development Guide/04 Creating the Brokerage.html b/03 Open Source/03 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/04 Creating the Brokerage.html
rename to 03 Open Source/03 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
diff --git a/03 Open Source/03 Brokerage Development Guide/05 Translating Symbol Conventions.html b/03 Open Source/03 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/05 Translating Symbol Conventions.html
rename to 03 Open Source/03 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
diff --git a/03 Open Source/03 Brokerage Development Guide/06 Describe Brokerage Limitations.html b/03 Open Source/03 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/06 Describe Brokerage Limitations.html
rename to 03 Open Source/03 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
diff --git a/03 Open Source/03 Brokerage Development Guide/07 Enable Live Data Streaming.html b/03 Open Source/03 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/07 Enable Live Data Streaming.html
rename to 03 Open Source/03 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
diff --git a/03 Open Source/03 Brokerage Development Guide/08 Enable Serving Historical Data.html b/03 Open Source/03 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/08 Enable Serving Historical Data.html
rename to 03 Open Source/03 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
diff --git a/03 Open Source/03 Brokerage Development Guide/09 Downloading Data.html b/03 Open Source/03 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/09 Downloading Data.html
rename to 03 Open Source/03 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
diff --git a/03 Open Source/03 Brokerage Development Guide/10 Brokerage Fee Structures.html b/03 Open Source/03 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/10 Brokerage Fee Structures.html
rename to 03 Open Source/03 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
diff --git a/03 Open Source/03 Brokerage Development Guide/11 Updating Algorithm API.html b/03 Open Source/03 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/11 Updating Algorithm API.html
rename to 03 Open Source/03 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html

From d07f01bd7b8b63508e3d1c0d2b5daa0251c6953e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 16:45:21 +0000
Subject: [PATCH 147/753] Automated push from server made by Jared Broad

---
 .../03 Step 1%3A Laying a Foundation.html           | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
index aa46b2a..48982b3 100644
--- a/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
+++ b/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
@@ -2,25 +2,30 @@
 The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.
 </p>
 
-<h4>Create Authentication Configuration</h4>
+<h4>1.1 Create Authentication Configuration</h4>
 <p>
 In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.
 </p>
 
-<h4>Create Brokerage Data</h4>
+<h4>1.2 Create Brokerage Data</h4>
 <p>
 By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.
 </p>
 
-<h4>Create Brokerage Model</h4>
+<h4>1.3 Create Brokerage Model</h4>
 <p>
 Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>.  Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.
 </p>
 
-<h4>Create Brokerage Instance in Factory</h4>
+<h4>1.4 Create Brokerage Instance in Factory</h4>
 <p>
 The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.
 </p>
 <p>
 Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start up process. 
+</p>
+
+<h4>1.5 Create Configuration Environment</h4>
+<p>
+In the config file LEAN has helper <em>environments</em> which group configuration flags together and override the root configuration values. You should make a <code>mybrokerage-live</code> environment for your brokerage which specifies the brokerage type name for <code>live-mode-brokerage</code>. You should copy the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L132">paper-trading brokerage</a> setup to start. You should set the <code>environment</code> <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L9">value</a> to your new brokerage environment for testing. 
 </p>
\ No newline at end of file

From cc44ed29499f56fa6d4550dbe088ef23156af39d Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 14:32:35 -0400
Subject: [PATCH 148/753] Added visual studio plugin tutorial

---
 .../01 Introduction.html}                                         | 0
 .../02 Installation.html}                                         | 0
 .../03 Plugin Features.html}                                      | 0
 .../04 Backtest Tool Window.html}                                 | 0
 .../05 Troubleshooting.html}                                      | 0
 .../02 How do I use the API File Provider.html                    | 0
 .../00.html                                                       | 0
 .../01 Introduction.html                                          | 0
 .../02 Guide Contents.html                                        | 0
 .../03 Step 1%3A Laying a Foundation.html                         | 0
 .../04 Step 2%3A Creating the Brokerage.html}                     | 0
 .../05 Step 3%3A Translating Symbol Conventions.html}             | 0
 .../06 Step 4%3A Describe Brokerage Limitations.html}             | 0
 .../07 Step 5%3A Enable Live Data Streaming.html                  | 0
 .../08 Step 6%3A Enable Serving Historical Data.html              | 0
 .../09 Step 7%3A Downloading Data.html                            | 0
 .../10 Step 8%3A Brokerage Fee Structures.html                    | 0
 .../11 Step 9%3A Updating Algorithm API.html                      | 0
 18 files changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/{03 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html => 02 Backtesting from Visual Studio/01 Introduction.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html => 02 Backtesting from Visual Studio/02 Installation.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html => 02 Backtesting from Visual Studio/03 Plugin Features.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html => 02 Backtesting from Visual Studio/04 Backtest Tool Window.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html => 02 Backtesting from Visual Studio/05 Troubleshooting.html} (100%)
 rename 03 Open Source/{02 Using the API File Provider => 03 Using the API File Provider}/02 How do I use the API File Provider.html (100%)
 rename 03 Open Source/{03 Brokerage Development Guide => 04 Brokerage Development Guide}/00.html (100%)
 rename 03 Open Source/{03 Brokerage Development Guide => 04 Brokerage Development Guide}/01 Introduction.html (100%)
 rename 03 Open Source/{03 Brokerage Development Guide => 04 Brokerage Development Guide}/02 Guide Contents.html (100%)
 rename 03 Open Source/{03 Brokerage Development Guide => 04 Brokerage Development Guide}/03 Step 1%3A Laying a Foundation.html (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/09 Step 7%3A Downloading Data.html => 04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html => 04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html} (100%)
 rename 03 Open Source/{03 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html => 04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html} (100%)
 create mode 100644 03 Open Source/04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
 create mode 100644 03 Open Source/04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
 create mode 100644 03 Open Source/04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
 create mode 100644 03 Open Source/04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
 create mode 100644 03 Open Source/04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html

diff --git a/03 Open Source/03 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
rename to 03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
diff --git a/03 Open Source/03 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
rename to 03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
diff --git a/03 Open Source/03 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
rename to 03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
diff --git a/03 Open Source/03 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
rename to 03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
diff --git a/03 Open Source/03 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
rename to 03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html
diff --git a/03 Open Source/02 Using the API File Provider/02 How do I use the API File Provider.html b/03 Open Source/03 Using the API File Provider/02 How do I use the API File Provider.html
similarity index 100%
rename from 03 Open Source/02 Using the API File Provider/02 How do I use the API File Provider.html
rename to 03 Open Source/03 Using the API File Provider/02 How do I use the API File Provider.html
diff --git a/03 Open Source/03 Brokerage Development Guide/00.html b/03 Open Source/04 Brokerage Development Guide/00.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/00.html
rename to 03 Open Source/04 Brokerage Development Guide/00.html
diff --git a/03 Open Source/03 Brokerage Development Guide/01 Introduction.html b/03 Open Source/04 Brokerage Development Guide/01 Introduction.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/01 Introduction.html
rename to 03 Open Source/04 Brokerage Development Guide/01 Introduction.html
diff --git a/03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/02 Guide Contents.html
rename to 03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html
diff --git a/03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
rename to 03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
diff --git a/03 Open Source/03 Brokerage Development Guide/09 Step 7%3A Downloading Data.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
rename to 03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
diff --git a/03 Open Source/03 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html b/03 Open Source/04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
rename to 03 Open Source/04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
diff --git a/03 Open Source/03 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html b/03 Open Source/04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
similarity index 100%
rename from 03 Open Source/03 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html
rename to 03 Open Source/04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
diff --git a/03 Open Source/04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html b/03 Open Source/04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html b/03 Open Source/04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html b/03 Open Source/04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html b/03 Open Source/04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html b/03 Open Source/04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html
new file mode 100644
index 0000000..e69de29

From 2263b5d82fdd507cea18e370198abd072219fcb3 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 18:39:13 +0000
Subject: [PATCH 149/753] Automated push from server made by Jared Broad

---
 .../00 Introduction/Introduction.html         | 26 ++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 56f5876..c1bc929 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -4,16 +4,36 @@
 a .tutorial-box { color:#747f8e; cursor: pointer; text-decoration: none; position: relative; margin-bottom: 20px}
 a.tutorial-link:hover  { text-decoration: none; }
 i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
-</style><a class="tutorial-link docs-internal-link" href="/tutorials/open-source/desktop-charting-with-lean">
+</style>
+
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/desktop-charting-with-lean">
   <div class="tutorial-box">    
     <h4><i class="fa fa-list-alt"></i> 100 Desktop Charting with LEAN</h4>    
     <p>Guide to using the desktop charting environment that comes with LEAN (UX v1.0).</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
-</a><a class="tutorial-link docs-internal-link" href="/tutorials/open-source/how-do-i-use-the-api-file-provider">
+</a>
+
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/backtesting-from-visual-studio">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-windows"></i> 101 Backtesting from Visual Studio</h4>    
+    <p>VIsual Studio plugin integrated with the QuantConnect API.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/how-do-i-use-the-api-file-provider">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-list-alt"></i> 101 Using the API File Provider</h4>    
+    <h4><i class="fa fa-list-alt"></i> 102 Using the API File Provider</h4>    
     <p>Configuring your installation to pull financial data from the QuantConnect website repository.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>
+
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/brokerage-development-guide">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-briefcase"></i> 301 Brokerage Development Guide</h4>    
+    <p>Guide to implementing your own brokerage in LEAN.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
\ No newline at end of file

From a5e0080e476bbcf3baddbf7e1c6492cf0d2d72b8 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 18:46:17 +0000
Subject: [PATCH 150/753] Automated push from server made by Jared Broad

---
 .../01 Introduction.html                                  | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index e69de29..7edd73d 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. 
+</p>
+
+<p>
+   You can download the plugin here: 
+    <a href=""><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a>
+</p>
\ No newline at end of file

From f696232f4d76f9c477c71f62f8fef109835ddc60 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 18:48:29 +0000
Subject: [PATCH 151/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html    | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index 7edd73d..e894e06 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -5,4 +5,6 @@
 <p>
    You can download the plugin here: 
     <a href=""><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a>
-</p>
\ No newline at end of file
+</p>
+
+<p><em>Visual Studio 2015 and 2017 are supported.</em></p>
\ No newline at end of file

From 7c9d8f275e3f1a319abca59652e72eb29f03bc43 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 18:52:18 +0000
Subject: [PATCH 152/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html  | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index e894e06..4c8505d 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -4,7 +4,5 @@
 
 <p>
    You can download the plugin here: 
-    <a href=""><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a>
-</p>
-
-<p><em>Visual Studio 2015 and 2017 are supported.</em></p>
\ No newline at end of file
+    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a>
+</p>
\ No newline at end of file

From c4bd8ec3cbf62aff0fe45bfae272ad549730c45a Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:02:06 +0000
Subject: [PATCH 153/753] Automated push from server made by Jared Broad

---
 .../02 Installation.html                      | 30 +++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index e69de29..bb984d7 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -0,0 +1,30 @@
+
+<p>
+To install the plugin simply build the binary supplied above; or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
+</p>
+
+<h4>Build</h4>
+<ol>
+    <li>Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.</li>
+    <li>Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.</li>
+    <li>Verify in the Visual Studio ‘Output’ panel the compilation succeeded with no errors.</li>
+</ol>
+
+<h4>Installation</h4>
+<p>
+If you have intstalled a previous version of the plugin you first need to remove this by your Tools Menu. You can find this in the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and uninstall. <em>Once uninstalled; restart Visual Studio.</em>
+</p>
+<p>
+After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. Execute ‘QuantConnect.VisualStudioPlugin.vsix’.
+</p>
+
+<h4>Setting Up Your Plugin</h4>
+<ol>
+    <li>Login to QuantConnect and navigate to your <a href="/account">My Account</a> page.</li>
+    <li>In the API panel, you will find your a ‘unique access token’ and your ‘user id’ which are required to connect to the API. Copy these to your clipboard.</li>
+    <li>From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your Api credentials and login.</li>
+</ol>
+<figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/loginBox.png"/>
+</figcaption>
+

From c81b05d0b40bad0e907774f73261f3fd9fe35ed3 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:02:47 +0000
Subject: [PATCH 154/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html    | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index 4c8505d..2706428 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -1,3 +1,7 @@
+<style>
+.backdrop { background-color: #ddd; padding: 15px; border: 1px solid #ccc; border-radius: 5px;}
+</style>
+
 <p>
 The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. 
 </p>

From 5d847ad187555b8f7a11f29cc37ff34cd5f3b409 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:04:22 +0000
Subject: [PATCH 155/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/02 Installation.html     | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index bb984d7..efe0c4d 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -28,3 +28,6 @@ <h4>Setting Up Your Plugin</h4>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/loginBox.png"/>
 </figcaption>
 
+<p>
+Manually logging in is only required the first time. After the first login the plugin will automatically login, using previously saved credentials.
+</p>
\ No newline at end of file

From bfb4190ab5eda6b1baee5314fafc8369c146aee3 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:21:40 +0000
Subject: [PATCH 156/753] Automated push from server made by Jared Broad

---
 .../03 Plugin Features.html                   | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
index e69de29..8a4607c 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html	
@@ -0,0 +1,46 @@
+<p>
+The Visual Studio plugin can currently save files to a project, compile the project, and backtest it in the cloud. Through the accompanying "tool window" it can also rename, open or add a note to a backtest, and create a new project.
+</p>
+
+<h4>Saving Files to QuantConnect</h4>
+<p>
+Save files from your local project to a QuantConnect project. You can save many files at a time.
+</p>
+<ol>
+ <li>Select the desired files with your mouse from the Solution Explorer tool window.</li>
+ <li>Right-click selection → ‘Save to QuantConnect’.</li>
+ <li>Select target project name:
+   <figcaption class="backdrop"><img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/selectProjectBox.png"/></figcaption>
+</li>
+<li>
+  Result:
+   <ul>
+    <li>If the operation succeeds you will be informed through the VS Status Bar.</li>
+    <li>If any error occurs you will be informed through VS Status Bar and with a popup box.</li>
+   </ul>
+   <figcaption class="backdrop"><img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/uploadFailedBox.png"/></figcaption>
+</li>
+</ol>
+
+<h4>Send for Backtesting</h4>
+<p>
+This feature allows you to upload one or more files to a target project, compile it and backtest it in the QuantConnect cloud.
+</p>
+<ol>
+ <li>Select the desired files you'd like to backtest.</li>
+ <li>Right-click → ‘Send for Backtesting’</li>
+ <li>Select the target project with the project list view.</li>
+ <li>Result:
+ <ul>
+    <li>If the operation succeeds you will be informed through the VS Status Bar and the target QC project will be opened using the default browser pointing at your new backtest.</li>
+    <li>If any error occurs you will be informed through VS Status Bar and with a popup box.</li>
+   </ul>
+   <figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/uploadFailedBox.png"/></figcaption>
+If the error is due to a compilation error, the pop-up box will contain the first compilation error.
+<br/><br/>
+<figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/backtestFailedBox.png"/></figcaption>
+If the error is due to a run-time issue during the backtesting process, the popup box will contain error details and the corresponding stack trace.
+
+</li>
\ No newline at end of file

From 2f22f798cff7f2de4e35f19df55ac3f4e15037dd Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:32:38 +0000
Subject: [PATCH 157/753] Automated push from server made by Jared Broad

---
 .../04 Backtest Tool Window.html              | 31 +++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
index e69de29..8066c56 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html	
@@ -0,0 +1,31 @@
+<p>
+The backtesting tool window utility allows you to monitor and control ongoing backtests, along with editing various properties of existing completed backtests.
+</p>
+<figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/toolWindow.png"/>
+</figcaption>
+<p>
+If you select a project ‘BuyTheDip_007’ using the tool windows combo box and launch a backtest using ‘Send For Backtesting’ for ‘BuyTheDip_007’ project, it will display the backtests progress in the tool window.
+</p>
+
+<h4>How do I get the Backtest Window?</h4>
+<p>
+From the Visual Studio IDE go to ‘View’ menu → ‘Other Windows’ → ‘QuantConnect’. If there are previous valid credentials, the tool window will auto login when open or when the user performs an action.
+</p>
+
+<h4>Available Backtest Window Features</h4>
+<ul>
+ <li><i class="fa fa-bar-chart"></i> Open Backtest: Open backtest viewer on QuantConnect.</li>
+ <li><i class="fa fa-refresh"></i> Refresh/Reload Project List</li> 
+ <li><i class="fa fa-trash"></i> Delete Backtest</li>
+ <li><i class="fa fa-pencil"></i> Edit Backtest: Rename or add notes to backtest results.
+  <figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/editBacktest.png"/>
+</figcaption>
+ </li>
+ <li><i class="fa fa-plus"></i> Create a New Project
+  <figcaption class="backdrop">
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/newProject.png"/>
+</figcaption>
+ </li>
+</ul>
\ No newline at end of file

From 56be49c7ff6834186f7d6c96a8cd0bbadb0ebb0c Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:34:52 +0000
Subject: [PATCH 158/753] Automated push from server made by Jared Broad

---
 .../05 Troubleshooting.html                                | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html
index e69de29..9312fcf 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html	
@@ -0,0 +1,7 @@
+<p>
+VisualStudio plugin can write log data to the VisualStudio activity log, but only if VisualStudio is started with the /log parameter switch. To debug the QuantConnect plugin start VisualStudio with the following command:
+</p>
+<pre>devenv /log &lt;path-to-log&gt;</pre>
+<p>
+See <a target="_BLANK" href="https://docs.microsoft.com/en-us/visualstudio/ide/reference/log-devenv-exe">Visual Studio Documentation</a> for more information.
+</p>
\ No newline at end of file

From 9b127751c05b3c939530cf7917aca52748ce6f93 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 15:37:34 -0400
Subject: [PATCH 159/753] Create 00.html

---
 03 Open Source/02 Backtesting from Visual Studio/00.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/02 Backtesting from Visual Studio/00.html

diff --git a/03 Open Source/02 Backtesting from Visual Studio/00.html b/03 Open Source/02 Backtesting from Visual Studio/00.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/03 Open Source/02 Backtesting from Visual Studio/00.html	
@@ -0,0 +1 @@
+

From ee266a7862d26a1d663fc0abd5b3372f981da87b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 11 Jul 2018 15:42:45 -0400
Subject: [PATCH 160/753] Add algorithm volatility selling

---
 .../01 Strategy Library.php                   |  8 ++++
 .../01 Introduction.html                      |  7 +++
 .../02 Method.html                            | 48 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 +++
 4 files changed, 69 insertions(+)
 create mode 100644 04 Strategy Library/25 Volatility Risk Premium Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html
 create mode 100644 04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index f62e4f8..c4e406c 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -166,6 +166,14 @@
         ],
         'description' => 'Goes long stocks with the lowest turnover and short on stocks with the highest turnover from the lowest market-cap quartile.'
     ],
+    [
+        'name' => 'Volatility Risk Premium Effect',
+        'link' => 'strategy-library/volatility-risk-premium-effect',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/20'
+        ],
+        'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/01 Introduction.html b/04 Strategy Library/25 Volatility Risk Premium Effect/01 Introduction.html
new file mode 100644
index 0000000..c517ab8
--- /dev/null
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/01 Introduction.html	
@@ -0,0 +1,7 @@
+<p>
+  Long volatility means that the value of your portfolio increases when the volatility goes up.
+  Short volatility means that you make money when the volatility goes down. The simplest example of volatility selling involves the sale of put and call contracts.
+  Traders often long volatility by holding the long position of put or call options for hedging purpose.
+  In contrast, the short volatility strategy expects to earn the systematic risk premium by selling options.
+  This algorithm will explore the risk premium effect in volatility selling.
+</p>
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html b/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html
new file mode 100644
index 0000000..fbf46c1
--- /dev/null
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html	
@@ -0,0 +1,48 @@
+<p>
+  This short volatility algorithm first prescreens the option contracts by the expiry and the strike.
+  To include the weekly contract, we use the universe function
+</p>
+<div class="section-example-container">
+<pre class="python">def Initialize(self):
+    option.SetFilter(self.UniverseFunc)
+  def UniverseFunc(self, universe):
+      return universe.IncludeWeeklys().Strikes(-20, 20).Expiration(timedelta(25), timedelta(35))
+</pre>
+</div>
+<p>
+  The algorithm selects contracts with one month until maturity so we choose a small range for expiration.
+</p>
+<p>
+  In <code>OnData()<code>, we divide the option chain into put and call options. Then we create two lists
+  <code>expiries</code> and <code>strikes</code> to save all available expiration dates and stike prices to facilitate
+  sorting and filtering.
+</p>
+<p>
+  The algorithm needs three option contracts with one month to the maturity: one ATM call, one ATM put to contruct the ATM straddle,
+  one 15% OTM put. As it's difficult to find the contract with the specified days to maturity and strikes,
+  we use <code>min()</code> to find the most closest contract.
+</p>
+<div class="section-example-container">
+<pre class="python">expiries = [i.Expiry for i in puts]
+# determine expiration date nearly 30 days
+expiry = min(expiries, key=lambda x: abs((x.date()-self.Time.date()).days-30))
+strikes = [i.Strike for i in puts]
+# determine at-the-money strike
+strike = min(strikes, key=lambda x: abs(x-underlying_price))
+# determine 15% out-of-the-money strike
+otm_strike = min(strikes, key = lambda x:abs(x-Decimal(0.85)*underlying_price))
+</pre>
+</div>
+<p>
+  From the above expiration date and strike price, we pick three option contracts
+</p>
+<div class="section-example-container">
+<pre class="python">self.atm_call = [i for i in calls if i.Expiry == expiry and i.Strike == strike]
+self.atm_put = [i for i in puts if i.Expiry == expiry and i.Strike == strike]
+self.otm_put = [i for i in puts if i.Expiry == expiry and i.Strike == otm_strike]
+</pre>
+</div>
+<p>
+  In trading, we sell the ATM straddle by selling one ATM call and one ATM put. Then we buy an OTM put option as insurance against a market crash.
+  Then we wait until the expiration and sell the underlying positions after option exercise and assignment. The portfolio is rebalanced once a month.
+</p>
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html
new file mode 100644
index 0000000..a0bce57
--- /dev/null
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_f0fca76a72227c5bee77d59bdbf5a53d.html"></iframe>
+    </div>
+</div>

From b654eab0071d5c1b6b251e52801e85caeae2f49b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:43:07 +0000
Subject: [PATCH 161/753] Automated push from server made by Jared Broad

---
 .../02 Installation.html                               | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index efe0c4d..a9dfa69 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -1,6 +1,6 @@
 
 <p>
-To install the plugin simply build the binary supplied above; or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
+To install the plugin simply build the binary supplied above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
 </p>
 
 <h4>Build</h4>
@@ -12,22 +12,22 @@ <h4>Build</h4>
 
 <h4>Installation</h4>
 <p>
-If you have intstalled a previous version of the plugin you first need to remove this by your Tools Menu. You can find this in the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and uninstall. <em>Once uninstalled; restart Visual Studio.</em>
+If you have intstalled a previous version of the plugin you first need to remove this first. You do this from the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and click uninstall. <em>Once uninstalled; remember to restart Visual Studio.</em>
 </p>
 <p>
-After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. Execute ‘QuantConnect.VisualStudioPlugin.vsix’.
+After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. If you built sucessfully you should be able to execute ‘QuantConnect.VisualStudioPlugin.vsix’.
 </p>
 
 <h4>Setting Up Your Plugin</h4>
 <ol>
     <li>Login to QuantConnect and navigate to your <a href="/account">My Account</a> page.</li>
     <li>In the API panel, you will find your a ‘unique access token’ and your ‘user id’ which are required to connect to the API. Copy these to your clipboard.</li>
-    <li>From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your Api credentials and login.</li>
+    <li>From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your API credentials and click Login.</li>
 </ol>
 <figcaption class="backdrop">
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/loginBox.png"/>
 </figcaption>
 
 <p>
-Manually logging in is only required the first time. After the first login the plugin will automatically login, using previously saved credentials.
+Manually logging in is only required the first time. After the first login, the plugin will automatically log in using previously saved credentials.
 </p>
\ No newline at end of file

From d58b262d4612d614c99a6c8f8b4c7ee51d487921 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:44:50 +0000
Subject: [PATCH 162/753] Automated push from server made by Jared Broad

---
 03 Open Source/02 Backtesting from Visual Studio/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/00.html b/03 Open Source/02 Backtesting from Visual Studio/00.html
index 8b13789..4b86dc6 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/00.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/00.html	
@@ -1 +1 @@
-
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio.png" />
\ No newline at end of file

From 82c7ca5b8aa599dd88707bfe6d4fd02bd83bea2e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:47:53 +0000
Subject: [PATCH 163/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/02 Installation.html     | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index a9dfa69..c4569aa 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -1,9 +1,8 @@
-
 <p>
 To install the plugin simply build the binary supplied above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
 </p>
 
-<h4>Build</h4>
+<h4>Building Plugin</h4>
 <ol>
     <li>Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.</li>
     <li>Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.</li>

From c6969d84c38927a218b053ddba901f7d27855618 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:49:39 +0000
Subject: [PATCH 164/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index 2706428..db9ecd9 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -3,7 +3,7 @@
 </style>
 
 <p>
-The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. 
+The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports <b>Visual Studio 2015 and 2017</b>.
 </p>
 
 <p>

From e22816d38ace1ec1366e841902a6f94f7e7e60c9 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:50:09 +0000
Subject: [PATCH 165/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/02 Installation.html   | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index c4569aa..cb51d27 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -1,8 +1,9 @@
+
 <p>
-To install the plugin simply build the binary supplied above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
+To install the plugin simply execute the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
 </p>
 
-<h4>Building Plugin</h4>
+<h4>Build</h4>
 <ol>
     <li>Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.</li>
     <li>Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.</li>

From f007bbfe6c2289c03ad0f13b5a0cda6a82e898cc Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 19:57:15 +0000
Subject: [PATCH 166/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/02 Installation.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index cb51d27..b00faf4 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -1,7 +1,7 @@
 
 <p>
 To install the plugin simply execute the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
-</p>
+</p> 
 
 <h4>Build</h4>
 <ol>

From e5e409c99f7d5dd83bf51123fce9e69a50ae19ff Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 11 Jul 2018 16:23:51 -0400
Subject: [PATCH 167/753] Fix html bug

---
 .../25 Volatility Risk Premium Effect/02 Method.html            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html b/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html
index fbf46c1..3bc10d3 100644
--- a/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html	
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/02 Method.html	
@@ -13,7 +13,7 @@
   The algorithm selects contracts with one month until maturity so we choose a small range for expiration.
 </p>
 <p>
-  In <code>OnData()<code>, we divide the option chain into put and call options. Then we create two lists
+  In <code>OnData()</code>, we divide the option chain into put and call options. Then we create two lists
   <code>expiries</code> and <code>strikes</code> to save all available expiration dates and stike prices to facilitate
   sorting and filtering.
 </p>

From 5440e4c92d23ae51e8e694c99accb9d9793de21e Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 16:59:41 -0400
Subject: [PATCH 168/753] Added quantpedia filter

---
 .../00 Strategy Library/02 Quantpedia.php     | 154 ++++++++++++++++++
 1 file changed, 154 insertions(+)
 create mode 100644 04 Strategy Library/00 Strategy Library/02 Quantpedia.php

diff --git a/04 Strategy Library/00 Strategy Library/02 Quantpedia.php b/04 Strategy Library/00 Strategy Library/02 Quantpedia.php
new file mode 100644
index 0000000..2d0ebd0
--- /dev/null
+++ b/04 Strategy Library/00 Strategy Library/02 Quantpedia.php	
@@ -0,0 +1,154 @@
+<?php
+/**
+ * Created by IntelliJ IDEA.
+ * User: Gustavo
+ * Date: 5/24/2018
+ * Time: 4:42 PM
+ */
+
+?>
+<div class="row">
+    <div class="search col-xs-12">
+    <h2>Quantpedia Strategy Implementations</h2>
+    </div>
+</div>
+<div class="row">
+    <div class="search col-xs-12">
+        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="SLSearch.OnSearchChange()" onkeyup="SLSearch.OnSearchChange()">
+    </div>
+</div>
+<br>
+<table id="strategy-library-table" class="table table-striped qc-table">
+    <thead>
+    <tr>
+        <th>
+            Strategy Name
+        </th>
+    </tr>
+    </thead>
+    <tbody>
+    <?php foreach ($strategyMap as $strategy) {
+        
+        if (!array_key_exists('sources', $strategy)) continue;
+        
+        $sources = '';
+        if (array_key_exists('sources', $strategy)) {
+            $sources .= '<p class="sources">';
+            $sources .= (count($strategy['sources']) > 1 ? 'Sources:' : 'Source:');
+            foreach ($strategy['sources'] as $key => $source) {
+                $sources .= " <a href=\"{$source}\" rel=\"nofollow\" >{$key}</a>,";
+            }
+
+            $sources = trim($sources, ',');
+            $sources .= '</p>';
+        }
+        ?>
+        <tr>
+            <td>
+                <a class="docs-internal-link" href="/tutorials/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
+                <p><?= $strategy['description'] ?></p>
+                <?= $sources ?>
+            </td>
+        </tr>
+    <?php } ?>
+    </tbody>
+</table>
+<style>
+    .documentation .page-sub-heading {
+        display: none;
+    }
+
+    .documentation #strategy-library-search-box {
+        margin: 30px 0;
+    }
+
+    .documentation #doc-content .pre-title-area > span {
+        display: none;
+    }
+
+    .documentation #doc-content .pre-title-area > span:first-child {
+        display: inline;
+    }
+
+    .documentation #doc-content #strategy-library-table tbody > tr > td > a {
+        font-size: 24px;
+        margin-top: 16px;
+        display: block;
+    }
+</style>
+<script type="application/javascript">
+    /**
+     * SLSearch static controller
+     */
+    var SLSearch = /** @class */ (function () {
+        function SLSearch() {
+        }
+
+        /**
+         * Handler for the vent of change in the strategy library search box
+         * @constructor
+         */
+        SLSearch.OnSearchChange = function () {
+            if (!SLSearch._BuildSearchMap())
+                return;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            var search = $('#strategy-library-search-box').val();
+            var options = {
+                shouldSort: true,
+                threshold: 0.8,
+                location: 0,
+                distance: 100,
+                maxPatternLength: 32,
+                minMatchCharLength: 3,
+                keys: [
+                    "text"
+                ]
+            };
+            console.log('starting search');
+            var result = SLSearch._StrategyLibraryMap;
+            if (search != '') {
+                var fuse = new Fuse(SLSearch._StrategyLibraryMap, options);
+                result = fuse.search(search);
+                if (result.length == 0) {
+                    var cols = table.find('thead th').length;
+                    result = [{
+                        html: "<td colspan='" + cols + "' class='text-center' > No matching result </td>",
+                        text: ''
+                    }];
+                }
+            }
+            var tableHtml = '';
+            for (var i in result) {
+                tableHtml += '<tr>' + result[i].html + '</tr>';
+            }
+            table.find('tbody').html(tableHtml);
+            console.log(result);
+        };
+        /**
+         * Builds the strategy library search map
+         * @private
+         */
+        SLSearch._BuildSearchMap = function () {
+            if (SLSearch._StrategyLibraryMap !== null)
+                return true;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            SLSearch._StrategyLibraryMap = [];
+            table.find('tbody > tr').each(function (i, el) {
+                var text = $(el).text();
+                text = text.replace(/(\s+|\n)/g, ' ');
+                text = text.replace(/(\s+|\n)/g, ' ');
+                SLSearch._StrategyLibraryMap.push({
+                    'html': $(el).html(),
+                    'text': text
+                });
+            });
+            return true;
+        };
+        SLSearch._StrategyLibraryMap = null;
+        return SLSearch;
+    }());
+</script>

From 3b3a5e73bc579a6208ad44f9fefa3a6b673f450b Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 17:06:54 -0400
Subject: [PATCH 169/753] Update 02 Quantpedia.php

---
 04 Strategy Library/00 Strategy Library/02 Quantpedia.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/02 Quantpedia.php b/04 Strategy Library/00 Strategy Library/02 Quantpedia.php
index 2d0ebd0..159a62a 100644
--- a/04 Strategy Library/00 Strategy Library/02 Quantpedia.php	
+++ b/04 Strategy Library/00 Strategy Library/02 Quantpedia.php	
@@ -1,7 +1,7 @@
 <?php
 /**
  * Created by IntelliJ IDEA.
- * User: Gustavo
+ * User: 
  * Date: 5/24/2018
  * Time: 4:42 PM
  */

From 29e02f61857e523e7b5b7da7dd8ca51cf5659314 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 17:12:34 -0400
Subject: [PATCH 170/753] Shuffle

---
 .../26 Quantpedia/02 Quantpedia.php           | 154 ++++++++++++++++++
 1 file changed, 154 insertions(+)
 create mode 100644 04 Strategy Library/26 Quantpedia/02 Quantpedia.php

diff --git a/04 Strategy Library/26 Quantpedia/02 Quantpedia.php b/04 Strategy Library/26 Quantpedia/02 Quantpedia.php
new file mode 100644
index 0000000..2d0ebd0
--- /dev/null
+++ b/04 Strategy Library/26 Quantpedia/02 Quantpedia.php	
@@ -0,0 +1,154 @@
+<?php
+/**
+ * Created by IntelliJ IDEA.
+ * User: Gustavo
+ * Date: 5/24/2018
+ * Time: 4:42 PM
+ */
+
+?>
+<div class="row">
+    <div class="search col-xs-12">
+    <h2>Quantpedia Strategy Implementations</h2>
+    </div>
+</div>
+<div class="row">
+    <div class="search col-xs-12">
+        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="SLSearch.OnSearchChange()" onkeyup="SLSearch.OnSearchChange()">
+    </div>
+</div>
+<br>
+<table id="strategy-library-table" class="table table-striped qc-table">
+    <thead>
+    <tr>
+        <th>
+            Strategy Name
+        </th>
+    </tr>
+    </thead>
+    <tbody>
+    <?php foreach ($strategyMap as $strategy) {
+        
+        if (!array_key_exists('sources', $strategy)) continue;
+        
+        $sources = '';
+        if (array_key_exists('sources', $strategy)) {
+            $sources .= '<p class="sources">';
+            $sources .= (count($strategy['sources']) > 1 ? 'Sources:' : 'Source:');
+            foreach ($strategy['sources'] as $key => $source) {
+                $sources .= " <a href=\"{$source}\" rel=\"nofollow\" >{$key}</a>,";
+            }
+
+            $sources = trim($sources, ',');
+            $sources .= '</p>';
+        }
+        ?>
+        <tr>
+            <td>
+                <a class="docs-internal-link" href="/tutorials/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
+                <p><?= $strategy['description'] ?></p>
+                <?= $sources ?>
+            </td>
+        </tr>
+    <?php } ?>
+    </tbody>
+</table>
+<style>
+    .documentation .page-sub-heading {
+        display: none;
+    }
+
+    .documentation #strategy-library-search-box {
+        margin: 30px 0;
+    }
+
+    .documentation #doc-content .pre-title-area > span {
+        display: none;
+    }
+
+    .documentation #doc-content .pre-title-area > span:first-child {
+        display: inline;
+    }
+
+    .documentation #doc-content #strategy-library-table tbody > tr > td > a {
+        font-size: 24px;
+        margin-top: 16px;
+        display: block;
+    }
+</style>
+<script type="application/javascript">
+    /**
+     * SLSearch static controller
+     */
+    var SLSearch = /** @class */ (function () {
+        function SLSearch() {
+        }
+
+        /**
+         * Handler for the vent of change in the strategy library search box
+         * @constructor
+         */
+        SLSearch.OnSearchChange = function () {
+            if (!SLSearch._BuildSearchMap())
+                return;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            var search = $('#strategy-library-search-box').val();
+            var options = {
+                shouldSort: true,
+                threshold: 0.8,
+                location: 0,
+                distance: 100,
+                maxPatternLength: 32,
+                minMatchCharLength: 3,
+                keys: [
+                    "text"
+                ]
+            };
+            console.log('starting search');
+            var result = SLSearch._StrategyLibraryMap;
+            if (search != '') {
+                var fuse = new Fuse(SLSearch._StrategyLibraryMap, options);
+                result = fuse.search(search);
+                if (result.length == 0) {
+                    var cols = table.find('thead th').length;
+                    result = [{
+                        html: "<td colspan='" + cols + "' class='text-center' > No matching result </td>",
+                        text: ''
+                    }];
+                }
+            }
+            var tableHtml = '';
+            for (var i in result) {
+                tableHtml += '<tr>' + result[i].html + '</tr>';
+            }
+            table.find('tbody').html(tableHtml);
+            console.log(result);
+        };
+        /**
+         * Builds the strategy library search map
+         * @private
+         */
+        SLSearch._BuildSearchMap = function () {
+            if (SLSearch._StrategyLibraryMap !== null)
+                return true;
+            var table = $('#strategy-library-table');
+            if (table.length == 0)
+                return false;
+            SLSearch._StrategyLibraryMap = [];
+            table.find('tbody > tr').each(function (i, el) {
+                var text = $(el).text();
+                text = text.replace(/(\s+|\n)/g, ' ');
+                text = text.replace(/(\s+|\n)/g, ' ');
+                SLSearch._StrategyLibraryMap.push({
+                    'html': $(el).html(),
+                    'text': text
+                });
+            });
+            return true;
+        };
+        SLSearch._StrategyLibraryMap = null;
+        return SLSearch;
+    }());
+</script>

From 943c7b9c7e0e22f4601910ed05fff0d0dc86b1bc Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Wed, 11 Jul 2018 17:13:08 -0400
Subject: [PATCH 171/753] delete redundant

---
 .../00 Strategy Library/02 Quantpedia.php     | 154 ------------------
 1 file changed, 154 deletions(-)
 delete mode 100644 04 Strategy Library/00 Strategy Library/02 Quantpedia.php

diff --git a/04 Strategy Library/00 Strategy Library/02 Quantpedia.php b/04 Strategy Library/00 Strategy Library/02 Quantpedia.php
deleted file mode 100644
index 159a62a..0000000
--- a/04 Strategy Library/00 Strategy Library/02 Quantpedia.php	
+++ /dev/null
@@ -1,154 +0,0 @@
-<?php
-/**
- * Created by IntelliJ IDEA.
- * User: 
- * Date: 5/24/2018
- * Time: 4:42 PM
- */
-
-?>
-<div class="row">
-    <div class="search col-xs-12">
-    <h2>Quantpedia Strategy Implementations</h2>
-    </div>
-</div>
-<div class="row">
-    <div class="search col-xs-12">
-        <input type="text" id="strategy-library-search-box" class="form-control input-lg" placeholder="Search Strategy Tutorials" onchange="SLSearch.OnSearchChange()" onkeyup="SLSearch.OnSearchChange()">
-    </div>
-</div>
-<br>
-<table id="strategy-library-table" class="table table-striped qc-table">
-    <thead>
-    <tr>
-        <th>
-            Strategy Name
-        </th>
-    </tr>
-    </thead>
-    <tbody>
-    <?php foreach ($strategyMap as $strategy) {
-        
-        if (!array_key_exists('sources', $strategy)) continue;
-        
-        $sources = '';
-        if (array_key_exists('sources', $strategy)) {
-            $sources .= '<p class="sources">';
-            $sources .= (count($strategy['sources']) > 1 ? 'Sources:' : 'Source:');
-            foreach ($strategy['sources'] as $key => $source) {
-                $sources .= " <a href=\"{$source}\" rel=\"nofollow\" >{$key}</a>,";
-            }
-
-            $sources = trim($sources, ',');
-            $sources .= '</p>';
-        }
-        ?>
-        <tr>
-            <td>
-                <a class="docs-internal-link" href="/tutorials/<?= $strategy['link'] ?>"><?= $strategy['name'] ?></a>
-                <p><?= $strategy['description'] ?></p>
-                <?= $sources ?>
-            </td>
-        </tr>
-    <?php } ?>
-    </tbody>
-</table>
-<style>
-    .documentation .page-sub-heading {
-        display: none;
-    }
-
-    .documentation #strategy-library-search-box {
-        margin: 30px 0;
-    }
-
-    .documentation #doc-content .pre-title-area > span {
-        display: none;
-    }
-
-    .documentation #doc-content .pre-title-area > span:first-child {
-        display: inline;
-    }
-
-    .documentation #doc-content #strategy-library-table tbody > tr > td > a {
-        font-size: 24px;
-        margin-top: 16px;
-        display: block;
-    }
-</style>
-<script type="application/javascript">
-    /**
-     * SLSearch static controller
-     */
-    var SLSearch = /** @class */ (function () {
-        function SLSearch() {
-        }
-
-        /**
-         * Handler for the vent of change in the strategy library search box
-         * @constructor
-         */
-        SLSearch.OnSearchChange = function () {
-            if (!SLSearch._BuildSearchMap())
-                return;
-            var table = $('#strategy-library-table');
-            if (table.length == 0)
-                return false;
-            var search = $('#strategy-library-search-box').val();
-            var options = {
-                shouldSort: true,
-                threshold: 0.8,
-                location: 0,
-                distance: 100,
-                maxPatternLength: 32,
-                minMatchCharLength: 3,
-                keys: [
-                    "text"
-                ]
-            };
-            console.log('starting search');
-            var result = SLSearch._StrategyLibraryMap;
-            if (search != '') {
-                var fuse = new Fuse(SLSearch._StrategyLibraryMap, options);
-                result = fuse.search(search);
-                if (result.length == 0) {
-                    var cols = table.find('thead th').length;
-                    result = [{
-                        html: "<td colspan='" + cols + "' class='text-center' > No matching result </td>",
-                        text: ''
-                    }];
-                }
-            }
-            var tableHtml = '';
-            for (var i in result) {
-                tableHtml += '<tr>' + result[i].html + '</tr>';
-            }
-            table.find('tbody').html(tableHtml);
-            console.log(result);
-        };
-        /**
-         * Builds the strategy library search map
-         * @private
-         */
-        SLSearch._BuildSearchMap = function () {
-            if (SLSearch._StrategyLibraryMap !== null)
-                return true;
-            var table = $('#strategy-library-table');
-            if (table.length == 0)
-                return false;
-            SLSearch._StrategyLibraryMap = [];
-            table.find('tbody > tr').each(function (i, el) {
-                var text = $(el).text();
-                text = text.replace(/(\s+|\n)/g, ' ');
-                text = text.replace(/(\s+|\n)/g, ' ');
-                SLSearch._StrategyLibraryMap.push({
-                    'html': $(el).html(),
-                    'text': text
-                });
-            });
-            return true;
-        };
-        SLSearch._StrategyLibraryMap = null;
-        return SLSearch;
-    }());
-</script>

From 8477cef272c5674e62539696c1a865a14753ddd5 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 11 Jul 2018 21:15:37 +0000
Subject: [PATCH 172/753] Automated push from server made by Jared Broad

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index c4e406c..cb0c9e7 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -1,7 +1,7 @@
 <?php
 /**
  * Created by IntelliJ IDEA.
- * User: Gustavo
+ * User: 
  * Date: 5/24/2018
  * Time: 4:42 PM
  */

From 5bfe7a4cfdda815fafe916a502e0515a14d2adee Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 12 Jul 2018 18:52:54 -0400
Subject: [PATCH 173/753] Add algorithm to explore the momentum effect in
 commodities futures

---
 .../01 Strategy Library.php                   | 10 +++-
 .../01 Introduction.html                      |  4 ++
 .../02 Method.html                            | 48 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 +++
 4 files changed, 67 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/27 Momentum Effect in Commodities Futures/01 Introduction.html
 create mode 100644 04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html
 create mode 100644 04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index cb0c9e7..7f8c1df 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -1,7 +1,7 @@
 <?php
 /**
  * Created by IntelliJ IDEA.
- * User: 
+ * User:
  * Date: 5/24/2018
  * Time: 4:42 PM
  */
@@ -174,6 +174,14 @@
         ],
         'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.'
     ],
+    [
+        'name' => 'Momentum Effect in Commodities Futures',
+        'link' => 'strategy-library/momentum-effect-in-commodities-futures',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/21'
+        ],
+        'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/01 Introduction.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/01 Introduction.html
new file mode 100644
index 0000000..21044a4
--- /dev/null
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Commodity futures are excellent portfolio diversifiers and some of them are an effective hedge against inflation.
+  This algorithm will explore the momentum effect in commodity futures with the momentum return.
+</p>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html
new file mode 100644
index 0000000..3bc10d3
--- /dev/null
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html	
@@ -0,0 +1,48 @@
+<p>
+  This short volatility algorithm first prescreens the option contracts by the expiry and the strike.
+  To include the weekly contract, we use the universe function
+</p>
+<div class="section-example-container">
+<pre class="python">def Initialize(self):
+    option.SetFilter(self.UniverseFunc)
+  def UniverseFunc(self, universe):
+      return universe.IncludeWeeklys().Strikes(-20, 20).Expiration(timedelta(25), timedelta(35))
+</pre>
+</div>
+<p>
+  The algorithm selects contracts with one month until maturity so we choose a small range for expiration.
+</p>
+<p>
+  In <code>OnData()</code>, we divide the option chain into put and call options. Then we create two lists
+  <code>expiries</code> and <code>strikes</code> to save all available expiration dates and stike prices to facilitate
+  sorting and filtering.
+</p>
+<p>
+  The algorithm needs three option contracts with one month to the maturity: one ATM call, one ATM put to contruct the ATM straddle,
+  one 15% OTM put. As it's difficult to find the contract with the specified days to maturity and strikes,
+  we use <code>min()</code> to find the most closest contract.
+</p>
+<div class="section-example-container">
+<pre class="python">expiries = [i.Expiry for i in puts]
+# determine expiration date nearly 30 days
+expiry = min(expiries, key=lambda x: abs((x.date()-self.Time.date()).days-30))
+strikes = [i.Strike for i in puts]
+# determine at-the-money strike
+strike = min(strikes, key=lambda x: abs(x-underlying_price))
+# determine 15% out-of-the-money strike
+otm_strike = min(strikes, key = lambda x:abs(x-Decimal(0.85)*underlying_price))
+</pre>
+</div>
+<p>
+  From the above expiration date and strike price, we pick three option contracts
+</p>
+<div class="section-example-container">
+<pre class="python">self.atm_call = [i for i in calls if i.Expiry == expiry and i.Strike == strike]
+self.atm_put = [i for i in puts if i.Expiry == expiry and i.Strike == strike]
+self.otm_put = [i for i in puts if i.Expiry == expiry and i.Strike == otm_strike]
+</pre>
+</div>
+<p>
+  In trading, we sell the ATM straddle by selling one ATM call and one ATM put. Then we buy an OTM put option as insurance against a market crash.
+  Then we wait until the expiration and sell the underlying positions after option exercise and assignment. The portfolio is rebalanced once a month.
+</p>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html
new file mode 100644
index 0000000..a0bce57
--- /dev/null
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_f0fca76a72227c5bee77d59bdbf5a53d.html"></iframe>
+    </div>
+</div>

From afee6643122dde76bad5b83a0b52f472d0849f90 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 12 Jul 2018 18:56:07 -0400
Subject: [PATCH 174/753] Update the method and the algorithm

---
 .../02 Method.html                            | 62 +++++++++----------
 .../04 Algorithm.html                         |  4 +-
 2 files changed, 30 insertions(+), 36 deletions(-)

diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html
index 3bc10d3..a2426ff 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html	
@@ -1,48 +1,42 @@
 <p>
-  This short volatility algorithm first prescreens the option contracts by the expiry and the strike.
-  To include the weekly contract, we use the universe function
-</p>
-<div class="section-example-container">
-<pre class="python">def Initialize(self):
-    option.SetFilter(self.UniverseFunc)
-  def UniverseFunc(self, universe):
-      return universe.IncludeWeeklys().Strikes(-20, 20).Expiration(timedelta(25), timedelta(35))
-</pre>
-</div>
-<p>
-  The algorithm selects contracts with one month until maturity so we choose a small range for expiration.
+  As the strategy needs the continuous futures contract, we import the custom data from Quandl.
+  We create a universe of tradable commodity futures from all available commodity futures traded on CME and ICE.
+  They are all liquid and active continuous contracts #1. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations.
+  The data resolution is daily.
 </p>
 <p>
-  In <code>OnData()</code>, we divide the option chain into put and call options. Then we create two lists
-  <code>expiries</code> and <code>strikes</code> to save all available expiration dates and stike prices to facilitate
-  sorting and filtering.
-</p>
-<p>
-  The algorithm needs three option contracts with one month to the maturity: one ATM call, one ATM put to contruct the ATM straddle,
-  one 15% OTM put. As it's difficult to find the contract with the specified days to maturity and strikes,
-  we use <code>min()</code> to find the most closest contract.
+  The first step is importing the data.
 </p>
 <div class="section-example-container">
-<pre class="python">expiries = [i.Expiry for i in puts]
-# determine expiration date nearly 30 days
-expiry = min(expiries, key=lambda x: abs((x.date()-self.Time.date()).days-30))
-strikes = [i.Strike for i in puts]
-# determine at-the-money strike
-strike = min(strikes, key=lambda x: abs(x-underlying_price))
-# determine 15% out-of-the-money strike
-otm_strike = min(strikes, key = lambda x:abs(x-Decimal(0.85)*underlying_price))
+<pre class="python">from QuantConnect.Python import PythonQuandl
+for symbol in self.symbols:
+    self.AddData(QuandlFutures, symbol, Resolution.Daily)
+
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "settle"
 </pre>
 </div>
 <p>
-  From the above expiration date and strike price, we pick three option contracts
+  Here we use the indicator <code>RateOfChange(period)<code> to simulate the momentum return. Here the period is 12 months.
+  As we are using the custom data, the indicator initialization should use the history request to update the value manually.
+  All indicators are saved in the dictionary <code>self.roc</code>.
 </p>
 <div class="section-example-container">
-<pre class="python">self.atm_call = [i for i in calls if i.Expiry == expiry and i.Strike == strike]
-self.atm_put = [i for i in puts if i.Expiry == expiry and i.Strike == strike]
-self.otm_put = [i for i in puts if i.Expiry == expiry and i.Strike == otm_strike]
+<pre class="python">self.roc = {}
+for symbol in self.symbols:
+    self.AddData(QuandlFutures, symbol, Resolution.Daily)
+    self.roc[symbol] = RateOfChange(period)
+    hist = self.History([symbol], 400, Resolution.Daily).loc[symbol]
+    for i in hist.itertuples():
+        self.roc[symbol].Update(i.Index, i.settle)
 </pre>
 </div>
 <p>
-  In trading, we sell the ATM straddle by selling one ATM call and one ATM put. Then we buy an OTM put option as insurance against a market crash.
-  Then we wait until the expiration and sell the underlying positions after option exercise and assignment. The portfolio is rebalanced once a month.
+  In <code>OnData(self, data)</code>, indicators for all futures contracts are updated every day with the settlement price.
+</p>
+<p>
+  We rank the contracts by the last 12-month return and divide them into quintiles.
+  In the trading part, the algorithm goes long on the quintile with the highest momentum return and goes short on the quintile with the lowest momentum return.
+  The portfolio is rebalanced each month.
 </p>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html
index a0bce57..a368a10 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html	
@@ -1,6 +1,6 @@
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_f0fca76a72227c5bee77d59bdbf5a53d.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_203a6729604c80a71b5c3b2baa2b3f69.html"></iframe>
     </div>
 </div>

From ab613748ba68aee076c3754350f451be271ea83f Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 12 Jul 2018 19:04:30 -0400
Subject: [PATCH 175/753] Create quantpedia.json

---
 quantpedia.json | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 quantpedia.json

diff --git a/quantpedia.json b/quantpedia.json
new file mode 100644
index 0000000..45e0bf4
--- /dev/null
+++ b/quantpedia.json
@@ -0,0 +1,16 @@
+{
+     1 : "6caef9a7716468be0d790b63b7f463f6",
+     2 : "1cb38e96cec6120fc85152da661605c1",
+     3 : "8f7b25a5e872436195cb27a815b5f7e1",
+     4 : "d31f2d20f4a060418985dd8c0c606c13",
+     5 : "798687a2edab75475f0c9095ecd71c1f",
+     7 : "34e8ae2763e7e2122634d4091120d320",
+     8 : "336bbb8e9e4a55a6adfa00c0d9f904bb",
+     12: "72532ccafeaa844c81aa75c5696b4b24",
+     13: "8ce50fdd465d8629d4c73135938477d9",
+     14: "ec30318cc245fe200ca1261b7f26f17b",
+     15: "bd83aa417032f8407382a1c065aa7511",
+     16: "afd2c9d67fd51d602bf8eac2ef28d712",
+     18: "ba1bb35b26896e2e710a510f62230a24",
+     20: "f0fca76a72227c5bee77d59bdbf5a53d"
+}

From 41808ac5ed5e7e1e59d7538802a9523de5f43463 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 13 Jul 2018 09:41:12 -0400
Subject: [PATCH 176/753] Update quantpedia json with algorithm 21

---
 quantpedia.json | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 quantpedia.json

diff --git a/quantpedia.json b/quantpedia.json
new file mode 100644
index 0000000..09f0c9c
--- /dev/null
+++ b/quantpedia.json
@@ -0,0 +1,18 @@
+{
+     1 : "6caef9a7716468be0d790b63b7f463f6",
+     2 : "1cb38e96cec6120fc85152da661605c1",
+     3 : "8f7b25a5e872436195cb27a815b5f7e1",
+     4 : "d31f2d20f4a060418985dd8c0c606c13",
+     5 : "798687a2edab75475f0c9095ecd71c1f",
+     7 : "34e8ae2763e7e2122634d4091120d320",
+     8 : "336bbb8e9e4a55a6adfa00c0d9f904bb",
+     12: "72532ccafeaa844c81aa75c5696b4b24",
+     13: "8ce50fdd465d8629d4c73135938477d9",
+     14: "ec30318cc245fe200ca1261b7f26f17b",
+     15: "bd83aa417032f8407382a1c065aa7511",
+     16: "afd2c9d67fd51d602bf8eac2ef28d712",
+     18: "ba1bb35b26896e2e710a510f62230a24",
+     20: "f0fca76a72227c5bee77d59bdbf5a53d",
+     21: "203a6729604c80a71b5c3b2baa2b3f69"
+
+}

From 02b5da0274621612b28d6c0192ba8a59a6fc65df Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 13 Jul 2018 18:33:56 -0400
Subject: [PATCH 177/753] Add algortihm Small Capitalization Stocks Premium
 Anomaly

---
 .../01 Strategy Library.php                   |  8 ++++++
 .../01 Introduction.html                      |  6 ++++
 .../02 Method.html                            | 28 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 ++++
 quantpedia.json                               |  3 +-
 5 files changed, 50 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/01 Introduction.html
 create mode 100644 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
 create mode 100644 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 7f8c1df..9957f5d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -182,6 +182,14 @@
         ],
         'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.'
     ],
+    [
+        'name' => 'Small Capitalization Stocks Premium Anomaly',
+        'link' => 'strategy-library/small-capitalization-stocks-premium-anomaly',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/25'
+        ],
+        'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/01 Introduction.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/01 Introduction.html
new file mode 100644
index 0000000..1e56e9b
--- /dev/null
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  Small caps are typically defined as companies with market caps that are less than $2 billion.
+  The advantage of investing in small cap companies is that they are young companies with significant growth potential.
+  However, the risk of failure is greater with small-cap stocks than with large-cap and mid-cap stocks.
+  In this algorithm, we will explore the performance of the small-capitalization investment.
+</p>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
new file mode 100644
index 0000000..0e4dce1
--- /dev/null
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
@@ -0,0 +1,28 @@
+<p>
+  The first step is corase universe selection. We create an investment universe with stocks that have fundmental data and has price greater than $5.
+</p>
+<div class="section-example-container">
+<pre class="python">self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.AdjustedPrice) > 5)]
+</pre>
+</div>
+<p>In fine universe selection, we sort the stocks in universe by the market capitalization and choose 10 stocks with the lowest market cap.
+</p>
+<div class="section-example-container">
+<pre class="python">def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)
+                                and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
+                                and (x.EarningReports.BasicEPS.TwelveMonths > 0)]
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        sorted_market_cap = sorted(fine, key=lambda x: x.MarketCap)
+        self.filtered_fine = [i.Symbol for i in sorted_market_cap[:20]]
+        self.yearly_rebalance = False
+        return self.filtered_fine
+    else:
+        return []
+</pre>
+</div>
+<p>
+   In <code>OnData()</code>, we buy 10 stocks in the list of lowest market-cap. The portfolio is rebalanced every year.
+</p>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html
new file mode 100644
index 0000000..5be3efc
--- /dev/null
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html	
@@ -0,0 +1,6 @@
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_da3bf5ee608fb8b7dc952372dd925542.html"></iframe>
+    </div>
+</div>
diff --git a/quantpedia.json b/quantpedia.json
index 09f0c9c..c87df39 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -13,6 +13,7 @@
      16: "afd2c9d67fd51d602bf8eac2ef28d712",
      18: "ba1bb35b26896e2e710a510f62230a24",
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
-     21: "203a6729604c80a71b5c3b2baa2b3f69"
+     21: "203a6729604c80a71b5c3b2baa2b3f69",
+     25: "da3bf5ee608fb8b7dc952372dd925542", 
 
 }

From 46020ec91cca581ad044e0867bb6f9aa55c06ac5 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 15:49:20 +0000
Subject: [PATCH 178/753] Automated push from server made by Jared Broad

---
 .../04 Brokerage Development Guide/00.html       | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/03 Open Source/04 Brokerage Development Guide/00.html b/03 Open Source/04 Brokerage Development Guide/00.html
index fcee4ea..3fdb751 100644
--- a/03 Open Source/04 Brokerage Development Guide/00.html	
+++ b/03 Open Source/04 Brokerage Development Guide/00.html	
@@ -6,6 +6,22 @@
     padding: 15px;
     color: #f5ae29;
 }
+.tip {
+    width: 100%;
+    border: 1px solid #f5ae29;
+    border-radius: 5px;
+    padding: 15px;
+}
+.tip i {
+    color: #f5ae29;
+}
+.tip-title { 
+ font-weight: bold;
+color: #f5ae29;
+margin-left: 5px;
+margin-right: 5px;
+}
+.tip p { display: inline; }
 </style>
 <div class="work-in-progress"> 
     <i class="fa fa-warning"></i> Updated July 11th, 2018: The Brokerage Development Guide is an active work in progress.

From 5fa5e266efe3eafe860890e11e1b068c2c230941 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 16:54:06 +0000
Subject: [PATCH 179/753] Automated push from server made by Jared Broad

---
 03 Open Source/04 Brokerage Development Guide/00.html | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/03 Open Source/04 Brokerage Development Guide/00.html b/03 Open Source/04 Brokerage Development Guide/00.html
index 3fdb751..4f7e92e 100644
--- a/03 Open Source/04 Brokerage Development Guide/00.html	
+++ b/03 Open Source/04 Brokerage Development Guide/00.html	
@@ -11,6 +11,7 @@
     border: 1px solid #f5ae29;
     border-radius: 5px;
     padding: 15px;
+    margin-bottom: 20px;
 }
 .tip i {
     color: #f5ae29;
@@ -22,6 +23,16 @@
 margin-right: 5px;
 }
 .tip p { display: inline; }
+table th i {
+    color: #f5ae29;
+}
+th.summary {
+   font-family: "Courier New"; 
+   font-weight: normal;
+}
+.table.qc-table tbody tr td {
+   text-align: left;
+}
 </style>
 <div class="work-in-progress"> 
     <i class="fa fa-warning"></i> Updated July 11th, 2018: The Brokerage Development Guide is an active work in progress.

From d5b3ab03d18b0c3e0ac7d98441d3ec1193c24244 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 16:54:16 +0000
Subject: [PATCH 180/753] Automated push from server made by Jared Broad

---
 .../03 Step 1%3A Laying a Foundation.html     | 61 ++++++++++++-------
 1 file changed, 40 insertions(+), 21 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
index 48982b3..c38f3fe 100644
--- a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
+++ b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
@@ -1,31 +1,50 @@
-<p>
-The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.
-</p>
+<p>The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.</p>
 
 <h4>1.1 Create Authentication Configuration</h4>
-<p>
-In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.
-</p>
+<p>In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.</p>
 
 <h4>1.2 Create Brokerage Data</h4>
-<p>
-By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.
-</p>
+<p>By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.</p>
 
 <h4>1.3 Create Brokerage Model</h4>
-<p>
-Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>.  Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.
-</p>
+<p>Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>. Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.</p>
 
 <h4>1.4 Create Brokerage Instance in Factory</h4>
-<p>
-The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.
-</p>
-<p>
-Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start up process. 
-</p>
+<p>The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.</p>
+<p>Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start up process.</p>
 
 <h4>1.5 Create Configuration Environment</h4>
-<p>
-In the config file LEAN has helper <em>environments</em> which group configuration flags together and override the root configuration values. You should make a <code>mybrokerage-live</code> environment for your brokerage which specifies the brokerage type name for <code>live-mode-brokerage</code>. You should copy the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L132">paper-trading brokerage</a> setup to start. You should set the <code>environment</code> <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L9">value</a> to your new brokerage environment for testing. 
-</p>
\ No newline at end of file
+<p>In the config file LEAN has helper <em>environments</em> which group configuration flags together and override the root configuration values. You should make a <code>mybrokerage-live</code> environment for your brokerage which specifies the brokerage type name for <code>live-mode-brokerage</code>. You should copy the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L132">paper-trading brokerage</a> setup to start. You should set the <code>environment</code> <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L9">value</a> to your new brokerage environment for testing.</p>
+<style> </style>
+<div class="tip">
+  <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
+  <p>In the <code>IBrokerageFactory</code> examples, you'll see code like this: <code>Composer.Instance.AddPart&lt;IDataQueueHandler&gt;(dataQueueHandler)</code>, which is adding parts to the "Composer". The Composer is a system in LEAN for loading types dynamically. In this case, it is adding an instance of the DataQueueHandler for the brokerage to the composer. You can think of the composer as a library, and adding parts is like adding books to its collection. But we'll come back to this later...</p>
+</div>
+
+<table class="table qc-table table-striped table-responsive summary">
+   <thead>
+     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerageFactory</th></tr>
+   </thead>
+   <tbody>
+      <tr><td width="30%">Primary Role</td><td>Create and initialize a brokerage instance.</td></tr>
+      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerage.cs</a></td></tr>
+      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs">GDAXBrokerageFactory.cs</a></td></tr>
+      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/">In Brokerages Folder in Brokerages Solution</a></td></tr>
+   </tbody>
+</table>
+
+<table class="table qc-table table-striped table-responsive">
+   <thead>
+     <tr><th colspan="2" class="summary"><i class="fa fa-check"></i> Stage 1: Checklist</th></tr>
+   </thead>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Configuration keys and placeholder values for brokerage authentication requirements.</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Created folder for brokerage in Brokerages solution; with <code>MyBrokerageFactory.cs</code>.</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Implemented all interfaces of the BrokerageFactory (some with stub implementations).</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Create a stub MyBrokerage.cs with Not Implemented exceptions.</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Create a stub MyBrokerageBrokerageModel.cs inheriting from DefaultBrokerageModel.</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Created a <code>mybrokerage</code> live configuration environment specifying your class.</td></tr>
+   <tr><td width="10px"><input type="checkbox"/></td><td>Set the <code>environment</code> configuration to your new brokerage environment.</td></tr>
+</table>
+
+
+

From 0859f062abb2ac4c10494bc172f6e8ef8b57a31f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 17:20:28 +0000
Subject: [PATCH 181/753] Automated push from server made by Jared Broad

---
 03 Open Source/04 Brokerage Development Guide/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/00.html b/03 Open Source/04 Brokerage Development Guide/00.html
index 4f7e92e..429f31f 100644
--- a/03 Open Source/04 Brokerage Development Guide/00.html	
+++ b/03 Open Source/04 Brokerage Development Guide/00.html	
@@ -35,5 +35,5 @@
 }
 </style>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Updated July 11th, 2018: The Brokerage Development Guide is an active work in progress.
+    <i class="fa fa-warning"></i> Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress.
 </div>
\ No newline at end of file

From f799b11253897453a6ca929542d19b506785af22 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 17:27:10 +0000
Subject: [PATCH 182/753] Automated push from server made by Jared Broad

---
 .../03 Step 1%3A Laying a Foundation.html                   | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
index c38f3fe..e8af1de 100644
--- a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
+++ b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
@@ -46,5 +46,7 @@ <h4>1.5 Create Configuration Environment</h4>
    <tr><td width="10px"><input type="checkbox"/></td><td>Set the <code>environment</code> configuration to your new brokerage environment.</td></tr>
 </table>
 
-
-
+<div class="tip">
+  <i class="fa fa-bolt"></i><span class="tip-title">Action:</span>
+  <p> Build the solution. Although <em>running</em> won't work the stub implementations should still build.</p>
+</div>
\ No newline at end of file

From 4401ee4f85797832b09671b5e24c5677a64a1a86 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 17:37:28 +0000
Subject: [PATCH 183/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html        | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index e69de29..6e867be 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -0,0 +1,13 @@
+<p>
+The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> holds the bulk of the core logic responsible for running the brokerage implementation. It has many important roles vital for the stability of a running algorithm. These include:
+</p>
+<ol>
+<li>Maintain Connection - Connect and maintain connection while algorithm running.</li>
+<li>Setup State - Initialize the algorithm portfolio, open orders and cashbook.</li>
+<li>Order Operations - Create, update and cancel orders.</li>
+<li>Order Events - Receive order fills and apply them to portfolio.</li>
+<li>Account Events - Track non-order events (cash deposits/removals).</li>
+<li>Brokerage Events - Interpret brokerage messages and act when required.</li>
+<li>Serve History Requests - Provide historical data on request.</li> 
+</ol>
+<p>Many smaller models described later <em>use</em> the Brokerage implementation internally so its best to start implementation of the <code>IBrokerage</code> now. Brokerage classes can get quite large so you should use a <code>partial</code> class modifier to break up the files in appropriate categories.</p>
\ No newline at end of file

From 89bb6ff4b7061b691c3679daf7dd37a21fe815ce Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 19:17:13 +0000
Subject: [PATCH 184/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html  | 37 +++++++++++++++++--
 1 file changed, 34 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index 6e867be..83631ad 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -1,5 +1,5 @@
 <p>
-The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> holds the bulk of the core logic responsible for running the brokerage implementation. It has many important roles vital for the stability of a running algorithm. These include:
+The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> holds the bulk of the core logic responsible for running the brokerage implementation. Many smaller models described later <em>use</em> the Brokerage implementation internally so its best to start implementation of the <code>IBrokerage</code> now. Brokerage classes can get quite large so you should use a <code>partial</code> class modifier to break up the files in appropriate categories. It has many important roles vital for the stability of a running algorithm. These include:
 </p>
 <ol>
 <li>Maintain Connection - Connect and maintain connection while algorithm running.</li>
@@ -9,5 +9,36 @@
 <li>Account Events - Track non-order events (cash deposits/removals).</li>
 <li>Brokerage Events - Interpret brokerage messages and act when required.</li>
 <li>Serve History Requests - Provide historical data on request.</li> 
-</ol>
-<p>Many smaller models described later <em>use</em> the Brokerage implementation internally so its best to start implementation of the <code>IBrokerage</code> now. Brokerage classes can get quite large so you should use a <code>partial</code> class modifier to break up the files in appropriate categories.</p>
\ No newline at end of file
+</ol> 
+<p>
+Often brokerages will have their own ticker styles, order class names, event names. Many of the methods in the brokerage implementation may simply be converting from the brokerage object format into LEAN format. You should plan accordingly to write neat code. 
+</p>
+
+<h4>2.1 Brokerage Requirements</h4>
+<p>QuantConnect is best used with <em>streaming or socket based</em> brokerage connections. Streaming brokerage implementations allow for the easiest translation of broker events into LEAN events. Without streaming order-events you will need to <em>poll</em> for to check for fills. In our experience this is fraught with additional risks and challenges.</p>
+
+<h4>2.2 Installing a Library</h4>
+<p>
+Most brokerages will provide a wrapper for their API. You should use this where possible as long as it has a permissive license. Although it is technically possible to embed an external github repository we've elected to not do this to make LEAN easier to install (submodules can be tricky for beginners). You should copy the library into its own subfolder of the brokerage implementation: <code>/Brokerages/MyBrokerage/BrokerLib/*</code>. 
+</p>
+
+<div class="tip">
+  <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
+  <p>Libraries will need to be <em>.NET Framework 4.6.2</em> compatible as LEAN is fully cross-platform via Mono. </p>
+</div>
+
+<p>
+LEAN <i class="fa fa-heart" style="color: red"></i> Open-Source. If you copy and paste code from an external source leave the comments and headers intact, and if they do not have a comment header be sure to <em>add</em> one to <em>each file</em> referencing the source. Let's keep the attributions in place.
+</p>
+
+<table class="table qc-table table-striped table-responsive summary">
+   <thead>
+     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerage</th></tr>
+   </thead>
+   <tbody>
+      <tr><td width="30%">Primary Role</td><td>Brokerage connection, orders and fill events.</td></tr>
+      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage.cs</a></td></tr>
+      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages/GDAX">GDAXBrokerage.cs</a></td></tr>
+      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages">QuantConnect.Brokerages.sln</a></td></tr>
+   </tbody>
+</table>
\ No newline at end of file

From f564cc5c649abe84a2cc808a5d719fba6e885970 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 19:19:34 +0000
Subject: [PATCH 185/753] Automated push from server made by Jared Broad

---
 .../03 Step 1%3A Laying a Foundation.html     | 31 ++++++++++++++-----
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
index e8af1de..4bee070 100644
--- a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
+++ b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
@@ -1,21 +1,36 @@
-<p>The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.</p>
+<p>
+The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.
+</p>
 
 <h4>1.1 Create Authentication Configuration</h4>
-<p>In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.</p>
+<p>
+In the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json">configuration file</a> add a few key-values with your brokerage configuration information. This will be used for most local debugging and testing as the default. E.g. <code>oanda-access-token</code> and <code>oanda-account-id</code>. These will be copied to the job packet which contains a matching field <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L43">BrokerageData</a>. This is a dictionary of &lt;string,string&gt; pairs.
+</p>
 
 <h4>1.2 Create Brokerage Data</h4>
-<p>By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.</p>
+<p>
+By default the <code>IBrokerageFactory.BrokerageData</code> implementation should load all required configuration from the config file using the <code>Config</code> class. E.g. <code>Config.Get("oanda-access-token")</code>. This can be a simple <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L47">pass through</a> to the config for most brokerages.
+</p>
 
 <h4>1.3 Create Brokerage Model</h4>
-<p>Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>. Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.</p>
+<p>
+Brokerage Models tell LEAN what order types a brokerage supports, whether we're allowed to update an order, and what transaction models to use for fills. It is important to do but something we can come back to later. For now, we should just create a stub implementation which we'll extend and improve later. This file <code>MyBrokerageBrokerageModel.cs</code> lives in the <code>/Common/Brokerages</code> folder. For now, you can make it an empty implementation inheriting from the <code>DefaultBrokerageModel</code>. See this example of a partially implemented model <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Brokerages/GDAXBrokerageModel.cs">here</a>. Set your empty placeholder model to the BrokerageModel <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs#L58">property</a>.
+</p>
 
 <h4>1.4 Create Brokerage Instance in Factory</h4>
-<p>The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.</p>
-<p>Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start up process.</p>
+<p>
+The Brokerage Factory uses a job packet to create an initialized brokerage instance. This happens in the <code>CreateBrokerage()</code> method. You should assume the <code>job</code> has the best source of data <i>not</i> the class BrokerageData property. The <code>BrokerageData</code> property on the factory are the starting default values sourced from config which can be overridden by a runtime job.
+</p>
+
+<p>
+Given our <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> implementation hasn't been started yet let's make a placeholder file for our brokerage with the methods stubbed out: <code>MyBrokerage.cs</code> in the <code>MyBrokerage</code> folder, in the <code>Brokerages</code> solution. All the methods should throw a <code>new NotImplementedException()</code> for now except for the constructor which should save any required authentication data to private variables. The <code>CreateBrokerage()</code> method should create a brokerage object but not <i>connect</i> to the brokerage. The connection is done later in the LEAN start-up process.
+</p>
 
 <h4>1.5 Create Configuration Environment</h4>
-<p>In the config file LEAN has helper <em>environments</em> which group configuration flags together and override the root configuration values. You should make a <code>mybrokerage-live</code> environment for your brokerage which specifies the brokerage type name for <code>live-mode-brokerage</code>. You should copy the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L132">paper-trading brokerage</a> setup to start. You should set the <code>environment</code> <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L9">value</a> to your new brokerage environment for testing.</p>
-<style> </style>
+<p>
+In the config file LEAN has helper <em>environments</em> which group configuration flags together and override the root configuration values. You should make a <code>mybrokerage-live</code> environment for your brokerage which specifies the brokerage type name for <code>live-mode-brokerage</code>. You should copy the <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L132">paper-trading brokerage</a> setup to start. You should set the <code>environment</code> <a href="https://github.com/QuantConnect/Lean/blob/master/Launcher/config.json#L9">value</a> to your new brokerage environment for testing.
+</p>
+
 <div class="tip">
   <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
   <p>In the <code>IBrokerageFactory</code> examples, you'll see code like this: <code>Composer.Instance.AddPart&lt;IDataQueueHandler&gt;(dataQueueHandler)</code>, which is adding parts to the "Composer". The Composer is a system in LEAN for loading types dynamically. In this case, it is adding an instance of the DataQueueHandler for the brokerage to the composer. You can think of the composer as a library, and adding parts is like adding books to its collection. But we'll come back to this later...</p>

From b0dbff8c5f25ba87f0694f9bc90418d5ecec11ed Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 19:42:59 +0000
Subject: [PATCH 186/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html  | 40 +++++++++++--------
 1 file changed, 24 insertions(+), 16 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index 83631ad..d80e3ef 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -1,3 +1,15 @@
+<table class="table qc-table table-striped table-responsive summary">
+   <thead>
+     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerage</th></tr>
+   </thead>
+   <tbody>
+      <tr><td width="30%">Primary Role</td><td>Brokerage connection, orders and fill events.</td></tr>
+      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage.cs</a></td></tr>
+      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages/GDAX">GDAXBrokerage.cs</a></td></tr>
+      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages">QuantConnect.Brokerages.sln</a></td></tr>
+   </tbody>
+</table>
+
 <p>
 The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage</a> holds the bulk of the core logic responsible for running the brokerage implementation. Many smaller models described later <em>use</em> the Brokerage implementation internally so its best to start implementation of the <code>IBrokerage</code> now. Brokerage classes can get quite large so you should use a <code>partial</code> class modifier to break up the files in appropriate categories. It has many important roles vital for the stability of a running algorithm. These include:
 </p>
@@ -14,6 +26,10 @@
 Often brokerages will have their own ticker styles, order class names, event names. Many of the methods in the brokerage implementation may simply be converting from the brokerage object format into LEAN format. You should plan accordingly to write neat code. 
 </p>
 
+<p>
+<b>Implementation Style</b>. This guide will focus mostly on implementing the brokerage step by step in LEAN; as its a more natural workflow for most people. You can also follow a more <em>test-driven-development</em> process by following the test harness. To do this create a new test class which extends from the base class in <code>/Tests/Brokerages/BrokerageTests.cs</code>. This test-framework tests all the methods for an IBrokerage implementation.
+</p>
+
 <h4>2.1 Brokerage Requirements</h4>
 <p>QuantConnect is best used with <em>streaming or socket based</em> brokerage connections. Streaming brokerage implementations allow for the easiest translation of broker events into LEAN events. Without streaming order-events you will need to <em>poll</em> for to check for fills. In our experience this is fraught with additional risks and challenges.</p>
 
@@ -21,24 +37,16 @@ <h4>2.2 Installing a Library</h4>
 <p>
 Most brokerages will provide a wrapper for their API. You should use this where possible as long as it has a permissive license. Although it is technically possible to embed an external github repository we've elected to not do this to make LEAN easier to install (submodules can be tricky for beginners). You should copy the library into its own subfolder of the brokerage implementation: <code>/Brokerages/MyBrokerage/BrokerLib/*</code>. 
 </p>
+<p>
+LEAN <i class="fa fa-heart" style="color: red"></i> Open-Source. If you copy and paste code from an external source leave the comments and headers intact, and if they do not have a comment header be sure to <em>add</em> one to <em>each file</em> referencing the source. Let's keep the attributions in place.
+</p>
 
 <div class="tip">
   <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
   <p>Libraries will need to be <em>.NET Framework 4.6.2</em> compatible as LEAN is fully cross-platform via Mono. </p>
 </div>
-
-<p>
-LEAN <i class="fa fa-heart" style="color: red"></i> Open-Source. If you copy and paste code from an external source leave the comments and headers intact, and if they do not have a comment header be sure to <em>add</em> one to <em>each file</em> referencing the source. Let's keep the attributions in place.
-</p>
-
-<table class="table qc-table table-striped table-responsive summary">
-   <thead>
-     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerage</th></tr>
-   </thead>
-   <tbody>
-      <tr><td width="30%">Primary Role</td><td>Brokerage connection, orders and fill events.</td></tr>
-      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerage.cs">IBrokerage.cs</a></td></tr>
-      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages/GDAX">GDAXBrokerage.cs</a></td></tr>
-      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/tree/master/Brokerages">QuantConnect.Brokerages.sln</a></td></tr>
-   </tbody>
-</table>
\ No newline at end of file
+ 
+<div class="tip">
+  <i class="fa fa-bolt"></i><span class="tip-title">Action:</span>
+  <p>Build the project again to make sure the library is compiling successfully. Its good to make sure your library is integrated successfully before continuing. </p>
+</div> 
\ No newline at end of file

From 52d8091519392aef4a788ddadd902fca67365d21 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 19:45:12 +0000
Subject: [PATCH 187/753] Automated push from server made by Jared Broad

---
 .../03 Step 1%3A Laying a Foundation.html     | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
index 4bee070..45ce511 100644
--- a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
+++ b/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html	
@@ -1,3 +1,15 @@
+<table class="table qc-table table-striped table-responsive summary">
+   <thead>
+     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerageFactory</th></tr>
+   </thead>
+   <tbody>
+      <tr><td width="30%">Primary Role</td><td>Create and initialize a brokerage instance.</td></tr>
+      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory.cs</a></td></tr>
+      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs">GDAXBrokerageFactory.cs</a></td></tr>
+      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/">In Brokerages Folder in Brokerages Solution</a></td></tr>
+   </tbody>
+</table>
+
 <p>
 The <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerageFactory</a> creates brokerage instances with a <a href="https://github.com/QuantConnect/Lean/blob/master/Common/Packets/LiveNodePacket.cs#L37">Job Packet</a> which configures LEAN. It contains the name of the selected brokerage to create, which is used to create the right BrokerageFactory type. The configuration <code>live-mode-brokerage</code> key is used to set the brokerage name.
 </p>
@@ -36,18 +48,6 @@ <h4>1.5 Create Configuration Environment</h4>
   <p>In the <code>IBrokerageFactory</code> examples, you'll see code like this: <code>Composer.Instance.AddPart&lt;IDataQueueHandler&gt;(dataQueueHandler)</code>, which is adding parts to the "Composer". The Composer is a system in LEAN for loading types dynamically. In this case, it is adding an instance of the DataQueueHandler for the brokerage to the composer. You can think of the composer as a library, and adding parts is like adding books to its collection. But we'll come back to this later...</p>
 </div>
 
-<table class="table qc-table table-striped table-responsive summary">
-   <thead>
-     <tr><th colspan="2" class="summary"><i class="fa fa-thumb-tack"></i> IBrokerageFactory</th></tr>
-   </thead>
-   <tbody>
-      <tr><td width="30%">Primary Role</td><td>Create and initialize a brokerage instance.</td></tr>
-      <tr><td>Interface</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Common/Interfaces/IBrokerageFactory.cs">IBrokerage.cs</a></td></tr>
-      <tr><td>Example</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerageFactory.cs">GDAXBrokerageFactory.cs</a></td></tr>
-      <tr><td>Target Location</td><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/">In Brokerages Folder in Brokerages Solution</a></td></tr>
-   </tbody>
-</table>
-
 <table class="table qc-table table-striped table-responsive">
    <thead>
      <tr><th colspan="2" class="summary"><i class="fa fa-check"></i> Stage 1: Checklist</th></tr>

From b2eab01757ecc043dc3e41a75aaf5bb0078b15c6 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 14 Jul 2018 23:45:00 +0000
Subject: [PATCH 188/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html  | 92 ++++++++++++++++++-
 1 file changed, 91 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index d80e3ef..192f443 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -26,6 +26,11 @@
 Often brokerages will have their own ticker styles, order class names, event names. Many of the methods in the brokerage implementation may simply be converting from the brokerage object format into LEAN format. You should plan accordingly to write neat code. 
 </p>
 
+<p>
+The brokerage will eventually implement 3 interfaces:
+<pre>class MyBrokerage : IBrokerage, IDataDownloader, IHistoryProvider { ... }</pre>
+</p>
+
 <p>
 <b>Implementation Style</b>. This guide will focus mostly on implementing the brokerage step by step in LEAN; as its a more natural workflow for most people. You can also follow a more <em>test-driven-development</em> process by following the test harness. To do this create a new test class which extends from the base class in <code>/Tests/Brokerages/BrokerageTests.cs</code>. This test-framework tests all the methods for an IBrokerage implementation.
 </p>
@@ -49,4 +54,89 @@ <h4>2.2 Installing a Library</h4>
 <div class="tip">
   <i class="fa fa-bolt"></i><span class="tip-title">Action:</span>
   <p>Build the project again to make sure the library is compiling successfully. Its good to make sure your library is integrated successfully before continuing. </p>
-</div> 
\ No newline at end of file
+</div> 
+
+<h4>2.4 IBrokerage Class Implementation</h4>
+<ul>
+   <li>
+     Base Class
+     <p>Using a base class is optional but allows you to reuse event methods we have provided. The <code>Brokerage</code> object implements these event handlers and marks the remaining items as <code>abstract</code>.</p>
+<p>LEAN provides an optional base class <code>BaseWebsocketsBrokerage</code> which seeks to connect and maintain a socket connection and pass messages to an event handler. As each socket connection is different consider carefully before using this class; it might be easier and more maintainable to simply maintain your own socket connection.</p>
+
+<div class="tip">
+  <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
+  <p>Brush up on the <code>partial</code> class keyword. This will help you break-up your class later.</p>
+</div>
+   </li>
+
+   <li>
+    Class Constructor
+     <p>Once the scaffolding brokerage methods are in place (overrides of the abstract base classes); you can focus on the class constructor. Here if you are using a brokerage SDK you can create a new instance of their library, and store it to a class variable for later use. You should make your constructor take all the arguments it needs. This is passed to you from code you implemented in the previous section, the <code>IBrokerageFactory</code>.</p>
+<table class="table qc-table summary">
+<thead><tr><th class="summary" colspan="2"><i class="fa fa-thumb-tack"></i> Class Constructor Examples</th></tr></thead>
+<tbody>
+<tr><td width="30%"><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/InteractiveBrokers/InteractiveBrokersBrokerage.cs#L191">Interactive Brokers</a></td><td>Launching external process to create brokerage.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Fxcm/FxcmBrokerage.cs#L92">FXCM</a></td><td>Simple example; saving  class variables and setting defaults.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Oanda/OandaBrokerage.cs#L55">OANDA</a></td><td>Creating SDK instance and assigning internal event handlers.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/BaseWebsocketsBrokerage.cs#L91">GDAX</a></td><td>Offload constructor work to BrokerageFactory and use <code>BaseWebsocketBrokerage</code> base class.</td></tr>
+</tbody>
+</table>
+   </li>
+
+   <li>
+    Name
+     <p>The <code>Name</code> property is a human readable brokerage name for debugging and logging. For US Equity regulated brokerages convention states this name generally ends in the word "Brokerage". </p>
+   </li>
+
+   <li>
+    Connect()
+     <p>The <code>Connect</code> method triggers logic for establishing a link to your brokerage. Normally we do not do this in the constructor as it would make algorithms and brokerages die in the BrokerageFactory process. For most brokerages this will be simply calling the connect method on your SDK library. </p>
+
+<table class="table qc-table summary">
+<thead><tr><th class="summary" colspan="2"><i class="fa fa-thumb-tack"></i> Connection Method Examples</th></tr></thead>
+<tbody>
+<tr><td width="30%"><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/InteractiveBrokers/InteractiveBrokersBrokerage.cs#L506">Interactive Brokers</a></td><td>Connecting to external process with brokerage SDK.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Fxcm/FxcmBrokerage.cs#L122">FXCM</a></td><td>Using SDK to create event handlers and connection thread.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Oanda/OandaBrokerage.cs#L85">OANDA</a></td><td>Simple example, calling SDK.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/BaseWebsocketsBrokerage.cs#L116">GDAX</a></td><td>Establish websocket connection and monitoring in a thread.</td></tr>
+</tbody>
+</table>
+
+<h4>Exceptions</h4>
+<p>
+In the event of a soft failure like internet connection lost, or server 502 errors you should create a new <code>BrokerageMessageEvent</code>. By doing this you allow the <em>algorithm</em> to handle the brokerage messages. For example: Interactive Brokers resets socket connections at different times globally, so users in other parts of the world can get disconnected at strange times of the day. Knowing this, they may elect to have their algorithm ignore specific disconnection attempts.
+</p>
+<p>
+In the event of a hard-failure such as the password being incorrect or an unsupported API method you should throw a real exception with details of the error. You can see examples of using both of these techniques in the <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Brokerages/Fxcm/FxcmBrokerage.cs#L210">FXCM Connect</a> method. 
+</p>
+   </li>
+
+   <li>
+    IsConnected
+     <p>A boolean property to indicate the state of the brokerage connection. Depending on your connection style this may be automatically handled for you and simply require passing back the value from your SDK. Alternatively you may need to maintain your own connection state flag in your brokerage.</p>
+   </li>
+
+   <li>
+    PlaceOrder()
+<p>
+Send a new LEAN order to the brokerage and report back the success or failure. The <code>PlaceOrder</code> method accepts a generic <code>Order</code> object which is the base class for all order types. The first step of <em>placing</em> an order is often to convert it from LEAN format into the Brokerage SDK required format. Brokerage implementations should aim to support as many LEAN order types as possible. There may be other order types in the brokerage but implementing them is considered out of scope of a rev-0 brokerage implementation.
+</p>
+<p>
+Once the order type is converted you should send it to your brokerage submit API. Often you will receive an immediate reply indicating the order was successfully placed.
+</p>
+<p>
+Part of the order conversion might be converting the brokerage <em>ticker</em> (e.g. LEAN name "EURUSD", OANDA name is "EUR/USD"). This is done with a <code>BrokerageSymbolMapper</code> class. We'll get to that in <a href="#Brokerage-Development-Guide-Step-3-Translating-Symbol-Conventions">Step 3</a>, for now you can pass a request for the brokerage ticker to the stub implementation. 
+</p>
+
+<div class="tip">
+  <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
+  <p>Create an internal method: <code>BrokerOrder ConvertOrder(Order order)</code> for conversion to brokerage orders. You will use this over and over again. Converting order types is an error prone process and its recommended to carefully review each order after you've completed a port. Remember to check each required brokerage order property, brokers like InteractiveBrokers have hundreds of properties.</p>
+</div>
+
+<div class="tip">
+  <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
+  <p>Use the <code>IsConnected</code> boolean to check if you're connected before placing the order. If not throw an exception to halt the algorithm.</p>
+</div>
+
+   </li>
+</ul>

From 005e9ffe0f2e0c4246470d43a9747e7286958007 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 15 Jul 2018 02:34:49 +0000
Subject: [PATCH 189/753] Automated push from server made by Jared Broad

---
 03 Open Source/04 Brokerage Development Guide/00.html | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/03 Open Source/04 Brokerage Development Guide/00.html b/03 Open Source/04 Brokerage Development Guide/00.html
index 429f31f..cedc442 100644
--- a/03 Open Source/04 Brokerage Development Guide/00.html	
+++ b/03 Open Source/04 Brokerage Development Guide/00.html	
@@ -12,6 +12,7 @@
     border-radius: 5px;
     padding: 15px;
     margin-bottom: 20px;
+    margin-top: 20px;
 }
 .tip i {
     color: #f5ae29;
@@ -33,6 +34,9 @@
 .table.qc-table tbody tr td {
    text-align: left;
 }
+.implementation {
+    font-family: "Courier New";
+}
 </style>
 <div class="work-in-progress"> 
     <i class="fa fa-warning"></i> Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress.

From 20c31f133f90e19774d243ba77ed3392de2191a7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 15 Jul 2018 02:38:41 +0000
Subject: [PATCH 190/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html  | 59 +++++++++++++++++--
 1 file changed, 53 insertions(+), 6 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index 192f443..7d8c256 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -59,7 +59,7 @@ <h4>2.2 Installing a Library</h4>
 <h4>2.4 IBrokerage Class Implementation</h4>
 <ul>
    <li>
-     Base Class
+     <span class="implementation">Base Class</span>
      <p>Using a base class is optional but allows you to reuse event methods we have provided. The <code>Brokerage</code> object implements these event handlers and marks the remaining items as <code>abstract</code>.</p>
 <p>LEAN provides an optional base class <code>BaseWebsocketsBrokerage</code> which seeks to connect and maintain a socket connection and pass messages to an event handler. As each socket connection is different consider carefully before using this class; it might be easier and more maintainable to simply maintain your own socket connection.</p>
 
@@ -70,7 +70,7 @@ <h4>2.4 IBrokerage Class Implementation</h4>
    </li>
 
    <li>
-    Class Constructor
+    <span class="implementation">Class Constructor</span>
      <p>Once the scaffolding brokerage methods are in place (overrides of the abstract base classes); you can focus on the class constructor. Here if you are using a brokerage SDK you can create a new instance of their library, and store it to a class variable for later use. You should make your constructor take all the arguments it needs. This is passed to you from code you implemented in the previous section, the <code>IBrokerageFactory</code>.</p>
 <table class="table qc-table summary">
 <thead><tr><th class="summary" colspan="2"><i class="fa fa-thumb-tack"></i> Class Constructor Examples</th></tr></thead>
@@ -84,12 +84,12 @@ <h4>2.4 IBrokerage Class Implementation</h4>
    </li>
 
    <li>
-    Name
+    <span class="implementation">Name</span>
      <p>The <code>Name</code> property is a human readable brokerage name for debugging and logging. For US Equity regulated brokerages convention states this name generally ends in the word "Brokerage". </p>
    </li>
 
    <li>
-    Connect()
+    <span class="implementation">Connect()</span>
      <p>The <code>Connect</code> method triggers logic for establishing a link to your brokerage. Normally we do not do this in the constructor as it would make algorithms and brokerages die in the BrokerageFactory process. For most brokerages this will be simply calling the connect method on your SDK library. </p>
 
 <table class="table qc-table summary">
@@ -112,12 +112,17 @@ <h4>Exceptions</h4>
    </li>
 
    <li>
-    IsConnected
+    <span class="implementation">Disconnect()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">IsConnected</span>
      <p>A boolean property to indicate the state of the brokerage connection. Depending on your connection style this may be automatically handled for you and simply require passing back the value from your SDK. Alternatively you may need to maintain your own connection state flag in your brokerage.</p>
    </li>
 
    <li>
-    PlaceOrder()
+    <span class="implementation">PlaceOrder()</span>
 <p>
 Send a new LEAN order to the brokerage and report back the success or failure. The <code>PlaceOrder</code> method accepts a generic <code>Order</code> object which is the base class for all order types. The first step of <em>placing</em> an order is often to convert it from LEAN format into the Brokerage SDK required format. Brokerage implementations should aim to support as many LEAN order types as possible. There may be other order types in the brokerage but implementing them is considered out of scope of a rev-0 brokerage implementation.
 </p>
@@ -139,4 +144,46 @@ <h4>Exceptions</h4>
 </div>
 
    </li>
+
+   <li>
+    <span class="implementation">UpdateOrder()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">CancelOrder()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">UpdateOrder()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">GetOpenOrders()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">GetAccountHoldings()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">GetCashBalance()</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">bool AccountInstantlyUpdated</span>
+<p></p>
+</li>
+
+   <li>
+    <span class="implementation">GetHistory(HistoryRequest request)</span>
+<p></p>
+</li>
+
+
 </ul>

From 813873ac25cfc42f45870f2b1b18d3a233402787 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 15 Jul 2018 03:18:16 +0000
Subject: [PATCH 191/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html  | 31 ++++++++++++-------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index 7d8c256..cd7078b 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -84,12 +84,12 @@ <h4>2.4 IBrokerage Class Implementation</h4>
    </li>
 
    <li>
-    <span class="implementation">Name</span>
+    <span class="implementation">string Name</span>
      <p>The <code>Name</code> property is a human readable brokerage name for debugging and logging. For US Equity regulated brokerages convention states this name generally ends in the word "Brokerage". </p>
    </li>
 
    <li>
-    <span class="implementation">Connect()</span>
+    <span class="implementation">void Connect()</span>
      <p>The <code>Connect</code> method triggers logic for establishing a link to your brokerage. Normally we do not do this in the constructor as it would make algorithms and brokerages die in the BrokerageFactory process. For most brokerages this will be simply calling the connect method on your SDK library. </p>
 
 <table class="table qc-table summary">
@@ -112,17 +112,17 @@ <h4>Exceptions</h4>
    </li>
 
    <li>
-    <span class="implementation">Disconnect()</span>
+    <span class="implementation">void Disconnect()</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">IsConnected</span>
+    <span class="implementation">bool IsConnected</span>
      <p>A boolean property to indicate the state of the brokerage connection. Depending on your connection style this may be automatically handled for you and simply require passing back the value from your SDK. Alternatively you may need to maintain your own connection state flag in your brokerage.</p>
    </li>
 
    <li>
-    <span class="implementation">PlaceOrder()</span>
+    <span class="implementation">bool PlaceOrder(Order order)</span>
 <p>
 Send a new LEAN order to the brokerage and report back the success or failure. The <code>PlaceOrder</code> method accepts a generic <code>Order</code> object which is the base class for all order types. The first step of <em>placing</em> an order is often to convert it from LEAN format into the Brokerage SDK required format. Brokerage implementations should aim to support as many LEAN order types as possible. There may be other order types in the brokerage but implementing them is considered out of scope of a rev-0 brokerage implementation.
 </p>
@@ -132,6 +132,9 @@ <h4>Exceptions</h4>
 <p>
 Part of the order conversion might be converting the brokerage <em>ticker</em> (e.g. LEAN name "EURUSD", OANDA name is "EUR/USD"). This is done with a <code>BrokerageSymbolMapper</code> class. We'll get to that in <a href="#Brokerage-Development-Guide-Step-3-Translating-Symbol-Conventions">Step 3</a>, for now you can pass a request for the brokerage ticker to the stub implementation. 
 </p>
+<p>
+The <code>PlaceOrder()</code> method returns true when the order is <em>accepted</em> to the brokerage. If it is invalid, immediately rejected or there is an internet outage you can return false.
+</p>
 
 <div class="tip">
   <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
@@ -146,32 +149,32 @@ <h4>Exceptions</h4>
    </li>
 
    <li>
-    <span class="implementation">UpdateOrder()</span>
+    <span class="implementation">bool UpdateOrder(Order order)</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">CancelOrder()</span>
+    <span class="implementation">bool CancelOrder(Order order)</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">UpdateOrder()</span>
+    <span class="implementation">bool UpdateOrder(Order order)</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">GetOpenOrders()</span>
+    <span class="implementation">List&lt;Order&gt; GetOpenOrders()</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">GetAccountHoldings()</span>
+    <span class="implementation">List&lt;Holding&gt; GetAccountHoldings()</span>
 <p></p>
 </li>
 
    <li>
-    <span class="implementation">GetCashBalance()</span>
+    <span class="implementation">List&lt;Cash&gt; GetCashBalance()</span>
 <p></p>
 </li>
 
@@ -181,9 +184,13 @@ <h4>Exceptions</h4>
 </li>
 
    <li>
-    <span class="implementation">GetHistory(HistoryRequest request)</span>
+    <span class="implementation">IEnumerable&lt;BaseData&gt; GetHistory(HistoryRequest request)</span>
 <p></p>
 </li>
 
+   <li>
+    <span class="implementation">bool AccountInstantlyUpdated</span>
+<p></p>
+</li>
 
 </ul>

From 727f3537ebac65e283ddfad434520a37d6d7e795 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 15 Jul 2018 04:30:03 +0000
Subject: [PATCH 192/753] Automated push from server made by Jared Broad

---
 .../04 Step 2%3A Creating the Brokerage.html       | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
index cd7078b..7cda62c 100644
--- a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
+++ b/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html	
@@ -113,7 +113,7 @@ <h4>Exceptions</h4>
 
    <li>
     <span class="implementation">void Disconnect()</span>
-<p></p>
+<p>Disconnect is called at the end of the algorithm before shutting down LEAN.</p>
 </li>
 
    <li>
@@ -145,12 +145,20 @@ <h4>Exceptions</h4>
   <i class="fa fa-lightbulb-o"></i><span class="tip-title">Tip:</span>
   <p>Use the <code>IsConnected</code> boolean to check if you're connected before placing the order. If not throw an exception to halt the algorithm.</p>
 </div>
-
    </li>
 
    <li>
     <span class="implementation">bool UpdateOrder(Order order)</span>
-<p></p>
+<p>Transmit an update request to the API and return true if it was successfully processed. There are no simple examples here! Updating an order is one of the most tricky part of the brokerage implementations. You can easily run into synchronization issues.</p>
+<table class="table qc-table summary">
+<thead><tr><th class="summary" colspan="2"><i class="fa fa-thumb-tack"></i> Update Order Examples</th></tr></thead>
+<tbody>
+<tr><td width="30%"><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/InteractiveBrokers/InteractiveBrokersBrokerage.cs#L300">Interactive Brokers</a></td><td>External application, updating multiple asset classes.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Fxcm/FxcmBrokerage.cs#L522">FXCM</a></td><td>Relatively simple example via async brokerage callback.</td></tr>
+<tr><td>OANDA <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Oanda/OandaRestApiV1.cs#L215">v1</a>, <a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/Oanda/OandaRestApiV20.cs#L218">v2</a></td><td>Complex example with 2 async APIs, multiple parameters.</td></tr>
+<tr><td><a href="https://github.com/QuantConnect/Lean/blob/master/Brokerages/GDAX/GDAXBrokerage.cs#L134">GDAX</a></td><td>Updating isn't supported; throw an exception.</td></tr>
+</tbody>
+</table>
 </li>
 
    <li>

From 81f84ab6eede80039e9a063911ed549f43688bcc Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 15 Jul 2018 04:35:29 +0000
Subject: [PATCH 193/753] Automated push from server made by Jared Broad

---
 .../04 Brokerage Development Guide/02 Guide Contents.html     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html
index 9db0ad6..73ae158 100644
--- a/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html	
+++ b/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html	
@@ -4,10 +4,10 @@
    </thead>
    <tbody>
        <tr>
-           <td>1.</td><td width="95%"><a href="#Brokerage-Development-Guide-Laying-a-Foundation">Laying a Foundation (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
+           <td>1.</td><td width="95%"><a href="#Brokerage-Development-Guide-Step-1-Laying-a-Foundation">Laying a Foundation (IBrokerageFactory)</a> <br/>Stub out the implementation and initialize a brokerage instance.</td>           
        </tr>
        <tr>
-           <td>2.</td><td width="95%"><a href="#">Creating The Brokerage (IBrokerage)</a> <br/>Installing key brokerage application logic, where possible using a brokerage SDK.</td>           
+           <td>2.</td><td width="95%"><a href="#Brokerage-Development-Guide-Step-2-Creating-the-Brokerage">Creating The Brokerage (IBrokerage)</a> <br/>Installing key brokerage application logic, where possible using a brokerage SDK.</td>           
        </tr>
        <tr>
            <td>3.</td><td width="95%"><a href="#">Translating Symbol Conventions (ISymbolMapper)</a> <br/>Translate brokerage specific tickers to LEAN format for a uniform algorithm design experience.</td>           

From 796afbc27367bc4330ca13b3b79dcd8b03c0388c Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 16 Jul 2018 09:39:44 -0400
Subject: [PATCH 194/753] Update 04 Algorithm.html

---
 07 Applied Options[]/04 Long Strangle/04 Algorithm.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html
index b4df8b4..78ab066 100755
--- a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
+++ b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_89f1f8e5f1ed5e78bd1b4cdf3ab796a1.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_89f1f8e5f1ed5e78bd1b4cdf3ab796a1.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e3c07881318d864a9ae25637d6d2d9d3.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e3c07881318d864a9ae25637d6d2d9d3.html"></iframe>
     </div>
 </div>

From aaeeae2e5c6fc6a5ccbd55739eca18e69cf2713d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 16 Jul 2018 09:47:35 -0400
Subject: [PATCH 195/753] Update 02 Method.html

---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
index 0e4dce1..148d56e 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
@@ -1,11 +1,11 @@
 <p>
-  The first step is corase universe selection. We create an investment universe with stocks that have fundmental data and has price greater than $5.
+  The first step is coarse universe selection. We create an investment universe with stocks that have fundmental data and with a price greater than $5.
 </p>
 <div class="section-example-container">
 <pre class="python">self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.AdjustedPrice) > 5)]
 </pre>
 </div>
-<p>In fine universe selection, we sort the stocks in universe by the market capitalization and choose 10 stocks with the lowest market cap.
+<p>In fine universe selection, we sort the stocks in the universe by the market capitalization and choose 10 stocks with the lowest market cap.
 </p>
 <div class="section-example-container">
 <pre class="python">def FineSelectionFunction(self, fine):

From b6a3c373c7eba12b3ac46394f3070ab72242a40f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 17 Jul 2018 19:40:10 +0000
Subject: [PATCH 196/753] Automated push from server made by Jared Broad

---
 04 Strategy Library/16 Overnight Anomaly/01 Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html b/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html
index fea63b4..d188ba7 100644
--- a/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html	
+++ b/04 Strategy Library/16 Overnight Anomaly/01 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
-Instead of collecting profit from intraday trading, this algorithm is trying to view the overnight returns.
+Instead of collecting profit from intraday trading, this algorithm tries to capture the overnight returns of the index.
 </p>

From ed1c63e42c915a742ff5bbcf566232502054e7f2 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 17 Jul 2018 15:43:29 -0400
Subject: [PATCH 197/753] Add algortihm commodity futures term structure

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  3 +
 .../02 Method.html                            | 61 +++++++++++++++++++
 .../03 Algorithm.html                         | 14 +++++
 .../04 References.html                        |  9 +++
 quantpedia.json                               |  3 +-
 6 files changed, 97 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/29 Term Structure Effect in Commodities/01 Introduction.html
 create mode 100644 04 Strategy Library/29 Term Structure Effect in Commodities/02 Method.html
 create mode 100644 04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
 create mode 100644 04 Strategy Library/29 Term Structure Effect in Commodities/04 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9957f5d..37a822e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -190,6 +190,14 @@
         ],
         'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
     ],
+    [
+        'name' => 'Term Structure Effect in Commodities',
+        'link' => 'strategy-library/term-structure-effect-in-commodities',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/22'
+        ],
+        'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/01 Introduction.html b/04 Strategy Library/29 Term Structure Effect in Commodities/01 Introduction.html
new file mode 100644
index 0000000..aa441da
--- /dev/null
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  The term structure of commodities usually refers to the difference between futures prices of different maturities at a given time point. The shape of the futures curve is essential to commodity hedgers and speculators as futures price serves as a forecast of future spot price. The futures price curve contains the information about futures supply and demand conditions. This algorithm will examine the role of term structure signals for the design of profitable trading strategies in commodity futures markets.
+</p>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/02 Method.html b/04 Strategy Library/29 Term Structure Effect in Commodities/02 Method.html
new file mode 100644
index 0000000..616312c
--- /dev/null
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/02 Method.html	
@@ -0,0 +1,61 @@
+<h4>Concepts of Futures</h4>
+<p>
+  Before starting the algorithm, we first introduce a few concepts related to commodity futures. Backwardation is defined as conditions when the futures price is below the current spot price and contango as conditions when the futures price is above the current spot price. The futures term structure curve is a representation of the backwardation/contango rate for the different maturities of futures contracts. This price gap between different maturity contracts is quantified as the roll return.
+  The contango/backwardation rates are calculated by taking the price difference between the spot market price and the futures contract price. This difference can be expressed
+  as a percentage over the time to expiration and then annualised to calculate an annual roll return.
+</p>
+\[R_t = \left[ln(P_{t,n})-ln(P_{t,d})\right]\times\frac{365}{N_{t,d}-N_{t,n}}\]
+<p>
+  Where \(P_{t,n}\) is the price of the nearest-to-maturity contract at time t. \(P_{t,d}\) is the price of the distant contract at time t. \(N_{t,n}\) is the number of days between time t and the maturity of the nearby contract and \(N_{t,d}\) is the number of days between time t and the maturity of the distant contract.
+</p>
+<h4>Calculation of Roll Return</h4>
+<p>
+  To calculate the roll return, first we sort the future chain by expiry and select the first two contracts as the nearest-to-maturity contract and the the distant contract.
+</p>
+<div class="section-example-container">
+<pre class="python">for symbol, chain in self.chains.items():
+    contracts = sorted(chain, key = lambda x: x.Expiry)
+    # R = (log(Pn) - log(Pd)) * 365 / (Td - Tn)
+    # R - Roll returns
+    # Pn - Nearest contract price
+    # Pd - Distant contract price
+    # Tn - Nearest contract expire date
+    # Pd - Distant contract expire date
+    near_contract = contracts[0]
+    distant_contract = contracts[-1]
+    price_near = near_contract.LastPrice if near_contract.LastPrice>0 else 0.5*float(near_contract.AskPrice+near_contract.BidPrice)
+    price_distant = distant_contract.LastPrice if distant_contract.LastPrice>0 else 0.5*float(distant_contract.AskPrice+distant_contract.BidPrice)
+    if distant_contract.Expiry == near_contract.Expiry:
+        self.Debug("ERROR: Near and distant contracts have the same expiry!" + str(near_contract))
+        return
+    expire_range = 365 / (distant_contract.Expiry - near_contract.Expiry).days
+    roll_returns[symbol] = (np.log(float(price_near)) - np.log(float(price_distant)))*expire_range
+</pre>
+</div>
+<h4>Backwardation and Contango</h4>
+<p>
+  In the next step, we will split the futures based on backwardation and contango. If the roll return is greater than 0, the term structure of commodity futures prices
+  is downward-sloping and so that the market is in backwardation. Conversely, a negative roll return signals an upward-sloping price curve and a contangoed market.
+</p>
+<div class="section-example-container">
+<pre class="python">positive_roll_returns = { symbol: returns for symbol, returns in roll_returns.items() if returns > 0 }
+negative_roll_returns = { symbol: returns for symbol, returns in roll_returns.items() if returns < 0 }
+backwardation = sorted(positive_roll_returns , key = lambda x: positive_roll_returns[x], reverse = True)[:quintile]
+contango = sorted(negative_roll_returns , key = lambda x: negative_roll_returns[x])[:quintile]
+</pre>
+</div>
+<h4>Algorithm Trade</h4>
+<p>
+  The algorithm buys 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.
+</p>
+<div class="section-example-container">
+<pre class="python">
+for short_symbol in contango:
+    sort = sorted(self.chains[short_symbol], key = lambda x: x.Expiry)
+    self.SetHoldings(sort[1].Symbol, -0.5/count)
+
+for long_symbol in backwardation:
+    sort = sorted(self.chains[long_symbol], key = lambda x: x.Expiry)
+    self.SetHoldings(sort[1].Symbol, 0.5/count)
+  </pre>
+  </div>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
new file mode 100644
index 0000000..85593ab
--- /dev/null
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -0,0 +1,14 @@
+<p>Python Algorithm</p>
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d7285b5353d51cd5bd033f205e5faf44.html"></iframe>
+    </div>
+</div>
+<p>Csharp Algorithm</p>
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a14a3ca9e9de51e5670f10cb7ec14a95.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/04 References.html b/04 Strategy Library/29 Term Structure Effect in Commodities/04 References.html
new file mode 100644
index 0000000..149276a
--- /dev/null
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/04 References.html	
@@ -0,0 +1,9 @@
+
+<ol>
+  <li>
+    Fuertes, Ana-Maria and Miffre, Joëlle and Rallis, Georgios, Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals (April 22, 2010). Journal of Banking and Finance 34, 2530-2548.<a href="https://ssrn.com/abstract=1127213"> Online Copy</a>
+  </li>
+  <li>
+    Jez Liberty, Roll Yield and Commodity Yield Curve (July 19th, 2010). Automated Trading System <a href="http://www.automated-trading-system.com/roll-yield-commodity-yield-curve/"> Online Copy</a>
+  </li>
+ </ol>
diff --git a/quantpedia.json b/quantpedia.json
index 8830380..b324c90 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -14,5 +14,6 @@
      18: "ba1bb35b26896e2e710a510f62230a24",
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
      21: "203a6729604c80a71b5c3b2baa2b3f69",
-     25: "da3bf5ee608fb8b7dc952372dd925542"
+     22: "d7285b5353d51cd5bd033f205e5faf44",
+     25: "da3bf5ee608fb8b7dc952372dd925542",
 }

From 5663c9555429b2d0568d41d54e2283cec9253bb0 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 17 Jul 2018 16:36:18 -0400
Subject: [PATCH 198/753] Add quantpedia source

---
 .../12 Asset Class Trend Following/04 Source.html            | 5 +++++
 04 Strategy Library/13 Asset Class Momentum/04 Source.html   | 5 +++++
 04 Strategy Library/14 Sector Momentum/04 Source.html        | 5 +++++
 04 Strategy Library/15 Short Term Reversal/04 Source.html    | 5 +++++
 04 Strategy Library/16 Overnight Anomaly/04 Source.html      | 5 +++++
 04 Strategy Library/17 Forex Momentum/04 Source.html         | 5 +++++
 .../18 Volatility Effect in Stocks/04 Source.html            | 5 +++++
 .../19 Pairs Trading with Stocks/04 Source.html              | 5 +++++
 04 Strategy Library/20 Forex Carry Trade/04 Source.html      | 5 +++++
 .../21 Momentum Effect in Stocks/04 Source.html              | 5 +++++
 .../05 Source.html                                           | 5 +++++
 .../{04 Algorithm.html => 03 Algorithm.html}                 | 0
 .../04 Source.html                                           | 5 +++++
 .../{04 Algorithm.html => 03 Algorithm.html}                 | 0
 .../24 Liquidity Effect in Stocks/04 Source.html             | 5 +++++
 .../{04 Algorithm.html => 03 Algorithm.html}                 | 0
 .../25 Volatility Risk Premium Effect/04 Source.html         | 5 +++++
 .../{04 Algorithm.html => 03 Algorithm.html}                 | 0
 .../27 Momentum Effect in Commodities Futures/04 Source.html | 5 +++++
 .../{04 Algorithm.html => 03 Algorithm.html}                 | 0
 .../04 Source.html                                           | 5 +++++
 .../29 Term Structure Effect in Commodities/05 Source.html   | 5 +++++
 22 files changed, 85 insertions(+)
 create mode 100644 04 Strategy Library/12 Asset Class Trend Following/04 Source.html
 create mode 100644 04 Strategy Library/13 Asset Class Momentum/04 Source.html
 create mode 100644 04 Strategy Library/14 Sector Momentum/04 Source.html
 create mode 100644 04 Strategy Library/15 Short Term Reversal/04 Source.html
 create mode 100644 04 Strategy Library/16 Overnight Anomaly/04 Source.html
 create mode 100644 04 Strategy Library/17 Forex Momentum/04 Source.html
 create mode 100644 04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html
 create mode 100644 04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html
 create mode 100644 04 Strategy Library/20 Forex Carry Trade/04 Source.html
 create mode 100644 04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html
 create mode 100644 04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html
 rename 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/{04 Algorithm.html => 03 Algorithm.html} (100%)
 create mode 100644 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html
 rename 04 Strategy Library/24 Liquidity Effect in Stocks/{04 Algorithm.html => 03 Algorithm.html} (100%)
 create mode 100644 04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html
 rename 04 Strategy Library/25 Volatility Risk Premium Effect/{04 Algorithm.html => 03 Algorithm.html} (100%)
 create mode 100644 04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html
 rename 04 Strategy Library/27 Momentum Effect in Commodities Futures/{04 Algorithm.html => 03 Algorithm.html} (100%)
 create mode 100644 04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html
 rename 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/{04 Algorithm.html => 03 Algorithm.html} (100%)
 create mode 100644 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html
 create mode 100644 04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html

diff --git a/04 Strategy Library/12 Asset Class Trend Following/04 Source.html b/04 Strategy Library/12 Asset Class Trend Following/04 Source.html
new file mode 100644
index 0000000..8362e4e
--- /dev/null
+++ b/04 Strategy Library/12 Asset Class Trend Following/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/16">Quantpedia - Asset Class Trend Following</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/13 Asset Class Momentum/04 Source.html b/04 Strategy Library/13 Asset Class Momentum/04 Source.html
new file mode 100644
index 0000000..c5a5ad0
--- /dev/null
+++ b/04 Strategy Library/13 Asset Class Momentum/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/2">Quantpedia - Asset Class Momentum(Rotational System)</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/14 Sector Momentum/04 Source.html b/04 Strategy Library/14 Sector Momentum/04 Source.html
new file mode 100644
index 0000000..a99b75d
--- /dev/null
+++ b/04 Strategy Library/14 Sector Momentum/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/3">Quantpedia - Sector Momentum</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/15 Short Term Reversal/04 Source.html b/04 Strategy Library/15 Short Term Reversal/04 Source.html
new file mode 100644
index 0000000..d403238
--- /dev/null
+++ b/04 Strategy Library/15 Short Term Reversal/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/13">Quantpedia - Short Term Reversal in Stocks</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/16 Overnight Anomaly/04 Source.html b/04 Strategy Library/16 Overnight Anomaly/04 Source.html
new file mode 100644
index 0000000..3fe783e
--- /dev/null
+++ b/04 Strategy Library/16 Overnight Anomaly/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/4">Quantpedia - Overnight Anomaly</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/17 Forex Momentum/04 Source.html b/04 Strategy Library/17 Forex Momentum/04 Source.html
new file mode 100644
index 0000000..0e20e2b
--- /dev/null
+++ b/04 Strategy Library/17 Forex Momentum/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/8">Quantpedia - FX Momentum</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html b/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..d206a79
--- /dev/null
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/7">Quantpedia - Volatility Effect in Stocks - Long-Only Version</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html b/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html
new file mode 100644
index 0000000..673ccec
--- /dev/null
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/12">Quantpedia - Pairs Trading with Stocks</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/20 Forex Carry Trade/04 Source.html b/04 Strategy Library/20 Forex Carry Trade/04 Source.html
new file mode 100644
index 0000000..29a3752
--- /dev/null
+++ b/04 Strategy Library/20 Forex Carry Trade/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/5">Quantpedia - FX Carry Trade</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html b/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..18aa3cc
--- /dev/null
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/14">Quantpedia - Momentum Effect in Stocks</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html
new file mode 100644
index 0000000..09fdcce
--- /dev/null
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/15">Quantpedia - Momentum Effect in Country Equity Indexes</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Algorithm.html
rename to 04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/03 Algorithm.html
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html
new file mode 100644
index 0000000..0751c5e
--- /dev/null
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/16">Quantpedia - Mean Reversion Effect in Country Equity Indexes</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html b/04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/24 Liquidity Effect in Stocks/04 Algorithm.html
rename to 04 Strategy Library/24 Liquidity Effect in Stocks/03 Algorithm.html
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..8f2766d
--- /dev/null
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/18">Quantpedia - Liquidity Effect in Stocks</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html b/04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/25 Volatility Risk Premium Effect/04 Algorithm.html
rename to 04 Strategy Library/25 Volatility Risk Premium Effect/03 Algorithm.html
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html
new file mode 100644
index 0000000..631fae6
--- /dev/null
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/20">Quantpedia - Volatility Risk Premium Effect</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Algorithm.html
rename to 04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html
new file mode 100644
index 0000000..e2dc9dc
--- /dev/null
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/21">Quantpedia - Momentum Effect in Commodities</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Algorithm.html
rename to 04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html
new file mode 100644
index 0000000..e5e2815
--- /dev/null
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/25">Quantpedia - Small Capitalization Stocks Premium Anomaly</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html b/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html
new file mode 100644
index 0000000..8f4a059
--- /dev/null
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/22">Quantpedia - Term Structure Effect in Commodities</a>
+</li>
+</ul>

From 1033cc46771d590517d0cf2cebca450a305e8c8c Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 17 Jul 2018 17:38:40 -0400
Subject: [PATCH 199/753] Modified the source file

---
 .../12 Asset Class Trend Following/04 Source.html               | 2 +-
 04 Strategy Library/13 Asset Class Momentum/04 Source.html      | 2 +-
 04 Strategy Library/14 Sector Momentum/04 Source.html           | 2 +-
 04 Strategy Library/15 Short Term Reversal/04 Source.html       | 2 +-
 04 Strategy Library/16 Overnight Anomaly/04 Source.html         | 2 +-
 04 Strategy Library/17 Forex Momentum/04 Source.html            | 2 +-
 .../18 Volatility Effect in Stocks/04 Source.html               | 2 +-
 04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html | 2 +-
 04 Strategy Library/20 Forex Carry Trade/04 Source.html         | 2 +-
 04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html | 2 +-
 .../22 Momentum Effect in Country Equity Indexes/05 Source.html | 2 +-
 .../04 Source.html                                              | 2 +-
 .../24 Liquidity Effect in Stocks/04 Source.html                | 2 +-
 .../25 Volatility Risk Premium Effect/04 Source.html            | 2 +-
 .../27 Momentum Effect in Commodities Futures/04 Source.html    | 2 +-
 .../04 Source.html                                              | 2 +-
 .../29 Term Structure Effect in Commodities/05 Source.html      | 2 +-
 17 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/04 Strategy Library/12 Asset Class Trend Following/04 Source.html b/04 Strategy Library/12 Asset Class Trend Following/04 Source.html
index 8362e4e..1c941c0 100644
--- a/04 Strategy Library/12 Asset Class Trend Following/04 Source.html	
+++ b/04 Strategy Library/12 Asset Class Trend Following/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/16">Quantpedia - Asset Class Trend Following</a>
+    <a href="https://quantpedia.com/Screener/Details/16" rel="nofollow">Quantpedia - Asset Class Trend Following</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/13 Asset Class Momentum/04 Source.html b/04 Strategy Library/13 Asset Class Momentum/04 Source.html
index c5a5ad0..ef94c60 100644
--- a/04 Strategy Library/13 Asset Class Momentum/04 Source.html	
+++ b/04 Strategy Library/13 Asset Class Momentum/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/2">Quantpedia - Asset Class Momentum(Rotational System)</a>
+    <a href="https://quantpedia.com/Screener/Details/2" rel="nofollow">Quantpedia - Asset Class Momentum(Rotational System)</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/14 Sector Momentum/04 Source.html b/04 Strategy Library/14 Sector Momentum/04 Source.html
index a99b75d..7023847 100644
--- a/04 Strategy Library/14 Sector Momentum/04 Source.html	
+++ b/04 Strategy Library/14 Sector Momentum/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/3">Quantpedia - Sector Momentum</a>
+    <a href="https://quantpedia.com/Screener/Details/3"  rel="nofollow">Quantpedia - Sector Momentum</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/15 Short Term Reversal/04 Source.html b/04 Strategy Library/15 Short Term Reversal/04 Source.html
index d403238..df7d579 100644
--- a/04 Strategy Library/15 Short Term Reversal/04 Source.html	
+++ b/04 Strategy Library/15 Short Term Reversal/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/13">Quantpedia - Short Term Reversal in Stocks</a>
+    <a href="https://quantpedia.com/Screener/Details/13" rel="nofollow">Quantpedia - Short Term Reversal in Stocks</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/16 Overnight Anomaly/04 Source.html b/04 Strategy Library/16 Overnight Anomaly/04 Source.html
index 3fe783e..d9a91da 100644
--- a/04 Strategy Library/16 Overnight Anomaly/04 Source.html	
+++ b/04 Strategy Library/16 Overnight Anomaly/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/4">Quantpedia - Overnight Anomaly</a>
+    <a href="https://quantpedia.com/Screener/Details/4" rel="nofollow">Quantpedia - Overnight Anomaly</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/17 Forex Momentum/04 Source.html b/04 Strategy Library/17 Forex Momentum/04 Source.html
index 0e20e2b..1b4d99a 100644
--- a/04 Strategy Library/17 Forex Momentum/04 Source.html	
+++ b/04 Strategy Library/17 Forex Momentum/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/8">Quantpedia - FX Momentum</a>
+    <a href="https://quantpedia.com/Screener/Details/8" rel="nofollow">Quantpedia - FX Momentum</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html b/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html
index d206a79..e9be339 100644
--- a/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html	
+++ b/04 Strategy Library/18 Volatility Effect in Stocks/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/7">Quantpedia - Volatility Effect in Stocks - Long-Only Version</a>
+    <a href="https://quantpedia.com/Screener/Details/7" rel="nofollow">Quantpedia - Volatility Effect in Stocks - Long-Only Version</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html b/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html
index 673ccec..e8be166 100644
--- a/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html	
+++ b/04 Strategy Library/19 Pairs Trading with Stocks/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/12">Quantpedia - Pairs Trading with Stocks</a>
+    <a href="https://quantpedia.com/Screener/Details/12" rel="nofollow">Quantpedia - Pairs Trading with Stocks</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/20 Forex Carry Trade/04 Source.html b/04 Strategy Library/20 Forex Carry Trade/04 Source.html
index 29a3752..18f54dd 100644
--- a/04 Strategy Library/20 Forex Carry Trade/04 Source.html	
+++ b/04 Strategy Library/20 Forex Carry Trade/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/5">Quantpedia - FX Carry Trade</a>
+    <a href="https://quantpedia.com/Screener/Details/5" rel="nofollow">Quantpedia - FX Carry Trade</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html b/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html
index 18aa3cc..f50729f 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/14">Quantpedia - Momentum Effect in Stocks</a>
+    <a href="https://quantpedia.com/Screener/Details/14" rel="nofollow">Quantpedia - Momentum Effect in Stocks</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html
index 09fdcce..2334ff4 100644
--- a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html	
+++ b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/15">Quantpedia - Momentum Effect in Country Equity Indexes</a>
+    <a href="https://quantpedia.com/Screener/Details/15" rel="nofollow">Quantpedia - Momentum Effect in Country Equity Indexes</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html
index 0751c5e..c4c7a89 100644
--- a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html	
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/16">Quantpedia - Mean Reversion Effect in Country Equity Indexes</a>
+    <a href="https://quantpedia.com/Screener/Details/16" rel="nofollow">Quantpedia - Mean Reversion Effect in Country Equity Indexes</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html
index 8f2766d..e09165f 100644
--- a/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html	
+++ b/04 Strategy Library/24 Liquidity Effect in Stocks/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/18">Quantpedia - Liquidity Effect in Stocks</a>
+    <a href="https://quantpedia.com/Screener/Details/18" rel="nofollow">Quantpedia - Liquidity Effect in Stocks</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html
index 631fae6..887095d 100644
--- a/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html	
+++ b/04 Strategy Library/25 Volatility Risk Premium Effect/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/20">Quantpedia - Volatility Risk Premium Effect</a>
+    <a href="https://quantpedia.com/Screener/Details/20" rel="nofollow">Quantpedia - Volatility Risk Premium Effect</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html
index e2dc9dc..7b964a7 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/21">Quantpedia - Momentum Effect in Commodities</a>
+    <a href="https://quantpedia.com/Screener/Details/21" rel="nofollow">Quantpedia - Momentum Effect in Commodities</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html
index e5e2815..b33e5d2 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/25">Quantpedia - Small Capitalization Stocks Premium Anomaly</a>
+    <a href="https://quantpedia.com/Screener/Details/25" rel="nofollow">Quantpedia - Small Capitalization Stocks Premium Anomaly</a>
 </li>
 </ul>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html b/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html
index 8f4a059..f270488 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/05 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/22">Quantpedia - Term Structure Effect in Commodities</a>
+    <a href="https://quantpedia.com/Screener/Details/22" rel="nofollow">Quantpedia - Term Structure Effect in Commodities</a>
 </li>
 </ul>

From b46d6b49322d0257c478175a9df2a776278c56b9 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 18 Jul 2018 18:36:41 -0400
Subject: [PATCH 200/753] Add new algorithm Book-to-Market Value Anomaly

---
 .../01 Strategy Library.php                   |  8 ++++++
 .../01 Introduction.html                      | 10 +++++++
 .../02 Method.html                            | 27 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++++
 .../04 Source.html                            |  5 ++++
 quantpedia.json                               |  1 +
 6 files changed, 57 insertions(+)
 create mode 100644 04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html
 create mode 100644 04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
 create mode 100644 04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html
 create mode 100644 04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 37a822e..d0b8980 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -198,6 +198,14 @@
         ],
         'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.'
     ],
+    [
+        'name' => 'Book-to-Market Value Anomaly',
+        'link' => 'strategy-library/book-to-market-value-anomaly',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/26'
+        ],
+        'description' => 'Quintile portfolios are formed based on the Book-to-Market ratio and the highest quintile is held for one year.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html
new file mode 100644
index 0000000..bad6aeb
--- /dev/null
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html	
@@ -0,0 +1,10 @@
+<p>
+  Book-to-market ratio is used to find the value of a company by comparing the book value of a firm to its market value. The definition of the book-to-market ratio is
+  \[Book\ to\ Market \ Ratio=\frac{Common\ Shareholders \ Equity}{Market \ Cap}=\frac{book \ value \ per \ share}{Market \ price \ per \ share}\]
+  Book value represents a company's assets minus its liabilities and sometimes is referred to as shareholders' equity.
+  Price-to-Book Ratio is defined as
+  \[Price \ to \ Book\ Ratio=\frac{Market \ price \ per \ share}{book \ value \ per \ share}\]
+  Therefore, we can see the Book-to-market ratio is the inverse of the P/B ratio.
+  The book-to-market ratio suggests how much investors are paying against each dollar of book value in the balance sheet. 
+  The bigger the ratio is, the more fundamentally cheap is the investigated company. This algorithm will create the portfolio with this factor.
+</p>
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
new file mode 100644
index 0000000..7191727
--- /dev/null
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
@@ -0,0 +1,27 @@
+<p>
+  To construct the universe, first we eliminate stocks which don't have fundmental data. In <code>FineSelectionFunction</code>,
+  we calculate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
+  to each fine fundamental object. The universe is narrowed to top 20% companies with the highest market cap. 
+</P>
+<p>
+  According to the algorithm, the portfolio is weighted based on market cap. We calculte the weight in <code>FineSelectionFunction</code> and save
+  them in <code>self.weights</code>.
+</P>
+<div class="section-example-container">
+<pre class="python">
+fine = [x for x in fine if (x.ValuationRatios.PBRatio > 0)]
+for i in fine:
+    i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len(fine)*0.2)]
+top_bm = sorted(top_market_cap, key = lambda x: 1 / x.ValuationRatios.PBRatio, reverse=True)[:int(len(top_market_cap)*0.2)]
+self.sorted_by_bm = [i.Symbol for i in top_bm]
+total_market_cap = np.sum([i.MarketCap for i in top_bm])
+self.weights = {}
+for i in top_bm:
+    self.weights[str(i.Symbol)] = i.MarketCap/total_market_cap
+return self.sorted_by_bm
+</pre>
+</div>
+<p>
+In the next step, we sort the stocks with the inverse of P/B ratio by descending order. Quintile portfolios are then formed based on the Book-to-Market ratio and the highest quintile is held for one year.
+</p>
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html
new file mode 100644
index 0000000..e0ed93e
--- /dev/null
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d8cd05d85b47eea4381d2ca20abf2f74.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html
new file mode 100644
index 0000000..cd8d0fa
--- /dev/null
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/26" rel="nofollow">Quantpedia - Value (Book-to-Market) Anomaly</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index b324c90..4bf68ba 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -16,4 +16,5 @@
      21: "203a6729604c80a71b5c3b2baa2b3f69",
      22: "d7285b5353d51cd5bd033f205e5faf44",
      25: "da3bf5ee608fb8b7dc952372dd925542",
+     26: "d8cd05d85b47eea4381d2ca20abf2f74",
 }

From 9d3abc43951cb195ccbfb58d8eebef20400815a9 Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Wed, 18 Jul 2018 19:23:54 -0400
Subject: [PATCH 201/753] Fix typo

---
 .../30 Book-to-Market Value Anomaly/02 Method.html           | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
index 7191727..057268b 100644
--- a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
@@ -1,10 +1,11 @@
 <p>
   To construct the universe, first we eliminate stocks which don't have fundmental data. In <code>FineSelectionFunction</code>,
-  we calculate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
+  we 
+  culate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
   to each fine fundamental object. The universe is narrowed to top 20% companies with the highest market cap. 
 </P>
 <p>
-  According to the algorithm, the portfolio is weighted based on market cap. We calculte the weight in <code>FineSelectionFunction</code> and save
+  According to the algorithm, the portfolio is weighted based on market cap. We calculate the weight in <code>FineSelectionFunction</code> and save
   them in <code>self.weights</code>.
 </P>
 <div class="section-example-container">

From ab74ae044bd2cf83230159ded46f92cdafc0136c Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Thu, 19 Jul 2018 08:44:47 -0400
Subject: [PATCH 202/753] Added LEAN Report Creator folder

---
 03 Open Source/04 Lean Report Creator/00.html                    | 1 +
 .../01 Introduction.html}                                        | 0
 .../02 Creating a Report.html}                                   | 0
 .../00.html                                                      | 0
 .../01 Introduction.html                                         | 0
 .../02 Guide Contents.html                                       | 0
 .../03 Step 1%3A Laying a Foundation.html                        | 0
 .../04 Step 2%3A Creating the Brokerage.html                     | 0
 .../05 Step 3%3A Translating Symbol Conventions.html}            | 0
 .../06 Step 4%3A Describe Brokerage Limitations.html}            | 0
 .../07 Step 5%3A Enable Live Data Streaming.html}                | 0
 .../08 Step 6%3A Enable Serving Historical Data.html}            | 0
 .../09 Step 7%3A Downloading Data.html}                          | 0
 .../10 Step 8%3A Brokerage Fee Structures.html                   | 0
 .../11 Step 9%3A Updating Algorithm API.html                     | 0
 15 files changed, 1 insertion(+)
 create mode 100644 03 Open Source/04 Lean Report Creator/00.html
 rename 03 Open Source/{04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html => 04 Lean Report Creator/01 Introduction.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html => 04 Lean Report Creator/02 Creating a Report.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide => 05 Brokerage Development Guide}/00.html (100%)
 rename 03 Open Source/{04 Brokerage Development Guide => 05 Brokerage Development Guide}/01 Introduction.html (100%)
 rename 03 Open Source/{04 Brokerage Development Guide => 05 Brokerage Development Guide}/02 Guide Contents.html (100%)
 rename 03 Open Source/{04 Brokerage Development Guide => 05 Brokerage Development Guide}/03 Step 1%3A Laying a Foundation.html (100%)
 rename 03 Open Source/{04 Brokerage Development Guide => 05 Brokerage Development Guide}/04 Step 2%3A Creating the Brokerage.html (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html => 05 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html => 05 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html => 05 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html => 05 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html} (100%)
 rename 03 Open Source/{04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html => 05 Brokerage Development Guide/09 Step 7%3A Downloading Data.html} (100%)
 create mode 100644 03 Open Source/05 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
 create mode 100644 03 Open Source/05 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html

diff --git a/03 Open Source/04 Lean Report Creator/00.html b/03 Open Source/04 Lean Report Creator/00.html
new file mode 100644
index 0000000..b80338c
--- /dev/null
+++ b/03 Open Source/04 Lean Report Creator/00.html	
@@ -0,0 +1 @@
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/" />
\ No newline at end of file
diff --git a/03 Open Source/04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html b/03 Open Source/04 Lean Report Creator/01 Introduction.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
rename to 03 Open Source/04 Lean Report Creator/01 Introduction.html
diff --git a/03 Open Source/04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
rename to 03 Open Source/04 Lean Report Creator/02 Creating a Report.html
diff --git a/03 Open Source/04 Brokerage Development Guide/00.html b/03 Open Source/05 Brokerage Development Guide/00.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/00.html
rename to 03 Open Source/05 Brokerage Development Guide/00.html
diff --git a/03 Open Source/04 Brokerage Development Guide/01 Introduction.html b/03 Open Source/05 Brokerage Development Guide/01 Introduction.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/01 Introduction.html
rename to 03 Open Source/05 Brokerage Development Guide/01 Introduction.html
diff --git a/03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html b/03 Open Source/05 Brokerage Development Guide/02 Guide Contents.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/02 Guide Contents.html
rename to 03 Open Source/05 Brokerage Development Guide/02 Guide Contents.html
diff --git a/03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html b/03 Open Source/05 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
rename to 03 Open Source/05 Brokerage Development Guide/03 Step 1%3A Laying a Foundation.html
diff --git a/03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html b/03 Open Source/05 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
rename to 03 Open Source/05 Brokerage Development Guide/04 Step 2%3A Creating the Brokerage.html
diff --git a/03 Open Source/04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html b/03 Open Source/05 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
rename to 03 Open Source/05 Brokerage Development Guide/05 Step 3%3A Translating Symbol Conventions.html
diff --git a/03 Open Source/04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html b/03 Open Source/05 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
rename to 03 Open Source/05 Brokerage Development Guide/06 Step 4%3A Describe Brokerage Limitations.html
diff --git a/03 Open Source/04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html b/03 Open Source/05 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
rename to 03 Open Source/05 Brokerage Development Guide/07 Step 5%3A Enable Live Data Streaming.html
diff --git a/03 Open Source/04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html b/03 Open Source/05 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
rename to 03 Open Source/05 Brokerage Development Guide/08 Step 6%3A Enable Serving Historical Data.html
diff --git a/03 Open Source/04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html b/03 Open Source/05 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
similarity index 100%
rename from 03 Open Source/04 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html
rename to 03 Open Source/05 Brokerage Development Guide/09 Step 7%3A Downloading Data.html
diff --git a/03 Open Source/05 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html b/03 Open Source/05 Brokerage Development Guide/10 Step 8%3A Brokerage Fee Structures.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/05 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html b/03 Open Source/05 Brokerage Development Guide/11 Step 9%3A Updating Algorithm API.html
new file mode 100644
index 0000000..e69de29

From a006e3a1162ecc8a8e33fa056b5581b28836babb Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Thu, 19 Jul 2018 08:50:28 -0400
Subject: [PATCH 203/753] Fix tutorial load issue

---
 03 Open Source/04 Lean Report Creator/01 Introduction.html     | 3 +++
 .../04 Lean Report Creator/02 Creating a Report.html           | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/03 Open Source/04 Lean Report Creator/01 Introduction.html b/03 Open Source/04 Lean Report Creator/01 Introduction.html
index e69de29..075d86d 100644
--- a/03 Open Source/04 Lean Report Creator/01 Introduction.html	
+++ b/03 Open Source/04 Lean Report Creator/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+
+</p>
\ No newline at end of file
diff --git a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html
index e69de29..075d86d 100644
--- a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
+++ b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
@@ -0,0 +1,3 @@
+<p>
+
+</p>
\ No newline at end of file

From fac32bcc3a452ff2ee302fb66f5ec03553908d73 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 19 Jul 2018 12:57:40 +0000
Subject: [PATCH 204/753] Automated push from server made by Jared Broad

---
 03 Open Source/04 Lean Report Creator/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/04 Lean Report Creator/00.html b/03 Open Source/04 Lean Report Creator/00.html
index b80338c..f5722bc 100644
--- a/03 Open Source/04 Lean Report Creator/00.html	
+++ b/03 Open Source/04 Lean Report Creator/00.html	
@@ -1 +1 @@
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/" />
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/20180615-drawdown-1024x543.png" />
\ No newline at end of file

From c4d54ab7b598e5c25244794aec42132d7c3bbb59 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 19 Jul 2018 13:02:13 +0000
Subject: [PATCH 205/753] Automated push from server made by Jared Broad

---
 03 Open Source/04 Lean Report Creator/01 Introduction.html | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/04 Lean Report Creator/01 Introduction.html b/03 Open Source/04 Lean Report Creator/01 Introduction.html
index 075d86d..74cffbc 100644
--- a/03 Open Source/04 Lean Report Creator/01 Introduction.html	
+++ b/03 Open Source/04 Lean Report Creator/01 Introduction.html	
@@ -1,3 +1,8 @@
 <p>
+The LEAN Report Creator is a report generated from backtesting-result objects and allows you to quickly create polished, professional-grade reports for each backtest (see a full <a href="https://cdn.quantconnect.com/blog/20180615-backtest-report.html">example report</a> generated by LRC). Our hope is that you can use these reports to share your strategy performance with prospective investors.
+</p>
+<p>
+Users working through the QuantConnect.com IDE can have these reports generated automatically at the end of a backtest. To generate one, look at the “Report” tab below your backtest result charts.
+</p>
+<img src="https://cdn.quantconnect.com/tutorials/i/20180615-terminal-report-1024x380.png" class="img-responsive">
 
-</p>
\ No newline at end of file

From f47a61e1ae3d7ff91b0035fd8ab4247ddc286f53 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 19 Jul 2018 13:21:30 +0000
Subject: [PATCH 206/753] Automated push from server made by Jared Broad

---
 .../02 Creating a Report.html                 | 35 +++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html
index 075d86d..d34f496 100644
--- a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
+++ b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
@@ -1,3 +1,38 @@
 <p>
+The LEAN Report Creator is a python script located in the <a href="https://github.com/QuantConnect/Lean/tree/master/PythonToolbox">PythonToolbox</a>. It is a command line tool which takes the json backtest result object as its input. You can run it like this:
+</p>
+<div class="section-example-container">
+<pre class="all">
+python CreateLeanReport.py--backtest=./sample.json --output=./report.html --user=data.json
+</pre>
+</div>
+<p>
+The program has three key options you should configure to your requirements:
+</p>
+<style> table.text-left > tbody > tr > td { text-align: left; } </style>
+<table class="table table-striped qc-table text-left">
+<thead>
+<tr>
+<th>Command Switch</th>
+<th>Description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><code>--backtest</code></td>
+<td>Location of the source backtest json file.</td>
+</tr>
+<tr>
+<td><code>--output</code></td>
+<td>Resulting output HTML file location.</td>
+</tr>
+<tr>
+<td><code>--user</code></td>
+<td>Json data object for the user data of the report (see <a href="https://github.com/QuantConnect/Lean/blob/master/PythonToolbox/user_data.json" target="_BLANK">example</a>).</td>
+</tr>
+</tbody>
+</table>
 
+<p>
+The program injects the images as base64 encoded objects into the HTML file to make it more portable and simpler to share with your partners. 
 </p>
\ No newline at end of file

From 7268698d274b2f15b40616abdafae3951319b865 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Thu, 19 Jul 2018 09:21:51 -0400
Subject: [PATCH 207/753] Added 2 new headings

---
 03 Open Source/04 Lean Report Creator/03 Source Backtest.html     | 0
 .../04 Lean Report Creator/04 Customizing Report Contents.html    | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 03 Open Source/04 Lean Report Creator/03 Source Backtest.html
 create mode 100644 03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html

diff --git a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html
new file mode 100644
index 0000000..e69de29
diff --git a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html
new file mode 100644
index 0000000..e69de29

From 31b9a8c708d14acc4af5fbd4134ca411a20cab20 Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 19 Jul 2018 12:21:38 -0400
Subject: [PATCH 208/753] adding algorithm turn of month in equity indexes

---
 .../01 Introduction.html                      |  3 ++
 .../02 Method.html                            | 29 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++++
 .../04 Source.html                            |  5 ++++
 4 files changed, 43 insertions(+)
 create mode 100644 04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html
 create mode 100644 04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html
 create mode 100644 04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html
 create mode 100644 04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html

diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html b/04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html
new file mode 100644
index 0000000..0102c22
--- /dev/null
+++ b/04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  The turn of the month is an effect on stock indices which states that stocks will rise during the last day before the end of the month and the first three days of each month. Researchers believe this significance comes as a result of pension funds receiving cash flows and reinvesting in the market, along with this period being a natural point for portfolio rebalancing between retail and professional investors. This algorithm is an approximation of the following strategy.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html b/04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html
new file mode 100644
index 0000000..a055225
--- /dev/null
+++ b/04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html	
@@ -0,0 +1,29 @@
+<p>
+  We start by creating scheduled events. The event at <code>MonthEnd()</code> will trigger the algorithm to buy SPY and the event at <code>MonthStart()</code> will start the process to sell SPY.  
+</p>
+<div class="section-example-container">
+<pre class="python">
+self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.rebalance)
+self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.purchase)
+</pre>
+</div>
+<p>
+  We will purchase the SPY immediately, and we will wait 3 trading days, as suggested, before liquidating our portfolio. Assigning <code>self.sell_flag</code> in the scheduled event handler will help us wait 3 days before executing the liquidate order in <code>OnData()</code>. The equity index is bought and sold every month.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def purchase(self):
+        self.SetHoldings("SPY", 1)
+    
+def rebalance(self):
+    self.sell_flag = True
+    
+def OnData(self, data):
+    if self.sell_flag:
+        self.days += 1
+        if self.days == 3:
+            self.Liquidate()
+            self.sell_flag = False
+            self.days = 0
+</pre>
+</div>
diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html b/04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html
new file mode 100644
index 0000000..a56b8e9
--- /dev/null
+++ b/04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ac6336b5911ac0d4848bae6546ea1dde.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html b/04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html
new file mode 100644
index 0000000..ac06e01
--- /dev/null
+++ b/04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/41"  rel="nofollow">Quantpedia - Turn of the Month in Equity Indexes</a>
+</li>
+</ul>
\ No newline at end of file

From edb7401c909d88759b26237f413dedbb4bf2df31 Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Thu, 19 Jul 2018 12:28:00 -0400
Subject: [PATCH 209/753] Update 02 Method.html

---
 .../30 Book-to-Market Value Anomaly/02 Method.html             | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
index 057268b..508d471 100644
--- a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
@@ -1,7 +1,6 @@
 <p>
   To construct the universe, first we eliminate stocks which don't have fundmental data. In <code>FineSelectionFunction</code>,
-  we 
-  culate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
+  we caculate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
   to each fine fundamental object. The universe is narrowed to top 20% companies with the highest market cap. 
 </P>
 <p>

From b53b0798d990bff0f2e91ca3e2195ed8f0df92ca Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Thu, 19 Jul 2018 12:28:57 -0400
Subject: [PATCH 210/753] Update 02 Method.html

---
 .../30 Book-to-Market Value Anomaly/02 Method.html              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
index 508d471..2af5ef5 100644
--- a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
+++ b/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html	
@@ -1,6 +1,6 @@
 <p>
   To construct the universe, first we eliminate stocks which don't have fundmental data. In <code>FineSelectionFunction</code>,
-  we caculate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
+  we calculate the market cap with PE ratio, earning per shares and shares outstanding and assign the property <code>MarketCap</code>
   to each fine fundamental object. The universe is narrowed to top 20% companies with the highest market cap. 
 </P>
 <p>

From bb2b2c8ce01f9ecf7fefbe97395e5bc6ea8b28c6 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 19 Jul 2018 12:51:00 -0400
Subject: [PATCH 211/753] Update 03 Algorithm.html

---
 .../29 Term Structure Effect in Commodities/03 Algorithm.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
index 85593ab..5ef48fa 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -5,7 +5,7 @@
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d7285b5353d51cd5bd033f205e5faf44.html"></iframe>
     </div>
 </div>
-<p>Csharp Algorithm</p>
+<p>CSharp Algorithm</p>
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">

From 098a02571a4ff334cda3a3f4048d361afdb83244 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 19 Jul 2018 12:51:40 -0400
Subject: [PATCH 212/753] Update 03 Algorithm.html

---
 .../03 Algorithm.html                                      | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
index 5ef48fa..1707718 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -1,14 +1,17 @@
-<p>Python Algorithm</p>
+<p>Algorithm</p>
+<span class="python">
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d7285b5353d51cd5bd033f205e5faf44.html"></iframe>
     </div>
 </div>
-<p>CSharp Algorithm</p>
+</span>
+<span class="csharp">
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a14a3ca9e9de51e5670f10cb7ec14a95.html"></iframe>
     </div>
 </div>
+</span>

From 84547f5a76093c7290c7da5eab4453b847d250b3 Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 19 Jul 2018 14:40:00 -0400
Subject: [PATCH 213/753] updated the indexes

---
 .../00 Strategy Library/01 Strategy Library.php           | 8 ++++++++
 quantpedia.json                                           | 3 ++-
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9957f5d..69d076f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -190,6 +190,14 @@
         ],
         'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
     ],
+    [
+        'name' => 'Turn of the Month in Equity Indexes',
+        'link' => 'strategy-library/turn-of-the-month-in-equity-indexes',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/41'
+        ],
+        'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.'
+    ],
 ];
 
 ?>
diff --git a/quantpedia.json b/quantpedia.json
index 8830380..048b41b 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -14,5 +14,6 @@
      18: "ba1bb35b26896e2e710a510f62230a24",
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
      21: "203a6729604c80a71b5c3b2baa2b3f69",
-     25: "da3bf5ee608fb8b7dc952372dd925542"
+     25: "da3bf5ee608fb8b7dc952372dd925542",
+     41: "ac6336b5911ac0d4848bae6546ea1dde"
 }

From 335ec5a8478758a9ede6c36e6bdad5d25664d9a6 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 19 Jul 2018 18:58:35 +0000
Subject: [PATCH 214/753] Automated push from server made by Jared Broad

---
 .../03 Source Backtest.html                   | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html
index e69de29..cb79e58 100644
--- a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
+++ b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
@@ -0,0 +1,25 @@
+<p>
+The <code>--backtest</code> switch refers to a JSON file created by the LEAN Engine. This holds all of the backtest data which is required for generating the report html. This data can be sourced for desktop or cloud backtests.
+</p>
+
+<h4>Desktop Backtesting Results</h4>
+<p>
+By default LEAN saves the backtest result data to disk inside of your LEAN launcher folder: <code>Lean/Launcher/bin/Debug/BasicTemplateAlgorithm.json</code>. It is saved there by the <a href="https://github.com/QuantConnect/Lean/blob/master/Engine/Results/BaseResultsHandler.cs#L35">BaseResultHandler.SaveResult</a> method. You can access it like this from the Python Toolbox:
+</p>
+<div class="section-example-container">
+<pre class="all">python CreateLeanReport.py --backtest=../Launcher/bin/Debug/BasicTemplateAlgorithm.json ....</pre>
+</div>
+
+<h4>Cloud Backtesting Results</h4>
+<p>
+Backtest processed reports can also be downloaded via API. The following example imports the QuantConnect API class; and uses it to download the final HTML report. To access the API you will need to know your API User Id and Token, which can be located on your <a href="/account">Account</a> page.
+</p>
+<div class="section-example-container">
+<pre class="all">
+>>> from IPython.core.display import display, HTML
+>>> from quantconnect.api import Api
+>>> api = Api(your-user-id, your-token)
+>>> lean_report = api.read_backtest_report(project-id, backtest-id)
+>>> display(HTML(lean_report['report']))
+</pre>
+</div>
\ No newline at end of file

From 53b1c2365cc1b1f5a57e4d7efcddf98ca328ae63 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 20 Jul 2018 12:56:55 +0000
Subject: [PATCH 215/753] Automated push from server made by Jared Broad

---
 .../04 Customizing Report Contents.html              | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html
index e69de29..c9a997f 100644
--- a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html	
+++ b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html	
@@ -0,0 +1,12 @@
+<p>
+The report imports a profile image and description of the strategy. This is stored inside a JSON file <code>user_data.json</code>. You can customize the contents of this file to automatically generate future reports with the new data:
+</p>
+<div class="section-example-container">
+<pre class="all">{
+    "authorName": "Joe Blogs",
+    "authorPicture": "AuthorProfile.png",
+    "authorBiography": "Put your biography here.",
+    "projectName": " -- Page title / project name --",
+    "projectDescription": " -- Longer description text area -- "
+}</pre>
+</div>
\ No newline at end of file

From f18eb123610e6272c5ff62ca9c11c452318c12a1 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 20 Jul 2018 14:59:51 +0000
Subject: [PATCH 216/753] Automated push from server made by Jared Broad

---
 03 Open Source/00 Introduction/Introduction.html | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index c1bc929..36de027 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -30,6 +30,14 @@ <h4><i class="fa fa-list-alt"></i> 102 Using the API File Provider</h4>
   </div>
 </a>
 
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/lean-report-creator">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-line-chart"></i> 103 Lean Report Creator</h4>    
+    <p>Create polished professional reports from backtest results.</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+
 <a class="tutorial-link docs-internal-link" href="/tutorials/open-source/brokerage-development-guide">
   <div class="tutorial-box">    
     <h4><i class="fa fa-briefcase"></i> 301 Brokerage Development Guide</h4>    

From 36e9567fd98ccca0a7a111622b984343580545c9 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 20 Jul 2018 11:17:42 -0400
Subject: [PATCH 217/753] Create 05 Interpreting Report Charts

---
 .../04 Lean Report Creator/05 Interpreting Report Charts       | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts

diff --git a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts
new file mode 100644
index 0000000..517c4ef
--- /dev/null
+++ b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts	
@@ -0,0 +1,3 @@
+<p>
+
+</p>

From 65600c427f359a0f57613096e649e84fdfc83f0f Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 20 Jul 2018 11:17:57 -0400
Subject: [PATCH 218/753] Rename 05 Interpreting Report Charts to 05
 Interpreting Report Charts.html

---
 ...erpreting Report Charts => 05 Interpreting Report Charts.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/04 Lean Report Creator/{05 Interpreting Report Charts => 05 Interpreting Report Charts.html} (100%)

diff --git a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html
similarity index 100%
rename from 03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts
rename to 03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html

From 3515f08a81442ba6c255390a20fbfcec1649e8f6 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 20 Jul 2018 15:48:18 +0000
Subject: [PATCH 219/753] Automated push from server made by Jared Broad

---
 .../05 Interpreting Report Charts.html        | 60 +++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html
index 517c4ef..a5d8f47 100644
--- a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html	
+++ b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html	
@@ -1,3 +1,63 @@
+<h4>Cumulative Return</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/cumulative-return.png" class="img-responsive">
 <p>
+This chart shows the cumulative returns for your strategy in orange and the benchmark in gray. 
+</p>
+
+<h4>Daily Return</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/daily-returns.png" class="img-responsive">
+<p>
+This chart shows the daily returns for your strategy. When the return is positive, a orange bar will show above the horizontal line; when the return is negative, a gray bar will show below the horizontal line.
+</p>
+
+<h4>Top 5 Drawdown Periods</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/drawdowns.png" class="img-responsive">
+<p>
+This chart shows the drawdown of each day. A certain day's drawdown is defined as the percentage of loss compared to the maximum value prior to this day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors.
+</p>
+
+<h4>Monthly Returns</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/monthly-returns.png" class="img-responsive">
+<p>
+This chart shows the return of each month. We convert original price series into monthly series, and calculate the returns of each month. The green color indicates positive return, the red color indicates negative return, and the greater the loss is, the darker the color is; the yellow color means the gain or loss is rather small; the white color means the month is not included in the backtest period. The values in the cells are in percentage.
+</p>
+
+
+<h4>Annual Returns</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/annual-returns.png" class="img-responsive">
+<p>This chart shows the return of each year. We calculate the total return within each year, shown by the blue bars. The red dotted line represents the average of the annual returns. If the backtest covers less than 12 months of a certain year, then the value in the chart is the actual return which is not annualized.
+</p>
 
+<h4>Crisis Events</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/crisis-lehman-brothers.png" class="img-responsive">
+<p>This group of charts shows the behaviors of both your strategy and the benchmark during a certain historical period. We set the value of your strategy the same as the benchmark at the beginning of each crisis event, and the lines represent the cumulative returns of your strategy and benchmark from the beginning of this crisis event. The report only draws the crisis event charts whose time periods are covered by your strategy.
+</p>
+
+<h4>Rolling Portfolio Beta to Equity</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/rolling-portfolio-beta-to-equity.png" class="img-responsive">
+<p>
+This chart shows the rolling portfolio beta to the benchmark. This chart is drawn based on daily data. Every day, we calculate the beta of your portfolio to the benchmark over the past 6 months (gray line) or 12 months (blue line).</p> 
+<p>
+A beta close to 1 means the strategy has a risk exposure similar to the benchmark. A beta higher than 1 means the strategy has more risk than the benchmark, a beta close to 0 means the strategy is "market neutral", which isn't much affected by market situation. Beta can also be negative - in this situation the the strategy has opposite risk exposure to the benchmark. We won't draw this chart when your backtest period is less than 12 months.
+</p>
+
+<h4>Rolling Sharpe Ratio</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/rolling-sharpe-ratio%286-month%29.png" class="img-responsive">
+<p>This chart shows the rolling sharpe ratio of your strategy. The rolling sharpe ratio is calculated on daily data, and annualized. Every day, we calculate the sharpe ratio of your portfolio over the past 6 months, and connect the sharpe ratios into a line. The red dotted line represents the mean value of the total sharpe ratios. We won't draw this chart when your backtest period is less than 6 months.
+</p>
+
+<h4>Net Holdings</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/net-holdings.png" class="img-responsive">
+<p>This chart shows the net holdings of your portfolio. The net holding is the aggregated weight of risky assets in your portfolio. It could be either positive (when your total position is long), negative (when your total position is short) or 0 (when you only hold cash). The net holding changes only if new order is fired. The chart is drawn based on minute data, which means we aggregate all the positions of a minute together.
+</p>
+
+<h4>Leverage</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/leverage.png" class="img-responsive">
+<p>This chart shows the leverage of your portfolio. The value of the leverage is always non-negative. When you only hold cash, the leverage is 0; a leverage smaller than 1 means you either long assets with money less than your portfolio value or short assets with total value less than your portfolio value; a leverage larger than 1 means you either borrow money to buy assets or short assets whose value is larger than your portfolio value. The leverage changes only if new order is fired.
+</p>
+
+<h4>Asset Allocations</h4>
+<img src="https://cdn.quantconnect.com/tutorials/i/asset-allocation-equity.png" class="img-responsive">
+<p>
+The asset allocation charts show a time-weighted average of each class of asset to your portfolio. When a certain asset has very small percentage and is too small to be shown in the pie chart, it will be incorporated into an "Others" category. The value of the percentage could be either positive or negative. 
 </p>

From 159f42d0ea6402308b2373673a3154b7cb6dd73a Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 20 Jul 2018 15:52:41 +0000
Subject: [PATCH 220/753] Automated push from server made by Jared Broad

---
 .../03 Source Backtest.html                   | 21 ++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html
index cb79e58..8a4bc3a 100644
--- a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
+++ b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
@@ -14,6 +14,8 @@ <h4>Cloud Backtesting Results</h4>
 <p>
 Backtest processed reports can also be downloaded via API. The following example imports the QuantConnect API class; and uses it to download the final HTML report. To access the API you will need to know your API User Id and Token, which can be located on your <a href="/account">Account</a> page.
 </p>
+
+<em>Option 1: Easy: Downloading Cloud Generated Reports:</em>
 <div class="section-example-container">
 <pre class="all">
 >>> from IPython.core.display import display, HTML
@@ -22,4 +24,21 @@ <h4>Cloud Backtesting Results</h4>
 >>> lean_report = api.read_backtest_report(project-id, backtest-id)
 >>> display(HTML(lean_report['report']))
 </pre>
-</div>
\ No newline at end of file
+</div>
+
+<em>Option 2: Harder: Downloading Cloud Generated Backtest Json Files:</em>
+<div class="section-example-container">
+<pre class="all">
+>>> from quantconnect.api import Api
+>>> from json import dump
+>>> api = Api(your-user-id, your-token)
+>>> data = api.read_backtest(project-id, backtest-id)
+>>> with open("./json/sample.json", 'w+') as fp:
+>>>    json.dump(data['result'], fp, ensure_ascii=False)
+</pre>
+</div>
+
+<p>
+You can update your report description by clicking on the Project Description button in your project panel. The report automatically uses your profile picture for the report image.
+</p>
+<img src="https://cdn.quantconnect.com/tutorials/i/project-description.png" class="img-responsive" />
\ No newline at end of file

From 43e800e4cd88db235fdc6c39e38a9744921af646 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 20 Jul 2018 18:26:54 -0400
Subject: [PATCH 221/753] Add algorithm Momentum Effect Combined with Term
 Structure in Commodities

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  5 ++
 .../02 Method.html                            | 62 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 .../01 Introduction.html                      |  0
 .../02 Method.html                            |  0
 .../03 Algorithm.html                         |  0
 .../04 Source.html                            |  0
 quantpedia.json                               |  2 +
 10 files changed, 88 insertions(+)
 create mode 100644 04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html
 create mode 100644 04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
 create mode 100644 04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html
 create mode 100644 04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/04 Source.html
 rename 04 Strategy Library/{30 Book-to-Market Value Anomaly => 31 Book-to-Market Value Anomaly}/01 Introduction.html (100%)
 rename 04 Strategy Library/{30 Book-to-Market Value Anomaly => 31 Book-to-Market Value Anomaly}/02 Method.html (100%)
 rename 04 Strategy Library/{30 Book-to-Market Value Anomaly => 31 Book-to-Market Value Anomaly}/03 Algorithm.html (100%)
 rename 04 Strategy Library/{30 Book-to-Market Value Anomaly => 31 Book-to-Market Value Anomaly}/04 Source.html (100%)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index d0b8980..92650de 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -198,6 +198,14 @@
         ],
         'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.'
     ],
+    [
+        'name' => 'Momentum Effect Combined with Term Structure in Commodities',
+        'link' => 'strategy-library/momentum-effect-combined-with-term-structure-in-commodities',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/23'
+        ],
+        'description' => 'Portfolios are formed based on roll returns and the algorithm goes long and short contracts with the highest and lowest one-month performance.'
+    ],
     [
         'name' => 'Book-to-Market Value Anomaly',
         'link' => 'strategy-library/book-to-market-value-anomaly',
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html
new file mode 100644
index 0000000..e45df3f
--- /dev/null
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  In the last two tutorials, we discussed the momentum anomaly and the term structure effect of commodity futures.
+  Term structure strategy selects contracts in deep contango and backwardation based on the roll return and the momentum strategy goes long past winners and short past losers based on the historical return.
+  This algorithm will combine both effects to exploit the trading signals in commodity futures.
+</p>
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
new file mode 100644
index 0000000..d1d6b8b
--- /dev/null
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -0,0 +1,62 @@
+<h4>Roll Return</h4>
+<p>
+  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains<code>.
+  First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
+  Contracts fall into <code>Med</code> level are eliminated from the universe. Next we'll calculate the mean return of contracts in <code>High</code> and <code>Low</code>.
+</p>
+<div class="section-example-container">
+<pre class="python">
+roll_return = {}
+for symbol, chain in self.chains.items():
+    contracts = sorted(chain, key = lambda x: x.Expiry)
+    expiry_nearest = contracts[0].Expiry
+    price_nearest = float(contracts[0].LastPrice) if contracts[0].LastPrice>0 else 0.5*float(contracts[0].AskPrice+contracts[0].BidPrice)
+    for x in contracts[1:]:
+        roll_return[x] = (price_nearest-float(x.LastPrice))*365 / (x.Expiry-expiry_nearest).days
+sorted_by_roll_return = sorted(roll_return, key = lambda x: roll_return[x], reverse =True)
+tertile = floor(1/3*len(sorted_by_roll_return))
+high = sorted_by_roll_return[:tertile]
+low = sorted_by_roll_return[-tertile:]
+</pre>
+</div>
+<h4>Mean Return</h4>
+<p>
+  The second filter is the historical mean return.  We sort the contracts in the High portfolio into two sub-portfolios (High-Winner and High-Loser) based on the mean return of the commodities over the past one month.
+High-Winner is thus made of the commodities that have both the highest roll-returns at the time of portfolio construction and the best past performance.
+Similarly, we sort the commodities in the Low portfolio into two sub-portfolios (Low-Winner and Low-Loser) based on their mean return over the past one months. Low-Loser contains, therefore, commodities that have both the lowest roll-returns at the time of portfolio construction and the worst past performance.
+</P>
+<div class="section-example-container">
+<pre class="python">
+mean_return_high = {}
+for i in high:
+    hist = self.History(i.Symbol, timedelta(days = 21), Resolution.Minute)
+    if hist.empty:
+        continue
+    hist_close = hist['close'][i.Expiry][i.Symbol.Value]
+    mean_return_high[i] = np.mean(hist_close.pct_change())
+high_winners = sorted(mean_return_high, key = lambda x: mean_return_high[x], reverse=True)[:int(len(high)*0.5)]
+
+mean_return_low = {}
+for i in low:
+    hist = self.History(i.Symbol, timedelta(days = 21), Resolution.Minute)
+    if hist.empty:
+        continue
+    hist_close = hist['close'][i.Expiry][i.Symbol.Value]
+    mean_return_low[i] = np.mean(hist_close.pct_change())
+low_losers = sorted(mean_return_low, key = lambda x: mean_return_low[x], reverse=True)[-int(len(low)*0.5):]
+</pre>
+</div>
+<p>
+The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the nect month, the strategy liquidates  the invested contracts and rebalance the portfolio.
+</p>
+<div class="section-example-container">
+<pre class="python">
+short_weight = 0.5/len(low_losers)
+for short in low_losers:
+    self.SetHoldings(short.Symbol, -short_weight)
+
+long_weight = 0.5/len(high_winners)
+for long in high_winners:
+    self.SetHoldings(long.Symbol, long_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html
new file mode 100644
index 0000000..91052ac
--- /dev/null
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_12a417567b5706fdff6cf6265506b98c.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/04 Source.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/04 Source.html
new file mode 100644
index 0000000..be5436f
--- /dev/null
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/23" rel="nofollow">Quantpedia - Momentum Effect Combined with Term Structure in Commodities</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/30 Book-to-Market Value Anomaly/01 Introduction.html
rename to 04 Strategy Library/31 Book-to-Market Value Anomaly/01 Introduction.html
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/02 Method.html
similarity index 100%
rename from 04 Strategy Library/30 Book-to-Market Value Anomaly/02 Method.html
rename to 04 Strategy Library/31 Book-to-Market Value Anomaly/02 Method.html
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/30 Book-to-Market Value Anomaly/03 Algorithm.html
rename to 04 Strategy Library/31 Book-to-Market Value Anomaly/03 Algorithm.html
diff --git a/04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/04 Source.html
similarity index 100%
rename from 04 Strategy Library/30 Book-to-Market Value Anomaly/04 Source.html
rename to 04 Strategy Library/31 Book-to-Market Value Anomaly/04 Source.html
diff --git a/quantpedia.json b/quantpedia.json
index 4bf68ba..0cfa238 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -15,6 +15,8 @@
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
      21: "203a6729604c80a71b5c3b2baa2b3f69",
      22: "d7285b5353d51cd5bd033f205e5faf44",
+     23: "12a417567b5706fdff6cf6265506b98c",
      25: "da3bf5ee608fb8b7dc952372dd925542",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
+
 }

From 24077c612fb66c59d0e0897d7a011058410aa966 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 23 Jul 2018 09:33:55 -0400
Subject: [PATCH 222/753] Update introduction

---
 .../01 Introduction.html                                       | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html
index e45df3f..2327552 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
@@ -1,5 +1,6 @@
 <p>
   In the last two tutorials, we discussed the momentum anomaly and the term structure effect of commodity futures.
-  Term structure strategy selects contracts in deep contango and backwardation based on the roll return and the momentum strategy goes long past winners and short past losers based on the historical return.
+  Term structure strategy selects contracts in deep contango and backwardation. In other words, the portfolio is created by buying each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.
+  The momentum strategy goes long commodity futures with the highest one-month momentum and shorts on futures with the lowest one-month momentum. Signals are based on the historical return.
   This algorithm will combine both effects to exploit the trading signals in commodity futures.
 </p>

From 272a72620306fd0f477571ce318d8c3f82023b1a Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 23 Jul 2018 11:34:57 -0400
Subject: [PATCH 223/753] Update introduction

---
 .../01 Introduction.html                                    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html
index 2327552..c0e6979 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
@@ -1,6 +1,6 @@
 <p>
-  In the last two tutorials, we discussed the momentum anomaly and the term structure effect of commodity futures.
-  Term structure strategy selects contracts in deep contango and backwardation. In other words, the portfolio is created by buying each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.
+  This algorithm will combine <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">the term structure effect</a> and
+  <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures" rel="nofollow">the momentum effect</a> to exploit the trading signals in commodity futures.
   The momentum strategy goes long commodity futures with the highest one-month momentum and shorts on futures with the lowest one-month momentum. Signals are based on the historical return.
-  This algorithm will combine both effects to exploit the trading signals in commodity futures.
+  While the portfolio in term structure strategy is created with futures contracts in deep contango and backwardation. It goes long commodities with the highest roll-returns and short commodities with the lowest roll-returns and holds the long-short positions for one month.
 </p>

From 1a9bd90f6894c44f31042467dc99f9a22f34d2ab Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 23 Jul 2018 16:49:51 -0400
Subject: [PATCH 224/753] Add algorithm Gold Market Timing

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  5 ++
 .../32 Gold Market Timing/02 Method.html      | 56 +++++++++++++++++++
 .../32 Gold Market Timing/03 Algorithm.html   |  6 ++
 .../32 Gold Market Timing/04 Source.html      |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 81 insertions(+)
 create mode 100644 04 Strategy Library/32 Gold Market Timing/01 Introduction.html
 create mode 100644 04 Strategy Library/32 Gold Market Timing/02 Method.html
 create mode 100644 04 Strategy Library/32 Gold Market Timing/03 Algorithm.html
 create mode 100644 04 Strategy Library/32 Gold Market Timing/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 92650de..c499502 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -214,6 +214,14 @@
         ],
         'description' => 'Quintile portfolios are formed based on the Book-to-Market ratio and the highest quintile is held for one year.'
     ],
+    [
+        'name' => 'Gold Market Timing',
+        'link' => 'strategy-library/gold-market-timing',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/43'
+        ],
+        'description' => 'Goes long gold when the Fed model shows that the market is undervalued (the earnings yield is higher than the bond yield and their ratio is at least 2).'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/32 Gold Market Timing/01 Introduction.html b/04 Strategy Library/32 Gold Market Timing/01 Introduction.html
new file mode 100644
index 0000000..ee38e33
--- /dev/null
+++ b/04 Strategy Library/32 Gold Market Timing/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  Gold plays an essential role as a diversifier due to its low or negative correlation to other asset classes.
+  The Fed model illustrates a relationship between stocks and bonds to judge whether the U.S. stock market is reasonably valued.
+  In this algorithm, we'll introduce the Fed model to the gold investment to determine the entry and exit points.
+</p>
diff --git a/04 Strategy Library/32 Gold Market Timing/02 Method.html b/04 Strategy Library/32 Gold Market Timing/02 Method.html
new file mode 100644
index 0000000..f002344
--- /dev/null
+++ b/04 Strategy Library/32 Gold Market Timing/02 Method.html	
@@ -0,0 +1,56 @@
+<h4>Fed Model</h4>
+<p>
+  Historically, the S&P 500 earning yield has a strong correlation with the 10-year Treasury note. There is the theory that if the forward earnings yield of the S&P 500 is higher than the 10-year government bond yield, stocks are undervalued and vice versa.
+</P>
+<p>
+  We import the custom fundamental data of government 10-year bond yield and S&P 500 earnings yield from Quandl.
+</P>
+<div class="section-example-container">
+<pre class="python">
+# United States Government 10-Year Bond Yield
+self.bond_yield = "YC/USA10Y"
+# S&P 500 Earnings Yield. Earnings Yield = trailing 12 month earnings divided by index price
+self.earnings_yield = "MULTPL/SP500_EARNINGS_YIELD_MONTH"
+# Gold Prices (Daily) - Currency USD (All values are national currency units per troy ounce)
+self.gold = "WGC/GOLD_DAILY_USD"
+# Add custom quandl data
+self.AddData(QuandlRate, self.bond_yield, Resolution.Daily, DateTimeZone.Utc, True)
+self.AddData(QuandlValue, self.earnings_yield, Resolution.Daily, DateTimeZone.Utc, True)
+self.AddData(QuandlValue, self.gold, Resolution.Daily, DateTimeZone.Utc, True)
+</pre>
+</div>
+
+<h4>Gold Investment</h4>
+<p>
+  With gold having a strong negative correlation to equity valuations, then gold prices will rise along with earnings yield, bond yield, and inflation.
+  Taking the above relationship a step further, this algorithm goes long gold when the Fed model shows the market undervalued otherwise liquidate the portfolio.
+  The undervalued market is defined as the earning yield is higher than the bond yield and their ratio is at least 2. Rebalancing is done on a monthly basis.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Rebalance(self):
+    if self.Securities[self.earnings_yield].Price == 0 or self.Securities[self.bond_yield].Price == 0: return
+    # Buy gold if E/P is higher than the bond yield and their ratio is at least 2
+    if self.Securities[self.earnings_yield].Price > self.Securities[self.bond_yield].Price * Decimal(2):
+        self.SetHoldings(self.gold, 0.9)
+    else:
+        self.Liquidate()
+</pre>
+</div>
+<h4>Custom Charting</h4>
+<p>
+  To demonstrate the relationship between government 10-year bond yield and S&P 500 earnings yield, we plot the two series in equity chart.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    yieldPlot = Chart("Yield Plot")
+    yieldPlot.AddSeries(Series("BondYield", SeriesType.Line, 0))
+    yieldPlot.AddSeries(Series("EarningsYield", SeriesType.Line, 0))
+    self.AddChart(yieldPlot)
+def OnData(self, data):
+    if data.ContainsKey(self.bond_yield) and data.ContainsKey(self.earnings_yield):
+        self.Plot("Yield Plot", "BondYield", data[self.bond_yield].Price)
+        self.Plot("Yield Plot", "EarningsYield", data[self.earnings_yield].Price)
+</pre>
+</div>
diff --git a/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html
new file mode 100644
index 0000000..414a43d
--- /dev/null
+++ b/04 Strategy Library/32 Gold Market Timing/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_707c5bb33fede32269a986370bf8a0a9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/32 Gold Market Timing/04 Source.html b/04 Strategy Library/32 Gold Market Timing/04 Source.html
new file mode 100644
index 0000000..bcb7163
--- /dev/null
+++ b/04 Strategy Library/32 Gold Market Timing/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/43" rel="nofollow">Quantpedia - Gold Market Timing</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 0cfa238..cdd72ff 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -18,5 +18,6 @@
      23: "12a417567b5706fdff6cf6265506b98c",
      25: "da3bf5ee608fb8b7dc952372dd925542",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
+     43: "707c5bb33fede32269a986370bf8a0a9",
 
 }

From 19b2443162f06212e0da528616f0f7104f3a5dec Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 23 Jul 2018 17:54:29 -0400
Subject: [PATCH 225/753] Update 01 Introduction.html

---
 .../01 Introduction.html                                      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html
index c0e6979..8af2089 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/01 Introduction.html	
@@ -1,6 +1,6 @@
 <p>
-  This algorithm will combine <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">the term structure effect</a> and
-  <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures" rel="nofollow">the momentum effect</a> to exploit the trading signals in commodity futures.
+  This algorithm will combine <a href="/tutorials/strategy-library/term-structure-effect-in-commodities">the term structure effect</a> and
+  <a href="/tutorials/strategy-library/momentum-effect-in-commodities-futures" rel="nofollow">the momentum effect</a> to exploit the trading signals in commodity futures.
   The momentum strategy goes long commodity futures with the highest one-month momentum and shorts on futures with the lowest one-month momentum. Signals are based on the historical return.
   While the portfolio in term structure strategy is created with futures contracts in deep contango and backwardation. It goes long commodities with the highest roll-returns and short commodities with the lowest roll-returns and holds the long-short positions for one month.
 </p>

From a3d8d007c3621a668a49f7e8a415e8b44693796e Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Mon, 23 Jul 2018 18:18:27 -0400
Subject: [PATCH 226/753] Update 02 Method.html

---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
index d1d6b8b..f749c6e 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -1,7 +1,7 @@
 <h4>Roll Return</h4>
 <p>
   The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains<code>.
-  First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
+  First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross-section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
   Contracts fall into <code>Med</code> level are eliminated from the universe. Next we'll calculate the mean return of contracts in <code>High</code> and <code>Low</code>.
 </p>
 <div class="section-example-container">
@@ -47,7 +47,7 @@ <h4>Mean Return</h4>
 </pre>
 </div>
 <p>
-The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the nect month, the strategy liquidates  the invested contracts and rebalance the portfolio.
+The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the next month, the strategy liquidates the contracts invested and rebalance the portfolio.
 </p>
 <div class="section-example-container">
 <pre class="python">

From b4ffa256edb552cb5508d7aa5c8c20ee7728a537 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 23 Jul 2018 18:49:10 -0400
Subject: [PATCH 227/753] Update 02 Method.html

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
index f749c6e..e6d7c4a 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -47,7 +47,7 @@ <h4>Mean Return</h4>
 </pre>
 </div>
 <p>
-The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the next month, the strategy liquidates the contracts invested and rebalance the portfolio.
+The combined strategy buys the High-Winner portfolio, shorts the Low-Loser portfolio and holds this position for one month. At the start of the next month, the strategy liquidates the contracts invested and rebalances the portfolio.
 </p>
 <div class="section-example-container">
 <pre class="python">

From 4f585d6b30b3bc8bae9bca662151159e9fe9a182 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 23 Jul 2018 19:00:23 -0400
Subject: [PATCH 228/753] Update 01 Introduction.html

---
 .../32 Gold Market Timing/01 Introduction.html                | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/04 Strategy Library/32 Gold Market Timing/01 Introduction.html b/04 Strategy Library/32 Gold Market Timing/01 Introduction.html
index ee38e33..af1149d 100644
--- a/04 Strategy Library/32 Gold Market Timing/01 Introduction.html	
+++ b/04 Strategy Library/32 Gold Market Timing/01 Introduction.html	
@@ -1,5 +1,3 @@
 <p>
-  Gold plays an essential role as a diversifier due to its low or negative correlation to other asset classes.
-  The Fed model illustrates a relationship between stocks and bonds to judge whether the U.S. stock market is reasonably valued.
-  In this algorithm, we'll introduce the Fed model to the gold investment to determine the entry and exit points.
+Gold plays an essential role as a diversifier due to its low or negative correlation to other asset classes. We explore "The Fed Model," a theory that bonds can be used to judge whether the  U.S. stock market is fairly valued and use this to determine entry and exit points for gold.
 </p>

From f1482968a99161eef70d65713e4143e20fda74e0 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 23 Jul 2018 19:01:23 -0400
Subject: [PATCH 229/753] Update 02 Method.html

---
 .../32 Gold Market Timing/02 Method.html               | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/32 Gold Market Timing/02 Method.html b/04 Strategy Library/32 Gold Market Timing/02 Method.html
index f002344..7aaeaaf 100644
--- a/04 Strategy Library/32 Gold Market Timing/02 Method.html	
+++ b/04 Strategy Library/32 Gold Market Timing/02 Method.html	
@@ -1,9 +1,9 @@
 <h4>Fed Model</h4>
 <p>
-  Historically, the S&P 500 earning yield has a strong correlation with the 10-year Treasury note. There is the theory that if the forward earnings yield of the S&P 500 is higher than the 10-year government bond yield, stocks are undervalued and vice versa.
+  Historically, the S&amp;P 500 earning yield has a strong correlation with the 10-year Treasury note. There is the theory that if the forward earnings yield of the S&amp;P 500 is higher than the 10-year government bond yield, stocks are undervalued and vice versa.
 </P>
 <p>
-  We import the custom fundamental data of government 10-year bond yield and S&P 500 earnings yield from Quandl.
+  We import the custom fundamental data of government 10-year bond yield and S&amp;P 500 earnings yield from Quandl.
 </P>
 <div class="section-example-container">
 <pre class="python">
@@ -22,9 +22,7 @@ <h4>Fed Model</h4>
 
 <h4>Gold Investment</h4>
 <p>
-  With gold having a strong negative correlation to equity valuations, then gold prices will rise along with earnings yield, bond yield, and inflation.
-  Taking the above relationship a step further, this algorithm goes long gold when the Fed model shows the market undervalued otherwise liquidate the portfolio.
-  The undervalued market is defined as the earning yield is higher than the bond yield and their ratio is at least 2. Rebalancing is done on a monthly basis.
+  With gold having a strong negative correlation to equity valuations, then gold prices will rise along with earnings yield, bond yield, and inflation. Taking the above relationship a step further, this algorithm goes long gold when the Fed model shows the market undervalued otherwise liquidate the portfolio. The undervalued market is defined as the earning yield is higher than the bond yield and their ratio is at least 2. Rebalancing is done on a monthly basis.
 </P>
 <div class="section-example-container">
 <pre class="python">
@@ -39,7 +37,7 @@ <h4>Gold Investment</h4>
 </div>
 <h4>Custom Charting</h4>
 <p>
-  To demonstrate the relationship between government 10-year bond yield and S&P 500 earnings yield, we plot the two series in equity chart.
+  To demonstrate the relationship between government 10-year bond yield and S&amp;P 500 earnings yield, we plot the two series in equity chart.
 </P>
 <div class="section-example-container">
 <pre class="python">

From d93d1eb1e8bbc40ca6f11d5a80e020639b32fb92 Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Wed, 25 Jul 2018 12:10:15 -0400
Subject: [PATCH 230/753] added algorithm quantpedia 44: paired switching

---
 .../01 Strategy Library.php                   |  8 ++++++
 .../33 Paired Switching/01 Introduction.html  |  3 +++
 .../33 Paired Switching/02 Method.html        | 26 +++++++++++++++++++
 .../33 Paired Switching/03 Algorithm.html     |  6 +++++
 .../33 Paired Switching/04 Source.html        |  5 ++++
 quantpedia.json                               |  3 ++-
 6 files changed, 50 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/33 Paired Switching/01 Introduction.html
 create mode 100644 04 Strategy Library/33 Paired Switching/02 Method.html
 create mode 100644 04 Strategy Library/33 Paired Switching/03 Algorithm.html
 create mode 100644 04 Strategy Library/33 Paired Switching/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9957f5d..adf2ad8 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -190,6 +190,14 @@
         ],
         'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
     ],
+    [
+        'name' => 'Paired Switching',
+        'link' => 'strategy-library/paired-switching',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/44'
+        ],
+        'description' => 'Goes long asset with better performance over the last period and rebalances portfolio every quarter.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/33 Paired Switching/01 Introduction.html b/04 Strategy Library/33 Paired Switching/01 Introduction.html
new file mode 100644
index 0000000..2175d7b
--- /dev/null
+++ b/04 Strategy Library/33 Paired Switching/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Paired switching is a strategy where in its simplest form, investors pick two assets that are negatively correlated and periodically switch position based on their relative performance. The idea behind this strategy is that if the assets are negatively correlated, then a traditional mixed portfolio might lead to a lower return than the return for the individual assets. If the negative correlation exists, switching positions could improve the performance of a portfolio where two assets are statically weighted.  
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/33 Paired Switching/02 Method.html b/04 Strategy Library/33 Paired Switching/02 Method.html
new file mode 100644
index 0000000..61d91f6
--- /dev/null
+++ b/04 Strategy Library/33 Paired Switching/02 Method.html	
@@ -0,0 +1,26 @@
+<p>
+  After two negatively correlated assets are determined, we will analyze them on a quarterly basis. The analysis will consist of retrieving historical prices for the two assets and calculating their performances over the prior quarter. We will buy the asset that yields a higher return during the period. The position is held for one quarter, and then the analysis is repeated.
+</p>
+<div class="section-example-container">
+<pre class="python">
+ def Rebalance(self):
+    self.months +=1
+    if(self.months%3==0):
+        history_call = self.History(self.Securities.Keys,timedelta(days=90))
+        if not history_call.empty:
+            first_bars = history_call.loc[self.first.Symbol.Value]
+            last_p1 = first_bars["close"].iloc[0]
+            second_bars = history_call.loc[self.second.Symbol.Value]
+            last_p2 = second_bars["close"].iloc[0]
+            first_performance = (float(self.Securities[self.first.Symbol].Price) - float(last_p1))/(float(self.Securities[self.first.Symbol].Price))
+            second_performance = (float(self.Securities[self.second.Symbol].Price) - float(last_p2))/(float(self.Securities[self.second.Symbol].Price))
+            if(first_performance > second_performance):
+                if(self.Securities[self.second.Symbol].Invested==True):
+                    self.Liquidate(self.second.Symbol)
+                self.SetHoldings(self.first.Symbol,1)
+            else:
+                if(self.Securities[self.first.Symbol].Invested==True):
+                    self.Liquidate(self.first.Symbol)
+                self.SetHoldings(self.second.Symbol,1)
+</pre>
+</div>
diff --git a/04 Strategy Library/33 Paired Switching/03 Algorithm.html b/04 Strategy Library/33 Paired Switching/03 Algorithm.html
new file mode 100644
index 0000000..16a1ec2
--- /dev/null
+++ b/04 Strategy Library/33 Paired Switching/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6fe04ea9049f2cf36d3c64ee672254ea.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/33 Paired Switching/04 Source.html b/04 Strategy Library/33 Paired Switching/04 Source.html
new file mode 100644
index 0000000..759542a
--- /dev/null
+++ b/04 Strategy Library/33 Paired Switching/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/44"  rel="nofollow">Quantpedia - Paired Switching</a>
+</li>
+</ul>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 8830380..7941ada 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -14,5 +14,6 @@
      18: "ba1bb35b26896e2e710a510f62230a24",
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
      21: "203a6729604c80a71b5c3b2baa2b3f69",
-     25: "da3bf5ee608fb8b7dc952372dd925542"
+     25: "da3bf5ee608fb8b7dc952372dd925542",
+     33: "6fe04ea9049f2cf36d3c64ee672254ea",
 }

From a9a43097c190c0f382fd579e0581a6ec80893a20 Mon Sep 17 00:00:00 2001
From: Gurumeher Sawhney <gs2605@nyu.edu>
Date: Wed, 25 Jul 2018 12:46:06 -0400
Subject: [PATCH 231/753] Update quantpedia.json

---
 quantpedia.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quantpedia.json b/quantpedia.json
index 7941ada..e8d9657 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -15,5 +15,5 @@
      20: "f0fca76a72227c5bee77d59bdbf5a53d",
      21: "203a6729604c80a71b5c3b2baa2b3f69",
      25: "da3bf5ee608fb8b7dc952372dd925542",
-     33: "6fe04ea9049f2cf36d3c64ee672254ea",
+     44: "6fe04ea9049f2cf36d3c64ee672254ea",
 }

From 8444e314134f89e1d76c5bf427c1bc5ce97cc2b4 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 25 Jul 2018 17:16:54 -0400
Subject: [PATCH 232/753] Update 02 Quantpedia.php

---
 04 Strategy Library/26 Quantpedia/02 Quantpedia.php | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/26 Quantpedia/02 Quantpedia.php b/04 Strategy Library/26 Quantpedia/02 Quantpedia.php
index 2d0ebd0..32dd198 100644
--- a/04 Strategy Library/26 Quantpedia/02 Quantpedia.php	
+++ b/04 Strategy Library/26 Quantpedia/02 Quantpedia.php	
@@ -9,7 +9,8 @@
 ?>
 <div class="row">
     <div class="search col-xs-12">
-    <h2>Quantpedia Strategy Implementations</h2>
+    <img src="https://cdn.quantconnect.com/tutorials/i/quantpedia.png" width="33%" style="margin-top: 40px"/>
+    <h2>Strategy Implementations</h2>
     </div>
 </div>
 <div class="row">

From 3a1808b4eff5cd0c6388d769d913872036be7edf Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 30 Jul 2018 16:26:11 -0400
Subject: [PATCH 233/753] Add Momentum-Short Term Reversal Strategy

---
 .../01 Strategy Library.php                   |  8 ++
 .../01 Introduction.html                      |  4 +
 .../02 Method.html                            | 88 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  3 +-
 6 files changed, 113 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/34 Momentum-Short Term Reversal Strategy/01 Introduction.html
 create mode 100644 04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html
 create mode 100644 04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html
 create mode 100644 04 Strategy Library/34 Momentum-Short Term Reversal Strategy/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 58e2118..4ceffcd 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -238,6 +238,14 @@
         ],
         'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.'
     ]
+    [
+        'name' => 'Momentum - Short Term Reversal Strategy',
+        'link' => 'strategy-library/momentum-short-term-reversal-strategy',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/51'
+        ],
+        'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.'
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/01 Introduction.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/01 Introduction.html
new file mode 100644
index 0000000..fcda95b
--- /dev/null
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Momentum effect is an anomaly in nearly every market. However, if a stock in the winner group is in the final stages of overreaction, it is not the best long opportunity because of the high probability of its reversal, and therefore profit reduction. Similarly, in the loser group, a stock which is in the final stages of overreaction is also not the best short opportunity because its reversal would lower the profit of short selling within a short time. Based on this logic,
+  this momentum-reversal strategy seeks to buy winners and sell losers that are less likely to be in the final stages of overreaction.
+</p>
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html
new file mode 100644
index 0000000..4311c0f
--- /dev/null
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html	
@@ -0,0 +1,88 @@
+<p>
+  All stocks on NYSE and NASDAQ are used as the investment universe. We create a class to save all variables for each symbol.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    def __init__(self, symbol):
+        self.symbol = symbol
+        self.window = RollingWindow[float](13)
+        self.GARR_ratio = None
+        self.yearly_return = None
+</pre>
+</div>
+<p>
+  To get the value for the above variables, we save the stock price at the start of each month in the rolling window for the last 12 months and calculate the monthly return and the yearly return.
+  Then we can compute the last month's geometric average rate of return (GARR) and the previous 12-month GARR. The formulas are as follows
+</P>
+\[GARR_{last\ 12\ months}=(1+R_1)^{1/12}\cdot(1+R_2)^{1/12}\cdot\cdot\cdot(1+R_{11})^{1/12}\cdot(1+R_{12})^{1/12}-1\]
+\[GARR_{last\ months}=(1+R_{last\ month})^{1/12}-1\]
+</P>
+<p>
+  We assign the value of two returns to each symbol in <code>SymbolData<code> class. All stocks are sorted based on their past 12-month return.
+  Stocks are then divided into 3 portfolios (top 30% - winner group, middle 40% and bottom 30% - loser group).  In the winner group, stocks are further
+  classified into two categories: the return-increasing winner and return-decreasing winner using the ratio of last month's geometric average rate of return (GARR) over the last 12 month GARR.
+</P>
+\[GARR\ Ratio= \frac{GARR_{last\ months} }{GARR_{last\ 12\ months}}\]
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    if self.month_start:
+        self.coarse = True
+        coarse = [i for i in coarse if i.AdjustedPrice > 10]
+        for i in coarse:
+            if i.Symbol not in self.SymbolPrice:
+                self.SymbolPrice[i.Symbol] = SymbolData(i.Symbol)
+            self.SymbolPrice[i.Symbol].window.Add(float(i.AdjustedPrice))
+            if self.SymbolPrice[i.Symbol].window.IsReady:
+                price = np.array([i for i in self.SymbolPrice[i.Symbol].window])
+                returns = (price[:-1]-price[1:])/price[1:]
+                self.SymbolPrice[i.Symbol].yearly_return = (price[0]-price[-1])/price[-1]
+                GARR_12 = np.prod([(1+i)**(1/12) for i in returns])-1
+                GARR_1 = (1+returns[0])**(1/12)-1
+                self.SymbolPrice[i.Symbol].GARR_ratio = GARR_1 / GARR_12
+</pre>
+</div>
+
+<p>
+  The decreasing-return winner group contains 13 stocks in winner group with the lowest GARR Ratio and vice-versa for the increasing-return winner group.
+  The loser group is divided into the increasing-return loser and the decreasing-return loser groups using a similar methodology. The return-increasing loser group
+  contains 15 stocks in loser group with the highest GARR Ratio.
+</P>
+<div class="section-example-container">
+<pre class="python">
+ReadySymbolPrice = {symbol: SymbolData for symbol, SymbolData in self.SymbolPrice.items() if SymbolData.window.IsReady}
+if ReadySymbolPrice and len(ReadySymbolPrice)>50:
+    # sort stocks in coarse by last 12-month return
+    sorted_by_return = sorted(ReadySymbolPrice, key = lambda x: ReadySymbolPrice[x].yearly_return)
+    # top 30% with the highest 12-month return goes into winner group
+    winner = sorted_by_return[:int(len(sorted_by_return)*0.3)]
+    # bottom 30% with the lowest 12-month return goes into loser group
+    loser = sorted_by_return[-int(len(sorted_by_return)*0.3):]
+    self.decrease_winner = sorted(winner, key = lambda x: ReadySymbolPrice[x].GARR_ratio)[:15]
+    self.increase_loser = sorted(loser, key = lambda x: ReadySymbolPrice[x].GARR_ratio)[-15:]
+    return self.decrease_winner+self.increase_loser
+</pre>
+</div>
+<p>
+The algorithm goes long stocks from the decreasing-return winner group and short stocks from the increasing-return loser group. The portfolio is created as equally weighted and rebalanced on a monthly basis.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if self.month_start and self.coarse:
+        self.month_start = False
+        self.coarse = False
+        if all([self.decrease_winner, self.increase_loser]):
+            stocks_invested = [x.Key for x in self.Portfolio]
+            for i in stocks_invested:
+                if i not in self.decrease_winner+self.increase_loser:
+                    self.Liquidate(i)
+            short_weight = 0.5/len(self.increase_loser)
+            for j in self.increase_loser:
+                self.SetHoldings(j, -short_weight)
+            long_weight = 0.5/len(self.decrease_winner)
+            for i in self.decrease_winner:
+                self.SetHoldings(i, long_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html
new file mode 100644
index 0000000..a38d444
--- /dev/null
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_b6d06b2074e4efe807c29135507fa2ab.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/04 Source.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/04 Source.html
new file mode 100644
index 0000000..0b1779c
--- /dev/null
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/51" rel="nofollow">Quantpedia - Momentum - Short Term Reversal Strategy</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 388660a..c979eb2 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -18,7 +18,8 @@
      23: "12a417567b5706fdff6cf6265506b98c",
      25: "da3bf5ee608fb8b7dc952372dd925542",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
-     43: "707c5bb33fede32269a986370bf8a0a9",
      41: "ac6336b5911ac0d4848bae6546ea1dde",
+     43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
+     51: "b6d06b2074e4efe807c29135507fa2ab",
 }

From 379caf9a3cb5a7c1812ca1b92559453a198a22cd Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 30 Jul 2018 16:27:34 -0400
Subject: [PATCH 234/753] Fix html

---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html
index c217215..b5578d7 100644
--- a/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html	
+++ b/04 Strategy Library/23 Mean Reversion Effect in Country Equity Indexes/02 Method.html	
@@ -1,8 +1,8 @@
 <p>
   The investment universe consists of 19 ETFs which invest in individual country equity indexes.
-  The strategy is going to long on the bottom four countries with the worst 36-month return and short on the top 4 countries with the best 36-month return. The helper indicator method <code>self.ROC(symbol, period, resolution)<code>
+  The strategy is going to long on the bottom four countries with the worst 36-month return and short on the top 4 countries with the best 36-month return. The helper indicator method <code>self.ROC(symbol, period, resolution)</code>
   is used to calculate 36-month return where the resolution is daily and the period is 36*21.
 </p>
 <p>
-  The portfolio is reweighted every three years. We schedule the event to fire every month. <code>self.months<code> is the variable to save the number of months, and the algorithm jumps the rebalance execution if the amount of passed months does not equate to 36.
+  The portfolio is reweighted every three years. We schedule the event to fire every month. <code>self.months</code> is the variable to save the number of months, and the algorithm jumps the rebalance execution if the amount of passed months does not equate to 36.
 </p>

From e94a99f2faf62083d7d1100abaaced55016eecea Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 30 Jul 2018 16:30:44 -0400
Subject: [PATCH 235/753] Fix html

---
 .../34 Momentum-Short Term Reversal Strategy/02 Method.html     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html
index 4311c0f..522b29b 100644
--- a/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html	
+++ b/04 Strategy Library/34 Momentum-Short Term Reversal Strategy/02 Method.html	
@@ -19,7 +19,7 @@
 \[GARR_{last\ months}=(1+R_{last\ month})^{1/12}-1\]
 </P>
 <p>
-  We assign the value of two returns to each symbol in <code>SymbolData<code> class. All stocks are sorted based on their past 12-month return.
+  We assign the value of two returns to each symbol in <code>SymbolData</code> class. All stocks are sorted based on their past 12-month return.
   Stocks are then divided into 3 portfolios (top 30% - winner group, middle 40% and bottom 30% - loser group).  In the winner group, stocks are further
   classified into two categories: the return-increasing winner and return-decreasing winner using the ratio of last month's geometric average rate of return (GARR) over the last 12 month GARR.
 </P>

From 93160552a539a1b4df4bb4ca9250dc75d9855889 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Mon, 30 Jul 2018 17:12:14 -0400
Subject: [PATCH 236/753] Fix bug in strategy library definition array.

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4ceffcd..4be520b 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -237,7 +237,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/41'
         ],
         'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.'
-    ]
+    ],
     [
         'name' => 'Momentum - Short Term Reversal Strategy',
         'link' => 'strategy-library/momentum-short-term-reversal-strategy',

From 240a80f1b12650f99b7318f2370621d190242251 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 30 Jul 2018 22:37:31 +0000
Subject: [PATCH 237/753] Automated push from server made by Jing Wu

---
 .../02 Brownian motion.html                                 | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index 29780ed..809e6bb 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -8,10 +8,10 @@
   Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
 </p>
 <p>
-  Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables {\(W_t\mid t\geq0} \)} indexed by time t. The one-dimensional Brownian motion is called Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
+  Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables \(W_t\mid t\geq0\) indexed by time t. The one-dimensional Brownian motion is called the Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
 </p>
 <ul>
-     <li>\({W_0=0\)</li>
+     <li>\(W_0=0\)</li>
      <li>For \(t\geq0\) and \(\Delta t\geq0\), the increment \(W_{t+\Delta t}-W_t\) is normally distributed with mean 0 and standard deviation \(\sqrt{\Delta t}\) .</li>
      <li>For any partitions \(0\leq t_1&lt;t_2&lt;\cdot\cdot\cdot &lt;t_n\), the increments \(W_{t_1}- W_{t_0},W_{t_2}-W_{t_1},\cdot\cdot\cdot, W_{t_n}-W_{t_{n-1}}\) are independent random variables.</li>
      <li>With probability 1, the function W(t) is continuous at t.</li>
@@ -185,7 +185,7 @@ <h3>1. Monte Carlo Simulation of European Options</h3>
 </p>
 <h2>Summary</h2>
 <p>
-  In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion.The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
+  In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion. The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
 </p>
 <p>
   In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.

From b713de3d2ab69748af32e8116f74ace176bdd5e8 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 30 Jul 2018 22:41:49 +0000
Subject: [PATCH 238/753] Automated push from server made by Jing Wu

---
 .../02 Brownian motion.html                                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index 809e6bb..d871bd1 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -8,7 +8,7 @@
   Markov process is a special case of the stochastic process. It means that only the current value of a random variable is relevant for future prediction. In general, we usually assume that the stock price follows a Markov stochastic process, which means only the current stock price is relevant for predicting the future price, it is not correlated with the past price.
 </p>
 <p>
-  Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables \(W_t\mid t\geq0\) indexed by time t. The one-dimensional Brownian motion is called the Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
+  Brownian motion is a particular type of Markov stochastic process or we can think of it as a family of random variables \(\left\{W_t\mid t\geq0\right\}\) indexed by time t. The one-dimensional Brownian motion is called the Wiener Process. (Brownian motion is n-dimensional Wiener processes which mean each dimension is just a standard Wiener process). It has the following properties:
 </p>
 <ul>
      <li>\(W_0=0\)</li>

From da33d38f949a54490bb1bff5ae5ee9040b2dfb1b Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 31 Jul 2018 15:25:17 -0400
Subject: [PATCH 239/753] Rename 04 Strategy Library/30 Turn of the Month in
 Equity Indexes/01 Introduction.html to 04 Strategy Library/35 Turn of the
 Month in Equity Indexes01 Introduction.html

---
 ... 35 Turn of the Month in Equity Indexes01 Introduction.html} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 04 Strategy Library/{30 Turn of the Month in Equity Indexes/01 Introduction.html => 35 Turn of the Month in Equity Indexes01 Introduction.html} (98%)

diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes01 Introduction.html
similarity index 98%
rename from 04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html
rename to 04 Strategy Library/35 Turn of the Month in Equity Indexes01 Introduction.html
index 0102c22..acf9adb 100644
--- a/04 Strategy Library/30 Turn of the Month in Equity Indexes/01 Introduction.html	
+++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes01 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
   The turn of the month is an effect on stock indices which states that stocks will rise during the last day before the end of the month and the first three days of each month. Researchers believe this significance comes as a result of pension funds receiving cash flows and reinvesting in the market, along with this period being a natural point for portfolio rebalancing between retail and professional investors. This algorithm is an approximation of the following strategy.
-</p>
\ No newline at end of file
+</p>

From 5485c2a6eb945fadf65279986b6fd359b82f93dc Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 31 Jul 2018 15:25:43 -0400
Subject: [PATCH 240/753] Rename 04 Strategy Library/35 Turn of the Month in
 Equity Indexes01 Introduction.html to 04 Strategy Library/35 Turn of the
 Month in Equity Indexes/01 Introduction.html

---
 .../01 Introduction.html}                                         | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{35 Turn of the Month in Equity Indexes01 Introduction.html => 35 Turn of the Month in Equity Indexes/01 Introduction.html} (100%)

diff --git a/04 Strategy Library/35 Turn of the Month in Equity Indexes01 Introduction.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/35 Turn of the Month in Equity Indexes01 Introduction.html
rename to 04 Strategy Library/35 Turn of the Month in Equity Indexes/01 Introduction.html

From 1f350919a6bdfd5d183ac00a8474fbd1b998f433 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 31 Jul 2018 15:25:51 -0400
Subject: [PATCH 241/753] Rename 04 Strategy Library/30 Turn of the Month in
 Equity Indexes/02 Method.html to 04 Strategy Library/35 Turn of the Month in
 Equity Indexes/02 Method.html

---
 .../02 Method.html                                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{30 Turn of the Month in Equity Indexes => 35 Turn of the Month in Equity Indexes}/02 Method.html (100%)

diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html
similarity index 100%
rename from 04 Strategy Library/30 Turn of the Month in Equity Indexes/02 Method.html
rename to 04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html

From 5d64fdeeae5ea867e69cdef2a702d23b7c3705e3 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 31 Jul 2018 15:26:04 -0400
Subject: [PATCH 242/753] Rename 04 Strategy Library/30 Turn of the Month in
 Equity Indexes/03 Algorithm.html to 04 Strategy Library/35 Turn of the Month
 in Equity Indexes/03 Algorithm.html

---
 .../03 Algorithm.html                                             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{30 Turn of the Month in Equity Indexes => 35 Turn of the Month in Equity Indexes}/03 Algorithm.html (100%)

diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/30 Turn of the Month in Equity Indexes/03 Algorithm.html
rename to 04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html

From fd4911c2da884fbe9d4461361b0477eb458e6d7d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 31 Jul 2018 15:26:16 -0400
Subject: [PATCH 243/753] Rename 04 Strategy Library/30 Turn of the Month in
 Equity Indexes/04 Source.html to 04 Strategy Library/35 Turn of the Month in
 Equity Indexes/04 Source.html

---
 .../04 Source.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 04 Strategy Library/{30 Turn of the Month in Equity Indexes => 35 Turn of the Month in Equity Indexes}/04 Source.html (96%)

diff --git a/04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/04 Source.html
similarity index 96%
rename from 04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html
rename to 04 Strategy Library/35 Turn of the Month in Equity Indexes/04 Source.html
index ac06e01..3d56eeb 100644
--- a/04 Strategy Library/30 Turn of the Month in Equity Indexes/04 Source.html	
+++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes/04 Source.html	
@@ -2,4 +2,4 @@
   <li>
     <a href="https://quantpedia.com/Screener/Details/41"  rel="nofollow">Quantpedia - Turn of the Month in Equity Indexes</a>
 </li>
-</ul>
\ No newline at end of file
+</ul>

From ec85427cd292e731c680d3bfd9c01b3cf25a37e0 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 1 Aug 2018 11:43:17 -0400
Subject: [PATCH 244/753] Add Sentiment and Style Rotation Algorithm

---
 .../01 Strategy Library.php                   |  10 +-
 .../01 Introduction.html                      |   4 +
 .../02 Method.html                            | 129 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 154 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4be520b..e4bafc0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -245,7 +245,15 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/51'
         ],
         'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.'
-    ]
+    ],
+    [
+        'name' => 'Sentiment and Style Rotation Effect in Stocks',
+        'link' => 'strategy-library/sentiment-and-style-rotation-effect-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/53'
+        ],
+        'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..654173f
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Growth stocks refer to high-quality, successful companies whose earnings are expected to continue growing at an above-average rate relative to the market.
+  Growth stocks generally have high price-to-earnings (P/E) ratios and high price-to-book(P/B) ratios. At times, growth stocks are considered expensive and overvalued. The value stocks refer to stocks which have high dividend payout ratios or low financial ratios such as P/E ratios and P/B ratios. The value stocks are often considered undervalued by the market. This algorithm will create the long-short positions based on the relation between investor sentiment and the performance of value stocks over growth stocks.
+</p>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..85c26c6
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
@@ -0,0 +1,129 @@
+<h4>The measure of investment sentiment</h4>
+<p>
+  To measure investors' sentiment, we use gauges: the CBOE equity put-call ratio and the market volatility (VIX) index.
+  The VIX index is constructed using the implied volatilities on S&P 500 index options and shows the market's expectation of 30-day volatility.
+  The CBOE equity put-call ratio is calculated by dividing the trading volume of CBOE equity put options by the trading volume of CBOE equity call options.
+  A rising put-call ratio means equity traders are buying more puts than calls and indicates a bearish sentiment in the market while a falling put-call ratio
+  is considered as the bullish market sentiment.
+</p>
+<p>
+  We import the daily VIX data from Quandl. CBOE provides the volume put-call ratio data from 11-01-2006 to present so we import the custom data from <a href="http://www.cboe.com/data/historical-options-data/volume-put-call-ratios">CBOE</a>.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SentimentAndStyleRotationAlgorithm(QCAlgorithm):
+  def Initialize(self):
+      self.SetStartDate(2010, 1, 1)
+      self.SetEndDate(2018, 7, 1)
+      self.SetCash(100000)
+      self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily)
+      self.AddData(CBOE, "PutCallRatio", Resolution.Daily)
+
+class QuandlVix(PythonQuandl):
+    '''Quandl VIX data class'''
+    def __init__(self):
+        self.ValueColumnName = "VIX Close"
+
+class CBOE(PythonData):
+    '''Cboe Equity Volume Put/Call Ratios (11-01-2006 to present) Custom Data Class'''
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/equitypc.csv", SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        if not (line.strip() and line[0].isdigit()): return None
+        index = CBOE()
+        index.Symbol = config.Symbol
+
+        try:
+            # Example File Format:
+            # DATE       CALL      PUT       TOTAL      P/C Ratio
+            # 11/1/06    976510    623929    1600439    0.64
+            data = line.split(',')
+            index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d")
+            index.Value = Decimal(data[4])
+
+        except ValueError:
+                return None
+
+        return index
+</pre>
+</div>
+<h4>The Measure of the Growth and Value Stocks</h4>
+<p>
+  All stocks on NYSE and NASDAQ are used as the investment universe. In <code>CoarseSelectionFunction</code>, we eliminate ETFs which don't have fundamental data.
+  In <code>FineSelectionFunction</code>, stocks are sorted into deciles based on a size measure - market capitalization.   We use only the first three size deciles
+  for the algorithm to avoid potential problems with small illiquid stocks.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.month_start:
+        self.selection = True
+
+        fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
+                                and i.EarningReports.BasicEPS.TwelveMonths>0
+                                and i.ValuationRatios.PERatio>0
+                                and i.ValuationRatios.PBRatio>0]
+        # Calculate the market cap and add the "MakretCap" property to fine universe object
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        # sort fine object by MarketCap
+        sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)]
+        decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)]
+        decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
+</pre>
+</div>
+</p>
+  In the next step, we subdivide each size decile into five portfolios based on the P/B ratio. For each of the first three size deciles, the value portfolio consists of all firms included in the quintile with the lowest P/B ratio, and the growth portfolio consists stocks with the highest P/B ratio.
+</p>
+<div class="section-example-container">
+<pre class="python">
+sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio)
+# The value portfolio consists of all firms included in the quintile with the lowest P/B ratio
+PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)]
+PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)]
+PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)]
+self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3]
+# The growth portfolio consists of all firms included in the quintile with the highest P/B ratio
+PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):]
+PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):]
+PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):]
+self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3]
+</pre>
+</div>
+
+<h4>The Relation Between Investor Sentiment and Equity Style</h4>
+<p>
+  According to the research paper from Lee and Song<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
+  value stocks tend to outperform growth stocks when the CBOE equity put-call ratio is relatively low, and the VIX is relatively high. The value portfolio significantly underperforms the growth portfolio when the put-call ratio and VIX are both high.
+  To convert the daily put-call ratio and VIX data into monthly value, we take an average over the recent one month and the previous six months.
+</p>
+<p>
+  If the recent monthly average CBOE put-call ratio is lower than its six-month average and the one-month average of VIX  is higher than its six-month average, the algorithm goes long on an equally weighted portfolio consisting of value stocks (the lowest P/B quintile) from the top three size deciles. If recent monthly average CBOE put-call ratio and the VIX index are both higher than their six-month average, the algorithm goes short the value stocks.
+  Otherwise, the algorithm goes long both value stocks and growth stocks. The position holding period is three months, and the portfolio is rebalanced every three months.
+</P>
+<div class="section-example-container">
+<pre class="python">
+stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+for i in stocks_invested:
+    if i not in self.value_portfolio+self.growth_portfolio:
+        self.Liquidate(i)
+
+if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value:
+    if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value:
+        long_weight = 1/len(self.value_portfolio)
+        for long in self.value_portfolio:
+            self.SetHoldings(long, long_weight)
+    elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value:
+        short_weight = 1/len(self.value_portfolio)
+        for short in self.value_portfolio:
+            self.SetHoldings(short, -short_weight)
+else:
+    long_weight = 1/len(self.value_portfolio+self.growth_portfolio)
+    for long in self.value_portfolio+self.growth_portfolio:
+        self.SetHoldings(long, long_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..9af8464
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e8a75429c6a43e969e4a9243290e05f4.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..d965226
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/53" rel="nofollow">Quantpedia - Sentiment and Style Rotation Effect in Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index c979eb2..a6accd1 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -22,4 +22,5 @@
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
+     53: "e8a75429c6a43e969e4a9243290e05f4",
 }

From 9f08a05e9a7876b011042318552bd504c8088b18 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 1 Aug 2018 11:49:29 -0400
Subject: [PATCH 245/753] Fix html

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
index 85c26c6..b38e5f5 100644
--- a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
@@ -74,7 +74,7 @@ <h4>The Measure of the Growth and Value Stocks</h4>
         decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
 </pre>
 </div>
-</p>
+<p>
   In the next step, we subdivide each size decile into five portfolios based on the P/B ratio. For each of the first three size deciles, the value portfolio consists of all firms included in the quintile with the lowest P/B ratio, and the growth portfolio consists stocks with the highest P/B ratio.
 </p>
 <div class="section-example-container">

From b9ab61d749627322a1e05df7d68b71af4854baaa Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 1 Aug 2018 19:16:31 +0000
Subject: [PATCH 246/753] Automated push from server made by Jing Wu

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
index e6d7c4a..59d56bb 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -1,6 +1,6 @@
 <h4>Roll Return</h4>
 <p>
-  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains<code>.
+  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains</code>.
   First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross-section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
   Contracts fall into <code>Med</code> level are eliminated from the universe. Next we'll calculate the mean return of contracts in <code>High</code> and <code>Low</code>.
 </p>

From eb24a1623288540d9ef3f9c985649dd11db9b92d Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 1 Aug 2018 22:02:47 +0000
Subject: [PATCH 247/753] Automated push from server made by Jared Broad

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
index b38e5f5..a29b638 100644
--- a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
@@ -97,7 +97,7 @@ <h4>The Measure of the Growth and Value Stocks</h4>
 
 <h4>The Relation Between Investor Sentiment and Equity Style</h4>
 <p>
-  According to the research paper from Lee and Song<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
+  According to the research paper from Lee and Song, <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
   value stocks tend to outperform growth stocks when the CBOE equity put-call ratio is relatively low, and the VIX is relatively high. The value portfolio significantly underperforms the growth portfolio when the put-call ratio and VIX are both high.
   To convert the daily put-call ratio and VIX data into monthly value, we take an average over the recent one month and the previous six months.
 </p>

From 6844326fbd30d70c7a19a23a3b1d2d25fb74dfb7 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 1 Aug 2018 18:59:23 -0400
Subject: [PATCH 248/753] Add Momentum and State of Market Filters Algorithm

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   7 ++
 .../02 Method.html                            | 114 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 141 insertions(+)
 create mode 100644 04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html
 create mode 100644 04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html
 create mode 100644 04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html
 create mode 100644 04 Strategy Library/37 Momentum and State of Market Filters/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e4bafc0..9dd8c51 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -254,6 +254,14 @@
         ],
         'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
     ],
+    [
+        'name' => 'Momentum and State of Market Filters',
+        'link' => 'strategy-library/momentum-and-state-of-market-filters',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
+        ],
+        'description' => 'goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html
new file mode 100644
index 0000000..706e404
--- /dev/null
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html	
@@ -0,0 +1,7 @@
+<p>
+  The momentum effect is one of the most used trading anomalies. It states that what was strongly going up in the near past will probably continue to go up shortly.
+  The momentum effect is strong, but it can suffer significant drawdowns sometimes.
+  There are research papers show that the momentum is sensible to the state of the market.
+  This state of the market can be defined in various ways like the sentiment, prior market returns and so on.
+  Therefore, this algorithm will combine the momentum effect with the market state filter to turn off the momentum trading in down market state times.
+</p>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html b/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html
new file mode 100644
index 0000000..5d68768
--- /dev/null
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html	
@@ -0,0 +1,114 @@
+<p>
+  As we know, the stock market index tracks the price changes of a select group of stocks and compiles those stock price changes into a single value.
+  For example, S&P500 is composed of only 500 large-cap stocks. A broad market index is characterized by including stocks from companies of all sizes(large, mid and small-cap based on their values).
+  The most popular U.S. broad market indexes include the Russell 3000, the Wilshire 5000 Total Market Index and the MSCI U.S. Broad Market Index. Those broad-based market indexes attempt to own the entire market
+  and their return can be a good benchmark of the current market state.
+</P>
+<div class="section-example-container">
+<pre class="python">
+class MomentumandStateofMarkeFiltersAlgorithm(QCAlgorithm):
+    def Initialize(self):
+        self.SetStartDate(2011, 1, 1)
+        self.SetEndDate(2018, 1, 1)
+        self.SetCash(100000)
+        # add Wilshire 5000 Total Market Index data from Dropbox
+        self.AddData(Wilshire5000, "W5000", Resolution.Daily)
+        # calculate the index yearly return
+        self.W5000Return = self.ROC("W5000", 252)
+        # initialize the RateOfChange indicator of Wilshire 5000 total market index
+        history = self.History(["W5000"], 500, Resolution.Daily)
+        for tuple in history.loc["W5000"].itertuples():
+            self.W5000Return.Update(tuple.Index, tuple.value)
+</pre>
+</div>
+<p>
+  In this algorithm, we choose the Wilshire 5000 Total Market Index to be the market state measure. For the period of index return, longer horizons should capture more dramatic changes in the state of the market, but longer horizons also reduce the number of observations of changes in the market's state. Here we choose 12 months return according to the paper <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299927">Market States and Momentum</a> from Guttierez, Cooper and Hameed.
+  The daily index price comes from the <a href="https://finance.yahoo.com/quote/%5EW5000/history?p=%5EW5000">Yahoo Finance</a>.
+</P>
+<div class="section-example-container">
+<pre class="python">
+class Wilshire5000(PythonData):
+    "Class to import Wilshire 5000 Total Market Index data from Dropbox"
+
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("https://www.dropbox.com/s/tfky2mm0dsqte3a/W5000.csv?dl=1",
+                                      SubscriptionTransportMedium.RemoteFile)
+    def Reader(self, config, line, date, isLiveMode):
+        if not (line.strip() and line[1].isdigit()): return None
+        index = Wilshire5000()
+        index.Symbol = config.Symbol
+        try:
+            # Example File Format: (Data starts from 01/04/2010)
+            # Date    Open         High         Low          Close        Adj Close    Volume
+            # 1/4/10  11549.13965  11749.37012  11549.13965  11743.54004  11743.54004  0
+            data = line.split(',')
+            index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+            index.Value = Decimal(data[5])
+        except:
+            return None
+        return index
+</pre>
+</div>
+
+<p>
+  The investment universe contains all stocks on NYSE and NASDAQ with a price higher than $1. We use the momentum indicator to gauge the momentum effect.
+  In <code>CoarseSelectionFunction</code>, the MOM indicator value for each symbol in coarse is updated with the adjusted price and saved in the dictionary <code>self.mom</code>.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    coarse = [x for x in coarse if (x.HasFundamentalData and x.AdjustedPrice > 1)]
+    for i in coarse:
+        if i.Symbol not in self.mom:
+            self.mom[i.Symbol] = SymbolData(i.Symbol, self.lookback)
+        self.mom[i.Symbol].MOM.Update(self.Time, i.AdjustedPrice)
+</pre>
+</div>
+<p>
+  When the indicator is ready, stocks are then sorted based on the previous six months momentum value.
+  Top 20 Stocks with the highest MOM are in the long stock list, 20 Stocks with the lowest MOM are in the short stock list.
+</p>
+<div class="section-example-container">
+<pre class="python">
+self.MOMReady = {symbol: SymbolData for symbol, SymbolData in self.mom.items() if SymbolData.MOM.IsReady}
+if self.MOMReady:
+    # sort stocks by 6 months' momentum
+    sortByMOM = sorted(self.MOMReady, key = lambda x: self.MOMReady[x].MOM.Current.Value, reverse = True)
+    self.long = sortByMOM[:20]
+    self.short = sortByMOM[-20:]
+    return self.long+self.short
+</pre>
+</div>
+
+<p>
+  The strategy is rebalanced monthly. At the beginning of each month, we identify the state of the market.
+  If the market’s one-year return is positive, we define the state of the market as "UP" otherwise the state is "DOWN".
+  When the market is in "UP" state, we go long on the previous six-month winners (highest Momentum) and goes short on the last six-month losers (lowest Momentum).
+  Stocks are equally weighted. If the market is in "DOWN" state, we liquidate all asset holdings and invest in the long-term Treasury bond ETF to control the downside risk.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if self.month_start and self.selection:
+        self.month_start = False
+        self.selection = False
+        if self.long is None or self.short is None: return
+        # if the previous 12 months return on the broad equity market was positive
+        if self.W5000Return.Current.Value > 0:
+            stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+            for i in stocks_invested:
+                if i not in self.long+self.short:
+                    self.Liquidate(i)
+            short_weight = 0.5/len(self.short)
+            # goes short on the prior six-month losers (lowest decile)
+            for short_symbol in self.short:
+                self.SetHoldings(short_symbol, -short_weight)
+            # goes long on the prior six-month winners (highest decile)
+            long_weight = 0.5/len(self.long)
+            for long_symbol in self.long:
+                self.SetHoldings(long_symbol, long_weight)
+        else:
+            self.Liquidate()
+            self.SetHoldings(self.tlt, 1)
+</pre>
+</div>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html
new file mode 100644
index 0000000..dbd0178
--- /dev/null
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a5e952d091e9df05b8f3415fae0ddd9e.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/04 Source.html b/04 Strategy Library/37 Momentum and State of Market Filters/04 Source.html
new file mode 100644
index 0000000..ad4f813
--- /dev/null
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/54" rel="nofollow">Quantpedia - Momentum and State of Market (Sentiment) Filters</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index a6accd1..bab6029 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -23,4 +23,5 @@
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
      53: "e8a75429c6a43e969e4a9243290e05f4",
+     54: "a5e952d091e9df05b8f3415fae0ddd9e",
 }

From 256e3ccf0b69c7776b1e76e130155c9e77c5a21b Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 1 Aug 2018 19:05:29 -0400
Subject: [PATCH 249/753] Update 01 Strategy Library.php

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9dd8c51..2703c45 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -260,7 +260,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
         ],
-        'description' => 'goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
+        'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
     ],
 ];
 

From 618eaeb965d38fb758b168534d01fb13bec6222a Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 1 Aug 2018 20:38:28 -0400
Subject: [PATCH 250/753] Update 05 Algorithm.html

---
 .../02 QuantConnect Options API/05 Algorithm.html               | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 43fce30..64b1d35 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
     </div>
 </div>

From 864bc3d6048922c491d63d3637bef7475032dc41 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 2 Aug 2018 10:02:23 -0400
Subject: [PATCH 251/753] Update method and introduction

---
 .../00 Strategy Library/01 Strategy Library.php           | 2 +-
 .../02 Method.html                                        | 2 +-
 .../02 Method.html                                        | 2 +-
 .../01 Introduction.html                                  | 6 ++----
 .../02 Method.html                                        | 8 ++++----
 .../03 Algorithm.html                                     | 2 +-
 .../02 QuantConnect Options API/05 Algorithm.html         | 2 +-
 quantpedia.json                                           | 2 +-
 8 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9dd8c51..2703c45 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -260,7 +260,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
         ],
-        'description' => 'goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
+        'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
     ],
 ];
 
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
index e6d7c4a..59d56bb 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -1,6 +1,6 @@
 <h4>Roll Return</h4>
 <p>
-  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains<code>.
+  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains</code>.
   First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross-section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
   Contracts fall into <code>Med</code> level are eliminated from the universe. Next we'll calculate the mean return of contracts in <code>High</code> and <code>Low</code>.
 </p>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
index b38e5f5..a29b638 100644
--- a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
@@ -97,7 +97,7 @@ <h4>The Measure of the Growth and Value Stocks</h4>
 
 <h4>The Relation Between Investor Sentiment and Equity Style</h4>
 <p>
-  According to the research paper from Lee and Song<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
+  According to the research paper from Lee and Song, <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
   value stocks tend to outperform growth stocks when the CBOE equity put-call ratio is relatively low, and the VIX is relatively high. The value portfolio significantly underperforms the growth portfolio when the put-call ratio and VIX are both high.
   To convert the daily put-call ratio and VIX data into monthly value, we take an average over the recent one month and the previous six months.
 </p>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html
index 706e404..83ab70a 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html	
@@ -1,7 +1,5 @@
 <p>
-  The momentum effect is one of the most used trading anomalies. It states that what was strongly going up in the near past will probably continue to go up shortly.
-  The momentum effect is strong, but it can suffer significant drawdowns sometimes.
-  There are research papers show that the momentum is sensible to the state of the market.
-  This state of the market can be defined in various ways like the sentiment, prior market returns and so on.
+  The momentum effect states that what was strongly going up in the near past will probably continue to go up shortly. It is one of the most used trading anomalies, but the strategy using only the momentum can suffer significant drawdowns sometimes. There are research papers show that the return of momentum strategy is sensible to the state of the market.
+  This state of the market can be defined in various ways like the investors' sentiment, prior market returns and so on.
   Therefore, this algorithm will combine the momentum effect with the market state filter to turn off the momentum trading in down market state times.
 </p>
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html b/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html
index 5d68768..31414d9 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/02 Method.html	
@@ -1,7 +1,7 @@
 <p>
-  As we know, the stock market index tracks the price changes of a select group of stocks and compiles those stock price changes into a single value.
-  For example, S&P500 is composed of only 500 large-cap stocks. A broad market index is characterized by including stocks from companies of all sizes(large, mid and small-cap based on their values).
-  The most popular U.S. broad market indexes include the Russell 3000, the Wilshire 5000 Total Market Index and the MSCI U.S. Broad Market Index. Those broad-based market indexes attempt to own the entire market
+  As we know, a stock market index tracks the price changes of a select group of stocks and compiles those stock price changes into a single value.
+  For example, S&amp;P500 is composed of only 500 large-cap stocks. A broad market index is characterized by including stocks from companies of all sizes(large, mid and small-cap based on their values).
+  The most popular U.S. broad market indexes include the Russell 3000, the Wilshire 5000 Total Market Index and the MSCI U.S. Broad Market Index. Those broad-based market indexes attempt to cover the entire market
   and their return can be a good benchmark of the current market state.
 </P>
 <div class="section-example-container">
@@ -31,7 +31,7 @@
     "Class to import Wilshire 5000 Total Market Index data from Dropbox"
 
     def GetSource(self, config, date, isLiveMode):
-        return SubscriptionDataSource("https://www.dropbox.com/s/tfky2mm0dsqte3a/W5000.csv?dl=1",
+        return SubscriptionDataSource("https://www.dropbox.com/s/z9rof4fr9cqzgpt/W5000.csv?dl=1",
                                       SubscriptionTransportMedium.RemoteFile)
     def Reader(self, config, line, date, isLiveMode):
         if not (line.strip() and line[1].isdigit()): return None
diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html
index dbd0178..c07b1c7 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a5e952d091e9df05b8f3415fae0ddd9e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_12769f4ad4513269d8f771c99046deb2.html"></iframe>
     </div>
 </div>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 43fce30..64b1d35 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index bab6029..0ecb0cc 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -23,5 +23,5 @@
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
      53: "e8a75429c6a43e969e4a9243290e05f4",
-     54: "a5e952d091e9df05b8f3415fae0ddd9e",
+     54: "12769f4ad4513269d8f771c99046deb2",
 }

From a1624e41f50e4fdda31a23fc16866c0c8ae56dee Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 2 Aug 2018 18:27:58 -0400
Subject: [PATCH 252/753] added algorithm 38: accrual anomaly

---
 .../01 Strategy Library.php                   |  8 +++
 .../38 Accrual Anomaly/01 Introduction.html   |  3 +
 .../38 Accrual Anomaly/02 Method.html         | 69 +++++++++++++++++++
 .../38 Accrual Anomaly/03 Algorithm.html      |  6 ++
 .../38 Accrual Anomaly/04 Source.html         |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 92 insertions(+)
 create mode 100644 04 Strategy Library/38 Accrual Anomaly/01 Introduction.html
 create mode 100644 04 Strategy Library/38 Accrual Anomaly/02 Method.html
 create mode 100644 04 Strategy Library/38 Accrual Anomaly/03 Algorithm.html
 create mode 100644 04 Strategy Library/38 Accrual Anomaly/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e4bafc0..c22f949 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -254,6 +254,14 @@
         ],
         'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
     ],
+    [
+        'name' => 'Accrual Anomaly',
+        'link' => 'strategy-library/accrual-anomaly',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/38'
+        ],
+        'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/38 Accrual Anomaly/01 Introduction.html b/04 Strategy Library/38 Accrual Anomaly/01 Introduction.html
new file mode 100644
index 0000000..3979090
--- /dev/null
+++ b/04 Strategy Library/38 Accrual Anomaly/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+ Accrual anomaly is based upon the reasoning that it is important to measure if a company's earnings are based on real cash inflow or on revenue recognition from questionable accounting practices. Since companies with lower levels of accruals have more certain real earnings, they should earn higher market returns. This strategy will take a long position in low accrual companies and a short position in high accrual accompanies. It is important to note that this strategy requires fundamental data from the current and past year in order to perform its analysis. As a result, it won't be implementable in live trading at this time.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/38 Accrual Anomaly/02 Method.html b/04 Strategy Library/38 Accrual Anomaly/02 Method.html
new file mode 100644
index 0000000..e800787
--- /dev/null
+++ b/04 Strategy Library/38 Accrual Anomaly/02 Method.html	
@@ -0,0 +1,69 @@
+<p>
+ The first step is coarse and fine universe selection. Using coarse selection, we create an investment universe with stocks that have fundamental data. The universes will be saved so that we can perform analysis on the annual changes of specific balance sheet data in the following year.
+</p>
+<div class="section-example-container">
+<pre class="python">
+ def CoarseSelectionFunction(self, coarse):
+        if self.yearly_rebalance:
+            self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)
+                                                            and (x.Market == "usa")]
+            return self.filtered_coarse
+        else: 
+            return []      
+    
+def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        fine = [x for x in fine if (float(x.FinancialStatements.BalanceSheet.CurrentAssets.Value) > 0) 
+                                and (float(x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value) > 0)
+                               and (float(x.FinancialStatements.BalanceSheet.CurrentLiabilities.Value) > 0)
+                                and (float(x.FinancialStatements.BalanceSheet.CurrentDebt.Value) > 0)
+                                and (float(x.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value) > 0)
+                                and (float(x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value) > 0)]
+        
+        if not self.previous_fine:
+            self.previous_fine = fine
+            self.yearly_rebalance = False
+            return []
+        else:
+            self.filtered_fine = self.CalculateAccruals(fine,self.previous_fine)
+            sorted_filter = sorted(self.filtered_fine, key=lambda x: x.bs_acc)
+            self.filtered_fine = [i.Symbol for i in sorted_filter]
+            self.previous_fine = fine
+            return self.filtered_fine
+    else:
+        return []
+</pre>
+</div>
+<p>
+  During fine universe selection we take the stocks from the previous and current year and calculate their balance sheet based accrual values. The stocks are then sorted in ascending order based upon the implemented formula. Note that we account for size difference across the sample firms by scaling the accruals by the average of the beginning and end-of-year book value of total assets.
+</p>
+<div class="section-example-container">
+<pre class="python">
+ def CalculateAccruals(self, current, previous):
+    accruals = []
+    for stock_data in current:
+        try:
+            prev_data = None
+            for x in previous:
+                if x.Symbol == stock_data.Symbol:
+                    prev_data = x
+                    break
+            
+            delta_assets = float(stock_data.FinancialStatements.BalanceSheet.CurrentAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentAssets.Value)
+            delta_cash = float(stock_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value)-float(prev_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value)
+            delta_liabilities = float(stock_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)
+            delta_debt = float(stock_data.FinancialStatements.BalanceSheet.CurrentDebt.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentDebt.Value)
+            delta_tax = float(stock_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value)-float(prev_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value)
+            dep = float(stock_data.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value)
+            avg_total = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)+float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/2
+
+            stock_data.bs_acc = ((delta_assets-delta_cash)-(delta_liabilities-delta_debt-delta_tax)-dep)/avg_total
+            accruals.append(stock_data)
+        except:
+            pass
+    return accruals
+</pre>
+</div>
+<p>
+   In <code>OnData()</code>, we short top decile of the stocks in the sorted list and long the bottom decile of stocks. The portfolio is rebalanced every year at the start of June.
+</p>
diff --git a/04 Strategy Library/38 Accrual Anomaly/03 Algorithm.html b/04 Strategy Library/38 Accrual Anomaly/03 Algorithm.html
new file mode 100644
index 0000000..0ed7669
--- /dev/null
+++ b/04 Strategy Library/38 Accrual Anomaly/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_55081a6fa81c88fd75579c2f838a1180.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/38 Accrual Anomaly/04 Source.html b/04 Strategy Library/38 Accrual Anomaly/04 Source.html
new file mode 100644
index 0000000..623fba7
--- /dev/null
+++ b/04 Strategy Library/38 Accrual Anomaly/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/38"  rel="nofollow">Quantpedia - Accrual Anomaly</a>
+</li>
+</ul>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index a6accd1..a2586ab 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -18,6 +18,7 @@
      23: "12a417567b5706fdff6cf6265506b98c",
      25: "da3bf5ee608fb8b7dc952372dd925542",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
+     38: "55081a6fa81c88fd75579c2f838a1180",
      41: "ac6336b5911ac0d4848bae6546ea1dde",
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",

From a4ebe7d04327733350f663247187b20746885b48 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 3 Aug 2018 18:44:57 -0400
Subject: [PATCH 253/753] Add Pairs Trading with Country ETFs Algorithm

---
 .../01 Strategy Library.php                   | 10 ++++-
 .../01 Introduction.html                      |  6 +++
 .../02 Method.html                            | 43 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++
 .../04 Source.html                            |  5 +++
 quantpedia.json                               |  1 +
 6 files changed, 70 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html
 create mode 100644 04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html
 create mode 100644 04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html
 create mode 100644 04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4be520b..6bed570 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -245,7 +245,15 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/51'
         ],
         'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.'
-    ]
+    ],
+    [
+        'name' => 'Pairs Trading with Country ETFs',
+        'link' => 'strategy-library/pairs-trading-with-country-etfs',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/55'
+        ],
+        'description' => 'Identifies the price divergence from two highly correlated country ETFs and takes a market neutral position.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html
new file mode 100644
index 0000000..f9bd0fd
--- /dev/null
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  Pairs trading is a non-directional, market-neutral trading strategy which seeks to identify the price divergence from two highly correlated assets.
+  Pairs trading assumes the co-movement and the mean reversion of two asset prices. It indicates that the price of selected assets
+  tend to move together, and when they diverge, we can exploit the investment opportunity by taking a market neutral position as we assume their price will tend to move back to the average price over time.
+  In this algorithm, we use a large family of international exchange-traded funds(ETFs) to examine the performance of the pairs trading strategy.
+</p>
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html
new file mode 100644
index 0000000..45e0d19
--- /dev/null
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html	
@@ -0,0 +1,43 @@
+<h4>The selection of pairs</h4>
+<p>
+  The investment universe consists of 25 international ETFs. The pair selection is based on a rolling time period called the formation period.
+  During each formation period, we save the history closing price of each ETF. Here we use a Python list-like container called<code>deque</code> to keep a fixed-length price series. From these prices, we compute normalized cumulative price indices which are comparable across the ETFs in the group.
+  According to the paper <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1958546">Pairs Trading on International ETFs</a> from Panagiotis, Dimitrios and Tao,
+  here the formation period is 120 days.
+</P>
+\[R_t^n=\prod_{1}^{t}(1+r_t^n)  \ \ \  for \ \ \ t=1,2,...,120 \]
+\[r_t^n=\frac{P_t}{P_{t-1}}-1\]
+<p>
+  Where \(r_t^n\) is the simple return of the n-th ETF. \(R_t^n\) is the index value.
+</p>
+<p>
+  For each formation period, we compute the average absolute distance among all pairs in the group and
+  then rank the distances from the smallest to largest to identify the trading opportunities.
+  The top 5 pairs with the smallest distance are used in the subsequent 20 day trading period.
+  The definition of distance is
+</P>
+\[D_t^{a,b}=\frac{1}{120}\sum_{t+1}^{t+120}\mid P_t^a-P_t^b\mid \ for \ all\ pairs \ a,b \]
+<p>
+  <div class="section-example-container">
+  <pre class="python">
+  class Pair:
+      def __init__(self, symbol_a, symbol_b, price_a, price_b):
+          self.symbol_a = symbol_a
+          self.symbol_b = symbol_b
+          self.price_a = np.array(price_a)
+          self.price_b = np.array(price_b)
+          # compute normalized cumulative price indices
+          self.index_a = np.cumprod(self.price_a[1:]/self.price_a[:-1])
+          self.index_b = np.cumprod(self.price_b[1:]/self.price_b[:-1])
+
+      def distance(self):
+          return 1/120*sum(abs(self.index_a -self.index_b))
+  </pre>
+  </div>
+
+<h4>The Definition of Divergence</h4>
+<p>
+  Now we consider the top 5 pairs. For each of the one month trading period, we compute the 120-day normalized cumulative price indices and compare them to a fraction of the distance value \(D_t^{a,b}\) for each pair (a,b). Assume the threshold is 0.5, if the index value of asset A is greater than asset B by 0.5 times the current distance \(D_t^{a,b}\),
+  then we sell asset A and buy asset B. The quantity of A and B is based on the dollar neutral. We invest the same amount of money in the long leg and the short leg, vice versa if the index value of asset B is greater than asset A by 0.5 times the distance. When the difference between index A and index B falls into the range \(\left(-0.5D_t^{a,b},0.5D_t^{a,b}\right)\),
+  we liquidate the pair. The above procedure is repeated for all top 5 pairs. At the start of next month, the new pairs are selected and trade for next month.
+</P>
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html
new file mode 100644
index 0000000..7cf2e86
--- /dev/null
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2d6bcad96d965a04cd7eac846d54f129.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html
new file mode 100644
index 0000000..e9da602
--- /dev/null
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/55" rel="nofollow">Quantpedia - Pairs Trading with Country ETFs</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index c979eb2..3817fc8 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -22,4 +22,5 @@
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
+     55: "2d6bcad96d965a04cd7eac846d54f129",
 }

From 86d517b39266b56ef934503c69b4dc7f5cf4a105 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 3 Aug 2018 18:49:10 -0400
Subject: [PATCH 254/753] Add code snippet

---
 .../02 Method.html                            | 35 ++++++++++++++++---
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html
index 45e0d19..a4a879f 100644
--- a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html	
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html	
@@ -18,8 +18,8 @@ <h4>The selection of pairs</h4>
 </P>
 \[D_t^{a,b}=\frac{1}{120}\sum_{t+1}^{t+120}\mid P_t^a-P_t^b\mid \ for \ all\ pairs \ a,b \]
 <p>
-  <div class="section-example-container">
-  <pre class="python">
+<div class="section-example-container">
+<pre class="python">
   class Pair:
       def __init__(self, symbol_a, symbol_b, price_a, price_b):
           self.symbol_a = symbol_a
@@ -32,8 +32,8 @@ <h4>The selection of pairs</h4>
 
       def distance(self):
           return 1/120*sum(abs(self.index_a -self.index_b))
-  </pre>
-  </div>
+</pre>
+</div>
 
 <h4>The Definition of Divergence</h4>
 <p>
@@ -41,3 +41,30 @@ <h4>The Definition of Divergence</h4>
   then we sell asset A and buy asset B. The quantity of A and B is based on the dollar neutral. We invest the same amount of money in the long leg and the short leg, vice versa if the index value of asset B is greater than asset A by 0.5 times the distance. When the difference between index A and index B falls into the range \(\left(-0.5D_t^{a,b},0.5D_t^{a,b}\right)\),
   we liquidate the pair. The above procedure is repeated for all top 5 pairs. At the start of next month, the new pairs are selected and trade for next month.
 </P>
+<div class="section-example-container">
+<pre class="python">
+for i in self.sorted_pairs:
+    pair = Pair(i[0], i[1], self.history_price[i[0].Value],  self.history_price[i[1].Value])
+    index_a = pair.index_a[-1]
+    index_b = pair.index_b[-1]
+    delta = pair.distance()
+    if index_a - index_b > self.threshold*delta:
+        if not self.Portfolio[pair.symbol_a].Invested and not self.Portfolio[pair.symbol_b].Invested:
+            ratio = self.Portfolio[pair.symbol_a].Price / self.Portfolio[pair.symbol_b].Price
+            quantity = int(self.CalculateOrderQuantity(pair.symbol_a, 0.2))
+            self.Sell(pair.symbol_a, quantity)
+            self.Buy(pair.symbol_b,  floor(ratio*quantity))
+
+    elif index_a - index_b < -self.threshold*delta:
+        if not self.Portfolio[pair.symbol_a].Invested and not self.Portfolio[pair.symbol_b].Invested:
+            ratio = self.Portfolio[pair.symbol_b].Price / self.Portfolio[pair.symbol_a].Price
+            quantity = int(self.CalculateOrderQuantity(pair.symbol_b, 0.2))
+            self.Sell(pair.symbol_b, quantity)
+            self.Buy(pair.symbol_a, floor(ratio*quantity))
+
+    # the position is closed when prices revert back
+    elif self.Portfolio[i[0]].Invested and self.Portfolio[i[1]].Invested:
+            self.Liquidate(pair.symbol_a)
+            self.Liquidate(pair.symbol_b)
+</pre>
+</div>

From 2eadde2a24a89e24725dafc8062dee392ef04c67 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 3 Aug 2018 18:50:47 -0400
Subject: [PATCH 255/753] Fix html

---
 .../37 Pairs Trading with Country ETFs/02 Method.html            | 1 -
 1 file changed, 1 deletion(-)

diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html
index a4a879f..8a84d14 100644
--- a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html	
+++ b/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html	
@@ -17,7 +17,6 @@ <h4>The selection of pairs</h4>
   The definition of distance is
 </P>
 \[D_t^{a,b}=\frac{1}{120}\sum_{t+1}^{t+120}\mid P_t^a-P_t^b\mid \ for \ all\ pairs \ a,b \]
-<p>
 <div class="section-example-container">
 <pre class="python">
   class Pair:

From 9a4873d44b2301ce37d716ee66b54efb613eeb9d Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Mon, 6 Aug 2018 17:53:21 -0400
Subject: [PATCH 256/753] added algorithm 52: asset growth effect

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  3 +
 .../39 Asset Growth Effect/02 Method.html     | 57 +++++++++++++++++++
 .../39 Asset Growth Effect/03 Algorithm.html  |  6 ++
 .../39 Asset Growth Effect/04 Source.html     |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 80 insertions(+)
 create mode 100644 04 Strategy Library/39 Asset Growth Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/39 Asset Growth Effect/02 Method.html
 create mode 100644 04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/39 Asset Growth Effect/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e4bafc0..e65dcfa 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -254,6 +254,14 @@
         ],
         'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
     ],
+    [
+        'name' => 'Asset Growth Effect',
+        'link' => 'strategy-library/asset-growth-effect',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/52'
+        ],
+        'description' => 'Creates long-short positions of stocks based on the annual change of their total assets.'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html b/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html
new file mode 100644
index 0000000..827a150
--- /dev/null
+++ b/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+ The asset growth effect is based upon the reasoning that high asset growth stocks underperform stocks with low asset growth. Some papers suggest that the return premium achieved by low asset growth stocks is consistent with the compensation for risk. Two arguments are that growth options are inherently more risky than assets in place and systematic market mispricing of growing businesses due to the extrapolation of past gains to growth for high asset growth companies. This strategy will take a long position in low asset growth companies, and a short position in high asset growth accompanies. It is important to note that this strategy requires fundamental data from the current and past year to perform its analysis. As a result, this strategy would need a year in live trading to determine signals.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/39 Asset Growth Effect/02 Method.html b/04 Strategy Library/39 Asset Growth Effect/02 Method.html
new file mode 100644
index 0000000..0335c63
--- /dev/null
+++ b/04 Strategy Library/39 Asset Growth Effect/02 Method.html	
@@ -0,0 +1,57 @@
+<p>
+ The first step is coarse and fine universe selection. Using coarse selection, we create an investment universe with all non-financial U.S. stocks listed on NYSE, AMEX, and NASDAQ that contain fundamental data. The universes will be saved so that we can perform analysis on the annual changes of the total assets in the following year.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+        if self.yearly_rebalance:
+            filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)
+                                                            and (x.Market == "usa")]
+            return filtered_coarse
+        else: 
+            return []
+
+def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        fine = [x for x in fine if x.FinancialStatements.BalanceSheet.TotalAssets.Value > 0
+                and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))
+                and (x.CompanyReference.IndustryTemplateCode!="B")
+                and (x.CompanyReference.IndustryTemplateCode!="I")]
+        if not self.previous_fine:
+            self.previous_fine = fine
+            self.yearly_rebalance = False
+            return []
+        else:
+            self.filtered_fine = self.Calculate(fine,self.previous_fine)
+            sorted_filter = sorted(self.filtered_fine, key=lambda x: x.delta_assets)
+            self.filtered_fine = [i.Symbol for i in sorted_filter]
+            self.previous_fine = fine
+            return self.filtered_fine
+    else:
+        return []
+</pre>
+</div>
+<p>
+  During fine universe selection we take the stocks from the previous and current year and calculate their total asset growth. The stocks are then sorted in ascending order based upon the implemented calculation. Note that we account for size difference across the sample firms by scaling the growth by last year's total assets.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Calculate(self, current, previous):
+        growth = []
+        for stock_data in current:
+            try:
+                prev_data = None
+                for x in previous:
+                    if x.Symbol == stock_data.Symbol:
+                        prev_data = x
+                        break
+                stock_data.delta_assets = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value) 
+                growth.append(stock_data)
+            except:
+                pass
+        return growth
+</pre>
+</div>
+<p>
+   In <code>OnData()</code>, we short top decile of the stocks in the sorted list and long the bottom decile of stocks. The portfolio is rebalanced every year at the end of June.
+</p>
diff --git a/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html b/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html
new file mode 100644
index 0000000..fbb66ad
--- /dev/null
+++ b/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b1b8f760c0fbf8b22ad67f6ddcafc89d.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/39 Asset Growth Effect/04 Source.html b/04 Strategy Library/39 Asset Growth Effect/04 Source.html
new file mode 100644
index 0000000..a9d5834
--- /dev/null
+++ b/04 Strategy Library/39 Asset Growth Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/52"  rel="nofollow">Quantpedia - Asset Growth Effect</a>
+</li>
+</ul>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index a6accd1..37526be 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -22,5 +22,6 @@
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
+     52: "b1b8f760c0fbf8b22ad67f6ddcafc89d",
      53: "e8a75429c6a43e969e4a9243290e05f4",
 }

From c58bd8d8da1bab39815598fc61014f63e3c39c89 Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Tue, 7 Aug 2018 09:21:35 -0400
Subject: [PATCH 257/753] updated backtest link

---
 04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html | 2 +-
 quantpedia.json                                              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html b/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html
index fbb66ad..04cf1c1 100644
--- a/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html	
+++ b/04 Strategy Library/39 Asset Growth Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b1b8f760c0fbf8b22ad67f6ddcafc89d.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_813285e2c02ffca1afa114e668301ea8.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 37526be..a7f5f7b 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -22,6 +22,6 @@
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
-     52: "b1b8f760c0fbf8b22ad67f6ddcafc89d",
+     52: "813285e2c02ffca1afa114e668301ea8",
      53: "e8a75429c6a43e969e4a9243290e05f4",
 }

From 69e8254c3efeb0f71a0693633857cd29283d85c0 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 7 Aug 2018 17:04:14 -0400
Subject: [PATCH 258/753] Update 01 Introduction.html

---
 .../01 Introduction.html                                      | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html
index 83ab70a..4d5fa7a 100644
--- a/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html	
+++ b/04 Strategy Library/37 Momentum and State of Market Filters/01 Introduction.html	
@@ -1,5 +1,7 @@
 <p>
-  The momentum effect states that what was strongly going up in the near past will probably continue to go up shortly. It is one of the most used trading anomalies, but the strategy using only the momentum can suffer significant drawdowns sometimes. There are research papers show that the return of momentum strategy is sensible to the state of the market.
+  The momentum effect states that what was strongly going up in the near past will probably continue to go up shortly. It is one of the most used trading anomalies, but the strategy using only the momentum can suffer significant drawdowns sometimes. 
+  
+  Some research papers show that the return of momentum strategies depend on the overall market conditions.
   This state of the market can be defined in various ways like the investors' sentiment, prior market returns and so on.
   Therefore, this algorithm will combine the momentum effect with the market state filter to turn off the momentum trading in down market state times.
 </p>

From ade7620646bb5bdc88d4eaec6d0843b173aa18b4 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 7 Aug 2018 17:20:30 -0400
Subject: [PATCH 259/753] Rename the folder

---
 .../01 Strategy Library.php                   |   9 ++
 .../02 Method.html                            |   2 +-
 .../01 Introduction.html                      |   4 +
 .../02 Method.html                            | 129 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 .../01 Introduction.html                      |   0
 .../02 Method.html                            |   0
 .../03 Algorithm.html                         |   0
 .../04 Source.html                            |   0
 .../05 Algorithm.html                         |   2 +-
 quantpedia.json                               |   4 +
 12 files changed, 159 insertions(+), 2 deletions(-)
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html
 rename 04 Strategy Library/{37 Pairs Trading with Country ETFs => 40 Pairs Trading with Country ETFs}/01 Introduction.html (100%)
 rename 04 Strategy Library/{37 Pairs Trading with Country ETFs => 40 Pairs Trading with Country ETFs}/02 Method.html (100%)
 rename 04 Strategy Library/{37 Pairs Trading with Country ETFs => 40 Pairs Trading with Country ETFs}/03 Algorithm.html (100%)
 rename 04 Strategy Library/{37 Pairs Trading with Country ETFs => 40 Pairs Trading with Country ETFs}/04 Source.html (100%)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6bed570..789fb0e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -247,12 +247,21 @@
         'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.'
     ],
     [
+<<<<<<< .merge_file_mEiqFp
         'name' => 'Pairs Trading with Country ETFs',
         'link' => 'strategy-library/pairs-trading-with-country-etfs',
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/55'
         ],
         'description' => 'Identifies the price divergence from two highly correlated country ETFs and takes a market neutral position.'
+=======
+        'name' => 'Sentiment and Style Rotation Effect in Stocks',
+        'link' => 'strategy-library/sentiment-and-style-rotation-effect-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/53'
+        ],
+        'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
+>>>>>>> .merge_file_aU2v6h
     ],
 ];
 
diff --git a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html
index e6d7c4a..59d56bb 100644
--- a/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
+++ b/04 Strategy Library/30 Momentum Effect Combined with Term Structure in Commodities/02 Method.html	
@@ -1,6 +1,6 @@
 <h4>Roll Return</h4>
 <p>
-  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains<code>.
+  The universe is created with 22 commodity futures. For multiple commodities, we save the future chain of each commodity in dictionary <code>self.chains</code>.
   First, we compute the roll-returns at the start of each month and sort the roll-return list. 1/3 breakpoints are used to split the cross-section of futures contracts into 3 portfolios, labeled <code>Low</code>, <code>Med</code> and <code>High</code>. For the formula of the roll-return, please see <a href="https://www.quantconnect.com/tutorials/strategy-library/term-structure-effect-in-commodities">Term Structure Effect in Commodities</a>.
   Contracts fall into <code>Med</code> level are eliminated from the universe. Next we'll calculate the mean return of contracts in <code>High</code> and <code>Low</code>.
 </p>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..654173f
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Growth stocks refer to high-quality, successful companies whose earnings are expected to continue growing at an above-average rate relative to the market.
+  Growth stocks generally have high price-to-earnings (P/E) ratios and high price-to-book(P/B) ratios. At times, growth stocks are considered expensive and overvalued. The value stocks refer to stocks which have high dividend payout ratios or low financial ratios such as P/E ratios and P/B ratios. The value stocks are often considered undervalued by the market. This algorithm will create the long-short positions based on the relation between investor sentiment and the performance of value stocks over growth stocks.
+</p>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..a29b638
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 Method.html	
@@ -0,0 +1,129 @@
+<h4>The measure of investment sentiment</h4>
+<p>
+  To measure investors' sentiment, we use gauges: the CBOE equity put-call ratio and the market volatility (VIX) index.
+  The VIX index is constructed using the implied volatilities on S&P 500 index options and shows the market's expectation of 30-day volatility.
+  The CBOE equity put-call ratio is calculated by dividing the trading volume of CBOE equity put options by the trading volume of CBOE equity call options.
+  A rising put-call ratio means equity traders are buying more puts than calls and indicates a bearish sentiment in the market while a falling put-call ratio
+  is considered as the bullish market sentiment.
+</p>
+<p>
+  We import the daily VIX data from Quandl. CBOE provides the volume put-call ratio data from 11-01-2006 to present so we import the custom data from <a href="http://www.cboe.com/data/historical-options-data/volume-put-call-ratios">CBOE</a>.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SentimentAndStyleRotationAlgorithm(QCAlgorithm):
+  def Initialize(self):
+      self.SetStartDate(2010, 1, 1)
+      self.SetEndDate(2018, 7, 1)
+      self.SetCash(100000)
+      self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily)
+      self.AddData(CBOE, "PutCallRatio", Resolution.Daily)
+
+class QuandlVix(PythonQuandl):
+    '''Quandl VIX data class'''
+    def __init__(self):
+        self.ValueColumnName = "VIX Close"
+
+class CBOE(PythonData):
+    '''Cboe Equity Volume Put/Call Ratios (11-01-2006 to present) Custom Data Class'''
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/equitypc.csv", SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        if not (line.strip() and line[0].isdigit()): return None
+        index = CBOE()
+        index.Symbol = config.Symbol
+
+        try:
+            # Example File Format:
+            # DATE       CALL      PUT       TOTAL      P/C Ratio
+            # 11/1/06    976510    623929    1600439    0.64
+            data = line.split(',')
+            index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d")
+            index.Value = Decimal(data[4])
+
+        except ValueError:
+                return None
+
+        return index
+</pre>
+</div>
+<h4>The Measure of the Growth and Value Stocks</h4>
+<p>
+  All stocks on NYSE and NASDAQ are used as the investment universe. In <code>CoarseSelectionFunction</code>, we eliminate ETFs which don't have fundamental data.
+  In <code>FineSelectionFunction</code>, stocks are sorted into deciles based on a size measure - market capitalization.   We use only the first three size deciles
+  for the algorithm to avoid potential problems with small illiquid stocks.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.month_start:
+        self.selection = True
+
+        fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
+                                and i.EarningReports.BasicEPS.TwelveMonths>0
+                                and i.ValuationRatios.PERatio>0
+                                and i.ValuationRatios.PBRatio>0]
+        # Calculate the market cap and add the "MakretCap" property to fine universe object
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        # sort fine object by MarketCap
+        sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)]
+        decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)]
+        decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
+</pre>
+</div>
+<p>
+  In the next step, we subdivide each size decile into five portfolios based on the P/B ratio. For each of the first three size deciles, the value portfolio consists of all firms included in the quintile with the lowest P/B ratio, and the growth portfolio consists stocks with the highest P/B ratio.
+</p>
+<div class="section-example-container">
+<pre class="python">
+sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio)
+# The value portfolio consists of all firms included in the quintile with the lowest P/B ratio
+PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)]
+PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)]
+PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)]
+self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3]
+# The growth portfolio consists of all firms included in the quintile with the highest P/B ratio
+PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):]
+PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):]
+PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):]
+self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3]
+</pre>
+</div>
+
+<h4>The Relation Between Investor Sentiment and Equity Style</h4>
+<p>
+  According to the research paper from Lee and Song, <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>,
+  value stocks tend to outperform growth stocks when the CBOE equity put-call ratio is relatively low, and the VIX is relatively high. The value portfolio significantly underperforms the growth portfolio when the put-call ratio and VIX are both high.
+  To convert the daily put-call ratio and VIX data into monthly value, we take an average over the recent one month and the previous six months.
+</p>
+<p>
+  If the recent monthly average CBOE put-call ratio is lower than its six-month average and the one-month average of VIX  is higher than its six-month average, the algorithm goes long on an equally weighted portfolio consisting of value stocks (the lowest P/B quintile) from the top three size deciles. If recent monthly average CBOE put-call ratio and the VIX index are both higher than their six-month average, the algorithm goes short the value stocks.
+  Otherwise, the algorithm goes long both value stocks and growth stocks. The position holding period is three months, and the portfolio is rebalanced every three months.
+</P>
+<div class="section-example-container">
+<pre class="python">
+stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+for i in stocks_invested:
+    if i not in self.value_portfolio+self.growth_portfolio:
+        self.Liquidate(i)
+
+if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value:
+    if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value:
+        long_weight = 1/len(self.value_portfolio)
+        for long in self.value_portfolio:
+            self.SetHoldings(long, long_weight)
+    elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value:
+        short_weight = 1/len(self.value_portfolio)
+        for short in self.value_portfolio:
+            self.SetHoldings(short, -short_weight)
+else:
+    long_weight = 1/len(self.value_portfolio+self.growth_portfolio)
+    for long in self.value_portfolio+self.growth_portfolio:
+        self.SetHoldings(long, long_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..9af8464
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e8a75429c6a43e969e4a9243290e05f4.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..d965226
--- /dev/null
+++ b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/53" rel="nofollow">Quantpedia - Sentiment and Style Rotation Effect in Stocks</a>
+</li>
+</ul>
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/37 Pairs Trading with Country ETFs/01 Introduction.html
rename to 04 Strategy Library/40 Pairs Trading with Country ETFs/01 Introduction.html
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/02 Method.html
similarity index 100%
rename from 04 Strategy Library/37 Pairs Trading with Country ETFs/02 Method.html
rename to 04 Strategy Library/40 Pairs Trading with Country ETFs/02 Method.html
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/37 Pairs Trading with Country ETFs/03 Algorithm.html
rename to 04 Strategy Library/40 Pairs Trading with Country ETFs/03 Algorithm.html
diff --git a/04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html b/04 Strategy Library/40 Pairs Trading with Country ETFs/04 Source.html
similarity index 100%
rename from 04 Strategy Library/37 Pairs Trading with Country ETFs/04 Source.html
rename to 04 Strategy Library/40 Pairs Trading with Country ETFs/04 Source.html
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 43fce30..64b1d35 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://beta.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 3817fc8..0e96c86 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -22,5 +22,9 @@
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",
+<<<<<<< .merge_file_JtOw6A
      55: "2d6bcad96d965a04cd7eac846d54f129",
+=======
+     53: "e8a75429c6a43e969e4a9243290e05f4",
+>>>>>>> .merge_file_1djOVF
 }

From 8b473a1def816a9b09fc7304123f0a8dbf0a546b Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Tue, 7 Aug 2018 18:27:46 -0400
Subject: [PATCH 260/753] edited the introduction

---
 .../39 Asset Growth Effect/01 Introduction.html              | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html b/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html
index 827a150..f9177b2 100644
--- a/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html	
+++ b/04 Strategy Library/39 Asset Growth Effect/01 Introduction.html	
@@ -1,3 +1,6 @@
 <p>
- The asset growth effect is based upon the reasoning that high asset growth stocks underperform stocks with low asset growth. Some papers suggest that the return premium achieved by low asset growth stocks is consistent with the compensation for risk. Two arguments are that growth options are inherently more risky than assets in place and systematic market mispricing of growing businesses due to the extrapolation of past gains to growth for high asset growth companies. This strategy will take a long position in low asset growth companies, and a short position in high asset growth accompanies. It is important to note that this strategy requires fundamental data from the current and past year to perform its analysis. As a result, this strategy would need a year in live trading to determine signals.
+ The asset growth effect is based upon the reasoning that high asset growth stocks underperform stocks with low asset growth. Some papers suggest that the return premium achieved by low asset growth stocks is consistent with the compensation for risk. There are two popular arguments to suggest this return premium. One arguments is that as firms grow, the asset mix of the firm becomes less risky as assets-in-place displace the value of assets the firm's expected to invest in the future. The second argument is that there is a systematic market mispricing of growing businesses due to the extrapolation of past gains to growth for high asset growth companies. 
+ </p>
+ <p>
+ This strategy will take a long position in low asset growth companies, and a short position in high asset growth accompanies. It is important to note that this strategy requires fundamental data from the current and past year to perform its analysis. As a result, this strategy would need a year in live trading to determine signals.
 </p>
\ No newline at end of file

From 87dcb659a46506a9cf148b514b27d9118de1009b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 7 Aug 2018 18:32:07 -0400
Subject: [PATCH 261/753] Add Momentum and Style Rotation Algorithm

---
 .../01 Strategy Library.php                   |  8 ++++
 .../01 Introduction.html                      |  5 +++
 .../02 Method.html                            | 44 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++
 .../04 Source.html                            |  5 +++
 quantpedia.json                               |  1 +
 6 files changed, 69 insertions(+)
 create mode 100644 04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html
 create mode 100644 04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 93fb414..8f9023d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -278,6 +278,14 @@
         ],
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.'
      ],
+     [
+         'name' => 'Momentum and Style Rotation Effect',
+         'link' => 'strategy-library/momentum-and-style-rotation-effect',
+         'sources' => [
+             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
+         ],
+         'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
+      ],
 
 ];
 
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html b/04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html
new file mode 100644
index 0000000..3d2db27
--- /dev/null
+++ b/04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  Based on the market cap, we can divide the stocks into large-cap, mid-cap and small-cap stocks. In each of those categories, we can subdivide them into value stocks and growth stocks. The growth stocks typically grow revenues faster than the market average and have relatively high P/E ratios and P/B ratios. The value stocks have relatively low P/E ratios and P/B ratios. There are six styles in total.
+  We've demonstrated various momentum strategies to generate excess returns at the firm, industry, and country level.
+  In this algorithm, we'll explore the momentum effect at the style index ETF level.
+</p>
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html b/04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html
new file mode 100644
index 0000000..2146e81
--- /dev/null
+++ b/04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html	
@@ -0,0 +1,44 @@
+<p>
+  We choose six index ETFs to represent different equity styles(small-cap value, mid-cap value, large-cap value,
+  small-cap growth, mid-cap growth, large-cap growth). After adding the assets, we save the momentum indicator in
+  the dictionary <code>self.mom</code> for each style. The formation period of 12-month is used to gauge the value of momentum.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2001, 1, 1)
+    self.SetEndDate(2018, 8, 1)
+    self.SetCash(100000)
+    self.tickers = [
+                    "IJJ", # iShares S&P MidCap 400 Value Index ETF
+                    "IJS", # iShares S&P SmallCap 600 Value ETF
+                    "IVE", # iShares S&P 500 Value Index ETF
+                    "IVW", # iShares S&P 500 Growth ETF
+                    "IJK", # iShares S&P Mid-Cap 400 Growth ETF
+                    "IJT", # iShares S&P Small-Cap 600 Growth ETF
+                    ]
+    self.symbols = []
+    for ticker in self.tickers:
+        self.symbols.append(self.AddEquity(ticker, Resolution.Daily).Symbol)
+    self.SetWarmUp(timedelta(days=12*20))
+    # save all momentum indicator in the dictionary
+    self.mom = {i:self.MOM(i, 12*20, Resolution.Daily) for i in self.symbols}
+</pre>
+</div>
+<p>
+  Six ETFs are ranked based on their prior 12-month performance in the formation period. The algorithm
+  goes long the top performing ETF and short the ETF at the bottom and holds the position for one month.
+  The portfolio is rebalanced at the start of next month.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Rebalance(self):
+    sorted_mom = sorted(self.mom, key = lambda x: self.mom[x].Current.Value)
+    invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+    for i in invested:
+        if i not in [sorted_mom[0], sorted_mom[1]]:
+            self.Liquidate(i)
+    self.SetHoldings(sorted_mom[0], -0.5)
+    self.SetHoldings(sorted_mom[-1], 0.5)
+</pre>
+</div>
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html b/04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html
new file mode 100644
index 0000000..d0bf935
--- /dev/null
+++ b/04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d0b1d400ad21477d83ae2b4f85616318.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html b/04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html
new file mode 100644
index 0000000..d09e0bf
--- /dev/null
+++ b/04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/91" rel="nofollow">Quantpedia - Momentum and Style Rotation Effect</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index b278606..8e285bf 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -26,4 +26,5 @@
      55: "2d6bcad96d965a04cd7eac846d54f129",
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
+     91: "d0b1d400ad21477d83ae2b4f85616318",
 }

From 87d0fbe29db4977c0f91c814c548c64ac169cd12 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 7 Aug 2018 18:43:10 -0400
Subject: [PATCH 262/753] Rename the folder

---
 .../01 Introduction.html                                          | 0
 .../02 Method.html                                                | 0
 .../03 Algorithm.html                                             | 0
 .../04 Source.html                                                | 0
 4 files changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{41 Momentum and Style Rotation Effect => 91 Momentum and Style Rotation Effect}/01 Introduction.html (100%)
 rename 04 Strategy Library/{41 Momentum and Style Rotation Effect => 91 Momentum and Style Rotation Effect}/02 Method.html (100%)
 rename 04 Strategy Library/{41 Momentum and Style Rotation Effect => 91 Momentum and Style Rotation Effect}/03 Algorithm.html (100%)
 rename 04 Strategy Library/{41 Momentum and Style Rotation Effect => 91 Momentum and Style Rotation Effect}/04 Source.html (100%)

diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/41 Momentum and Style Rotation Effect/01 Introduction.html
rename to 04 Strategy Library/91 Momentum and Style Rotation Effect/01 Introduction.html
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html
similarity index 100%
rename from 04 Strategy Library/41 Momentum and Style Rotation Effect/02 Method.html
rename to 04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/41 Momentum and Style Rotation Effect/03 Algorithm.html
rename to 04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html
diff --git a/04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/04 Source.html
similarity index 100%
rename from 04 Strategy Library/41 Momentum and Style Rotation Effect/04 Source.html
rename to 04 Strategy Library/91 Momentum and Style Rotation Effect/04 Source.html

From a491073c6dadf9d195e9558cb04b7cbffcaa7dc3 Mon Sep 17 00:00:00 2001
From: Gustavo <gaviles@users.noreply.github.com>
Date: Tue, 7 Aug 2018 18:56:17 -0400
Subject: [PATCH 263/753] Add missing opening bracket

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 1 +
 1 file changed, 1 insertion(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 84af220..68e9e9f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -270,6 +270,7 @@
         ],
         'description' => 'Creates long-short positions of stocks based on the annual change of their total assets.'
     ],
+    [
         'name' => 'Momentum and State of Market Filters',
         'link' => 'strategy-library/momentum-and-state-of-market-filters',
         'sources' => [

From 9ea382d7b705fdf9780e27566586b8892c7c4a63 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 8 Aug 2018 18:04:39 -0400
Subject: [PATCH 264/753] Add option expiration week algorithm

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      | 11 ++++
 .../02 Method.html                            | 50 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 81 insertions(+)
 create mode 100644 04 Strategy Library/102 Option Expiration Week Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/102 Option Expiration Week Effect/02 Method.html
 create mode 100644 04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/102 Option Expiration Week Effect/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 68e9e9f..b046c4d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -294,6 +294,14 @@
          ],
          'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
       ],
+      [
+          'name' => 'Option Expiration Week Effect',
+          'link' => 'strategy-library/option-expiration -week-effect',
+          'sources' => [
+              'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
+          ],
+          'description' => 'Goes long S&P 100 index ETF during option expiration week and stays in cash during other days.'
+       ],
 ];
 
 ?>
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/01 Introduction.html b/04 Strategy Library/102 Option Expiration Week Effect/01 Introduction.html
new file mode 100644
index 0000000..2a0c77c
--- /dev/null
+++ b/04 Strategy Library/102 Option Expiration Week Effect/01 Introduction.html	
@@ -0,0 +1,11 @@
+<p>
+  The expiration date for an options contract is the time when the contract is no longer valid.
+  The expiration date for listed stock options in the United States is usually the third Friday of the contract month, which is the month when the contract expires.
+  When that Friday falls on a holiday, the expiration date is on Thursday immediately before the third Friday.
+  The options expiration week is a week before options expiration.
+</P>
+<p>
+  During option-expiration weeks, a reduction occurs in option open interest as the near-term options approach their expiration and then expire.
+  According to a research from Chris and Licheng <a href="https://www.sciencedirect.com/science/article/pii/S0378426613003051">Returns and option activity over the option-expiration week for S&P 100 stocks</a>,
+  Large-cap stocks with actively traded options tend to have higher average weekly returns during option-expiration weeks. In this algorithm, we'll use the real market data to explore the option expiration week effect.
+</p>
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html b/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html
new file mode 100644
index 0000000..06adf98
--- /dev/null
+++ b/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html	
@@ -0,0 +1,50 @@
+<p>
+  S&amp;P 100 index includes 102 leading U.S. stocks with exchange-listed options. The constituents represent almost 51% of the market capitalization of the U.S. equity market.
+  Here we trade the S&amp;P 100 index ETF as a portfolio of U.S. large-cap stocks.
+</p>
+<p>
+  In the next step, we add the options to get their expiration dates. The range of expiration dates should bigger enough to include the
+  the contract which expires in this month.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2007, 1, 1)
+    self.SetEndDate(2018, 8, 1)
+    self.SetCash(10000)
+    self.AddEquity("OEF", Resolution.Minute)
+    option = self.AddOption("OEF")
+    option.SetFilter(-3, 3, timedelta(0), timedelta(days = 60))
+</pre>
+</div>
+<p>
+  To get a list of expiration dates from the contracts in the current option chain, we can use <code>TradingCalendar</code> object.
+  It allows us to filter the calendar days by the type <code>TradingDayType.OptionExpiration</code> and the start as well as the end date.
+  The expiry at the top of the list is the most recent expiration date.
+</p>
+<p>
+  The long position is opened at the start of the expiration week. Therefore, we use the scheduled event to fire the rebalance method every Monday.
+  If the current Monday is in the expiration week, we long the S&amp;P 100 index ETF.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Rebalance(self):
+    calendar = self.TradingCalendar.GetDaysByType(TradingDayType.OptionExpiration, self.Time, self.EndDate)
+    expiries = [i.Date for i in calendar]
+    if len(expiries) == 0: return
+    self.lastest_expiry = expiries[0]
+
+    if (self.lastest_expiry - self.Time).days <= 5:
+        self.SetHoldings("OEF", 1)
+</pre>
+</div>
+<p>
+   The algorithm stays in cash during days out of the options expiration week.
+ </P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, slice):
+    if self.Time.date() == self.lastest_expiry.date():
+        self.Liquidate()
+</pre>
+</div>
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html
new file mode 100644
index 0000000..6804f0c
--- /dev/null
+++ b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_eb454693f736028041f116bd905b8520.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/04 Source.html b/04 Strategy Library/102 Option Expiration Week Effect/04 Source.html
new file mode 100644
index 0000000..3a2585c
--- /dev/null
+++ b/04 Strategy Library/102 Option Expiration Week Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/102" rel="nofollow">Quantpedia - Option Expiration Week Effect</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index c399f15..31e4d5f 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,4 +28,5 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
+     102: "eb454693f736028041f116bd905b8520",
 }

From 7b22fded023bed8034a05fee194e038e9950c61f Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 9 Aug 2018 09:34:54 -0400
Subject: [PATCH 265/753] added algorithm 152

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  3 +
 .../02 Method.html                            | 56 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 79 insertions(+)
 create mode 100644 04 Strategy Library/152 Momentum Effect in REITs/01 Introduction.html
 create mode 100644 04 Strategy Library/152 Momentum Effect in REITs/02 Method.html
 create mode 100644 04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html
 create mode 100644 04 Strategy Library/152 Momentum Effect in REITs/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 68e9e9f..5cbeda0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -294,6 +294,14 @@
          ],
          'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
       ],
+      [
+         'name' => 'Momentum Effect in REITs',
+         'link' => 'strategy-library/momentum-effect-in-reits',
+         'sources' => [
+             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
+         ],
+         'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.'
+      ],
 ];
 
 ?>
diff --git a/04 Strategy Library/152 Momentum Effect in REITs/01 Introduction.html b/04 Strategy Library/152 Momentum Effect in REITs/01 Introduction.html
new file mode 100644
index 0000000..4cf8481
--- /dev/null
+++ b/04 Strategy Library/152 Momentum Effect in REITs/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  The momentum effect is a classic anomaly that says what was strongly going up in the past will probably continue to go up in the near future.  The calculation performed uses the rate of change in price movements. We see this effect in REITs through studies where REITs with the highest annual past performance beat lower performing trusts. This strategy will take a long position in companies with the strongest momentum and rebalance quarterly.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html b/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html
new file mode 100644
index 0000000..c72b473
--- /dev/null
+++ b/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html	
@@ -0,0 +1,56 @@
+<p>
+  The first step is coarse and fine universe selection. During coarse selection, we create an investment universe with securities in the USA and during the fine selection we take all available REITs by using the Morningstar field <code>IsREIT</code>. 
+</p>
+<p>
+  In the fine selection we also calculate each REIT's past 11-month return one-month lagged and rank them. This is used to determine the top tercile of portfolio.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+        if self.quarterly_rebalance:
+            self.filtered_coarse = [x.Symbol for x in coarse if (x.Market == "usa")]
+            return self.filtered_coarse
+        else: 
+            return []      
+    
+def FineSelectionFunction(self, fine):
+    if self.quarterly_rebalance:
+        fine = [x for x in fine if (x.CompanyReference.IsREIT == 1)] 
+        
+        start = self.Time-timedelta(days = 365)
+        end = self.Time-timedelta(days = 30)
+        for x in fine:
+            hist = self.History([x.Symbol],start,end,Resolution.Daily)
+            if not hist.empty:
+                start_price = hist["close"].iloc[0]
+                end_price = hist["close"].iloc[-1]
+                x.momentum = (start_price-end_price)/start_price
+        
+        fine = [x for x in fine if hasattr(x, 'momentum')]
+        sorted_filter = sorted(fine, key=lambda x: x.momentum)
+        self.filtered_fine = [i.Symbol for i in sorted_filter]
+        return self.filtered_fine
+    else:
+        return []
+</pre>
+</div>
+<p>
+   In <code>OnData()</code>, we buy the stocks in the best performing tercile for three months and the portfolio is rebalanced every three months.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+        if not self.quarterly_rebalance: return 
+        if self.filtered_fine:
+            portfolio_size = int(len(self.filtered_fine)/3)
+            long_stocks = self.filtered_fine[-portfolio_size:]
+            stocks_invested = [x.Key for x in self.Portfolio]
+            for i in stocks_invested:
+                if i not in long_stocks:
+                    self.Liquidate(i) 
+                elif i in long_stocks:
+                    self.SetHoldings(i, 1/(portfolio_size))
+            self.quarterly_rebalance = False
+            self.filtered_fine = False
+</pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html b/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html
new file mode 100644
index 0000000..55aa78e
--- /dev/null
+++ b/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_292a5ec88c2706279536fb6a081af80b.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/152 Momentum Effect in REITs/04 Source.html b/04 Strategy Library/152 Momentum Effect in REITs/04 Source.html
new file mode 100644
index 0000000..50d2daa
--- /dev/null
+++ b/04 Strategy Library/152 Momentum Effect in REITs/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/152"  rel="nofollow">Quantpedia - Momentum Effect in REITs</a>
+</li>
+</ul>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index c399f15..dc80bdb 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,4 +28,5 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
+     152: "292a5ec88c2706279536fb6a081af80b",
 }

From 5b8f4da97c5a9f7d0ee1a87335c44ef0770333eb Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 9 Aug 2018 09:35:38 -0400
Subject: [PATCH 266/753] Update algorithm

---
 .../00 Strategy Library/01 Strategy Library.php               | 4 ++--
 .../102 Option Expiration Week Effect/02 Method.html          | 1 +
 .../102 Option Expiration Week Effect/03 Algorithm.html       | 2 +-
 quantpedia.json                                               | 2 +-
 4 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index b046c4d..65e7b72 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -296,11 +296,11 @@
       ],
       [
           'name' => 'Option Expiration Week Effect',
-          'link' => 'strategy-library/option-expiration -week-effect',
+          'link' => 'strategy-library/option-expiration-week-effect',
           'sources' => [
               'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
           ],
-          'description' => 'Goes long S&P 100 index ETF during option expiration week and stays in cash during other days.'
+          'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
        ],
 ];
 
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html b/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html
index 06adf98..e89806b 100644
--- a/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html	
+++ b/04 Strategy Library/102 Option Expiration Week Effect/02 Method.html	
@@ -15,6 +15,7 @@
     self.AddEquity("OEF", Resolution.Minute)
     option = self.AddOption("OEF")
     option.SetFilter(-3, 3, timedelta(0), timedelta(days = 60))
+    self.SetBenchmark("OEF")
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html
index 6804f0c..516d50a 100644
--- a/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
+++ b/04 Strategy Library/102 Option Expiration Week Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_eb454693f736028041f116bd905b8520.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_cd2d187e44a00c7b19f64aee8b0895d9.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 31e4d5f..fe8be92 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,5 +28,5 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
-     102: "eb454693f736028041f116bd905b8520",
+     102: "cd2d187e44a00c7b19f64aee8b0895d9",
 }

From 3958dd5d4bf30392814d7f010afbad96a7015570 Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 9 Aug 2018 11:33:43 -0400
Subject: [PATCH 267/753] adding edits to algorithm, method and quantpedia.json

---
 .../152 Momentum Effect in REITs/02 Method.html        | 10 ++++++----
 .../152 Momentum Effect in REITs/03 Algorithm.html     |  2 +-
 quantpedia.json                                        |  4 ++++
 3 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html b/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html
index c72b473..4473eb4 100644
--- a/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html	
+++ b/04 Strategy Library/152 Momentum Effect in REITs/02 Method.html	
@@ -1,14 +1,16 @@
 <p>
-  The first step is coarse and fine universe selection. During coarse selection, we create an investment universe with securities in the USA and during the fine selection we take all available REITs by using the Morningstar field <code>IsREIT</code>. 
+  The first step is coarse and fine universe selection. During coarse selection, we create an investment universe of stocks that have prices greater than $1, contain fundamental data and do not have a very low trading volume for liquidity purposes. During the fine selection we take all available REITs by using the Morningstar field <code>IsREIT</code>. 
 </p>
 <p>
   In the fine selection we also calculate each REIT's past 11-month return one-month lagged and rank them. This is used to determine the top tercile of portfolio.
 </p>
 <div class="section-example-container">
 <pre class="python">
-def CoarseSelectionFunction(self, coarse):
+ def CoarseSelectionFunction(self, coarse):
         if self.quarterly_rebalance:
-            self.filtered_coarse = [x.Symbol for x in coarse if (x.Market == "usa")]
+            self.filtered_coarse = [x.Symbol for x in coarse if (float(x.Price) > 1)
+                                                            and (x.HasFundamentalData)
+                                                            and float(x.Volume) > 10000]
             return self.filtered_coarse
         else: 
             return []      
@@ -24,7 +26,7 @@
             if not hist.empty:
                 start_price = hist["close"].iloc[0]
                 end_price = hist["close"].iloc[-1]
-                x.momentum = (start_price-end_price)/start_price
+                x.momentum = (end_price-start_price)/start_price
         
         fine = [x for x in fine if hasattr(x, 'momentum')]
         sorted_filter = sorted(fine, key=lambda x: x.momentum)
diff --git a/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html b/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html
index 55aa78e..ad991fb 100644
--- a/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html	
+++ b/04 Strategy Library/152 Momentum Effect in REITs/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_292a5ec88c2706279536fb6a081af80b.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b35034509d95c173fed1c0adcecc5be0.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index aa29926..b0aa114 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,6 +28,10 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
+<<<<<<< Updated upstream
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
 	 152: "292a5ec88c2706279536fb6a081af80b",
+=======
+     152: "b35034509d95c173fed1c0adcecc5be0",
+>>>>>>> Stashed changes
 }

From a3aa275a32b552b50e512f48cd7c7a7926b88c1f Mon Sep 17 00:00:00 2001
From: gurusawhney <gs2605@nyu.edu>
Date: Thu, 9 Aug 2018 11:37:06 -0400
Subject: [PATCH 268/753] fixed duplicate 152 in json

---
 quantpedia.json | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/quantpedia.json b/quantpedia.json
index b0aa114..bbda0aa 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,10 +28,6 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
-<<<<<<< Updated upstream
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
-	 152: "292a5ec88c2706279536fb6a081af80b",
-=======
      152: "b35034509d95c173fed1c0adcecc5be0",
->>>>>>> Stashed changes
 }

From e79d47d447f433ecee119c9531ce26ba1979567e Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 9 Aug 2018 18:44:28 -0400
Subject: [PATCH 269/753] Add WTI BRENT Spread Trade Algortihm

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   5 +
 .../02 Method.html                            | 121 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 146 insertions(+)
 create mode 100644 04 Strategy Library/100 Trading with WTI BRENT Spread/01 Introduction.html
 create mode 100644 04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html
 create mode 100644 04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html
 create mode 100644 04 Strategy Library/100 Trading with WTI BRENT Spread/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 68e9e9f..3570902 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -294,6 +294,14 @@
          ],
          'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
       ],
+      [
+          'name' => 'Trading with WTI BRENT Spread',
+          'link' => 'strategy-library/trading-with-wti-brent-spread',
+          'sources' => [
+              'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
+          ],
+          'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.'
+       ],
 ];
 
 ?>
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/01 Introduction.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 Introduction.html
new file mode 100644
index 0000000..d18875d
--- /dev/null
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The WTI-Brent spread is the difference between the prices of two types of crude oil: West Texas Intermediate (WTI) on the long side and Brent Crude (Brent) on the short side.
+  For years, the price difference between the two has only been a few dollars on average. As both oils are very similar, their spread shows signs of strong predictability and usually oscillates around some average value.
+  Therefore, it is possible to use deviations from the fair spread value to bet on convergence back to fair value. Here we present a trading strategy based on the price deviations of the spread.
+</P>
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html
new file mode 100644
index 0000000..e6f9598
--- /dev/null
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html	
@@ -0,0 +1,121 @@
+<p>
+  We download the WTI and Brent crude oil price from <a href="www.macrotrends.net" rel="nofollow">Macrotrends</a> and import the csv file with the custom data downloader.
+  The unit is dollars per barrel.
+</p>
+<div class="section-example-container">
+<pre class="python">
+  class WTI(PythonData):
+      "Class to import WTI Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/jpie3z6j0stp97d/wti-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = WTI()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+
+  class BRENT(PythonData):
+      "Class to import BRENT Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/w380c4n7xjmdqxl/brent-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = BRENT()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+</pre>
+</div>
+<p>
+  The spread is defined as the difference between WTI price and Brent price. Next, we need to calculate the moving average of the spread series.
+  using the the indicator <code>SimpleMovingAverage</code>. As the indicator uses the price difference instead of the price series,
+  we need to manually initialize the indicator with the history request.
+</p>
+<div class="section-example-container">
+<pre class="python">
+self.SpreadSMA = SimpleMovingAverage(20)
+hist = self.History(["WTI", "BRENT"], 400, Resolution.Daily)["value"].unstack(level=0).dropna()
+hist_20days = hist[-20:]
+spread = (hist_20days["WTI"] - hist_20days["BRENT"]).dropna()
+for index, value in spread.items():
+    self.SpreadSMA.Update(index, value)
+</pre>
+</div>
+<p>
+  To get the fair value of the spread, we perform the linear regression between WTI and Brent price over the last one year history price.
+</P>
+ \[P_{Brent}=\beta \cdot P_{WTI}+\alpha\]
+<p>
+   Then the fair value of the spread is
+</P>
+\[Fair \ Spread =(1-\beta)\cdot CurrentPrice_{WTI}-\alpha\]
+</P>
+<div class="section-example-container">
+<pre class="python">
+hist_one_year = hist[-252:]
+X = hist_one_year["WTI"][:, np.newaxis]
+y = hist_one_year["BRENT"]
+self.regr = linear_model.LinearRegression()
+self.regr.fit(X, y)
+</pre>
+</div>
+<p>
+  The fair value is calculated every day. If the current spread value is above SMA 20 then we enter a short position in the spread on close (betting that the spread will decrease to fair value represented by SMA 20).
+  The trade is closed at the close of the trading day when the spread crosses below fair value. If the current spread value is below SMA 20 then we enter a long position betting that the spread will increase and the trade is closed at the close of the trading day when the spread crosses above fair value.
+</P>
+
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if not (data.ContainsKey("WTI") and data.ContainsKey("BRENT")): return
+    self.Plot("Spread Plot", "Spread", data["WTI"].Price - data["BRENT"].Price)
+
+    self.SpreadSMA.Update(self.Time, data["WTI"].Price - data["BRENT"].Price)
+    if not self.SpreadSMA.IsReady: return
+    spread = self.Securities["WTI"].Price - self.Securities["BRENT"].Price
+    fair_value =self.Securities["WTI"].Price - Decimal(self.regr.predict(self.Securities["WTI"].Price)[0])
+
+    if spread > self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong):
+        self.SetHoldings("WTI", -0.5)
+        self.SetHoldings("BRENT", 0.5)
+        self.Plot("Spread Plot", "Long Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    elif spread < self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort):
+        self.SetHoldings("WTI", 0.5)
+        self.SetHoldings("BRENT", -0.5)
+        self.Plot("Spread Plot", "Short Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    if self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong and spread < fair_value:
+        self.Liquidate()
+
+    if self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort and spread > fair_value:
+        self.Liquidate()
+</pre>
+</div>
+<p>
+  To demonstrate the trend of the spread series, we add the spread plot and mark the spread long/short point on the spread curve.
+</P>
+<div class="section-example-container">
+<pre class="python">
+spreadPlot = Chart("Spread Plot")
+spreadPlot.AddSeries(Series("Spread", SeriesType.Line, 0))
+spreadPlot.AddSeries(Series("Long Spread Trade", SeriesType.Scatter, 0))
+spreadPlot.AddSeries(Series("Short Spread Trade", SeriesType.Scatter, 0))
+self.AddChart(spreadPlot)
+</pre>
+</div>
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html
new file mode 100644
index 0000000..b909114
--- /dev/null
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2691b0f4e604049a81275708313610c6.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/04 Source.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 Source.html
new file mode 100644
index 0000000..5cbb24e
--- /dev/null
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/100" rel="nofollow">Quantpedia - Trading WTI/BRENT Spread</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index c399f15..41b68d1 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -28,4 +28,5 @@
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
+     100: "2691b0f4e604049a81275708313610c6",
 }

From 3fc36653400d14bddbb3f67188dcde7b0a4b0469 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 13 Aug 2018 09:48:40 -0400
Subject: [PATCH 270/753] Add January Effect in Stocks Algorithm

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  5 ++
 .../02 Method.html                            | 69 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 94 insertions(+)
 create mode 100644 04 Strategy Library/114 January Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/114 January Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/114 January Effect in Stocks/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e44ae13..e40fbbb 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -310,6 +310,14 @@
           ],
           'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
        ],
+       [
+           'name' => 'January Effect in Stocks',
+           'link' => 'strategy-library/january-effect-in-stocks',
+           'sources' => [
+               'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/114'
+           ],
+           'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.'
+        ],
 ];
 
 ?>
diff --git a/04 Strategy Library/114 January Effect in Stocks/01 Introduction.html b/04 Strategy Library/114 January Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..cb4b453
--- /dev/null
+++ b/04 Strategy Library/114 January Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The January effect is a calendar anomaly saying that small-cap stocks returns in January are especially strong.
+  The most common explanation of this phenomenon is that individual investors, who are income tax-sensitive and who disproportionately hold small stocks, sell stocks for tax reasons at year end and reinvest during the first month of the year.
+  In this algorithm, we will explore the January effect in the stock market.
+</P>
diff --git a/04 Strategy Library/114 January Effect in Stocks/02 Method.html b/04 Strategy Library/114 January Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..ae1f295
--- /dev/null
+++ b/04 Strategy Library/114 January Effect in Stocks/02 Method.html	
@@ -0,0 +1,69 @@
+<p>
+  The investment universe consists of US-listed companies. A minimum stock price filter is used to avoid penny stocks. To avoid stocks that are not liquid enough, we select 1000 stocks with the highest dollar volume.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    if self.monthly_rebalance:
+        self.coarse = True
+        coarse = [x for x in coarse if (x.AdjustedPrice > 10)]
+        topDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)[:1000]
+        return [i.Symbol for i in topDollarVolume]
+    else:
+        return []
+</pre>
+</div>
+<p>
+  In <code>FineSelectionFunction(self, fine)</code>, we calculate the market cap value with shares outstanding, earning per shares and the PE ratio.
+  Then stocks are sorted by their market capitalization. The top 10 stocks are selected as the large-cap group and the bottom 10 stocks belong to the small-cap group.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.monthly_rebalance:
+        fine =[i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
+                              and i.EarningReports.BasicEPS.TwelveMonths>0
+                              and i.ValuationRatios.PERatio>0]
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        sorted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        symbols = [i.Symbol for i in sorted_market_cap]
+        self.top_market_cap = symbols[:10]
+        self.bottom_market_cap = symbols[-10:]
+        return self.top_market_cap + self.bottom_market_cap
+    else:
+        return []
+</pre>
+</div>
+<p>
+  The algorithm invests into small-cap stocks at the beginning of each January and stays invested in large-cap stocks for rest of the year.
+  The portfolio is rebalanced every month.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  def OnData(self, data):
+      if not (self.monthly_rebalance and self.coarse): return
+      self.coarse = False
+      self.monthly_rebalance = False
+      stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+      # invest in small cap stocks at the beginning of each January
+      if self.Time.month == 1:
+          # liquidate stocks not in the small-cap group
+          for i in stocks_invested:
+              if i not in self.bottom_market_cap:
+                  self.Liquidate(i)
+          weight = 1/len(self.bottom_market_cap)
+          for i in self.bottom_market_cap:
+              self.SetHoldings(i, weight)
+      # invest in large cap stocks for rest of the year
+      else:
+          # liquidate stocks not in the large-cap group
+          for i in stocks_invested:
+              if i not in self.top_market_cap:
+                  self.Liquidate(i)
+
+          weight = 1/len(self.top_market_cap)
+          for i in self.top_market_cap:
+              self.SetHoldings(i, weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..b909114
--- /dev/null
+++ b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2691b0f4e604049a81275708313610c6.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/114 January Effect in Stocks/04 Source.html b/04 Strategy Library/114 January Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..7f85cc6
--- /dev/null
+++ b/04 Strategy Library/114 January Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/114" rel="nofollow">Quantpedia - January Effect in Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index bbda0aa..1a60aba 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -29,5 +29,6 @@
      54: "12769f4ad4513269d8f771c99046deb2",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
+     114: "2691b0f4e604049a81275708313610c6",
      152: "b35034509d95c173fed1c0adcecc5be0",
 }

From 62016581ec3b15c784b6f2dbdbf765989769e3eb Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 16 Aug 2018 10:11:05 -0400
Subject: [PATCH 271/753] Add earnings quality multi-factor long-short strategy

---
 .../01 Strategy Library.php                   |   8 +
 .../01 Introduction.html                      |   5 +
 .../02 Method.html                            | 157 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 182 insertions(+)
 create mode 100644 04 Strategy Library/229 Earnings Quality Factor/01 Introduction.html
 create mode 100644 04 Strategy Library/229 Earnings Quality Factor/02 Method.html
 create mode 100644 04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html
 create mode 100644 04 Strategy Library/229 Earnings Quality Factor/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e44ae13..4f3594e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -310,6 +310,14 @@
           ],
           'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
        ],
+       [
+           'name' => 'Earnings Quality Factor',
+           'link' => 'strategy-library/earnings-quality-factor',
+           'sources' => [
+               'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
+           ],
+           'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.'
+        ],
 ];
 
 ?>
diff --git a/04 Strategy Library/229 Earnings Quality Factor/01 Introduction.html b/04 Strategy Library/229 Earnings Quality Factor/01 Introduction.html
new file mode 100644
index 0000000..c809686
--- /dev/null
+++ b/04 Strategy Library/229 Earnings Quality Factor/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  Multiple factors can explain abnormal equity returns which could be used to build profitable equity long-short strategy.
+  The most common factors are momentum, short-term reversal, market value, size factors and so on.
+  In this tutorial, we will choose a few factors associated with earnings quality to investigate the return premium on stocks.
+</P>
diff --git a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html
new file mode 100644
index 0000000..5096b21
--- /dev/null
+++ b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
@@ -0,0 +1,157 @@
+<h4>Earnings Quality Metrics</h4>
+<p>
+  Earnings quality refers to the ability of reported earnings to predict a company's future earnings.
+  The earnings should be attributable to higher sales or lower costs, rather than artificial profits created by accounting anomalies or tricks such as inflation of inventories or changing depreciation.
+  It is one of the most critical measures in financial reporting systems. High earnings quality indicates a healthy development in the firm's business and can improve the market efficiency.
+</P>
+<p>
+  The first metric of earnings quality is accruals. It is defined by cash flow relative to reported earnings. The high-quality earnings firms are characterized by low accruals while the low-quality firms are characterized by high accruals.
+  The formula is
+</P>
+\[Accruals= ( \Delta CA - \Delta Cash) - ( \Delta CL - \Delta STD - \Delta ITP) - Dep\]
+<p>Where</P>
+  <ul>
+       <li>\(\Delta CA\) = annual change in current assets</li>
+       <li>(\Delta Cash\) = change in cash and cash equivalents</li>
+       <li>(\Delta CL\) = change in current liabilities</li>
+       <li>(\Delta STD\) = change in debt included in current liabilities</li>
+       <li>(\Delta ITP\) =  change in income taxes payable</li>
+       <li>(\Delta Dep\) annual depreciation and amortization expense</li>
+  </ul>
+<p>
+  We use an annual change reported for two consecutive fiscal years.
+  To calculate the accruals, we save the fine fundamental object in the last year and calculate the difference in the next year.
+  The algorithm will start to trade after one-year initialization.
+</p>
+  <div class="section-example-container">
+  <pre class="python">
+    def CalculateAccruals(self, current, previous):
+        accruals = []
+        for stock_data in current:
+            #compares this and last year's fine fundamental objects
+            try:
+                prev_data = None
+                for x in previous:
+                    if x.Symbol == stock_data.Symbol:
+                        prev_data = x
+                        break
+
+                #calculates the balance sheet accruals and adds the property to the fine fundamental object
+                delta_assets = float(stock_data.FinancialStatements.BalanceSheet.CurrentAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentAssets.Value)
+                delta_cash = float(stock_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value)-float(prev_data.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value)
+                delta_liabilities = float(stock_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentLiabilities.Value)
+                delta_debt = float(stock_data.FinancialStatements.BalanceSheet.CurrentDebt.Value)-float(prev_data.FinancialStatements.BalanceSheet.CurrentDebt.Value)
+                delta_tax = float(stock_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value)-float(prev_data.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value)
+                dep = float(stock_data.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value)
+                avg_total = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)+float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/2
+                #accounts for the size difference
+                stock_data.Accrual = ((delta_assets-delta_cash)-(delta_liabilities-delta_debt-delta_tax)-dep)/avg_total
+                accruals.append(stock_data)
+            except:
+                #value in current universe does not exist in the previous universe
+                pass
+        return accruals
+  </pre>
+  </div>
+<p>
+  The second metric is cash flow to assets ratio. It is calculated by dividing cash flows from operations by the average total assets. Firms with high cash flow to total assets are of high earnings quality.
+  The other two metrics are Return on Equity(ROE) and Debt to Assets(DA) which are available properties of fine fundamental objects. The Higher the ROE ratio, the better the earnings quality.
+  Low debt to assets means low leverage. It leads to more stable earnings and less dependence on the current financing conditions in the economy. Therefore, low debt to assets ratios is a signal of high earnings quality.
+  Before calculating those variables, we should make sure their values are all positive.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        #filters out the non-financial companies that don't contain the necessary data
+        fine = [x for x in fine if (x.CompanyReference.IndustryTemplateCode != "B")
+                                and (x.FinancialStatements.BalanceSheet.CurrentAssets.Value > 0)
+                                and (x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value > 0)
+                                and (x.FinancialStatements.BalanceSheet.CurrentLiabilities.Value > 0)
+                                and (x.FinancialStatements.BalanceSheet.CurrentDebt.Value > 0)
+                                and (x.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value > 0)
+                                and (x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value > 0)]
+
+        if not self.previous_fine:
+            # will wait one year in order to have the historical fundamental data
+            self.previous_fine = fine
+            self.yearly_rebalance = False
+            return []
+        else:
+            # calculate the accrual for each stock
+            fine = self.CalculateAccruals(fine, self.previous_fine)
+            filtered_fine = [x for x in fine if (x.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value> 0)
+                                            and (x.EarningReports.BasicEPS.Value>0)
+                                            and (x.EarningReports.BasicAverageShares.Value>0)
+                                            and (x.OperationRatios.DebttoAssets.Value>0)
+                                            and (x.OperationRatios.ROE.Value>0)]
+            for i in filtered_fine:
+                # cash flow to assets
+                i.CFA = i.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value/(i.EarningReports.BasicEPS.Value * i.EarningReports.BasicAverageShares.Value)
+                # debt to assets
+                i.DA = i.OperationRatios.DebttoAssets.Value
+                # return on equity
+                i.ROE = i.OperationRatios.ROE.Value
+</pre>
+</div>
+
+<h4>The Scoring System</h4>
+<p>
+  The investment universe consists of all non-financial stocks from NYSE, Amex and Nasdaq. Next, we will build a composite scoring system to rank the stocks in the universe.
+  The first step is sorting stocks by four factors respectively. “good” quality has a high score, so ideally a stock has low accruals, low debt to assets, high ROE, and high cash flow to assets will be allocated high score.
+  Therefore, we sort the accruals and debt to assets ratio in descending orders and sort the ROE and cash flow to assets ratios in ascending order.
+  Then the score of each stock is the sum of rank in four factors.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  # sort stocks by four factors respectively
+   sortedByAccrual = sorted(filtered_fine, key=lambda x: x.Accrual, reverse=True) # high score with low accrual
+   sortedByCFA = sorted(filtered_fine, key=lambda x: x.CFA)                       # high score with high CFA
+   sortedByDA = sorted(filtered_fine, key=lambda x: x.DA, reverse=True)           # high score with low leverage
+   sortedByROE = sorted(filtered_fine, key=lambda x: x.ROE)                       # high score with high ROE
+   # create dict to save the score for each stock
+   score_dict = {}
+   # assign a score to each stock according to their rank with different factors
+   for i,obj in enumerate(sortedByAccrual):
+       scoreAccrual = i
+       scoreCFA = sortedByCFA.index(obj)
+       scoreDA = sortedByDA.index(obj)
+       scoreROE = sortedByROE.index(obj)
+       score = scoreAccrual + scoreCFA + scoreDA + scoreROE
+       score_dict[obj.Symbol] = score
+
+   sortedByScore = sorted(score_dict, key = lambda x: score_dict[x], reverse = True)
+   # long stocks with the top score (> 30%) and short stocks with the bottom score (< 70%)
+   self.long = sortedByScore[:int(0.3*len(sortedByScore))]
+   self.short = sortedByScore[-int(0.3*len(sortedByScore)):]
+</pre>
+</div>
+<h4>Trade and Rebalance</h4>
+<p>
+  Based on the composite factor score, the algorithm goes long the top 30% of high score stocks and short the bottom 30% of low score stocks.
+  Final factor portfolio is formed at the end of each June and is rebalanced yearly.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if not self.yearly_rebalance: return
+    if self.long and self.short:
+        long_stocks = [x.Key for x in self.Portfolio if x.Value.IsLong]
+        short_stocks = [x.Key for x in self.Portfolio if x.Value.IsShort]
+        # liquidate the stocks not in the filtered long/short list
+        for long in long_stocks:
+            if long not in self.long:
+                self.Liquidate(long)
+
+        for short in short_stocks:
+            if short not in self.short:
+                self.Liquidate(short)
+
+        long_weight = 0.8/len(self.long)
+        for i in self.long:
+            self.SetHoldings(i, long_weight)
+        short_weight = 0.8/len(self.short)
+        for i in self.short:
+            self.SetHoldings(i, -short_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html
new file mode 100644
index 0000000..706ddb2
--- /dev/null
+++ b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_49156e0c7cc09e415b66dc1437c5a7a9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/229 Earnings Quality Factor/04 Source.html b/04 Strategy Library/229 Earnings Quality Factor/04 Source.html
new file mode 100644
index 0000000..b2455b8
--- /dev/null
+++ b/04 Strategy Library/229 Earnings Quality Factor/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/229" rel="nofollow">Quantpedia - Earnings Quality Factor</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index bbda0aa..0e3537b 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -30,4 +30,5 @@
      91: "d0b1d400ad21477d83ae2b4f85616318",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      152: "b35034509d95c173fed1c0adcecc5be0",
+     229: "49156e0c7cc09e415b66dc1437c5a7a9",
 }

From 94e7e3cd344645cc6721c70aad4c795618acdb4f Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 16 Aug 2018 15:38:39 -0400
Subject: [PATCH 272/753] Update the backtest

---
 .../02 Method.html                            | 22 +++++++++----------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html
index 5096b21..7d677df 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
@@ -65,12 +65,12 @@ <h4>Earnings Quality Metrics</h4>
     if self.yearly_rebalance:
         #filters out the non-financial companies that don't contain the necessary data
         fine = [x for x in fine if (x.CompanyReference.IndustryTemplateCode != "B")
-                                and (x.FinancialStatements.BalanceSheet.CurrentAssets.Value > 0)
-                                and (x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value > 0)
-                                and (x.FinancialStatements.BalanceSheet.CurrentLiabilities.Value > 0)
-                                and (x.FinancialStatements.BalanceSheet.CurrentDebt.Value > 0)
-                                and (x.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value > 0)
-                                and (x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value > 0)]
+                                and (x.FinancialStatements.BalanceSheet.CurrentAssets.Value != 0)
+                                and (x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.Value != 0)
+                                and (x.FinancialStatements.BalanceSheet.CurrentLiabilities.Value != 0)
+                                and (x.FinancialStatements.BalanceSheet.CurrentDebt.Value != 0)
+                                and (x.FinancialStatements.BalanceSheet.IncomeTaxPayable.Value != 0)
+                                and (x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.Value != 0)]
 
         if not self.previous_fine:
             # will wait one year in order to have the historical fundamental data
@@ -80,11 +80,11 @@ <h4>Earnings Quality Metrics</h4>
         else:
             # calculate the accrual for each stock
             fine = self.CalculateAccruals(fine, self.previous_fine)
-            filtered_fine = [x for x in fine if (x.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value> 0)
-                                            and (x.EarningReports.BasicEPS.Value>0)
-                                            and (x.EarningReports.BasicAverageShares.Value>0)
-                                            and (x.OperationRatios.DebttoAssets.Value>0)
-                                            and (x.OperationRatios.ROE.Value>0)]
+            filtered_fine = [x for x in fine if (x.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value != 0)
+                                            and (x.EarningReports.BasicEPS.Value != 0)
+                                            and (x.EarningReports.BasicAverageShares.Value != 0)
+                                            and (x.OperationRatios.DebttoAssets.Value != 0)
+                                            and (x.OperationRatios.ROE.Value != 0)]
             for i in filtered_fine:
                 # cash flow to assets
                 i.CFA = i.FinancialStatements.CashFlowStatement.OperatingCashFlow.Value/(i.EarningReports.BasicEPS.Value * i.EarningReports.BasicAverageShares.Value)
diff --git a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html
index 706ddb2..3bc488d 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_49156e0c7cc09e415b66dc1437c5a7a9.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5544552803512ca667342d5011dedd1d.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 1090cc7..848901d 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -32,5 +32,5 @@
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      114: "2691b0f4e604049a81275708313610c6",
      152: "b35034509d95c173fed1c0adcecc5be0",
-     229: "49156e0c7cc09e415b66dc1437c5a7a9",
+     229: "5544552803512ca667342d5011dedd1d",
 }

From 0cac2be905919ccaedb2837fe6e847a2b0645734 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 17 Aug 2018 20:38:54 +0000
Subject: [PATCH 273/753] Automated push from server made by Jing Wu

---
 .../229 Earnings Quality Factor/02 Method.html         | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html
index 7d677df..1790ed8 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
@@ -12,11 +12,11 @@ <h4>Earnings Quality Metrics</h4>
 <p>Where</P>
   <ul>
        <li>\(\Delta CA\) = annual change in current assets</li>
-       <li>(\Delta Cash\) = change in cash and cash equivalents</li>
-       <li>(\Delta CL\) = change in current liabilities</li>
-       <li>(\Delta STD\) = change in debt included in current liabilities</li>
-       <li>(\Delta ITP\) =  change in income taxes payable</li>
-       <li>(\Delta Dep\) annual depreciation and amortization expense</li>
+       <li>\(\Delta Cash\) = change in cash and cash equivalents</li>
+       <li>\(\Delta CL\) = change in current liabilities</li>
+       <li>\(\Delta STD\) = change in debt included in current liabilities</li>
+       <li>\(\Delta ITP\) =  change in income taxes payable</li>
+       <li>\(\Delta Dep\) annual depreciation and amortization expense</li>
   </ul>
 <p>
   We use an annual change reported for two consecutive fiscal years.

From 65d5ae19500977d9b55c4b1c2ae6721bf0ce3a1b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 17 Aug 2018 20:40:13 +0000
Subject: [PATCH 274/753] Automated push from server made by Jing Wu

---
 .../229 Earnings Quality Factor/02 Method.html         | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html
index 1790ed8..f3222d1 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
@@ -12,11 +12,11 @@ <h4>Earnings Quality Metrics</h4>
 <p>Where</P>
   <ul>
        <li>\(\Delta CA\) = annual change in current assets</li>
-       <li>\(\Delta Cash\) = change in cash and cash equivalents</li>
-       <li>\(\Delta CL\) = change in current liabilities</li>
-       <li>\(\Delta STD\) = change in debt included in current liabilities</li>
-       <li>\(\Delta ITP\) =  change in income taxes payable</li>
-       <li>\(\Delta Dep\) annual depreciation and amortization expense</li>
+       <li>(\Delta Cash\) = change in cash and cash equivalents</li>
+       <li>(\Delta CL\) = change in current liabilities</li>
+       <li>(\Delta STD\) = change in debt included in current liabilities</li>
+       <li>(\Delta ITP\) =  change in income taxes payable</li>
+       <li>(\Delta Dep\) = annual depreciation and amortization expense</li>
   </ul>
 <p>
   We use an annual change reported for two consecutive fiscal years.

From e36fbc5395220ae6dc2f11f4ef19208196614a13 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Sat, 18 Aug 2018 13:46:04 -0400
Subject: [PATCH 275/753] Add Momentum Combined with Volatility Strategy

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   4 +
 .../02 Method.html                            | 134 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 158 insertions(+)
 create mode 100644 04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index a813668..92e573f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -334,6 +334,14 @@
            ],
            'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.'
         ],
+        [
+            'name' => 'Momentum and Reversal Combined with Volatility Effect in Stocks',
+            'link' => 'strategy-library/momentum-and-reversal-combined-with-volatility-effect-in-stocks',
+            'sources' => [
+                'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
+            ],
+            'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
+         ],
 ];
 
 ?>
diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/01 Introduction.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/01 Introduction.html
new file mode 100644
index 0000000..bccce7f
--- /dev/null
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  Momentum is a well-known strategy that buys stocks with the best return over the past three to twelve months and sells stocks with the worst performances over the same time horizon.
+  The reversal strategy buys the stocks with relatively low returns and sells stocks with high returns. In this algorithm, we will develop a long-short strategy combining the momentum/reversal effect with the realized volatility. 
+</P>
diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/02 Method.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/02 Method.html
new file mode 100644
index 0000000..94a7056
--- /dev/null
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/02 Method.html	
@@ -0,0 +1,134 @@
+<h4>The Universe Initial Filter</h4>
+<p>
+  The investment universe consists of NYSE, AMEX and NASDAQ stocks with prices higher than $5 per share.
+  In <code>FineSelectionFunction</code>, we divide the universe into two equal halves by size of the company.
+  Here size is defined as the share price times the number of shares outstanding.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    # update the price of stocks in universe everyday
+    for i in coarse:
+        if i.Symbol not in self.dataDict:
+            self.dataDict[i.Symbol] = SymbolData(i.Symbol, self.lookback)
+        self.dataDict[i.Symbol].Update(i.AdjustedPrice)
+
+    if self.monthly_rebalance:
+        # drop stocks which have no fundamental data or have too low prices
+        filteredCoarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
+        return filteredCoarse
+    else:
+        return []
+
+def FineSelectionFunction(self, fine):
+
+    if self.monthly_rebalance:
+        sortedFine = sorted(fine, key = lambda x: x.EarningReports.BasicAverageShares.Value * self.dataDict[x.Symbol].Price, reverse=True)
+        # select stocks with large size
+        topFine = sortedFine[:int(0.5*len(sortedFine))]
+        self.filteredFine = [x.Symbol for x in topFine]
+        return self.filteredFine
+    else:
+        return []
+</pre>
+</div>
+<h4>The Realized Return and Volatility</h4>
+<p>
+  At the beginning of each month, realized returns and realized (annualized) volatilities are calculated for each stock.
+  The realized volatility refers to the historical volatility. The formula of the realized volatility \(\sigma\) is
+</p>
+\[R_{avg}=\frac{\sum_{i=1}^n R_i}{n}\]
+\[\sigma=\sqrt{\frac{\sum_{i=1}^n(R_i-R_{avg})^2}{n-1}}\]
+<p>
+   To annualize the volatility, we multiply the 1-day volatility by the square root of the number of trading days in a year – in our case square root of 252.
+</p>
+<p>
+  A 6-month warm-up period is required to initialize the history price for stocks in the universe. We create the class
+  <code>SymbolData</code> to save all required variables associated with a single stock.
+  One week (5 trading days) prior to the beginning of each month is skipped to avoid biases due to microstructures.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  class SymbolData:
+    '''Contains data specific to a symbol required by this model'''
+
+    def __init__(self, symbol, lookback):
+        self.symbol = symbol
+        # self.History = RollingWindow[Decimal](lookback)
+        self.History = deque(maxlen=lookback)
+        self.Price = None
+
+    def Update(self, value):
+        # update yesterday's close price
+        self.Price = value
+        # update the history price series
+        self.History.append(float(value))
+        # self.History.Add(value)
+
+    def IsReady(self):
+        return len(self.History) == self.History.maxlen
+
+    def Volatility(self):
+        # one week (5 trading days) prior to the beginning of each month is skipped
+        prices = np.array(self.History)[:-5]
+        returns = (prices[1:]-prices[:-1])/prices[:-1]
+        # calculate the annualized realized volatility
+        return np.std(returns)*np.sqrt(252)
+
+    def Return(self):
+        # one week (5 trading days) prior to the beginning of each month is skipped
+        prices = np.array(self.History)[:-5]
+        # calculate the annualized realized return
+        return (prices[-1]-prices[0])/prices[0]
+</pre>
+</div>
+
+<p>
+  After the warm-up period, the historical price series is ready. Stocks are sorted into quintiles based on their realized volatility.
+  Stocks in the top 20% highest volatility are further sorted into quintiles by their six-month realized returns.
+  The algorithm goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if self.monthly_rebalance and self.filteredFine:
+        filtered_data = {symbol: symbolData for (symbol, symbolData) in self.dataDict.items() if symbol in self.filteredFine and symbolData.IsReady()}
+        self.filteredFine = None
+        self.monthly_rebalance = False
+        if len(filtered_data) < 100: return
+        # sort the universe by volatility and select stocks in the top high volatility quintile
+        sortedByVol = sorted(filtered_data.items(), key=lambda x: x[1].Volatility(), reverse = True)[:int(0.2*len(filtered_data))]
+        sortedByVol = dict(sortedByVol)
+        # sort the stocks in top-quintile by realized return
+        sortedByReturn = sorted(sortedByVol, key = lambda x: sortedByVol[x].Return(), reverse = True)
+        long = sortedByReturn[:int(0.2*len(sortedByReturn))]
+        short = sortedByReturn[-int(0.2*len(sortedByReturn)):]
+</pre>
+</div>
+<h4>Portfolio Rebalance and Trade</h4>
+<p>
+  The methodology of Jegadeesh and Titamn (1993) is used to rebalance the portfolio.
+  Specifically, at the beginning of each month, stocks are sorted into quintiles based on their realized returns and equally weighted portfolios are formed to be held for the next six months.
+  This sorting and portfolio formation procedure is performed each month. In any given month t, the strategy holds 6 portfolios that are selected in the current month as well as the previous 5 months.
+  Therefore 1/6 of the portfolio is rebalanced every month. We save those 6 portfolios in a deque list <code>self.portfolios</code> and the list is updated every month. The portfolio of the current month is added while the portfolio selected from six months ago is removed from the list.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.portfolios = deque(maxlen=6)
+def OnData(self, data):
+    self.portfolios.append(short+long)
+    # 1/6 of the portfolio is rebalanced every month
+    if len(self.portfolios) == self.portfolios.maxlen:
+        for i in list(self.portfolios)[0]:
+            self.Liquidate(i)
+    # stocks are equally weighted and held for 6 months
+    short_weight = 1/len(short)
+    for i in short:
+        self.SetHoldings(i, -1/6*short_weight)
+
+    long_weight = 1/len(long)
+    for i in long:
+        self.SetHoldings(i, 1/6*long_weight)
+  </pre>
+  </div>
diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..e3a87f0
--- /dev/null
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_886a9f73bc29142f14fcb2c2d01404a9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/04 Source.html b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/04 Source.html
new file mode 100644
index 0000000..54db2a0
--- /dev/null
+++ b/04 Strategy Library/155 Momentum and Reversal Combined with Volatility Effect in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/155" rel="nofollow">Quantpedia - Momentum and Reversal Combined with Volatility Effect in Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 848901d..654ba49 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,6 +31,7 @@
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      114: "2691b0f4e604049a81275708313610c6",
+     155: "886a9f73bc29142f14fcb2c2d01404a9",
      152: "b35034509d95c173fed1c0adcecc5be0",
      229: "5544552803512ca667342d5011dedd1d",
 }

From f9a2f14f136f1d1f2c27c2064c2042d6874d45b1 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 18 Aug 2018 18:34:22 +0000
Subject: [PATCH 276/753] Automated push from server made by Jing Wu

---
 .../03 Pairs Trading-Copula vs Cointegration/01 Abstract.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html
index 4937cd0..d95036a 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 Abstract.html	
@@ -3,5 +3,5 @@
 </p>
 
 <p>
-  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a></em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012). The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
+  We will discuss the basic framework of copula from the mathematical perspective and explain how to apply the approach in pairs trading. The implementation of the algorithm is based on the paper <em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">Trading strategies with copulas</a></em> from Stander Y, Marais D, Botha I(2013). We compare the performance of the copula pairs trading strategy with the co-integration pairs trading method based on the paper <em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">Statistical arbitrage trading strategies and high-frequency trading</a></em> from Hanson T A, Hall J R. (2012). The co-integration technique assumes a co-integration relationship between paired equities to identify profitable trading opportunities. The empirical results suggest that the copula-based strategy is more profitable than the traditional pairs trading techniques.
 </p>

From 8bb667ee0e5e269c80cb6d6a8c29321dd1d9d4e8 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 20 Aug 2018 15:16:14 +0000
Subject: [PATCH 277/753] Automated push from server made by Jing Wu

---
 .../229 Earnings Quality Factor/02 Method.html         | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html
index f3222d1..cbce69f 100644
--- a/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
+++ b/04 Strategy Library/229 Earnings Quality Factor/02 Method.html	
@@ -12,11 +12,11 @@ <h4>Earnings Quality Metrics</h4>
 <p>Where</P>
   <ul>
        <li>\(\Delta CA\) = annual change in current assets</li>
-       <li>(\Delta Cash\) = change in cash and cash equivalents</li>
-       <li>(\Delta CL\) = change in current liabilities</li>
-       <li>(\Delta STD\) = change in debt included in current liabilities</li>
-       <li>(\Delta ITP\) =  change in income taxes payable</li>
-       <li>(\Delta Dep\) = annual depreciation and amortization expense</li>
+       <li>\(\Delta Cash\) = change in cash and cash equivalents</li>
+       <li>\(\Delta CL\) = change in current liabilities</li>
+       <li>\(\Delta STD\) = change in debt included in current liabilities</li>
+       <li>\(\Delta ITP\) =  change in income taxes payable</li>
+       <li>\(\Delta Dep\) = annual depreciation and amortization expense</li>
   </ul>
 <p>
   We use an annual change reported for two consecutive fiscal years.

From 0ef277c4f44b50d70cab81df288f9b863118d8e9 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 21 Aug 2018 15:49:32 -0400
Subject: [PATCH 278/753] Add January Barometer algorithm

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   4 +
 .../113 January Barometer/02 Method.html      | 134 ++++++++++++++++++
 .../113 January Barometer/03 Algorithm.html   |   6 +
 .../113 January Barometer/04 Source.html      |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 158 insertions(+)
 create mode 100644 04 Strategy Library/113 January Barometer/01 Introduction.html
 create mode 100644 04 Strategy Library/113 January Barometer/02 Method.html
 create mode 100644 04 Strategy Library/113 January Barometer/03 Algorithm.html
 create mode 100644 04 Strategy Library/113 January Barometer/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 92e573f..36bed20 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -342,6 +342,14 @@
             ],
             'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
          ],
+         [
+             'name' => 'January Barometer',
+             'link' => 'strategy-library/january-barometer',
+             'sources' => [
+                 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
+             ],
+             'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills.'
+          ],
 ];
 
 ?>
diff --git a/04 Strategy Library/113 January Barometer/01 Introduction.html b/04 Strategy Library/113 January Barometer/01 Introduction.html
new file mode 100644
index 0000000..5b1687c
--- /dev/null
+++ b/04 Strategy Library/113 January Barometer/01 Introduction.html	
@@ -0,0 +1,4 @@
+<p>
+  January barometer is a calendar anomaly saying that February to December returns on equity index could be foretold by the January performance of equity index - a strong January shows strong rest of the year and otherwise.
+  This algorithm is going to explore the estimation effect of January barometer in equity index market.
+</P>
diff --git a/04 Strategy Library/113 January Barometer/02 Method.html b/04 Strategy Library/113 January Barometer/02 Method.html
new file mode 100644
index 0000000..94a7056
--- /dev/null
+++ b/04 Strategy Library/113 January Barometer/02 Method.html	
@@ -0,0 +1,134 @@
+<h4>The Universe Initial Filter</h4>
+<p>
+  The investment universe consists of NYSE, AMEX and NASDAQ stocks with prices higher than $5 per share.
+  In <code>FineSelectionFunction</code>, we divide the universe into two equal halves by size of the company.
+  Here size is defined as the share price times the number of shares outstanding.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    # update the price of stocks in universe everyday
+    for i in coarse:
+        if i.Symbol not in self.dataDict:
+            self.dataDict[i.Symbol] = SymbolData(i.Symbol, self.lookback)
+        self.dataDict[i.Symbol].Update(i.AdjustedPrice)
+
+    if self.monthly_rebalance:
+        # drop stocks which have no fundamental data or have too low prices
+        filteredCoarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
+        return filteredCoarse
+    else:
+        return []
+
+def FineSelectionFunction(self, fine):
+
+    if self.monthly_rebalance:
+        sortedFine = sorted(fine, key = lambda x: x.EarningReports.BasicAverageShares.Value * self.dataDict[x.Symbol].Price, reverse=True)
+        # select stocks with large size
+        topFine = sortedFine[:int(0.5*len(sortedFine))]
+        self.filteredFine = [x.Symbol for x in topFine]
+        return self.filteredFine
+    else:
+        return []
+</pre>
+</div>
+<h4>The Realized Return and Volatility</h4>
+<p>
+  At the beginning of each month, realized returns and realized (annualized) volatilities are calculated for each stock.
+  The realized volatility refers to the historical volatility. The formula of the realized volatility \(\sigma\) is
+</p>
+\[R_{avg}=\frac{\sum_{i=1}^n R_i}{n}\]
+\[\sigma=\sqrt{\frac{\sum_{i=1}^n(R_i-R_{avg})^2}{n-1}}\]
+<p>
+   To annualize the volatility, we multiply the 1-day volatility by the square root of the number of trading days in a year – in our case square root of 252.
+</p>
+<p>
+  A 6-month warm-up period is required to initialize the history price for stocks in the universe. We create the class
+  <code>SymbolData</code> to save all required variables associated with a single stock.
+  One week (5 trading days) prior to the beginning of each month is skipped to avoid biases due to microstructures.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  class SymbolData:
+    '''Contains data specific to a symbol required by this model'''
+
+    def __init__(self, symbol, lookback):
+        self.symbol = symbol
+        # self.History = RollingWindow[Decimal](lookback)
+        self.History = deque(maxlen=lookback)
+        self.Price = None
+
+    def Update(self, value):
+        # update yesterday's close price
+        self.Price = value
+        # update the history price series
+        self.History.append(float(value))
+        # self.History.Add(value)
+
+    def IsReady(self):
+        return len(self.History) == self.History.maxlen
+
+    def Volatility(self):
+        # one week (5 trading days) prior to the beginning of each month is skipped
+        prices = np.array(self.History)[:-5]
+        returns = (prices[1:]-prices[:-1])/prices[:-1]
+        # calculate the annualized realized volatility
+        return np.std(returns)*np.sqrt(252)
+
+    def Return(self):
+        # one week (5 trading days) prior to the beginning of each month is skipped
+        prices = np.array(self.History)[:-5]
+        # calculate the annualized realized return
+        return (prices[-1]-prices[0])/prices[0]
+</pre>
+</div>
+
+<p>
+  After the warm-up period, the historical price series is ready. Stocks are sorted into quintiles based on their realized volatility.
+  Stocks in the top 20% highest volatility are further sorted into quintiles by their six-month realized returns.
+  The algorithm goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if self.monthly_rebalance and self.filteredFine:
+        filtered_data = {symbol: symbolData for (symbol, symbolData) in self.dataDict.items() if symbol in self.filteredFine and symbolData.IsReady()}
+        self.filteredFine = None
+        self.monthly_rebalance = False
+        if len(filtered_data) < 100: return
+        # sort the universe by volatility and select stocks in the top high volatility quintile
+        sortedByVol = sorted(filtered_data.items(), key=lambda x: x[1].Volatility(), reverse = True)[:int(0.2*len(filtered_data))]
+        sortedByVol = dict(sortedByVol)
+        # sort the stocks in top-quintile by realized return
+        sortedByReturn = sorted(sortedByVol, key = lambda x: sortedByVol[x].Return(), reverse = True)
+        long = sortedByReturn[:int(0.2*len(sortedByReturn))]
+        short = sortedByReturn[-int(0.2*len(sortedByReturn)):]
+</pre>
+</div>
+<h4>Portfolio Rebalance and Trade</h4>
+<p>
+  The methodology of Jegadeesh and Titamn (1993) is used to rebalance the portfolio.
+  Specifically, at the beginning of each month, stocks are sorted into quintiles based on their realized returns and equally weighted portfolios are formed to be held for the next six months.
+  This sorting and portfolio formation procedure is performed each month. In any given month t, the strategy holds 6 portfolios that are selected in the current month as well as the previous 5 months.
+  Therefore 1/6 of the portfolio is rebalanced every month. We save those 6 portfolios in a deque list <code>self.portfolios</code> and the list is updated every month. The portfolio of the current month is added while the portfolio selected from six months ago is removed from the list.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.portfolios = deque(maxlen=6)
+def OnData(self, data):
+    self.portfolios.append(short+long)
+    # 1/6 of the portfolio is rebalanced every month
+    if len(self.portfolios) == self.portfolios.maxlen:
+        for i in list(self.portfolios)[0]:
+            self.Liquidate(i)
+    # stocks are equally weighted and held for 6 months
+    short_weight = 1/len(short)
+    for i in short:
+        self.SetHoldings(i, -1/6*short_weight)
+
+    long_weight = 1/len(long)
+    for i in long:
+        self.SetHoldings(i, 1/6*long_weight)
+  </pre>
+  </div>
diff --git a/04 Strategy Library/113 January Barometer/03 Algorithm.html b/04 Strategy Library/113 January Barometer/03 Algorithm.html
new file mode 100644
index 0000000..e3a87f0
--- /dev/null
+++ b/04 Strategy Library/113 January Barometer/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_886a9f73bc29142f14fcb2c2d01404a9.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/113 January Barometer/04 Source.html b/04 Strategy Library/113 January Barometer/04 Source.html
new file mode 100644
index 0000000..54db2a0
--- /dev/null
+++ b/04 Strategy Library/113 January Barometer/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/155" rel="nofollow">Quantpedia - Momentum and Reversal Combined with Volatility Effect in Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 654ba49..814479b 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,4 +34,5 @@
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      152: "b35034509d95c173fed1c0adcecc5be0",
      229: "5544552803512ca667342d5011dedd1d",
+     113: "9b1291a5f08dcc07df86363e46144084",
 }

From ed37ce5ad1ec1b5d5f9efcb628fcfee864f79102 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 21 Aug 2018 15:51:58 -0400
Subject: [PATCH 279/753] Updates the backtest

---
 .../114 January Effect in Stocks/03 Algorithm.html              | 2 +-
 quantpedia.json                                                 | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html
index b909114..f94dcba 100644
--- a/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/114 January Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2691b0f4e604049a81275708313610c6.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_7a404f43e9191ac5bce9a9446572cb98.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 814479b..aaa2782 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -30,7 +30,7 @@
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
-     114: "2691b0f4e604049a81275708313610c6",
+     114: "7a404f43e9191ac5bce9a9446572cb98",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      152: "b35034509d95c173fed1c0adcecc5be0",
      229: "5544552803512ca667342d5011dedd1d",

From d1a0be1c121396731685f2054879a4c4d9fe652f Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 21 Aug 2018 15:56:42 -0400
Subject: [PATCH 280/753] Updates tutorial

---
 .../01 Introduction.html                      |   4 +-
 .../113 January Barometer/02 Method.html      | 142 ++----------------
 .../113 January Barometer/03 Algorithm.html   |   2 +-
 .../113 January Barometer/04 Source.html      |   2 +-
 4 files changed, 18 insertions(+), 132 deletions(-)

diff --git a/04 Strategy Library/113 January Barometer/01 Introduction.html b/04 Strategy Library/113 January Barometer/01 Introduction.html
index 5b1687c..4148671 100644
--- a/04 Strategy Library/113 January Barometer/01 Introduction.html	
+++ b/04 Strategy Library/113 January Barometer/01 Introduction.html	
@@ -1,4 +1,4 @@
 <p>
-  January barometer is a calendar anomaly saying that February to December returns on equity index could be foretold by the January performance of equity index - a strong January shows strong rest of the year and otherwise.
-  This algorithm is going to explore the estimation effect of January barometer in equity index market.
+  January barometer is a calendar anomaly saying that the January performance of equity index could foretell February to December returns on equity index - a strong January shows strong rest of the year and otherwise.
+  This algorithm is going to explore the estimation effect of the January barometer in equity index market.
 </P>
diff --git a/04 Strategy Library/113 January Barometer/02 Method.html b/04 Strategy Library/113 January Barometer/02 Method.html
index 94a7056..c8d3623 100644
--- a/04 Strategy Library/113 January Barometer/02 Method.html	
+++ b/04 Strategy Library/113 January Barometer/02 Method.html	
@@ -1,134 +1,20 @@
-<h4>The Universe Initial Filter</h4>
 <p>
-  The investment universe consists of NYSE, AMEX and NASDAQ stocks with prices higher than $5 per share.
-  In <code>FineSelectionFunction</code>, we divide the universe into two equal halves by size of the company.
-  Here size is defined as the share price times the number of shares outstanding.
+  We use the S&amp;P500 ETF as the underlying. In January, the algorithm buys SPY and hold until the end of January.
+  At the end of January, we calculate the January return, if the return is greater than zero, the algorithm will continue to hold the SPY. If the January return is negative instead, the algorithm will liquidate the SPY asset and invest in the treasury bill for the rest of the year. The portfolio is rebalanced every year in January.
 </p>
 <div class="section-example-container">
 <pre class="python">
-def CoarseSelectionFunction(self, coarse):
-    # update the price of stocks in universe everyday
-    for i in coarse:
-        if i.Symbol not in self.dataDict:
-            self.dataDict[i.Symbol] = SymbolData(i.Symbol, self.lookback)
-        self.dataDict[i.Symbol].Update(i.AdjustedPrice)
-
-    if self.monthly_rebalance:
-        # drop stocks which have no fundamental data or have too low prices
-        filteredCoarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.Price) > 5)]
-        return filteredCoarse
-    else:
-        return []
-
-def FineSelectionFunction(self, fine):
-
-    if self.monthly_rebalance:
-        sortedFine = sorted(fine, key = lambda x: x.EarningReports.BasicAverageShares.Value * self.dataDict[x.Symbol].Price, reverse=True)
-        # select stocks with large size
-        topFine = sortedFine[:int(0.5*len(sortedFine))]
-        self.filteredFine = [x.Symbol for x in topFine]
-        return self.filteredFine
-    else:
-        return []
+def Rebalance(self):
+    if self.Time.month == 1:
+        self.Liquidate("BIL")
+        self.SetHoldings("SPY", 1)
+        self.startPrice = self.Securities["SPY"].Price
+    if self.Time.month == 2 and self.startPrice is not None:
+        returns = (self.Securities["SPY"].Price - self.startPrice)/self.startPrice
+        if returns > 0:
+            self.SetHoldings("SPY", 1)
+        else:
+            self.Liquidate("SPY")
+            self.SetHoldings("BIL", 1)
 </pre>
 </div>
-<h4>The Realized Return and Volatility</h4>
-<p>
-  At the beginning of each month, realized returns and realized (annualized) volatilities are calculated for each stock.
-  The realized volatility refers to the historical volatility. The formula of the realized volatility \(\sigma\) is
-</p>
-\[R_{avg}=\frac{\sum_{i=1}^n R_i}{n}\]
-\[\sigma=\sqrt{\frac{\sum_{i=1}^n(R_i-R_{avg})^2}{n-1}}\]
-<p>
-   To annualize the volatility, we multiply the 1-day volatility by the square root of the number of trading days in a year – in our case square root of 252.
-</p>
-<p>
-  A 6-month warm-up period is required to initialize the history price for stocks in the universe. We create the class
-  <code>SymbolData</code> to save all required variables associated with a single stock.
-  One week (5 trading days) prior to the beginning of each month is skipped to avoid biases due to microstructures.
-</P>
-<div class="section-example-container">
-<pre class="python">
-  class SymbolData:
-    '''Contains data specific to a symbol required by this model'''
-
-    def __init__(self, symbol, lookback):
-        self.symbol = symbol
-        # self.History = RollingWindow[Decimal](lookback)
-        self.History = deque(maxlen=lookback)
-        self.Price = None
-
-    def Update(self, value):
-        # update yesterday's close price
-        self.Price = value
-        # update the history price series
-        self.History.append(float(value))
-        # self.History.Add(value)
-
-    def IsReady(self):
-        return len(self.History) == self.History.maxlen
-
-    def Volatility(self):
-        # one week (5 trading days) prior to the beginning of each month is skipped
-        prices = np.array(self.History)[:-5]
-        returns = (prices[1:]-prices[:-1])/prices[:-1]
-        # calculate the annualized realized volatility
-        return np.std(returns)*np.sqrt(252)
-
-    def Return(self):
-        # one week (5 trading days) prior to the beginning of each month is skipped
-        prices = np.array(self.History)[:-5]
-        # calculate the annualized realized return
-        return (prices[-1]-prices[0])/prices[0]
-</pre>
-</div>
-
-<p>
-  After the warm-up period, the historical price series is ready. Stocks are sorted into quintiles based on their realized volatility.
-  Stocks in the top 20% highest volatility are further sorted into quintiles by their six-month realized returns.
-  The algorithm goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.
-</p>
-<div class="section-example-container">
-<pre class="python">
-def OnData(self, data):
-    if self.monthly_rebalance and self.filteredFine:
-        filtered_data = {symbol: symbolData for (symbol, symbolData) in self.dataDict.items() if symbol in self.filteredFine and symbolData.IsReady()}
-        self.filteredFine = None
-        self.monthly_rebalance = False
-        if len(filtered_data) < 100: return
-        # sort the universe by volatility and select stocks in the top high volatility quintile
-        sortedByVol = sorted(filtered_data.items(), key=lambda x: x[1].Volatility(), reverse = True)[:int(0.2*len(filtered_data))]
-        sortedByVol = dict(sortedByVol)
-        # sort the stocks in top-quintile by realized return
-        sortedByReturn = sorted(sortedByVol, key = lambda x: sortedByVol[x].Return(), reverse = True)
-        long = sortedByReturn[:int(0.2*len(sortedByReturn))]
-        short = sortedByReturn[-int(0.2*len(sortedByReturn)):]
-</pre>
-</div>
-<h4>Portfolio Rebalance and Trade</h4>
-<p>
-  The methodology of Jegadeesh and Titamn (1993) is used to rebalance the portfolio.
-  Specifically, at the beginning of each month, stocks are sorted into quintiles based on their realized returns and equally weighted portfolios are formed to be held for the next six months.
-  This sorting and portfolio formation procedure is performed each month. In any given month t, the strategy holds 6 portfolios that are selected in the current month as well as the previous 5 months.
-  Therefore 1/6 of the portfolio is rebalanced every month. We save those 6 portfolios in a deque list <code>self.portfolios</code> and the list is updated every month. The portfolio of the current month is added while the portfolio selected from six months ago is removed from the list.
-</p>
-<div class="section-example-container">
-<pre class="python">
-def Initialize(self):
-    self.portfolios = deque(maxlen=6)
-def OnData(self, data):
-    self.portfolios.append(short+long)
-    # 1/6 of the portfolio is rebalanced every month
-    if len(self.portfolios) == self.portfolios.maxlen:
-        for i in list(self.portfolios)[0]:
-            self.Liquidate(i)
-    # stocks are equally weighted and held for 6 months
-    short_weight = 1/len(short)
-    for i in short:
-        self.SetHoldings(i, -1/6*short_weight)
-
-    long_weight = 1/len(long)
-    for i in long:
-        self.SetHoldings(i, 1/6*long_weight)
-  </pre>
-  </div>
diff --git a/04 Strategy Library/113 January Barometer/03 Algorithm.html b/04 Strategy Library/113 January Barometer/03 Algorithm.html
index e3a87f0..be69ba3 100644
--- a/04 Strategy Library/113 January Barometer/03 Algorithm.html	
+++ b/04 Strategy Library/113 January Barometer/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_886a9f73bc29142f14fcb2c2d01404a9.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9b1291a5f08dcc07df86363e46144084.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/113 January Barometer/04 Source.html b/04 Strategy Library/113 January Barometer/04 Source.html
index 54db2a0..8a2d56e 100644
--- a/04 Strategy Library/113 January Barometer/04 Source.html	
+++ b/04 Strategy Library/113 January Barometer/04 Source.html	
@@ -1,5 +1,5 @@
 <ul>
   <li>
-    <a href="https://quantpedia.com/Screener/Details/155" rel="nofollow">Quantpedia - Momentum and Reversal Combined with Volatility Effect in Stocks</a>
+    <a href="https://quantpedia.com/Screener/Details/113" rel="nofollow">Quantpedia - January Barometer</a>
 </li>
 </ul>

From a614e4060b2a0410b85043e1c9d8557121ddbae7 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 22 Aug 2018 09:29:23 -0400
Subject: [PATCH 281/753] Add algorithm ROA effect within stocks

---
 .../01 Strategy Library.php                   |  8 ++
 .../01 Introduction.html                      |  5 ++
 .../02 Method.html                            | 74 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 99 insertions(+)
 create mode 100644 04 Strategy Library/199 ROA Effect within Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/199 ROA Effect within Stocks/02 Method.html
 create mode 100644 04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/199 ROA Effect within Stocks/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 92e573f..286dfa5 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -342,6 +342,14 @@
             ],
             'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
          ],
+         [
+             'name' => 'ROA Effect within Stocks',
+             'link' => 'strategy-library/roa-effect-within-stocks',
+             'sources' => [
+                 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
+             ],
+             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowerst RPA from each market capitalization group.'
+          ],
 ];
 
 ?>
diff --git a/04 Strategy Library/199 ROA Effect within Stocks/01 Introduction.html b/04 Strategy Library/199 ROA Effect within Stocks/01 Introduction.html
new file mode 100644
index 0000000..1848e9e
--- /dev/null
+++ b/04 Strategy Library/199 ROA Effect within Stocks/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  Return on Equity(ROE) is defined as the ratio of net income over shareholders equity,
+  where shareholders’ equity is the difference between a company's total assets and total liabilities. Shareholders' equity is often referred to as the book value of the company.
+  ROE is a measure of how efficiently a company uses its assets to produce earnings. It can explain many anomalies related to earnings and profitability. This algorithm will build a long-short portfolio with ROE factor.
+</p>
diff --git a/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html
new file mode 100644
index 0000000..2aa2344
--- /dev/null
+++ b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html	
@@ -0,0 +1,74 @@
+<p>
+  The investment universe contains all stocks on NYSE and AMEX and Nasdaq.
+  In <code>CoarseSelectionFunction</code>, we eliminated ETFa which does not have fundamental data.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    if self.monthly_rebalance:
+        self.coarse = True
+        filteredCoarse = [x.Symbol for x in coarse if x.HasFundamentalData]
+        return filteredCoarse
+    else:
+        return []
+</pre>
+</div>
+<p>
+  In <code>FineSelectionFunction</code>, stocks with sales greater than 10 milion USD are selected.
+  Then we calculate the market cap for those stocks and sort them into two groups: Big size group with the higher market cap and small size group with the lower market cap. Each half is then divided into deciles based on Return on assets (ROA).
+</P>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.monthly_rebalance:
+        fine =[i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths != 0
+                              and i.EarningReports.BasicEPS.TwelveMonths != 0
+                              and i.ValuationRatios.PERatio != 0
+                              # sales is greater than 10 million
+                              and i.ValuationRatios.SalesPerShare*i.EarningReports.DilutedAverageShares.Value > 10000000
+                              and i.OperationRatios.ROA.Value != 0]
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        # sort into 2 halves based on market capitalization
+        sorted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        top = sorted_market_cap[:int(len(sorted_market_cap)*0.5)]
+        bottom = sorted_market_cap[-int(len(sorted_market_cap)*0.5):]
+        # each half is then divided into deciles based on Return on Assets (ROA)
+        sortedTopByROA = sorted(top, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
+        sortedBottomByROA = sorted(bottom, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
+        # long top decile from each market capitalization group
+        long = sortedTopByROA[:int(len(sortedTopByROA)*0.05)] + sortedBottomByROA[:int(len(sortedTopByROA)*0.05)]
+        self.longStocks = [i.Symbol for i in long]
+        # short bottom decile from each market capitalization group
+        short = sortedTopByROA[-int(len(sortedTopByROA)*0.05):] + sortedBottomByROA[-int(len(sortedTopByROA)*0.05):]
+        self.shortStocks = [i.Symbol for i in short]
+
+        return self.longStocks+self.shortStocks
+    else:
+        return []
+</pre>
+</div>
+<p>
+  The algorithm goes long top decile from each market capitalization group and short bottom deciles.
+  The strategy is rebalanced monthly and stocks are equally weighted.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  def OnData(self, data):
+      if not (self.monthly_rebalance and self.coarse): return
+      self.coarse = False
+      self.monthly_rebalance = False
+      stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+      for i in stocks_invested:
+          if i not in self.longStocks+self.shortStocks:
+              self.Liquidate(i)
+
+      long_weight = 0.5/len(self.longStocks)
+      for i in self.longStocks:
+          self.SetHoldings(i, long_weight)
+
+      short_weight = 0.5/len(self.shortStocks)
+      for i in self.shortStocks:
+          self.SetHoldings(i, -short_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html
new file mode 100644
index 0000000..c75531f
--- /dev/null
+++ b/04 Strategy Library/199 ROA Effect within Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2deff750ba4eff5bf2f2138ecffb4a7c.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/199 ROA Effect within Stocks/04 Source.html b/04 Strategy Library/199 ROA Effect within Stocks/04 Source.html
new file mode 100644
index 0000000..137b5de
--- /dev/null
+++ b/04 Strategy Library/199 ROA Effect within Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/199" rel="nofollow">Quantpedia - ROA Effect within Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 654ba49..4fa28bb 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,4 +34,5 @@
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      152: "b35034509d95c173fed1c0adcecc5be0",
      229: "5544552803512ca667342d5011dedd1d",
+     199: "2deff750ba4eff5bf2f2138ecffb4a7c",
 }

From 2fd750d0cc67fb884da1118f8a39c2b39fd629be Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 22 Aug 2018 09:32:46 -0400
Subject: [PATCH 282/753] Update tutorial

---
 .../199 ROA Effect within Stocks/02 Method.html           | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html
index 2aa2344..16dcca2 100644
--- a/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html	
+++ b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html	
@@ -29,7 +29,7 @@
                               and i.OperationRatios.ROA.Value != 0]
         for i in fine:
             i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
-        # sort into 2 halves based on market capitalization
+        # sort into 2 halfs based on market capitalization
         sorted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
         top = sorted_market_cap[:int(len(sorted_market_cap)*0.5)]
         bottom = sorted_market_cap[-int(len(sorted_market_cap)*0.5):]
@@ -37,10 +37,10 @@
         sortedTopByROA = sorted(top, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
         sortedBottomByROA = sorted(bottom, key = lambda x: x.OperationRatios.ROA.Value, reverse = True)
         # long top decile from each market capitalization group
-        long = sortedTopByROA[:int(len(sortedTopByROA)*0.05)] + sortedBottomByROA[:int(len(sortedTopByROA)*0.05)]
+        long = sortedTopByROA[:int(len(sortedTopByROA)*0.1)] + sortedBottomByROA[:int(len(sortedTopByROA)*0.1)]
         self.longStocks = [i.Symbol for i in long]
         # short bottom decile from each market capitalization group
-        short = sortedTopByROA[-int(len(sortedTopByROA)*0.05):] + sortedBottomByROA[-int(len(sortedTopByROA)*0.05):]
+        short = sortedTopByROA[-int(len(sortedTopByROA)*0.1):] + sortedBottomByROA[-int(len(sortedTopByROA)*0.1):]
         self.shortStocks = [i.Symbol for i in short]
 
         return self.longStocks+self.shortStocks
@@ -49,7 +49,7 @@
 </pre>
 </div>
 <p>
-  The algorithm goes long top decile from each market capitalization group and short bottom deciles.
+  The algorithm goes long the top decile from each market capitalization group and short the bottom decile.
   The strategy is rebalanced monthly and stocks are equally weighted.
 </P>
 <div class="section-example-container">

From b3546151ae28e3c8becbf34d030573da4946b9cf Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 22 Aug 2018 10:42:35 -0400
Subject: [PATCH 283/753] update tutorial

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 286dfa5..6ea2be9 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -348,7 +348,7 @@
              'sources' => [
                  'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
              ],
-             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowerst RPA from each market capitalization group.'
+             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowerst ROA from each market capitalization group.'
           ],
 ];
 

From 983a6c0d4fa07f4333ed26f138e24b6940751453 Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Wed, 22 Aug 2018 10:56:15 -0400
Subject: [PATCH 284/753] Update 01 Strategy Library.php

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 3b64bc7..d0b0032 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -348,7 +348,7 @@
              'sources' => [
                  'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
              ],
-             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowerst ROA from each market capitalization group.'
+             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.'
          ],
          [
              'name' => 'January Barometer',

From d288975ab6fa0d423a50d61f68c1b249c121d2ac Mon Sep 17 00:00:00 2001
From: Jing Wu <msjingwu@outlook.com>
Date: Wed, 22 Aug 2018 10:56:44 -0400
Subject: [PATCH 285/753] Update 02 Method.html

---
 04 Strategy Library/199 ROA Effect within Stocks/02 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html
index 16dcca2..774d359 100644
--- a/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html	
+++ b/04 Strategy Library/199 ROA Effect within Stocks/02 Method.html	
@@ -1,6 +1,6 @@
 <p>
   The investment universe contains all stocks on NYSE and AMEX and Nasdaq.
-  In <code>CoarseSelectionFunction</code>, we eliminated ETFa which does not have fundamental data.
+  In <code>CoarseSelectionFunction</code>, we eliminated ETFs which does not have fundamental data.
 </P>
 <div class="section-example-container">
 <pre class="python">

From 9e6e8ee44c0b79f7399de6c5f084c7a50b1a919e Mon Sep 17 00:00:00 2001
From: Gustavo <g.aviles.segovia@gmail.com>
Date: Wed, 22 Aug 2018 11:50:19 -0400
Subject: [PATCH 286/753] Fix spacing and bug closing the description with the
 wrong string character.

---
 .../01 Strategy Library.php                   | 146 +++++++++---------
 1 file changed, 73 insertions(+), 73 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index d0b0032..0e261ed 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -285,79 +285,79 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/38'
         ],
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.'
-     ],
-     [
-         'name' => 'Momentum and Style Rotation Effect',
-         'link' => 'strategy-library/momentum-and-style-rotation-effect',
-         'sources' => [
-             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
-         ],
-         'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
-      ],
-      [
-          'name' => 'Trading with WTI BRENT Spread',
-          'link' => 'strategy-library/trading-with-wti-brent-spread',
-          'sources' => [
-              'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
-          ],
-          'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.'
-      ],
-      [
-         'name' => 'Momentum Effect in REITs',
-         'link' => 'strategy-library/momentum-effect-in-reits',
-         'sources' => [
-             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
-         ],
-         'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.'
-      ],
-      [
-          'name' => 'Option Expiration Week Effect',
-          'link' => 'strategy-library/option-expiration-week-effect',
-          'sources' => [
-              'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
-          ],
-          'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
-       ],
-       [
-           'name' => 'Earnings Quality Factor',
-           'link' => 'strategy-library/earnings-quality-factor',
-           'sources' => [
-               'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
-           ],
-           'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.'
-       ],
-       [
-           'name' => 'January Effect in Stocks',
-           'link' => 'strategy-library/january-effect-in-stocks',
-           'sources' => [
-               'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/114'
-           ],
-           'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.'
-        ],
-        [
-            'name' => 'Momentum and Reversal Combined with Volatility Effect in Stocks',
-            'link' => 'strategy-library/momentum-and-reversal-combined-with-volatility-effect-in-stocks',
-            'sources' => [
-                'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
-            ],
-            'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
-         ],
-         [
-             'name' => 'ROA Effect within Stocks',
-             'link' => 'strategy-library/roa-effect-within-stocks',
-             'sources' => [
-                 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
-             ],
-             'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.'
-         ],
-         [
-             'name' => 'January Barometer',
-             'link' => 'strategy-library/january-barometer',
-             'sources' => [
-                 'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
-             ],
-             'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills.'
-          ],
+    ],
+    [
+        'name' => 'Momentum and Style Rotation Effect',
+        'link' => 'strategy-library/momentum-and-style-rotation-effect',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
+        ],
+        'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
+    ],
+    [
+        'name' => 'Trading with WTI BRENT Spread',
+        'link' => 'strategy-library/trading-with-wti-brent-spread',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
+        ],
+        'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.'
+    ],
+    [
+        'name' => 'Momentum Effect in REITs',
+        'link' => 'strategy-library/momentum-effect-in-reits',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
+        ],
+        'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.'
+    ],
+    [
+        'name' => 'Option Expiration Week Effect',
+        'link' => 'strategy-library/option-expiration-week-effect',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
+        ],
+        'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
+    ],
+    [
+        'name' => 'Earnings Quality Factor',
+        'link' => 'strategy-library/earnings-quality-factor',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
+        ],
+        'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.'
+    ],
+    [
+        'name' => 'January Effect in Stocks',
+        'link' => 'strategy-library/january-effect-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/114'
+        ],
+        'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.'
+    ],
+    [
+        'name' => 'Momentum and Reversal Combined with Volatility Effect in Stocks',
+        'link' => 'strategy-library/momentum-and-reversal-combined-with-volatility-effect-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
+        ],
+        'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
+    ],
+    [
+        'name' => 'ROA Effect within Stocks',
+        'link' => 'strategy-library/roa-effect-within-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
+        ],
+        'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.'
+    ],
+    [
+        'name' => 'January Barometer',
+        'link' => 'strategy-library/january-barometer',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
+        ],
+        'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills."
+    ]
 ];
 
 ?>

From c98382f4f92dbd4bbb408d8af9aebd2b799431fb Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 22 Aug 2018 16:24:29 -0400
Subject: [PATCH 287/753] Add algorithm VIX Predicts Stock Index Returns

---
 .../01 Strategy Library.php                   | 10 +++-
 .../01 Introduction.html                      |  6 +++
 .../02 Method.html                            | 49 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 +++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  7 +--
 6 files changed, 79 insertions(+), 4 deletions(-)
 create mode 100644 04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html
 create mode 100644 04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html
 create mode 100644 04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html
 create mode 100644 04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 0e261ed..d0279d5 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -357,7 +357,15 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
         ],
         'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills."
-    ]
+    ],
+    [
+        'name' => 'VIX Predicts Stock Index Returns',
+        'link' => 'strategy-library/vix-predicts-stock-index-returns',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/58'
+        ],
+        'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html
new file mode 100644
index 0000000..a519011
--- /dev/null
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  VIX index is the weighted average of the implied volatilities computed from a total of eight near-the-money, nearby and second nearby American option contracts on the underlying S&amp;P100 index. The index is calculated on an intraday basis by the CBOE.
+  The extreme levels of the VIX index are a strong indicator of equity index returns.
+  One could anticipate that the higher the fear in the markets, indicated by the VIX index, the higher the subsequent returns on the broad equity index.
+  In this algorithm, we will explore if VIX index is the forward-looking indicator of future stock index returns.
+</p>
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html
new file mode 100644
index 0000000..3564d32
--- /dev/null
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html	
@@ -0,0 +1,49 @@
+<p>
+  To assess if the implied volatility turns out to be relevant trading signals for
+  long and short positions, we must first define what we mean by large or small implied volatility levels.
+  At any given time, we create 20 equally spaced percentiles for the history close price of VIX in the last two years.
+  By 20 equally spaced percentiles, it means the 5%, 10%, . . . , 95% percentiles. Then the current VIX index price is compared to these percentiles and ranked accordingly.
+</P>
+<p>
+  We import the daily VIX data from Quandl. The correspondent stock index is S&amp;P100 index. We use the iShares S&P 100 ETF <code>OEF</code>
+  In the first step, we create a rolling window to save the history VIX price. The history request is used to initialize the rolling window.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2006, 1, 1)
+    self.SetEndDate(2018, 8, 1)
+    self.SetCash(100000)
+    self.AddEquity("OEF", Resolution.Daily)
+    self.vix = 'CBOE/VIX'
+    self.AddData(QuandlVix, self.vix, Resolution.Daily)
+    self.window = RollingWindow[float](252*2)
+    hist = self.History([self.vix], 1000, Resolution.Daily)
+    for close in hist.loc[self.vix]['vix close']:
+        self.window.Add(close)
+
+class QuandlVix(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "VIX Close"
+</pre>
+</div>
+
+<p>
+  The rolling past 2-year history of the VIX index is then split to create 20 equally spaced percentiles.
+  The algorithm goes long on the equity index if the VIX index on the current day is higher than on any other day during the rolling 2-year window or if the VIX index value is in the highest 2 percentile boxes.
+  If the VIX index on the current day is lower than on any other day during the rolling 2-year window or if the VIX index value is in the lowest two percentile boxes. The algorithm goes short on the equity index.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if not data.ContainsKey(self.vix): return
+    self.window.Add(self.Securities[self.vix].Price)
+    if not self.window.IsReady: return
+    history_close = [i for i in self.window]
+
+    if self.Securities[self.vix].Price > np.percentile(history_close, 90):
+        self.SetHoldings("OEF", 1)
+    elif self.Securities[self.vix].Price < np.percentile(history_close, 10):
+        self.SetHoldings("OEF", -1)
+</pre>
+</div>
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html
new file mode 100644
index 0000000..f8ca3b6
--- /dev/null
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6f1f8cae87849a2aa65aa318f6554dfc.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html
new file mode 100644
index 0000000..1afea81
--- /dev/null
+++ b/04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/58" rel="nofollow">Quantpedia - VIX Predicts Stock Index Returns</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index d4529d4..8f76bec 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -27,13 +27,14 @@
      55: "2d6bcad96d965a04cd7eac846d54f129",
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
+     58: "6f1f8cae87849a2aa65aa318f6554dfc",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
+     113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",
-     155: "886a9f73bc29142f14fcb2c2d01404a9",
      152: "b35034509d95c173fed1c0adcecc5be0",
-     229: "5544552803512ca667342d5011dedd1d",
+     155: "886a9f73bc29142f14fcb2c2d01404a9",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
-     113: "9b1291a5f08dcc07df86363e46144084",
+     229: "5544552803512ca667342d5011dedd1d",
 }

From 2533d2954cff6e11a09f11b46f4317869e3b1447 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 23 Aug 2018 14:28:49 -0400
Subject: [PATCH 288/753] Add algorithm Lunar Cycle in Equity Market

---
 .../01 Strategy Library.php                   |  8 +++
 .../01 Introduction.html                      |  8 +++
 .../02 Method.html                            | 65 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 93 insertions(+)
 create mode 100644 04 Strategy Library/61 Lunar Cycle in Equity Market/01 Introduction.html
 create mode 100644 04 Strategy Library/61 Lunar Cycle in Equity Market/02 Method.html
 create mode 100644 04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html
 create mode 100644 04 Strategy Library/61 Lunar Cycle in Equity Market/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 0e261ed..63153c7 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -357,6 +357,14 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
         ],
         'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills."
+    ],
+    [
+        'name' => 'Lunar Cycle in Equity Market',
+        'link' => 'strategy-library/lunar-cycle-in-equity-market',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/61'
+        ],
+        'description' => "Goes long in emerging market index ETF 7 days before the new moon and switch to a short position on emerging market index ETF 7 days before the full moon."
     ]
 ];
 
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/01 Introduction.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/01 Introduction.html
new file mode 100644
index 0000000..49a362d
--- /dev/null
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/01 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+  The lunar phase is the shape of the direct sunlight portion of the Moon as viewed from Earth.
+  The lunar phases gradually and cyclically change over the period of a month.
+  Lunar phases have proven effects on human biology and psychology.
+  Investors are subject to various psychological and behavioral biases and mood fluctuations.
+  Therefore if lunar phases affect mood, by extension, these phases may affect investor behavior and thus asset prices.
+  This tutorial we will build an algorithm based upon the Moon phase.
+</P>
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/02 Method.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/02 Method.html
new file mode 100644
index 0000000..450d0cf
--- /dev/null
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/02 Method.html	
@@ -0,0 +1,65 @@
+<p>
+  The lunar calendar data is obtained from the United States Naval Observatory (USNO) website. We import the custom data as a CSV file.
+  The four phases of the lunar cycle are New Moon, First Quarter, Full Moon and Last Quarter. They are in string format in the data file so
+  we convert the string names to numbers as New Moon(0), First Quarter(1), Full Moon(2) and Last Quarter(3).
+</P>
+<div class="section-example-container">
+<pre class="python">
+class MoonPhase(PythonData):
+    "Class to import Phases of the Moon data from Dropbox"
+
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("https://www.dropbox.com/s/q9rt06tpfjlvymt/MoonPhase.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        index = MoonPhase()
+        index.Symbol = config.Symbol
+        try:
+            # Example File Format: (Data starts from 01/07/2004)
+            # date                 phase
+            # 2004 Jan 07 15:40    Full Moon
+            data = line.split(',')
+            if data[0] == "date": return None
+            index.Time = datetime.strptime(data[0], "%Y %b %d %H:%M").replace(hour=0, minute=0)
+            if data[1] == "New Moon":
+                index.Value = 0
+            elif data[1] == "First Quarter":
+                index.Value = 1
+            elif data[1] == "Full Moon":
+                index.Value = 2
+            elif data[1] == "Last Quarter":
+                index.Value = 3
+        except:
+            return None
+
+        return index
+</pre>
+</div>
+
+<p>
+  Research shows that the effect is strongest in emerging markets.
+  Here we choose the iShares MSCI Emerging Markets Index ETF <code>EEM</code>.
+  The algorithm therefore goes long in emerging market index ETF 7 days before the new moon and switch to a short position on ETF 7 days before the full moon.
+  The benchmark is set to <code>EEM</code>.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2004, 1, 1)
+    self.SetEndDate(2018, 8, 1)
+    self.SetCash(100000)
+    # import the custom data
+    self.AddData(MoonPhase, "phase", Resolution.Daily)
+    self.AddEquity("EEM", Resolution.Daily)
+    self.SetBenchmark("EEM")
+
+def OnData(self, data):
+    # long in emerging market index ETF 7 days before the new moon (It's the Last Quarter)
+    if self.Securities["phase"].Price == 3 and not self.Portfolio["EEM"].IsLong:
+        self.SetHoldings("EEM", 1)
+    # short on emerging market index ETF 7 days before the full moon (It's the First Quarter)
+    elif self.Securities["phase"].Price == 1 and not self.Portfolio["EEM"].IsShort:
+        self.SetHoldings("EEM", -1)
+</pre>
+</div>
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html
new file mode 100644
index 0000000..55f69ca
--- /dev/null
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_17611f8cf05678b9bd09d98ba3581854.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/61 Lunar Cycle in Equity Market/04 Source.html b/04 Strategy Library/61 Lunar Cycle in Equity Market/04 Source.html
new file mode 100644
index 0000000..2a93cea
--- /dev/null
+++ b/04 Strategy Library/61 Lunar Cycle in Equity Market/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/61" rel="nofollow">Quantpedia - Lunar Cycle in Equity Market</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index d4529d4..c2fe8c5 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -27,6 +27,7 @@
      55: "2d6bcad96d965a04cd7eac846d54f129",
      53: "e8a75429c6a43e969e4a9243290e05f4",
      54: "12769f4ad4513269d8f771c99046deb2",
+     61: "17611f8cf05678b9bd09d98ba3581854",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 471c66143eea0f3d5d12fb5006d67fe2f943d60b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 28 Aug 2018 09:37:17 -0400
Subject: [PATCH 289/753] Add Algorithm Combining Momentum Effect with Volume

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   5 +
 .../02 Method.html                            | 108 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 133 insertions(+)
 create mode 100644 04 Strategy Library/66 Combining Momentum Effect with Volume/01 Introduction.html
 create mode 100644 04 Strategy Library/66 Combining Momentum Effect with Volume/02 Method.html
 create mode 100644 04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html
 create mode 100644 04 Strategy Library/66 Combining Momentum Effect with Volume/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 5821310..f28e807 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -374,6 +374,14 @@
         ],
         'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history."
     ],
+    [
+        'name' => 'Combining Momentum Effect with Volume',
+        'link' => 'strategy-library/combining-momentum-effect-with-volume',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/66'
+        ],
+        'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/01 Introduction.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/01 Introduction.html
new file mode 100644
index 0000000..0b90427
--- /dev/null
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The Momentum traders take on a long or short position in the stock, in the hopes that the momentum will continue in the same direction.
+  The momentum strategy has a higher degree of volatility than most other strategies.
+  In the tutorial, we'll introduce the trading volume factor to enhance the portfolio return and control the risk of momentum strategy.
+</P>
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/02 Method.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/02 Method.html
new file mode 100644
index 0000000..a2593a9
--- /dev/null
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/02 Method.html	
@@ -0,0 +1,108 @@
+<p>
+  The investment universe consists of all stocks on NYSE and NASDAQ. The momentum is usually defined by the stock return in the last N months.
+  The indicator <code>RateOfChange</code> is constructed to represent the return. The lookback period is one year.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  class SymbolData:
+      def __init__(self, symbol, lookback):
+          self.Symbol = symbol
+          self.ROC = RateOfChange(lookback)
+          self.Volume = None
+</pre>
+</div>
+<p>
+  The ROC indicator and volume are updated everyday in <code>CoarseSelectionFunction</code>. Securities which do not have fundamental data are eliminated.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+  def CoarseSelectionFunction(self, coarse):
+      for i in coarse:
+          if i.Symbol not in self.dataDict:
+              self.dataDict[i.Symbol] = SymbolData(i.Symbol, self.lookback)
+          self.dataDict[i.Symbol].ROC.Update(i.EndTime, i.AdjustedPrice)
+          self.dataDict[i.Symbol].Volume = i.Volume
+
+      if self.monthly_rebalance:
+          # drop stocks which have no fundamental data
+          filteredCoarse = [x for x in coarse if (x.HasFundamentalData)]
+          return [i.Symbol for i in filteredCoarse]
+      else:
+          return []
+</pre>
+</div>
+<p>
+  Trading volume (turnover) serves as a useful indicator of the level of investor interest in stocks.
+  The number of sellers tends to exceed the number of buyers when the stock falls into disfavor, which will lead to a falling share price.
+  When a stock is popular, the number of buyers exceeds the number of sellers, the price tends to rise.
+  As a result, a firm’s turnover may be a measure of investors' interest in the firm's stock so it could help to identify the future trend of stocks.
+  Here the turnover is calculated as the ratio of the number of shares traded each day to the number of shares outstanding.
+</p>
+<p>
+  In <code>FineSelectionFunction</code>, after the indicator is ready for all symbols in dictionary <code>self.dataDict</code>, turnover is calculated
+  with the <code>Volume</code> and <code>EarningReports.BasicAverageShares</code>.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.monthly_rebalance:
+        dataReady = {symbol: symbolData for (symbol, symbolData) in self.dataDict.items() if symbolData.ROC.IsReady}
+        if len(dataReady) < 100:
+            self.filteredFine = []
+        else:
+            sortedFine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths != 0 and i.Symbol in dataReady]
+            sortedFineSymbols = [i.Symbol for i in sortedFine]
+            filteredData = {symbol: symbolData for (symbol, symbolData) in dataReady.items() if symbol in sortedFineSymbols}
+            for i in sortedFine:
+                if i.Symbol in filteredData and filteredData[i.Symbol].Volume != 0:
+                    filteredData[i.Symbol].Turnover = i.EarningReports.BasicAverageShares.ThreeMonths / filteredData[i.Symbol].Volume
+</pre>
+</div>
+<p>
+  Stocks are sorted into deciles every month based on previous 12-month returns.
+  Each momentum decile is then divided into terciles based on turnover. The long and short portfolios are constructed with the highest turnover from the top momentum decile and the highest volume from the bottom momentum decile respectively.
+</P>
+<div class="section-example-container">
+<pre class="python">
+for i in sortedFine:
+    if i.Symbol in filteredData and filteredData[i.Symbol].Volume != 0:
+        filteredData[i.Symbol].Turnover = i.EarningReports.BasicAverageShares.ThreeMonths / filteredData[i.Symbol].Volume
+sortedByROC = sorted(filteredData.values(), key = lambda x: x.ROC.Current.Value, reverse = True)
+topROC = sortedByROC[:int(len(sortedByROC)*0.2)]
+bottomROC = sortedByROC[-int(len(sortedByROC)*0.2):]
+HighTurnoverTopROC = sorted(topROC, key = lambda x: x.Turnover, reverse = True)
+HighTurnoverBottomROC = sorted(bottomROC, key = lambda x: x.Turnover, reverse = True)
+self.long =  [i.Symbol for i in HighTurnoverTopROC[:int(len(HighTurnoverTopROC)*0.01)]]
+self.short = [i.Symbol for i in HighTurnoverBottomROC[:int(len(HighTurnoverBottomROC)*0.01)]]
+self.filteredFine = self.long + self.short
+self.portfolios.append(self.filteredFine)
+</pre>
+</div>
+<p>
+  A long-short portfolio is held for three months, and then it is rebalanced.
+  Therefore, the investor buys 1/3 of its portfolio for three consecutive months and rebalances 1/3 of its portfolio each month.
+  We save each portfolio in deque list <code>self.portfolios</code> and liquidate the portfolio three-months ago when rebalancing.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if self.monthly_rebalance and self.filteredFine:
+     self.filteredFine = None
+     self.monthly_rebalance = False
+
+     # 1/3 of the portfolio is rebalanced every month
+     if len(self.portfolios) == self.portfolios.maxlen:
+         for i in list(self.portfolios)[0]:
+             self.Liquidate(i)
+
+     # stocks are equally weighted and held for 3 months
+     short_weight = 1/len(self.short)
+     for i in self.short:
+         self.SetHoldings(i, -1/3*short_weight)
+
+     long_weight = 1/len(self.long)
+     for i in self.long:
+         self.SetHoldings(i, 1/3*long_weight)
+</pre>
+</div>
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html
new file mode 100644
index 0000000..3956745
--- /dev/null
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ace0e17cc133feceaf4aa83035309116.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/66 Combining Momentum Effect with Volume/04 Source.html b/04 Strategy Library/66 Combining Momentum Effect with Volume/04 Source.html
new file mode 100644
index 0000000..44c69e4
--- /dev/null
+++ b/04 Strategy Library/66 Combining Momentum Effect with Volume/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/61" rel="nofollow">Quantpedia - Combining Momentum Effect with Volume</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index b7c8fe9..042f486 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -29,6 +29,7 @@
      54: "12769f4ad4513269d8f771c99046deb2",
      58: "6f1f8cae87849a2aa65aa318f6554dfc",
      61: "17611f8cf05678b9bd09d98ba3581854",
+     66: "ace0e17cc133feceaf4aa83035309116",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 74a801de7710d144b59495e111d4eb7997818d10 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 30 Aug 2018 18:59:13 -0400
Subject: [PATCH 290/753] Add Short Term Reversal with Futures

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   6 +
 .../02 Method.html                            | 108 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 134 insertions(+)
 create mode 100644 04 Strategy Library/71 Short Term Reversal with Futures/01 Introduction.html
 create mode 100644 04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html
 create mode 100644 04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html
 create mode 100644 04 Strategy Library/71 Short Term Reversal with Futures/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index f28e807..570260c 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -382,6 +382,14 @@
         ],
         'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile."
     ],
+    [
+        'name' => 'Short Term Reversal with Futures',
+        'link' => 'strategy-library/short-term-reversal-with-futures',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/71'
+        ],
+        'description' => "Goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/01 Introduction.html b/04 Strategy Library/71 Short Term Reversal with Futures/01 Introduction.html
new file mode 100644
index 0000000..68196fe
--- /dev/null
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  The short-term reversal strategy buys stocks which are past losers and sells stocks which are past winners.
+  It is commonly used in the equity market. This algorithm will explore the reversal effect in the futures market.
+  Research also suggests that trading volume contains information about future market movements. The algorithm will
+  be constructed with both the volume and return reversal effect.
+</P>
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html b/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html
new file mode 100644
index 0000000..9138438
--- /dev/null
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html	
@@ -0,0 +1,108 @@
+<p>
+  The daily continuous futures data is from <a href="https://www.quandl.com/databases/SCF" rel="nofollow">Quandl</a>.
+  The investment universe consists of 24 types of US futures contracts: 4 currencies, five financial indices, eight agricultural products, and seven commodities.
+  The contracts are all front month contracts.
+</P>
+<div class="section-example-container">
+<pre class="python">
+class Futures(PythonData):
+    # import the futures custom data from dropbox
+    def GetSource(self, config, date, isLiveMode):
+        source = "your_custom_source_url"
+        return SubscriptionDataSource(source, SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        futures = Futures()
+        futures.Symbol = config.Symbol
+        data = line.split(',')
+        if data[0] == "date": return None
+        futures.Time = datetime.strptime(data[0], "%m/%d/%y")
+        futures.Value = float(data[4])
+        futures["settle"] = float(data[4]) # add the settle price
+        futures["volume"] = float(data[5]) # add the volume
+        futures["open_interest"] = float(data[6]) # add the open interest
+        return futures
+</pre>
+</div>
+<p>
+  The algorithm uses a weekly time frame(Wednesday-Wednesday interval). Therefore the <code>self.Schedule.On()</code> will fire the trading every Wednesday.
+  To compare the weekly change of volume and open interest, we create the <code>deque</code> list with the length to be two to save the history data frame of
+  the most recent week and the week before.
+</p>
+<div class="section-example-container">
+<pre class="python">
+  def Initialize(self):
+      self.SetStartDate(2017, 7, 10)
+      self.SetEndDate(2018, 8, 1)
+      self.SetCash(10000000)
+
+      self.tickers = ["CME_SF1_EF", "CME_MP1_FF",  "CME_CD1_EF", "CME_ED8_FF",               # Currencies
+                      "CME_NQ1_EF", "CME_MD1_EF", "CME_ES1_EF", "CME_YM1_EF", "CME_NK1_EF",  # Financial indices
+                      "CME_C1_EF", "CME_SM1_EF",  "ICE_CC1_EF",  "CME_LC1_EF",               # Agricultural product
+                      "CME_KW1_EF", "CME_S1_EF", "ICE_KC1_EF", "CME_LC1_EF",
+                      "CME_HG1_FF",  "CME_GC1_EF", "SHFE_AL1_EF",  "SHFE_CU1_EF",            # Commodities
+                      "CME_CL1_EF", "ICE_T1_FR", "CME_HO1_EF"]
+      self.length = len(self.tickers)
+      for ticker in self.tickers:
+          self.AddData(Futures, ticker)
+      # create the deque list to save the weekly history dataframe
+      self.window = deque(maxlen=2)
+      # rebalance the portfolio every week on Wednesday
+      self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday, DayOfWeek.Wednesday), self.TimeRules.At(0, 0), self.WeeklyTrade)
+</pre>
+</div>
+<p>
+  In the weekly rebalance function, weekly history data frame is saved. The contract is defined as the high(low) volume contract if the contract's volume changes between the period from t-1 to t and period from t-2 to t-1 is above(below) the median volume change of all contracts,
+  In addition, all contracts are also assigned to either high-open interest (top 50% of changes in open interest) or low-open interest groups (bottom 50% of changes in open interest) based on lagged changes in open interest between the period from t-1 to t and period from t-2 to t-1.
+  We created the method <code>CalculateChange</code> to calculate the increment value and rank the contracts based on the weekly history data frame.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+  def WeeklyTrade(self):
+      hist = self.History(self.tickers, self.Time-timedelta(days=7), self.Time,Resolution.Daily)
+      self.window.append(hist)
+      if len(self.window) == self.window.maxlen:
+          hist_t2 = self.window[0] # the weekly history dataframe two week ago
+          hist_t1 = self.window[1] # the weekly history dataframe a week ago
+          top_vol, bottom_vol = self.CalculateChange("volume", hist_t2, hist_t1)
+          top_OI, bottom_OI = self.CalculateChange("open_interest", hist_t2, hist_t1)
+</pre>
+</div>
+
+<div class="section-example-container">
+<pre class="python">
+  def CalculateChange(self, column_name, hist_t2, hist_t1):
+      # calculate the weekly change and sort by the colume value
+      value_t2 = hist_t2[column_name].unstack(level=0).sum(axis=0)
+      value_t1 = hist_t1[column_name].unstack(level=0).sum(axis=0)
+      delta = (value_t1 - value_t2).sort_values(ascending=True)
+      top = list(delta[:int(self.length*0.5)].index)
+      bottom = list(delta[-int(self.length*0.5):].index)
+      return top, bottom
+</pre>
+</div>
+
+<p>
+  We take the intersection of the top volume group and bottom open interest group. The trading candidates are selected from this group.
+  Next futures in the intersection are sorted based on the return in the previous week.
+  The algorithm goes long on futures from the high-volume, low-open interest group with the lowest return and shorts the contract with the highest return.
+</P>
+<div class="section-example-container">
+<pre class="python">
+# the intersection of top volume group and bottom open interest group
+trade_group = list(set(top_vol) & set(bottom_OI))
+returns = {}
+for ticker in trade_group:
+    res = (hist_t1.loc[ticker]["settle"][-1] - hist_t2.loc[ticker]["settle"][-1]) / hist_t2.loc[ticker]["settle"][-1]
+    returns[ticker] = res
+
+sortedByReturn = sorted(returns, key = lambda x: returns[x])
+
+if len(sortedByReturn) >= 2:
+    if self.Portfolio.Invested:
+        self.Liquidate()
+    self.SetHoldings(sortedByReturn[-1], -0.3)
+    self.SetHoldings(sortedByReturn[0], 0.3)
+</pre>
+</div>
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html
new file mode 100644
index 0000000..430008f
--- /dev/null
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_124aa2fdf4ae34bc022a00b54c26b10d.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/04 Source.html b/04 Strategy Library/71 Short Term Reversal with Futures/04 Source.html
new file mode 100644
index 0000000..168dc1b
--- /dev/null
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/71" rel="nofollow">Quantpedia - Short Term Reversal with Futures</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 042f486..858c1b4 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -30,6 +30,7 @@
      58: "6f1f8cae87849a2aa65aa318f6554dfc",
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
+     71: "124aa2fdf4ae34bc022a00b54c26b10d",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 42716f64b7c84c07a410acbabb9b0c688819b3a5 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 30 Aug 2018 19:04:49 -0400
Subject: [PATCH 291/753] Update the method

---
 .../71 Short Term Reversal with Futures/02 Method.html          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html b/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html
index 9138438..6b0c293 100644
--- a/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html	
+++ b/04 Strategy Library/71 Short Term Reversal with Futures/02 Method.html	
@@ -1,5 +1,5 @@
 <p>
-  The daily continuous futures data is from <a href="https://www.quandl.com/databases/SCF" rel="nofollow">Quandl</a>.
+  The daily continuous futures data with the adjusted price is from <a href="https://www.quandl.com/databases/SCF" rel="nofollow">Quandl</a>.
   The investment universe consists of 24 types of US futures contracts: 4 currencies, five financial indices, eight agricultural products, and seven commodities.
   The contracts are all front month contracts.
 </P>

From 056856e5679292db6029662c231283cb2e950699 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 3 Sep 2018 10:55:25 -0400
Subject: [PATCH 292/753] Add Algorithm  Pre-Holiday Effect

---
 .../01 Strategy Library.php                   |  8 +++++++
 .../01 Introduction.html                      |  6 +++++
 .../83 Pre-Holiday Effect/02 Method.html      | 23 +++++++++++++++++++
 .../83 Pre-Holiday Effect/03 Algorithm.html   |  6 +++++
 .../83 Pre-Holiday Effect/04 Source.html      |  5 ++++
 quantpedia.json                               |  1 +
 6 files changed, 49 insertions(+)
 create mode 100644 04 Strategy Library/83 Pre-Holiday Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/83 Pre-Holiday Effect/02 Method.html
 create mode 100644 04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/83 Pre-Holiday Effect/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 570260c..80e4915 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -390,6 +390,14 @@
         ],
         'description' => "Goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week."
     ],
+    [
+        'name' => 'Pre-holiday Effect',
+        'link' => 'strategy-library/pre-holiday-effect',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/83'
+        ],
+        'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/01 Introduction.html b/04 Strategy Library/83 Pre-Holiday Effect/01 Introduction.html
new file mode 100644
index 0000000..824943d
--- /dev/null
+++ b/04 Strategy Library/83 Pre-Holiday Effect/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  Pre-holiday days on the market are often characterized with lower liquidity as a lot of market participants are not involved in the market or they lower their exposure.
+  Historical research shows that stock prices often behave in a specific manner in each of the two trading days preceding these holidays.
+  This anomaly in equities is often called the pre-holiday effect. It is the market in-efficiency for short-term traders to gain on the final trading day before a holiday.
+  In this algorithm, we'll construct a simple strategy to exploit this pre-holiday effect in the equity market.
+</P>
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html b/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html
new file mode 100644
index 0000000..abf0bea
--- /dev/null
+++ b/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html	
@@ -0,0 +1,23 @@
+<p>
+  <code>TradingCalendar</code> class can help us find all the available holidays during a period of time.
+  <code>GetDaysByType</code> returns trading days of the specified <code>TradingDayType</code> that contains trading events associated with the range of dates.
+  Here we choose the type to be <code>PublicHoliday</code> and list all holidays from today to the next two days.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    calendar = self.TradingCalendar.GetDaysByType(TradingDayType.PublicHoliday, self.Time, self.Time+timedelta(days=2))
+    holidays = [i.Date for i in calendar]
+</pre>
+</div>
+<p>
+  The investment vehicle is SPDR S&amp;P500 ETF. The algorithm will trade the ETF if there are holidays within the next two days and stays in cash during other trading days.
+</P>
+<div class="section-example-container">
+<pre class="python">
+if not self.Portfolio.Invested and len(holidays)>0:
+    self.SetHoldings("SPY", 1)
+elif self.Portfolio.Invested and len(holidays)==0:
+    self.Liquidate()
+</pre>
+</div>
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
new file mode 100644
index 0000000..fee4a49
--- /dev/null
+++ b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_555ce2d13390674be60983178ed45a15.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/04 Source.html b/04 Strategy Library/83 Pre-Holiday Effect/04 Source.html
new file mode 100644
index 0000000..7859f12
--- /dev/null
+++ b/04 Strategy Library/83 Pre-Holiday Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/83" rel="nofollow">Quantpedia - Pre-Holiday Effect</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 858c1b4..0ab792e 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,6 +31,7 @@
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
+     83: "5544552803512ca667342d5011dedd1d",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 9fa4992e7725a61d1bafafa833c270bc3a23043b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 3 Sep 2018 10:57:34 -0400
Subject: [PATCH 293/753] Update backtest

---
 quantpedia.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quantpedia.json b/quantpedia.json
index 0ab792e..d15bc2c 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,7 +31,7 @@
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
-     83: "5544552803512ca667342d5011dedd1d",
+     83: "555ce2d13390674be60983178ed45a15",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From ec47d37caa4b7120fa897361b09037a7d348eb06 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 3 Sep 2018 11:12:14 -0400
Subject: [PATCH 294/753] Update backtest

---
 04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html | 2 +-
 quantpedia.json                                             | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
index fee4a49..8f691ea 100644
--- a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
+++ b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_555ce2d13390674be60983178ed45a15.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_110fa54e4a0cb765cb268c21ac08507e.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index d15bc2c..99214e4 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,7 +31,7 @@
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
-     83: "555ce2d13390674be60983178ed45a15",
+     83: "110fa54e4a0cb765cb268c21ac08507e",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 3363666cb530cc6e201d8f792c4960fbe54091e4 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 3 Sep 2018 11:48:42 -0400
Subject: [PATCH 295/753] Update backtest

---
 .../83 Pre-Holiday Effect/02 Method.html             | 12 ++++++++----
 .../83 Pre-Holiday Effect/03 Algorithm.html          |  2 +-
 quantpedia.json                                      |  2 +-
 3 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html b/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html
index abf0bea..d1c4444 100644
--- a/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html	
+++ b/04 Strategy Library/83 Pre-Holiday Effect/02 Method.html	
@@ -1,13 +1,17 @@
 <p>
   <code>TradingCalendar</code> class can help us find all the available holidays during a period of time.
   <code>GetDaysByType</code> returns trading days of the specified <code>TradingDayType</code> that contains trading events associated with the range of dates.
-  Here we choose the type to be <code>PublicHoliday</code> and list all holidays from today to the next two days.
+  Here we choose the type to be <code>TradingDayType.PublicHoliday</code> and list all holidays from today to the next two days. As <code>PublicHoliday</code> includes weekends,
+  we use the type <code>TradingDayType.Weekend</code> to subtract weekend holidays.
 </P>
 <div class="section-example-container">
 <pre class="python">
-def OnData(self, data):
-    calendar = self.TradingCalendar.GetDaysByType(TradingDayType.PublicHoliday, self.Time, self.Time+timedelta(days=2))
-    holidays = [i.Date for i in calendar]
+  def OnData(self, data):
+      calendar1 = self.TradingCalendar.GetDaysByType(TradingDayType.PublicHoliday, self.Time, self.Time+timedelta(days=2))
+      calendar2 = self.TradingCalendar.GetDaysByType(TradingDayType.Weekend, self.Time, self.Time+timedelta(days=2))
+      holidays = [i.Date for i in calendar1]
+      weekends = [i.Date for i in calendar2]
+      public_holidays = list(set(holidays) - set(weekends))
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html
index 8f691ea..b59109e 100644
--- a/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
+++ b/04 Strategy Library/83 Pre-Holiday Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_110fa54e4a0cb765cb268c21ac08507e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_fdfcddd132eaf55039d867c03efe3012.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 99214e4..efd9562 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,7 +31,7 @@
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
-     83: "110fa54e4a0cb765cb268c21ac08507e",
+     83: "fdfcddd132eaf55039d867c03efe3012",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 6de8171930c5afb60ee3c7fa8a15effa85a08fcd Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 12 Sep 2018 10:11:49 -0400
Subject: [PATCH 296/753] Add algorithm term structure of VIX futures

---
 .../01 Strategy Library.php                   |   8 +
 .../01 Introduction.html                      |   8 +
 .../02 Method.html                            | 166 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 194 insertions(+)
 create mode 100644 04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html
 create mode 100644 04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
 create mode 100644 04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
 create mode 100644 04 Strategy Library/198 Exploiting Term Structure of VIX Futures/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 80e4915..44a988f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -398,6 +398,14 @@
         ],
         'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days."
     ],
+    [
+        'name' => 'Term Structure of VIX Futures',
+        'link' => 'strategy-library/term-structure-of-vix-futures',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/198'
+        ],
+        'description' => "Buys or sells the nearest VIX futures based on the daily roll and hedge against the open positions with E-mini S&amp;P500 futures"
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html
new file mode 100644
index 0000000..4a932b1
--- /dev/null
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+  The Chicago Board Options Exchange (CBOE) introduced the Volatility Index (VIX) in 1993 to provide a measure of the implied volatility of 30-day, at the money S&P 100 index options.
+  Volatility has become a widely accepted asset class since the introduction of the VIX futures contracts in 2004. VIX futures are often used for hedging purpose because of its negative correlation with the equity market return. When we talk about the term structure of futures, we often refer to the forward curve.
+  The VIX forward curve consists of VIX futures prices at various delivery times in the future.
+  Academic research states that volatility follows a mean reverting process. When the VIX futures curve is upward sloped (in contango),
+  the VIX is expected to rise because it is low relative to long-term average levels and vice versa for the downward sloped VIX future curve.
+  In this study, we will create a strategy with the term structure effect of VIX futures and hedge the term structure risk with the S&amp;P500 futures.
+</P>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
new file mode 100644
index 0000000..2c630ad
--- /dev/null
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
@@ -0,0 +1,166 @@
+<p>
+  The trading strategy uses VIX futures as a trading vehicle and S&P E-mini for hedging purposes. The spot VIX price data and
+  the continuous front contract price of VIX and E-mini S&amp;P500 futures in the daily resolution are from Quandl.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2014, 1, 1)   # Set Start Date
+    self.SetEndDate(2018, 9, 1)     # Set End Date
+    self.SetCash(10000000)          # Set Strategy Cash
+    self.vix = "CBOE/VIX"
+    self.vx1 = "CHRIS/CBOE_VX1"
+    self.es1 = "CHRIS/CME_ES1"
+    self.AddData(QuandlVix, self.vix, Resolution.Daily)           # Add Quandl VIX price (daily)
+    self.AddData(QuandlFutures, self.vx1, Resolution.Daily)       # Add Quandl VIX front month futures data (daily)
+    self.AddData(QuandlFutures, self.es1, Resolution.Daily)       # Add Quandl E-mini S&P500 front month futures data (daily)
+    # Add VIX futures contract data
+    self.AddFuture(Futures.Indices.VIX).SetFilter(timedelta(0), timedelta(days=180))
+    # Add E-mini S&P500 futures contract data
+    self.AddFuture(Futures.Indices.SP500EMini).SetFilter(timedelta(0), timedelta(days=180))
+</pre>
+</div>
+<p>
+  Futures basis is defined as the difference between the underlying product cash price and futures contract price at a given time.
+</P>
+\[Basis_t=S_t-F_t\]
+<p>
+  Where \(S_t\) is the spot VIX price, \(F_t\) is the VIX futures price. The basis is in contango means the futures price is higher than the spot price.
+  The opposite of Contango is Backwardation. It refers to the market condition in which the futures price is less than the spot price.
+</p>
+\[Contango=S_t<F_t\]
+\[Backwardation=S_t>F_t\]
+<p>
+  We select the nearest VIX and E-mini futures with at least ten trading days to maturity in the futures chains.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    # select the nearest VIX and E-mini S&P500 futures with at least 10 trading days to maturity
+    # if the front contract expires, roll forward to the next nearest contract
+    for chain in data.FutureChains:
+        if chain.Key.Value == "VX":
+            if self.front_VX is None or ((self.front_VX.Expiry-self.Time).days <= 1):
+                contracts = list(filter(lambda x: x.Expiry >= self.Time + timedelta(days = 10), chain.Value))
+                self.front_VX = sorted(contracts, key = lambda x: x.Expiry)[0]
+        if chain.Key.Value == "ES":
+            if self.front_ES is None or ((self.front_ES.Expiry-self.Time).days <= 1):
+                contracts = list(filter(lambda x: x.Expiry >= self.Time + timedelta(days = 10), chain.Value))
+                self.front_ES = sorted(contracts, key = lambda x: x.Expiry)[0]
+</pre>
+</div>
+<p>
+  While this trading strategy takes advantage of the roll by selling VIX futures at a premium to the VIX and by buying VIX futures at a discount to the VIX,
+  it is exposed to the potentially substantial risks associated with adverse moves in the VIX futures curve. However, as the tendency of VIX futures prices to move inversely to equity returns,
+  much of this risk can be hedged by open the mini-S&P 500 futures position in the same direction.
+</P>
+<p>
+  The number of mini-S&amp;P futures contracts to buy or sell per VIX futures contract is based on the hedge ratio estimates.
+  The hedge ratios are constructed from regressions of VIX futures price changes on a constant and
+  on contemporaneous percentage changes of the front mini-S&P 500 futures contract both alone and
+  multiplied by the number of business days that the VIX futures contract is from the settlement, as shown below.
+</p>
+\[\Delta P^{VX}_t=\beta_0+\beta_1* Return^{ES}_t+\beta_2*(Return^{ES}_t*TimeToSettlement^{VX}_t)+\mu_t\]
+<p>
+  After we get the parameters \(beta_0\), \(beta_1\) and \(beta_2\), the formula for the hedge ratio is
+</P>
+\[HR_t=\frac{\beta_1*1000+\beta_2*TimeToSettlement_{t-1}*1000}{0.01*P^{ES}_{t-1}*50}\]
+<div class="section-example-container">
+<pre class="python">
+def CalculateHedgeRatio(self, price_VX, price_ES, days_to_maturity):
+    # the regression method to calculate the hedge ratio
+    price_VX = np.array(price_VX)
+    price_ES = np.array(price_ES)
+    delta_VX = np.diff(price_VX)
+    res_ES = np.diff(price_ES)/price_ES[:-1]*100
+    tts = np.array(days_to_maturity)[1:]
+    df = pd.DataFrame({"delta_VX":delta_VX, "SPRET":res_ES, "product":res_ES*tts}).dropna()
+    # remove rows with zero value
+    df = df[(df != 0).all(1)]
+    y = df['delta_VX'].astype(float)
+    X = df[['SPRET', "product"]].astype(float)
+    X = sm.add_constant(X)
+    model = sm.OLS(y, X).fit()
+    beta_1 = model.params[1]
+    beta_2 = model.params[2]
+    hedge_ratio = abs((1000*beta_1 + beta_2*((self.front_VX.Expiry-self.Time).days)*1000)/(0.01*50*float(self.Securities[self.es1].Price)))
+    return hedge_ratio
+</pre>
+</div>
+<p>
+  Our daily futures price data is from the continuous front contract. To measure the time to settlement, we use the <code>TradingCalendar</code> to get the available VIX futures expiries.
+  The algorithm to print the settlement date can be found here <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_21cd4e541e2c3366303adf894848cd5d.html">Expiry Generator</a>.
+  By changing the algorithm start date, we can get a list of expiration dates of VIX futures from 2011 to 2018. In the next step we save those DateTime objects in <code>expiry.py</code> in pandas data frame.
+  We populate the date index with the backward fill method to make it easy to calculate the time to settlement.
+</P>
+<p>
+  To perform the regression, we save the history price in deque list and update the list every day.
+</P>
+<div class="section-example-container">
+<pre class="python">
+# the rolling window to save the front month VX future price
+self.price_VX = deque(maxlen=252)
+# the rolling window to save the front month ES future price
+self.price_ES = deque(maxlen=252)
+# the rolling window to save the time-to-maturity of the contract
+self.days_to_maturity = deque(maxlen=252)
+# initialize the deque list
+for index, row in df.iterrows():
+    self.price_VX.append(row[self.vx1])
+    self.price_ES.append(row[self.es1])
+    self.days_to_maturity.append((row['expiry']-index).days)
+</pre>
+</div>
+
+<p>
+  The daily roll is defined as the difference between the front VIX futures price and the VIX, divided by the number of business days until the VIX futures contract settles.
+  Short VIX futures positions are entered when the VIX futures basis is in contango and the daily roll exceeds 0.10 and
+  long VIX futures positions are entered when the VIX futures basis is in backwardation and the daily roll is less than -0.10.
+</P>
+<div class="section-example-container">
+<pre class="python">
+daily_roll = (self.Securities[self.vx1].Price - self.Securities[self.vix].Price)/(self.front_VX.Expiry-self.Time).days
+if not self.Portfolio[self.front_VX.Symbol].Invested:
+    # Long if the contract is in contango with adaily roll greater than 0.10
+    if daily_roll > 0.1:
+        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
+        self.SetHoldings(self.front_VX.Symbol, 0.5)
+        self.SetHoldings(self.front_ES.Symbol, 0.5*hedge_ratio)
+    # Short if the contract is in backwardation with adaily roll less than -0.10
+    elif daily_roll < -0.1:
+        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
+        self.SetHoldings(self.front_VX.Symbol, -0.5)
+        self.SetHoldings(self.front_ES.Symbol, -0.5*hedge_ratio)
+</pre>
+</div>
+<p>
+  Trades are exited when the motivating conditions no longer exist.
+  The exit condition is defined as the daily roll being less than 0.05 for short trades and higher than -0.05 VIX futures points for long trades.
+  If these exit conditions are not triggered, trades are exited two days before the contract expires.
+</P>
+<div class="section-example-container">
+<pre class="python">
+# exit if the daily roll being less than 0.05 if holding long positions
+ if self.Portfolio[self.front_VX.Symbol].IsLong and daily_roll < 0.05:
+     self.Liquidate()
+     self.front_VX = None
+     self.front_ES = None
+     return
+
+ # exit if the daily roll being greater than -0.05 if holding short positions
+ if self.Portfolio[self.front_VX.Symbol].IsShort and daily_roll > -0.05:
+     self.Liquidate()
+     self.front_VX = None
+     self.front_ES = None
+     return
+
+if self.front_VX and self.front_ES:
+# if these exit conditions are not triggered, trades are exited two days before it expires
+if self.Portfolio[self.front_VX.Symbol].Invested and self.Portfolio[self.front_ES.Symbol].Invested:
+ if (self.front_VX.Expiry-self.Time).days <=2 or (self.front_ES.Expiry-self.Time).days <=2:
+     self.Liquidate()
+     self.front_VX = None
+     self.front_ES = None
+     return
+   </pre>
+   </div>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
new file mode 100644
index 0000000..af168bf
--- /dev/null
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2a9f432b6c073ba7edc1922cb905009.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/04 Source.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/04 Source.html
new file mode 100644
index 0000000..fc12d36
--- /dev/null
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/198" rel="nofollow">Quantpedia - Exploiting Term Structure of VIX Futures</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index efd9562..7875b76 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -39,6 +39,7 @@
      114: "7a404f43e9191ac5bce9a9446572cb98",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
+     198: "a2a9f432b6c073ba7edc1922cb905009",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      229: "5544552803512ca667342d5011dedd1d",
 }

From 89eeddca4738f7fca128735f77a2a3c4c6b10e88 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 12 Sep 2018 15:27:52 -0400
Subject: [PATCH 297/753] Update backtest

---
 .../01 Introduction.html                      |  2 +-
 .../02 Method.html                            | 22 ++++++++++++++-----
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 4 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html
index 4a932b1..f975737 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/01 Introduction.html	
@@ -1,5 +1,5 @@
 <p>
-  The Chicago Board Options Exchange (CBOE) introduced the Volatility Index (VIX) in 1993 to provide a measure of the implied volatility of 30-day, at the money S&P 100 index options.
+  The Chicago Board Options Exchange (CBOE) introduced the Volatility Index (VIX) in 1993 to provide a measure of the implied volatility of 30-day, at the money S&amp;P 100 index options.
   Volatility has become a widely accepted asset class since the introduction of the VIX futures contracts in 2004. VIX futures are often used for hedging purpose because of its negative correlation with the equity market return. When we talk about the term structure of futures, we often refer to the forward curve.
   The VIX forward curve consists of VIX futures prices at various delivery times in the future.
   Academic research states that volatility follows a mean reverting process. When the VIX futures curve is upward sloped (in contango),
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
index 2c630ad..41df415 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
@@ -52,12 +52,12 @@
 <p>
   While this trading strategy takes advantage of the roll by selling VIX futures at a premium to the VIX and by buying VIX futures at a discount to the VIX,
   it is exposed to the potentially substantial risks associated with adverse moves in the VIX futures curve. However, as the tendency of VIX futures prices to move inversely to equity returns,
-  much of this risk can be hedged by open the mini-S&P 500 futures position in the same direction.
+  much of this risk can be hedged by open the E-mini S&amp;P 500 futures position in the same direction.
 </P>
 <p>
   The number of mini-S&amp;P futures contracts to buy or sell per VIX futures contract is based on the hedge ratio estimates.
   The hedge ratios are constructed from regressions of VIX futures price changes on a constant and
-  on contemporaneous percentage changes of the front mini-S&P 500 futures contract both alone and
+  on contemporaneous percentage changes of the front mini-S&amp;P 500 futures contract both alone and
   multiplied by the number of business days that the VIX futures contract is from the settlement, as shown below.
 </p>
 \[\Delta P^{VX}_t=\beta_0+\beta_1* Return^{ES}_t+\beta_2*(Return^{ES}_t*TimeToSettlement^{VX}_t)+\mu_t\]
@@ -88,11 +88,23 @@
 </pre>
 </div>
 <p>
-  Our daily futures price data is from the continuous front contract. To measure the time to settlement, we use the <code>TradingCalendar</code> to get the available VIX futures expiries.
-  The algorithm to print the settlement date can be found here <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_21cd4e541e2c3366303adf894848cd5d.html">Expiry Generator</a>.
-  By changing the algorithm start date, we can get a list of expiration dates of VIX futures from 2011 to 2018. In the next step we save those DateTime objects in <code>expiry.py</code> in pandas data frame.
+  Our daily futures price data is from the continuous front contract. To measure the time to settlement, we import the custom data of the VIX futures expiration dates from 2011 to 2018.
   We populate the date index with the backward fill method to make it easy to calculate the time to settlement.
 </P>
+<div class="section-example-container">
+<pre class="python">
+# import the futures expiry calendar
+url = "https://www.dropbox.com/s/5k4rbuzfsfn3w0h/expiry.csv?dl=1"
+df_date = pd.read_csv(url, index_col = 'date')
+# convert the index and expiry column to datetime format
+df_date.index = pd.to_datetime(df_date.index)
+df_date['expiry']=pd.to_datetime(df_date['expiry'])
+idx = pd.date_range('03-16-2011', '04-19-2019')
+# populate the date index 
+expiry_date = df_date.reindex(idx, method='bfill')
+df = pd.concat([settle, expiry_date], axis=1, join='inner')
+</pre>
+</div>
 <p>
   To perform the regression, we save the history price in deque list and update the list every day.
 </P>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
index af168bf..18eebdf 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2a9f432b6c073ba7edc1922cb905009.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_42ec586c02a582da48fa3d0be00043c7.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 7875b76..4c36517 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -39,7 +39,7 @@
      114: "7a404f43e9191ac5bce9a9446572cb98",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
-     198: "a2a9f432b6c073ba7edc1922cb905009",
+     198: "42ec586c02a582da48fa3d0be00043c7",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      229: "5544552803512ca667342d5011dedd1d",
 }

From c2ecbcd97348573d9b0844412203d982e3fbb965 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 12 Sep 2018 19:03:41 -0400
Subject: [PATCH 298/753] Update backtest

---
 .../02 Method.html                            | 49 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 27 insertions(+), 26 deletions(-)

diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
index 41df415..3cce496 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
@@ -100,7 +100,7 @@
 df_date.index = pd.to_datetime(df_date.index)
 df_date['expiry']=pd.to_datetime(df_date['expiry'])
 idx = pd.date_range('03-16-2011', '04-19-2019')
-# populate the date index 
+# populate the date index
 expiry_date = df_date.reindex(idx, method='bfill')
 df = pd.concat([settle, expiry_date], axis=1, join='inner')
 </pre>
@@ -131,18 +131,19 @@
 </P>
 <div class="section-example-container">
 <pre class="python">
+# calculate the daily roll
 daily_roll = (self.Securities[self.vx1].Price - self.Securities[self.vix].Price)/(self.front_VX.Expiry-self.Time).days
 if not self.Portfolio[self.front_VX.Symbol].Invested:
-    # Long if the contract is in contango with adaily roll greater than 0.10
+    # Short if the contract is in contango with adaily roll greater than 0.10
     if daily_roll > 0.1:
-        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
-        self.SetHoldings(self.front_VX.Symbol, 0.5)
-        self.SetHoldings(self.front_ES.Symbol, 0.5*hedge_ratio)
-    # Short if the contract is in backwardation with adaily roll less than -0.10
-    elif daily_roll < -0.1:
         hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
         self.SetHoldings(self.front_VX.Symbol, -0.5)
         self.SetHoldings(self.front_ES.Symbol, -0.5*hedge_ratio)
+    # Long if the contract is in backwardation with adaily roll less than -0.10
+    elif daily_roll < -0.1:
+        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
+        self.SetHoldings(self.front_VX.Symbol, 0.5)
+        self.SetHoldings(self.front_ES.Symbol, 0.5*hedge_ratio)
 </pre>
 </div>
 <p>
@@ -152,27 +153,27 @@
 </P>
 <div class="section-example-container">
 <pre class="python">
-# exit if the daily roll being less than 0.05 if holding long positions
- if self.Portfolio[self.front_VX.Symbol].IsLong and daily_roll < 0.05:
-     self.Liquidate()
-     self.front_VX = None
-     self.front_ES = None
-     return
+# exit if the daily roll being less than 0.05 if holding short positions
+if self.Portfolio[self.front_VX.Symbol].IsShort and daily_roll < 0.05:
+    self.Liquidate()
+    self.front_VX = None
+    self.front_ES = None
+    return
 
- # exit if the daily roll being greater than -0.05 if holding short positions
- if self.Portfolio[self.front_VX.Symbol].IsShort and daily_roll > -0.05:
-     self.Liquidate()
-     self.front_VX = None
-     self.front_ES = None
-     return
+# exit if the daily roll being greater than -0.05 if holding long positions
+if self.Portfolio[self.front_VX.Symbol].IsLong and daily_roll > -0.05:
+    self.Liquidate()
+    self.front_VX = None
+    self.front_ES = None
+    return
 
 if self.front_VX and self.front_ES:
 # if these exit conditions are not triggered, trades are exited two days before it expires
 if self.Portfolio[self.front_VX.Symbol].Invested and self.Portfolio[self.front_ES.Symbol].Invested:
- if (self.front_VX.Expiry-self.Time).days <=2 or (self.front_ES.Expiry-self.Time).days <=2:
-     self.Liquidate()
-     self.front_VX = None
-     self.front_ES = None
-     return
+  if (self.front_VX.Expiry-self.Time).days <=2 or (self.front_ES.Expiry-self.Time).days <=2:
+      self.Liquidate()
+      self.front_VX = None
+      self.front_ES = None
+      return
    </pre>
    </div>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
index 18eebdf..51e5f5c 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_42ec586c02a582da48fa3d0be00043c7.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ba6e621c07abbe02645a7537d9909a88.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 4c36517..8ac6ba3 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -39,7 +39,7 @@
      114: "7a404f43e9191ac5bce9a9446572cb98",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
-     198: "42ec586c02a582da48fa3d0be00043c7",
+     198: "ba6e621c07abbe02645a7537d9909a88",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      229: "5544552803512ca667342d5011dedd1d",
 }

From c8979c4c557cbdf5d8083fcf41c9b432dc6212c4 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 13 Sep 2018 13:35:15 -0400
Subject: [PATCH 299/753] Add chart

---
 .../02 Method.html                            | 60 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 33 insertions(+), 31 deletions(-)

diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
index 3cce496..5d07400 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
@@ -5,15 +5,12 @@
 <div class="section-example-container">
 <pre class="python">
 def Initialize(self):
-    self.SetStartDate(2014, 1, 1)   # Set Start Date
+    self.SetStartDate(2011, 1, 1)   # Set Start Date
     self.SetEndDate(2018, 9, 1)     # Set End Date
     self.SetCash(10000000)          # Set Strategy Cash
-    self.vix = "CBOE/VIX"
-    self.vx1 = "CHRIS/CBOE_VX1"
-    self.es1 = "CHRIS/CME_ES1"
-    self.AddData(QuandlVix, self.vix, Resolution.Daily)           # Add Quandl VIX price (daily)
-    self.AddData(QuandlFutures, self.vx1, Resolution.Daily)       # Add Quandl VIX front month futures data (daily)
-    self.AddData(QuandlFutures, self.es1, Resolution.Daily)       # Add Quandl E-mini S&P500 front month futures data (daily)
+    self.vix = self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily).Symbol              # Add Quandl VIX price (daily)
+    self.vx1 = self.AddData(QuandlFutures, "CHRIS/CBOE_VX1", Resolution.Daily).Symbol    # Add Quandl VIX front month futures data (daily)
+    self.es1 = self.AddData(QuandlFutures, "CHRIS/CME_ES1", Resolution.Daily).Symbol     # Add Quandl E-mini S&P500 front month futures data (daily)
     # Add VIX futures contract data
     self.AddFuture(Futures.Indices.VIX).SetFilter(timedelta(0), timedelta(days=180))
     # Add E-mini S&P500 futures contract data
@@ -39,11 +36,11 @@
     # select the nearest VIX and E-mini S&P500 futures with at least 10 trading days to maturity
     # if the front contract expires, roll forward to the next nearest contract
     for chain in data.FutureChains:
-        if chain.Key.Value == "VX":
+        if chain.Key.Value == Futures.Indices.VIX:
             if self.front_VX is None or ((self.front_VX.Expiry-self.Time).days <= 1):
                 contracts = list(filter(lambda x: x.Expiry >= self.Time + timedelta(days = 10), chain.Value))
                 self.front_VX = sorted(contracts, key = lambda x: x.Expiry)[0]
-        if chain.Key.Value == "ES":
+        if chain.Key.Value == Futures.Indices.SP500EMini:
             if self.front_ES is None or ((self.front_ES.Expiry-self.Time).days <= 1):
                 contracts = list(filter(lambda x: x.Expiry >= self.Time + timedelta(days = 10), chain.Value))
                 self.front_ES = sorted(contracts, key = lambda x: x.Expiry)[0]
@@ -67,24 +64,24 @@
 \[HR_t=\frac{\beta_1*1000+\beta_2*TimeToSettlement_{t-1}*1000}{0.01*P^{ES}_{t-1}*50}\]
 <div class="section-example-container">
 <pre class="python">
-def CalculateHedgeRatio(self, price_VX, price_ES, days_to_maturity):
-    # the regression method to calculate the hedge ratio
-    price_VX = np.array(price_VX)
-    price_ES = np.array(price_ES)
-    delta_VX = np.diff(price_VX)
-    res_ES = np.diff(price_ES)/price_ES[:-1]*100
-    tts = np.array(days_to_maturity)[1:]
-    df = pd.DataFrame({"delta_VX":delta_VX, "SPRET":res_ES, "product":res_ES*tts}).dropna()
-    # remove rows with zero value
-    df = df[(df != 0).all(1)]
-    y = df['delta_VX'].astype(float)
-    X = df[['SPRET', "product"]].astype(float)
-    X = sm.add_constant(X)
-    model = sm.OLS(y, X).fit()
-    beta_1 = model.params[1]
-    beta_2 = model.params[2]
-    hedge_ratio = abs((1000*beta_1 + beta_2*((self.front_VX.Expiry-self.Time).days)*1000)/(0.01*50*float(self.Securities[self.es1].Price)))
-    return hedge_ratio
+  def CalculateHedgeRatio(self):
+      price_VX = np.array(self.price_VX)
+      price_ES = np.array(self.price_ES)
+      delta_VX = np.diff(price_VX)
+      res_ES = np.diff(price_ES)/price_ES[:-1]*100
+      tts = np.array(self.days_to_maturity)[1:]
+      df = pd.DataFrame({"delta_VX":delta_VX, "SPRET":res_ES, "product":res_ES*tts}).dropna()
+      # remove rows with zero value
+      df = df[(df != 0).all(1)]
+      y = df['delta_VX'].astype(float)
+      X = df[['SPRET', "product"]].astype(float)
+      X = sm.add_constant(X)
+      model = sm.OLS(y, X).fit()
+      beta_1 = model.params[1]
+      beta_2 = model.params[2]
+      hedge_ratio = abs((1000*beta_1 + beta_2*((self.front_VX.Expiry-self.Time).days)*1000)/(0.01*50*float(self.Securities[self.es1].Price)))
+      self.Plot("Trade", "Hedge Ratio", hedge_ratio)
+      return hedge_ratio
 </pre>
 </div>
 <p>
@@ -133,15 +130,20 @@
 <pre class="python">
 # calculate the daily roll
 daily_roll = (self.Securities[self.vx1].Price - self.Securities[self.vix].Price)/(self.front_VX.Expiry-self.Time).days
+self.Plot("Trade", "VIX", self.Securities[self.vix].Price)
+self.Plot("Trade", "VIX Futures", self.Securities[self.vx1].Price)
+self.Plot("Trade", "Daily Roll", daily_roll)
 if not self.Portfolio[self.front_VX.Symbol].Invested:
     # Short if the contract is in contango with adaily roll greater than 0.10
     if daily_roll > 0.1:
-        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
+        hedge_ratio = self.CalculateHedgeRatio()
+        self.Plot("Trade", "Sell", self.Securities[self.vx1].Price)
         self.SetHoldings(self.front_VX.Symbol, -0.5)
         self.SetHoldings(self.front_ES.Symbol, -0.5*hedge_ratio)
     # Long if the contract is in backwardation with adaily roll less than -0.10
     elif daily_roll < -0.1:
-        hedge_ratio = self.CalculateHedgeRatio(self.price_VX, self.price_ES, self.days_to_maturity)
+        hedge_ratio = self.CalculateHedgeRatio()
+        self.Plot("Trade", "Buy", self.Securities[self.vx1].Price)
         self.SetHoldings(self.front_VX.Symbol, 0.5)
         self.SetHoldings(self.front_ES.Symbol, 0.5*hedge_ratio)
 </pre>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
index 51e5f5c..b702a4f 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ba6e621c07abbe02645a7537d9909a88.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_59a110653538ebcbfccacbe237c33091.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 8ac6ba3..1652ce6 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -39,7 +39,7 @@
      114: "7a404f43e9191ac5bce9a9446572cb98",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
-     198: "ba6e621c07abbe02645a7537d9909a88",
+     198: "59a110653538ebcbfccacbe237c33091",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      229: "5544552803512ca667342d5011dedd1d",
 }

From 4e9bc07b3429fc8e18ed4de0f904ded842b06a4c Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 13 Sep 2018 14:45:24 -0400
Subject: [PATCH 300/753] Edit the title

---
 .../00 Strategy Library/01 Strategy Library.php               | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 44a988f..79c201e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -399,8 +399,8 @@
         'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days."
     ],
     [
-        'name' => 'Term Structure of VIX Futures',
-        'link' => 'strategy-library/term-structure-of-vix-futures',
+        'name' => 'Exploiting Term Structure of VIX Futures',
+        'link' => 'strategy-library/exploiting-term-structure-of-vix-futures',
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/198'
         ],

From d06e1b4089a9ed5cb5b637f471d020b1ba19eac0 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 14 Sep 2018 18:08:30 -0400
Subject: [PATCH 301/753] Add algorithm beta factors in stocks

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |  10 ++
 .../77 Beta Factors in Stocks/02 Method.html  | 125 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../77 Beta Factors in Stocks/04 Source.html  |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 155 insertions(+)
 create mode 100644 04 Strategy Library/77 Beta Factors in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/77 Beta Factors in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html
 create mode 100644 04 Strategy Library/77 Beta Factors in Stocks/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 80e4915..03d814a 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -398,6 +398,14 @@
         ],
         'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days."
     ],
+    [
+        'name' => 'Beta Factors in Stocks',
+        'link' => 'strategy-library/beta-factors-in-stocks',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/77'
+        ],
+        'description' => "Goes long stocks with the bottom beta and short stocks with the top beta, securities are weighted by the ranked betas."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/01 Introduction.html b/04 Strategy Library/77 Beta Factors in Stocks/01 Introduction.html
new file mode 100644
index 0000000..4b193a4
--- /dev/null
+++ b/04 Strategy Library/77 Beta Factors in Stocks/01 Introduction.html	
@@ -0,0 +1,10 @@
+<p>
+  Beta is a statistical measure of a stock's volatility in relation to the market. Stock analysts use this measure to get a sense of stocks' risk profiles.
+  It is also a key component of the capital asset pricing model (CAPM), A stock's price variability is essential to consider when assessing risk. It represents the co-movement instead of the volatility. Therefore, it is possible for a stock to have zero beta and higher volatility than the market.
+</p>
+<p>
+  In the real world, some investors are prohibited from using leverage and other investors’ leverage is limited by margin requirements.
+  Therefore, their only way to achieve higher returns is to buy more risky stocks, which would cause the overvaluation of higher-beta stocks.
+  This behavior suggests that high-beta (risky) stocks should deliver lower risk-adjusted returns than low-beta stocks.
+  In this algorithm, we'll use the leverage to explore the inefficiency of the beta factor.
+</p>
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/02 Method.html b/04 Strategy Library/77 Beta Factors in Stocks/02 Method.html
new file mode 100644
index 0000000..e043cf6
--- /dev/null
+++ b/04 Strategy Library/77 Beta Factors in Stocks/02 Method.html	
@@ -0,0 +1,125 @@
+<p>
+  The investment universe consists of all stocks in Nasdaq and NYSE.
+  We use the Wilshire 5000 Total Market Index which covers all stocks actively traded in the United States.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+    self.SetStartDate(2011, 1, 1)
+    self.SetEndDate(2018, 9, 1)
+    self.SetCash(1000000)
+    self.UniverseSettings.Resolution = Resolution.Daily
+    self.AddUniverse(self.CoarseSelectionFunction)
+    self.AddEquity("SPY", Resolution.Daily)
+    # add Wilshire 5000 Total Market Index data from Dropbox
+    self.AddData(Wilshire5000, "W5000", Resolution.Daily)
+    self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), self.rebalance)
+    self.data = {}
+    self.monthly_rebalance = False
+    self.long = None
+    self.short = None
+    self.market_price = deque(maxlen=253)
+    hist = self.History(["W5000"], 400, Resolution.Daily)
+    for i in hist.loc["W5000"].itertuples():
+        self.market_price.append(i.value)
+</pre>
+</div>
+<p>
+  The formula for calculating beta is the covariance of the return of an asset with the return of the market divided by the variance of the return of the market over a certain period.
+</P>
+\[\beta_i=\frac{cov(R_i,R_{m})}{Var(R_m)}\]
+<p>
+  We choose the 1-year rolling window as the period in the beta calculation. We created the <code>SymbolData</code> class to update the rolling window of return
+  and the calculation of beta.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  class SymbolData:
+      def __init__(self, symbol):
+          self.Symbol = symbol
+          self.window = RollingWindow[Decimal](2)
+          self.returns = deque(maxlen=252)
+
+      def Update(self, price):
+          if price != 0:
+              self.window.Add(price)
+              if self.window.IsReady:
+                  self.returns.append((self.window[0]-self.window[1])/self.window[1])
+
+      def IsReady(self):
+          return len(self.returns) == self.returns.maxlen
+
+      def beta(self, market_ret):
+          asset_return = np.array(self.returns, dtype=np.float32)
+          market_return = np.array(market_ret, dtype=np.float32)
+          return np.cov(asset_return, market_return)[0][1]/np.var(market_return)
+</pre>
+</div>
+<p>
+  In <code>CoarseSelectionFunction</code>, we filter the stocks which price is lower than five as they are not active in the market.
+  When the return rolling window is ready, Stocks are then ranked in ascending order on the basis of their estimated beta.
+  The algorithm goes long on five stocks at the bottom beta list and short on five stocks at the top beta list.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  def CoarseSelectionFunction(self, coarse):
+
+      if self.Securities["W5000"].Price is not None:
+          self.market_price.append(self.Securities["W5000"].Price)
+      for i in coarse:
+          if i.Symbol not in self.data:
+              self.data[i.Symbol] = SymbolData(i.Symbol)
+          self.data[i.Symbol].Update(i.AdjustedPrice)
+
+      if self.monthly_rebalance:
+          sortedByPrice = [i.Symbol for i in coarse if i.AdjustedPrice>5]
+          ready_data = {symbol: data for symbol, data in self.data.items() if symbol in sortedByPrice and data.IsReady()}
+          if len(ready_data) > 20:
+              self.market_return = np.diff(np.array(self.market_price))/np.array(self.market_price)[:-1]
+              # sort the dictionary in ascending order by beta value
+              sorted_beta = sorted(ready_data, key = lambda x: ready_data[x].beta(self.market_return))
+              self.long = sorted_beta[:5]
+              self.short = sorted_beta[-5:]
+              return self.long+self.short
+          else:
+              self.monthly_rebalance = False
+              return []
+      else:
+          return []
+</pre>
+</div>
+<p>
+  In each portfolio, securities are weighted by the ranked betas. Lower-beta stocks have larger weights in the low-beta portfolio and higher-beta
+  stocks have larger weights in the high-beta portfolio. The portfolios are rebalanced every calendar month.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+      if not self.monthly_rebalance: return
+      if self.long is None or self.short is None: return
+
+      long_invested = [x.Key for x in self.Portfolio if x.Value.IsLong]
+      short_invested = [x.Key for x in self.Portfolio if x.Value.IsShort]
+
+      for i in long_invested:
+          if i not in self.long:
+              self.Liquidate(i)
+
+      for i in short_invested:
+          if i not in self.short:
+              self.Liquidate(i)
+
+      long_scale_factor = 0.5/sum(range(1,len(self.long)+1))
+      for rank, symbol in enumerate(self.long):
+          self.SetHoldings(symbol, (len(self.long)-rank+1)*long_scale_factor)
+
+      short_scale_factor = 0.5/sum(range(1,len(self.long)+1))
+      for rank, symbol in enumerate(self.short):
+          self.SetHoldings(symbol, -(rank+1)*short_scale_factor)
+
+
+      self.monthly_rebalance = False
+      self.long = None
+      self.short = None
+</pre>
+</div>
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html
new file mode 100644
index 0000000..c9839cd
--- /dev/null
+++ b/04 Strategy Library/77 Beta Factors in Stocks/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_0483e5a7094604254ab37eda8b5141b8.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/77 Beta Factors in Stocks/04 Source.html b/04 Strategy Library/77 Beta Factors in Stocks/04 Source.html
new file mode 100644
index 0000000..f65d0bf
--- /dev/null
+++ b/04 Strategy Library/77 Beta Factors in Stocks/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/77" rel="nofollow">Quantpedia - Beta Factor in Stocks</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index efd9562..4481975 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -31,6 +31,7 @@
      61: "17611f8cf05678b9bd09d98ba3581854",
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
+     77: "0483e5a7094604254ab37eda8b5141b8",
      83: "fdfcddd132eaf55039d867c03efe3012",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",

From bda1def597628ca81609eb7317951db22efb10ac Mon Sep 17 00:00:00 2001
From: DataBoyTX <loki_austin@sbcglobal.net>
Date: Mon, 17 Sep 2018 15:26:03 -0500
Subject: [PATCH 302/753] Change Symbol GOOG to AAPL - fixes typo

In this simple example on how to compute intrinsic value and time value of a single option, two different symbols are used, which is confusing, and I am pretty sure it's just typo.  Simply change GOOG to AAPL
---
 .../01 General Features of Options/04 The Value of Options.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html
index 975eb99..f0877cc 100755
--- a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
@@ -12,5 +12,5 @@
 \[Time Value= Premium-Intrinsic Value\]
 </div>
 <p>
-For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of GOOG is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
+For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
 </p>

From 76e286a0df0c7c2190902fea8c405736cac22f3c Mon Sep 17 00:00:00 2001
From: Thomas Cook <loki_austin@sbcglobal.net>
Date: Thu, 20 Sep 2018 14:43:37 -0500
Subject: [PATCH 303/753] fixes to links to API docs

fixes links to AddOption and Option class API docs
---
 .../02 QuantConnect Options API/02 Add Options.html           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
index 0ab569f..fa84171 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
@@ -10,7 +10,7 @@
 </thead>
 <tbody>
 <tr>
-<td><a href="https://www.quantconnect.com/lean/docs#topic117.html">AddOption</a>(underlying,
+<td><a href="https://www.quantconnect.com/lean/docs#topic123.html">AddOption</a>(underlying,
 resolution,
 fillDataForward)</td>
 <td><strong>underlying(string):</strong> The underlying equity symbol
@@ -31,5 +31,5 @@
 </pre>
 </div>
 <p>
-  The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic19245.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
+  The return value of AddOption method is an <a href="https://www.quantconnect.com/lean/docs#topic27616.html">option security object</a> , please refer to the link for detailed properties and methods of option class.
 </p>

From d5cc77f7ec031384eb63bf7e0cc8f98e82073568 Mon Sep 17 00:00:00 2001
From: Thomas Cook <loki_austin@sbcglobal.net>
Date: Thu, 20 Sep 2018 14:57:17 -0500
Subject: [PATCH 304/753] fix links to API and fix some grammar

---
 .../02 QuantConnect Options API/03 Filter Contracts.html    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html
index 02b6927..be1a2be 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/03 Filter Contracts.html	
@@ -1,5 +1,5 @@
 <p>
-  After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic22332.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
+  After adding the options for specific underlying stock,  you can set the filter criteria with <a href="https://www.quantconnect.com/lean/docs#topic27633.html">SetFilter</a> method to pull contracts using the specified min and max strike and expiration range values you need for a given symbol.
 </p>
 
 <table class="table qc-table">
@@ -22,10 +22,10 @@
 </tbody>
 </table>
 <p>
-  Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative values with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K might not being $100 since rarely will option be ATM exactly). Then  filter will looks for options with strikes between and including (777.5 + 2.5*2, 777.5 - 2.5*1). The time to expiration of these options are restricted within 90 days from now on. </span>
+  Here parameters <em>min Strike</em> and <em>max Strike</em> are the relative multipliers with respect to the market price. We use Google(NASDAQ: GOOG)<span lang="EN-GB"> as an example to describe the filter criteria. If today is 01/03/2017, the market price of underlying stock is $776, the strike prices of GOOG options are spaced $2.5. Then <code>SetFilter(-1, +2, timedelta(0), timedelta(90))</code> will fist look up at the money contracts with strike being K=$777.5 (Here K is 777.5 the next closest multiple to the underlying price of 775 since rarely will an option be ATM exactly). Then SetFilter will look for options with strikes between and including ( 777.5 - 2.5*1, 777.5 + 2.5*2). The time to expiration of these options are restricted within 90 days from now on. </span>
 </p>
 <p>
-  For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there is no contracts satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
+  For the strike, the exchange normally chooses the strike prices at which options can be written so that they are spaced $2.50, $5, or $10 apart. Typically the spacing is $2.50 when the stock price is between $5 and $25, $5 when the stock price is between $25 and $200, and $10 for stock prices above $200. So you should carefully choose the parameters of <em>min strike</em> and <em>max Strike</em> in case there are no contracts that satisfy the filter criteria if the range is too small, less than the minimum units of strike prices change.
 </p>
 <p>
   For the expiry, there are many expiration dates that apply to the different series of options. An option cycle is the pattern of months in which options contracts expire. There are three kinds of common option cycles. The options on the January cycle have contracts available in the first month of each quarter (January, April, July and October). Options assigned to the February cycle use the middle month of each quarter (February, May, August and November). And options in the March cycle have options available during the last month of each quarter (March, June, September and December). In addition, individual stock options typically expire in the current month and the subsequent month.

From 1e1fa1751c0903fbd4253fb789e382aac9f1791a Mon Sep 17 00:00:00 2001
From: Thomas Cook <loki_austin@sbcglobal.net>
Date: Thu, 20 Sep 2018 15:00:05 -0500
Subject: [PATCH 305/753] fix links to API docs

---
 .../02 QuantConnect Options API/04 Select Contracts.html  | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index 9b62ccc..cbf5dbc 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -1,11 +1,11 @@
 <p>
-  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
+  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic8772.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
 
-<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
+<a href="https://www.quantconnect.com/lean/docs#topic24223.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
 
-<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
+<a href="https://www.quantconnect.com/lean/docs#topic24199.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
 
-<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
+<a href="https://www.quantconnect.com/lean/docs#topic24232.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
 </p>
 
 <table class="table qc-table">

From 2e35498ae598cb96fd1151e95eefa30c7df0f1db Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 20 Sep 2018 16:00:47 -0400
Subject: [PATCH 306/753] Add algorithm monthly cycle in cross section stock
 returns

---
 .../01 Strategy Library.php                   |  8 ++
 .../01 Introduction.html                      |  5 ++
 .../02 Method.html                            | 74 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 5 files changed, 98 insertions(+)
 create mode 100644 04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/01 Introduction.html
 create mode 100644 04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/02 Method.html
 create mode 100644 04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html
 create mode 100644 04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 79c201e..b88566b 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -406,6 +406,14 @@
         ],
         'description' => "Buys or sells the nearest VIX futures based on the daily roll and hedge against the open positions with E-mini S&amp;P500 futures"
     ],
+    [
+        'name' => '12 Month Cycle in Cross-Section of Stocks Returns',
+        'link' => 'strategy-library/12-month-cycle-in-cross-section-of-stocks-returns',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
+        ],
+        'description' => "Goes long in stocks from the winner of and shorts stocks from loser according to their performance in January one year ago"
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/01 Introduction.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/01 Introduction.html
new file mode 100644
index 0000000..e5ef779
--- /dev/null
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  January effect in stocks market says that stocks perform especially well in the first month of the year. This seasonal effect might lead us to think
+  that stocks performed well in the last year's January will perform well in this year's January. In this tutorial, we're going to implement a strategy based
+  on this yearly seasonal effect.
+</p>
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/02 Method.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/02 Method.html
new file mode 100644
index 0000000..8663ff7
--- /dev/null
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/02 Method.html	
@@ -0,0 +1,74 @@
+<p>
+  The investment universe consists of all stocks from AMEX and NYSE. Firstly, in <code>CoarseSelectionFunction</code>, we remove stocks which do not have fundamental data.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+        if self.monthly_rebalance:
+            coarse = [x for x in coarse if (x.HasFundamentalData)
+                                        and (x.Market == "usa")]
+            return [i.Symbol for i in coarse]
+        else:
+            return []
+</pre>
+</div>
+<p>
+  Next, we are going to filter the top 30% of stocks based on their market cap.
+  In implementation, the market cap is calculated with the <code>BasicAverageShares</code>, <code>BasicEPS</code> and <code>PERatio</code>.
+  Then we request the history price 12 months ago and 11 months ago to calculate the January return of the last year.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.monthly_rebalance:
+        fine =[i for i in fine if ((i.SecurityReference.ExchangeId == "NYS") or (i.SecurityReference.ExchangeId == "ASE"))]
+        self.filtered_fine = []
+
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.TwelveMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+            history_start = self.History([i.Symbol], TimeSpan.FromDays(365))
+            history_end = self.History([i.Symbol],TimeSpan.FromDays(335))
+            if not history_start.empty and not history_end.empty:
+                i.Returns = float(history_end.iloc[0]["close"] - history_start.iloc[0]["close"])
+                self.filtered_fine.append(i)
+
+        size = int(len(fine)*.3)
+        self.filtered_fine = sorted(self.filtered_fine, key = lambda x: x.MarketCap, reverse=True)
+        self.filtered_fine = self.filtered_fine[:size]
+        self.filtered_fine = sorted(self.filtered_fine, key = lambda x: x.Returns, reverse=True)
+        symbols = [i.Symbol for i in self.filtered_fine]
+        self.filtered_fine = symbols
+        return symbols
+    else:
+        return []
+</pre>
+</div>
+<p>
+  Every month, stocks are grouped into ten portfolios with the equal number of stocks in each portfolio according to their performance in January one year ago.
+  Investors go long in stocks from the winner decile and shorts stocks from loser decile. The portfolio is equally weighted and rebalanced every month.
+</p>
+
+</P>
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+       if not (self.monthly_rebalance): return
+       if not (self.filtered_fine): return
+       self.monthly_rebalance = False
+
+       portfolio_size = int(len(self.filtered_fine)/10)
+       short_stocks = self.filtered_fine[-portfolio_size:]
+       long_stocks = self.filtered_fine[:portfolio_size]
+       stocks_invested = [x.Key for x in self.Portfolio]
+       for i in stocks_invested:
+           #liquidate the stocks not in the filtered balance sheet accrual list
+           if i not in self.filtered_fine:
+               self.Liquidate(i)
+           #long the stocks in the list
+           elif i in long_stocks:
+               self.SetHoldings(i, 1/(portfolio_size*2))
+           #short the stocks in the list
+           elif i in short_stocks:
+               self.SetHoldings(i,-1/(portfolio_size*2))
+</pre>
+</div>
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html
new file mode 100644
index 0000000..6189f9c
--- /dev/null
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_1f9f254c932d8e7ae85e803affd52a7b.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/04 Source.html b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/04 Source.html
new file mode 100644
index 0000000..53aad28
--- /dev/null
+++ b/04 Strategy Library/125 12 Month Cycle in Cross-Section of Stocks Returns/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/125" rel="nofollow">Quantpedia - 12 Month Cycle in Cross-Section of Stocks Returns</a>
+</li>
+</ul>

From 29baa56f5ffdbef3779cfb602dacd6fc8d7ff90d Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Thu, 20 Sep 2018 16:14:11 -0400
Subject: [PATCH 307/753] Update json file

---
 quantpedia.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/quantpedia.json b/quantpedia.json
index 1652ce6..af6943b 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -37,6 +37,7 @@
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",
+     125: "1f9f254c932d8e7ae85e803affd52a7b",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      198: "59a110653538ebcbfccacbe237c33091",

From 8cfd5e0d1bc42074805b375d3771479703657f45 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 24 Sep 2018 10:30:51 -0400
Subject: [PATCH 308/753] Add algorithm Momentum Effect in Stocks in Small
 Portfolios

---
 .../01 Strategy Library.php                   | 10 ++-
 .../01 Introduction.html                      |  6 ++
 .../02 Method.html                            | 68 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 95 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 Introduction.html
 create mode 100644 04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html
 create mode 100644 04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html
 create mode 100644 04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e7b7d24..2bfb941 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -420,7 +420,15 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
         ],
-        'description' => "Goes long in stocks from the winner of and shorts stocks from loser according to their performance in January one year ago"
+        'description' => "Goes long in stocks from the winner of and shorts stocks from loser according to their performance in January one year ago."
+    ],
+    [
+        'name' => 'Momentum Effect in Stocks in Small Portfolios',
+        'link' => 'strategy-library/momentum-effect-in-stocks-in-small-portfolios',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/162'
+        ],
+        'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year."
     ],
 ];
 
diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 Introduction.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 Introduction.html
new file mode 100644
index 0000000..d0d909e
--- /dev/null
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  The main reason for the momentum anomaly is the behavioral biases of the investor like underreaction and confirmation bias.
+  Momentum strategy usually uses portfolios filled by thousands of stocks to compute the momentum factor return.
+  This is not possible for small retail investors with small portfolios. They are constrained compared to big hedge funds and cannot diversify so well.
+  In this tutorial, we'll construct a small portfolio consisting of up to 50 stocks to check the effect of momentum.
+</p>
diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html
new file mode 100644
index 0000000..07d8281
--- /dev/null
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html	
@@ -0,0 +1,68 @@
+<p>
+  The investment universe consists of all US listed companies. Stocks which have no fundamental data are ruled out from the universe.
+</P>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    if self.yearly_rebalance:
+        # drop stocks which have no fundamental data
+        self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
+        return self.filtered_coarse
+    else:
+        return []
+</pre>
+</div>
+<p>
+  In <code>FineSelectionFunction</code>, stocks with the lowest market capitalization (25% of the universe) are excluded due to liquidity reasons.
+  The momentum is defined as the stock market return over the previous 12 months. Momentum profits are calculated by ranking companies on the basis of yearly return.
+  The ranking period is one year.
+</P>
+<div class="section-example-container">
+<pre class="python">
+  def FineSelectionFunction(self, fine):
+      if self.yearly_rebalance:
+          # Calculate the yearly return and market cap
+          for i in fine:
+              i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+          top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len(fine)*0.75)]
+          has_return = []
+          for i in top_market_cap:
+              history = self.History([i.Symbol], timedelta(days=365), Resolution.Daily)
+              if not history.empty:
+                  close = history.loc[str(i.Symbol)]['close']
+                  i.returns = (close[0]-close[-1])/close[-1]
+                  has_return.append(i)
+          sorted_by_return = sorted(has_return, key = lambda x: x.returns)
+          self.long = [i.Symbol for i in sorted_by_return[-10:]]
+          self.short = [i.Symbol for i in sorted_by_return[:10]]
+
+          return self.long+self.short
+      else:
+          return []
+</pre>
+</div>
+<p>
+  The investor goes long in the ten stocks with the highest performance and goes short in the ten stocks with the lowest performance.
+  The portfolio is equally weighted and rebalanced yearly.
+</p>
+
+</P>
+<div class="section-example-container">
+<pre class="python">
+  def OnData(self, data):
+      if not self.yearly_rebalance: return
+      if self.long and self.short:
+          stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+          # liquidate stocks not in the trading list
+          for i in stocks_invested:
+              if i not in self.long+self.short:
+                  self.Liquidate(i)
+          for i in self.short:
+              self.SetHoldings(i, -0.5/len(self.short))
+          for i in self.long:
+              self.SetHoldings(i, 0.5/len(self.long))
+          self.long = None
+          self.short = None
+          self.yearly_rebalance = False
+</pre>
+</div>
diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html
new file mode 100644
index 0000000..6bbcd8e
--- /dev/null
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5761c1817a9dcb805be487f9e32adfa1.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 Source.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 Source.html
new file mode 100644
index 0000000..0cfa5d6
--- /dev/null
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/162" rel="nofollow">Quantpedia - Momentum Effect in Stocks in Small Portfolios</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 0f4cb34..d278b43 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -41,6 +41,7 @@
      125: "1f9f254c932d8e7ae85e803affd52a7b",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
+     162: "5761c1817a9dcb805be487f9e32adfa1",
      198: "59a110653538ebcbfccacbe237c33091",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      229: "5544552803512ca667342d5011dedd1d",

From 6a76f076f848c6e6cff1d7a5f303604c7bc36264 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 25 Sep 2018 12:20:15 -0400
Subject: [PATCH 309/753] Update backtest

---
 .../06 Iron Condor/02 Implementation.html     | 24 +++++++++++--------
 .../06 Iron Condor/04 Algorithm.html          |  4 ++--
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/07 Applied Options[]/06 Iron Condor/02 Implementation.html b/07 Applied Options[]/06 Iron Condor/02 Implementation.html
index 37ffc44..7441745 100755
--- a/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
+++ b/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
@@ -3,16 +3,20 @@
 </p>
 <div class="section-example-container">
 
-<pre class="python">def Initialize(self):
-	self.SetStartDate(2017, 2, 1)
-	self.SetEndDate(2017, 3, 31)
-	self.SetCash(150000)
-	equity = self.AddEquity("GOOG", Resolution.Minute)
-	option = self.AddOption("GOOG", Resolution.Minute)
-	self.symbol = option.Symbol
-	option.SetFilter(-20, 20, timedelta(0), timedelta(40))
-	# use the underlying equity GOOG as the benchmark
-	self.SetBenchmark(equity.Symbol)
+<pre class="python">
+def Initialize(self):
+		self.SetStartDate(2017, 2, 1)
+		self.SetEndDate(2017, 3, 1)
+		self.SetCash(500000)
+		equity = self.AddEquity("GOOG", Resolution.Minute)
+		option = self.AddOption("GOOG", Resolution.Minute)
+		self.symbol = option.Symbol
+		option.SetFilter(self.UniverseFunc)
+		# use the underlying equity GOOG as the benchmark
+		self.SetBenchmark(equity.Symbol)
+
+def UniverseFunc(self, universe):
+    return universe.IncludeWeeklys().Strikes(-15, 15).Expiration(timedelta(0), timedelta(40))
 </pre>
 </div>
 <p>
diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
index 7fdc852..cf2d154 100755
--- a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e95a3d8feac3b92b3f39233f00e09ae5.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c2a81c53ec674492e16d3e8da52baecd.html"></iframe>
     </div>
 </div>
 <p>
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_66bf622f52dbb2a6cb56ccf992d64765.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e496915dea9a5b0efcd70d0f77309261.html"></iframe>
     </div>
 </div>

From 06e14770cabeade5bb9e79d6eb8296bc586ef7e1 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 25 Sep 2018 12:56:37 -0400
Subject: [PATCH 310/753] Update the algorithm description

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2bfb941..072f0ba 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -420,7 +420,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
         ],
-        'description' => "Goes long in stocks from the winner of and shorts stocks from loser according to their performance in January one year ago."
+        'description' => "Reviews the returns from last January, going long on the top 10% winners and short the bottom 10%."
     ],
     [
         'name' => 'Momentum Effect in Stocks in Small Portfolios',

From 912e76cc9aa7b16ffd8a86bfb4e0ef9750fb7e58 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 25 Sep 2018 13:04:10 -0400
Subject: [PATCH 311/753] Update the method

---
 .../02 Method.html                                        | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html
index 07d8281..e614895 100644
--- a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html	
+++ b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 Method.html	
@@ -1,6 +1,6 @@
 <p>
   The investment universe consists of all US listed companies. Stocks which have no fundamental data are ruled out from the universe.
-</P>
+</p>
 <div class="section-example-container">
 <pre class="python">
 def CoarseSelectionFunction(self, coarse):
@@ -13,10 +13,10 @@
 </pre>
 </div>
 <p>
-  In <code>FineSelectionFunction</code>, stocks with the lowest market capitalization (25% of the universe) are excluded due to liquidity reasons.
+  In <code>FineSelectionFunction</code>, stocks with the lowest market capitalization (25% of the universe) are excluded due to low liquidity.
   The momentum is defined as the stock market return over the previous 12 months. Momentum profits are calculated by ranking companies on the basis of yearly return.
   The ranking period is one year.
-</P>
+</p>
 <div class="section-example-container">
 <pre class="python">
   def FineSelectionFunction(self, fine):
@@ -45,8 +45,6 @@
   The investor goes long in the ten stocks with the highest performance and goes short in the ten stocks with the lowest performance.
   The portfolio is equally weighted and rebalanced yearly.
 </p>
-
-</P>
 <div class="section-example-container">
 <pre class="python">
   def OnData(self, data):

From ef8469523dfe4b036eec137d08c881c83b7674b9 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 26 Sep 2018 21:16:03 -0400
Subject: [PATCH 312/753] Add algorithm Value Effect within Countries

---
 .../01 Strategy Library.php                   |  8 ++
 .../01 Introduction.html                      |  5 ++
 .../02 Method.html                            | 87 +++++++++++++++++++
 .../03 Algorithm.html                         |  6 ++
 .../04 Source.html                            |  5 ++
 quantpedia.json                               |  1 +
 6 files changed, 112 insertions(+)
 create mode 100644 04 Strategy Library/207 Value Effect within Countries/01 Introduction.html
 create mode 100644 04 Strategy Library/207 Value Effect within Countries/02 Method.html
 create mode 100644 04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html
 create mode 100644 04 Strategy Library/207 Value Effect within Countries/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 072f0ba..7e37f67 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -430,6 +430,14 @@
         ],
         'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year."
     ],
+    [
+        'name' => 'Value Effect within Countries',
+        'link' => 'strategy-library/value-effect-within-countries',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/207'
+        ],
+        'description' => "invests in the cheapest 33% of country ETFs according to CAPE ratios."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html b/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html
new file mode 100644
index 0000000..66cb373
--- /dev/null
+++ b/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+  The equity valuation has a predictive ability for future equity returns. There are various methodologies to evaluate whether
+  the equity is undervalued or overvalued using metrics like price-to-earnings (P/E), return on equity (ROE), dividend yield, book-to-equity and so on.
+  In this algorithm, we are going to use a ten-year normalized earnings metrics invented by Yale University professor Robert Shiller to find the fair value of the equity market.
+</p>
diff --git a/04 Strategy Library/207 Value Effect within Countries/02 Method.html b/04 Strategy Library/207 Value Effect within Countries/02 Method.html
new file mode 100644
index 0000000..e29c28d
--- /dev/null
+++ b/04 Strategy Library/207 Value Effect within Countries/02 Method.html	
@@ -0,0 +1,87 @@
+<p>
+  This new metric is called the cyclically adjusted price-to-earnings ratio (CAPE). The basic idea is comparing the stock prices with earnings smoothed across multiple years.
+  Using backward-looking earnings allows us to smooth out the economic cycle as well as the price fluctuations.
+  By definition, CAPE is the price divided by the average of ten years of earnings (moving average), adjusted for inflation.
+</p>
+<p>
+  The investment universe consists of 22 countries with easily accessible equity markets via ETFs.
+  We import the custom CAPE ratio(Shiller PE Ratio) data of those 22 countries and create a dictionary to save the corresponding country ETF.
+  The CAPE ratio data is in monthly resolution and it starts from January 2000.
+</p>
+<div class="section-example-container">
+<pre class="python">
+  class CAPE(PythonData):
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/fcv8x1xeqamg5lx/CAPERatio.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = CAPE()
+          index.Symbol = config.Symbol
+          # data format
+          # Date       Canada  UK     United States  France    Germany   Italy    Spain ...
+          # 1/31/00    45.7    25.08  42.18          55.94     51.35     54.34    32.16 ...
+          data = line.split(',')
+          index.Time = datetime.strptime(data[0], "%m/%d/%y")
+          symbols = Symbols().tickers
+          for key, value in symbols.items():
+              index[key] = float(data[value[0]]) if data[value[0]] else None
+          return index
+
+
+  class Symbols:
+      def __init__(self):
+          # the indiex is the country name
+          # the first element of the value is the column number of CAPE ratio value in custom dataset
+          # the second element of the value is the corresponding country ETF
+
+          self.tickers = {"Canada":[1, "XIC"],          # S&P/TSX Composite Index: iShares S&P TSX Capped Cmpst Indx Fnd
+                          "Uk":[2, "EWU"],              # FTSE 100 Index: iShares MSCI United Kingdom ETF
+                          "Us":[3, "SPY"],              # S&P 500 Index: SPDR S&P 500 ETF
+                          "France":[4, "EWQ"],          # CAC 40 Index: iShares MSCI France ETF
+                          "Germany":[5, "EWG"],         # HDAX Index: iShares MSCI Germany ETF
+                          "Italy":[6, "EWI"],           # FTSE MIB Index: iShares MSCI Italy ETF
+                          "Spain":[7, "EWP"],           # IBEX 35 Index: iShares MSCI Spain ETF
+                          "Russia":[8, "ERUS"],         # RTS Index: iShares MSCI Russia ETF
+                          "India":[9, "INDY"],          # NIFTY 50 Index: iShares India 50 ETF
+                          "Japan":[10, "EWJ"],          # All Public Companies: iShares MSCI Japan ETF
+                          "Singapore":[11, "EWS"],      # STI Index:  iShares MSCI Singapore ETF
+                          "Korea":[12,"EWY"],           # KOSPI Index: iShares MSCI South Korea ETF
+                          "China":[13, "MCHI"],         # SSE Composite: iShares MSCI China Index Fund
+                          "Hongkong":[14, "EWH"],       # Hang Seng Index: iShares MSCI Hong Kong Index Fund
+                          "Brazil":[15, "EWZ"],         # Indice Bovespa (Ibovespa): iShares MSCI Brazil ETF
+                          "Mexico":[16, "EWW"],         # &P/BMV IPC Index: iShares MSCI Mexico ETF
+                          "Southafrica":[17, "EZA"],    # FTSE/JSE CAP Top 40 Index: iShares MSCI South Africa ETF
+                          "Australia":[18, "EWA"],      # ASX All Ordinaries Index: iShares MSCI Australia ETF
+                          "Turkey":[19, "TUR"],         # BIST 100: iShares MSCI Turkey ETF
+                          "Poland":[20, "EPOL"],        # WIG Index: iShares MSCI Poland ETF
+                          "Indonesia":[21, "EIDO"],     # IDX Composite: iShares MSCI Indonesia ETF
+                          "Philippines":[22, "EPHE"]}   # PSE Composite:  iShares MSCI Philippines Investable
+</pre>
+</div>
+<p>
+  According to the academic research of Shiller and Campbell using market data from the S&P index, the lower the CAPE, the higher the investors' likely return from equities.
+  Therefore, the algorithm then invests in the cheapest 33% of countries from the sample with the lowest CAPE ratio if those countries have a CAPE below 15 (the algorithm holds 0% cash instead of countries with a CAPE higher than 15).
+  The portfolio is equally weighted and rebalanced monthly.
+</p>
+<div class="section-example-container">
+<pre class="python">
+  def Rebalance(self):
+      self.cape = {}
+      for key, value in self.symbols.items():
+          cape = getattr(self.slice["CAPE"], key)
+          if cape is not None:
+              self.cape[value[1]] = cape
+      sorted_cape = sorted(self.cape, key = lambda x: self.cape[x])
+      # invests the cheapest 33% of countries if those countries have a CAPE below 15
+      lowest_cape = sorted_cape[:int(1/3*len(sorted_cape))]
+      long_list = [i for i in lowest_cape if self.cape[i]<15]
+      invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+      for i in invested:
+          if i.Value not in long_list:
+              self.Liquidate(i)
+      for i in long_list:
+          self.SetHoldings(i, 1/len(long_list))
+</pre>
+</div>
diff --git a/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html
new file mode 100644
index 0000000..b581b49
--- /dev/null
+++ b/04 Strategy Library/207 Value Effect within Countries/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9bcf7ac117397af393ca59f795c4abdd.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/207 Value Effect within Countries/04 Source.html b/04 Strategy Library/207 Value Effect within Countries/04 Source.html
new file mode 100644
index 0000000..66df9ad
--- /dev/null
+++ b/04 Strategy Library/207 Value Effect within Countries/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/207" rel="nofollow">Quantpedia - Value Effect within Countries</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index d278b43..c81f0a9 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -44,5 +44,6 @@
      162: "5761c1817a9dcb805be487f9e32adfa1",
      198: "59a110653538ebcbfccacbe237c33091",
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
+     207: "9bcf7ac117397af393ca59f795c4abdd",
      229: "5544552803512ca667342d5011dedd1d",
 }

From 5cb9b0a278086ed1ab2cd2bd2fba19d9c1f3c604 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 28 Sep 2018 10:30:16 -0400
Subject: [PATCH 313/753] Update tutorial

---
 .../00 Strategy Library/01 Strategy Library.php     |  2 +-
 .../01 Introduction.html                            |  4 ++--
 .../02 Method.html                                  | 13 +++++++------
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 7e37f67..4720eec 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -436,7 +436,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/207'
         ],
-        'description' => "invests in the cheapest 33% of country ETFs according to CAPE ratios."
+        'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios."
     ],
 ];
 
diff --git a/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html b/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html
index 66cb373..2f1fbdc 100644
--- a/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html	
+++ b/04 Strategy Library/207 Value Effect within Countries/01 Introduction.html	
@@ -1,5 +1,5 @@
 <p>
-  The equity valuation has a predictive ability for future equity returns. There are various methodologies to evaluate whether
+  Equity valuation may be a predictive signal for future equity return. There are various methodologies to evaluate whether
   the equity is undervalued or overvalued using metrics like price-to-earnings (P/E), return on equity (ROE), dividend yield, book-to-equity and so on.
-  In this algorithm, we are going to use a ten-year normalized earnings metrics invented by Yale University professor Robert Shiller to find the fair value of the equity market.
+  In this algorithm, we use a ten-year normalized earnings metrics invented by Yale University professor Robert Shiller to find the fair value of the equity market.
 </p>
diff --git a/04 Strategy Library/207 Value Effect within Countries/02 Method.html b/04 Strategy Library/207 Value Effect within Countries/02 Method.html
index e29c28d..a6db2f9 100644
--- a/04 Strategy Library/207 Value Effect within Countries/02 Method.html	
+++ b/04 Strategy Library/207 Value Effect within Countries/02 Method.html	
@@ -1,12 +1,12 @@
 <p>
-  This new metric is called the cyclically adjusted price-to-earnings ratio (CAPE). The basic idea is comparing the stock prices with earnings smoothed across multiple years.
-  Using backward-looking earnings allows us to smooth out the economic cycle as well as the price fluctuations.
-  By definition, CAPE is the price divided by the average of ten years of earnings (moving average), adjusted for inflation.
+  The cyclically adjusted price-to-earnings ratio (CAPE) compares the stock prices with earnings smoothed across multiple years.
+  It is the price divided by the average of ten years of earnings (moving average), adjusted for inflation.
+  The backward-looking earnings smooth out the economic cycle as well as the price fluctuations.
 </p>
 <p>
   The investment universe consists of 22 countries with easily accessible equity markets via ETFs.
   We import the custom CAPE ratio(Shiller PE Ratio) data of those 22 countries and create a dictionary to save the corresponding country ETF.
-  The CAPE ratio data is in monthly resolution and it starts from January 2000.
+  This data from Quandl is in monthly resolution and starts January 2000.
 </p>
 <div class="section-example-container">
 <pre class="python">
@@ -61,8 +61,9 @@
 </pre>
 </div>
 <p>
-  According to the academic research of Shiller and Campbell using market data from the S&P index, the lower the CAPE, the higher the investors' likely return from equities.
-  Therefore, the algorithm then invests in the cheapest 33% of countries from the sample with the lowest CAPE ratio if those countries have a CAPE below 15 (the algorithm holds 0% cash instead of countries with a CAPE higher than 15).
+  According to the academic research of Shiller and Campbell using market data from the S&amp;P index, the lower the CAPE, the higher the investors' likely return from equities.
+  Therefore, the algorithm then invests in the cheapest 33% of countries from the sample with the lowest CAPE ratio if those countries have a CAPE below 15.
+  If there are no countries with CAPE lower than 15, the algorithm holds cash instead of country ETFs.
   The portfolio is equally weighted and rebalanced monthly.
 </p>
 <div class="section-example-container">

From 7f62c98746698cc40102bb3017bfe085dde61d96 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 28 Sep 2018 19:23:17 -0400
Subject: [PATCH 314/753] Update the algorithm

---
 04 Strategy Library/15 Short Term Reversal/03 Algorithm.html | 2 +-
 quantpedia.json                                              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html
index 648b728..09e4c6f 100644
--- a/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
+++ b/04 Strategy Library/15 Short Term Reversal/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8ce50fdd465d8629d4c73135938477d9.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_05d0edd633b1438852d1d641af0224ae.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index d278b43..270c8d5 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -7,7 +7,7 @@
      7 : "34e8ae2763e7e2122634d4091120d320",
      8 : "336bbb8e9e4a55a6adfa00c0d9f904bb",
      12: "72532ccafeaa844c81aa75c5696b4b24",
-     13: "8ce50fdd465d8629d4c73135938477d9",
+     13: "05d0edd633b1438852d1d641af0224ae",
      14: "ec30318cc245fe200ca1261b7f26f17b",
      15: "bd83aa417032f8407382a1c065aa7511",
      16: "afd2c9d67fd51d602bf8eac2ef28d712",

From 31ac36289c495fb7ec13256791fe51c9f27eab2c Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 2 Oct 2018 18:33:08 -0400
Subject: [PATCH 315/753] Update the tutorial

---
 .../02 Rate of Return.html                                | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index c9641b3..8affb26 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -32,12 +32,12 @@ <h4>Single-period Return</h4>
   The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
 </p>
 <p>
-  Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now!
+  Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B for 3 months and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now. Since there are four quarters in a year,the annual return of strategy B is
 </p>
-\[(1+r)^3 = 1+0.65\]
-\[ r = \sqrt[3]{1.65} - 1\ = 0.18167\]
+\[(1+r)^4 = 1+0.65\]
+\[ r = \sqrt[4]{1.65} - 1\ = 0.133368\]
 <p>
-  Strategy A has an higher compounding annual return!
+  Strategy A has an higher compounding annual return when we compare 0.133 and 0.2.
 </p>
 <h4>Logarithm Return</h4>
 <p>

From ebd7e33e37680f27cde74d6de405a5710cb1110a Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 2 Oct 2018 18:35:17 -0400
Subject: [PATCH 316/753] Update the tutorial

---
 .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 8affb26..33272aa 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -37,7 +37,7 @@ <h4>Single-period Return</h4>
 \[(1+r)^4 = 1+0.65\]
 \[ r = \sqrt[4]{1.65} - 1\ = 0.133368\]
 <p>
-  Strategy A has an higher compounding annual return when we compare 0.133 and 0.2.
+  Strategy A has an higher compounding annual return when we compare 0.133 with 0.2.
 </p>
 <h4>Logarithm Return</h4>
 <p>

From 7bb85e291673d45e3fd124827c400644b98b4a3b Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Tue, 2 Oct 2018 18:45:16 -0400
Subject: [PATCH 317/753] Update tutorial

---
 .../02 Rate of Return.html                                | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 33272aa..cf56c0f 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -32,12 +32,12 @@ <h4>Single-period Return</h4>
   The rate of return we calculate here is called <strong>cumulative return</strong> or<strong> overall return</strong>. It measures the total return of this asset over a period of time.
 </p>
 <p>
-  Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B for 3 months and the cumulative return is 65%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now. Since there are four quarters in a year,the annual return of strategy B is
+  Now consider the following situation: we have two strategies: strategy A and strategy B. We backtested strategy A for 1 years and the cumulative return is 20%, while we backtested strategy B for 3 months(one quarter) and the cumulative return is 6%. Which strategy has a high rate of return? Our commonly used method is to convert all the returns into <strong>compounding annual return</strong>, regardless of the investing horizon of each strategy. We can compare the returns of strategies with different time horizon now. Since there are four quarters in a year,the annual return of strategy B is
 </p>
-\[(1+r)^4 = 1+0.65\]
-\[ r = \sqrt[4]{1.65} - 1\ = 0.133368\]
+\[(1+0.06)^4 = 1+r\]
+\[ r = 0.262\]
 <p>
-  Strategy A has an higher compounding annual return when we compare 0.133 with 0.2.
+  Strategy B has an higher compounding annual return when we compare 0.26 with 0.2.
 </p>
 <h4>Logarithm Return</h4>
 <p>

From c7fb8f34eccf4621a0c2296f93a6240bda263e60 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Wed, 3 Oct 2018 14:13:18 -0400
Subject: [PATCH 318/753] Update tutorial

---
 .../02 Rate of Return.html                       | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index cf56c0f..2bcb3c5 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -37,19 +37,19 @@ <h4>Single-period Return</h4>
 \[(1+0.06)^4 = 1+r\]
 \[ r = 0.262\]
 <p>
-  Strategy B has an higher compounding annual return when we compare 0.26 with 0.2.
+  Strategy B has an higher compounding annual return when we compare 26% with 20%.
 </p>
 <h4>Logarithm Return</h4>
 <p>
-  We introduced compounding annual return above, which is a kind of <strong>effective rate of return</strong>. You can regard it as a 'hypothetical return'. Strategy B might never have a 18.167% rate of rate annually during the 3-year backtesting period. However, if we assume that the strategy has a 18.167% rate of return every year, it has the same cumulative return over the 3 years. As we mentioned previously, if we assume a strategy is quarterly compounding, the relation between quarterly effective rate of return and annual return is:
+  In the above example, strategy A has 6% return over three months. Nominally, the annual return would be 3*6% = 18%.
+  This nominal annual interest rate is called the stated annual interest rate.
+  It is calculated as the periodic interest rate times the number of periods per year. It works according to the simple interest and does not take into account the compounding periods, while the effective annual interest rate is 26% as we calculated above and it does account for intra-year compounding.
+  The effective annual interest rate is an essential tool that allows the evaluation of the real return on investment.
+  If we assume the number of compounding periods in one year is n, the formula to convert the stated annual interest rate to the effective annual interest rate is
 </p>
-\[(1+\frac{r}{4})^4 = 1+r\]
+\[r_{effective}=(1+\frac{r_{nominal}}{n})^n-1\]
 <p>
-  More generally, if the times of compounding in one year is \(n\) and the annual rate of return is \(r\), the relation is given by:
-</p>
-\[(1+\frac{r}{n})^n = 1+r\]
-<p>
-  Now imagine the stock markets. The prices of your assets is changing every second, or even every millisecond. If the times of compounding, or n, approach to infinite, this is called <strong>continuous compounding</strong>. The calculation formula is given below:
+  Now imagine the price of asset is changing every second or even every millisecond, the period of compounding n approaches infinite. This is called <strong>continuous compounding</strong>. The calculation formula is given below:
 </p>
 \[\lim_{n \to \infty }(1+\frac{r}{n})^n = e^r\]
 <p>

From 9a32c2a63f21709cc1af641dbbf9ee08e2a5c726 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Mon, 8 Oct 2018 15:35:26 -0400
Subject: [PATCH 319/753] Update tutorial 02

---
 .../04 Results.html                                  | 12 +++++-------
 .../06 Algorithm.html                                |  2 +-
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html
index 0ec705b..ccd73cf 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/04 Results.html	
@@ -1,5 +1,7 @@
 <p>
-  The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016. We can see the results are fairly close to those from the source paper with a R-squared value of 3.1% compared to the paper's 3.89%. Our momentum coefficient, ρ, is 0.0344 compared to the paper's 0.042. We obtained 0.9955 mean reversion coefficient (1 - 0.0045), and the paper got 0.9859.
+  The following regression output is obtained by backtesting the time period from Jun 2013 to Jun 2016.
+  From the regression statistics, the R-squared value is 1.4% which is smaller than the paper value 3.89%.
+  Our momentum coefficient, ρ, is 0.0633 compared to the paper's 0.042. We obtained 1.0350 mean reversion coefficient (1 + 0.0350), and the paper got 0.9859.
 </p>
 
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-1" />
@@ -10,10 +12,6 @@
 
 <h3>Backtest Sensitivity Results</h3>
 <p>
-  We performed some rough period sensitivity analysis in different time periods and summarized the results as the following table:
-</p>
-
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-1.png" alt="Tutorial02-mean-reversion-2" />
-<p>
-  The compound annual returns are quite stable, however, the paper claimed that the average return is 27.5% vs our 11% achieved. This difference can be accounted for by the data time span and currency pairs.
+  We performed some rough period sensitivity analysis in different time period(from 2015 to 2018) and summarized the results as the following table:
 </p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-mean-reversion-2.png" alt="Tutorial02-mean-reversion-2" />
diff --git a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html
index 3c5b8f8..5c357f4 100755
--- a/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
+++ b/04 Strategy Library/02 Combining Mean Reversion and Momentum in Forex Market/06 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c1eea4df64cf986eba481f5e5e376e8a.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_73883ad731bcac43e1a94b3983b5f8ca.html"></iframe>
     </div>
 </div>

From c06a3e46160ad275fb1b72dca14be0e2cb76b6a3 Mon Sep 17 00:00:00 2001
From: Jing Wu <wujingwj@outlook.com>
Date: Fri, 19 Oct 2018 11:05:32 -0400
Subject: [PATCH 320/753] Add algorithm 78 Beta Factor in Country Equity
 Indexes

---
 .../01 Strategy Library.php                   |   8 ++
 .../01 Introduction.html                      |   6 +
 .../02 Method.html                            | 113 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 Source.html                            |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 139 insertions(+)
 create mode 100644 04 Strategy Library/78 Beta Factor in Country Equity Indexes/01 Introduction.html
 create mode 100644 04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html
 create mode 100644 04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html
 create mode 100644 04 Strategy Library/78 Beta Factor in Country Equity Indexes/04 Source.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4720eec..f7b809d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -438,6 +438,14 @@
         ],
         'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios."
     ],
+    [
+        'name' => 'Beta Factor in Country Equity Indexes',
+        'link' => 'strategy-library/beta-factor-in-country-equity-indexes',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
+        ],
+        'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs."
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/01 Introduction.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/01 Introduction.html
new file mode 100644
index 0000000..6d6f826
--- /dev/null
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/01 Introduction.html	
@@ -0,0 +1,6 @@
+<p>
+  Some investors are prohibited from using leverage and other investors’ leverage is limited by margin requirements.
+  Their only way to achieve higher returns is to buy more risky stocks which makes these assets more expensive.
+  High-beta and risky assets should therefore deliver lower risk-adjusted returns than low-beta assets.
+  Investors could exploit this inefficiency by using ETFs. This algorithm is going to explore this phenomenon.
+</p>
diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html
new file mode 100644
index 0000000..34d213c
--- /dev/null
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html	
@@ -0,0 +1,113 @@
+<p>
+  The implementation of this algorithm uses the <a href="https://www.quantconnect.com/docs/algorithm-framework/overview">alpha framework</a>.
+  The algorithm picks 35 country indexes ETFs as the trading universe. As the symbols in the universe don't change over time,
+  we use the <code>ManualUniverseSelectionModel</code> to subscribe the daily data for those symbols.
+</p>
+<p>
+  Beta is a statistical measure of a stock's volatility in relation to the market.
+  Stock analysts use this measure to get a sense of stocks' risk profiles.
+  The formula for calculating beta is the covariance of the return of an asset with the return of the market divided by the variance of the return of the market over a certain period.
+</p>
+\[\beta_i=\frac{cov(R_i,R_{m})}{Var(R_m)}\]
+<div class="section-example-container">
+<pre class="python">
+def beta(self, asset_return, market_return):
+    asset_return = np.array(asset_return, dtype=np.float32)
+    market_return = np.array(market_return, dtype=np.float32)
+    return np.cov(asset_return, market_return)[0][1]/np.var(market_return)
+</pre>
+</div>
+<p>
+  We use S&amp;P500 ETF as the market measure. The beta for each country is calculated with respect to the SPY using a 1-year rolling window.
+  <code>SymbolData</code> class save the one-year rolling window price data.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    def __init__(self, symbol):
+        self.Symbol = symbol
+        self.Price = deque(maxlen=253)
+</pre>
+</div>
+<p>
+  <code>self.assets<code> is a dictionary to save the price series for each country ETF. The key is the ETF symbol.
+  For new symbols added to the algorithm, we request the one-year history data to initialize the price series.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        if added.Symbol.Value == "SPY":
+            self.market_price = deque(maxlen=253)
+            hist_SPY = algorithm.History(["SPY"], 500, Resolution.Daily)
+            for i in hist_SPY.loc["SPY"].itertuples():
+                self.market_price.append(i.close)
+
+        if added not in self.assets and added.Symbol.Value != "SPY":
+            hist = algorithm.History([added.Symbol.Value], 500, Resolution.Daily)
+            if not hist.empty:
+                self.assets[added.Symbol] = SymbolData(added)
+                for i in hist.loc[added.Symbol.Value].itertuples():
+                    self.assets[added.Symbol].Price.append(i.close)
+
+    for removed in changes.RemovedSecurities:
+        self.assets.pop(removed.Symbol)
+</pre>
+</div>
+<p>
+  In the Alpha model, <code>Update(self, algorithm, data)<code> method updates this model with the latest data from the algorithm.
+  This method is called each time the algorithm receives data for subscribed securities. In this method, we update the price series with new trade bars.
+  The price series is converted to return series. We plug the market return and the country ETF return into the beta formula.
+  ETFs are then ranked in ascending order by their estimated beta. The ranked ETFs are assigned to one of two portfolios: low beta and high beta.
+  Each portfolio contains a quarter of the total assets.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    if data.ContainsKey("SPY"):
+        self.market_price.append(float(algorithm.Securities["SPY"].Price))
+    for key, value in self.assets.items():
+        if data.ContainsKey(key):
+            value.Price.append(float(algorithm.Securities[key].Price))
+    insights = []
+    if self.month != algorithm.Time.month:
+        self.month = algorithm.Time.month
+        beta_values = {}
+        market_return = np.diff(np.array(self.market_price))/np.array(self.market_price)[:-1]
+        long = None
+        for key, value in self.assets.items():
+            if key != "SPY" and len(value.Price) == value.Price.maxlen:
+                asset_return = np.diff(np.array(value.Price))/np.array(value.Price)[:-1]
+                beta_values[key] = self.beta(asset_return, market_return)
+        sorted_by_beta = sorted(beta_values, key = lambda x: beta_values[x])
+</pre>
+</div>
+<p>
+  The algorithm shorts the high-beta portfolio and longs the low-beta portfolio. Securities are rebalanced every calendar month.
+  <code>PortfolioConstructionModel</code> is set to emit the target weight monthly.
+  The time period of insight is from the current day to the end of the calendar month.
+  In Python calendar library, <code>calendar.monthrange(year, month)</code> returns the number of days in a month for the specified year and month.
+  The insight direction is set to be flat for symbols removed from the long/short list at the end of the month.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+long = sorted_by_beta[:int(0.25*len(sorted_by_beta))]
+short = sorted_by_beta[-int(0.25*len(sorted_by_beta)):]
+# day: the weekday of first day of the month
+# num_days: number of days in month
+day, num_days = calendar.monthrange(algorithm.Time.year, algorithm.Time.month)
+insight_period = num_days - algorithm.Time.day - 1
+if long and short:
+    invested = [x.Key for x in algorithm.Portfolio if x.Value.Invested]
+    for i in invested:
+        if algorithm.Portfolio[i].IsLong and i not in long:
+            insights.append(Insight.Price(i, timedelta(days=1), InsightDirection.Flat))
+        if algorithm.Portfolio[i].IsShort and i not in short:
+            insights.append(Insight.Price(i, timedelta(days=1), InsightDirection.Flat))
+    for i in long:
+        insights.append(Insight.Price(i, timedelta(days=insight_period), InsightDirection.Up))
+    for i in short:
+        insights.append(Insight.Price(i, timedelta(days=insight_period), InsightDirection.Down))
+</pre>
+</div>
diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html
new file mode 100644
index 0000000..36e3092
--- /dev/null
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_27fb5f05b0e48f488f0994d8d83ddc77.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/04 Source.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/04 Source.html
new file mode 100644
index 0000000..3c11e0b
--- /dev/null
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/78" rel="nofollow">Quantpedia - Beta Factor in Country Equity Indexes</a>
+</li>
+</ul>
diff --git a/quantpedia.json b/quantpedia.json
index 57aa4c2..2d6bbe9 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -32,6 +32,7 @@
      66: "ace0e17cc133feceaf4aa83035309116",
      71: "124aa2fdf4ae34bc022a00b54c26b10d",
      77: "0483e5a7094604254ab37eda8b5141b8",
+     78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
      91: "d0b1d400ad21477d83ae2b4f85616318",
      100: "2691b0f4e604049a81275708313610c6",

From ed38179b672e510544a1a7a6d815a66d4401aa15 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 26 Oct 2018 14:47:54 +0000
Subject: [PATCH 321/753] Automated push from server made by Jing Wu

---
 .../78 Beta Factor in Country Equity Indexes/02 Method.html   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html
index 34d213c..d2c9c93 100644
--- a/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html	
+++ b/04 Strategy Library/78 Beta Factor in Country Equity Indexes/02 Method.html	
@@ -30,7 +30,7 @@
 </pre>
 </div>
 <p>
-  <code>self.assets<code> is a dictionary to save the price series for each country ETF. The key is the ETF symbol.
+  <code>self.assets</code> is a dictionary to save the price series for each country ETF. The key is the ETF symbol.
   For new symbols added to the algorithm, we request the one-year history data to initialize the price series.
 </p>
 <div class="section-example-container">
@@ -55,7 +55,7 @@
 </pre>
 </div>
 <p>
-  In the Alpha model, <code>Update(self, algorithm, data)<code> method updates this model with the latest data from the algorithm.
+  In the Alpha model, <code>Update(self, algorithm, data)</code> method updates this model with the latest data from the algorithm.
   This method is called each time the algorithm receives data for subscribed securities. In this method, we update the price series with new trade bars.
   The price series is converted to return series. We plug the market return and the country ETF return into the beta formula.
   ETFs are then ranked in ascending order by their estimated beta. The ranked ETFs are assigned to one of two portfolios: low beta and high beta.

From 5677680b2b0da0bafe35447573fce07adaa486a6 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 5 Nov 2018 21:02:35 +0000
Subject: [PATCH 322/753] Automated push from server made by Jing Wu

---
 .../03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index f9b1045..038e110 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_343dc596a484a20a579ae77c79a12495.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_59d99772a1736de98db5f14e2e548ce8.html"></iframe>
     </div>
 </div>
 

From 15ed70aa67d69692a142307b4e437f0f1ed39178 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 12 Nov 2018 14:54:27 +0000
Subject: [PATCH 323/753] Automated push from server made by Jing Wu

---
 .../02 Method.html                            | 29 +++++++++----------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index 63e4b63..a666b03 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -9,22 +9,23 @@ <h3>Step 1: Initialization</h3>
 
 <div class="section-example-container">
 
-<pre class="python">def Initialize(self):
-        self.SetStartDate(2002, 1, 3)
-        self.SetEndDate(2016, 12, 1)
+<pre class="python">    def Initialize(self):
+        self.SetStartDate(2005, 1, 1)
+        self.SetEndDate(2017, 5, 10)
         self.SetCash(1000000)
-
+        
         self.UniverseSettings.Resolution = Resolution.Daily
         self.AddUniverse(self.CoarseSelectionFunction)
         self._numberOfSymbols = 100
-        self._numberOfTradings = 0.1 * self._numberOfSymbols
-
+        self._numberOfTradings = int(0.1 * self._numberOfSymbols)
+        
         self._numOfWeeks = 0
         self._LastDay = -1
         self._ifWarmUp = False
-
+        
         self._stocks = []
-        self._values = {}</pre>
+        self._values = {}
+</pre>
 </div>
 <p>
   Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
@@ -32,13 +33,11 @@ <h3>Step 1: Initialization</h3>
 
 <div class="section-example-container">
 
-<pre class="python">def CoarseSelectionFunction(self, coarse):
-        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-        top100 = sortedByDollarVolume[:self._numberOfSymbols]
-        list = List[Symbol]()
-        for x in top100:
-                list.Add(x.Symbol)
-        return list
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+     sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+     top100 = sortedByDollarVolume[:self._numberOfSymbols]
+     return [i.Symbol for i in top100]
 </pre>
 </div>
 <h3>Step 2:Warm Up</h3>

From 007a8ec677475911034a4a59eb302b61bbc3ba59 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 13 Nov 2018 06:22:17 +0000
Subject: [PATCH 324/753] Automated push from server made by Jing Wu

---
 .../03 Local Volatility.html                          | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index 68bc274..3092bdf 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -3,15 +3,16 @@ <h4>1. Definition</h4>
   The constant volatility assumption in BSM model is not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. There exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
 </p>
 <p>
-  The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
+  The local volatility of the underlying assets is a deterministic function of assets price and the time t.
 </p>
 \[\sigma=\sigma(S_t,t)\]
-
-Therefore with local volatility model, the stochastic process followed by the stock price is
-
+<p>
+Therefore with the local volatility model, the stochastic process followed by the stock price is
+</p>
 \[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
-
+<p>
 If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness from the stock price: \(W_t\).
+</p>
 <h4>2. Model Calibration</h4>
 <p>
   How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.

From 69c9ee6115283846a9d15c146644993003db0cfc Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Tue, 13 Nov 2018 06:24:01 +0000
Subject: [PATCH 325/753] Automated push from server made by Jing Wu

---
 .../03 Local Volatility.html                          | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index 3092bdf..9bd6f87 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -3,16 +3,15 @@ <h4>1. Definition</h4>
   The constant volatility assumption in BSM model is not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. There exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
 </p>
 <p>
-  The local volatility of the underlying assets is a deterministic function of assets price and the time t.
+  The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
 </p>
 \[\sigma=\sigma(S_t,t)\]
-<p>
+
 Therefore with the local volatility model, the stochastic process followed by the stock price is
-</p>
+
 \[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
-<p>
-If \(\sigma(S_t,t)=\sigmaS_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness from the stock price: \(W_t\).
-</p>
+
+If \(\sigma(S_t,t)=\sigma S_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness from the stock price: \(W_t\).
 <h4>2. Model Calibration</h4>
 <p>
   How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.

From 59d5e13a0e3592f4f66e43544e6f6737b273f065 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 14 Nov 2018 16:52:44 +0000
Subject: [PATCH 326/753] Automated push from server made by Jing Wu

---
 .../03 Local Volatility.html                               | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index 9bd6f87..0840ef5 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -6,12 +6,13 @@ <h4>1. Definition</h4>
   The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
 </p>
 \[\sigma=\sigma(S_t,t)\]
-
+<p>
 Therefore with the local volatility model, the stochastic process followed by the stock price is
-
+</p>
 \[\text d S_t=\mu S_t\ \text d t+\sigma(S_t,t)\text d W_t\]
-
+<p>
 If \(\sigma(S_t,t)=\sigma S_t\), then this is the case of BSM model with constant volatility\(\sigma\). Here we are not assuming the volatility is constant but a function of the asset price so the stock’s log-returns are not normally distributed anymore. There is only one source of randomness from the stock price: \(W_t\).
+</p>
 <h4>2. Model Calibration</h4>
 <p>
   How to obtain the function \(\sigma(S_t,t)\)? Finding the function \(\sigma(S_t,t)\) is known as the calibration of local volatility model. This is answered by the following Dupire's formula.

From 1c17dfb82bf06763ecc0d8792a087578c8fde1dc Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 14 Nov 2018 17:32:29 +0000
Subject: [PATCH 327/753] Automated push from server made by Jing Wu

---
 .../03 Local Volatility.html                                  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index 0840ef5..635786c 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -23,10 +23,10 @@ <h4>2. Model Calibration</h4>
   If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with different combinations of strike prices and expiration dates. For a given date, time(t) and the underlying stock price(St), a local volatility is derived from the equation that options price calculated with the local volatility equals to the market options price.
 </p>
 <p>
-  Usually, we can only get a limited number of contracts with a few strikes and maturities, we can follow steps below to get the local volatility estimation:
+  Usually, we can only get a limited number of contracts with a few strikes and maturities, we can follow the steps below to get the local volatility estimation:
 <ul>
      <li>First, use the available quoted price to calculate the implied volatilities.</li>
-     <li>Appy interpolation method to produce a smooth implied volatility surface.</li>
+     <li>Apply interpolation method to produce a smooth implied volatility surface.</li>
      <li>Plug implied volatilities into BSM model to get all the market prices of European calls.</li>
      <li>Calculate the local volatility according to Dupire formula. To avoid taking derivatives, we could use finite differences to approximate the derivative.</li>
 </ul>

From 9aa5939abfc164d32c18ac69a78f09fcc93ec700 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Wed, 14 Nov 2018 17:33:01 +0000
Subject: [PATCH 328/753] Automated push from server made by Jing Wu

---
 .../03 Local Volatility.html                                | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html
index 635786c..64f161e 100755
--- a/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
+++ b/06 Introduction to Options[]/08 Local Volatility and Stochastic Volatility/03 Local Volatility.html	
@@ -3,7 +3,7 @@ <h4>1. Definition</h4>
   The constant volatility assumption in BSM model is not reasonable in most of the options pricing. The volatility skew tells us that the stock’s log-returns are actually not normally distributed. There exist some other kinds of distributions of stock price so that if we use this distribution to price the options, it would give the accurate options' price as we see in the market. The local volatility is implied in this non-normal distribution. Let's look at the definition of local volatility.
 </p>
 <p>
-  The the local volatility of the underlying assets is a deterministic function of assets price and the time t.
+  The local volatility of the underlying assets is a deterministic function of assets price and the time t.
 </p>
 \[\sigma=\sigma(S_t,t)\]
 <p>
@@ -23,10 +23,10 @@ <h4>2. Model Calibration</h4>
   If we assume that we know all the observed market price of European call options C(T, K) for every strike K and every maturity T.  Local volatility model calculates volatilities for a set of options with different combinations of strike prices and expiration dates. For a given date, time(t) and the underlying stock price(St), a local volatility is derived from the equation that options price calculated with the local volatility equals to the market options price.
 </p>
 <p>
-  Usually, we can only get a limited number of contracts with a few strikes and maturities, we can follow the steps below to get the local volatility estimation:
+  Usually, we can only get a limited number of contracts with a few strikes and maturities, we can follow steps below to get the local volatility estimation:
 <ul>
      <li>First, use the available quoted price to calculate the implied volatilities.</li>
-     <li>Apply interpolation method to produce a smooth implied volatility surface.</li>
+     <li>Appy interpolation method to produce a smooth implied volatility surface.</li>
      <li>Plug implied volatilities into BSM model to get all the market prices of European calls.</li>
      <li>Calculate the local volatility according to Dupire formula. To avoid taking derivatives, we could use finite differences to approximate the derivative.</li>
 </ul>

From f74be9fdc55b3cea2efd330b5c67037035d2d583 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 20 Dec 2018 15:42:32 -0800
Subject: [PATCH 329/753] Update README.md

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index f49f107..371ab1a 100644
--- a/README.md
+++ b/README.md
@@ -13,8 +13,6 @@ Lean Engine is an open-source fully managed C# algorithmic trading engine built
 
 Please submit new tutorial requests as an issue to the [Tutorial Repository][5]. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed! 
 
-We've made a simple editor to help with drafting HTML tutorials: https://www.quantconnect.com/tutorials/editor. Once you've drafted the content there copy the code into a pull-request for the tutorials website.
-
 ## Mailing List ##
 
 The mailing list for the project can be found on [Google Groups][6]

From 08abb6559f28deaf6161b838a63fd534d78ee175 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Fri, 4 Jan 2019 11:04:38 -0800
Subject: [PATCH 330/753] Update 03 Full Python Example.html

Updates self._stopLimitTicket. Previous version times out when backtesting. Testing code with new line and it works.
---
 .../03 Tracking and Managing Orders/03 Full Python Example.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html
index 20d1cf2..e53941f 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/03 Full Python Example.html	
@@ -43,7 +43,7 @@
                 -100,
                 slice["SPY"].Close * Decimal(0.9),
                 slice["SPY"].Close * Decimal(0.8),
-                "stop market")
+                "stop limit")
 
 
     def OnEndOfDay(self):

From 5aee1bdf8bf3d4d1f8c68e18ba034ece7d933568 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Fri, 4 Jan 2019 11:22:19 -0800
Subject: [PATCH 331/753] Update 04 Select Contracts.html

Some small punctuation/sentence structure fixes
---
 .../04 Select Contracts.html                              | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index 9b62ccc..f734be3 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -1,11 +1,11 @@
 <p>
-  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need use property <code>Slice.OptionChains</code> to request options data for this slice.
+  For QuantConnect API, <a href="https://www.quantconnect.com/lean/docs#topic3838.html">Slice</a> class provides a data structure for all of an algorithm's data at a single time step. So you need to use the property <code>Slice.OptionChains</code> to request options data for this slice.
 
-<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of  OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) <code>Value</code>(the option chain).
+<a href="https://www.quantconnect.com/lean/docs#topic19269.html">OptionChains</a> is a collection of OptionChain keyed by the option's underlying symbol. The elements in <code>Slice.OptionChains</code> have properties <code>Key</code>(the underlying symbol object) and <code>Value</code>(the option chain).
 
-<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, It is a list of option contracts.
+<a href="https://www.quantconnect.com/lean/docs#topic19245.html">OptionChain</a>  represents an entire chain of option contracts for a single underlying security. In other words, it is a list of option contracts.
 
-<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in option chain, you can use the following statements to check different options properties.
+<a href="https://www.quantconnect.com/lean/docs#topic19279.html">OptionContract</a> defines a single option contract at a specific expiration and strike price. For any contract x in the option chain, you can use the following statements to check different options properties.
 </p>
 
 <table class="table qc-table">

From 90d6571f268c2555b4ca43d47e0bc6addad113c4 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Fri, 4 Jan 2019 11:36:47 -0800
Subject: [PATCH 332/753] Update 04 Select Contracts.html

---
 .../04 Using Options in QuantConnect/04 Select Contracts.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index f734be3..8f31d28 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -153,7 +153,7 @@
 </table>
 
 <p>
-  Here we give an example of how to find ATM, ITM OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
+  Here we give an example of how to find ATM, ITM and OTM contracts for trading. First, we need to extract the OptionChain from OptionChains according to symbols we added in Initialize step. Secondly, we extract ATM, ITM and OTM contracts by using UnderlyingLastPrice and Strike properties. Note here the strikes of ATM options are not exactly the same as the market price of underlying stocks, thus here we first sort the contracts by the absolute values of the difference between the UnderlyingLastPrice and the Strike. Then we choose the contract with the minimum absolute value as the ATM contract.
 </p>
 
 <div class="section-example-container">

From d9d1f6ad61cdf5ee0f8c91c95b2e8b423d63ff81 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 7 Jan 2019 09:44:42 -0800
Subject: [PATCH 333/753] Update 02 Fetching Data.html

---
 .../05 Pandas-Resampling and DataFrame/02 Fetching Data.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html
index d859d77..325ecd6 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
@@ -1,5 +1,5 @@
 <p>
-  Here we use data from the yahoo_finance API.
+  Here we use the Quandl API to retrieve data.
 </p>
 <div class="section-example-container">
 

From 38e86b376780fb9bddc52a3312031ce0f9fd967b Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 7 Jan 2019 09:56:39 -0800
Subject: [PATCH 334/753] Update 05 Summary.html

Tutorial section 'Introduction to Financial Python' appears twice on website. Removed html code from summary.html to fix the problem.
---
 .../05 Summary.html                           | 912 ------------------
 1 file changed, 912 deletions(-)

diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html
index 6ab6e8a..0dce45e 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
@@ -1,915 +1,3 @@
-<p>
-  In the last chapter we had a glimpse of Pandas. In this chapter we will learn about resampling methods and the DataFrame object, which is a powerful tool for financial data analysis.
-</p>
-<p>
-  Here we use data from the yahoo_finance API.
-</p>
-<div class="section-example-container">
-
-<pre class="python">import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
-</pre>
-</div>
-<p>
-  We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
-</p>
-<div class="section-example-container">
-
-<pre class="python">aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table['Adj. Close']['2017']
-print aapl
-</pre>
-</div>
-
-<p>
-  Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
-</p>
-<div class="section-example-container">
-
-<pre class="python">print aapl['2017-3']
-Date
-2017-03-01    138.657681
-2017-03-02    137.834404
-2017-03-03    138.647762
-2017-03-06    138.211326
-2017-03-07    138.389868
-2017-03-08    137.874080
-2017-03-09    137.556672
-2017-03-10    138.012946
-2017-03-13    138.072460
-2017-03-14    137.864161
-2017-03-15    139.322254
-2017-03-16    139.550391
-2017-03-17    138.856061
-2017-03-20    140.314154
-2017-03-21    138.707276
-2017-03-22    140.274478
-2017-03-23    139.778528
-2017-03-24    139.500796
-2017-03-27    139.738852
-2017-03-28    142.635200
-2017-03-29    142.952608
-2017-03-30    142.764147
-2017-03-31    142.496334
-</pre>
-</div>
-
-<p>
-  Or in several consecutive months:
-</p>
-<div class="section-example-container">
-
-<pre class="python">aapl['2017-2':'2017-4']
-</pre>
-</div>
-
-<p>
-  .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
-</p>
-<div class="section-example-container">
-
-<pre class="python">print aapl.head()
-print aapl.tail(10)
-</pre>
-</div>
-<p>
-  The output:
-</p>
-<div class="section-example-container">
-
-<pre class="python">
-Date
-2017-01-03    114.715378
-2017-01-04    114.586983
-2017-01-05    115.169696
-2017-01-06    116.453639
-2017-01-09    117.520300
-Name: Adj. Close, dtype: float64
-Date
-2017-08-08    159.433108
-2017-08-09    160.409148
-2017-08-10    155.270000
-2017-08-11    157.480000
-2017-08-14    159.850000
-2017-08-15    161.600000
-2017-08-16    160.950000
-2017-08-17    157.870000
-2017-08-18    157.500000
-2017-08-21    157.210000
-Name: Adj. Close, dtype: float64
-</pre>
-</div>
-
-<h2>Resampling</h2>
-<p>
-  <code>series.resample(freq)</code> is a class called "DatetimeIndexResampler" which groups data in a Series object into regular time intervals. The argument "freq" determines the length of each interval.
-</p>
-<p>
-  <code>series.resample.mean()</code> is a complete statement that groups data into intervals, and then compute the mean of each interval. For example, if we want to aggregate the daily data into monthly data by mean:
-</p>
-<div class="section-example-container">
-
-<pre class="python">by_month = aapl.resample('M').mean()
-print by_month
-Date
-2017-01-31    118.093136
-2017-02-28    132.456268
-2017-03-31    139.478802
-2017-04-30    141.728436
-2017-05-31    151.386305
-2017-06-30    147.233064
-2017-07-31    147.706190
-2017-08-31    157.444303
-</pre>
-</div>
-
-<p>
-  We can also aggregate the data by week:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">by_week = aapl.resample('W').mean()
-print by_week.head()
-
-Date
-2017-01-31    120.932434
-2017-02-28    136.551200
-2017-03-31    143.532630
-2017-04-30    144.179981
-2017-05-31    156.100000
-2017-06-30    155.450000
-2017-07-31    153.460000
-</pre>
-</div>
-
-<p>
-  We can choose almost any frequency by using the format 'nf', where 'n' is an integer and 'f' is M for month, W for week and D for day.
-</p>
-<div class="section-example-container">
-
-<pre class="python">three_day = aapl.resample('3D').mean()
-two_week  = aapl.resample('2W').mean()
-two_month = aapl.resample('2M').mean()
-</pre>
-</div>
-
-<p>
-  Besides the mean() method, other methods can also be used with the resampler:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">std = aapl.resample('W').std()    # standard deviation
-max = aapl.resample('W').max()    # maximum value
-min = aapl.resample('W').min()    # minimum value
-</pre>
-</div>
-
-<p>
-  Often we want to calculate monthly returns of a stock, based on prices on the last day of each month. To fetch those prices, we use the series.resample.agg() method:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">last_day = aapl.resample('M').agg(lambda x: x[-1])
-print last_day
-Date
-2017-01-31    119.851150
-2017-02-28    135.880362
-2017-03-31    142.496334
-2017-04-30    142.486415
-2017-05-31    152.142689
-2017-06-30    143.438008
-2017-07-31    148.248489
-2017-08-31    157.210000
-</pre>
-</div>
-
-<p>
-  Or directly calculate the monthly rates of return using the data for the first day and the last day:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">monthly_return = aapl.resample('M').agg(lambda x: x[-1]/x[1] - 1)
-print monthly_return
-
-Date
-2017-01-31    0.045940
-2017-02-28    0.070409
-2017-03-31    0.033823
-2017-04-30   -0.007736
-2017-05-31    0.039829
-2017-06-30   -0.073528
-2017-07-31    0.033035
-2017-08-31    0.004505
-</pre>
-</div>
-
-<p>
-  Series object also provides us some convenient methods to do some quick calculation.
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">print monthly_return.mean()
-print monthly_return.std()
-print monthly_return.max()
-[out]: 0.0208974076157
-       0.0476398315185
-       0.0704090212384
-</pre>
-</div>
-
-<p>
-  Another two methods frequently used on Series are .diff() and .pct_change(). The former calculates the difference between consecutive elements, and the latter calculates the percentage change.
-</p>
-<div class="section-example-container">
-
-<pre class="python">print last_day.diff()
-print last_day.pct_change()
-
-Date
-2017-01-31          NaN
-2017-02-28    16.029211
-2017-03-31     6.615972
-2017-04-30    -0.009919
-2017-05-31     9.656274
-2017-06-30    -8.704681
-2017-07-31     4.810482
-2017-08-31     8.961511
-Freq: M, Name: Adj. Close, dtype: float64
-Date
-2017-01-31         NaN
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449
-</pre>
-</div>
-
-<p>
-  Notice that we induced a NaN value while calculating percentage changes i.e. returns.
-</p>
-
-<p>
-  When dealing with NaN values, we usually either removing the data point or fill it with a specific value. Here we fill it with 0:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">daily_return = last_day.pct_change()
-print daily_return.fillna(0)
-
-Date
-2017-01-31    0.000000
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449
-</pre>
-</div>
-
-<p>
-  Alternatively, we can fill a NaN with the next fitted value. This is called 'backward fill', or 'bfill' in short:
-</p>
-<div class="section-example-container">
-
-<pre class="python">daily_return = last_day.pct_change()
-print daily_return.fillna(method = 'bfill')
-
-Date
-2017-01-31    0.133743
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.033537
-2017-08-31    0.060449
-</pre>
-</div>
-
-<p>
-  As expected, since there is a 'backward fill' method, there must be a 'forward fill' method, or 'ffill' in short. However we can't use it here because the NaN is the first value.
-</p>
-
-<p>
-  We can also simply remove NaN values by <strong>.dropna()</strong>
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">daily_return = last_day.pct_change().dropna()
-print daily_return
-
-Date
-2017-02-28    0.133743
-2017-03-31    0.048690
-2017-04-30   -0.000070
-2017-05-31    0.067770
-2017-06-30   -0.057214
-2017-07-31    0.038050
-</pre>
-</div>
-
-<h2>DataFrame</h2>
-
-<p>
-  The <strong>DataFrame</strong> is the most commonly used data structure in Pandas. It is essentially a table, just like an Excel spreadsheet.
-</p>
-<p>
-  More precisely, a DataFrame is a collection of Series objects, each of which may contain different data types. A DataFrame can be created from various data types: dictionary, 2-D numpy.ndarray, a Series or another DataFrame.
-</p>
-<h3>Create DataFrames</h3>
-<p>
-  The most common method of creating a DataFrame is passing a dictionary:
-</p>
-<div class="section-example-container">
-
-<pre class="python">dict = {'AAPL': [143.5,  144.09, 142.73, 144.18, 143.77],
-        'GOOG': [898.7,  911.71, 906.69, 918.59, 926.99],
-        'IBM':  [155.58, 153.67, 152.36, 152.94, 153.49]}
-dates = pd.date_range('2017-07-03', periods = 5, freq = 'D')
-df = pd.DataFrame(dict, index = dates)
-print df
-              AAPL    GOOG     IBM
-2017-07-03  143.50  898.70  155.58
-2017-07-04  144.09  911.71  153.67
-2017-07-05  142.73  906.69  152.36
-2017-07-06  144.18  918.59  152.94
-2017-07-07  143.77  926.99  153.49
-</pre>
-</div>
-
-<h3>Manipulating DataFrames</h3>
-
-<p>
-  We can fetch values in a DataFrame by columns and index. Each column in a DataFrame is essentially a Pandas Series. We can fetch a column by square brackets: <strong>df['column_name']</strong>
-</p>
-<p>
-  If a column name contains no spaces, then we can also use df.column_name to fetch a column:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">df = aapl_table
-print df.Close.tail(5)
-print df['Adj. Volume'].tail(5)
-Date
-2017-07-24    152.09
-2017-07-25    152.74
-2017-07-26    153.46
-2017-07-27    150.56
-2017-07-28    149.50
-Name: Close, dtype: float64
-Date
-2017-07-24    21122730.0
-2017-07-25    18612649.0
-2017-07-26    15172136.0
-2017-07-27    32175875.0
-2017-07-28    16832947.0
-Name: Adj. Volume, dtype: float64
-</pre>
-</div>
-
-<p>
-  All the methods we applied to a Series index such as iloc[], loc[] and resampling methods, can also be applied to a DataFrame:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">aapl_2016 = df['2016']
-aapl_month = aapl_2016.resample('M').agg(lambda x: x[-1])
-print aapl_month
-</pre>
-</div>
-
-<div class="section-example-container">
-
-<pre class="python">
-              Open      High     Low   Close      Volume  Ex-Dividend  \
-Date
-2016-01-31   94.79   97.3400   94.35   97.34  64416504.0          0.0
-2016-02-29   96.86   98.2300   96.65   96.69  35216277.0          0.0
-2016-03-31  109.72  109.9000  108.88  108.99  25888449.0          0.0
-2016-04-30   93.99   94.7200   92.51   93.74  68531478.0          0.0
-2016-05-31   99.60  100.4000   98.82   99.86  42307212.0          0.0
-2016-06-30   94.44   95.7700   94.30   95.60  35836356.0          0.0
-2016-07-31  104.19  104.5500  103.68  104.21  27733688.0          0.0
-2016-08-31  105.66  106.5699  105.64  106.10  29662406.0          0.0
-2016-09-30  112.46  113.3700  111.80  113.05  36379106.0          0.0
-2016-10-31  113.65  114.2300  113.20  113.54  26419398.0          0.0
-2016-11-30  111.56  112.2000  110.27  110.52  36162258.0          0.0
-2016-12-31  116.65  117.2000  115.43  115.82  30586265.0          0.0
-
-            Split Ratio   Adj. Open   Adj. High    Adj. Low  Adj. Close  \
-Date
-2016-01-31          1.0   91.952819   94.426495   91.525989   94.426495
-2016-02-29          1.0   94.466655   95.802804   94.261844   94.300856
-2016-03-31          1.0  107.008893  107.184446  106.189649  106.296931
-2016-04-30          1.0   91.667571   92.379533   90.224141   91.423748
-2016-05-31          1.0   97.732787   98.517789   96.967410   97.987913
-2016-06-30          1.0   92.669522   93.974588   92.532147   93.807775
-2016-07-31          1.0  102.236738  102.589989  101.736299  102.256363
-2016-08-31          1.0  104.237384  105.135033  104.217653  104.671460
-2016-09-30          1.0  110.945828  111.843576  110.294715  111.527885
-2016-10-31          1.0  112.119806  112.691997  111.675865  112.011287
-2016-11-30          1.0  110.629129  111.263789  109.349893  109.597807
-2016-12-31          1.0  115.676657  116.222068  114.466837  114.853583
-
-            Adj. Volume
-Date
-2016-01-31   64416504.0
-2016-02-29   35216277.0
-2016-03-31   25888449.0
-2016-04-30   68531478.0
-2016-05-31   42307212.0
-2016-06-30   35836356.0
-2016-07-31   27733688.0
-2016-08-31   29662406.0
-2016-09-30   36379106.0
-2016-10-31   26419398.0
-2016-11-30   36162258.0
-2016-12-31   30586265.0
-</pre>
-</div>
-
-<p>
-  We may select certain columns of a DataFrame using their names:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">aapl_bar = aapl_month[['Open', 'High', 'Low', Close']]
-print aapl_bar
-
-              Open      High     Low   Close
-Date
-2016-01-31   94.79   97.3400   94.35   97.34
-2016-02-29   96.86   98.2300   96.65   96.69
-2016-03-31  109.72  109.9000  108.88  108.99
-2016-04-30   93.99   94.7200   92.51   93.74
-2016-05-31   99.60  100.4000   98.82   99.86
-2016-06-30   94.44   95.7700   94.30   95.60
-2016-07-31  104.19  104.5500  103.68  104.21
-2016-08-31  105.66  106.5699  105.64  106.10
-2016-09-30  112.46  113.3700  111.80  113.05
-2016-10-31  113.65  114.2300  113.20  113.54
-2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82
-</pre>
-</div>
-
-<p>
-  We can even specify both rows and columns using loc[]. The row indices and column names are separated by a comma:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">print aapl_month.loc['2016-03':'2016-06', ['Open', 'High', 'Low', 'Close']]
-
-              Open    High     Low   Close
-Date
-2016-03-31  109.72  109.90  108.88  108.99
-2016-04-30   93.99   94.72   92.51   93.74
-2016-05-31   99.60  100.40   98.82   99.86
-2016-06-30   94.44   95.77   94.30   95.60
-</pre>
-</div>
-
-<p>
-  The subset methods in DataFrame is quite useful. By writing logical statements in square brackets, we can make customized subsets:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">above = aapl_bar[aapl_bar.Close &gt; np.mean(aapl_bar.Close)]
-print above
-
-              Open      High     Low   Close
-Date
-2016-03-31  109.72  109.9000  108.88  108.99
-2016-08-31  105.66  106.5699  105.64  106.10
-2016-09-30  112.46  113.3700  111.80  113.05
-2016-10-31  113.65  114.2300  113.20  113.54
-2016-11-30  111.56  112.2000  110.27  110.52
-2016-12-31  116.65  117.2000  115.43  115.82
-</pre>
-</div>
-
-<h3>Data Validation</h3>
-
-<p>
-  As mentioned, all methods that apply to a Series can also be applied to a DataFrame. Here we add a new column to an existing DataFrame:
-</p>
-<div class="section-example-container">
-
-<pre class="python">aapl_bar['rate_return'] = aapl_bar.Close.pct_change()
-print aapl_bar
-
-              Open      High     Low   Close  rate_return
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955
-</pre>
-</div>
-
-<p>
-  Here the calculation introduced a NaN value. If the DataFrame is large, we would not be able to observe it. <strong>isnull()</strong> provides a convenient way to check abnormal values.
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">missing = aapl_bar.isnull()
-print missing
-print '---------------------------------------------'
-print missing.describe()
-
-             Open   High    Low  Close rate_return
-Date
-2016-01-31  False  False  False  False        True
-2016-02-29  False  False  False  False       False
-2016-03-31  False  False  False  False       False
-2016-04-30  False  False  False  False       False
-2016-05-31  False  False  False  False       False
-2016-06-30  False  False  False  False       False
-2016-07-31  False  False  False  False       False
-2016-08-31  False  False  False  False       False
-2016-09-30  False  False  False  False       False
-2016-10-31  False  False  False  False       False
-2016-11-30  False  False  False  False       False
-2016-12-31  False  False  False  False       False
-
----------------------------------------------
-
-         Open   High    Low  Close rate_return
-count      12     12     12     12          12
-unique      1      1      1      1           2
-top     False  False  False  False       False
-freq       12     12     12     12          11
-</pre>
-</div>
-
-<p>
-  The row labelled "unique" indicates the number of unique values in each column. Since the "rate_return" column has 2 unique values, it has at least one missing value.
-</p>
-<p>
-  We can deduce the number of missing values by comparing "count" with "freq". There are 12 counts and 11 False values, so there is one True value which corresponds to the missing value.
-</p>
-<p>
-  We can also find the rows with missing values easily:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">print missing[missing.rate_return == True]
-
-             Open   High    Low  Close rate_return
-Date
-2016-01-31  False  False  False  False        True
-</pre>
-</div>
-
-<p>
-  Usually when dealing with missing data, we either delete the whole row or fill it with some value. As we introduced in the Series chapter, the same method <strong>dropna()</strong> and <strong>fillna()</strong> can be applied to a DataFrame.
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">drop = aapl_bar.dropna()
-print drop
-print '\n--------------------------------------------------\n'
-fill = aapl_bar.fillna(0)
-print fill
-
-              Open      High     Low   Close  rate_return
-Date
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955
-
---------------------------------------------------
-
-              Open      High     Low   Close  rate_return
-Date
-2016-01-31   94.79   97.3400   94.35   97.34     0.000000
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955
-</pre>
-</div>
-
-<h3>DataFrame Concat</h3>
-<p>
-  We have seen how to extract a Series from a dataFrame. Now we need to consider how to merge a Series or a DataFrame into another one.
-</p>
-<p>
-  In Pandas, the function <strong>concat()</strong> allows us to merge multiple Series into a DataFrame:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">s1 = pd.Series([143.5, 144.09, 142.73, 144.18, 143.77], name = 'AAPL')
-s2 = pd.Series([898.7, 911.71, 906.69, 918.59, 926.99], name = 'GOOG')
-data_frame = pd.concat([s1, s2], axis = 1)
-print data_frame
-
-     AAPL    GOOG
-0  143.50  898.70
-1  144.09  911.71
-2  142.73  906.69
-3  144.18  918.59
-4  143.77  926.99
-</pre>
-</div>
-
-<p>
-  The "axis = 1" parameter will join two DataFrames by columns:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">log_price = np.log(aapl_bar.Close)
-log_price.name = 'log_price'
-print log_price
-print '\n--------------------------------------------\n'
-concat = pd.concat([aapl_bar, log_price], axis = 1)
-print concat
-
-Date
-2016-01-31    4.578210
-2016-02-29    4.571510
-2016-03-31    4.691256
-2016-04-30    4.540525
-2016-05-31    4.603769
-2016-06-30    4.560173
-2016-07-31    4.646408
-2016-08-31    4.664382
-2016-09-30    4.727830
-2016-10-31    4.732155
-2016-11-30    4.705197
-2016-12-31    4.752037
-Freq: M, Name: log_price, dtype: float64
-
---------------------------------------------
-
-              Open      High     Low   Close  rate_return  log_price
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN   4.578210
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678   4.571510
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211   4.691256
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921   4.540525
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287   4.603769
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660   4.560173
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063   4.646408
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136   4.664382
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504   4.727830
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334   4.732155
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599   4.705197
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955   4.752037
-</pre>
-</div>
-
-<p>
-  We can also join two DataFrames by rows. Consider these two DataFrames:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">df_volume = aapl_table.loc['2016-10':'2017-04', ['Volume', 'Split Ratio']].resample('M').agg(lambda x: x[-1])
-print df_volume
-print '\n-------------------------------------------\n'
-df_2017 = aapl_table.loc['2016-10':'2017-04', ['Open', 'High', 'Low', 'Close']].resample('M').agg(lambda x: x[-1])
-print df_2017
-
-                Volume  Split Ratio
-Date
-2016-10-31  26419398.0          1.0
-2016-11-30  36162258.0          1.0
-2016-12-31  30586265.0          1.0
-2017-01-31  49200993.0          1.0
-2017-02-28  23482860.0          1.0
-2017-03-31  19661651.0          1.0
-2017-04-30  20247187.0          1.0
-
--------------------------------------------
-
-              Open     High     Low   Close
-Date
-2016-10-31  113.65  114.230  113.20  113.54
-2016-11-30  111.56  112.200  110.27  110.52
-2016-12-31  116.65  117.200  115.43  115.82
-2017-01-31  121.15  121.390  120.62  121.35
-2017-02-28  137.08  137.435  136.70  136.99
-2017-03-31  143.72  144.270  143.01  143.66
-2017-04-30  144.09  144.300  143.27  143.65
-</pre>
-</div>
-
-<p>
-  Now we merge the DataFrames with our DataFrame 'aapl_bar'
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1)
-print concat
-
-              Open      High     Low   Close  rate_return      Volume  \
-Date
-2016-01-31   94.79   97.3400   94.35   97.34          NaN         NaN
-2016-02-29   96.86   98.2300   96.65   96.69    -0.006678         NaN
-2016-03-31  109.72  109.9000  108.88  108.99     0.127211         NaN
-2016-04-30   93.99   94.7200   92.51   93.74    -0.139921         NaN
-2016-05-31   99.60  100.4000   98.82   99.86     0.065287         NaN
-2016-06-30   94.44   95.7700   94.30   95.60    -0.042660         NaN
-2016-07-31  104.19  104.5500  103.68  104.21     0.090063         NaN
-2016-08-31  105.66  106.5699  105.64  106.10     0.018136         NaN
-2016-09-30  112.46  113.3700  111.80  113.05     0.065504         NaN
-2016-10-31  113.65  114.2300  113.20  113.54     0.004334  26419398.0
-2016-11-30  111.56  112.2000  110.27  110.52    -0.026599  36162258.0
-2016-12-31  116.65  117.2000  115.43  115.82     0.047955  30586265.0
-2017-01-31     NaN       NaN     NaN     NaN          NaN  49200993.0
-2017-02-28     NaN       NaN     NaN     NaN          NaN  23482860.0
-2017-03-31     NaN       NaN     NaN     NaN          NaN  19661651.0
-2017-04-30     NaN       NaN     NaN     NaN          NaN  20247187.0
-
-            Split Ratio
-Date
-2016-01-31          NaN
-2016-02-29          NaN
-2016-03-31          NaN
-2016-04-30          NaN
-2016-05-31          NaN
-2016-06-30          NaN
-2016-07-31          NaN
-2016-08-31          NaN
-2016-09-30          NaN
-2016-10-31          1.0
-2016-11-30          1.0
-2016-12-31          1.0
-2017-01-31          1.0
-2017-02-28          1.0
-2017-03-31          1.0
-2017-04-30          1.0
-</pre>
-</div>
-
-<p>
-  By default the DataFrame are joined with all of the data. This default options results in zero information loss. We can also merge them by intersection, this is called 'inner join':
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">concat = pd.concat([aapl_bar, df_volume], axis = 1, join = 'inner')
-print concat
-
-              Open    High     Low   Close  rate_return      Volume  \
-Date
-2016-10-31  113.65  114.23  113.20  113.54     0.004334  26419398.0
-2016-11-30  111.56  112.20  110.27  110.52    -0.026599  36162258.0
-2016-12-31  116.65  117.20  115.43  115.82     0.047955  30586265.0
-
-            Split Ratio
-Date
-2016-10-31          1.0
-2016-11-30          1.0
-2016-12-31          1.0
-
-</pre>
-</div>
-<p>
-  Only the intersection part was left if use 'inner join' method. Now let's try to append a DataFrame to another one:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">append = aapl_bar.append(df_2017)
-print append
-             Close      High     Low    Open  rate_return
-Date
-2016-01-31   97.34   97.3400   94.35   94.79          NaN
-2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.54  114.2300  113.20  113.65          NaN
-2016-11-30  110.52  112.2000  110.27  111.56          NaN
-2016-12-31  115.82  117.2000  115.43  116.65          NaN
-2017-01-31  121.35  121.3900  120.62  121.15          NaN
-2017-02-28  136.99  137.4350  136.70  137.08          NaN
-2017-03-31  143.66  144.2700  143.01  143.72          NaN
-2017-04-30  143.65  144.3000  143.27  144.09          NaN
-
-</pre>
-</div>
-<p>
-  'Append' is essentially to concat two DataFrames by axis = 0, thus here is an alternative way to append:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">concat = pd.concat([aapl_bar, df_2017], axis = 0)
-print concat
-             Close      High     Low    Open  rate_return
-Date
-2016-01-31   97.34   97.3400   94.35   94.79          NaN
-2016-02-29   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.54  114.2300  113.20  113.65          NaN
-2016-11-30  110.52  112.2000  110.27  111.56          NaN
-2016-12-31  115.82  117.2000  115.43  116.65          NaN
-2017-01-31  121.35  121.3900  120.62  121.15          NaN
-2017-02-28  136.99  137.4350  136.70  137.08          NaN
-2017-03-31  143.66  144.2700  143.01  143.72          NaN
-2017-04-30  143.65  144.3000  143.27  144.09          NaN
-</pre>
-</div>
-<p>
-  Please note that if the two DataFrame have some columns with the same column names, these columns are considered to be the same and will be merged. It's very important to have the right column names. If we change a column names here:
-</p>
-<div class="section-example-container">
-
-<pre class="python">df_2017.columns = ['Change', 'High', 'Low', 'Close']
-concat = pd.concat([aapl_bar, df_2017], axis = 0)
-print concat
-
-            Change   Close      High     Low    Open  rate_return
-Date
-2016-01-31     NaN   97.34   97.3400   94.35   94.79          NaN
-2016-02-29     NaN   96.69   98.2300   96.65   96.86    -0.006678
-2016-03-31     NaN  108.99  109.9000  108.88  109.72     0.127211
-2016-04-30     NaN   93.74   94.7200   92.51   93.99    -0.139921
-2016-05-31     NaN   99.86  100.4000   98.82   99.60     0.065287
-2016-06-30     NaN   95.60   95.7700   94.30   94.44    -0.042660
-2016-07-31     NaN  104.21  104.5500  103.68  104.19     0.090063
-2016-08-31     NaN  106.10  106.5699  105.64  105.66     0.018136
-2016-09-30     NaN  113.05  113.3700  111.80  112.46     0.065504
-2016-10-31     NaN  113.54  114.2300  113.20  113.65     0.004334
-2016-11-30     NaN  110.52  112.2000  110.27  111.56    -0.026599
-2016-12-31     NaN  115.82  117.2000  115.43  116.65     0.047955
-2016-10-31  113.65  113.54  114.2300  113.20     NaN          NaN
-2016-11-30  111.56  110.52  112.2000  110.27     NaN          NaN
-2016-12-31  116.65  115.82  117.2000  115.43     NaN          NaN
-2017-01-31  121.15  121.35  121.3900  120.62     NaN          NaN
-2017-02-28  137.08  136.99  137.4350  136.70     NaN          NaN
-2017-03-31  143.72  143.66  144.2700  143.01     NaN          NaN
-2017-04-30  144.09  143.65  144.3000  143.27     NaN          NaN
-</pre>
-</div>
-<p>
-  Since the column name of 'Open' has been changed, the new DataFrame has an new column named 'Change'.
-</p>
 <h2>Summary</h2>
 <p>
   Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.

From 9d1697272749dabafff2ecc5368e4fac87bd831d Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 7 Jan 2019 11:12:20 -0800
Subject: [PATCH 335/753] Removed additional header

---
 .../05 Pandas-Resampling and DataFrame/05 Summary.html           | 1 -
 1 file changed, 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html
index 0dce45e..6ba7e57 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/05 Summary.html	
@@ -1,4 +1,3 @@
-<h2>Summary</h2>
 <p>
   Hereby we introduced the most import part of python: resampling and DataFrame manipulation. We only introduced the most commonly used method in Financial data analysis. There are also many methods used in data mining, which are also beneficial. You can always check the <a href="https://pandas.pydata.org/pandas-docs/stable/index.html">Pandas</a> official documentations for help.
 </p>

From afc63f434662912075be8f8f9ee02c98da1e443b Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 11:30:51 -0800
Subject: [PATCH 336/753] Update 02 Brownian motion.html

Updated Brownian motion function to better represent the true formula (W0 instead of 0, just to help reinforce this idea for those who are new to it) and to change 'steps' argument to 'N', added variable instantiation (N, T, dt) and updated a few arguments in 'for' loop so that code is executable when copy-and-pasted. Nothing intrinsically different, just changes to make code executable when sourced
---
 .../02 Brownian motion.html                          | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index d871bd1..2f104ad 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -33,16 +33,20 @@
     T: total time
     N: The total number of steps
     """
-    W0 = 0
+    W0 = [0]
     dt = T/float(N)
     # simulate the increments by normal random variable generator
-    increments = np.random.normal(0, 1*np.sqrt(dt), steps)
-    W = [0] + list(np.cumsum(increments))
+    increments = np.random.normal(0, 1*np.sqrt(dt), N)
+    W = W0 + list(np.cumsum(increments))
     return W
+
+N = 1000
+T = 10
+dt = T / float(N)
 t = np.linspace(0.0, N*dt, N+1)
 plt.figure(figsize=(15,10))
 for i in range(5):
-    W = wiener_process(10, 1000)
+    W = wiener_process(T, N)
     plt.plot(t, W)
     plt.xlabel('time')
     plt.ylabel('W')

From f9a38e355122d19d857fd12399ee0c4ec6977b15 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 11:51:13 -0800
Subject: [PATCH 337/753] Update 03 Stochastic process followed by stock
 price.html

Some text editing -- noticed that this section needed a bit of tidying up, and that emphasis needed to be placed on sections of text that referenced portions of the equations presented
---
 ...Stochastic process followed by stock price.html | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html
index 1383d72..13c6b87 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html	
@@ -5,16 +5,16 @@
   On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
 </p>
 <p>
-  In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
+  In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price by using the stochastic differential equation (SDE):
 </p>
 
 \[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
 <p>
-  Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
+  Where \(dS_t\) is the change in the stock price over a short time period from \(t\) to \(t+\Delta t\). \(μ\) is the drift term and can be deemed as the annual expected level of the stock return, \(σ\) is the annual volatility of the stock. Here \(dS_t\) at different time \(t\) are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
 </p>
 
 <p>
-  Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
+  Now in the above equation, the drift and variance rate of stock price \(S\) is not only correlated with time \(t\) but also a function of both \(S\) itself and time \(t\).
 </p>
 
 <p>
@@ -22,17 +22,17 @@
 </p>
 \[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
 <p>
-  We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
+  We can also derive the process that \(ln(S_t)\) follows (here we just give the result, but the derivation uses the Ito Lemma):
 </p>
 \[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
 <p>
-  Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln(S) during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
+  Here \(ln(S_t)\) follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in \(ln(S_t)\) during time interval \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
 </p>
 \[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
 
 \[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
 <p>
-  Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
+  Now the logarithm stock price follows the normal distribution. We write (2) into discrete approximation form as:
 </p>
 \[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
 <p>Equivalently</p>
@@ -43,5 +43,5 @@
 </p>
 \[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
 <p>
-  According to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
+  According to the above equation, we know the stock price at time \(T\) should always greater than 0 and the problem of having a negative price is fixed. Thus we say the stock price \(S\) is a Geometric Brownian motion because the logarithm of \(S\) follows a Brownian motion.
 </p>

From 0e94a233164a80ff6bd585f9a23f04e29bdcb135 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 11:54:02 -0800
Subject: [PATCH 338/753] Update 04 Monte Carlo Simulation of Stock Price.html

---
 .../04 Monte Carlo Simulation of Stock Price.html               | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html
index 5037557..184bb1f 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/04 Monte Carlo Simulation of Stock Price.html	
@@ -1,5 +1,5 @@
 <p>
-  We apply this technique to model stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
+  We apply this technique to model stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price European options. Here we use Google as an example. Suppose today is 07/31/2017 and that the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the \(\mu\) and \(sigma\) parameters. (Note, we do not need to concern with the detailed determinants of \(\mu\) because the value of options written on a stock is, in general, is independent of \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with \(∆t\) = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
 </p>
 <div class="section-example-container">
 

From bab531a7fca5cab2d3a4eb27ebdcd3a5dbc9a9bd Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 11:57:55 -0800
Subject: [PATCH 339/753] Update 05 Monte Carlo Simulation of European
 Options.html

---
 .../05 Monte Carlo Simulation of European Options.html          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html
index 97012b7..5a438a9 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/05 Monte Carlo Simulation of European Options.html	
@@ -28,7 +28,7 @@
             payoff = max(0,strike-st)
         payoff_sum += payoff
     premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
- return premium
+    return premium
 mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
 </pre>
 </div>

From 08bf5d463b0916b11fe1fbafc5e8570820f0d473 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 13:37:37 -0800
Subject: [PATCH 340/753] Update 01 Definition.html

---
 07 Applied Options[]/01 Covered Call/01 Definition.html | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/07 Applied Options[]/01 Covered Call/01 Definition.html b/07 Applied Options[]/01 Covered Call/01 Definition.html
index 882627b..3c3a6a3 100755
--- a/07 Applied Options[]/01 Covered Call/01 Definition.html	
+++ b/07 Applied Options[]/01 Covered Call/01 Definition.html	
@@ -1,8 +1,8 @@
 <p>
-A Covered Call is an options strategy that involves both underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to gain profits from option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract is exercised you will sell the stock at the strike price but still keep the premium. The risk of a Covered Call also comes from the long stock position, which could drop.
+A Covered Call is an options strategy that involves both the underlying stock and an options contract. The trader buys (or already owns) a stock, then sells call options for the same amount of stock. The aim of the Covered Call is to profits from the option premium by selling calls written on the stock you already owned. At any time for US options or at expiration for European options, if the stock moves below the strike price, you can keep the premium and still maintain the stock position. If the price moves above the strike, the options contract will be exercised and you will have to sell the stock at the strike price but you will still keep the premium. The risk of a Covered Call also comes from the long stock position whose price could drop.
 </p>
 <p>
-The payoff  is as follows:
+The payoff is as follows:
 </p>
 <div class="section-example-container">
 
@@ -21,7 +21,7 @@
   payoff_covered_call = np.sum([payoff_short_call, payoff_long_stock], axis=0)
   plt.figure(figsize=(20,11))
   plt.plot(price, payoff_short_call, label = 'short call')
-  plt.plot(price, payoff_long_stock, label = 'long stock')
+  plt.plot(price, payoff_long_stock, label = 'underlying stock')
   plt.plot(price, payoff_covered_call, label = 'covered call')
   plt.legend(fontsize = 20)
   plt.xlabel('Stock Price at Expiry',fontsize = 15)

From 2687fdae212a00f46d10d9fd7f041944da8a0d9e Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 13:47:34 -0800
Subject: [PATCH 341/753] Update 02 Implementation.html

---
 07 Applied Options[]/01 Covered Call/02 Implementation.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/01 Covered Call/02 Implementation.html b/07 Applied Options[]/01 Covered Call/02 Implementation.html
index 2583e0d..a4bcc62 100755
--- a/07 Applied Options[]/01 Covered Call/02 Implementation.html	
+++ b/07 Applied Options[]/01 Covered Call/02 Implementation.html	
@@ -21,7 +21,7 @@
   In the initialization process, you need to add a coarse selection for the options contracts. Here we use <code>option.SetFilter(-3, +3, timedelta(0), timedelta(60))</code> to implement the coarse selection process. If today's market price of underlying stock is $159, the strike prices of IBM options are spaced $5. Then SetFilter will look up the most at the money contract with the strike being K=$160 (Here K might not be $159 since rarely will option be ATM exactly). The filter will look for options with strikes between and including (160-5*3, 160+5*3). The time to expiration of these options is restricted within 60 days from now on.
 </p>
 <p>
-  Step 2: Choose the Call Options Contract: Purchasing the underlying stock for 100 shares or an integral multiple of 100 shares. For options contract, one contract represents 100 shares of stock.
+  Step 2: Choose the Call Options Contract: Purchasing the underlying stock for 100 shares or an integer multiple of 100 shares. For options contract, one contract represents 100 shares of stock.
 </p>
 <p>
   Step 3: Filter out the call options from candidate contracts.

From 2fa4a7360feba275d46a46c4f640574c6de70443 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 13:53:12 -0800
Subject: [PATCH 342/753] Update 03 Summary.html

---
 07 Applied Options[]/01 Covered Call/03 Summary.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/01 Covered Call/03 Summary.html b/07 Applied Options[]/01 Covered Call/03 Summary.html
index 92e1b92..0f8bef9 100755
--- a/07 Applied Options[]/01 Covered Call/03 Summary.html	
+++ b/07 Applied Options[]/01 Covered Call/03 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-From the above strategy we can see,  the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small. The Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, stock gains up to the strike price, and accrued dividends during the stock holding period. But in a bullish market, you will lose the potential gains over the strike price.
+From the above strategy we can see that the share price of IBM had been increasing during the backtesting period from January 2016 to June 2016. The drawdown is small -- the Covered Call strategy performs better than a simple buy-and-hold strategy of underlying stock in a bearish market. The benefit of Covered Call is that you keep the premium, any gains from the underlying price increase up to the strike price, and accrued dividends during the stock holding period. In a bullish market, however, you miss out on any gains if the underlying stock price breaches the strike price.
 </p>

From 42b08957a702c53f9d2110eec560dc36cb5e5d90 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:07:17 -0800
Subject: [PATCH 343/753] Update 01 Definition.html

---
 .../02 Bull Call Spread/01 Definition.html                | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html
index 9b8de43..0418abb 100755
--- a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
@@ -1,11 +1,11 @@
 <p>
- Bull Call Spread is an option strategy involved with two call option contracts with the same expiration but different strikes. The strategy buys the call options with a lower strike and sells the same amount of call options with a higher strike price.
+ Bull Call Spread is an options strategy involving two call option contracts with the same expiration but different strikes. The strategy buys one call option with a lower strike and sells another call option with a higher strike price.
 </p>
 <p>
- This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss from the dropping price of the stock but still create a ceiling to the profit while the price is increasing.
+ This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss resulting from a drop in the price of the underlying stock but still creates a ceiling to the profit while the underlying price is increasing.
 </p>
 <p>
- Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900, the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
+ Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
 </p>
 <div class="section-example-container">
 
@@ -32,5 +32,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial02-bull-call-spread.png" alt="Tutorial02-bull-call-spread"/>
 <p>
- From the payoff plot we can see, the maximum profit of the strategy is the difference between call option strike price sold and call option strike price purchased minus the difference between buying lower strike call premium and selling higher strike call premium.
+ From the payoff plot we can see, the maximum payoff of the strategy is the difference between the option strike prices minus the difference between the premiums.
 </p>

From 86b5f3f4f79ca7ba6e709013b612310903c9639c Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:14:06 -0800
Subject: [PATCH 344/753] Update 02 Implementation.html

---
 .../02 Bull Call Spread/02 Implementation.html                | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html
index 974201f..b486aab 100755
--- a/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/02 Implementation.html	
@@ -61,7 +61,3 @@
 	self.TradeOptions(optionchain)
 </pre>
 </div>
-<h2>Summary</h2>
-<p>
-This strategy can be implemented when you have a moderate outlook on the stock because the payoff reaches its maximum when the price is between the strike price range of two traded options. The strategy protects the downside move of the stock price. But when the price is above the higher strike, the profit is capped.
-</p>

From 6a723c70f805aea68cae816b1903015f34df6a5a Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:34:40 -0800
Subject: [PATCH 345/753] Update 01 Definition.html

---
 07 Applied Options[]/03 Long Straddle/01 Definition.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/03 Long Straddle/01 Definition.html b/07 Applied Options[]/03 Long Straddle/01 Definition.html
index 9f8525e..8604edc 100755
--- a/07 Applied Options[]/03 Long Straddle/01 Definition.html	
+++ b/07 Applied Options[]/03 Long Straddle/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
-	Long Straddle is an option strategy involved with the long position of a call option and a put option. Both of them have the same strike price and the same expiration date. This strategy aims to gain profit when you feel that a stock was about to make a big move, but are not sure which way the price will move.
+	Long Straddle is an options trading strategy involving the going long in both a call and a put option, where both options have the same underlying asset, strike price and expiration date. This strategy aims to profit from volatile movements in the underlying stock, either positive or negative.
 </p>
 <div class="section-example-container">
 
@@ -25,5 +25,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-long-straddle.png" alt="long straddle strategy payoff"/>
 <p>
- Given this plot, if the stock price moves sharply at the expiration either up or down, the Long Straddle will collect positive profit. the potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial, because the stock price can fall to zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be reached if the stock price is exactly equal to the strike price at expiration. Both options will expire worthless.
+ Given this plot, if the stock price moves significantly away from the strike price in either direction, the Long Straddle will profit. The potential profit is unlimited on the upside because the stock price can rise indefinitely. On the downside, the potential profit is substantial but limited since the stock price can't fall below zero. The potential loss is limited to the premium of both call and put options. The maximum loss will be realized if the stock price is exactly equal to the strike price at expiration, and both options will expire worthless.
 </p>

From 2ac1199e9c09926628190ef695f9b7b3f0583107 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:36:42 -0800
Subject: [PATCH 346/753] Update 02 Implementation.html

---
 .../03 Long Straddle/02 Implementation.html               | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/07 Applied Options[]/03 Long Straddle/02 Implementation.html b/07 Applied Options[]/03 Long Straddle/02 Implementation.html
index afa998a..63d0380 100755
--- a/07 Applied Options[]/03 Long Straddle/02 Implementation.html	
+++ b/07 Applied Options[]/03 Long Straddle/02 Implementation.html	
@@ -1,5 +1,5 @@
 <p>
- Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration between 30 to 60 days from now on.
+ Step 1: Initialize your algorithm and filter the contract. Here we choose the time to expiration to be between 30 to 60 days.
 </p>
 <div class="section-example-container">
 
@@ -17,18 +17,18 @@
 </pre>
 </div>
 <p>
- Step 2: Sorted the option chain by expiration and choose the furthest date to filter the call contract. Then sorted those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
+ Step 2: Sort the option chain by expiration and choose the furthest date to filter the call contract. Then sort those call contracts by their strike price and choose the call contracts with the highest strike price to trade.
 </p>
 <div class="section-example-container">
 
 <pre class="python">for i in optionchain:
 	if i.Key != self.symbol: continue
 	chain = i.Value
-	# sorted the optionchain by expiration date and choose the furthest date
+	# sort the optionchain by expiration date and choose the furthest date
 	expiry = sorted(chain,key = lambda x: x.Expiry, reverse=True)[0].Expiry
 	# filter the call options from the contracts expires on that date
 	call = [i for i in chain if i.Expiry == expiry and i.Right == 0]
-	# sorted the contracts according to their strike prices
+	# sort the contracts according to their strike prices
 	call_contracts = sorted(call,key = lambda x: x.Strike)
 	if len(call_contracts) == 0: continue
 	self.call = call_contracts[0]

From 78ad131f04242fcf0674ca00967b8769620028a1 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:54:52 -0800
Subject: [PATCH 347/753] Update 01 Definition.html

---
 07 Applied Options[]/04 Long Strangle/01 Definition.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/04 Long Strangle/01 Definition.html b/07 Applied Options[]/04 Long Strangle/01 Definition.html
index e5691db..4e3c5d7 100755
--- a/07 Applied Options[]/04 Long Strangle/01 Definition.html	
+++ b/07 Applied Options[]/04 Long Strangle/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
- Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call of the same underlying stock and expiration date. Similar to Long Straddle, Long Strangle has unlimited profit and limited risk and can be applied if traders think some major news events may cause the stock to make a sharp move either up or down during the life of the options. But it differs from Long Straddle in that the call strike is above the put strike.
+ A Long Strangle is an options trading strategy that involves the simultaneous buying of an out-of-the-money put and an out-of-the-money call with the same underlying stock and expiration date. Similar to a Long Straddle, the Long Strangle has unlimited profit and limited risk, and can be applied if traders think the underlying asset will become volatile and move significantly in either direction. It differs from Long Straddle, however, in that the call strike is above the put strike.
 </p>
 <div class="section-example-container">
 
@@ -27,5 +27,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial04-long-strangle.png" alt="long strangle strategy payoff"/>
 <p>
- From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both a put and a call option. The maximum loss occurs when the stock price falls between the strike price of two options. All options are worthless on expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
+ From the payoff plot, the most that you can lose in a Long Strangle is the total premium you pay for holding the long position of both options. The maximum loss occurs when the stock price falls between the strike price of two options, in which case both options are worthless at expiration. The maximum gain of Long Strangle is unlimited for upside move because a stock's price has no maximum threshold.
 </p>

From d5bece598b1338a5858ba18b6708721c99e1a3ae Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 14:59:16 -0800
Subject: [PATCH 348/753] Update 03 Summary.html

---
 07 Applied Options[]/04 Long Strangle/03 Summary.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/04 Long Strangle/03 Summary.html b/07 Applied Options[]/04 Long Strangle/03 Summary.html
index de22af7..f567b6c 100755
--- a/07 Applied Options[]/04 Long Strangle/03 Summary.html	
+++ b/07 Applied Options[]/04 Long Strangle/03 Summary.html	
@@ -1,6 +1,6 @@
 <p>
-	In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16. Therefore we conclude the call option is exercised then we get 100 long stocks position. The put option expires worthless.
+	In this algorithm, at time 0, we buy OTM call with strike price 870 and OTM put with strike price 795. The share price of GOOG at time 0 is $832.8. At the expiry, the share price of GOOG is $930.16, and so the call option is exercised and we get 100 shares of GOOG while the put option expires worthless.
 </p>
 <p>
-	You can enter into Long Strangle if you have no clear idea of market direction but forecast there will be a great movement in the underlying asset. As the options you buy are all out of the money, that reduces the cost of this strategy. But because the call and the put options are all out of the money, the stock will need to move even more significantly than long straddle to gain positive profit from this strategy.
+	You can enter into a Long Strangle if you have no clear idea of market direction but forecast that there will be a great movement in the underlying asset. As the options you buy are all out of the money, the premium cost of entering this position is low. However, because the call and the put options are all out of the money, the stock will need to move even more significantly than in a long straddle in order to profit from this strategy.
 </p>

From d52a562994a6cc3bdf775ae85b629759f426e4d6 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:17:46 -0800
Subject: [PATCH 349/753] Update 01 Definition.html

---
 07 Applied Options[]/05 Butterfly Spread/01 Definition.html | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/07 Applied Options[]/05 Butterfly Spread/01 Definition.html b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html
index 905e999..6d03a55 100755
--- a/07 Applied Options[]/05 Butterfly Spread/01 Definition.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
-	Butterfly Spread strategy involves four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
+	A Butterfly Spread strategy involves trading four option contracts with the same expiration but three different strike prices. There are four kinds of Butterfly Spread:
 </p>
 <table class="table qc-table">
 <thead>
@@ -28,10 +28,10 @@
 </tbody>
 </table>
 <p>
- Butterfly Spread strategy consists of three legs with a total of four options. In this tutorial, we take Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
+ A Butterfly Spread consists of three legs with a total of four options. In this tutorial, we use the Long Butterfly Spread as an example: long one ITM call, short two ATM calls and long one OTM call. All the calls have the same expiration. On the other hand, the middle strike is halfway between the lower and the higher strikes.
 </p>
 <p>
-	The aim of Butterfly Spread strategy is to gain profits when traders think that the underlying stock will not rise or fall much by expiration.
+	The aim of a Butterfly Spread strategy is for a trader to profit from marginal price changes in the underlying stock in either direction.
 </p>
 <div class="section-example-container">
 

From 0c19468decc0551696ab5c475f7d3d45bc7b48d0 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:21:53 -0800
Subject: [PATCH 350/753] Update 02 Implementation.html

---
 .../05 Butterfly Spread/02 Implementation.html              | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html
index 2508d10..99e677e 100755
--- a/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/02 Implementation.html	
@@ -1,5 +1,5 @@
 <p>
-	Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days from now on. Since ITM call strike and OTM call strike are symmetrical with ATM call strike. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0.
+	Step 1: Initialize your algorithm including setting the start date and the end date, setting the cash and implement a coarse selection of option contract. <code>SetFilter(-9, 9, timedelta(30), timedelta(60))</code> helps us choose the contracts which expire in 30 to 60 days. For the strike parameter, the first parameter is the minimum strike rank relative to market price,  the second parameter is the maximum strike rank relative to market price. The rank of ATM contract is 0. Here we need to choose 2 numbers which are symmetrical with 0 to get strike prices for both ITM and OTM contracts.
 </p>
 <div class="section-example-container">
 
@@ -16,7 +16,7 @@
 </pre>
 </div>
 <p>
-	Step 2: Second we need to choose an expiration date for all the contracts in Butterfly Spread strategy. Then filter out all the call options expire on that date.
+	Step 2: Second we need to choose an expiration date for all the contracts, then filter out all the call options that expire on that date.
 </p>
 <div class="section-example-container">
 
@@ -30,7 +30,7 @@
 </pre>
 </div>
 <p>
-	Step 3: Sort the call options by their strike price in ascending order. Then the ATM option is the one which has the smallest absolute value of the difference between the strike price and the underlying asset price. The OTM option is the last one in this call options list which has the highest strike price. The corresponding ITM option is the first one in the list which has the lowest strike price.
+	Step 3: Sort the call options by their strike price in ascending order. Then choose the ATM option to be the one with the smallest absolute difference between the strike price and the underlying asset price. The OTM option is the last one in the call options list with the highest strike price. The corresponding ITM option is the first one in the list with the lowest strike price.
 </p>
 <div class="section-example-container">
 

From 674f36bf5b5d08e8f0470bab0e0a9984c7d8a892 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:25:13 -0800
Subject: [PATCH 351/753] Update 03 Summary.html

---
 07 Applied Options[]/05 Butterfly Spread/03 Summary.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/05 Butterfly Spread/03 Summary.html b/07 Applied Options[]/05 Butterfly Spread/03 Summary.html
index facfc5b..052e555 100755
--- a/07 Applied Options[]/05 Butterfly Spread/03 Summary.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/03 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-	From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option strike at $855, 1 ITM call option strike at $810 and sell 2 ATM options strike at 835. At the expiry 05/19/2017, the share price is $930, then the long positions of the ITM option and the OTM option are all exercised, we buy 100 GOOG  shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised. We have to sell 200  GOOG shares to option holder at $835. Then we no longer hold positions after the expiration of this Long Call Butterfly Spread. Since the stock price had a sharp increase during the life of options, we make no profits from this strategy.
+	From the following algorithm, at time 0, the GOOG share price is $832.8. We purchase 1 OTM call option with strike price $855, 1 ITM call option with strike price $810 and sell 2 ATM options with strike prices at 835. At the expiry 05/19/2017, the share price is $930 and so the long positions of the ITM option and the OTM option are exercised: we buy 100 GOOG shares at $810 and buy another 100 shares at $855. At the same time, the 2 short positions of the ATM option are also exercised: we sell 200  GOOG shares to the option holder at $835. After the expiration date, we don't hold any shares of the underlying. Since the stock price had a sharp increase during the life of options, we fail to profit from a Long Call Butterfly Spread.
 </p>

From cf60b2cb585d6cd8d5841797006765846a532f70 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:44:40 -0800
Subject: [PATCH 352/753] Update 01 Definition.html

---
 07 Applied Options[]/06 Iron Condor/01 Definition.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/06 Iron Condor/01 Definition.html b/07 Applied Options[]/06 Iron Condor/01 Definition.html
index 1cc2aa6..5301009 100755
--- a/07 Applied Options[]/06 Iron Condor/01 Definition.html	
+++ b/07 Applied Options[]/06 Iron Condor/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
-	Iron Condor is an option strategy which involves four option contracts.All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
+	An Iron Condor is an option strategy which involves four option contracts. All options have the same expiration date but different strike prices, where generally the spread in the put strike prices is the same as the spread in the calls. The order of strike for four contracts is A &gt; B &gt; C &gt; D.
 </p>
 <table class="table qc-table">
 <thead>
@@ -66,5 +66,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial06-iron-condor.png" alt="iron condor strategy payoff"/>
 <p>
-	Here the strike price is A(750), B(780), C(820) and D(850). From the payoff plot, the maximum profit all come from the options premium because the deeper OTM options are cheaper than the less deep ones. It is reached when the stock price is between higher put strike and lower call strike. They are two short positions. In this condition, all the options expire worthless.  The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D.Consequently, The two call options are in the money but the two puts expire worthless.
+	Here the strike price is A (750), B (780), C (820) and D (850). As seen in the payoff plot, the maximum profit comes from the options premium because the deeper OTM options are cheaper than the shallower ones. It is reached when the stock price is between the higher put strike and lower call strike (the two short positions) and the options all expire worthless. The maximum loss of Iron Condor is reached when the stock price is lower than the lowest strike A, then the two call options become worthless, the two puts are in the money. For the other condition, the stock price is higher than the highest strike D. Consequently, the two call options are in the money but the two puts expire worthless.
 </p>

From 12805fd251eae4f40a9f41f7165e5ed9ed8dfa94 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:55:06 -0800
Subject: [PATCH 353/753] Update 02 Implementation.html

---
 .../06 Iron Condor/02 Implementation.html                 | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/07 Applied Options[]/06 Iron Condor/02 Implementation.html b/07 Applied Options[]/06 Iron Condor/02 Implementation.html
index 7441745..f9c8e34 100755
--- a/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
+++ b/07 Applied Options[]/06 Iron Condor/02 Implementation.html	
@@ -1,5 +1,5 @@
 <p>
-	Step 1: Initialize your algorithm which involves setting the start date and the end date, setting the cash for your algorithm and implement the coarse selection of option contracts.
+	Step 1: Initialize your algorithm by setting the start date, the end date and the cash for your algorithm. Then, implement the coarse selection of option contracts.
 </p>
 <div class="section-example-container">
 
@@ -20,7 +20,7 @@
 </pre>
 </div>
 <p>
-	Step 2: Break the candidate options into two parts: call and put options.
+	Step 2: Break the candidate options into two parts: calls and puts.
 </p>
 <div class="section-example-container">
 
@@ -33,7 +33,7 @@
 </pre>
 </div>
 <p>
-	Step 3: Sort the call and put options by their strike price respectively.
+	Step 3: Sort the call and put options by their strike prices.
 </p>
 <div class="section-example-container">
 
@@ -42,7 +42,7 @@
 </pre>
 </div>
 <p>
-	Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria,<code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. we choose the first in the put option list as the OTM put with the lower strike, choose the 15 contract as the OTM put with the higher strike.
+	Step 4: Choose the corresponding options and trade them. In this algorithm, according to criteria, <code>SetFilter(-20, 20, timedelta(0), timedelta(40))</code> for put option list, there are 41 contracts in total: 1 ATM put, 20 OTM puts and 20 ITM puts. We choose the first in the put option list as the OTM put with the lower strike and the 15th contract as the OTM put with the higher strike.
 </p>
 <div class="section-example-container">
 

From c56a3ade1245ed86740b4878d9df60e2ca80d481 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 15:56:20 -0800
Subject: [PATCH 354/753] Update 03 Summary.html

---
 07 Applied Options[]/06 Iron Condor/03 Summary.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/06 Iron Condor/03 Summary.html b/07 Applied Options[]/06 Iron Condor/03 Summary.html
index c8b3b02..8766e51 100755
--- a/07 Applied Options[]/06 Iron Condor/03 Summary.html	
+++ b/07 Applied Options[]/06 Iron Condor/03 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-	The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we get the short position of 100 GOOG shares.
+	The iron condor is an option strategy that earns money as long as the underlying asset price does move out of a predetermined price range. In this algorithm, that range is $775 to $827.5. At 02/01/2017, we long $750 put at $1.25 and short $775 put at $3.5. At the same time, we short $827.5 call at$2.3 and long $850 call at $0.75. At this moment, the GOOG share price is $799.55 all the options are out the money. At expiration date 02/17/2017, the share price of GOOG is $828.07. The long put, short put and long call expire worthless. The short call is exercised. Thus we wind up going short 100 shares of GOOG.
 </p>

From cbd3e3915fce5f194fc906f229badb4bb5b8d9f4 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 16:03:31 -0800
Subject: [PATCH 355/753] Update 01 Definition.html

---
 07 Applied Options[]/07 Iron Butterfly/01 Definition.html | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/07 Applied Options[]/07 Iron Butterfly/01 Definition.html b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html
index 73c4d69..59eeb97 100755
--- a/07 Applied Options[]/07 Iron Butterfly/01 Definition.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
-	Iron Butterfly is an option strategy which involves four option contracts. All options have the same expiration date. The order of strike for four contracts is A &gt; B &gt; C.
+	The Iron Butterfly is an option strategy which involves four option contracts, all of which have the same expiration date. The order of strike prices for the four contracts is A &gt; B &gt; C.
 </p>
 <table class="table qc-table">
 <thead>
@@ -28,7 +28,7 @@
 </tbody>
 </table>
 <p>
-	Similar to the Iron Condor, Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility meaning that traders gain profits from this strategy if the stock price changes within a small range. Iron Butterfly has a more narrow range for the price to move up or down compared with Iron Condor.
+	Similar to the Iron Condor, the Iron Butterfly is a limited risk, limited profit trading strategy. It profits from lower volatility, meaning that traders profit if the stock price has marginal movement within a small range. The Iron Butterfly has a more narrow range for the price to move up or down compared with the Iron Condor.
 </p>
 <div class="section-example-container">
 
@@ -65,8 +65,8 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-iron-butterfly.png" alt="iron butterfly strategy payoff"/>
 <p>
-	From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of ATM options. In this condition, all options expire worthless and you keep all premiums received. We can see the Iron Butterfly has more narrow structures than the Iron Condor. However, the profit can be higher than with the Iron Condor as you receive more premium by selling ATM options than OTM options.
+	From the payoff plot, the maximum gain is simply the net credit you received when you buy and sell 4 options. This occurs if the stock price is exactly the same as the strike price of the ATM options. In this situation, all options expire worthless and you keep all premiums received. Although the Iron Butterfly has a narrower structure than the Iron Condor, the profit can be higher than with the Iron Condor as you receive a higher premium by selling ATM options than OTM options.
 </p>
 <p>
-	The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two conditions, two puts or two calls are exercised and the other two options expire worthless.
+	The maximum loss occurs if the underlying price is either below the OTM put strike or above the OTM call strike. In these two situations, two puts or two calls are exercised and the other two options expire worthless.
 </p>

From 7ea060fc3f9970775b26c5e47cc2b938f0b90dce Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 16:10:48 -0800
Subject: [PATCH 356/753] Update 02 Implementation.html

---
 .../07 Iron Butterfly/02 Implementation.html                | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html
index 3631f5f..c0092a5 100755
--- a/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/02 Implementation.html	
@@ -1,5 +1,5 @@
 <p>
-	Step 1: Initialize your algorithm which includes setting the start date and end date, setting the cash and implement the coarse selection of option contracts.
+	Step 1: Initialize your algorithm by setting the start date, end date and the cash. Then, implement the coarse selection of the options contracts.
 </p>
 <div class="section-example-container">
 
@@ -30,7 +30,7 @@
 </pre>
 </div>
 <p>
-	Step 3: Sort the call and put options according to their strike price respectively. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now on.Then for call option, the first 10 contracts are in the money, the last 10 contracts are out of the money. The middle one is at the money option. For put options, the first 10 contracts are out of the money, the last 10 contracts are in the money.
+	Step 3: Sort the call and put options according to their strike prices. <code>option.SetFilter(-10, 10, timedelta(0), timedelta(30))</code> helps us choose 21 call options and 21 put options which expire within 30 days from now. Then for the call options, the first 10 contracts are in the money and the last 10 contracts are out of the money. The middle one is an at the money option. For the put options, the first 10 contracts are out of the money and the last 10 contracts are in the money.
 </p>
 <div class="section-example-container">
 
@@ -40,7 +40,7 @@
 </pre>
 </div>
 <p>
-	Step 4: Find the specific contracts to trade. At the money options have the minimum absolute value of the difference between the underlying price and the strike price.
+	Step 4: Find the specific contracts to trade. At the money options have the minimum absolute difference between the underlying price and the strike price.
 </p>
 <div class="section-example-container">
 

From f7b3fa3a2c958c7b2ca82ea3458a1eb97cb5f9c9 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 16:11:44 -0800
Subject: [PATCH 357/753] Update 03 Summary.html

---
 07 Applied Options[]/07 Iron Butterfly/03 Summary.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/07 Iron Butterfly/03 Summary.html b/07 Applied Options[]/07 Iron Butterfly/03 Summary.html
index 85595a2..61dcef0 100755
--- a/07 Applied Options[]/07 Iron Butterfly/03 Summary.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/03 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-	In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of ATM call, after expiration we hold 100 short positions of GOOG stock.
+	In this algorithm, on 04/03/2017, the share price of Google is $832.8. We buy OTM put(strike = $805) at $2, OTM call(strike = $860) and sell an ATM call and an ATM put. At the expiry 04/21/2017, the share price is $841.53. The ATM call is exercised and the other 3 options expire worthless. As we hold the short position of the ATM call, after expiration we will be short 100 shares of GOOG.
 </p>

From aa78b16346249611525c88ca17261c6118a07c2c Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 16:16:22 -0800
Subject: [PATCH 358/753] Update 01 Definition.html

---
 07 Applied Options[]/08 Protective Collar/01 Definition.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/08 Protective Collar/01 Definition.html b/07 Applied Options[]/08 Protective Collar/01 Definition.html
index 68eb983..3d98e6e 100755
--- a/07 Applied Options[]/08 Protective Collar/01 Definition.html	
+++ b/07 Applied Options[]/08 Protective Collar/01 Definition.html	
@@ -1,5 +1,5 @@
 <p>
-	Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy with the purchase of an additional put option. It is being used if the trader is writing covered calls but wish to protect himself from an unexpected downside sharp move in the price of the underlying security. As a tradeoff, the profit will become limited compared with the covered call strategy.
+	A Protective Collar is an option strategy that involves both the underlying stock and two option contracts. The trader buys (or already owns) a stock, then buys an out-the-money put option and sells an out-the-money call option.  It is similar to the covered call strategy but with the purchase of an additional put option. This strategy is employed if the trader is writing covered calls but wishes to protect himself from an unexpected drop in the price of the underlying security. As a tradeoff for loss-protection, the profit is limited compared with the Covered Call strategy.
 </p>
 <div class="section-example-container">
 
@@ -32,5 +32,5 @@
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-protective-collar.png" alt="protective collar strategy payoff"/>
 <p>
-	According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset add the net credit from the premium. It occurs when the stock price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with limit risk and limit profit.
+	According to the payoff plot, the maximum profit is the strike price of short call minus the purchase price of the underlying asset plus the net credit from the premium. It occurs when the underlying price is beyond the strike price of the short call option. The maximum loss is the purchase price of the underlying asset minus the strike price of the long put minus the net credit from the premium. It occurs when the stock price is below the strike price of the long put. It is a strategy with both limited risk and limited profit.
 </p>

From 1052c384c0ea99b64db7f96e157b2be59fbcf7a1 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Mon, 7 Jan 2019 16:19:06 -0800
Subject: [PATCH 359/753] Update 02 Implementation.html

---
 .../08 Protective Collar/02 Implementation.html               | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/07 Applied Options[]/08 Protective Collar/02 Implementation.html b/07 Applied Options[]/08 Protective Collar/02 Implementation.html
index 57e2c1a..8b6e530 100755
--- a/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
+++ b/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
@@ -1,5 +1,5 @@
 <p>
-	Step 1: Initialize your algorithm that involves setting the start date and the end date, setting the cash and implement the coarse selection of option contracts.
+	Step 1: Initialize your algorithm by setting the start date, end date and cash. Then implement the coarse selection of options contracts.
 </p>
 <div class="section-example-container">
 
@@ -46,7 +46,7 @@
 </pre>
 </div>
 <p>
-	Step 4: In Ondata, if there is no assets in portfolio, we buy the undelying stocks. After that, we trade the options which equivalent to the amount of your stocks holding. (one option contracts equals 100 undelying shares).
+	Step 4: In Ondata, if there are no assets in our portfolio, we buy the undelying stock. After that, we trade the options written on the same number of shares of underlying asset (one option contracts equals 100 undelying shares).
 </p>
 <div class="section-example-container">
 

From ceeab0a2a4639add4eaa6928afc978cb2fd75313 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Tue, 8 Jan 2019 17:16:50 -0800
Subject: [PATCH 360/753] Update 02 Brownian motion.html

I think this will fix our duplicate sections problem
---
 .../02 Brownian motion.html                                 | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index 2f104ad..dd6c077 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -63,7 +63,7 @@
 <p>
   Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with the mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
 </p>
-<h3>2. The stochastic process followed by stock price</h3>
+<h3> Modeling Stock Price as a Stochastic Process</h3>
 <p>
   Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
 </p>
@@ -116,7 +116,7 @@ <h2>Monte Carlo Method</h2>
 <p>
   Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
 </p>
-<h3>1. Monte Carlo Simulation of Stock Price</h3>
+<h3>Monte Carlo Simulation of Stock Price</h3>
 <p>
   We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
 </p>
@@ -149,7 +149,7 @@ <h3>1. Monte Carlo Simulation of Stock Price</h3>
 </pre>
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
-<h3>1. Monte Carlo Simulation of European Options</h3>
+<h3>Monte Carlo Simulation of European Options</h3>
 <p>
   Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
 </p>

From 70ef2520cc97a536ba9800f92489e2f89b0678f2 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Tue, 8 Jan 2019 17:18:11 -0800
Subject: [PATCH 361/753] Update 02 Brownian motion.html

---
 .../02 Brownian motion.html                   | 131 ------------------
 1 file changed, 131 deletions(-)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html
index dd6c077..f0cf290 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/02 Brownian motion.html	
@@ -63,134 +63,3 @@
 <p>
   Where ε follows the standard normal distribution N(0,1). Thus \(\Delta x\) has normal distribution with the mean being \(a\Delta t\), variance being \(b^2\Delta t\). And because \(\Delta t\) is independent, from time 0 to time T, the change in the value of x is just the sum of \(\Delta x\) in each small time interval. It means that the change in the value of x follows the normal distribution \(N(aT,b^2T)\).
 </p>
-<h3> Modeling Stock Price as a Stochastic Process</h3>
-<p>
-  Then we go back to the stock price. If we use the generalized Wiener process to model the stock price, for example, the share price of a stock is $30 now, then the percentage change in price from now to say the end of this year is normally distributed with constant mean and variance. But there is a problem that for normal distribution assumption of return, there is the possibility that the stock price will be negative this is unrealistic for stock prices. For example, this percentage change could be -1.1, which means the stock price at the end of the year is -$3.
-</p>
-<p>
-  On the other hand, if the spot price is small for a stock, then it tends to have small increments in price over a given time interval, on the contrary, stocks with high prices tend to have much larger increments in price on the same interval. But Wiener process has a variance which depends on just the time interval but not on the price itself. Thus it is not appropriate to assume that a stock price follows a generalized Wiener process with constant drift rate and variance rate.
-</p>
-<p>
-  In order to characterize the dynamics of a stock price process and fix this problem, we model the proportional increase in the stock price as follows(This kind of equation is often called the stochastic differential equation (SDE)):
-</p>
-
-\[\text dS_t=\mu S_t\ \text d t+\sigma S_t\ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(1)\]
-<p>
-  Where \(dS_t\) is the change in the stock price over a short time period from t to\(t+\Delta t\). μ is the drift term and can be deemed as the annual expected level of the stock return, σ is the annual volatility of the stock. Here \(dS_t\) at different time t are independent, which means today's price change is independent with tomorrows change. \(W_t\) is a standard Wiener process (1) we discussed above.
-</p>
-
-<p>
-  Now in the above equation, the drift and variance rate of stock price S is not only correlated with time t but also a function of both S itself and time t.
-</p>
-
-<p>
-  The discrete approximation form of (1) is
-</p>
-\[\Delta S=\mu S\Delta t+\sigma S\epsilon\sqrt{\Delta t}\]
-<p>
-  We can also derive the process that \(lnS_t\) follows is(Here we just give the result, the derivation should use the Ito Lemma):
-</p>
-\[\text d\ ln(S_t)=(\mu-\frac{\sigma^2}{2}) \ \text d t+\sigma \ \text dW_t \cdot\cdot\cdot\cdot\cdot\cdot(2)\]
-<p>
-  Here ln S follows a generalized Wiener process as which has constant drift rate and variance rate. That means the change in ln(S) during time interval  \(\Delta t\) is normally distributed. This is the lognormal property of stock prices.
-</p>
-\[lnS_{t+\Delta t}-lnS_t\sim N\left[(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-
-\[lnS_{t+\Delta t}\sim N\left[\text ln{S_t}+(\mu-\frac{\sigma^2}{2})\Delta t,\sigma^2\Delta t\right]\]
-<p>
-  Now the logarithm stock price follows the normal distribution.We write (2) into discrete approximation form as:
-</p>
-\[ln(S_{t+\Delta t})-ln(S_{t})=(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\]
-<p>Equivalently</p>
-
-\[S_{t+\Delta t}=S_{t}\exp\left[(\mu-\frac{\sigma^2}{2})\ \Delta t+\sigma \epsilon\sqrt{\Delta t}\right]\cdot\cdot\cdot\cdot\cdot\cdot(3)\]
-<p>
-  If we change t to 0 and change \(\Delta t\) to T, we get
-</p>
-\[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
-<p>
-  According to the above equation, we know the stock price at time T should always greater than 0, the problem for negative price is fixed. Thus we say stock price S is a Geometric Brownian motion because the logarithm of S follows a Brownian motion.
-</p>
-
-<h2>Monte Carlo Method</h2>
-<p>
-  Monte Carlo method divides a time interval into many small time steps and randomly sampling possible paths for the variable, then repeats this process many times attempting to predict all the possible future outcomes of random variables.
-</p>
-<h3>Monte Carlo Simulation of Stock Price</h3>
-<p>
-  We apply this technique to modeling stock prices in order to look at the potential evolution of stock prices over time and then demonstrate how to price the European options. Here we use Google as an example. Suppose today is 07/31/2017, the share price of GOOG is $930.5. We use the historical close price from 01/2015 to 07/2017 to compute the historical annual return and volatility as the mu and sigma parameters. (Note, we do not need to concern with the detailed determinants of 􏰯\(\mu\) because the value of options written on a stock is, in general, independent of􏰯 \(\mu\). In contrast, the stock price volatility \(\sigma\) is of crucial importance to the determination of the value of options). Consider GOOG that pays no-dividends, has an expected return 39.64% per annum with continuous compounding and a volatility of 23.44% per annum. Observe today’s price $903.5 per share and with ∆t = 0.001 yr. Then we apply Monte Carlo to simulate 500 price paths in the next three months.
-</p>
-<div class="section-example-container">
-
-<pre class="python">
-import quandl
-quandl.ApiConfig.api_key = 'NxTUTAQswbKs5ybBbwfK'
-data = quandl.get('WIKI/GOOG')
-close = data['2015-01':'2017-07']['Adj. Close']
-annual_return = (close[-1]/close[1])** (365.0/len(close)) - 1
-annual_vol = (close/close.shift(1)-1)[1:].std()*np.sqrt(252)
-mu = annual_return # 0.39644
-sigma = annual_vol # 0.2344
-s0 = close[-1] # 903.5
-T = 3.0/12
-delta_t = 0.001
-num_reps = 500
-steps = T/delta_t
-plt.figure(figsize=(15,10))
-for j in range(num_reps):
-    price_path = [s0]
-    st = s0
-    for i in range(int(steps)):
-        st = st*e**((mu-0.5*sigma**2)*delta_t + sigma*np.sqrt(delta_t)*np.random.normal(0, 1))
-        price_path.append(st)
-    plt.plot(price_path)
-plt.ylabel('stock price',fontsize=15)
-plt.xlabel('steps',fontsize=15)
-</pre>
-</div>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial03-stock-price-path.png" alt="stock price path"/>
-<h3>Monte Carlo Simulation of European Options</h3>
-<p>
-  Monte Carlo simulation is a commonly used method for derivatives pricing where the payoff is dependent on the history of the underlying asset or where there are several underlying variables.
-</p>
-<p>
-  The essence of using Monte Carlo method to price the options is to simulate the possible paths for stock prices then we can get all the possible value of stock price at expiration.
-</p>
-<ul>
-     <li>First, we need to divide the maturity T of options into N small time intervals, the length of each time interval is \(\Delta t\), N is the number of steps</li>
-     <li>Sample the possible random paths for the stock price according to equation (3) and get the stock price \(S_T\)at maturity T.</li>
-     <li>Calculate the payoff of options according to the \(S_T\)</li>
-     <li>Discount the payoff at the risk-free rate to get one estimate of options' price</li>
-     <li>Repeat the step 1 to 4 for a reasonable number of times and get many estimates of options price and then the average of these price estimates is the final options price.</li>
-</ul>
-<p>
-  There is one thing you need to note that in option pricing, Monte Carlo simulations use the risk-neutral valuation result. More specifically, sample the paths to obtain the expected payoff in a risk-neutral world (The expected annual return rate and the risk-free annual rate should be the same in order to get the correct estimation of the option) and then discount this payoff at the risk-neutral rate.
-</p>
-<div class="section-example-container">
-
-<pre class="python">
-def mc_euro_options(option_type,s0,strike,maturity,r,sigma,num_reps):
-    payoff_sum = 0
-    for j in range(num_reps):
-        st = s0
-        st = st*e**((r-0.5*sigma**2)*maturity + sigma*np.sqrt(maturity)*np.random.normal(0, 1))
-        if option_type == 'c':
-            payoff = max(0,st-strike)
-        elif option_type == 'p':
-            payoff = max(0,strike-st)
-        payoff_sum += payoff
-    premium = (payoff_sum/float(num_reps))*e**(-r*maturity)
- return premium
-mc_euro_options('c',927.96,785,100.0/252,0.01,0.23,500)
-</pre>
-</div>
-<p>
-  Suppose the risk-free rate is 1%. A European call option with strike price being $785 and expires in 100 days, the spot price is $927, the premium by using Monte Carlo method is $149.
-</p>
-<h2>Summary</h2>
-<p>
-  In this chapter, we modeled stock price with the stochastic process. The stochastic process usually assumed for a stock price is geometric Brownian motion. The Black–Scholes–Merton model, which we cover in the next chapter, is based on the geometric Brownian motion assumption. Under this process, the logarithm of stock return in a small period of time is normally distributed and the returns in two nonoverlapping periods are independent.
-</p>
-<p>
-  In the second part of the tutorial,  we applied Monte Carlo method to simulate the stock price in order to gain an intuitive understanding of the stochastic process followed by stock price. Then we discussed how to use Monte Carlo method to price the options based on the underlying prices paths.
-</p>

From b739991aa3749aff5b1f648fcf692eab584f5666 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Tue, 8 Jan 2019 17:20:10 -0800
Subject: [PATCH 362/753] Update and rename 03 Stochastic process followed by
 stock price.html to 03 Modeling Stock Price as a Stochastic Process.html

---
 ...tml => 03 Modeling Stock Price as a Stochastic Process.html} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/{03 Stochastic process followed by stock price.html => 03 Modeling Stock Price as a Stochastic Process.html} (94%)

diff --git a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Modeling Stock Price as a Stochastic Process.html
similarity index 94%
rename from 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html
rename to 06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Modeling Stock Price as a Stochastic Process.html
index 13c6b87..8cec45a 100755
--- a/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Stochastic process followed by stock price.html	
+++ b/06 Introduction to Options[]/04 Stochastic Processes and Monte Carlo Method/03 Modeling Stock Price as a Stochastic Process.html	
@@ -43,5 +43,5 @@
 </p>
 \[S_{T}=S_{0}\exp\left[(\mu-\frac{\sigma^2}{2})\ T+\sigma \epsilon\sqrt{T}\right]\]
 <p>
-  According to the above equation, we know the stock price at time \(T\) should always greater than 0 and the problem of having a negative price is fixed. Thus we say the stock price \(S\) is a Geometric Brownian motion because the logarithm of \(S\) follows a Brownian motion.
+  According to the above equation, we know the stock price at time \(T\) should always be greater than 0 and the problem of having a negative price is fixed. Thus we say the stock price \(S\) is a Geometric Brownian motion because the logarithm of \(S\) follows a Brownian motion.
 </p>

From e42ca05b8713030aa883075f593c86c73bc0143c Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 9 Jan 2019 09:25:40 -0800
Subject: [PATCH 363/753] Update 02 Implementation.html

---
 .../08 Protective Collar/02 Implementation.html                 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/08 Protective Collar/02 Implementation.html b/07 Applied Options[]/08 Protective Collar/02 Implementation.html
index 8b6e530..0e3c4c7 100755
--- a/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
+++ b/07 Applied Options[]/08 Protective Collar/02 Implementation.html	
@@ -46,7 +46,7 @@
 </pre>
 </div>
 <p>
-	Step 4: In Ondata, if there are no assets in our portfolio, we buy the undelying stock. After that, we trade the options written on the same number of shares of underlying asset (one option contracts equals 100 undelying shares).
+	Step 4: In OnData, if there are no assets in our portfolio, we buy the undelying stock. After that, we trade the options written on the same number of shares of underlying asset (one option contracts equals 100 undelying shares).
 </p>
 <div class="section-example-container">
 

From 45b08113db07d8141759b3c441cdd5b75269c100 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Wed, 9 Jan 2019 16:03:51 -0800
Subject: [PATCH 364/753] Fixed sector momentum strategy

---
 04 Strategy Library/14 Sector Momentum/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html
index 6095e1b..0cb1b39 100644
--- a/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/14 Sector Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f7b25a5e872436195cb27a815b5f7e1.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4191ffdf07f9d7dec396f148a20b3eef.html"></iframe>
     </div>
 </div>

From 3e55af3bd2056c125d54e57a21c444797b1ccedc Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Thu, 10 Jan 2019 09:05:26 -0800
Subject: [PATCH 365/753] Backtest reference changed for Sector Momentum

---
 quantpedia.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quantpedia.json b/quantpedia.json
index 2d6bbe9..e1d900e 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -1,7 +1,7 @@
 {
      1 : "6caef9a7716468be0d790b63b7f463f6",
      2 : "1cb38e96cec6120fc85152da661605c1",
-     3 : "8f7b25a5e872436195cb27a815b5f7e1",
+     3 : "4191ffdf07f9d7dec396f148a20b3eef",
      4 : "d31f2d20f4a060418985dd8c0c606c13",
      5 : "798687a2edab75475f0c9095ecd71c1f",
      7 : "34e8ae2763e7e2122634d4091120d320",

From 75ddd2d481f903192f5a3bbdfd519b0a04e3026e Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 18 Jan 2019 09:35:06 -0800
Subject: [PATCH 366/753] Update 01 Abstract.html

---
 .../10 Short-Term Reversal Strategy in Stocks/01 Abstract.html  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
index 19ef597..eae0057 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -2,7 +2,7 @@
   This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
 </p>
 <p>
-  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the?lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
+  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
   In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
   In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
 </p>

From e2266f906805aa9461d376f01c0b6ceb62347016 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 18 Jan 2019 11:43:42 -0800
Subject: [PATCH 367/753] Added experience level tags

---
 .../01 Strategy Library.php                   | 169 ++++++++++++------
 1 file changed, 113 insertions(+), 56 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index f7b809d..7cf2a99 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -10,57 +10,68 @@
     [
         'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
         'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
-        'description' => 'Applies CAPM model to rank Dow Jones 30 companies.'
+        'description' => 'Applies CAPM model to rank Dow Jones 30 companies.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Combining Mean Reversion and Momentum in Forex Market',
         'link' => 'strategy-library/combining-mean-reversion-and-momentum-in-forex-market',
-        'description' => 'Combines momentum and mean reversion techniques in the forex markets.'
+        'description' => 'Combines momentum and mean reversion techniques in the forex markets.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Pairs Trading-Copula vs Cointegration',
         'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
-        'description' => 'Applies Copula and Cointergration method to pairs trading.'
+        'description' => 'Applies Copula and Cointergration method to pairs trading.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'The Dynamic Breakout II Strategy',
         'link' => 'strategy-library/the-dynamic-breakout-ii-strategy',
-        'description' => 'A demonstration of dynamic breakout II strategy.'
+        'description' => 'A demonstration of dynamic breakout II strategy.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Dual Thrust Trading Algorithm',
         'link' => 'strategy-library/dual-thrust-trading-algorithm',
-        'description' => 'A demontration of Dual Thrust Intraday strategy.'
+        'description' => 'A demontration of Dual Thrust Intraday strategy.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Can Crude Oil Predict Equity Returns',
         'link' => 'strategy-library/can-crude-oil-predict-equity-returns',
-        'description' => 'Applies regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.'
+        'description' => 'Applies regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach',
         'link' => 'strategy-library/intraday-dynamic-pairs-trading-using-correlation-and-cointegration-approach',
-        'description' => 'A high frequency pairs trading algorithm based on cointegration.'
+        'description' => 'A high frequency pairs trading algorithm based on cointegration.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
         'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
-        'description' => 'Applies high frequency filter to the momentum strategy.'
+        'description' => 'Applies high frequency filter to the momentum strategy.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Stock Selection Strategy Based on Fundamental Factors',
         'link' => 'strategy-library/stock-selection-strategy-based-on-fundamental-factors',
-        'description' => 'MorningStar Fundamental factors universe selection algorithm.'
+        'description' => 'MorningStar Fundamental factors universe selection algorithm.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Short-Term Reversal Strategy in Stocks',
         'link' => 'strategy-library/short-term-reversal-strategy-in-stocks',
-        'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action.'
+        'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Fundamental Factor Long Short Strategy',
         'link' => 'strategy-library/fundamental-factor-long-short-strategy',
-        'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors.'
+        'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Asset Class Trend Following',
@@ -68,7 +79,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/1'
         ],
-        'description' => 'Selects ETFs over ten-month moving average and assigns an equally weighted allocation.'
+        'description' => 'Selects ETFs over ten-month moving average and assigns an equally weighted allocation.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Asset Class Momentum',
@@ -76,7 +88,9 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/2'
         ],
-        'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.'
+        'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.',
+        'tags'=>'Beginner'
+        
     ],
     [
         'name' => 'Sector Momentum',
@@ -84,7 +98,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/3'
         ],
-        'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.'
+        'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Overnight Anomaly',
@@ -92,7 +107,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/4'
         ],
-        'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.'
+        'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Forex Carry Trade',
@@ -100,7 +116,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/5'
         ],
-        'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.'
+        'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Volatility Effect in Stocks',
@@ -108,7 +125,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/7'
         ],
-        'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility in the past one year.'
+        'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility in the past one year.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Forex Momentum',
@@ -116,7 +134,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/8'
         ],
-        'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.'
+        'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Pairs Trading with Stocks',
@@ -124,7 +143,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/12'
         ],
-        'description' => 'Looks for the security that minimizes the sum of squared deviations and long-short position is opened when pair prices have diverged by multiple of standard deviations.'
+        'description' => 'Looks for the security that minimizes the sum of squared deviations and long-short position is opened when pair prices have diverged by multiple of standard deviations.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Short Term Reversal',
@@ -132,7 +152,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/13'
         ],
-        'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.'
+        'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum Effect in Stocks',
@@ -148,7 +169,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/15'
         ],
-        'description' => 'Goes long stocks with the best 12-month momentum in the country equity indexes ETFs.'
+        'description' => 'Goes long stocks with the best 12-month momentum in the country equity indexes ETFs.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Mean Reversion Effect in Country Equity Indexes',
@@ -164,7 +186,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/18'
         ],
-        'description' => 'Goes long stocks with the lowest turnover and short on stocks with the highest turnover from the lowest market-cap quartile.'
+        'description' => 'Goes long stocks with the lowest turnover and short on stocks with the highest turnover from the lowest market-cap quartile.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Volatility Risk Premium Effect',
@@ -172,7 +195,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/20'
         ],
-        'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.'
+        'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Momentum Effect in Commodities Futures',
@@ -180,7 +204,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/21'
         ],
-        'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.'
+        'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Small Capitalization Stocks Premium Anomaly',
@@ -196,7 +221,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/44'
         ],
-        'description' => 'Goes long asset with better performance over the last period and rebalances portfolio every quarter.'
+        'description' => 'Goes long asset with better performance over the last period and rebalances portfolio every quarter.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Term Structure Effect in Commodities',
@@ -204,7 +230,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/22'
         ],
-        'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.'
+        'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Momentum Effect Combined with Term Structure in Commodities',
@@ -212,7 +239,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/23'
         ],
-        'description' => 'Portfolios are formed based on roll returns and the algorithm goes long and short contracts with the highest and lowest one-month performance.'
+        'description' => 'Portfolios are formed based on roll returns and the algorithm goes long and short contracts with the highest and lowest one-month performance.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Book-to-Market Value Anomaly',
@@ -220,7 +248,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/26'
         ],
-        'description' => 'Quintile portfolios are formed based on the Book-to-Market ratio and the highest quintile is held for one year.'
+        'description' => 'Quintile portfolios are formed based on the Book-to-Market ratio and the highest quintile is held for one year.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Gold Market Timing',
@@ -228,7 +257,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/43'
         ],
-        'description' => 'Goes long gold when the Fed model shows that the market is undervalued (the earnings yield is higher than the bond yield and their ratio is at least 2).'
+        'description' => 'Goes long gold when the Fed model shows that the market is undervalued (the earnings yield is higher than the bond yield and their ratio is at least 2).',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Turn of the Month in Equity Indexes',
@@ -236,7 +266,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/41'
         ],
-        'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.'
+        'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Momentum - Short Term Reversal Strategy',
@@ -244,7 +275,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/51'
         ],
-        'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.'
+        'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Pairs Trading with Country ETFs',
@@ -252,7 +284,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/55'
         ],
-        'description' => 'Identifies the price divergence from two highly correlated country ETFs and takes a market neutral position.'
+        'description' => 'Identifies the price divergence from two highly correlated country ETFs and takes a market neutral position.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Sentiment and Style Rotation Effect in Stocks',
@@ -260,7 +293,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/53'
         ],
-        'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.'
+        'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.',
+        'tags'=>'Expert'
     ],
     [
         'name' => 'Asset Growth Effect',
@@ -268,7 +302,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/52'
         ],
-        'description' => 'Creates long-short positions of stocks based on the annual change of their total assets.'
+        'description' => 'Creates long-short positions of stocks based on the annual change of their total assets.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum and State of Market Filters',
@@ -276,7 +311,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
         ],
-        'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.'
+        'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Accrual Anomaly',
@@ -284,7 +320,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/38'
         ],
-        'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.'
+        'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum and Style Rotation Effect',
@@ -292,7 +329,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
         ],
-        'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.'
+        'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.',
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Trading with WTI BRENT Spread',
@@ -300,7 +338,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
         ],
-        'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.'
+        'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum Effect in REITs',
@@ -308,7 +347,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
         ],
-        'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.'
+        'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Option Expiration Week Effect',
@@ -316,7 +356,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
         ],
-        'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.'
+        'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Earnings Quality Factor',
@@ -324,7 +365,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
         ],
-        'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.'
+        'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'January Effect in Stocks',
@@ -332,7 +374,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/114'
         ],
-        'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.'
+        'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum and Reversal Combined with Volatility Effect in Stocks',
@@ -340,7 +383,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
         ],
-        'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.'
+        'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'ROA Effect within Stocks',
@@ -348,7 +392,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
         ],
-        'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.'
+        'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.',
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'January Barometer',
@@ -356,7 +401,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
         ],
-        'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills."
+        'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills.",
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Lunar Cycle in Equity Market',
@@ -364,7 +410,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/61'
         ],
-        'description' => "Goes long in emerging market index ETF 7 days before the new moon and switch to a short position on emerging market index ETF 7 days before the full moon."
+        'description' => "Goes long in emerging market index ETF 7 days before the new moon and switch to a short position on emerging market index ETF 7 days before the full moon.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'VIX Predicts Stock Index Returns',
@@ -372,7 +419,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/58'
         ],
-        'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history."
+        'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history.",
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Combining Momentum Effect with Volume',
@@ -380,7 +428,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/66'
         ],
-        'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile."
+        'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Short Term Reversal with Futures',
@@ -388,7 +437,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/71'
         ],
-        'description' => "Goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week."
+        'description' => "Goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Pre-holiday Effect',
@@ -396,7 +446,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/83'
         ],
-        'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days."
+        'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days.",
+        'tags'=>'Beginner'
     ],
     [
         'name' => 'Beta Factors in Stocks',
@@ -404,7 +455,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/77'
         ],
-        'description' => "Goes long stocks with the bottom beta and short stocks with the top beta, securities are weighted by the ranked betas."
+        'description' => "Goes long stocks with the bottom beta and short stocks with the top beta, securities are weighted by the ranked betas.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Exploiting Term Structure of VIX Futures',
@@ -412,7 +464,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/198'
         ],
-        'description' => "Buys or sells the nearest VIX futures based on the daily roll and hedge against the open positions with E-mini S&amp;P500 futures."
+        'description' => "Buys or sells the nearest VIX futures based on the daily roll and hedge against the open positions with E-mini S&amp;P500 futures.",
+        'tags'=>'Expert'
     ],
     [
         'name' => '12 Month Cycle in Cross-Section of Stocks Returns',
@@ -420,7 +473,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
         ],
-        'description' => "Reviews the returns from last January, going long on the top 10% winners and short the bottom 10%."
+        'description' => "Reviews the returns from last January, going long on the top 10% winners and short the bottom 10%.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Momentum Effect in Stocks in Small Portfolios',
@@ -428,7 +482,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/162'
         ],
-        'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year."
+        'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Value Effect within Countries',
@@ -436,7 +491,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/207'
         ],
-        'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios."
+        'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios.",
+        'tags'=>'Intermediate'
     ],
     [
         'name' => 'Beta Factor in Country Equity Indexes',
@@ -444,7 +500,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
         ],
-        'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs."
+        'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
+        'tags'=>'Intermediate'
     ],
 ];
 

From 57816b1d6343375d5949855e2d117da9af007da0 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 18:05:57 +0000
Subject: [PATCH 368/753] Automated push from server made by Jared Broad

---
 .../02 QuantConnect Options API/02 Add Options.html           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
index fa84171..a1d5a37 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
@@ -25,8 +25,8 @@
     self.SetStartDate(2017, 01, 01)  #Set Start Date
     self.SetEndDate(2017, 06, 30)  #Set End Date
     self.SetCash(50000)  #Set Strategy Cash
-    equity = self.AddEquity("GOOG", Resolution.Daily) # Add the underlying stock: Google
-    option = self.AddOption("GOOG", Resolution.Daily) # Add the option corresponding to underlying stock
+    equity = self.AddEquity("GOOG", Resolution.Minute) # Add the underlying stock: Google
+    option = self.AddOption("GOOG", Resolution.Minute) # Add the option corresponding to underlying stock
     self.symbol = option.Symbol
 </pre>
 </div>

From e805b4d6a14cd908e2a6aefacc35376c93c17151 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 11:25:53 -0800
Subject: [PATCH 369/753] Create placeholder for random data

---
 03 Open Source/06 Generating Random Backtest Data/00.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/00.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/00.html b/03 Open Source/06 Generating Random Backtest Data/00.html
new file mode 100644
index 0000000..48280ba
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/00.html	
@@ -0,0 +1 @@
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/20180615-drawdown-1024x543.png" />

From f744d68b4ea5c38f7b6eee7c6ec8bb9b64a7c758 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 11:31:37 -0800
Subject: [PATCH 370/753] Create 01 Introduction.html

---
 .../06 Generating Random Backtest Data/01 Introduction.html    | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/01 Introduction.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html
new file mode 100644
index 0000000..d2ff1db
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+Introduction
+</p>

From 02ae01327cba728464baca739f1d2d5d60015a33 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 11:33:55 -0800
Subject: [PATCH 371/753] Update 00.html

---
 03 Open Source/06 Generating Random Backtest Data/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/00.html b/03 Open Source/06 Generating Random Backtest Data/00.html
index 48280ba..fb95fe4 100644
--- a/03 Open Source/06 Generating Random Backtest Data/00.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/00.html	
@@ -1 +1 @@
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/20180615-drawdown-1024x543.png" />
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/random-data.png" />

From d6085ae267cbf2e513f0c6606a99bd32ec49b8b1 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 19:40:14 +0000
Subject: [PATCH 372/753] Automated push from server made by Jared Broad

---
 .../06 Generating Random Backtest Data/01 Introduction.html  | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html
index d2ff1db..b7d6fcb 100644
--- a/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html	
@@ -1,3 +1,2 @@
-<p>
-Introduction
-</p>
+<p>Real market data is often expensive and governed by difficult redistribution licenses. QuantConnect wanted to give the LEAN users a way to design and test their algorithms, without needing to download and&nbsp; buy real financial data. The RandomDataGenerator seeks to address that by randomly generating data in the LEAN format, for the various asset types we support.</p>
+ 

From 34682f6ec06fab3f07c4d965437fb7dab1a40221 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 11:41:21 -0800
Subject: [PATCH 373/753] Create 02 Supported Assets and Resolutions.html

---
 .../02 Supported Assets and Resolutions.html                   | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html b/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html
new file mode 100644
index 0000000..c7bc153
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html	
@@ -0,0 +1,3 @@
+<p>
+The Random Data Generator currently supports the following asset types and resolutions:
+</p>

From f707b6544f090e3e997da9510a6110619cca9248 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 19:41:49 +0000
Subject: [PATCH 374/753] Automated push from server made by Jared Broad

---
 .../06 Generating Random Backtest Data/01 Introduction.html     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html
index b7d6fcb..51fc885 100644
--- a/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/01 Introduction.html	
@@ -1,2 +1,2 @@
-<p>Real market data is often expensive and governed by difficult redistribution licenses. QuantConnect wanted to give the LEAN users a way to design and test their algorithms, without needing to download and&nbsp; buy real financial data. The RandomDataGenerator seeks to address that by randomly generating data in the LEAN format, for the various asset types we support.</p>
+<p>Real market data is often expensive and governed by difficult redistribution licenses. QuantConnect wanted to give the LEAN users a way to design and test their algorithms, without needing to download and buy real financial data. The RandomDataGenerator seeks to address that by randomly generating data in the LEAN format, for the various asset types we support.</p>
  

From 37bc104b5546824c393f7018ea3708848afead0f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 20:09:30 +0000
Subject: [PATCH 375/753] Automated push from server made by Jared Broad

---
 .../02 Supported Assets and Resolutions.html  | 30 +++++++++++++++++--
 1 file changed, 27 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html b/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html
index c7bc153..c171ccb 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html	
@@ -1,3 +1,27 @@
-<p>
-The Random Data Generator currently supports the following asset types and resolutions:
-</p>
+<p>The Random Data Generator currently supports the following asset types and resolutions:</p>
+
+<table class="table qc-table table-itemized table-reflow">
+<thead>
+<tr>
+<th colspan="2">Supported Data Types and Resolutions</th></tr></thead>
+<tbody>
+    <tr>
+        <td width="30%">Equity <br/>Trade Only</td>
+        <td><code>Resolutions Tick, Second, Minute, Hour and Daily</code><p>All Equity data resolutions are supported.</p></td>
+    </tr>
+    <tr>
+        <td>Forex, CFD<br/>Quotes Only</td>
+        <td>
+                <code>Resolutions Tick, Second, Minute, Hour and Daily</code></code>
+                <p>Forex and CFD generator supports quote (spread) data in all resolutions.</p></td>
+    </tr>
+    <tr>
+        <td>Futures, Crypto<br/> Trades and Quotes</td>
+        <td><code>Resolutions Tick, Second, Minute, Hour and Daily</code><p>All Futures and crypto data types and resolutions are supported</p></td>
+    </tr>
+    <tr>
+        <td>Options<br/> Trades and Quotes</td>
+        <td><code>Resolution Minute</code><p>Options are partially supported. Currently option random data is generated independent of a random underlying asset making its usefulness limited. This will be fixed by <a href="https://github.com/QuantConnect/Lean/issues/2824">Github Issue #2824</a>.</p></td>
+    </tr>
+</tbody>
+</table>
\ No newline at end of file

From e4f76bdb0282a6edb4871009385fcf17c606a36c Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 12:25:27 -0800
Subject: [PATCH 376/753] Rename 02 Supported Assets and Resolutions.html to 04
 Supported Assets and Resolutions.html

---
 ...esolutions.html => 04 Supported Assets and Resolutions.html} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 03 Open Source/06 Generating Random Backtest Data/{02 Supported Assets and Resolutions.html => 04 Supported Assets and Resolutions.html} (99%)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html b/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html
similarity index 99%
rename from 03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html
rename to 03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html
index c171ccb..1a0830b 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Supported Assets and Resolutions.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html	
@@ -24,4 +24,4 @@
         <td><code>Resolution Minute</code><p>Options are partially supported. Currently option random data is generated independent of a random underlying asset making its usefulness limited. This will be fixed by <a href="https://github.com/QuantConnect/Lean/issues/2824">Github Issue #2824</a>.</p></td>
     </tr>
 </tbody>
-</table>
\ No newline at end of file
+</table>

From faee2b59d7a8d3833ed77df1c09c7365bb8f8912 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 12:25:52 -0800
Subject: [PATCH 377/753] Create 02 Using the Random Data Generator.html

---
 .../02 Using the Random Data Generator.html                      | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html
new file mode 100644
index 0000000..3c339c5
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
@@ -0,0 +1 @@
+<p>Using it.</p>

From 5625d0175a1ba4aaf6ad0243b0c3fd9dd9e8b2c8 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 12:26:35 -0800
Subject: [PATCH 378/753] Create 03 Command Parameters.html

---
 .../03 Command Parameters.html                                 | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html b/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html
new file mode 100644
index 0000000..a841baf
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html	
@@ -0,0 +1,3 @@
+<p>
+Options Commands
+</p>

From 735d0a754e3affe7ad4925bffdde308eed049e51 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 20:59:06 +0000
Subject: [PATCH 379/753] Automated push from server made by Jared Broad

---
 .../02 Using the Random Data Generator.html      | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html
index 3c339c5..2b320e5 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
@@ -1 +1,15 @@
-<p>Using it.</p>
+<p>The LEAN Random Data Generator is located in the LEAN ToolBox and can be accessed by executing the <code>QuantConnect.ToolBox.exe</code> CLI program.</p>
+<p>We recommend <a href="https://www.github.com/QuantConnect/Lean">cloning LEAN</a> and building from the source code directly from Github. However, if this is not possible for you we have made a snapshot of the build available for your convenience here: <br> <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/2019-01-19-QuantConnect.ToolBox.Binaries.zip"><i class="fa fa-cloud-download"></i> 2019-01-19-QuantConnect.ToolBox.Binaries.zip</a>
+</p>
+
+<p>To run the ToolBox open a command window and execute it from the command line. To execute it you will need C# or Mono installed:</p>
+
+Windows:
+<pre> .\QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
+
+Linux and Mac:
+<pre>mono QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
+
+<p>
+The Random Data Generator has many parameters you can configure to generate the various datasets LEAN supports. Please see the next section for documentation on these parameters.
+</p>
\ No newline at end of file

From dce40eef21cfe15a7c5871a47763f62f5217ec53 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 21:03:19 +0000
Subject: [PATCH 380/753] Automated push from server made by Jared Broad

---
 .../02 Using the Random Data Generator.html                   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html
index 2b320e5..e823f56 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
@@ -5,10 +5,10 @@
 <p>To run the ToolBox open a command window and execute it from the command line. To execute it you will need C# or Mono installed:</p>
 
 Windows:
-<pre> .\QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
+<pre class="prettyprint"> .\QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
 
 Linux and Mac:
-<pre>mono QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
+<pre class="prettyprint">mono QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
 
 <p>
 The Random Data Generator has many parameters you can configure to generate the various datasets LEAN supports. Please see the next section for documentation on these parameters.

From 000aa56b869018b865de1dd6e6a5abff2fae776d Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 21:56:58 +0000
Subject: [PATCH 381/753] Automated push from server made by Jared Broad

---
 .../03 Command Parameters.html                | 80 ++++++++++++++++++-
 1 file changed, 77 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html b/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html
index a841baf..9580486 100644
--- a/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/03 Command Parameters.html	
@@ -1,3 +1,77 @@
-<p>
-Options Commands
-</p>
+<table class="table qc-table table-itemized table-reflow">
+<thead>
+<tr>
+<th colspan="2">Random Data Generator Parameters</th></tr></thead>
+<tbody>
+    <tr>
+        <td width="30%">security-type</td>
+        <td>
+            <p>
+                Security type to generate.<br/>
+                Possible Values: <code>Equity, Crypto, Forex, Cfd, Option, Future</code><br/>
+                Default: Equity
+            </p>
+            Example: <code>--security-type=Equity</code>
+        </td>
+    </tr>
+    <tr>
+        <td>resolution</td>
+        <td>
+            <p>
+                Resolution of data to store.<br/>
+                Possible Values: <code>Tick, Second, Minute, Hour, Daily</code><br/>
+                Default: Minute
+            </p>
+            Example: <code>--resolution=Minute</code>
+        </td>
+    </tr>
+    <tr>
+        <td>from-date</td>
+        <td>
+            <p>
+                Start date for the data to generate: format yyyy-mm-dd.<br/>
+            </p>
+            Example: <code>--from-date=2018-01-01</code>
+        </td>
+    </tr>
+    <tr>
+        <td>to-date</td>
+        <td>
+            <p>
+                Final date of the generated data: format yyyy-mm-dd.<br/>
+            </p>
+            Example: <code>--to-date=2019-01-01</code>
+        </td>
+    </tr>
+    <tr>
+        <td>Symbol Count</td>
+        <td>
+            <p>
+                Number of tickers to generate<br/>
+            </p>
+            Example: <code>--symbol-count=100</code>
+        </td>
+    </tr>
+    <tr>
+        <td>data-density</td>
+        <td>
+            <p>
+                Tick density of the data generated to model low volume data.<br/>
+                Possible Values: <code>Dense, Sparse, VerySparse</code><br/>
+                Default: Dense
+            </p>
+            Example: <code>--density=Dense</code>
+        </td>
+    </tr>
+    <tr>
+        <td>include-coarse</td>
+        <td>
+            <p>
+                Generate the coarse universe data when generating Equity data.<br/>
+                Default: true
+            </p>
+            Example: <code>--include-coarse=true</code>
+        </td>
+    </tr>
+</tbody>
+</table>
\ No newline at end of file

From a60e910cbb2066a48c3877290dfc97946b084ede Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 22:01:41 +0000
Subject: [PATCH 382/753] Automated push from server made by Jared Broad

---
 .../02 Using the Random Data Generator.html     | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html
index e823f56..aaeae74 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
@@ -4,12 +4,25 @@
 
 <p>To run the ToolBox open a command window and execute it from the command line. To execute it you will need C# or Mono installed:</p>
 
-Windows:
+<p>Windows:</p>
 <pre class="prettyprint"> .\QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
 
-Linux and Mac:
+<p>Linux and Mac:</p>
 <pre class="prettyprint">mono QuantConnect.ToolBox.exe --app=randomdatagenerator</pre>
 
+<p>Complete Example:</p>
+<pre class="prettyprint">
+QuantConnect.ToolBox.exe 
+    --app=RDG 
+    --resolution=Minute 
+    --from-date=2018-01-01 
+    --to-date=2019-01-01 
+    --market=usa 
+    --data-density=Dense 
+    --include-coarse=true 
+    --symbol-count=100 
+</pre>
+
 <p>
 The Random Data Generator has many parameters you can configure to generate the various datasets LEAN supports. Please see the next section for documentation on these parameters.
 </p>
\ No newline at end of file

From fd7bd51984a706e4d5d49f5de0928c6d2da4b227 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sat, 19 Jan 2019 14:17:40 -0800
Subject: [PATCH 383/753] Create 05 Final Notes.html

---
 .../05 Final Notes.html                                | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 03 Open Source/06 Generating Random Backtest Data/05 Final Notes.html

diff --git a/03 Open Source/06 Generating Random Backtest Data/05 Final Notes.html b/03 Open Source/06 Generating Random Backtest Data/05 Final Notes.html
new file mode 100644
index 0000000..b44db0c
--- /dev/null
+++ b/03 Open Source/06 Generating Random Backtest Data/05 Final Notes.html	
@@ -0,0 +1,10 @@
+<p>
+The Random Data Generator uses Brownian motion to generate the data prices. You can see how these prices and ticker strings 
+are generated in the 
+<a href="https://github.com/QuantConnect/Lean/blob/master/ToolBox/RandomDataGenerator/RandomValueGenerator.cs#L13">RandomValueGenerator class</a>.
+</p>
+
+<p>
+If you'd like to use a different system for providing the randomness you can implement your own <code>IRandomValueGenerator</code> or 
+extend the <code>RandomValueGenerator</code> class.
+</p>

From 7c298fe0ae75bd2720d4a10cee66684554443d9b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 22:27:29 +0000
Subject: [PATCH 384/753] Automated push from server made by Jared Broad

---
 .../02 Using the Random Data Generator.html                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html
index aaeae74..c86faaf 100644
--- a/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/02 Using the Random Data Generator.html	
@@ -13,7 +13,7 @@
 <p>Complete Example:</p>
 <pre class="prettyprint">
 QuantConnect.ToolBox.exe 
-    --app=RDG 
+    --app=randomdatagenerator 
     --resolution=Minute 
     --from-date=2018-01-01 
     --to-date=2019-01-01 

From 95d02bddabb0f92824296b7a8a48b34545243085 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sat, 19 Jan 2019 23:02:05 +0000
Subject: [PATCH 385/753] Automated push from server made by Jared Broad

---
 .../04 Supported Assets and Resolutions.html                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html b/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html
index 1a0830b..7892a03 100644
--- a/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html	
+++ b/03 Open Source/06 Generating Random Backtest Data/04 Supported Assets and Resolutions.html	
@@ -21,7 +21,7 @@
     </tr>
     <tr>
         <td>Options<br/> Trades and Quotes</td>
-        <td><code>Resolution Minute</code><p>Options are partially supported. Currently option random data is generated independent of a random underlying asset making its usefulness limited. This will be fixed by <a href="https://github.com/QuantConnect/Lean/issues/2824">Github Issue #2824</a>.</p></td>
+        <td><code>Resolution Minute</code><p>Options are partially supported. Currently option random data is generated independent of a random underlying asset making its usefulness limited. This will be fixed by <a href="https://github.com/QuantConnect/Lean/issues/2839">Github Issue #2839</a>.</p></td>
     </tr>
 </tbody>
 </table>

From 48a8e1dd3ccddcbf01d9dd3a84d27f368dac563a Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 14:14:15 -0800
Subject: [PATCH 386/753] Create 00.html

---
 .../01 Desktop Charting with LEAN/00.html     | 43 +++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 03 Open Source/01 Desktop Charting with LEAN/00.html

diff --git a/03 Open Source/01 Desktop Charting with LEAN/00.html b/03 Open Source/01 Desktop Charting with LEAN/00.html
new file mode 100644
index 0000000..df8b08a
--- /dev/null
+++ b/03 Open Source/01 Desktop Charting with LEAN/00.html	
@@ -0,0 +1,43 @@
+<style>
+.work-in-progress {
+    width: 100%;
+    border: 1px solid #f5ae29;
+    border-radius: 5px;
+    padding: 15px;
+    color: #f5ae29;
+}
+.tip {
+    width: 100%;
+    border: 1px solid #f5ae29;
+    border-radius: 5px;
+    padding: 15px;
+    margin-bottom: 20px;
+    margin-top: 20px;
+}
+.tip i {
+    color: #f5ae29;
+}
+.tip-title { 
+ font-weight: bold;
+color: #f5ae29;
+margin-left: 5px;
+margin-right: 5px;
+}
+.tip p { display: inline; }
+table th i {
+    color: #f5ae29;
+}
+th.summary {
+   font-family: "Courier New"; 
+   font-weight: normal;
+}
+.table.qc-table tbody tr td {
+   text-align: left;
+}
+.implementation {
+    font-family: "Courier New";
+}
+</style>
+<div class="work-in-progress"> 
+    <i class="fa fa-warning"></i> Desktop Charting v1 works in Windows but it is no longer supported. Version 2 (Electron) is a work in progress and will be published in 2019.
+</div>

From 19eb5c1fc8f30ef69495d79a233e606b0928bbd8 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 14:15:25 -0800
Subject: [PATCH 387/753] Rename 03 Open Source/01 Desktop Charting with
 LEAN/00.html to 03 Open Source/07 Desktop Charting with LEAN/00.html

---
 .../00.html                                                       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/{01 Desktop Charting with LEAN => 07 Desktop Charting with LEAN}/00.html (100%)

diff --git a/03 Open Source/01 Desktop Charting with LEAN/00.html b/03 Open Source/07 Desktop Charting with LEAN/00.html
similarity index 100%
rename from 03 Open Source/01 Desktop Charting with LEAN/00.html
rename to 03 Open Source/07 Desktop Charting with LEAN/00.html

From b9c105e5128aa67287be6dfc6dc84a56634d835d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 14:15:59 -0800
Subject: [PATCH 388/753] Rename 03 Open Source/01 Desktop Charting with
 LEAN/01 Desktop Charting with LEAN.html to 03 Open Source/07 Desktop Charting
 with LEAN/01 Desktop Charting with LEAN.html

---
 .../01 Desktop Charting with LEAN.html                            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 03 Open Source/{01 Desktop Charting with LEAN => 07 Desktop Charting with LEAN}/01 Desktop Charting with LEAN.html (100%)

diff --git a/03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
similarity index 100%
rename from 03 Open Source/01 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
rename to 03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html

From 9d6dd30d19b069cf996fee0d219b824dc1e9bb37 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 20 Jan 2019 23:19:22 +0000
Subject: [PATCH 389/753] Automated push from server made by Jared Broad

---
 03 Open Source/07 Desktop Charting with LEAN/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/07 Desktop Charting with LEAN/00.html b/03 Open Source/07 Desktop Charting with LEAN/00.html
index df8b08a..9a35478 100644
--- a/03 Open Source/07 Desktop Charting with LEAN/00.html	
+++ b/03 Open Source/07 Desktop Charting with LEAN/00.html	
@@ -39,5 +39,5 @@
 }
 </style>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Desktop Charting v1 works in Windows but it is no longer supported. Version 2 (Electron) is a work in progress and will be published in 2019.
+    <i class="fa fa-warning"></i> Note: 2019-01-20: Desktop Charting v1 works in Windows but it is no longer supported. Version 2 (Electron) is a work in progress and will be published in 2019. 
 </div>

From 17e764e74e529c92c7fff505144cf4f11c5631df Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 16:31:35 -0800
Subject: [PATCH 390/753] Create 00.html

---
 01 API Tutorials/05 Create a BootCamp Tutorial/00.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Create a BootCamp Tutorial/00.html

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/00.html b/01 API Tutorials/05 Create a BootCamp Tutorial/00.html
new file mode 100644
index 0000000..5579bca
--- /dev/null
+++ b/01 API Tutorials/05 Create a BootCamp Tutorial/00.html	
@@ -0,0 +1 @@
+<img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png"/>

From 79e07c2267e4219097ec1a0d901f7d1a1177d0fc Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 16:32:56 -0800
Subject: [PATCH 391/753] Create 01 Introduction.html

---
 .../05 Create a BootCamp Tutorial/01 Introduction.html         | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html b/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html
new file mode 100644
index 0000000..c3ccecd
--- /dev/null
+++ b/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+This tutorial seeks to answer how to create a BootCamp Course. BootCamp is an interactive training environment for learning how to use the QuantConnect API. Each day hundreds of people go through BootCamp, learning new parts of the LEAN API. 
+</p>

From 72ee9b54f117f131af277eded93915b6586924cf Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 21 Jan 2019 00:36:22 +0000
Subject: [PATCH 392/753] Automated push from server made by Jared Broad

---
 01 API Tutorials/05 Create a BootCamp Tutorial/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/00.html b/01 API Tutorials/05 Create a BootCamp Tutorial/00.html
index 5579bca..28f560b 100644
--- a/01 API Tutorials/05 Create a BootCamp Tutorial/00.html	
+++ b/01 API Tutorials/05 Create a BootCamp Tutorial/00.html	
@@ -1 +1 @@
-<img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png"/>
+<img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png" class="img-responsive"/>

From 38cdb7b04bb223329e3aab2317035a5601d77325 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 21 Jan 2019 02:03:52 +0000
Subject: [PATCH 393/753] Automated push from server made by Jared Broad

---
 .../05 Create a BootCamp Tutorial/01 Introduction.html        | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html b/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html
index c3ccecd..fd3aa89 100644
--- a/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html	
+++ b/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
-This tutorial seeks to answer how to create a BootCamp Course. BootCamp is an interactive training environment for learning how to use the QuantConnect API. Each day hundreds of people go through BootCamp, learning new parts of the LEAN API. 
-</p>
+BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
+</p>
\ No newline at end of file

From 67801251a2e23015a377e4f2e2c3ed4cda03bc39 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:04:51 -0800
Subject: [PATCH 394/753] Rename 01 API Tutorials/05 Create a BootCamp
 Tutorial/00.html to 01 API Tutorials/05 Creating BootCamp Tutorials/00.html

---
 .../00.html                                                       | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 01 API Tutorials/{05 Create a BootCamp Tutorial => 05 Creating BootCamp Tutorials}/00.html (100%)

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/00.html b/01 API Tutorials/05 Creating BootCamp Tutorials/00.html
similarity index 100%
rename from 01 API Tutorials/05 Create a BootCamp Tutorial/00.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/00.html

From 10f285ff1bcecaa27accca5b856b0340a0234c29 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:05:22 -0800
Subject: [PATCH 395/753] Rename 01 API Tutorials/05 Create a BootCamp
 Tutorial/01 Introduction.html to 01 API Tutorials/05 Creating BootCamp
 Tutorials/01 Introduction.html

---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 01 API Tutorials/{05 Create a BootCamp Tutorial => 05 Creating BootCamp Tutorials}/01 Introduction.html (98%)

diff --git a/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html b/01 API Tutorials/05 Creating BootCamp Tutorials/01 Introduction.html
similarity index 98%
rename from 01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/01 Introduction.html
index fd3aa89..7b5b122 100644
--- a/01 API Tutorials/05 Create a BootCamp Tutorial/01 Introduction.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/01 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
 BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
-</p>
\ No newline at end of file
+</p>

From d9a365328f7f4d8f981bf809572db94d165060b4 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:05:49 -0800
Subject: [PATCH 396/753] Create 02 BootCamp Structure.html

---
 .../02 BootCamp Structure.html                             | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
new file mode 100644
index 0000000..b3e0cb6
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -0,0 +1,7 @@
+<p>
+BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.
+</p>
+<p>
+A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
+A <i>Lesson</i> focuses on the implementation of a single algorithm. A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
+</p>

From 8f624481cd58740097974599db10b63b72648bed Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:09:10 -0800
Subject: [PATCH 397/753] Update 02 BootCamp Structure.html

---
 .../02 BootCamp Structure.html                               | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index b3e0cb6..3399c51 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -2,6 +2,7 @@
 BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.
 </p>
 <p>
-A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
-A <i>Lesson</i> focuses on the implementation of a single algorithm. A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
+  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
+  A <i>Lesson</i> focuses on the implementation of a single algorithm. 
+  A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
 </p>

From 45551a572a5514e75acae73032229cfc3fa458ce Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 21 Jan 2019 02:12:46 +0000
Subject: [PATCH 398/753] Automated push from server made by Jared Broad

---
 .../02 BootCamp Structure.html                             | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index 3399c51..dd00a61 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -1,8 +1,7 @@
 <p>
-BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.
-</p>
-<p>
-  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
+BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
   A <i>Lesson</i> focuses on the implementation of a single algorithm. 
   A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
 </p>
+
+<p>We have build a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>
\ No newline at end of file

From 5744e42b608333869d7dc0cf725701b0e39e97df Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:13:07 -0800
Subject: [PATCH 399/753] Update 02 BootCamp Structure.html

---
 .../05 Creating BootCamp Tutorials/02 BootCamp Structure.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index dd00a61..2a9bff0 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -4,4 +4,4 @@
   A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
 </p>
 
-<p>We have build a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>
\ No newline at end of file
+<p>We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>

From 094400967822e7bb53852a58a4cc7d6f6bd387a5 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 21 Jan 2019 02:19:07 +0000
Subject: [PATCH 400/753] Automated push from server made by Jared Broad

---
 .../05 Creating BootCamp Tutorials/02 BootCamp Structure.html   | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index 2a9bff0..26b1e38 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -4,4 +4,6 @@
   A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
 </p>
 
+<p>Course: Algorithmic Trading with Equities > Lesson: Buy and Hold Algorithm > Task: Setting Cash</p>
+
 <p>We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>

From 0b14f59a81b9c2413d6db9c44438ec6e642ffa7e Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:19:29 -0800
Subject: [PATCH 401/753] Create 03 Requirements.html

---
 .../05 Creating BootCamp Tutorials/03 Requirements.html          | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html b/01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html	
@@ -0,0 +1 @@
+

From 52d76ec0255612accef21e3c03a39c6fae674684 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:22:11 -0800
Subject: [PATCH 402/753] Create 04 Step 1 Create a Lesson Plan.html

---
 .../04 Step 1 Create a Lesson Plan.html                          | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html b/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html	
@@ -0,0 +1 @@
+

From eadca3d66b46633464d743aa255b7430356db9da Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:22:36 -0800
Subject: [PATCH 403/753] Create 05 Step 2 Writing a Lesson.html

---
 .../05 Step 2 Writing a Lesson.html                              | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html b/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html	
@@ -0,0 +1 @@
+

From 9602459a0b732b0abdef6b703602044f3096d968 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:23:20 -0800
Subject: [PATCH 404/753] Create 06 Step 3 Building BootCamp Tests.html

---
 .../06 Step 3 Building BootCamp Tests.html                       | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html b/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html	
@@ -0,0 +1 @@
+

From 4836ebfc0811f3e44b1314761646e2aedbf4cdb2 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:23:57 -0800
Subject: [PATCH 405/753] Create 07 Step 4 Submitting Lesson for Review.html

---
 .../07 Step 4 Submitting Lesson for Review.html                  | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html b/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html	
@@ -0,0 +1 @@
+

From 4fe9bcbf2a1552a32aef12b0d687c665c3be49c3 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 20 Jan 2019 18:24:43 -0800
Subject: [PATCH 406/753] Create 08 Summary.html

---
 01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html b/01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html	
@@ -0,0 +1 @@
+

From b29f0a7bd2144abe5544c01472fe4ef9889c1e4f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 21 Jan 2019 21:32:53 +0000
Subject: [PATCH 407/753] Automated push from server made by Jared Broad

---
 .../02 BootCamp Structure.html                               | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index 26b1e38..22d6ce0 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -4,6 +4,9 @@
   A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
 </p>
 
-<p>Course: Algorithmic Trading with Equities > Lesson: Buy and Hold Algorithm > Task: Setting Cash</p>
+<ul>
+   <li>Course: Algorithmic Trading with Equities 
+      <ul><li>Lesson: Buy and Hold Algorithm <ul><li>Task: Setting Cash</il></ul> </li></ul></li>
+</ul>
 
 <p>We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>

From 21c83494d8716a9911055770c271c6b519ebfb28 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:03:36 -0800
Subject: [PATCH 408/753] Create 92 Price Earnings Anamoly

---
 04 Strategy Library/92 Price Earnings Anamoly | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly

diff --git a/04 Strategy Library/92 Price Earnings Anamoly b/04 Strategy Library/92 Price Earnings Anamoly
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly	
@@ -0,0 +1 @@
+

From b70ce01ac550ccda30d1d9801b06208464fe15c5 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:12:09 -0800
Subject: [PATCH 409/753] Delete 92 Price Earnings Anamoly

---
 04 Strategy Library/92 Price Earnings Anamoly | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 04 Strategy Library/92 Price Earnings Anamoly

diff --git a/04 Strategy Library/92 Price Earnings Anamoly b/04 Strategy Library/92 Price Earnings Anamoly
deleted file mode 100644
index 8b13789..0000000
--- a/04 Strategy Library/92 Price Earnings Anamoly	
+++ /dev/null
@@ -1 +0,0 @@
-

From 4cbe331dcc463657c0415bedc69fc01eec99dd46 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:12:32 -0800
Subject: [PATCH 410/753] Create Introduction.html

---
 .../92 Price Earnings Anamoly/Introduction.html      | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly/Introduction.html

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/Introduction.html b/04 Strategy Library/92 Price Earnings Anamoly/Introduction.html
new file mode 100644
index 0000000..e54ad00
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly/Introduction.html	
@@ -0,0 +1,12 @@
+<p>
+The price/earnings ratio, also known as the P/E ratio, is the ratio of a company's
+stock price to the company's earnings per share.
+</p>
+<p>
+  \[Price \ to \ Earnings\ Ratio=\frac{Market \ price \ per \ share}{Earnings \ per \ share \ from \ the \ most \ recent \ financial \ year}\]
+</p>
+<p>
+The P/E ratio is often used by investors to determine the valuation of a company's stock.
+Research suggests that a portfolio that consists of stocks with relatively low P/E 
+ratio outperforms a portfolio that consists of stocks with relatively high P/E ratio.
+</p>

From 193c68d9e1fef068cf9ff1135565b8d135099aa7 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:13:08 -0800
Subject: [PATCH 411/753] Create Method.html

---
 .../92 Price Earnings Anamoly/Method.html     | 44 +++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly/Method.html

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/Method.html b/04 Strategy Library/92 Price Earnings Anamoly/Method.html
new file mode 100644
index 0000000..5afb1b1
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly/Method.html	
@@ -0,0 +1,44 @@
+<p>
+We pick the 10 stocks that have the lowest P/E ratio in our universe, at the beginning of each year, 
+and we invest an equal amount of capital in each stock.
+</p>
+ 
+<p>
+To construct the universe, first, we eliminate stocks which don't have fundamental data and
+stocks that have a lower price than $5 per share, using the <code>CoarseSelectionFunction</code>.
+</p>
+ 
+<div class="section-example-container">
+<pre class="python">
+    def CoarseSelectionFunction(self, coarse):
+        if self.yearly_rebalance:
+            # drop stocks which have no fundamental data and have low price
+            CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
+            sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
+            top = sortedByDollarVolume[:self._NumCoarseStocks]
+            return [i.Symbol for i in top]
+        else: 
+            return [] 
+</pre>
+</div>
+ 
+<p>
+Then, in the <code>FineSelectionFunction</code>, we retrieve the list of 25 stocks, that have the lowest P/E ratio.
+</p>
+ 
+<div class="section-example-container">
+<pre class="python">
+ 
+    def FineSelectionFunction(self, fine):
+        if self.yearly_rebalance:
+            fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)]
+            for i in fine:
+                i.PERatio = float(i.ValuationRatios.PERatio)
+            sortedPERatio = sorted(fine, key=lambda x: x.PERatio,reverse=True)
+            self.filtered_fine = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
+            self.yearly_rebalance = False
+            return self.filtered_fine
+        else:
+            return []
+</pre>
+</div>

From 9fe19b62fa0c0e5407d8556aa8afad6505288818 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:13:56 -0800
Subject: [PATCH 412/753] Rename Introduction.html to 01 Introduction.html

---
 .../{Introduction.html => 01 Introduction.html}                   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/92 Price Earnings Anamoly/{Introduction.html => 01 Introduction.html} (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/Introduction.html b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/Introduction.html
rename to 04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html

From dd7392400dee85207e3f13ed6a4174b3cb95c761 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:14:14 -0800
Subject: [PATCH 413/753] Rename Method.html to 02 Method.html

---
 .../92 Price Earnings Anamoly/{Method.html => 02 Method.html}     | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/92 Price Earnings Anamoly/{Method.html => 02 Method.html} (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/Method.html b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/Method.html
rename to 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html

From c96b96c7efd61693d0344594ac22684ac55149f7 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:22:05 -0800
Subject: [PATCH 414/753] Create 03 Algorithm.html

---
 .../92 Price Earnings Anamoly/03 Algorithm.html             | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html
new file mode 100644
index 0000000..037bb5c
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html	
@@ -0,0 +1,6 @@
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_16a9f69cbcd2c2465ebe4f785d7f4395.html"></iframe>
+    </div>
+</div>

From 9734b770037097e0f7a548e1414d9557d6886bcd Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:22:59 -0800
Subject: [PATCH 415/753] Create 04 References.html

---
 .../92 Price Earnings Anamoly/04 References.html             | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly/04 References.html

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 References.html b/04 Strategy Library/92 Price Earnings Anamoly/04 References.html
new file mode 100644
index 0000000..ef93d18
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly/04 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Persson E, Ståhlberg C (2006). P/E and EV/EBITDA Investment Strategies vs. the Market (Master's thesis, Linköping University, Linköping, Sweden).  <a href="https://www.diva-portal.org/smash/get/diva2:23073/FULLTEXT01.pdf">Online Copy</a>
+  </li>
+</ol>

From e17275cf90e88f5497e8cca620b461861328fc0d Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:23:35 -0800
Subject: [PATCH 416/753] Update 02 Method.html

---
 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
index 5afb1b1..3af1fa6 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
@@ -23,7 +23,7 @@
 </div>
  
 <p>
-Then, in the <code>FineSelectionFunction</code>, we retrieve the list of 25 stocks, that have the lowest P/E ratio.
+Then, in the <code>FineSelectionFunction</code>, we retrieve the list of 10 stocks, that have the lowest P/E ratio.
 </p>
  
 <div class="section-example-container">

From 36742c5744cbb2ee3af7a9bc2693ccf724ea3f0f Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:35:33 -0800
Subject: [PATCH 417/753] Update 01 Introduction.html

---
 .../92 Price Earnings Anamoly/01 Introduction.html            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html
index e54ad00..dc5ef3d 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
@@ -1,6 +1,6 @@
 <p>
-The price/earnings ratio, also known as the P/E ratio, is the ratio of a company's
-stock price to the company's earnings per share.
+The Price to Earnings ratio, also known as the P/E ratio, is the ratio of a company's
+market price per share to the company's earnings per share.
 </p>
 <p>
   \[Price \ to \ Earnings\ Ratio=\frac{Market \ price \ per \ share}{Earnings \ per \ share \ from \ the \ most \ recent \ financial \ year}\]

From 79583c23fab0b0090b7293b74cb6189e2d65a2f8 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:47:20 -0800
Subject: [PATCH 418/753] Update 02 Method.html

---
 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
index 3af1fa6..c48f822 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
@@ -42,3 +42,7 @@
             return []
 </pre>
 </div>
+
+<p>
+In <code>OnData()</code>, we buy the 10 stocks in the list of stocks that have the lowest P/E ratio. The portfolio is rebalanced every year.
+</p>

From f0da972e9d434fdd9e587572fbee95cbf6be635a Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:51:03 -0800
Subject: [PATCH 419/753] Update 02 Method.html

---
 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
index c48f822..7011c7e 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
@@ -44,5 +44,5 @@
 </div>
 
 <p>
-In <code>OnData()</code>, we buy the 10 stocks in the list of stocks that have the lowest P/E ratio. The portfolio is rebalanced every year.
+In <code>OnData()</code>, we buy the 10 stocks that have the lowest P/E ratio in our universe. The portfolio is rebalanced every year.
 </p>

From e6fdfb39bf1abc5db587e51722787566b0afac6a Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 09:56:34 -0800
Subject: [PATCH 420/753] Update 01 Strategy Library.php

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 7cf2a99..c221bcd 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -503,6 +503,15 @@
         'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
         'tags'=>'Intermediate'
     ],
+    [
+        'name' => 'Low Price to Earnings Effect',
+        'link' => 'strategy-library/beta-factor-in-country-equity-indexes',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
+        ],
+        'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
+        'tags'=>'Intermediate'
+    ],
 ];
 
 ?>

From 2af59b392116974e4f551f33b782751bb3640542 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 11:26:02 -0800
Subject: [PATCH 421/753] Update 01 Strategy Library.php

---
 .../01 Strategy Library.php                   | 132 +++++++++---------
 1 file changed, 67 insertions(+), 65 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index c221bcd..f907431 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -11,67 +11,67 @@
         'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
         'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
         'description' => 'Applies CAPM model to rank Dow Jones 30 companies.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Momentum, Equities'
     ],
     [
         'name' => 'Combining Mean Reversion and Momentum in Forex Market',
         'link' => 'strategy-library/combining-mean-reversion-and-momentum-in-forex-market',
         'description' => 'Combines momentum and mean reversion techniques in the forex markets.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Mean Reversion, Momentum, Forex'
     ],
     [
         'name' => 'Pairs Trading-Copula vs Cointegration',
         'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
         'description' => 'Applies Copula and Cointergration method to pairs trading.',
-        'tags'=>'Expert'
+        'tags'=>'Expert, Pairs Trading, ETFs'
     ],
     [
         'name' => 'The Dynamic Breakout II Strategy',
         'link' => 'strategy-library/the-dynamic-breakout-ii-strategy',
         'description' => 'A demonstration of dynamic breakout II strategy.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Breakout, Forex'
     ],
     [
         'name' => 'Dual Thrust Trading Algorithm',
         'link' => 'strategy-library/dual-thrust-trading-algorithm',
         'description' => 'A demontration of Dual Thrust Intraday strategy.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Breakout, Equities'
     ],
     [
         'name' => 'Can Crude Oil Predict Equity Returns',
         'link' => 'strategy-library/can-crude-oil-predict-equity-returns',
         'description' => 'Applies regression method to predict the return from the stock market and compare it to the short-term U.S. T-bill rate.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Momentum,ETFs'
     ],
     [
         'name' => 'Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach',
         'link' => 'strategy-library/intraday-dynamic-pairs-trading-using-correlation-and-cointegration-approach',
         'description' => 'A high frequency pairs trading algorithm based on cointegration.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Pairs Trading,Equities'
     ],
     [
         'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
         'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
         'description' => 'Applies high frequency filter to the momentum strategy.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Momentum,Forex'
     ],
     [
         'name' => 'Stock Selection Strategy Based on Fundamental Factors',
         'link' => 'strategy-library/stock-selection-strategy-based-on-fundamental-factors',
         'description' => 'MorningStar Fundamental factors universe selection algorithm.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Fundamental Factors,Equities'
     ],
     [
         'name' => 'Short-Term Reversal Strategy in Stocks',
         'link' => 'strategy-library/short-term-reversal-strategy-in-stocks',
         'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, Mean Reversion, Equities'
     ],
     [
         'name' => 'Fundamental Factor Long Short Strategy',
         'link' => 'strategy-library/fundamental-factor-long-short-strategy',
         'description' => 'A basic monthly rebalance long short algorithm based on fundamental factors.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Fundamental Factors,Long Short,Equities'
     ],
     [
         'name' => 'Asset Class Trend Following',
@@ -80,7 +80,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/1'
         ],
         'description' => 'Selects ETFs over ten-month moving average and assigns an equally weighted allocation.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Momentum,ETFs'
     ],
     [
         'name' => 'Asset Class Momentum',
@@ -89,7 +89,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/2'
         ],
         'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Momentum, ETFs'
         
     ],
     [
@@ -99,7 +99,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/3'
         ],
         'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Momentum, ETFs'
     ],
     [
         'name' => 'Overnight Anomaly',
@@ -108,7 +108,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/4'
         ],
         'description' => 'Buy SPY ETF at its closing price and sell it at the opening each day.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,ETFs'
     ],
     [
         'name' => 'Forex Carry Trade',
@@ -117,7 +117,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/5'
         ],
         'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Long Short, Forex'
     ],
     [
         'name' => 'Volatility Effect in Stocks',
@@ -126,7 +126,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/7'
         ],
         'description' => 'Constructs equally weighted portfolios by selecting stocks with the lowest volatility in the past one year.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Equities'
     ],
     [
         'name' => 'Forex Momentum',
@@ -135,7 +135,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/8'
         ],
         'description' => 'Goes long currencies with strongest 12 month momentum against USD and goes short currencies with the lowest 12 month momentum against USD.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Momentum,Forex'
     ],
     [
         'name' => 'Pairs Trading with Stocks',
@@ -144,7 +144,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/12'
         ],
         'description' => 'Looks for the security that minimizes the sum of squared deviations and long-short position is opened when pair prices have diverged by multiple of standard deviations.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Pairs Trading,Mean Reversion,Equities'
     ],
     [
         'name' => 'Short Term Reversal',
@@ -153,7 +153,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/13'
         ],
         'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Mean Reversion,Equities'
     ],
     [
         'name' => 'Momentum Effect in Stocks',
@@ -162,6 +162,8 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/14'
         ],
         'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe.'
+        'tags'=>'Beginner,Momentum,Equities'
+
     ],
     [
         'name' => 'Momentum Effect in Country Equity Indexes',
@@ -170,7 +172,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/15'
         ],
         'description' => 'Goes long stocks with the best 12-month momentum in the country equity indexes ETFs.',
-        'tags'=>'Intermediate'
+        'tags'=>'Beginner,Momentum,ETFs'
     ],
     [
         'name' => 'Mean Reversion Effect in Country Equity Indexes',
@@ -178,7 +180,9 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/16'
         ],
-        'description' => 'Goes long country equity indexes ETFs with the worst 36-month return and short ETFs with the best 36-month return.'
+        'description' => 'Goes long country equity indexes ETFs with the worst 36-month return and short ETFs with the best 36-month return.',
+        'tags'=>'Beginner,Long Short,Mean Reversion,ETFs'
+
     ],
     [
         'name' => 'Liquidity Effect in Stocks',
@@ -187,7 +191,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/18'
         ],
         'description' => 'Goes long stocks with the lowest turnover and short on stocks with the highest turnover from the lowest market-cap quartile.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'Volatility Risk Premium Effect',
@@ -196,7 +200,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/20'
         ],
         'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Short Volatility, Options'
     ],
     [
         'name' => 'Momentum Effect in Commodities Futures',
@@ -205,7 +209,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/21'
         ],
         'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, Futures'
     ],
     [
         'name' => 'Small Capitalization Stocks Premium Anomaly',
@@ -213,6 +217,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/25'
         ],
+         'tags'=>'Intermediate,Fundamental Factors,Equities'
         'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
     ],
     [
@@ -222,7 +227,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/44'
         ],
         'description' => 'Goes long asset with better performance over the last period and rebalances portfolio every quarter.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Pairs Trading,Equities'
     ],
     [
         'name' => 'Term Structure Effect in Commodities',
@@ -231,7 +236,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/22'
         ],
         'description' => 'Buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest roll-returns and holds the long-short positions for one month.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Long Short,Futures'
     ],
     [
         'name' => 'Momentum Effect Combined with Term Structure in Commodities',
@@ -240,7 +245,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/23'
         ],
         'description' => 'Portfolios are formed based on roll returns and the algorithm goes long and short contracts with the highest and lowest one-month performance.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Long Short,Futures'
     ],
     [
         'name' => 'Book-to-Market Value Anomaly',
@@ -249,7 +254,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/26'
         ],
         'description' => 'Quintile portfolios are formed based on the Book-to-Market ratio and the highest quintile is held for one year.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Fundamental Factors,Equities'
     ],
     [
         'name' => 'Gold Market Timing',
@@ -258,7 +263,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/43'
         ],
         'description' => 'Goes long gold when the Fed model shows that the market is undervalued (the earnings yield is higher than the bond yield and their ratio is at least 2).',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Mean Reversion,ETFs'
     ],
     [
         'name' => 'Turn of the Month in Equity Indexes',
@@ -267,7 +272,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/41'
         ],
         'description' => 'Buys SPY the day before the end of the month and liquidates position on 3rd trading day of new month.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,ETFs'
     ],
     [
         'name' => 'Momentum - Short Term Reversal Strategy',
@@ -276,7 +281,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/51'
         ],
         'description' => 'Goes long stocks with the decreasing return from the winner group and short stocks with the increasing return from the loser group.',
-        'tags'=>'Beginner'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'Pairs Trading with Country ETFs',
@@ -285,7 +290,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/55'
         ],
         'description' => 'Identifies the price divergence from two highly correlated country ETFs and takes a market neutral position.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Pairs Trading,ETFs'
     ],
     [
         'name' => 'Sentiment and Style Rotation Effect in Stocks',
@@ -294,7 +299,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/53'
         ],
         'description' => 'Creates long-short positions of growth and value stocks based on the investment sentiment.',
-        'tags'=>'Expert'
+        'tags'=>'Expert,Long Short,Fundamental Factors,Equities'
     ],
     [
         'name' => 'Asset Growth Effect',
@@ -303,7 +308,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/52'
         ],
         'description' => 'Creates long-short positions of stocks based on the annual change of their total assets.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, Fundamental Factors,Equities'
     ],
     [
         'name' => 'Momentum and State of Market Filters',
@@ -312,7 +317,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
         ],
         'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Momentum, Equities'
     ],
     [
         'name' => 'Accrual Anomaly',
@@ -321,7 +326,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/38'
         ],
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Equities'
     ],
     [
         'name' => 'Momentum and Style Rotation Effect',
@@ -330,7 +335,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
         ],
         'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.',
-        'tags'=>'Beginner'
+        'tags'=>'Beginner, Long Short, ETFs'
     ],
     [
         'name' => 'Trading with WTI BRENT Spread',
@@ -339,7 +344,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
         ],
         'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Mean Reversion, Commodities'
     ],
     [
         'name' => 'Momentum Effect in REITs',
@@ -347,8 +352,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
         ],
-        'description' => 'Trecile portfolios are formed based on momentum and the best performing portfolio is held.',
-        'tags'=>'Intermediate'
+        'description' => 'Tercile portfolios are formed based on momentum and the best performing portfolio is held.',
+        'tags'=>'Intermediate, Momentum, ETFs'
     ],
     [
         'name' => 'Option Expiration Week Effect',
@@ -357,7 +362,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/102'
         ],
         'description' => 'Goes long S&amp;P 100 index ETF during option expiration week and stays in cash during other days.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Options'
     ],
     [
         'name' => 'Earnings Quality Factor',
@@ -366,7 +371,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
         ],
         'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, Fundamental Factors, Equities'
     ],
     [
         'name' => 'January Effect in Stocks',
@@ -375,7 +380,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/114'
         ],
         'description' => 'Invests into small cap stocks at the beginning of each January and stays invested in large cap stocks for rest of the year.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Equities'
     ],
     [
         'name' => 'Momentum and Reversal Combined with Volatility Effect in Stocks',
@@ -384,7 +389,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
         ],
         'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Equities'
     ],
     [
         'name' => 'ROA Effect within Stocks',
@@ -393,7 +398,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
         ],
         'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.',
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Fundamental Factors, Equities'
     ],
     [
         'name' => 'January Barometer',
@@ -402,7 +407,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
         ],
         'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills.",
-        'tags'=>'Beginner'
+        'tags'=>'Beginner, Equities'
     ],
     [
         'name' => 'Lunar Cycle in Equity Market',
@@ -411,7 +416,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/61'
         ],
         'description' => "Goes long in emerging market index ETF 7 days before the new moon and switch to a short position on emerging market index ETF 7 days before the full moon.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,ETFs'
     ],
     [
         'name' => 'VIX Predicts Stock Index Returns',
@@ -420,7 +425,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/58'
         ],
         'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history.",
-        'tags'=>'Beginner'
+        'tags'=>'Beginner, Equities'
     ],
     [
         'name' => 'Combining Momentum Effect with Volume',
@@ -429,7 +434,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/66'
         ],
         'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Long Short, Equities'
     ],
     [
         'name' => 'Short Term Reversal with Futures',
@@ -438,7 +443,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/71'
         ],
         'description' => "Goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short,Futures'
     ],
     [
         'name' => 'Pre-holiday Effect',
@@ -447,7 +452,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/83'
         ],
         'description' => "Invests in equity market 2 days preceding holiday days and stays in cash during the other trading days.",
-        'tags'=>'Beginner'
+        'tags'=>'Beginner,Equities'
     ],
     [
         'name' => 'Beta Factors in Stocks',
@@ -456,7 +461,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/77'
         ],
         'description' => "Goes long stocks with the bottom beta and short stocks with the top beta, securities are weighted by the ranked betas.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'Exploiting Term Structure of VIX Futures',
@@ -465,7 +470,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/198'
         ],
         'description' => "Buys or sells the nearest VIX futures based on the daily roll and hedge against the open positions with E-mini S&amp;P500 futures.",
-        'tags'=>'Expert'
+        'tags'=>'Expert,Futures'
     ],
     [
         'name' => '12 Month Cycle in Cross-Section of Stocks Returns',
@@ -474,7 +479,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
         ],
         'description' => "Reviews the returns from last January, going long on the top 10% winners and short the bottom 10%.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, Momentum Equities'
     ],
     [
         'name' => 'Momentum Effect in Stocks in Small Portfolios',
@@ -483,7 +488,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/162'
         ],
         'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Momentum, Equities'
     ],
     [
         'name' => 'Value Effect within Countries',
@@ -492,7 +497,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/207'
         ],
         'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate, Fundamental Factors, ETFs'
     ],
     [
         'name' => 'Beta Factor in Country Equity Indexes',
@@ -501,16 +506,13 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
         ],
         'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
-        'tags'=>'Intermediate'
+        'tags'=>'Intermediate,Long Short, ETFs'
     ],
     [
-        'name' => 'Low Price to Earnings Effect',
-        'link' => 'strategy-library/beta-factor-in-country-equity-indexes',
-        'sources' => [
-            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
-        ],
-        'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
-        'tags'=>'Intermediate'
+        'name' => 'Price to Earnings Anamoly',
+        'link' => 'strategy-library/price-earnings-anamoly',
+        'description' => "Invests in stocks with low P/E ratio.",
+        'tags'=>'Beginner, Fundamental Factors, Equities'
     ],
 ];
 

From 47d708d02d11028cee353abebb2622e45a1874de Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 11:29:37 -0800
Subject: [PATCH 422/753] Update 01 Strategy Library.php

---
 .../01 Strategy Library.php                   | 56 +++++++++----------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index f907431..e35107d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -11,31 +11,31 @@
         'name' => 'CAPM Alpha Ranking Strategy on Dow 30 Companies',
         'link' => 'strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies',
         'description' => 'Applies CAPM model to rank Dow Jones 30 companies.',
-        'tags'=>'Intermediate, Momentum, Equities'
+        'tags'=>'Intermediate,Momentum,Equities'
     ],
     [
         'name' => 'Combining Mean Reversion and Momentum in Forex Market',
         'link' => 'strategy-library/combining-mean-reversion-and-momentum-in-forex-market',
         'description' => 'Combines momentum and mean reversion techniques in the forex markets.',
-        'tags'=>'Intermediate, Mean Reversion, Momentum, Forex'
+        'tags'=>'Intermediate,Mean Reversion,Momentum,Forex'
     ],
     [
         'name' => 'Pairs Trading-Copula vs Cointegration',
         'link' => 'strategy-library/pairs-trading-copula-vs-cointegration',
         'description' => 'Applies Copula and Cointergration method to pairs trading.',
-        'tags'=>'Expert, Pairs Trading, ETFs'
+        'tags'=>'Expert,Pairs Trading,ETFs'
     ],
     [
         'name' => 'The Dynamic Breakout II Strategy',
         'link' => 'strategy-library/the-dynamic-breakout-ii-strategy',
         'description' => 'A demonstration of dynamic breakout II strategy.',
-        'tags'=>'Intermediate, Breakout, Forex'
+        'tags'=>'Intermediate,Breakout,Forex'
     ],
     [
         'name' => 'Dual Thrust Trading Algorithm',
         'link' => 'strategy-library/dual-thrust-trading-algorithm',
         'description' => 'A demontration of Dual Thrust Intraday strategy.',
-        'tags'=>'Intermediate, Breakout, Equities'
+        'tags'=>'Intermediate,Breakout,Equities'
     ],
     [
         'name' => 'Can Crude Oil Predict Equity Returns',
@@ -65,7 +65,7 @@
         'name' => 'Short-Term Reversal Strategy in Stocks',
         'link' => 'strategy-library/short-term-reversal-strategy-in-stocks',
         'description' => 'A short term reversal algorithm which gives the opposite signal by analyzing recent period price action.',
-        'tags'=>'Intermediate,Long Short, Mean Reversion, Equities'
+        'tags'=>'Intermediate,Long Short,Mean Reversion,Equities'
     ],
     [
         'name' => 'Fundamental Factor Long Short Strategy',
@@ -89,7 +89,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/2'
         ],
         'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.',
-        'tags'=>'Beginner,Momentum, ETFs'
+        'tags'=>'Beginner,Momentum,ETFs'
         
     ],
     [
@@ -99,7 +99,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/3'
         ],
         'description' => 'Selects ETFs in different sectors with the highest momentum and assigns an equally weighted allocation.',
-        'tags'=>'Beginner,Momentum, ETFs'
+        'tags'=>'Beginner,Momentum,ETFs'
     ],
     [
         'name' => 'Overnight Anomaly',
@@ -117,7 +117,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/5'
         ],
         'description' => 'Goes long the currency with the highest central bank interest rate and goes short the currency with the lowest interest rate.',
-        'tags'=>'Beginner,Long Short, Forex'
+        'tags'=>'Beginner,Long Short,Forex'
     ],
     [
         'name' => 'Volatility Effect in Stocks',
@@ -153,7 +153,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/13'
         ],
         'description' => 'Goes long stocks with the lowest return in the previous month and goes short stocks with the greatest return from the previous month.',
-        'tags'=>'Intermediate, Long Short, Mean Reversion,Equities'
+        'tags'=>'Intermediate,Long Short,Mean Reversion,Equities'
     ],
     [
         'name' => 'Momentum Effect in Stocks',
@@ -200,7 +200,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/20'
         ],
         'description' => 'Sells at-the-money straddle with one month until maturity and buys an offsetting 15% out-of-the-money puts each month.',
-        'tags'=>'Expert,Short Volatility, Options'
+        'tags'=>'Expert,Short Volatility,Options'
     ],
     [
         'name' => 'Momentum Effect in Commodities Futures',
@@ -209,7 +209,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/21'
         ],
         'description' => 'Goes long commodity futures with the highest momentum and short on futures with the lowest momentum.',
-        'tags'=>'Intermediate,Long Short, Futures'
+        'tags'=>'Intermediate,Long Short,Futures'
     ],
     [
         'name' => 'Small Capitalization Stocks Premium Anomaly',
@@ -317,7 +317,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/54'
         ],
         'description' => 'Goes long and short stocks with the highest and lowest six-month momentum respectively if the previous 12 months return on the broad market index was positive.',
-        'tags'=>'Intermediate, Long Short, Momentum, Equities'
+        'tags'=>'Intermediate,Long Short,Momentum,Equities'
     ],
     [
         'name' => 'Accrual Anomaly',
@@ -326,7 +326,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/38'
         ],
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.',
-        'tags'=>'Intermediate, Long Short, Equities'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'Momentum and Style Rotation Effect',
@@ -335,7 +335,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/91'
         ],
         'description' => 'Goes long style index ETF with the highest 12-month momentum and short ETF with the lowest 12-month momentum.',
-        'tags'=>'Beginner, Long Short, ETFs'
+        'tags'=>'Beginner,Long Short,ETFs'
     ],
     [
         'name' => 'Trading with WTI BRENT Spread',
@@ -344,7 +344,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/100'
         ],
         'description' => 'Goes long the spread if the spread is below 20-day moving average and short if the spread is above 20-day moving average.',
-        'tags'=>'Intermediate, Mean Reversion, Commodities'
+        'tags'=>'Intermediate,Mean Reversion,Commodities'
     ],
     [
         'name' => 'Momentum Effect in REITs',
@@ -353,7 +353,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/152'
         ],
         'description' => 'Tercile portfolios are formed based on momentum and the best performing portfolio is held.',
-        'tags'=>'Intermediate, Momentum, ETFs'
+        'tags'=>'Intermediate,Momentum,ETFs'
     ],
     [
         'name' => 'Option Expiration Week Effect',
@@ -371,7 +371,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/229'
         ],
         'description' => 'Goes long stocks with high earnings quality and short stocks with low earnings quality based on composite factor score.',
-        'tags'=>'Intermediate,Long Short, Fundamental Factors, Equities'
+        'tags'=>'Intermediate,Long Short,Fundamental Factors,Equities'
     ],
     [
         'name' => 'January Effect in Stocks',
@@ -389,7 +389,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/155'
         ],
         'description' => 'Goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest performing quintile from the highest volatility group.',
-        'tags'=>'Intermediate, Long Short, Equities'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'ROA Effect within Stocks',
@@ -398,7 +398,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/199'
         ],
         'description' => 'Goes long on stocks with highest ROA and short stocks with the lowest ROA from each market capitalization group.',
-        'tags'=>'Intermediate, Long Short, Fundamental Factors, Equities'
+        'tags'=>'Intermediate,Long Short,Fundamental Factors,Equities'
     ],
     [
         'name' => 'January Barometer',
@@ -407,7 +407,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/113'
         ],
         'description' => "Invested in equity market with ETF only if January return is positive otherwise switch investments to T-Bills.",
-        'tags'=>'Beginner, Equities'
+        'tags'=>'Beginner,Equities'
     ],
     [
         'name' => 'Lunar Cycle in Equity Market',
@@ -425,7 +425,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/58'
         ],
         'description' => "Goes long on equity index ETF if the VIX is in the highest percentile short if VIX is in the lowest percentile in the last two-year history.",
-        'tags'=>'Beginner, Equities'
+        'tags'=>'Beginner,Equities'
     ],
     [
         'name' => 'Combining Momentum Effect with Volume',
@@ -434,7 +434,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/66'
         ],
         'description' => "Goes long stocks with the highest volume from the top momentum decile and short stocks with the highest volume from the bottom momentum decile.",
-        'tags'=>'Intermediate, Long Short, Equities'
+        'tags'=>'Intermediate,Long Short,Equities'
     ],
     [
         'name' => 'Short Term Reversal with Futures',
@@ -479,7 +479,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/125'
         ],
         'description' => "Reviews the returns from last January, going long on the top 10% winners and short the bottom 10%.",
-        'tags'=>'Intermediate,Long Short, Momentum Equities'
+        'tags'=>'Intermediate,Long Short,Momentum Equities'
     ],
     [
         'name' => 'Momentum Effect in Stocks in Small Portfolios',
@@ -488,7 +488,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/162'
         ],
         'description' => "Goes long in the 10 stocks with the highest performance and goes short in the 10 stocks with the lowest performance in the previous one year.",
-        'tags'=>'Intermediate, Momentum, Equities'
+        'tags'=>'Intermediate, Momentum,Equities'
     ],
     [
         'name' => 'Value Effect within Countries',
@@ -497,7 +497,7 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/207'
         ],
         'description' => "Invests in the cheapest 33% of country ETFs according to CAPE ratios.",
-        'tags'=>'Intermediate, Fundamental Factors, ETFs'
+        'tags'=>'Intermediate,Fundamental Factors,ETFs'
     ],
     [
         'name' => 'Beta Factor in Country Equity Indexes',
@@ -506,13 +506,13 @@
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/78'
         ],
         'description' => "Goes long on the low-beta portfolio and short on the high-beta portfolio in country indexes ETFs.",
-        'tags'=>'Intermediate,Long Short, ETFs'
+        'tags'=>'Intermediate,Long Short,ETFs'
     ],
     [
         'name' => 'Price to Earnings Anamoly',
         'link' => 'strategy-library/price-earnings-anamoly',
         'description' => "Invests in stocks with low P/E ratio.",
-        'tags'=>'Beginner, Fundamental Factors, Equities'
+        'tags'=>'Beginner,Fundamental Factors,Equities'
     ],
 ];
 

From 17733250891821da42ae5edca055478c991cb0ee Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 11:30:20 -0800
Subject: [PATCH 423/753] Update 01 Introduction.html

---
 .../92 Price Earnings Anamoly/01 Introduction.html              | 2 --
 1 file changed, 2 deletions(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html
index dc5ef3d..76c2373 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
@@ -7,6 +7,4 @@
 </p>
 <p>
 The P/E ratio is often used by investors to determine the valuation of a company's stock.
-Research suggests that a portfolio that consists of stocks with relatively low P/E 
-ratio outperforms a portfolio that consists of stocks with relatively high P/E ratio.
 </p>

From 4d12688680388c9110a0e69307c3b815588550bf Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 12:00:54 -0800
Subject: [PATCH 424/753] Update 01 Introduction.html

---
 .../92 Price Earnings Anamoly/01 Introduction.html             | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html
index 76c2373..c4cde16 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html	
@@ -6,5 +6,6 @@
   \[Price \ to \ Earnings\ Ratio=\frac{Market \ price \ per \ share}{Earnings \ per \ share \ from \ the \ most \ recent \ financial \ year}\]
 </p>
 <p>
-The P/E ratio is often used by investors to determine the valuation of a company's stock.
+The P/E ratio is often used by investors to determine the valuation of a company's stock. Research suggests that a portfolio that consists of stocks with relatively low P/E 
+ratio outperforms a portfolio that consists of stocks with relatively high P/E ratio.
 </p>

From 9e305d88191466071c19f4a105a8ff6df30f6c09 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Tue, 22 Jan 2019 12:09:52 -0800
Subject: [PATCH 425/753] Update 02 Method.html

---
 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
index 7011c7e..91ab301 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html	
@@ -44,5 +44,5 @@
 </div>
 
 <p>
-In <code>OnData()</code>, we buy the 10 stocks that have the lowest P/E ratio in our universe. The portfolio is rebalanced every year.
+In <code>OnData()</code>, we buy the 10 stocks that have the lowest P/E ratio in our universe. We rebalance the portfolio at the beginning of each year.
 </p>

From 9e9dfa92acb09c485d9b811c5feb3d9c40d8ae3c Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Tue, 22 Jan 2019 13:35:18 -0800
Subject: [PATCH 426/753] Fix library object.

---
 .../00 Strategy Library/01 Strategy Library.php                | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e35107d..28a967f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -161,9 +161,8 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/14'
         ],
-        'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe.'
+        'description' => 'Goes long stocks with the best 12-month momentum in the large-cap universe.',
         'tags'=>'Beginner,Momentum,Equities'
-
     ],
     [
         'name' => 'Momentum Effect in Country Equity Indexes',

From 2cf8fb2b48d2cb5aabb1426a806ef2d70fd309ab Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Tue, 22 Jan 2019 13:37:52 -0800
Subject: [PATCH 427/753] Update 01 Strategy Library.php

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 28a967f..5ba4d96 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -216,7 +216,7 @@
         'sources' => [
             'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/25'
         ],
-         'tags'=>'Intermediate,Fundamental Factors,Equities'
+         'tags'=>'Intermediate,Fundamental Factors,Equities',
         'description' => 'Goes long stocks with the lowest market capitalization and rebalances the portfolio once a year.'
     ],
     [

From a9fab1e4480688ffce89556f04625c3b972beae5 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:02:15 -0800
Subject: [PATCH 428/753] Create 00.html

---
 03 Open Source/01 Debugging Python in Visual Studio/00.html | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/00.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/00.html b/03 Open Source/01 Debugging Python in Visual Studio/00.html
new file mode 100644
index 0000000..9ca8528
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/00.html	
@@ -0,0 +1,3 @@
+<p>
+Debugging python in visual studio
+</p>

From b946b9ba12ed8fdf7341e286b59baac543b6fd19 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:03:10 -0800
Subject: [PATCH 429/753] Create 01 Introduction.html

---
 .../01 Introduction.html                                     | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html
new file mode 100644
index 0000000..2babb61
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
@@ -0,0 +1,5 @@
+<p>
+ - Often want to debug
+ - VS makes this easy
+ - In this tutorial we'll show you how to debug your algorithm in LEAN from Visual Studio.
+</p>

From 843d6d84a8e9d569e6d1b9f4599ce5699021fb5d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:03:43 -0800
Subject: [PATCH 430/753] Create 02 Requirements.html

---
 .../01 Debugging Python in Visual Studio/02 Requirements.html | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html
new file mode 100644
index 0000000..d21e1d4
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html	
@@ -0,0 +1,4 @@
+<p>
+ - Before we get started you'll need to install the following software
+ 1,2,3,4,5 -> Links.
+</p>

From db18e68db42f307eace4995b2344e0b682c0316a Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:04:58 -0800
Subject: [PATCH 431/753] Create 03 Installing LEAN with Python.html

---
 .../03 Installing LEAN with Python.html                  | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html
new file mode 100644
index 0000000..da8971d
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html	
@@ -0,0 +1,9 @@
+<p>
+First step is to install LEAN and get it running with python algorithms
+</p>
+<p>
+To do this install : - - - - - 
+</p>
+<p>
+Run the BasicTemplateALgorithm.py successfully. Screen Shot oof the successful run.
+</p>

From a4c2746eebb32e2a0d5e5e48775aa79d800fe944 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:05:32 -0800
Subject: [PATCH 432/753] Create 04 Attaching the Debugger.html

---
 .../04 Attaching the Debugger.html                             | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
new file mode 100644
index 0000000..f4e6589
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -0,0 +1,3 @@
+<p>
+Run and attach.
+</p>

From 1abb73bd25781726ff0addf9532dd19598323e55 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:05:42 -0800
Subject: [PATCH 433/753] Create 05 Limitations.html

---
 .../01 Debugging Python in Visual Studio/05 Limitations.html     | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
@@ -0,0 +1 @@
+

From e43ff91effa84ef23a48fceef769c16dcd2831a4 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:05:58 -0800
Subject: [PATCH 434/753] Create 06 Summary.html

---
 .../01 Debugging Python in Visual Studio/06 Summary.html       | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html
new file mode 100644
index 0000000..bb95ae7
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+Success!
+</p>

From e5a5acc1f7e57c423ea4aa04d1915ca1dfa2be45 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 23 Jan 2019 16:23:23 -0800
Subject: [PATCH 435/753] Delete 02 Requirements.html

---
 .../01 Debugging Python in Visual Studio/02 Requirements.html | 4 ----
 1 file changed, 4 deletions(-)
 delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html
deleted file mode 100644
index d21e1d4..0000000
--- a/03 Open Source/01 Debugging Python in Visual Studio/02 Requirements.html	
+++ /dev/null
@@ -1,4 +0,0 @@
-<p>
- - Before we get started you'll need to install the following software
- 1,2,3,4,5 -> Links.
-</p>

From 402ef6d663c7f2ff2fdce9ee69bfdce1f250a73e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 04:54:07 +0000
Subject: [PATCH 436/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/02 Installation.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
index b00faf4..e97282f 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
@@ -1,6 +1,6 @@
 
 <p>
-To install the plugin simply execute the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
+To install the plugin simply execute ("double click") the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
 </p> 
 
 <h4>Build</h4>

From 85b450526ecc535235a8023c2f9f23bff16631d7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:18:59 +0000
Subject: [PATCH 437/753] Automated push from server made by Alexandre Catarino

---
 .../01 Debugging Python in Visual Studio/06 Summary.html  | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html
index bb95ae7..6e32755 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
@@ -1,3 +1,9 @@
 <p>
-Success!
+In this tutorial, we have leaned how to debug python algorithms in Visual Studio.
+</p>
+<p>
+The process is not as smooth as debugging pure python or C# code and have some limitations.
+</p>
+<p>
+Nevertheless, ...
 </p>

From acbb3929abeffedf71800e8778a5d42bee6b6ad0 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:19:06 +0000
Subject: [PATCH 438/753] Automated push from server made by Alexandre Catarino

---
 .../05 Limitations.html                              | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
index 8b13789..26db83c 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
@@ -1 +1,11 @@
-
+<p>Unfortunately, this debugging scenario entails some limitations.</p>
+<p>
+It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined Lean (e.g., SetHoldings).
+</p>
+<p>
+Debugging the same method is not smooth since we need to detach and reattach the process every time we leave the method we are debugging.
+<ul style="list-style-type:disc;">
+  <li>Detach Lean by clicking on Debug -> Detach All</li>
+  <li>Reattach Lean by clicking on Debug -> Reattach to Process</li>
+  </ul>
+</p>

From 9c3377568b48cbbe17dde4c3f5040eb97b8e01b4 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:19:13 +0000
Subject: [PATCH 439/753] Automated push from server made by Alexandre Catarino

---
 .../04 Attaching the Debugger.html            | 55 ++++++++++++++++++-
 1 file changed, 54 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index f4e6589..c74057d 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -1,3 +1,56 @@
 <p>
-Run and attach.
+The process that we will use to attach the python debugger requires that we run Lean from the command line and attach the process.
 </p>
+<p>
+First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:
+<div class="section-example-container" >
+<pre class="python" >
+while not sys.gettrace():
+    time.sleep(0.01)
+</pre>
+</div> 
+These statements will hold the algorithm execution until Lean is attached to the python debugger. 
+</p>
+<p>
+For example, place them in the OnData method to enable a breakpoint on the SetHoldings statement.
+<div class="section-example-container" >
+<pre class="python" >
+import sys, time
+
+class BasicTemplateAlgorithm(QCAlgorithm):
+    def Initialize(self):
+        self.SetStartDate(2013,10, 7)
+        self.SetEndDate(2013,10,11)
+        self.SetCash(100000)
+        self.AddEquity("SPY", Resolution.Second)
+        self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
+
+    def OnData(self, data):
+        while not sys.gettrace():
+            time.sleep(0.01)
+
+        if not self.Portfolio.Invested:
+            self.SetHoldings("SPY", 1)
+</pre>
+</div>
+</p>
+<p>
+Rebuild Lean and run it from the command line:
+<div class="section-example-container" >
+<pre>
+C:\Users\Guest> cd .\Lean\Launcher\bin\Debug\
+C:\Users\Guest\Lean\Launcher\bin\Debug> .\QuantConnect.Lean.Launcher.exe
+</pre>
+</div>
+</p>
+<p>
+Finally, attach the debugger to the process 
+<ul style="list-style-type:disc;">
+  <li>Open the Attach to Process dialog by clicking on Debug -> Attach to Process...</li>
+  <li>In "Attach to", click on Select button and select "Python code"</li>
+  <li>In "Available processes", select "QuantConnect.Lean.Launcher.exe" and click "Attach" button</li>
+</ul>
+At this moment the code execution will stop on the breakpoint placed on the SetHoldings statement.
+</p>
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif"/>
\ No newline at end of file

From 696be07cb3382661eefe0e3e10eb37de68744ce2 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:19:21 +0000
Subject: [PATCH 440/753] Automated push from server made by Alexandre Catarino

---
 .../03 Installing LEAN with Python.html       | 32 +++++++++++++++++--
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html
index da8971d..9bd47a5 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html	
@@ -1,9 +1,35 @@
 <p>
-First step is to install LEAN and get it running with python algorithms
+The first step is to install LEAN and get it running with python algorithms.
 </p>
 <p>
-To do this install : - - - - - 
+To do this install:
+<ul style="list-style-type:disc;">
+  <li>Download Lean at the <a href="https://github.com/QuantConnect/Lean">open-source project page</a>.</li>
+  <li>Install <a href="https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx">Visual Studio</a>.</li>
+  <li>Open <mark>QuantConnect.Lean.sln</mark> in Visual Studio</li>
+  <li>Build the solution by clicking Build Menu -> Build Solution</li>
+  <li>Press <mark>F5</mark> to run</li>
+</ul>
 </p>
 <p>
-Run the BasicTemplateALgorithm.py successfully. Screen Shot oof the successful run.
+By default, Lean will run the C# version of BasicTemplateFrameworkAlgorithm.
 </p>
+<p>
+Next step is to run the python version of BasicTemplateAlgorithm successfully.
+</p>
+<p>
+To run that algorithm:
+<ul style="list-style-type:disc;">
+  <li>Edit config.json with the following paramenters:</li>
+  <div class="section-example-container">
+  <pre>
+      "algorithm-type-name": "BasicTemplateAlgorithm",
+      "algorithm-language": "Python",
+      "algorithm-location": "../../../Algorithm.Python/BasicTemplateAlgorithm.py",
+  </pre>
+  </div>
+  <li>Build the solution by clicking Build Menu -> Build Solution</li>
+  <li>Press <mark>F5</mark> to run</li>
+</ul>
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python-success.png" />

From a0b24d4a8b40db99779754e504cf4854b98f18d3 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:19:26 +0000
Subject: [PATCH 441/753] Automated push from server made by Alexandre Catarino

---
 .../01 Introduction.html                           | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html
index 2babb61..609b454 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
@@ -1,5 +1,13 @@
+<style>
+.backdrop { background-color: #ddd; padding: 15px; border: 1px solid #ccc; border-radius: 5px;}
+</style>
+
 <p>
- - Often want to debug
- - VS makes this easy
- - In this tutorial we'll show you how to debug your algorithm in LEAN from Visual Studio.
+    We often want to use an IDE for algorithm development because it provides comprehensive facilities such as a source code editor and a debugger. QuantConnect delivers a robust online source code editor, but not a debugger. For some algorithm developers, a debugger is an essential tool; therefore they opt to work offline with full-featured IDE.
+</p>
+<p>
+    Visual Studio is a full-featured IDE that makes debugger easy. Unlike other solutions that only allows debugging a single process/language, we can use it to debug a python algorithm in Lean (C# engine).
+</p>
+<p>
+    In this tutorial, we'll show you how to debug your algorithm in LEAN from Visual Studio.
 </p>

From 73f60bba26d6a8a6e1167bdd06131607c74eeb83 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 21:19:32 +0000
Subject: [PATCH 442/753] Automated push from server made by Alexandre Catarino

---
 03 Open Source/01 Debugging Python in Visual Studio/00.html | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/00.html b/03 Open Source/01 Debugging Python in Visual Studio/00.html
index 9ca8528..aa2e91f 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/00.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/00.html	
@@ -1,3 +1 @@
-<p>
-Debugging python in visual studio
-</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python-cover.png" />
\ No newline at end of file

From 1f0892b68d4801934126d09ea9b7870b42059845 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 24 Jan 2019 14:05:33 -0800
Subject: [PATCH 443/753] Rename 04 Step 1 Create a Lesson Plan.html to 04 Step
 1%3A Create a Lesson Plan.html

---
 ... a Lesson Plan.html => 04 Step 1%3A Create a Lesson Plan.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 01 API Tutorials/05 Creating BootCamp Tutorials/{04 Step 1 Create a Lesson Plan.html => 04 Step 1%3A Create a Lesson Plan.html} (100%)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html b/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1 Create a Lesson Plan.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html

From ab3bc327c8d5ab007eeee254fda23316f137040f Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 24 Jan 2019 14:26:14 -0800
Subject: [PATCH 444/753] Rename 05 Step 2 Writing a Lesson.html to 05 Step
 2%3A Writing a Lesson.html

---
 ...2 Writing a Lesson.html => 05 Step 2%3A Writing a Lesson.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 01 API Tutorials/05 Creating BootCamp Tutorials/{05 Step 2 Writing a Lesson.html => 05 Step 2%3A Writing a Lesson.html} (100%)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html b/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2 Writing a Lesson.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html

From 85ea924d34306a6d96d8fe87fc94e2692df259c4 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 24 Jan 2019 14:26:24 -0800
Subject: [PATCH 445/753] Rename 06 Step 3 Building BootCamp Tests.html to 06
 Step 3%3A Building BootCamp Tests.html

---
 ...tCamp Tests.html => 06 Step 3%3A Building BootCamp Tests.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 01 API Tutorials/05 Creating BootCamp Tutorials/{06 Step 3 Building BootCamp Tests.html => 06 Step 3%3A Building BootCamp Tests.html} (100%)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html b/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3 Building BootCamp Tests.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html

From b651de39e115e190d38e9519db152b18e15e20a2 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 24 Jan 2019 14:26:34 -0800
Subject: [PATCH 446/753] Rename 07 Step 4 Submitting Lesson for Review.html to
 07 Step 4%3A Submitting Lesson for Review.html

---
 ...Review.html => 07 Step 4%3A Submitting Lesson for Review.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 01 API Tutorials/05 Creating BootCamp Tutorials/{07 Step 4 Submitting Lesson for Review.html => 07 Step 4%3A Submitting Lesson for Review.html} (100%)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html b/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4 Submitting Lesson for Review.html
rename to 01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html

From ada8c506f80ca8acedf992ccbdc188deb9b442fd Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 24 Jan 2019 14:29:01 -0800
Subject: [PATCH 447/753] Update 02 BootCamp Structure.html

---
 .../02 BootCamp Structure.html                       | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
index 22d6ce0..b9a4491 100644
--- a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -6,7 +6,15 @@
 
 <ul>
    <li>Course: Algorithmic Trading with Equities 
-      <ul><li>Lesson: Buy and Hold Algorithm <ul><li>Task: Setting Cash</il></ul> </li></ul></li>
+      <ul><li>Lesson: Buy and Hold Algorithm 
+         <ul><li>Task: Setting Cash</il></ul> 
+      </li></ul>
+   </li>
 </ul>
 
-<p>We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires another Course please let us know and we'll create one for you.</p>
+<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task.</p>
+
+<p>
+  We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires 
+  another <i>Course</i> please let us know and we'll create one for you.
+</p>

From 650b214cf044f950bc7fe31f5890cb67cb9b11fb Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 23:04:44 +0000
Subject: [PATCH 448/753] Automated push from server made by Alexandre Catarino

---
 .../01 Debugging Python in Visual Studio/01 Introduction.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html
index 609b454..c854c52 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
@@ -6,7 +6,7 @@
     We often want to use an IDE for algorithm development because it provides comprehensive facilities such as a source code editor and a debugger. QuantConnect delivers a robust online source code editor, but not a debugger. For some algorithm developers, a debugger is an essential tool; therefore they opt to work offline with full-featured IDE.
 </p>
 <p>
-    Visual Studio is a full-featured IDE that makes debugger easy. Unlike other solutions that only allows debugging a single process/language, we can use it to debug a python algorithm in Lean (C# engine).
+    Visual Studio is a full-featured IDE that makes debugging<span style="color: rgb(36, 41, 46); font-family: -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;, &quot;Segoe UI Symbol&quot;; font-size: 14px; text-align: start;">&nbsp;</span>easy. Unlike other solutions that only allows debugging a single process/language, we can use it to debug a python algorithm in Lean (C# engine).
 </p>
 <p>
     In this tutorial, we'll show you how to debug your algorithm in LEAN from Visual Studio.

From ed710c4098ccd23d4a8fbf485aea4999c48d3a82 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 23:05:40 +0000
Subject: [PATCH 449/753] Automated push from server made by Alexandre Catarino

---
 .../04 Attaching the Debugger.html            | 51 ++++++-------------
 1 file changed, 15 insertions(+), 36 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index c74057d..e9e814d 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -1,21 +1,11 @@
-<p>
-The process that we will use to attach the python debugger requires that we run Lean from the command line and attach the process.
-</p>
-<p>
-First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:
-<div class="section-example-container" >
-<pre class="python" >
-while not sys.gettrace():
+<p>The process that we will use to attach the python debugger requires that we run Lean from the command line and attach the process.</p>
+<p>First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:</p><div class="section-example-container">
+<pre class="python">while not sys.gettrace():
     time.sleep(0.01)
 </pre>
-</div> 
-These statements will hold the algorithm execution until Lean is attached to the python debugger. 
-</p>
-<p>
-For example, place them in the OnData method to enable a breakpoint on the SetHoldings statement.
-<div class="section-example-container" >
-<pre class="python" >
-import sys, time
+</div>These statements will hold the algorithm execution until Lean is attached to the python debugger.<p></p>
+<p>For example, place them in the OnData method to enable a breakpoint on the SetHoldings statement.</p><div class="section-example-container">
+<pre class="python">import sys, time
 
 class BasicTemplateAlgorithm(QCAlgorithm):
     def Initialize(self):
@@ -23,7 +13,7 @@
         self.SetEndDate(2013,10,11)
         self.SetCash(100000)
         self.AddEquity("SPY", Resolution.Second)
-        self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
+        self.Debug("numpy test &gt;&gt;&gt; print numpy.pi: " + str(np.pi))
 
     def OnData(self, data):
         while not sys.gettrace():
@@ -33,24 +23,13 @@
             self.SetHoldings("SPY", 1)
 </pre>
 </div>
-</p>
-<p>
-Rebuild Lean and run it from the command line:
-<div class="section-example-container" >
-<pre>
-C:\Users\Guest> cd .\Lean\Launcher\bin\Debug\
-C:\Users\Guest\Lean\Launcher\bin\Debug> .\QuantConnect.Lean.Launcher.exe
-</pre>
-</div>
-</p>
-<p>
-Finally, attach the debugger to the process 
-<ul style="list-style-type:disc;">
-  <li>Open the Attach to Process dialog by clicking on Debug -> Attach to Process...</li>
+
+<p></p>
+<p>Finally, attach the debugger to the process</p><ul style="list-style-type:disc;">
+  <li>Run Lean without debugging by clicking on Debug -&gt; Start Without Debugging</li>
+  <li>Open the Attach to Process dialog by clicking on Debug -&gt; Attach to Process...</li>
   <li>In "Attach to", click on Select button and select "Python code"</li>
   <li>In "Available processes", select "QuantConnect.Lean.Launcher.exe" and click "Attach" button</li>
-</ul>
-At this moment the code execution will stop on the breakpoint placed on the SetHoldings statement.
-</p>
-</p>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif"/>
\ No newline at end of file
+</ul>At this moment the code execution will stop on the breakpoint placed on the SetHoldings statement.<p></p>
+<p></p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif">
\ No newline at end of file

From f3608eda14e2ed67586928ede936e64466a69656 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 23:06:09 +0000
Subject: [PATCH 450/753] Automated push from server made by Alexandre Catarino

---
 .../05 Limitations.html                                | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
index 26db83c..87e460e 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
@@ -1,11 +1,11 @@
 <p>Unfortunately, this debugging scenario entails some limitations.</p>
 <p>
-It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined Lean (e.g., SetHoldings).
+It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).
 </p>
 <p>
 Debugging the same method is not smooth since we need to detach and reattach the process every time we leave the method we are debugging.
-<ul style="list-style-type:disc;">
-  <li>Detach Lean by clicking on Debug -> Detach All</li>
-  <li>Reattach Lean by clicking on Debug -> Reattach to Process</li>
+</p><ul style="list-style-type:disc;">
+  <li>Detach Lean by clicking on Debug -&gt; Detach All</li>
+  <li>Reattach Lean by clicking on Debug -&gt; Reattach to Process</li>
   </ul>
-</p>
+<p></p>

From c2e1cdca8b495d1f4cae1ce0b3d77960e5553249 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 24 Jan 2019 23:17:10 +0000
Subject: [PATCH 451/753] Automated push from server made by Alexandre Catarino

---
 .../01 Debugging Python in Visual Studio/06 Summary.html      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html
index 6e32755..44c8d06 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
@@ -1,9 +1,9 @@
 <p>
-In this tutorial, we have leaned how to debug python algorithms in Visual Studio.
+In this tutorial, we have learned how to debug python algorithms in Visual Studio.
 </p>
 <p>
 The process is not as smooth as debugging pure python or C# code and have some limitations.
 </p>
 <p>
-Nevertheless, ...
+Nevertheless, knowing how to debug python algorithms in Lean can give you a new level of understanding of their functionality and allow for easier implementation of the refined code in the IDE.
 </p>

From 0f8be13d81230cda6c480273f65fb228885a7deb Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 Jan 2019 00:01:00 +0000
Subject: [PATCH 452/753] Automated push from server made by Jared Broad

---
 .../01 Debugging Python in Visual Studio/06 Summary.html  | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html
index 44c8d06..88aa936 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html	
@@ -1,9 +1,3 @@
 <p>
-In this tutorial, we have learned how to debug python algorithms in Visual Studio.
-</p>
-<p>
-The process is not as smooth as debugging pure python or C# code and have some limitations.
-</p>
-<p>
-Nevertheless, knowing how to debug python algorithms in Lean can give you a new level of understanding of their functionality and allow for easier implementation of the refined code in the IDE.
+In this tutorial, we have learned how to debug python algorithms in Visual Studio. It is not quite as smooth as native python yet, but knowing how to debug python algorithms in LEAN can give you a new level of understanding of how they work and allow for easier debugging of your strategy.
 </p>

From ef88c2de5b4fcdfa3e77ea1ec6c360a49b21337c Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 Jan 2019 00:03:45 +0000
Subject: [PATCH 453/753] Automated push from server made by Jared Broad

---
 .../04 Attaching the Debugger.html            | 22 ++++++++++++++-----
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index e9e814d..d372b5d 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -1,10 +1,18 @@
 <p>The process that we will use to attach the python debugger requires that we run Lean from the command line and attach the process.</p>
-<p>First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:</p><div class="section-example-container">
+
+<p>First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:</p>
+
+<div class="section-example-container">
 <pre class="python">while not sys.gettrace():
     time.sleep(0.01)
 </pre>
-</div>These statements will hold the algorithm execution until Lean is attached to the python debugger.<p></p>
-<p>For example, place them in the OnData method to enable a breakpoint on the SetHoldings statement.</p><div class="section-example-container">
+</div>
+
+<p>These statements will hold the algorithm execution until Lean is attached to the python debugger.</p>
+
+<p>For example, place them in the OnData() method to enable a breakpoint on the SetHoldings statement.</p>
+
+<div class="section-example-container">
 <pre class="python">import sys, time
 
 class BasicTemplateAlgorithm(QCAlgorithm):
@@ -25,11 +33,13 @@
 </div>
 
 <p></p>
-<p>Finally, attach the debugger to the process</p><ul style="list-style-type:disc;">
+<p>Finally, attach the debugger to the process:</p>
+<ul style="list-style-type:disc;">
   <li>Run Lean without debugging by clicking on Debug -&gt; Start Without Debugging</li>
   <li>Open the Attach to Process dialog by clicking on Debug -&gt; Attach to Process...</li>
   <li>In "Attach to", click on Select button and select "Python code"</li>
   <li>In "Available processes", select "QuantConnect.Lean.Launcher.exe" and click "Attach" button</li>
-</ul>At this moment the code execution will stop on the breakpoint placed on the SetHoldings statement.<p></p>
-<p></p>
+</ul>
+
+<p>Once you attach the process the code execution will stop on the breakpoint placed on the SetHoldings statement.</p>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif">
\ No newline at end of file

From e36f9e12714a45ff49bb6787dfb6bbcb8a14e51f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 Jan 2019 00:07:55 +0000
Subject: [PATCH 454/753] Automated push from server made by Jared Broad

---
 .../03 Installing LEAN with Python.html         | 17 ++++++-----------
 1 file changed, 6 insertions(+), 11 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html
index 9bd47a5..ba680ab 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html	
@@ -3,20 +3,15 @@
 </p>
 <p>
 To do this install:
-<ul style="list-style-type:disc;">
-  <li>Download Lean at the <a href="https://github.com/QuantConnect/Lean">open-source project page</a>.</li>
+<ul>
+  <li>Download LEAN from <a href="https://github.com/QuantConnect/Lean">Github</a>.</li>
   <li>Install <a href="https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx">Visual Studio</a>.</li>
-  <li>Open <mark>QuantConnect.Lean.sln</mark> in Visual Studio</li>
-  <li>Build the solution by clicking Build Menu -> Build Solution</li>
-  <li>Press <mark>F5</mark> to run</li>
+  <li>Open <mark>QuantConnect.Lean.sln</mark> in Visual Studio.</li>
+  <li>Build the solution by clicking Build Menu -> Build Solution.</li>
+  <li>Press <mark>F5</mark> to run LEAN.</li>
 </ul>
 </p>
-<p>
-By default, Lean will run the C# version of BasicTemplateFrameworkAlgorithm.
-</p>
-<p>
-Next step is to run the python version of BasicTemplateAlgorithm successfully.
-</p>
+<p>By default, Lean will run the C# version of BasicTemplateAlgorithm. From here the next step is to run the python version of the BasicTemplateAlgorithm successfully.</p>
 <p>
 To run that algorithm:
 <ul style="list-style-type:disc;">

From 04e06a85e776d224fc01b39f78ae4e218f616637 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 25 Jan 2019 03:41:32 +0000
Subject: [PATCH 455/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html   | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index db9ecd9..fca9347 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -8,5 +8,6 @@
 
 <p>
    You can download the plugin here: 
-    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a>
-</p>
\ No newline at end of file
+    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a> *<br/>
+<small style="font-size: 0.8em">* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.</small>
+</p>

From 858d9db782c5c7d494483f48855c95466dee0235 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 12:35:57 -0800
Subject: [PATCH 456/753] Rename 04 References.html to 05 References.html

---
 .../{04 References.html => 05 References.html}                    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/92 Price Earnings Anamoly/{04 References.html => 05 References.html} (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 References.html b/04 Strategy Library/92 Price Earnings Anamoly/05 References.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/04 References.html
rename to 04 Strategy Library/92 Price Earnings Anamoly/05 References.html

From e7efff06c48c06821f806c65c5784528aebfb5b9 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 13:31:34 -0800
Subject: [PATCH 457/753] Update 05 References.html

---
 .../92 Price Earnings Anamoly/05 References.html              | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/05 References.html b/04 Strategy Library/92 Price Earnings Anamoly/05 References.html
index ef93d18..b9f2706 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/05 References.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/05 References.html	
@@ -2,4 +2,8 @@
   <li>
     Persson E, Ståhlberg C (2006). P/E and EV/EBITDA Investment Strategies vs. the Market (Master's thesis, Linköping University, Linköping, Sweden).  <a href="https://www.diva-portal.org/smash/get/diva2:23073/FULLTEXT01.pdf">Online Copy</a>
   </li>
+  
+  <li>
+    Fama, Eugene F. and French, Kenneth R., Multifactor Explanations of Asset Pricing Anomalies. J. OF FINANCE, Vol. 51 No. 1, March 1996. Available at SSRN: https://ssrn.com/abstract=7365
+</li>
 </ol>

From 6993888da8225395f025cf0b0d9b5069625b00a4 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 14:01:25 -0800
Subject: [PATCH 458/753] Create 04 Summary.html

---
 .../92 Price Earnings Anamoly/04 Summary.html            | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html
new file mode 100644
index 0000000..89debff
--- /dev/null
+++ b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html	
@@ -0,0 +1,9 @@
+<p>
+The portfolio significantly outperforms the benchmark, S&P 500, during the two years backtest period, from the beginning of the year 2016 to the end of the year 2017.
+</p>
+<p>
+In theory, there is an additional factor, to the small P/E ratio factor, contributing to the excess returns of the portfolio, relative to the benchmark. This additional factor is known as the size factor, where size in this respect, is a market capitalization of a stock. 
+</p>
+<p>
+Ranking portfolios on market capitalization and subsequently tracking their performance has demonstrated that portfolio of stocks with a small market capitalization outperforms a portfolio of stocks with large market capitalization. Another investment strategy in the strategy library, <a href="https://www.quantconnect.com/tutorials/strategy-library/small-capitalization-stocks-premium-anomaly">Small Market Capitalization Stock Premium</a>, demonstrates how to form a portfolio that only consists of stocks with relatively low market capitalization.
+</p>

From c2a5e3ad285ca2c20bf9a0f0849ee25957fd65be Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 14:02:57 -0800
Subject: [PATCH 459/753] Update 04 Summary.html

---
 04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html
index 89debff..cdcf3a5 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html	
@@ -2,6 +2,9 @@
 The portfolio significantly outperforms the benchmark, S&P 500, during the two years backtest period, from the beginning of the year 2016 to the end of the year 2017.
 </p>
 <p>
+The universe of stocks is filtered using dollar volume and price criteria. Most of the stocks chosen by the algorithm during the backtest period are small market capitalization stocks.
+</p>
+<p>
 In theory, there is an additional factor, to the small P/E ratio factor, contributing to the excess returns of the portfolio, relative to the benchmark. This additional factor is known as the size factor, where size in this respect, is a market capitalization of a stock. 
 </p>
 <p>

From 33b1ba5d80459397025ab5ac10278bc5900b10d4 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 14:03:58 -0800
Subject: [PATCH 460/753] Rename 04 Source.html to 05 Source.html

---
 .../{04 Source.html => 05 Source.html}                            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/31 Book-to-Market Value Anomaly/{04 Source.html => 05 Source.html} (100%)

diff --git a/04 Strategy Library/31 Book-to-Market Value Anomaly/04 Source.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/05 Source.html
similarity index 100%
rename from 04 Strategy Library/31 Book-to-Market Value Anomaly/04 Source.html
rename to 04 Strategy Library/31 Book-to-Market Value Anomaly/05 Source.html

From bfc1538a70e09bf2f74c4848d698c7f718c60651 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 14:06:07 -0800
Subject: [PATCH 461/753] Create 04 Summary.html

---
 .../31 Book-to-Market Value Anomaly/04 Summary.html      | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 04 Strategy Library/31 Book-to-Market Value Anomaly/04 Summary.html

diff --git a/04 Strategy Library/31 Book-to-Market Value Anomaly/04 Summary.html b/04 Strategy Library/31 Book-to-Market Value Anomaly/04 Summary.html
new file mode 100644
index 0000000..7d4d947
--- /dev/null
+++ b/04 Strategy Library/31 Book-to-Market Value Anomaly/04 Summary.html	
@@ -0,0 +1,9 @@
+<p>
+The portfolio underperforms relatively to the benchmark, S&P 500, during the backtest period.
+</p>
+<p>
+In general, stocks that have a low B/P ratio are considered to be value stocks, and similarly, stocks that have a high B/P ratio are referred to as growth stocks.
+</p>
+<p>
+During a bull market, which is associated with high GDP growth, value stocks tend to underperform growth stocks, as investors are optimistic about future earnings growth.
+</p>

From d44bff513b1c410ec49723059ca510a3548570a1 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Fri, 25 Jan 2019 14:08:17 -0800
Subject: [PATCH 462/753] Update 04 Summary.html

---
 04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html
index cdcf3a5..e3fdaad 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html	
@@ -5,7 +5,7 @@
 The universe of stocks is filtered using dollar volume and price criteria. Most of the stocks chosen by the algorithm during the backtest period are small market capitalization stocks.
 </p>
 <p>
-In theory, there is an additional factor, to the small P/E ratio factor, contributing to the excess returns of the portfolio, relative to the benchmark. This additional factor is known as the size factor, where size in this respect, is a market capitalization of a stock. 
+In theory, there is an additional factor, to the small P/E ratio factor, contributing to the excess returns of the portfolio, relative to the benchmark. This additional factor is known as the size factor, where size in this respect, is the market capitalization of a stock. 
 </p>
 <p>
 Ranking portfolios on market capitalization and subsequently tracking their performance has demonstrated that portfolio of stocks with a small market capitalization outperforms a portfolio of stocks with large market capitalization. Another investment strategy in the strategy library, <a href="https://www.quantconnect.com/tutorials/strategy-library/small-capitalization-stocks-premium-anomaly">Small Market Capitalization Stock Premium</a>, demonstrates how to form a portfolio that only consists of stocks with relatively low market capitalization.

From dfe859b681298115dad8364a253cc6bba6f5e9f6 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:30:31 -0800
Subject: [PATCH 463/753] Update 03 Algorithm.html

---
 04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html
index 037bb5c..98ca2eb 100644
--- a/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_16a9f69cbcd2c2465ebe4f785d7f4395.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1dba1ec267628169d3c4a06b94e5b107.html"></iframe>
     </div>
 </div>

From ce4b3022aa1a4a097ca6aa9e3122d8ccc4c6b359 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:37:52 -0800
Subject: [PATCH 464/753] Spelling

---
 .../00 Strategy Library/01 Strategy Library.php               | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 5ba4d96..e855425 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -508,8 +508,8 @@
         'tags'=>'Intermediate,Long Short,ETFs'
     ],
     [
-        'name' => 'Price to Earnings Anamoly',
-        'link' => 'strategy-library/price-earnings-anamoly',
+        'name' => 'Price to Earnings Anomaly',
+        'link' => 'strategy-library/price-earnings-anomaly',
         'description' => "Invests in stocks with low P/E ratio.",
         'tags'=>'Beginner,Fundamental Factors,Equities'
     ],

From 5268df6479c73a57af52e3c81252552e13ce4665 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:38:52 -0800
Subject: [PATCH 465/753] Rename 04 Strategy Library/92 Price Earnings
 Anamoly/01 Introduction.html to 04 Strategy Library/92 Price Earnings
 Anomaly/01 Introduction.html

---
 .../01 Introduction.html                                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{92 Price Earnings Anamoly => 92 Price Earnings Anomaly}/01 Introduction.html (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html b/04 Strategy Library/92 Price Earnings Anomaly/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/01 Introduction.html
rename to 04 Strategy Library/92 Price Earnings Anomaly/01 Introduction.html

From 2efaef80c4f805c2a4d1793f88116e658ac8f61d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:39:20 -0800
Subject: [PATCH 466/753] Rename 04 Strategy Library/92 Price Earnings
 Anamoly/02 Method.html to 04 Strategy Library/92 Price Earnings Anomaly/02
 Method.html

---
 .../02 Method.html                                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{92 Price Earnings Anamoly => 92 Price Earnings Anomaly}/02 Method.html (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/02 Method.html b/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/02 Method.html
rename to 04 Strategy Library/92 Price Earnings Anomaly/02 Method.html

From fdd754442f9334f984c3ad0f504b3ea534c6b655 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:39:43 -0800
Subject: [PATCH 467/753] Rename 04 Strategy Library/92 Price Earnings
 Anamoly/03 Algorithm.html to 04 Strategy Library/92 Price Earnings Anomaly/03
 Algorithm.html

---
 .../03 Algorithm.html                                             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{92 Price Earnings Anamoly => 92 Price Earnings Anomaly}/03 Algorithm.html (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/03 Algorithm.html
rename to 04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html

From 60df026ac2c0b0e5432cf5996245fdc8a7596591 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:40:04 -0800
Subject: [PATCH 468/753] Rename 04 Strategy Library/92 Price Earnings
 Anamoly/04 Summary.html to 04 Strategy Library/92 Price Earnings Anomaly/04
 Summary.html

---
 .../04 Summary.html                                               | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{92 Price Earnings Anamoly => 92 Price Earnings Anomaly}/04 Summary.html (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html b/04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/04 Summary.html
rename to 04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html

From 299ac222762af1208f11ad7277fac3274e0d77ca Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:42:21 -0800
Subject: [PATCH 469/753] Rename 04 Strategy Library/92 Price Earnings
 Anamoly/05 References.html to 04 Strategy Library/92 Price Earnings
 Anomaly/05 References.html

---
 .../05 References.html                                            | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{92 Price Earnings Anamoly => 92 Price Earnings Anomaly}/05 References.html (100%)

diff --git a/04 Strategy Library/92 Price Earnings Anamoly/05 References.html b/04 Strategy Library/92 Price Earnings Anomaly/05 References.html
similarity index 100%
rename from 04 Strategy Library/92 Price Earnings Anamoly/05 References.html
rename to 04 Strategy Library/92 Price Earnings Anomaly/05 References.html

From b263641e6f5b93bb8e8a2a9adda340b1f8d6e99e Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 27 Jan 2019 12:43:32 -0800
Subject: [PATCH 470/753] Update 03 Algorithm.html

---
 04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
index 98ca2eb..820b4ff 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1dba1ec267628169d3c4a06b94e5b107.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_28c47727b202b88fd00d9b0b52dadedf.html"></iframe>
     </div>
 </div>

From acd98783f172bf87cf9f67e772884a1a9ef74b64 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 28 Jan 2019 10:19:26 -0800
Subject: [PATCH 471/753] Update 02 Method.html

---
 .../100 Trading with WTI BRENT Spread/02 Method.html          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html
index e6f9598..fdcce3d 100644
--- a/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html	
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 Method.html	
@@ -88,11 +88,11 @@
     self.SpreadSMA.Update(self.Time, data["WTI"].Price - data["BRENT"].Price)
     if not self.SpreadSMA.IsReady: return
     spread = self.Securities["WTI"].Price - self.Securities["BRENT"].Price
-    fair_value =self.Securities["WTI"].Price - Decimal(self.regr.predict(self.Securities["WTI"].Price)[0])
+    fair_value =self.Securities["WTI"].Price - Decimal(self.regr.predict([[self.Securities["WTI"].Price]])[0])
 
     if spread > self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong):
         self.SetHoldings("WTI", -0.5)
-        self.SetHoldings("BRENT", 0.5)
+        self.SetHoldings("BRENT", 0.5) 
         self.Plot("Spread Plot", "Long Spread Trade", data["WTI"].Price - data["BRENT"].Price)
 
     elif spread < self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort):

From 539eab8f822224a80d1883cdc5a1d80b8165bcc6 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 28 Jan 2019 10:24:20 -0800
Subject: [PATCH 472/753] Added new link for backtest

---
 .../100 Trading with WTI BRENT Spread/03 Algorithm.html         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html
index b909114..15151d8 100644
--- a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
+++ b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2691b0f4e604049a81275708313610c6.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5ee5507fef6bf190ae533ce05ccaa785.html"></iframe>
     </div>
 </div>

From 7e03efcfa36fdaa0d0c746b24d04ad5ab6e60d27 Mon Sep 17 00:00:00 2001
From: HalldorAndersen <46326847+HalldorAndersen@users.noreply.github.com>
Date: Mon, 28 Jan 2019 10:29:07 -0800
Subject: [PATCH 473/753] Updated link for WTI/Brent strategy

---
 quantpedia.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quantpedia.json b/quantpedia.json
index e1d900e..cd0aa80 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -35,7 +35,7 @@
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
      91: "d0b1d400ad21477d83ae2b4f85616318",
-     100: "2691b0f4e604049a81275708313610c6",
+     100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",

From 8b8f5e6799eba9ae8c3d334813e1a1f5893907d4 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 31 Jan 2019 22:17:22 +0000
Subject: [PATCH 474/753] Automated push from server made by Alexandre Catarino

---
 .../04 Attaching the Debugger.html            | 42 +++++++++++--------
 1 file changed, 25 insertions(+), 17 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index d372b5d..b044b14 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -1,19 +1,27 @@
-<p>The process that we will use to attach the python debugger requires that we run Lean from the command line and attach the process.</p>
+<p>The process that we will use to attach the python debugger requires that we run Lean <b>without</b> debugging and attach the process.</p>
 
-<p>First, we will add the <b>sys</b> and <b>time</b> libraries, and the following statements:</p>
+<p>First, we will add the <b>ptvsd</b> library, and the following statements:</p>
 
-<div class="section-example-container">
-<pre class="python">while not sys.gettrace():
-    time.sleep(0.01)
-</pre>
-</div>
+<div class="section-example-container"><pre class="python">
+import ptvsd
+ptvsd.enable_attach()
+ptvsd.wait_for_attach()
+</pre></div>
 
 <p>These statements will hold the algorithm execution until Lean is attached to the python debugger.</p>
-
-<p>For example, place them in the OnData() method to enable a breakpoint on the SetHoldings statement.</p>
+<p>The breakpoint is set by the following statement:</p>
+<div class="section-example-container"><pre class="python">ptvsd.break_into_debugger()</pre></div>
+<p>For example, place it in the OnData() method:</p>
 
 <div class="section-example-container">
-<pre class="python">import sys, time
+<pre class="python">import ptvsd
+ptvsd.enable_attach()
+print(f'''Python Tool for Visual Studio Debugger {ptvsd.__version__}
+Please attach the python debugger:
+- In Visual Studio, select Debug > Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.
+- For Connection type, select Python remote (ptvsd)
+- In the Connection target box, select tcp://localhost:5678/ and click "Attach" button''')
+ptvsd.wait_for_attach()
 
 class BasicTemplateAlgorithm(QCAlgorithm):
     def Initialize(self):
@@ -21,12 +29,12 @@
         self.SetEndDate(2013,10,11)
         self.SetCash(100000)
         self.AddEquity("SPY", Resolution.Second)
-        self.Debug("numpy test &gt;&gt;&gt; print numpy.pi: " + str(np.pi))
+        self.Debug(f"numpy test &gt;&gt;&gt; print numpy.pi: {np.pi}")
 
     def OnData(self, data):
-        while not sys.gettrace():
-            time.sleep(0.01)
+        ptvsd.break_into_debugger()
 
+        close = data["SPY"].Close
         if not self.Portfolio.Invested:
             self.SetHoldings("SPY", 1)
 </pre>
@@ -36,10 +44,10 @@
 <p>Finally, attach the debugger to the process:</p>
 <ul style="list-style-type:disc;">
   <li>Run Lean without debugging by clicking on Debug -&gt; Start Without Debugging</li>
-  <li>Open the Attach to Process dialog by clicking on Debug -&gt; Attach to Process...</li>
-  <li>In "Attach to", click on Select button and select "Python code"</li>
-  <li>In "Available processes", select "QuantConnect.Lean.Launcher.exe" and click "Attach" button</li>
+  <li>In Visual Studio, select Debug -&gt; Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.</li>
+  <li>For Connection type, select Python remote (ptvsd)</li>
+  <li>In the Connection target box, select <mark>tcp://localhost:5678/</mark> and click "Attach" button</li>
 </ul>
 
-<p>Once you attach the process the code execution will stop on the breakpoint placed on the SetHoldings statement.</p>
+<p>Once you attach the process the code execution will stop after <mark>ptvsd.break_into_debugger()</mark> statement.</p>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif">
\ No newline at end of file

From 2e369a4bc043a1fdcdb8ba1b8a055bcf00a3c713 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 31 Jan 2019 22:25:00 +0000
Subject: [PATCH 475/753] Automated push from server made by Alexandre Catarino

---
 .../05 Limitations.html                              | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
index 87e460e..36b8529 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
@@ -1,11 +1,3 @@
 <p>Unfortunately, this debugging scenario entails some limitations.</p>
-<p>
-It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).
-</p>
-<p>
-Debugging the same method is not smooth since we need to detach and reattach the process every time we leave the method we are debugging.
-</p><ul style="list-style-type:disc;">
-  <li>Detach Lean by clicking on Debug -&gt; Detach All</li>
-  <li>Reattach Lean by clicking on Debug -&gt; Reattach to Process</li>
-  </ul>
-<p></p>
+<p>It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).</p>
+<p>The algorithm will always stop after <mark>ptvsd.break_into_debugger()</mark> call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks.</p>

From 05c8d17ff472289e9919503b101800c75621759e Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Thu, 31 Jan 2019 14:38:43 -0800
Subject: [PATCH 476/753] Create 02 Prerequisites.html

---
 .../01 Debugging Python in Visual Studio/02 Prerequisites.html   | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html
new file mode 100644
index 0000000..2fabf3d
--- /dev/null
+++ b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html	
@@ -0,0 +1 @@
+<p>prerequisites</p>

From 7aed3743aad8ca6d96f62e27769a0c49b42350f0 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 31 Jan 2019 22:46:47 +0000
Subject: [PATCH 477/753] Automated push from server made by Alexandre Catarino

---
 .../02 Prerequisites.html                                     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html
index 2fabf3d..5f525e7 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html	
@@ -1 +1,3 @@
-<p>prerequisites</p>
+<p>Python Tools for Visual Studio debug (<b><a href="https://github.com/Microsoft/ptvsd">ptvsd</a></b>) server python library. It can be easily installed using <b>pip</b>:</p>
+<div class="section-example-container"><pre class="python">pip install ptvsd</pre></div>
+

From 4afe3a30da9514126f393bc0f1213562eedcfb84 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 1 Feb 2019 17:33:02 +0000
Subject: [PATCH 478/753] Automated push from server made by Alexandre Catarino

---
 03 Open Source/01 Debugging Python in Visual Studio/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/00.html b/03 Open Source/01 Debugging Python in Visual Studio/00.html
index aa2e91f..c2005ec 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/00.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/00.html	
@@ -1 +1 @@
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python-cover.png" />
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python-cover-rev2.png" />
\ No newline at end of file

From 93ba40d9dfc3ffd6676836d8b4e6aedee7006aad Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 1 Feb 2019 17:33:47 +0000
Subject: [PATCH 479/753] Automated push from server made by Alexandre Catarino

---
 .../04 Attaching the Debugger.html                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index b044b14..791250c 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -50,4 +50,4 @@
 </ul>
 
 <p>Once you attach the process the code execution will stop after <mark>ptvsd.break_into_debugger()</mark> statement.</p>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python.gif">
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio-python-rev2.gif">
\ No newline at end of file

From cca4c887e4e276ed85ab1c3b960684de299a3f25 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 1 Feb 2019 17:51:46 +0000
Subject: [PATCH 480/753] Automated push from server made by Alexandre Catarino

---
 .../04 Attaching the Debugger.html                         | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
index 791250c..aaf8b00 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
@@ -2,14 +2,13 @@
 
 <p>First, we will add the <b>ptvsd</b> library, and the following statements:</p>
 
-<div class="section-example-container"><pre class="python">
-import ptvsd
+<div class="section-example-container"><pre class="python">import ptvsd
 ptvsd.enable_attach()
 ptvsd.wait_for_attach()
 </pre></div>
 
 <p>These statements will hold the algorithm execution until Lean is attached to the python debugger.</p>
-<p>The breakpoint is set by the following statement:</p>
+<p>The following statement sets the breakpoint:</p>
 <div class="section-example-container"><pre class="python">ptvsd.break_into_debugger()</pre></div>
 <p>For example, place it in the OnData() method:</p>
 
@@ -18,7 +17,7 @@
 ptvsd.enable_attach()
 print(f'''Python Tool for Visual Studio Debugger {ptvsd.__version__}
 Please attach the python debugger:
-- In Visual Studio, select Debug > Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.
+- In Visual Studio, select Debug &gt; Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.
 - For Connection type, select Python remote (ptvsd)
 - In the Connection target box, select tcp://localhost:5678/ and click "Attach" button''')
 ptvsd.wait_for_attach()

From 8f323899af73af8b1f506fd38e4c27a4784fe4ee Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 1 Feb 2019 17:58:07 +0000
Subject: [PATCH 481/753] Automated push from server made by Alexandre Catarino

---
 .../01 Debugging Python in Visual Studio/05 Limitations.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
index 36b8529..566ac60 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
+++ b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
@@ -1,3 +1,3 @@
 <p>Unfortunately, this debugging scenario entails some limitations.</p>
 <p>It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).</p>
-<p>The algorithm will always stop after <mark>ptvsd.break_into_debugger()</mark> call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks.</p>
+<p>The algorithm will always stop after <mark>ptvsd.break_into_debugger()</mark> call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks. In order to resume the algorithm execution without breakpoints, we need to detach the process (Debug -&gt; Detach All).</p>

From 81019adfd21137caa54b6a2d1f7d74d0ac2177ff Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 14:49:11 -0800
Subject: [PATCH 482/753] Rename 04 Strategy Library/58 VIX Predicts Stock
 Index Returns /01 Introduction.html to 04 Strategy Library/58 VIX Predicts
 Stock Index Returns/01 Introduction.html

---
 .../01 Introduction.html                                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{58 VIX Predicts Stock Index Returns  => 58 VIX Predicts Stock Index Returns}/01 Introduction.html (100%)

diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/58 VIX Predicts Stock Index Returns /01 Introduction.html
rename to 04 Strategy Library/58 VIX Predicts Stock Index Returns/01 Introduction.html

From de26515636d3bfc3534fd51227af26a092b05b08 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 14:51:28 -0800
Subject: [PATCH 483/753] Rename 04 Strategy Library/58 VIX Predicts Stock
 Index Returns /02 Method.html to 04 Strategy Library/58 VIX Predicts Stock
 Index Returns/02 Method.html

---
 .../02 Method.html                                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{58 VIX Predicts Stock Index Returns  => 58 VIX Predicts Stock Index Returns}/02 Method.html (100%)

diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/02 Method.html
similarity index 100%
rename from 04 Strategy Library/58 VIX Predicts Stock Index Returns /02 Method.html
rename to 04 Strategy Library/58 VIX Predicts Stock Index Returns/02 Method.html

From 83d3607c4c308dfa333faa4694ba66215ddc8f0a Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 14:52:17 -0800
Subject: [PATCH 484/753] Rename 04 Strategy Library/58 VIX Predicts Stock
 Index Returns /03 Algorithm.html to 04 Strategy Library/58 VIX Predicts Stock
 Index Returns/03 Algorithm.html

---
 .../03 Algorithm.html                                             | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{58 VIX Predicts Stock Index Returns  => 58 VIX Predicts Stock Index Returns}/03 Algorithm.html (100%)

diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/58 VIX Predicts Stock Index Returns /03 Algorithm.html
rename to 04 Strategy Library/58 VIX Predicts Stock Index Returns/03 Algorithm.html

From 5c48165d3ec7db46cda302a3d4a20b0a3d6d76ac Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 14:53:19 -0800
Subject: [PATCH 485/753] Rename 04 Strategy Library/58 VIX Predicts Stock
 Index Returns /04 Source.html to 04 Strategy Library/58 VIX Predicts Stock
 Index Returns/04 Source.html

---
 .../04 Source.html                                                | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{58 VIX Predicts Stock Index Returns  => 58 VIX Predicts Stock Index Returns}/04 Source.html (100%)

diff --git a/04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html b/04 Strategy Library/58 VIX Predicts Stock Index Returns/04 Source.html
similarity index 100%
rename from 04 Strategy Library/58 VIX Predicts Stock Index Returns /04 Source.html
rename to 04 Strategy Library/58 VIX Predicts Stock Index Returns/04 Source.html

From 9cb7a03fe4298034b2bf640f366b89cf9f0fb175 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 15:09:40 -0800
Subject: [PATCH 486/753] Created meta section

---
 .../01 Creating BootCamp Tutorials}/00.html                       | 0
 .../01 Creating BootCamp Tutorials}/01 Introduction.html          | 0
 .../01 Creating BootCamp Tutorials}/02 BootCamp Structure.html    | 0
 .../01 Creating BootCamp Tutorials}/03 Requirements.html          | 0
 .../04 Step 1%3A Create a Lesson Plan.html                        | 0
 .../05 Step 2%3A Writing a Lesson.html                            | 0
 .../06 Step 3%3A Building BootCamp Tests.html                     | 0
 .../07 Step 4%3A Submitting Lesson for Review.html                | 0
 .../01 Creating BootCamp Tutorials}/08 Summary.html               | 0
 9 files changed, 0 insertions(+), 0 deletions(-)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/00.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/01 Introduction.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/02 BootCamp Structure.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/03 Requirements.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/04 Step 1%3A Create a Lesson Plan.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/05 Step 2%3A Writing a Lesson.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/06 Step 3%3A Building BootCamp Tests.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/07 Step 4%3A Submitting Lesson for Review.html (100%)
 rename {01 API Tutorials/05 Creating BootCamp Tutorials => 08 Meta/01 Creating BootCamp Tutorials}/08 Summary.html (100%)

diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/00.html
rename to 08 Meta/01 Creating BootCamp Tutorials/00.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/01 Introduction.html
rename to 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/02 BootCamp Structure.html
rename to 08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/03 Requirements.html
rename to 08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
rename to 08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
rename to 08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
rename to 08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
rename to 08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
diff --git a/01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html
similarity index 100%
rename from 01 API Tutorials/05 Creating BootCamp Tutorials/08 Summary.html
rename to 08 Meta/01 Creating BootCamp Tutorials/08 Summary.html

From b07f4e86e7ef24608d5ff8be12077ec9ca0a0fc1 Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Sun, 3 Feb 2019 16:02:43 -0800
Subject: [PATCH 487/753] Temp removed until have time to write meta tutorial

---
 .../01 Creating BootCamp Tutorials/00.html    |  1 -
 .../01 Introduction.html                      |  3 ---
 .../02 BootCamp Structure.html                | 20 -------------------
 .../03 Requirements.html                      |  1 -
 .../04 Step 1%3A Create a Lesson Plan.html    |  1 -
 .../05 Step 2%3A Writing a Lesson.html        |  1 -
 .../06 Step 3%3A Building BootCamp Tests.html |  1 -
 ...tep 4%3A Submitting Lesson for Review.html |  1 -
 .../08 Summary.html                           |  1 -
 9 files changed, 30 deletions(-)
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/00.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/08 Summary.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
deleted file mode 100644
index 28f560b..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/00.html	
+++ /dev/null
@@ -1 +0,0 @@
-<img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png" class="img-responsive"/>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
deleted file mode 100644
index 7b5b122..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>
-BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
-</p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
deleted file mode 100644
index b9a4491..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ /dev/null
@@ -1,20 +0,0 @@
-<p>
-BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
-  A <i>Lesson</i> focuses on the implementation of a single algorithm. 
-  A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
-</p>
-
-<ul>
-   <li>Course: Algorithmic Trading with Equities 
-      <ul><li>Lesson: Buy and Hold Algorithm 
-         <ul><li>Task: Setting Cash</il></ul> 
-      </li></ul>
-   </li>
-</ul>
-
-<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task.</p>
-
-<p>
-  We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires 
-  another <i>Course</i> please let us know and we'll create one for you.
-</p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html	
+++ /dev/null
@@ -1 +0,0 @@
-

From 0ca777f3c4e5d9214748857d3c90c4d9e47b0fd0 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web-beta.c.continual-bay-418.internal>
Date: Mon, 4 Feb 2019 20:17:30 +0000
Subject: [PATCH 488/753] Automated push from server made by Jared Broad

---
 03 Open Source/00 Introduction/Introduction.html | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 36de027..d895560 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -38,6 +38,14 @@ <h4><i class="fa fa-line-chart"></i> 103 Lean Report Creator</h4>
   </div>
 </a>
 
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/generating-random-backtest-data">
+  <div class="tutorial-box">    
+    <h4><i class="fa fa-database"></i> 201 Generating Random Data</h4>    
+    <p>Generate random data to use for backtesting with LEAN</p>
+    <i class="fa fa-external-link tutorial-link-icon"></i>
+  </div>
+</a>
+
 <a class="tutorial-link docs-internal-link" href="/tutorials/open-source/brokerage-development-guide">
   <div class="tutorial-box">    
     <h4><i class="fa fa-briefcase"></i> 301 Brokerage Development Guide</h4>    

From 7915a4fb7088fdb177105790df7a49392fa87a0d Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Thu, 21 Feb 2019 14:22:41 -0800
Subject: [PATCH 489/753] Dual Threat strategy updates

---
 .../05 Dual Thrust Trading Algorithm/03 Conclusion.html        | 3 +--
 .../05 Dual Thrust Trading Algorithm/04 Algorithm.html         | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html
index b50deaa..62ab919 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/03 Conclusion.html	
@@ -1,4 +1,3 @@
 <p>
-  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of 0.906 and the drawdown is 25%.  As expected the strategy works well in a trending market. The strategy seemed to detect most of the market turning points and beat the market from 2009 to 2017.
-  This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than k2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
+  We tested this strategy on the S&amp;P 500 ETF SPY from 2004 to 2017. During the backtesting period, SPY resulted in a Sharpe Ratio of -0.37 and the drawdown is 65.7%. This is just a basic implementation and the parameters K1 and K2 are fixed. The strategy can be further extended to Futures and Forex markets. When K1 is smaller than K2, buy signals are prone to trigger and vice versa. To improve the model we can apply technical indicators to judge the trend of price and adjust the value of two parameters dynamically and apply risk control in the position sizing.
 </p>
diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index bb58a27..e2eb1e7 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ffe3fbed009f9502f145e96b81638575.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_24873b7b97dfc314f05e28fac8c9c3fe.html"></iframe>
     </div>
 </div>

From 372d46aa9b5d553a0e9092801cdc583308d97997 Mon Sep 17 00:00:00 2001
From: Jon Blankenship <jon@jonblankenship.com>
Date: Fri, 22 Feb 2019 20:57:44 -0600
Subject: [PATCH 490/753] Fix typos in Rate of Return, Mean and Variance
 tutorial

---
 .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +-
 .../06 Rate of Return, Mean and Variance/05 Summary.html        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 2bcb3c5..9204847 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -41,7 +41,7 @@ <h4>Single-period Return</h4>
 </p>
 <h4>Logarithm Return</h4>
 <p>
-  In the above example, strategy A has 6% return over three months. Nominally, the annual return would be 3*6% = 18%.
+  In the above example, strategy A has 6% return over three months. Nominally, the annual return would be 4*6% = 24%.
   This nominal annual interest rate is called the stated annual interest rate.
   It is calculated as the periodic interest rate times the number of periods per year. It works according to the simple interest and does not take into account the compounding periods, while the effective annual interest rate is 26% as we calculated above and it does account for intra-year compounding.
   The effective annual interest rate is an essential tool that allows the evaluation of the real return on investment.
diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html
index 6c1a074..1cc6f60 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/05 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-  We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those comcepts intensively in our later chapter.
+  We introduced different types of rate of return in this chapter, which could be a little bit tricky when we calculate them. Mean and standard deviation are also very important concepts when we conduct hypothesis test or measure the risk associated with a asset. We will use those concepts intensively in our later chapter.
 </p>

From 407dcec13d15b8801e4f2b52d62acc2b0ca5e304 Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Mon, 25 Feb 2019 09:22:57 -0800
Subject: [PATCH 491/753] Added data key check to handle NoneTypes

---
 .../05 Dual Thrust Trading Algorithm/04 Algorithm.html          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index e2eb1e7..196ca2a 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_24873b7b97dfc314f05e28fac8c9c3fe.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ebe64d611bd2abd527eafd7d834571fa.html"></iframe>
     </div>
 </div>

From 3d28afa20e32502216c3f63faf9acae70a266b0b Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Mon, 25 Feb 2019 09:54:09 -0800
Subject: [PATCH 492/753] Updated data slice check

---
 .../05 Dual Thrust Trading Algorithm/04 Algorithm.html          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html
index 196ca2a..bd1b269 100755
--- a/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
+++ b/04 Strategy Library/05 Dual Thrust Trading Algorithm/04 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ebe64d611bd2abd527eafd7d834571fa.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e407bc1fd64025bb31fa18d0b3c3d8da.html"></iframe>
     </div>
 </div>

From 20497939adfc887d47eaa69dc2ded8342de64022 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 29 Apr 2019 01:11:53 +0000
Subject: [PATCH 493/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index fca9347..62c2cf4 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -3,7 +3,7 @@
 </style>
 
 <p>
-The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports <b>Visual Studio 2015 and 2017</b>.
+The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports <b>Visual Studio 2015, 2017 and 2019</b>.
 </p>
 
 <p>

From f2b96b97c656d306e63e8d7c2f68e59a49ffbedd Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 29 Apr 2019 01:17:17 +0000
Subject: [PATCH 494/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index 62c2cf4..c94ca54 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -8,6 +8,6 @@
 
 <p>
    You can download the plugin here: 
-    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a> *<br/>
+    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_rev1.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a> *<br/>
 <small style="font-size: 0.8em">* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.</small>
 </p>

From 5d8b16944e04c55ab36e77ea5321b5c4b8ed0194 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 29 Apr 2019 13:21:45 +0000
Subject: [PATCH 495/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index c94ca54..8d29485 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -8,6 +8,6 @@
 
 <p>
    You can download the plugin here: 
-    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_rev1.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin.vsix</a> *<br/>
+    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_20190429.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin_20190429.vsix</a> *<br/>
 <small style="font-size: 0.8em">* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.</small>
 </p>

From 8f7a5c034067785b62dc8585dc188c197a5bf6ae Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Tue, 14 May 2019 18:05:22 -0700
Subject: [PATCH 496/753] Codes fixed in tutorial strategy

---
 .../27 Momentum Effect in Commodities Futures/02 Method.html  | 4 ++--
 .../03 Algorithm.html                                         | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html
index a2426ff..9870587 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/02 Method.html	
@@ -14,7 +14,7 @@
 
 class QuandlFutures(PythonQuandl):
     def __init__(self):
-        self.ValueColumnName = "settle"
+        self.ValueColumnName = "Settle"
 </pre>
 </div>
 <p>
@@ -29,7 +29,7 @@
     self.roc[symbol] = RateOfChange(period)
     hist = self.History([symbol], 400, Resolution.Daily).loc[symbol]
     for i in hist.itertuples():
-        self.roc[symbol].Update(i.Index, i.settle)
+        self.roc[symbol].Update(i.Index, i.value)
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html
index a368a10..635c7b2 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_203a6729604c80a71b5c3b2baa2b3f69.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0414f4f614c36b28af3a0883bad4b018.html"></iframe>
     </div>
 </div>

From d90f8e96ef045a4aa2e9d59bc75e18ad1b4156cc Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Wed, 15 May 2019 17:16:25 -0700
Subject: [PATCH 497/753] 118 Time Series Momentum Effect

New write-up for Time Series Momentum Effect (Quantpedia 118: https://quantpedia.com/Screener/Details/118 )
---
 .../01 Introduction.html                      | 11 +++
 .../02 Method.html                            | 82 +++++++++++++++++++
 .../03 Algorithm.html                         |  7 ++
 .../04 Source.html                            |  5 ++
 4 files changed, 105 insertions(+)
 create mode 100644 04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
 create mode 100644 04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/118 Time Series Momentum Effect/04 Source.html

diff --git a/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html
new file mode 100644
index 0000000..bc1c7b6
--- /dev/null
+++ b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html	
@@ -0,0 +1,11 @@
+<p>
+Time series momentum is related to, but different from the phenomenon known as “momentum” in the finance literature, which is primarily cross-sectional in nature.
+The momentum literature focuses on the relative performance of securities in the cross section, finding that securities that recently outperformed their peers over the past 3 to 12 months continue to outperform their peers on average over the next month. 
+Therefore, the usual strategy is to sort the returns of all the assets and choose the top ones to invest.
+Rather than focus on the relative returns of securities in the cross section, time series momentum focuses purely on a security’s own past return.
+Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the contract if it is positive and short if negative. 
+The position size is set to be inversely proportional to the their volatility. 
+A univariate <a href="https://www.investopedia.com/terms/g/generalalizedautogregressiveconditionalheteroskedasticity.asp">GARCH</a> model could be used to estimate volatility. 
+However, other simple models could probably be easily used with good results (for example, the easiest one would be using historical volatility). 
+The portfolio is rebalanced monthly. For the sake of simplicity, we use hitorical volatility to estimate volatility.
+</p>
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
new file mode 100644
index 0000000..888e4a9
--- /dev/null
+++ b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
@@ -0,0 +1,82 @@
+<p>
+  As the strategy needs the continuous futures contract, we import the custom data from Quandl.
+  We create a universe of tradable commodity futures from liquid commodity futures traded on CME and ICE and CBOT, you could add more using Quandl API.
+  They are all liquid and active financial derivatives. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations.
+  The data resolution is daily.
+</p>
+<p>
+  The first step is importing the data.
+</p>
+<div class="section-example-container">
+<pre class="python">from QuantConnect.Python import PythonQuandl
+for symbol in self.symbols_Settle:
+    self.AddData(QuandlFutures, symbol, Resolution.Daily)
+
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "Settle"
+</pre>
+</div>
+<p>
+  Here we use the indicator <code>RateOfChange(period)</code> to simulate the momentum return. Here the period is 12 months.
+  As we are using the custom data, the indicator initialization should use the history request to update the value manually.
+  All indicators are saved in the dictionary <code>self.roc</code>.
+</p>
+<div class="section-example-container">
+<pre class="python">self.roc = {}
+for symbol in self.symbols:
+    self.AddData(QuandlFutures, symbol, Resolution.Daily)
+    self.roc[symbol] = RateOfChange(period)
+    hist = self.History([symbol], 300, Resolution.Daily).loc[symbol]
+    for i in hist.itertuples():
+        self.roc[symbol].Update(i.Index, i.value)
+</pre>
+</div>
+<p>
+  In <code>OnData(self, data)</code>, indicators for all futures contracts are updated every day with the settlement price.
+</p>
+<p>
+  We choose instruments with positive 12-month momentum to long, and instrucments with negative 12-month momentum to short.
+</p>
+<div class="section-example-container">
+    <pre class="python">
+        self.long = [symbol for symbol in self.roc if self.roc[symbol].Current.Value > 0]
+        self.short =  [symbol for symbol in self.roc if self.roc[symbol].Current.Value < 0]
+    </pre>
+</div>
+
+<p>
+    The weights are inversely proportional to the contracts' <a href="https://www.investopedia.com/terms/v/volatility.asp">volatility</a>. As we stated in the introduction, the volatility can be estimated by GARCH model or histrocial volatility. Here, for the sake of simplity, we use historical volatility here. The <code> volatility </code> here is a dictionary containging the reciprocal 252-day historical volatility.
+</p>
+
+<div class="section-example-container">
+    <pre class="python">
+        hist = self.History(self.Symbol(symbol), 252, Resolution.Daily).loc[symbol]['value']
+        log_return = np.log(hist/hist.shift()).dropna().tolist()
+        volatility[symbol] = 1/np.std(log_return,ddof = 1)
+    </pre>
+</div>
+
+
+<p>
+    Then we determine the weights for every contract and then trade.
+
+</p>
+<div class="section-example-container">
+    <pre class="python">
+for long in self.long:
+    weights_long[long] = volatility[long]/sum(volatility.values())
+for short in self.short:
+    weights_short[short] = volatility[short]/sum(volatility.values())
+        
+for long in self.long:
+    self.SetHoldings(long, 0.5*weights_long[long]/sum(weights_long.values()))
+for short in self.short:
+    self.SetHoldings(short, -0.5*weights_short[short]/sum(weights_short.values()))
+    </pre>
+</div>
+
+
+
+
+
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html b/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html
new file mode 100644
index 0000000..f391d83
--- /dev/null
+++ b/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html	
@@ -0,0 +1,7 @@
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" 
+        src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_816f7cef5ec9f6b7917af376d4df6eac.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/04 Source.html b/04 Strategy Library/118 Time Series Momentum Effect/04 Source.html
new file mode 100644
index 0000000..f82cd1e
--- /dev/null
+++ b/04 Strategy Library/118 Time Series Momentum Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/118" rel="nofollow">Quantpedia - Time Series Momentum Effect</a>
+</li>
+</ul>

From 9a8d669171d8985d65f65b969b6c3f6c71828be8 Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Thu, 16 May 2019 18:14:25 -0700
Subject: [PATCH 498/753] Revised on 5/16 after Jack's review

Thanks to Jack for his review!
---
 .../01 Introduction.html                          |  9 ++++-----
 .../02 Method.html                                | 15 +++++++--------
 2 files changed, 11 insertions(+), 13 deletions(-)

diff --git a/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html
index bc1c7b6..b34d06b 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html	
@@ -1,11 +1,10 @@
 <p>
 Time series momentum is related to, but different from the phenomenon known as “momentum” in the finance literature, which is primarily cross-sectional in nature.
-The momentum literature focuses on the relative performance of securities in the cross section, finding that securities that recently outperformed their peers over the past 3 to 12 months continue to outperform their peers on average over the next month. 
-Therefore, the usual strategy is to sort the returns of all the assets and choose the top ones to invest.
-Rather than focus on the relative returns of securities in the cross section, time series momentum focuses purely on a security’s own past return.
+The momentum literature focuses on the relative performance of securities in the cross section, finding that securities that recently outperformed their peers over the past 3 to 12 months continue to do so on average over the next month. 
+Rather than focus on the relative returns of securities in the cross section, this time series momentum strategy focuses purely on the past returns of each individual futures contract.
 Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the contract if it is positive and short if negative. 
-The position size is set to be inversely proportional to the their volatility. 
+The position size is set to be inversely proportional to the <a href="https://www.investopedia.com/terms/v/volatility.asp">volatility</a> of the security's returns.
 A univariate <a href="https://www.investopedia.com/terms/g/generalalizedautogregressiveconditionalheteroskedasticity.asp">GARCH</a> model could be used to estimate volatility. 
 However, other simple models could probably be easily used with good results (for example, the easiest one would be using historical volatility). 
-The portfolio is rebalanced monthly. For the sake of simplicity, we use hitorical volatility to estimate volatility.
+The portfolio is rebalanced monthly and, for the sake of simplicity, we will use historical volatility.
 </p>
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
index 888e4a9..782f5ca 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
@@ -1,8 +1,7 @@
 <p>
   As the strategy needs the continuous futures contract, we import the custom data from Quandl.
-  We create a universe of tradable commodity futures from liquid commodity futures traded on CME and ICE and CBOT, you could add more using Quandl API.
-  They are all liquid and active financial derivatives. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations.
-  The data resolution is daily.
+  We create a universe of highly liquid commodity futures that are traded on CME, ICE and CBOT (more contracts can be added using the Quandl API).
+  We will use Quandl's daily, non-adjusted price data, which is based on spot-month continuous contract calculations.
 </p>
 <p>
   The first step is importing the data.
@@ -18,8 +17,8 @@
 </pre>
 </div>
 <p>
-  Here we use the indicator <code>RateOfChange(period)</code> to simulate the momentum return. Here the period is 12 months.
-  As we are using the custom data, the indicator initialization should use the history request to update the value manually.
+  Here we use a 12-month <code>RateOfChange(period)</code> indicator to simulate the momentum return. Here the period is 12 months.
+  As we are using the custom data, the indicator initialization should use a historical data request and update the value manually.
   All indicators are saved in the dictionary <code>self.roc</code>.
 </p>
 <div class="section-example-container">
@@ -36,7 +35,7 @@
   In <code>OnData(self, data)</code>, indicators for all futures contracts are updated every day with the settlement price.
 </p>
 <p>
-  We choose instruments with positive 12-month momentum to long, and instrucments with negative 12-month momentum to short.
+  We take a long position in instruments with positive 12-month momentum and short positions with negative momentum.
 </p>
 <div class="section-example-container">
     <pre class="python">
@@ -46,7 +45,7 @@
 </div>
 
 <p>
-    The weights are inversely proportional to the contracts' <a href="https://www.investopedia.com/terms/v/volatility.asp">volatility</a>. As we stated in the introduction, the volatility can be estimated by GARCH model or histrocial volatility. Here, for the sake of simplity, we use historical volatility here. The <code> volatility </code> here is a dictionary containging the reciprocal 252-day historical volatility.
+  As we stated in the introduction, a security's volatility can be estimated using a GARCH model or historical volatility. For the sake of simplicity, we use historical volatility. The <code>volatility</code> here is a dictionary containing the reciprocal of 252-day historical volatility.
 </p>
 
 <div class="section-example-container">
@@ -59,7 +58,7 @@
 
 
 <p>
-    Then we determine the weights for every contract and then trade.
+    Then we determine the weights for every contract and place our orders.
 
 </p>
 <div class="section-example-container">

From 9638508f3020c2d8ef7af1095f434a3c02532f2b Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Tue, 21 May 2019 09:07:42 -0700
Subject: [PATCH 499/753] Second changes after Jack's review

Thanks to Jack's second review
---
 .../118 Time Series Momentum Effect/02 Method.html            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
index 782f5ca..d05a031 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
@@ -17,7 +17,7 @@
 </pre>
 </div>
 <p>
-  Here we use a 12-month <code>RateOfChange(period)</code> indicator to simulate the momentum return. Here the period is 12 months.
+  Here we use a 12-month <code>RateOfChange(period)</code> indicator to simulate the momentum return.
   As we are using the custom data, the indicator initialization should use a historical data request and update the value manually.
   All indicators are saved in the dictionary <code>self.roc</code>.
 </p>
@@ -35,7 +35,7 @@
   In <code>OnData(self, data)</code>, indicators for all futures contracts are updated every day with the settlement price.
 </p>
 <p>
-  We take a long position in instruments with positive 12-month momentum and short positions with negative momentum.
+  We take a long position in instruments with positive 12-month momentum and short positions in those with negative momentum.
 </p>
 <div class="section-example-container">
     <pre class="python">

From 9cece26cc02173a8e2d14c36029f10f77f09fe91 Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Wed, 22 May 2019 18:17:23 -0700
Subject: [PATCH 500/753] Fixed URL retrieve bug

Updated strategy to use Download method to retrieve and then parse Dropbox CSV into pandas DataFrame. Changed embedded backtest to reflect this.
---
 .../02 Method.html                                | 15 ++++++++-------
 .../03 Algorithm.html                             |  2 +-
 2 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html
index 5d07400..3463891 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/02 Method.html	
@@ -92,14 +92,15 @@
 <pre class="python">
 # import the futures expiry calendar
 url = "https://www.dropbox.com/s/5k4rbuzfsfn3w0h/expiry.csv?dl=1"
-df_date = pd.read_csv(url, index_col = 'date')
-# convert the index and expiry column to datetime format
+data = self.Download(url).split('\r\n')
+expiry = [x.split(',')[1] for x in data][1:]
+date = [x.split(',')[0] for x in data][1:]
+df_date = pd.DataFrame(expiry, index = date, columns = ['expiry'])
 df_date.index = pd.to_datetime(df_date.index)
-df_date['expiry']=pd.to_datetime(df_date['expiry'])
-idx = pd.date_range('03-16-2011', '04-19-2019')
-# populate the date index
-expiry_date = df_date.reindex(idx, method='bfill')
-df = pd.concat([settle, expiry_date], axis=1, join='inner')
+df_date['expiry'] = pd.to_datetime(df_date['expiry'])
+idx = pd.date_range('01-01-2011', '04-19-2019')
+# populate the date index and backward fill the dataframe    
+return df_date.reindex(idx, method='bfill')
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html
index b702a4f..41e0455 100644
--- a/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
+++ b/04 Strategy Library/198 Exploiting Term Structure of VIX Futures/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_59a110653538ebcbfccacbe237c33091.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_bb547a1b0c188880d3cf351aef0317f1.html"></iframe>
     </div>
 </div>

From 9529f8191d0416a8a0037d9c3cc7c09b9c471db5 Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Fri, 24 May 2019 11:38:44 -0700
Subject: [PATCH 501/753] New write-up: 52-weeks high effect

Thanks Jack Simonson for his help!
---
 .../01 Introduction.html                      | 15 +++
 .../88 52-Weeks High Effect/02 Method.html    | 99 +++++++++++++++++++
 .../88 52-Weeks High Effect/03 Algorithm.html |  7 ++
 .../88 52-Weeks High Effect/04 Source.html    |  5 +
 4 files changed, 126 insertions(+)
 create mode 100644 04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html
 create mode 100644 04 Strategy Library/88 52-Weeks High Effect/02 Method.html
 create mode 100644 04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html
 create mode 100644 04 Strategy Library/88 52-Weeks High Effect/04 Source.html

diff --git a/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html b/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html
new file mode 100644
index 0000000..781cbf2
--- /dev/null
+++ b/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html	
@@ -0,0 +1,15 @@
+<p>
+    The “52-week high effect” states that stocks with prices close to the 52-week highs have better subsequent returns than stocks with prices far from the 52-week highs. 
+    Investors use the 52-week high as an “anchor” which they value stocks against. 
+    When stock prices are near the 52-week high, investors are unwilling to bid the price all the way to the fundamental value. 
+    As a result, investors under-react when stock prices approach the 52-week high, and this creates the 52-week high effect. 
+    
+    
+    This tutorial strategy consists of 500 most liquid stocks which are assigned into 11 industry sectors. 
+    We calculate each stock's ratio between its current price and 52-weeks high, and get the weighted ratios for each industry
+    sector (the weights are stocks' market capitalizations). Then we pick the industry with highest(lowest) weighted ratio to long(short). The investment weights are equal within the industry sector, 
+    and rebalance happens every month. There are two key points in this strategy: <br>
+    1.To initialize a rolling window to track each stock's history prices so that we have access to its 52-weeks high;<br>
+    2.To build a dictionary to store each stock's market capitalization as we will use it as weights when calculating weighted ratios for each industry.
+
+</p>
diff --git a/04 Strategy Library/88 52-Weeks High Effect/02 Method.html b/04 Strategy Library/88 52-Weeks High Effect/02 Method.html
new file mode 100644
index 0000000..75b08ad
--- /dev/null
+++ b/04 Strategy Library/88 52-Weeks High Effect/02 Method.html	
@@ -0,0 +1,99 @@
+<p>
+  <h4>Initialization of universe </h4>
+  We use coarse and fundamental selection as our universe selection. 
+  Here, <code>x.AssetClassification.MorningstarSectorCode</code> is used to assign each stock into different sectors. 
+  More available objects for use in QuantConnect Fine Universe filter can be found <a href="https://www.quantconnect.com/docs/data-library/fundamentals">here</a>.
+<p>
+<div class="section-example-container">
+    <pre class="python">
+        self.BasicMaterialsIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.BasicMaterials]
+        self.ConsumerCyclicalIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
+        self.FinancialServicesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices]
+        self.RealEstateIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate]
+        self.ConsumerDefensiveIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
+        self.HealthcareIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare]
+        self.UtilitiesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Utilities]
+        self.CommunicationServicesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.CommunicationServices]
+        self.EnergyIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy]
+        self.IndustrialsIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Industrials]
+        self.TechnologyIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
+        
+    </pre>
+</div>
+<p>
+  <h4>Calculation of market capitalization</h4>
+  In FineSelectionFunction, we calculate the market capitalization for each stock in our universe.
+</p>
+  <div class="section-example-container">
+      <pre class="python">
+        hist = self.History(self.symbols, 252, Resolution.Daily)
+        for x in fineFiltered:
+            if str(x.Symbol) in hist.index.get_level_values(0):   #If the historical data is available
+                self.mktCap[x.Symbol] = float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][-1]
+            else:
+                del self.symbols[self.symbols.index(x.Symbol)]    # Delete if we can't find any historical data
+                for industry in self.industry_list:
+                    if x in industry: del industry[industry.index(x)]
+      </pre>
+  </div>
+
+  <p>
+      <h4>Rolling window of stock prices</h4>
+      We also initialize rolling windows of 252 days for each stock in FineSelectionFunction so that we can use <code>max(self.windows[symbol])</code> to track its 52-weeks high.
+    </p>
+      <div class="section-example-container">
+          <pre class="python">
+            for symbol in self.symbols:
+            if not symbol in self.windows.keys():
+                self.windows[symbol] = RollingWindow[Decimal](252)
+            if str(symbol) in hist.index.get_level_values(0):
+                for historical_close in hist.loc[str(symbol)]['close']:
+                    self.windows[symbol].Add(historical_close)
+                    
+          </pre>
+      </div>
+
+
+      <p>
+          <h4>Order placements and rebalance</h4>
+          To find out the weighted ratios of each industry sector, we need to retrieve the ratios and weights.
+          The weights are each stock's market capitalization we allocated in <code>self.mktCap</code>.
+        </p>
+          <div class="section-example-container">
+              <pre class="python">
+                  weights = np.array([self.mktCap[x.Symbol]/mktCap_sum for x in industry_list[i]])
+              </pre>
+          </div>
+        <p>
+          The ratio is the current prices divided by 52-weeks highs:
+          <div class="section-example-container">
+              <pre class="python">
+                curPrice = np.array([self.windows[x.Symbol][0] for x in (self.industry_list[i])])            
+                fiftyTwoHigh = np.array( [ max(self.windows[x.Symbol]) for x in self.industry_list[i] ] )    
+                ratios = curPrice/fiftyTwoHigh                                                          
+                weights = np.array([self.mktCap[x.Symbol]/mktCap_sum for x in self.industry_list[i]])
+                </pre>
+
+              </div>
+        </p>
+        Add each industry's weighted score into <code>weighted_scores</code> and simply pick the industry with highest(lowest) score to long(short). Within the target industries, the investment weights are equal.
+
+        <p>
+          <div class="section-example-container">
+              <pre class="python">
+                  weighted_scores = []
+                  for i in range(0,len(self.industry_list)):
+                    weighted_scores.append(sum(ratios*weights))
+                  
+                  long_industry = industry_list[np.argmax(weighted_scores)]
+                  short_industry = industry_list[np.argmin(weighted_scores)]
+                  for x in long_industry:
+                      self.SetHoldings(x.Symbol, 0.5/len(long_industry))
+                  for x in short_industry:
+                      self.SetHoldings(x.Symbol, -0.5/len(short_industry))  
+
+                </pre>
+
+              </div>
+        </p>
+
diff --git a/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html b/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html
new file mode 100644
index 0000000..bb95e12
--- /dev/null
+++ b/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html	
@@ -0,0 +1,7 @@
+div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" 
+        src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c762e6ef9fce0ef91288520a97a73f10.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/88 52-Weeks High Effect/04 Source.html b/04 Strategy Library/88 52-Weeks High Effect/04 Source.html
new file mode 100644
index 0000000..263d587
--- /dev/null
+++ b/04 Strategy Library/88 52-Weeks High Effect/04 Source.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/88" rel="nofollow">Quantpedia - 52-Weeks High Effect in Stocks</a>
+</li>
+</ul>

From fd870f03e2a1b87de4b8f8b57ab09a938b42ec9f Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Fri, 24 May 2019 11:40:52 -0700
Subject: [PATCH 502/753] Revert "New write-up: 52-weeks high effect"

This reverts commit 9529f8191d0416a8a0037d9c3cc7c09b9c471db5.
---
 .../01 Introduction.html                      | 15 ---
 .../88 52-Weeks High Effect/02 Method.html    | 99 -------------------
 .../88 52-Weeks High Effect/03 Algorithm.html |  7 --
 .../88 52-Weeks High Effect/04 Source.html    |  5 -
 4 files changed, 126 deletions(-)
 delete mode 100644 04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html
 delete mode 100644 04 Strategy Library/88 52-Weeks High Effect/02 Method.html
 delete mode 100644 04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html
 delete mode 100644 04 Strategy Library/88 52-Weeks High Effect/04 Source.html

diff --git a/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html b/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html
deleted file mode 100644
index 781cbf2..0000000
--- a/04 Strategy Library/88 52-Weeks High Effect/01 Introduction.html	
+++ /dev/null
@@ -1,15 +0,0 @@
-<p>
-    The “52-week high effect” states that stocks with prices close to the 52-week highs have better subsequent returns than stocks with prices far from the 52-week highs. 
-    Investors use the 52-week high as an “anchor” which they value stocks against. 
-    When stock prices are near the 52-week high, investors are unwilling to bid the price all the way to the fundamental value. 
-    As a result, investors under-react when stock prices approach the 52-week high, and this creates the 52-week high effect. 
-    
-    
-    This tutorial strategy consists of 500 most liquid stocks which are assigned into 11 industry sectors. 
-    We calculate each stock's ratio between its current price and 52-weeks high, and get the weighted ratios for each industry
-    sector (the weights are stocks' market capitalizations). Then we pick the industry with highest(lowest) weighted ratio to long(short). The investment weights are equal within the industry sector, 
-    and rebalance happens every month. There are two key points in this strategy: <br>
-    1.To initialize a rolling window to track each stock's history prices so that we have access to its 52-weeks high;<br>
-    2.To build a dictionary to store each stock's market capitalization as we will use it as weights when calculating weighted ratios for each industry.
-
-</p>
diff --git a/04 Strategy Library/88 52-Weeks High Effect/02 Method.html b/04 Strategy Library/88 52-Weeks High Effect/02 Method.html
deleted file mode 100644
index 75b08ad..0000000
--- a/04 Strategy Library/88 52-Weeks High Effect/02 Method.html	
+++ /dev/null
@@ -1,99 +0,0 @@
-<p>
-  <h4>Initialization of universe </h4>
-  We use coarse and fundamental selection as our universe selection. 
-  Here, <code>x.AssetClassification.MorningstarSectorCode</code> is used to assign each stock into different sectors. 
-  More available objects for use in QuantConnect Fine Universe filter can be found <a href="https://www.quantconnect.com/docs/data-library/fundamentals">here</a>.
-<p>
-<div class="section-example-container">
-    <pre class="python">
-        self.BasicMaterialsIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.BasicMaterials]
-        self.ConsumerCyclicalIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerCyclical]
-        self.FinancialServicesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.FinancialServices]
-        self.RealEstateIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.RealEstate]
-        self.ConsumerDefensiveIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.ConsumerDefensive]
-        self.HealthcareIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Healthcare]
-        self.UtilitiesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Utilities]
-        self.CommunicationServicesIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.CommunicationServices]
-        self.EnergyIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy]
-        self.IndustrialsIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Industrials]
-        self.TechnologyIndustry = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
-        
-    </pre>
-</div>
-<p>
-  <h4>Calculation of market capitalization</h4>
-  In FineSelectionFunction, we calculate the market capitalization for each stock in our universe.
-</p>
-  <div class="section-example-container">
-      <pre class="python">
-        hist = self.History(self.symbols, 252, Resolution.Daily)
-        for x in fineFiltered:
-            if str(x.Symbol) in hist.index.get_level_values(0):   #If the historical data is available
-                self.mktCap[x.Symbol] = float(x.EarningReports.BasicAverageShares.ThreeMonths) * hist.loc[str(x.Symbol)]['close'][-1]
-            else:
-                del self.symbols[self.symbols.index(x.Symbol)]    # Delete if we can't find any historical data
-                for industry in self.industry_list:
-                    if x in industry: del industry[industry.index(x)]
-      </pre>
-  </div>
-
-  <p>
-      <h4>Rolling window of stock prices</h4>
-      We also initialize rolling windows of 252 days for each stock in FineSelectionFunction so that we can use <code>max(self.windows[symbol])</code> to track its 52-weeks high.
-    </p>
-      <div class="section-example-container">
-          <pre class="python">
-            for symbol in self.symbols:
-            if not symbol in self.windows.keys():
-                self.windows[symbol] = RollingWindow[Decimal](252)
-            if str(symbol) in hist.index.get_level_values(0):
-                for historical_close in hist.loc[str(symbol)]['close']:
-                    self.windows[symbol].Add(historical_close)
-                    
-          </pre>
-      </div>
-
-
-      <p>
-          <h4>Order placements and rebalance</h4>
-          To find out the weighted ratios of each industry sector, we need to retrieve the ratios and weights.
-          The weights are each stock's market capitalization we allocated in <code>self.mktCap</code>.
-        </p>
-          <div class="section-example-container">
-              <pre class="python">
-                  weights = np.array([self.mktCap[x.Symbol]/mktCap_sum for x in industry_list[i]])
-              </pre>
-          </div>
-        <p>
-          The ratio is the current prices divided by 52-weeks highs:
-          <div class="section-example-container">
-              <pre class="python">
-                curPrice = np.array([self.windows[x.Symbol][0] for x in (self.industry_list[i])])            
-                fiftyTwoHigh = np.array( [ max(self.windows[x.Symbol]) for x in self.industry_list[i] ] )    
-                ratios = curPrice/fiftyTwoHigh                                                          
-                weights = np.array([self.mktCap[x.Symbol]/mktCap_sum for x in self.industry_list[i]])
-                </pre>
-
-              </div>
-        </p>
-        Add each industry's weighted score into <code>weighted_scores</code> and simply pick the industry with highest(lowest) score to long(short). Within the target industries, the investment weights are equal.
-
-        <p>
-          <div class="section-example-container">
-              <pre class="python">
-                  weighted_scores = []
-                  for i in range(0,len(self.industry_list)):
-                    weighted_scores.append(sum(ratios*weights))
-                  
-                  long_industry = industry_list[np.argmax(weighted_scores)]
-                  short_industry = industry_list[np.argmin(weighted_scores)]
-                  for x in long_industry:
-                      self.SetHoldings(x.Symbol, 0.5/len(long_industry))
-                  for x in short_industry:
-                      self.SetHoldings(x.Symbol, -0.5/len(short_industry))  
-
-                </pre>
-
-              </div>
-        </p>
-
diff --git a/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html b/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html
deleted file mode 100644
index bb95e12..0000000
--- a/04 Strategy Library/88 52-Weeks High Effect/03 Algorithm.html	
+++ /dev/null
@@ -1,7 +0,0 @@
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" 
-        src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c762e6ef9fce0ef91288520a97a73f10.html"></iframe>
-    </div>
-</div>
diff --git a/04 Strategy Library/88 52-Weeks High Effect/04 Source.html b/04 Strategy Library/88 52-Weeks High Effect/04 Source.html
deleted file mode 100644
index 263d587..0000000
--- a/04 Strategy Library/88 52-Weeks High Effect/04 Source.html	
+++ /dev/null
@@ -1,5 +0,0 @@
-<ul>
-  <li>
-    <a href="https://quantpedia.com/Screener/Details/88" rel="nofollow">Quantpedia - 52-Weeks High Effect in Stocks</a>
-</li>
-</ul>

From be330c04b5667b49eecac3607a79c00160a73438 Mon Sep 17 00:00:00 2001
From: Xiyuan Liu <xyliurams@gmail.com>
Date: Tue, 28 May 2019 08:04:03 -0700
Subject: [PATCH 503/753] Revised after Alex's reviews

Thank you very much, Alex.
---
 .../01 Introduction.html                      |  4 +-
 .../02 Method.html                            | 61 ++++++-------------
 .../03 Algorithm.html                         |  2 +-
 3 files changed, 23 insertions(+), 44 deletions(-)

diff --git a/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html
index b34d06b..8161ee5 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/01 Introduction.html	
@@ -5,6 +5,6 @@
 Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the contract if it is positive and short if negative. 
 The position size is set to be inversely proportional to the <a href="https://www.investopedia.com/terms/v/volatility.asp">volatility</a> of the security's returns.
 A univariate <a href="https://www.investopedia.com/terms/g/generalalizedautogregressiveconditionalheteroskedasticity.asp">GARCH</a> model could be used to estimate volatility. 
-However, other simple models could probably be easily used with good results (for example, the easiest one would be using historical volatility). 
-The portfolio is rebalanced monthly and, for the sake of simplicity, we will use historical volatility.
+However, other simple models could probably be easily used with good results (for example, the easiest one would be using historical volatility). For the sake of simplicity, we will use historical volatility.
+The portfolio is rebalanced monthly.
 </p>
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
index d05a031..4427513 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
@@ -8,7 +8,7 @@
 </p>
 <div class="section-example-container">
 <pre class="python">from QuantConnect.Python import PythonQuandl
-for symbol in self.symbols_Settle:
+for symbol in self.symbols  :
     self.AddData(QuandlFutures, symbol, Resolution.Daily)
 
 class QuandlFutures(PythonQuandl):
@@ -17,65 +17,44 @@
 </pre>
 </div>
 <p>
-  Here we use a 12-month <code>RateOfChange(period)</code> indicator to simulate the momentum return.
-  As we are using the custom data, the indicator initialization should use a historical data request and update the value manually.
+  Here we use a 12-month <code>RateOfChange(period)</code> indicator to simulate the momentum returns.
   All indicators are saved in the dictionary <code>self.roc</code>.
 </p>
 <div class="section-example-container">
-<pre class="python">self.roc = {}
+<pre class="python">
+self.period = 252
+self.roc = {}
 for symbol in self.symbols:
-    self.AddData(QuandlFutures, symbol, Resolution.Daily)
-    self.roc[symbol] = RateOfChange(period)
-    hist = self.History([symbol], 300, Resolution.Daily).loc[symbol]
-    for i in hist.itertuples():
-        self.roc[symbol].Update(i.Index, i.value)
+  self.roc[symbol] = self.ROC(symbol, self.period)   #Initialize ROC indicator : ROC is short for RateofChange
+
 </pre>
 </div>
-<p>
-  In <code>OnData(self, data)</code>, indicators for all futures contracts are updated every day with the settlement price.
-</p>
-<p>
-  We take a long position in instruments with positive 12-month momentum and short positions in those with negative momentum.
-</p>
-<div class="section-example-container">
-    <pre class="python">
-        self.long = [symbol for symbol in self.roc if self.roc[symbol].Current.Value > 0]
-        self.short =  [symbol for symbol in self.roc if self.roc[symbol].Current.Value < 0]
-    </pre>
-</div>
 
 <p>
-  As we stated in the introduction, a security's volatility can be estimated using a GARCH model or historical volatility. For the sake of simplicity, we use historical volatility. The <code>volatility</code> here is a dictionary containing the reciprocal of 252-day historical volatility.
+  We use hisotry request to obtain historical prices. Here, <code>history</code> is the daily returns, which will be used to calculate volatilities.
 </p>
-
 <div class="section-example-container">
     <pre class="python">
-        hist = self.History(self.Symbol(symbol), 252, Resolution.Daily).loc[symbol]['value']
-        log_return = np.log(hist/hist.shift()).dropna().tolist()
-        volatility[symbol] = 1/np.std(log_return,ddof = 1)
+        history = self.History(self.symbols, self.period, Resolution.Daily)
+        history = history.value.unstack(level=0).pct_change().dropna()
     </pre>
 </div>
 
-
-<p>
-    Then we determine the weights for every contract and place our orders.
-
+<p>Then we calculate the historical volatilities and place orders. 
+  Note that the weights are inversely proportional to volatilities and <code>np.sign</code> determines whether to long or short.
 </p>
+
 <div class="section-example-container">
     <pre class="python">
-for long in self.long:
-    weights_long[long] = volatility[long]/sum(volatility.values())
-for short in self.short:
-    weights_short[short] = volatility[short]/sum(volatility.values())
-        
-for long in self.long:
-    self.SetHoldings(long, 0.5*weights_long[long]/sum(weights_long.values()))
-for short in self.short:
-    self.SetHoldings(short, -0.5*weights_short[short]/sum(weights_short.values()))
+        vol_inv = 1 / history.std(ddof=1)
+        vol_sum = vol_inv.sum()
+        weights = (vol_inv / vol_sum).fillna(0).to_dict()
+        self.Liquidate()
+        for symbol, roc in self.roc.items():
+            percentage = np.sign(roc.Current.Value) * weights[symbol] *.5
+            self.SetHoldings(symbol, percentage)
     </pre>
 </div>
 
 
 
-
-
diff --git a/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html b/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html
index f391d83..61b8d16 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/03 Algorithm.html	
@@ -2,6 +2,6 @@
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" 
-        src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_816f7cef5ec9f6b7917af376d4df6eac.html"></iframe>
+        src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9ba152609837b6af5f3eec0afe66b8aa.html"></iframe>
     </div>
 </div>

From 6fd56d3e926efc8adf422d97b50986c37ba56db5 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 3 Jun 2019 14:01:15 -0700
Subject: [PATCH 504/753] Update 02 Method.html

---
 .../118 Time Series Momentum Effect/02 Method.html              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html
index 4427513..3e2ec91 100644
--- a/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
+++ b/04 Strategy Library/118 Time Series Momentum Effect/02 Method.html	
@@ -31,7 +31,7 @@
 </div>
 
 <p>
-  We use hisotry request to obtain historical prices. Here, <code>history</code> is the daily returns, which will be used to calculate volatilities.
+  We use history request to obtain historical prices. Here, <code>history</code> is the daily returns, which will be used to calculate volatilities.
 </p>
 <div class="section-example-container">
     <pre class="python">

From 91d0ca53db23c5abe06af9864f0fe691d4d8e9ab Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 09:25:25 -0700
Subject: [PATCH 505/753] Revert "Temp removed until have time to write meta
 tutorial"

This reverts commit b07f4e86e7ef24608d5ff8be12077ec9ca0a0fc1.
---
 .../01 Creating BootCamp Tutorials/00.html    |  1 +
 .../01 Introduction.html                      |  3 +++
 .../02 BootCamp Structure.html                | 20 +++++++++++++++++++
 .../03 Requirements.html                      |  1 +
 .../04 Step 1%3A Create a Lesson Plan.html    |  1 +
 .../05 Step 2%3A Writing a Lesson.html        |  1 +
 .../06 Step 3%3A Building BootCamp Tests.html |  1 +
 ...tep 4%3A Submitting Lesson for Review.html |  1 +
 .../08 Summary.html                           |  1 +
 9 files changed, 30 insertions(+)
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/00.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/08 Summary.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
new file mode 100644
index 0000000..28f560b
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/00.html	
@@ -0,0 +1 @@
+<img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png" class="img-responsive"/>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
new file mode 100644
index 0000000..7b5b122
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
+</p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
new file mode 100644
index 0000000..b9a4491
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -0,0 +1,20 @@
+<p>
+BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
+  A <i>Lesson</i> focuses on the implementation of a single algorithm. 
+  A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
+</p>
+
+<ul>
+   <li>Course: Algorithmic Trading with Equities 
+      <ul><li>Lesson: Buy and Hold Algorithm 
+         <ul><li>Task: Setting Cash</il></ul> 
+      </li></ul>
+   </li>
+</ul>
+
+<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task.</p>
+
+<p>
+  We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires 
+  another <i>Course</i> please let us know and we'll create one for you.
+</p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html	
@@ -0,0 +1 @@
+
diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html	
@@ -0,0 +1 @@
+
diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html	
@@ -0,0 +1 @@
+
diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html	
@@ -0,0 +1 @@
+
diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html	
@@ -0,0 +1 @@
+
diff --git a/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html	
@@ -0,0 +1 @@
+

From fee14b1eda6b28c23f96331b23c09f558fe270c7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 17:08:18 +0000
Subject: [PATCH 506/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
index 7b5b122..fd3aa89 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
@@ -1,3 +1,3 @@
 <p>
 BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
-</p>
+</p>
\ No newline at end of file

From fdb5ee9914e1311dc5719b87a34e38e236da6d1b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 17:56:38 +0000
Subject: [PATCH 507/753] Automated push from server made by Jared Broad

---
 .../02 BootCamp Structure.html                       | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
index b9a4491..f08d745 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -1,11 +1,13 @@
 <p>
-BootCamp is divided into <i>Courses</i>, <i>Lessons</i> and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. 
-  A <i>Lesson</i> focuses on the implementation of a single algorithm. 
-  A <i>Task</i> breaks up the algorithm implementing into tiny steps easily implemented, aiming to guide the user through the coding process. 
+BootCamp is divided into <i>Courses</i>, <i>Lessons</i>, and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. A <i>Lesson</i> focuses on the implementation of a single algorithm.   A <i>Task</i> breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design. 
 </p>
+<p>The central guiding principle of BootCamp lessons is they are focused on the implementation of a single algorithmic trading strategy. </p>
+<style>
+ .nested-list li, .nested-list ul { margin-top: 0px !important; margin-bottom: 0px !important; padding-bottom: 0px !important; }
+</style>
 
-<ul>
-   <li>Course: Algorithmic Trading with Equities 
+<ul class="nested-list">
+   <li>Course: Algorithmic Trading with Equities
       <ul><li>Lesson: Buy and Hold Algorithm 
          <ul><li>Task: Setting Cash</il></ul> 
       </li></ul>

From 1044c38b1d8758f80cb58d0c6030750eb9c3aebf Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 18:47:37 +0000
Subject: [PATCH 508/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
index fd3aa89..88f3288 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
@@ -1,3 +1,7 @@
 <p>
-BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. Start January 2019, QuantConnect is opening up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
+BootCamp is an interactive, education system for teaching the community the QuantConnect API, and the structure for building an algorithm. In January 2019, QuantConnect opened up the BootCamp technology to allow contributions of tutorials from the community! This guide seeks to show you how to create a BootCamp tutorial.
+</p>
+
+<p>
+Education and access to the information required to compete is a the cornerstone of QuantConnect's mission for transparency, and the democratization of finance. With your help we can train the community on how to make the best algorithms possible. 
 </p>
\ No newline at end of file

From d88092c08a4e739bc585a7edd265ddc7e9b2140b Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 11:48:08 -0700
Subject: [PATCH 509/753] Update 01 Introduction.html

---
 08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html
index 88f3288..a854c3f 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/01 Introduction.html	
@@ -3,5 +3,5 @@
 </p>
 
 <p>
-Education and access to the information required to compete is a the cornerstone of QuantConnect's mission for transparency, and the democratization of finance. With your help we can train the community on how to make the best algorithms possible. 
-</p>
\ No newline at end of file
+Education and access to the information required to compete is a the cornerstone of QuantConnect's mission for radical openness, and the democratization of finance. With your help we can train the community on how to make the best algorithms possible. 
+</p>

From e77b499b62361d149534eda0d147765c6d65fb9b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 19:55:08 +0000
Subject: [PATCH 510/753] Automated push from server made by Jared Broad

---
 .../03 Requirements.html                             | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
index 8b13789..b0fa32c 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html	
@@ -1 +1,11 @@
-
+<p>
+Ensuring high-quality writing in BootCamp lessons is important to maintain the interest and motivation of the community. The skills required to build a BootCamp lesson cover several areas of expertise:
+</p>
+<ol>
+<li>QuantConnect API: Contributors must have knowledge of the QuantConnect API along with the correct way to implement the desired behavior.</li>
+<li>Programming Polygot: BootCamp algorithms are provided in both C# and Python. The text documentation is written in HTML, and the task validation code is performed in JavaScript. You should be able to code neatly in all four languages.</li>
+<li>Principles of Algorithmic Trading: Domain expertise in finance is important to be able to build thoughtful and educational content.</li>
+</ol> 
+<p>
+We highly recommended working closely with the QuantConnect team as you design and code the BootCamp lessons to get continuous feedback to ensure its deployed.
+</p>
\ No newline at end of file

From b107c36f24651b9d8eb6d3e6da94aa6fc3652532 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 12:55:53 -0700
Subject: [PATCH 511/753] Update and rename 04 Step 1%3A Create a Lesson
 Plan.html to 04 Create a Lesson Plan.html

---
 .../01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html  | 1 +
 .../04 Step 1%3A Create a Lesson Plan.html                       | 1 -
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html
new file mode 100644
index 0000000..31ee882
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html	
@@ -0,0 +1 @@
+<p></p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/04 Step 1%3A Create a Lesson Plan.html	
+++ /dev/null
@@ -1 +0,0 @@
-

From 1d0aa470b7a6bed5aa7ee0e8a2d3ee55ab8aad4e Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 12:56:21 -0700
Subject: [PATCH 512/753] Update and rename 05 Step 2%3A Writing a Lesson.html
 to 05 Writing a Lesson.html

---
 .../05 Step 2%3A Writing a Lesson.html                          | 1 -
 08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html | 2 ++
 2 files changed, 2 insertions(+), 1 deletion(-)
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Step 2%3A Writing a Lesson.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html
new file mode 100644
index 0000000..f5222ee
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html	
@@ -0,0 +1,2 @@
+<p>
+</p>

From fb00840053ff7acaf241b654e5fbd8c91474777e Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 12:59:09 -0700
Subject: [PATCH 513/753] Update and rename 06 Step 3%3A Building BootCamp
 Tests.html to 06 Building BootCamp Tests.html

---
 .../06 Building BootCamp Tests.html                        | 7 +++++++
 .../06 Step 3%3A Building BootCamp Tests.html              | 1 -
 2 files changed, 7 insertions(+), 1 deletion(-)
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html
new file mode 100644
index 0000000..97a2953
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html	
@@ -0,0 +1,7 @@
+<p>
+Validators
+  Partial Class
+  Validating algorithm output
+  
+  Examples of validation
+</p>
diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Step 3%3A Building BootCamp Tests.html	
+++ /dev/null
@@ -1 +0,0 @@
-

From c8b0940c6b7c286aaff1ecc5ae8ea22ef88ce7ad Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:00:29 -0700
Subject: [PATCH 514/753] Update and rename 07 Step 4%3A Submitting Lesson for
 Review.html to 07 Submitting Lesson for Review.html

---
 .../07 Step 4%3A Submitting Lesson for Review.html             | 1 -
 .../07 Submitting Lesson for Review.html                       | 3 +++
 2 files changed, 3 insertions(+), 1 deletion(-)
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/07 Step 4%3A Submitting Lesson for Review.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html
new file mode 100644
index 0000000..b514e77
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html	
@@ -0,0 +1,3 @@
+<p>
+Lesson submission
+</p>

From fca3d8eee85b8e5bd7d3c249a4f245d848cf2204 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:54:28 -0700
Subject: [PATCH 515/753] Update 04 Create a Lesson Plan.html

---
 .../04 Create a Lesson Plan.html                     | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html
index 31ee882..39ab03b 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html	
@@ -1 +1,11 @@
-<p></p>
+<p>
+  Implement algorithm,
+  Simplify as much as possible,
+  break into concepts for implementation,
+  draft bullets for each concept, 
+  split into tasks which can be accomplished with 1-2 lines of code,
+  include snippets of code and documentation needed to achieve the task - similar but not identical to the answer,
+  
+  Task structure,
+  
+</p>

From fd0894d8977c991d9756756b99fb723e9f15b7d7 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:55:49 -0700
Subject: [PATCH 516/753] Update 05 Writing a Lesson.html

---
 .../01 Creating BootCamp Tutorials/05 Writing a Lesson.html  | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html
index f5222ee..658cc88 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html	
@@ -1,2 +1,7 @@
 <p>
+   Task structure,
+  bulleting out the concepts and tasks,
+  translating to HTML and full tasks.
+  
+  
 </p>

From a9e5d45b9ad78745a12e66c80a9d378b86dc76cb Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:58:29 -0700
Subject: [PATCH 517/753] Create 07 Style Guide.html

---
 08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html b/08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html
new file mode 100644
index 0000000..5fc6ae1
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html	
@@ -0,0 +1,7 @@
+<p>
+H4 titles, verbs describing strategy step
+Brief Short sentences,
+Aim for no more than 3 todo's per task
+Hint should give enough information to complete the task.
+technical grammar, present tense. 
+</p>

From 5fb6b3265eb7da0bf39d3cbba0db55570114a675 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:58:47 -0700
Subject: [PATCH 518/753] Rename 07 Submitting Lesson for Review.html to 08
 Submitting Lesson for Review.html

---
 ...esson for Review.html => 08 Submitting Lesson for Review.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{07 Submitting Lesson for Review.html => 08 Submitting Lesson for Review.html} (100%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/08 Submitting Lesson for Review.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/07 Submitting Lesson for Review.html
rename to 08 Meta/01 Creating BootCamp Tutorials/08 Submitting Lesson for Review.html

From 68f4a0cec39fe1d8dea3ceab0110db629294be0d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 13:59:00 -0700
Subject: [PATCH 519/753] Update and rename 08 Summary.html to 09 Summary.html

---
 08 Meta/01 Creating BootCamp Tutorials/08 Summary.html | 1 -
 08 Meta/01 Creating BootCamp Tutorials/09 Summary.html | 3 +++
 2 files changed, 3 insertions(+), 1 deletion(-)
 delete mode 100644 08 Meta/01 Creating BootCamp Tutorials/08 Summary.html
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/09 Summary.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html
deleted file mode 100644
index 8b13789..0000000
--- a/08 Meta/01 Creating BootCamp Tutorials/08 Summary.html	
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/08 Meta/01 Creating BootCamp Tutorials/09 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/09 Summary.html
new file mode 100644
index 0000000..517c4ef
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/09 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+
+</p>

From 2e8394139a3f40c7fd6c89d1da2b8d48fe74490b Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 21:46:21 +0000
Subject: [PATCH 520/753] Automated push from server made by Jared Broad

---
 .../02 BootCamp Structure.html                            | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
index f08d745..75d20d6 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
@@ -14,9 +14,5 @@
    </li>
 </ul>
 
-<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task.</p>
-
-<p>
-  We have built a system for the community to design and submitting BootCamp Lessons and Tasks. If you think your content requires 
-  another <i>Course</i> please let us know and we'll create one for you.
-</p>
+<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task. We have built a system for the community to design and submit BootCamp Lessons and Tasks. If you think your content requires another <i>Course</i> please let us know and we'll create one for you.
+</p>
\ No newline at end of file

From 0cb9053f6eb5e71716d764673ffcd3da3ab74734 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 14:46:58 -0700
Subject: [PATCH 521/753] Rename 03 Requirements.html to 02 Requirements.html

---
 .../{03 Requirements.html => 02 Requirements.html}              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{03 Requirements.html => 02 Requirements.html} (99%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html
similarity index 99%
rename from 08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html
rename to 08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html
index b0fa32c..15ff07b 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/03 Requirements.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html	
@@ -8,4 +8,4 @@
 </ol> 
 <p>
 We highly recommended working closely with the QuantConnect team as you design and code the BootCamp lessons to get continuous feedback to ensure its deployed.
-</p>
\ No newline at end of file
+</p>

From 9cdc5d8dfaf54502e05971377d7cbf959ab14126 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 14:47:08 -0700
Subject: [PATCH 522/753] Rename 02 BootCamp Structure.html to 04 BootCamp
 Structure.html

---
 .../{02 BootCamp Structure.html => 04 BootCamp Structure.html}  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{02 BootCamp Structure.html => 04 BootCamp Structure.html} (99%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html
similarity index 99%
rename from 08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html
rename to 08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html
index 75d20d6..d3b135e 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/02 BootCamp Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html	
@@ -15,4 +15,4 @@
 </ul>
 
 <p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task. We have built a system for the community to design and submit BootCamp Lessons and Tasks. If you think your content requires another <i>Course</i> please let us know and we'll create one for you.
-</p>
\ No newline at end of file
+</p>

From f8c69003d3e11f5a20dc9c25954d890b08980cf7 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 14:47:44 -0700
Subject: [PATCH 523/753] Create 03 Compensation.html

---
 08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html

diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html
new file mode 100644
index 0000000..517c4ef
--- /dev/null
+++ b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html	
@@ -0,0 +1,3 @@
+<p>
+
+</p>

From 818d7df58199f5c28132339d137014c695918b0c Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 14:49:20 -0700
Subject: [PATCH 524/753] Shuffle titles

---
 ...{04 Create a Lesson Plan.html => 05 Create a Lesson Plan.html} | 0
 .../{05 Writing a Lesson.html => 06 Writing a Lesson.html}        | 0
 ...ilding BootCamp Tests.html => 07 Building BootCamp Tests.html} | 0
 .../{07 Style Guide.html => 08 Style Guide.html}                  | 0
 ...esson for Review.html => 09 Submitting Lesson for Review.html} | 0
 .../{09 Summary.html => 10 Summary.html}                          | 0
 6 files changed, 0 insertions(+), 0 deletions(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{04 Create a Lesson Plan.html => 05 Create a Lesson Plan.html} (100%)
 rename 08 Meta/01 Creating BootCamp Tutorials/{05 Writing a Lesson.html => 06 Writing a Lesson.html} (100%)
 rename 08 Meta/01 Creating BootCamp Tutorials/{06 Building BootCamp Tests.html => 07 Building BootCamp Tests.html} (100%)
 rename 08 Meta/01 Creating BootCamp Tutorials/{07 Style Guide.html => 08 Style Guide.html} (100%)
 rename 08 Meta/01 Creating BootCamp Tutorials/{08 Submitting Lesson for Review.html => 09 Submitting Lesson for Review.html} (100%)
 rename 08 Meta/01 Creating BootCamp Tutorials/{09 Summary.html => 10 Summary.html} (100%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/05 Create a Lesson Plan.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/04 Create a Lesson Plan.html
rename to 08 Meta/01 Creating BootCamp Tutorials/05 Create a Lesson Plan.html
diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/05 Writing a Lesson.html
rename to 08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/07 Building BootCamp Tests.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/06 Building BootCamp Tests.html
rename to 08 Meta/01 Creating BootCamp Tutorials/07 Building BootCamp Tests.html
diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html b/08 Meta/01 Creating BootCamp Tutorials/08 Style Guide.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/07 Style Guide.html
rename to 08 Meta/01 Creating BootCamp Tutorials/08 Style Guide.html
diff --git a/08 Meta/01 Creating BootCamp Tutorials/08 Submitting Lesson for Review.html b/08 Meta/01 Creating BootCamp Tutorials/09 Submitting Lesson for Review.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/08 Submitting Lesson for Review.html
rename to 08 Meta/01 Creating BootCamp Tutorials/09 Submitting Lesson for Review.html
diff --git a/08 Meta/01 Creating BootCamp Tutorials/09 Summary.html b/08 Meta/01 Creating BootCamp Tutorials/10 Summary.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/09 Summary.html
rename to 08 Meta/01 Creating BootCamp Tutorials/10 Summary.html

From 12639bf4c8ee1aa08b5b196d862a22448fb1abcd Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 22:03:53 +0000
Subject: [PATCH 525/753] Automated push from server made by Jared Broad

---
 .../03 Compensation.html                      | 38 ++++++++++++++++++-
 1 file changed, 37 insertions(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html
index 517c4ef..fe6f402 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html	
@@ -1,3 +1,39 @@
 <p>
-
+QuantConnect is offering contributing authors compensation for their BootCamp Lesson submissions. Depending on the complexity and topics covered a lesson ranges from $2,000 to $4,000USD. 
 </p>
+
+	<table class="table">
+		<thead>
+			<tr>
+				<th style="width: 30%;">
+					Difficulty
+				</th>
+				<th>
+					Compensation
+				</th>
+				<th>
+					Estimated Effort
+				</th>
+			</tr>
+		</thead>
+		<tbody>
+			<tr>
+				<td><p>Beginner</p></td>
+				<td><p>$2,000USD</p></td>
+				<td><p>3-5 days fulltime work</p></td>
+			</tr>
+			<tr>
+				<td><p>Intermediate</p></td>
+				<td><p>$3,000USD ()</p></td>
+				<td><p>4-6 days fulltime work</p></td>
+			</tr>
+			<tr>
+				<td><p>Advanced</p></td>
+				<td><p>$4,000USD</p></td>
+				<td><p>5-8 days fulltime work</p></td>
+			</tr>
+               </tbody>
+</table>
+<p>
+Before starting a lesson reach out to the QuantConnect team for approval on the lesson concept. We are seeking to provide diverse and complementing content for the community. If you're interested in creating BootCamp lessons but don't have a topic in mind <a href="http://www.quantconnect.com/contact?subject=bootcamp">get in touch with us</a>!
+</p>
\ No newline at end of file

From 94420141d6ccfebb2abf883a7016fe5adc02b267 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 22:04:05 +0000
Subject: [PATCH 526/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html
index fe6f402..4835b4c 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/03 Compensation.html	
@@ -24,7 +24,7 @@
 			</tr>
 			<tr>
 				<td><p>Intermediate</p></td>
-				<td><p>$3,000USD ()</p></td>
+				<td><p>$3,000USD</p></td>
 				<td><p>4-6 days fulltime work</p></td>
 			</tr>
 			<tr>

From a3cf7d88417dac84bb9073d247db2083f487dfc1 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 15:16:49 -0700
Subject: [PATCH 527/753] Rename 05 Create a Lesson Plan.html to 05 Planning
 Your Lesson.html

---
 ...{05 Create a Lesson Plan.html => 05 Planning Your Lesson.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{05 Create a Lesson Plan.html => 05 Planning Your Lesson.html} (100%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Create a Lesson Plan.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/05 Create a Lesson Plan.html
rename to 08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html

From e79b1101fed510181d0d6df60ef44cb1e234fd79 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 22:41:54 +0000
Subject: [PATCH 528/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html                 | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 39ab03b..2864977 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -1,3 +1,19 @@
+<p>
+Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step.
+</p>
+
+<h4>1. Strategy Selection and Implementation</h4>
+<p>
+Every BootCamp lesson is focused on an algorithmic strategy's implementation. The first step to planning a lesson is choosing a strategy which does not overlap with any of the existing BootCamp topics. This can be incrementally more difficult but should introduce new concepts.
+</p>
+
+<p>
+After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
+</p>
+
+<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order, then finally you can make the algorithm stop move by updating the stop price.</p>
+
+
 <p>
   Implement algorithm,
   Simplify as much as possible,

From 40baabf9d3c8727a99fdbc0a62f940f431292232 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 22:55:09 +0000
Subject: [PATCH 529/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html                  | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 2864977..ad0dc1e 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -11,17 +11,4 @@ <h4>1. Strategy Selection and Implementation</h4>
 After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
 </p>
 
-<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order, then finally you can make the algorithm stop move by updating the stop price.</p>
-
-
-<p>
-  Implement algorithm,
-  Simplify as much as possible,
-  break into concepts for implementation,
-  draft bullets for each concept, 
-  split into tasks which can be accomplished with 1-2 lines of code,
-  include snippets of code and documentation needed to achieve the task - similar but not identical to the answer,
-  
-  Task structure,
-  
-</p>
+<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price.</p>
\ No newline at end of file

From 91f1a7f9864015fefb2fbdd2b8ab20a7a7561d24 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 23 Jun 2019 23:06:29 +0000
Subject: [PATCH 530/753] Automated push from server made by Jared Broad

---
 .../04 BootCamp Structure.html                | 34 +++++++++++++++++--
 1 file changed, 32 insertions(+), 2 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html
index d3b135e..f25ea6a 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html	
@@ -1,7 +1,10 @@
 <p>
 BootCamp is divided into <i>Courses</i>, <i>Lessons</i>, and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. A <i>Lesson</i> focuses on the implementation of a single algorithm.   A <i>Task</i> breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design. 
 </p>
-<p>The central guiding principle of BootCamp lessons is they are focused on the implementation of a single algorithmic trading strategy. </p>
+
+
+<h4>Core Principle</h4>
+<p>The central guiding principle of BootCamp lessons is they are focused on the implementation of a single algorithmic trading strategy. At each level of the system, the titles should be directly related to the implementation of the strategy, not the concepts it is covering.</p>
 <style>
  .nested-list li, .nested-list ul { margin-top: 0px !important; margin-bottom: 0px !important; padding-bottom: 0px !important; }
 </style>
@@ -14,5 +17,32 @@
    </li>
 </ul>
 
-<p>A lesson should be comprised of about 6-12 tasks, each building upon the lessons learned from the previous task. We have built a system for the community to design and submit BootCamp Lessons and Tasks. If you think your content requires another <i>Course</i> please let us know and we'll create one for you.
+<p>
+A lesson should be comprised of about 6-12 tasks, where each task builds upon the lessons learned from the previous task. We have built a system for the community to design and submit BootCamp Lessons and Tasks. If you think your content requires another <i>Course</i> please let us know and we'll create one for you.
+</p>
+
+<h4>Course</h4>
+<p>
+A course is a collection of <i>Lessons</i> in a similar difficulty class, relating to a similar topic. The table below shows the <i>Courses</i> QuantConnect is hoping to create over 2019.
+</p>
+	<table class="table">
+		<thead>
+			<tr>
+				<th style="width: 85%;">
+					Course Name
+				</th>
+				<th style="width: 15%;">
+					Difficulty
+				</th> 
+			</tr>
+		</thead>
+		<tbody>
+			<tr>
+<td>
+<p>
+Algorithmic Trading With Equities 101<br/> <em>Implementation </em>
 </p>
+ </td>
+				<td><p>Beginner</p></td> 
+			</tr>
+</table>
\ No newline at end of file

From f6facc164471daddef597dd41b21f2fe54300d04 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 17:01:07 -0700
Subject: [PATCH 531/753] Rename 07 Building BootCamp Tests.html to 07 Building
 Code Validators.html

---
 ...lding BootCamp Tests.html => 07 Building Code Validators.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{07 Building BootCamp Tests.html => 07 Building Code Validators.html} (100%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/07 Building BootCamp Tests.html b/08 Meta/01 Creating BootCamp Tutorials/07 Building Code Validators.html
similarity index 100%
rename from 08 Meta/01 Creating BootCamp Tutorials/07 Building BootCamp Tests.html
rename to 08 Meta/01 Creating BootCamp Tutorials/07 Building Code Validators.html

From 17bfdf1f181ce7a21a6306fb1c2ac74595592a31 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 23 Jun 2019 17:02:21 -0700
Subject: [PATCH 532/753] Rename 04 BootCamp Structure.html to 04 Content
 Structure.html

---
 .../{04 BootCamp Structure.html => 04 Content Structure.html}   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename 08 Meta/01 Creating BootCamp Tutorials/{04 BootCamp Structure.html => 04 Content Structure.html} (99%)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html
similarity index 99%
rename from 08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html
rename to 08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html
index f25ea6a..c6d006b 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/04 BootCamp Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html	
@@ -45,4 +45,4 @@ <h4>Course</h4>
  </td>
 				<td><p>Beginner</p></td> 
 			</tr>
-</table>
\ No newline at end of file
+</table>

From 7d8a97b998614913ff5a13f1545560c280af4162 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 24 Jun 2019 17:20:40 +0000
Subject: [PATCH 533/753] Automated push from server made by Jared Broad

---
 .../04 Content Structure.html                 | 35 ++++++-------------
 1 file changed, 10 insertions(+), 25 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html
index c6d006b..ba95097 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html	
@@ -4,7 +4,7 @@
 
 
 <h4>Core Principle</h4>
-<p>The central guiding principle of BootCamp lessons is they are focused on the implementation of a single algorithmic trading strategy. At each level of the system, the titles should be directly related to the implementation of the strategy, not the concepts it is covering.</p>
+<p>The central guiding principle of BootCamp lessons is they are focused on the implementation of a single algorithmic trading strategy. At each level of the system, the titles should be directly related to the implementation of the strategy, not the concepts it is covering. This is actually quite difficult, but one way to help to get into this mindset is to focus on teaching how to implement the strategy.</p>
 <style>
  .nested-list li, .nested-list ul { margin-top: 0px !important; margin-bottom: 0px !important; padding-bottom: 0px !important; }
 </style>
@@ -12,37 +12,22 @@ <h4>Core Principle</h4>
 <ul class="nested-list">
    <li>Course: Algorithmic Trading with Equities
       <ul><li>Lesson: Buy and Hold Algorithm 
-         <ul><li>Task: Setting Cash</il></ul> 
+         <ul><li>Task: Initializing Your Algorithm -> Set Cash</il></ul> 
       </li></ul>
    </li>
 </ul>
 
+<h4>Course</h4>
 <p>
-A lesson should be comprised of about 6-12 tasks, where each task builds upon the lessons learned from the previous task. We have built a system for the community to design and submit BootCamp Lessons and Tasks. If you think your content requires another <i>Course</i> please let us know and we'll create one for you.
+A course is a collection of <i>Lessons</i> at a similar difficulty level, relating to a similar strategy topic. If you have a Boot Camp submission which requires another <i>Course</i> please let us know and we'll create one for you.
 </p>
 
-<h4>Course</h4>
+<h4>Lessons</h4>
 <p>
-A course is a collection of <i>Lessons</i> in a similar difficulty class, relating to a similar topic. The table below shows the <i>Courses</i> QuantConnect is hoping to create over 2019.
+A Lesson is a collection of tasks aiming to implement a single algorithmic strategy step by step. A lesson should be comprised of about 6-12 tasks, where each task builds upon the lessons learned from the previous task. We have built a system for the community to design and submit Boot Camp lessons and tasks.
 </p>
-	<table class="table">
-		<thead>
-			<tr>
-				<th style="width: 85%;">
-					Course Name
-				</th>
-				<th style="width: 15%;">
-					Difficulty
-				</th> 
-			</tr>
-		</thead>
-		<tbody>
-			<tr>
-<td>
+
+<h4>Tasks</h4>
 <p>
-Algorithmic Trading With Equities 101<br/> <em>Implementation </em>
-</p>
- </td>
-				<td><p>Beginner</p></td> 
-			</tr>
-</table>
+A Task is the atomic unit of the Boot Camp system. It is a single step required to be completed in the pathway to an algorithm. Tasks are grouped with a subheading according to algorithm implementation concept.
+</p>
\ No newline at end of file

From ea854568f0780d9424b362eea5ffcfe08e191b41 Mon Sep 17 00:00:00 2001
From: Daniel <735673488@qq.com>
Date: Thu, 27 Jun 2019 16:31:46 -0700
Subject: [PATCH 534/753] New Strategy

---
 .../01 Introduction.html                      |   7 +
 .../02 Strategy Theory.html                   |   3 +
 .../03 Method.html                            | 124 ++++++++++++++++++
 .../04 Conclusion.html                        |   7 +
 .../05 Algorithm.html                         |  29 ++++
 .../06 References.html                        |  11 ++
 6 files changed, 181 insertions(+)
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html
 create mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html

diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html
new file mode 100644
index 0000000..c180a4a
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html	
@@ -0,0 +1,7 @@
+<p>
+	In this research, we investigate the Seasonality and Market Session effect in the E-Mini S&P 500 futures. A recent article, Market Seasonality Study
+, argues that there is a seasonality effect in the futures market, specifically relating to E-Mini S&P 500 futures. The study argues that it's trading strategy implemented in the period from November to May lends itself to a bull market (Bullish Season), while the trading days between June and October (Bearish Season) hold far less profit. 
+
+	As for Session effect, another article, Seasonality S&P Market Session
+, shows that, after splitting each trading day into 8 basic sessions, the Night Session (18:00 - 5:30) provides a much greater profit for a single contract of the E-mini S&P 500 futures, compared to other sessions, both on Bullish Season and Bearish Season. Additionally, the Afternoon Session (12:15 - 14:00) and the Close Session (14:00 - 15:15) perform well in the Bullish Season too, and so does the Afternoon Session (12:15 - 14:00) in the Bearish Season. Therefore, it is natural to come up with a combined strategy which aggregates the top three profitable bullish and the top two profitable bearish strategies to see if we can maximize the returns.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html
new file mode 100644
index 0000000..c47adc2
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html	
@@ -0,0 +1,3 @@
+<p>
+	The logic of this strategy is to buy at the open of the session and close that trade at the conclusion of the session. In this tutorial, we will show the implementation of the algorithm on QuantConnect, and study the performance of Combined Strategy, and the performance of Night Session Strategy on Bearish Season and Bullish Season. 
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
new file mode 100644
index 0000000..5b56aeb
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
@@ -0,0 +1,124 @@
+<h3>Step 1:  Initialization</h3>
+
+<p>
+	We set up an 8-year long backtest period from 2008-01-01 to 2015-12-31. Then, we choose the E-mini S&P 500 futures, sort them in ascending order according to days to expiration and pick the nearest expiration contract. After that, we set the Bullish Season and the Bearish Season based on months and set the Night Session based on time of a day. We trigger the open and the close of trades with self.Schedule.On() function.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">
+	self.SetStartDate(2008, 1, 1)  # Set Start Date
+    self.SetEndDate(2015, 12, 31) # Set End Date
+    self.SetCash(100000)  # Set Strategy Cash
+        
+    future = self.AddFuture(Futures.Indices.SP500EMini) # S&P E-mini future contracts
+    future.SetFilter(timedelta(0), timedelta(180)) # Filter according to Expiry date
+    
+    # Set benchmark
+    benchmark = self.AddEquity("SPY")
+    self.SetBenchmark(benchmark.Symbol)
+    
+    # Set season
+    # Bullish Season: Nov - May; 
+    # Bearish Season: Jun - Oct
+    
+    # self.bullSeason = {11, 12, 1, 2, 3, 4, 5} # Bullish Season
+    
+    self.bearSeason = {6, 7, 8, 9, 10} # Bearish Season
+    
+    # Overnight trading strategy
+    
+    
+    # Bullish Season: buy at 18:00, sell at 5:30; buy at 13:15, sell at 15:15
+    # Bearlish Season: buy at 18:00, sell at 5:30; buy at 13:15, sell at 14:00
+    
+    
+    # Night: 1800 - 530
+    # Afternoon: 1315 - 1400
+    # Close: 1400 - 1515
+    
+    # Night session
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(18, 0), Action(self.Enter))
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(5, 30), Action(self.Exit))
+    
+    # Afternoon session
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 15), Action(self.Enter))
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.ExitException))
+    
+    # Close session
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.EnterException))
+    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(15, 15), Action(self.Exit))
+
+</pre>
+
+</div>
+
+<h3>Step 2:  Set up the Trading Signals</h3>
+
+<p>
+	For Combined Strategy, in Bullish Season, we buy the contract at 13:15, sell at 15:15 ( Afternoon Session + Close Session) and then buy again at 18:00 and sell it at 5:30 the next day (Night Session); in Bearish Season, we buy the contract at 13:15, sell at 14:00 ( Afternoon Session only) and then buy again at 18:00 and sell it at 5:30 the next day (Night Session). So there are two trades for every trading day. To realize this, we just need to check the month of the trading time and judge when to close the first trade (at 15:15 or at 14:00). 
+</p>
+
+<p>
+	For Night Session Strategy in Bullish Season or Bearish Season, we only buy at 18:00 and sell at 5:30 the next day, so there is only one trade for every trading day. This can also be realized by similar ideas (See code snippet for details).
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">
+    def OnData(self, data):
+        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+            Arguments:
+                data: Slice object keyed by symbol containing the stock data
+        '''
+        # self.Debug(f'Month: {self.Time.date().month}')
+        self.data = data
+        pass
+    
+    def Enter(self):
+        if not self.Portfolio.Invested:
+            
+            for chain in self.data.FutureChains:
+                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
+                
+                # self.Debug(f'Num of Contracts {self.Time}: {len(contracts)}')
+                
+                if len(contracts) == 0:
+                    continue
+                
+                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
+
+                # long
+                self.MarketOrder(front.Symbol, 1)
+    
+    def Exit(self):
+        if self.Portfolio.Invested:
+            # short
+            self.Liquidate()
+            
+    def EnterException(self):
+        isBear = self.Time.date().month in self.bearSeason
+        
+        if (not self.Portfolio.Invested) and isBear:
+            
+            for chain in self.data.FutureChains:
+                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
+
+                if len(contracts) == 0:
+                    continue
+                
+                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
+
+                # long
+                self.MarketOrder(front.Symbol, 1)
+    
+    def ExitException(self):
+        isBear = self.Time.date().month in self.bearSeason
+        
+        if self.Portfolio.Invested and isBear:
+            # short
+            self.Liquidate()	   
+</pre>
+
+</div>
+
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
new file mode 100644
index 0000000..0a8cd20
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
@@ -0,0 +1,7 @@
+<p>
+For Night Session Strategy, both in the Bullish Season and the Bearish Season, the Night Session effect (well trading performance if buy at its open and sell at its end) for E-mini S&P 500 futures exists based on an 8-year long backtest. Particularly, on the bearish Season, we gain a 13%  return only with 5% maximum drawdown. 
+
+For the Combined Strategy, its performance is not good, which is not consistent with the two articles on Seasonality and Market Session Effect. The reason is in the Afternoon Session and Close Session, the return is not as high as expected, and hence the Combined Strategy underperforms compared to the Night Session-only strategy.
+
+For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html
new file mode 100644
index 0000000..10d9687
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html	
@@ -0,0 +1,29 @@
+<p>
+	Backtest for Combined Strategy
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fcc074b7939a0fcb6ba9fc82d0935b06.html"></iframe>
+    </div>
+</div>
+
+<p>
+	Backtest for Night Session Strategy in Bearish Season
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2e2fec3a3fb010d260e342cca7a71f7e.html"></iframe>
+    </div>
+</div>
+
+<p>
+	Backtest for Night Session Strategy in Bullish Season
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b141a4532f76c5d638aa17110763e4b6.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html
new file mode 100644
index 0000000..d1f73bc
--- /dev/null
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html	
@@ -0,0 +1,11 @@
+<ol>
+  <li>
+    <a href="http://easylanguagemastery.com/market-seasonality-study/">Market Seasonality Study
+
+</a>
+  </li>
+  <li>
+    <a href="http://easylanguagemastery.com/seasonality-sp-market-session/">Seasonality S&P Market Session
+</a>
+  </li>
+</ol>

From 28f910b18f7ccbc498d28d02096285ee1e02fdea Mon Sep 17 00:00:00 2001
From: Daniel <735673488@qq.com>
Date: Fri, 28 Jun 2019 10:55:08 -0700
Subject: [PATCH 535/753] Re-edit

Edit the text to be more proper and accurate.
---
 .../01 Introduction.html                      | 23 +++++-
 .../02 Strategy Theory.html                   |  2 +-
 .../03 Method.html                            | 81 ++++++++-----------
 .../04 Conclusion.html                        |  6 +-
 .../06 References.html                        |  7 +-
 5 files changed, 57 insertions(+), 62 deletions(-)

diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html
index c180a4a..ec204bf 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html	
@@ -1,7 +1,22 @@
 <p>
-	In this research, we investigate the Seasonality and Market Session effect in the E-Mini S&P 500 futures. A recent article, Market Seasonality Study
-, argues that there is a seasonality effect in the futures market, specifically relating to E-Mini S&P 500 futures. The study argues that it's trading strategy implemented in the period from November to May lends itself to a bull market (Bullish Season), while the trading days between June and October (Bearish Season) hold far less profit. 
+	In this research, we investigate the Seasonality and Market Session effect in the E-Mini S&P 500 futures. A recent article, Market Seasonality Study, argues that there is a seasonality effect in the S&P 500. The study argues that it's trading strategy implemented in the period from November to May lends itself to a bull market (Bullish Season), while the trading days between June and October (Bearish Season) hold far less profit. 
+</p>
 
-	As for Session effect, another article, Seasonality S&P Market Session
-, shows that, after splitting each trading day into 8 basic sessions, the Night Session (18:00 - 5:30) provides a much greater profit for a single contract of the E-mini S&P 500 futures, compared to other sessions, both on Bullish Season and Bearish Season. Additionally, the Afternoon Session (12:15 - 14:00) and the Close Session (14:00 - 15:15) perform well in the Bullish Season too, and so does the Afternoon Session (12:15 - 14:00) in the Bearish Season. Therefore, it is natural to come up with a combined strategy which aggregates the top three profitable bullish and the top two profitable bearish strategies to see if we can maximize the returns.
+<p>
+	As for Session effect, another article, Seasonality S&P Market Session, argues that a trading day can be broken up into eight distinct sessions:
+</p>
+
+<ul>
+	<li>Pre-Market: Between 05:30 and 08:30</li>
+	<li>Open: Between 08:30 and 09:00</li>
+	<li>Morning: Between 09:00 and 11:30</li>
+	<li>Lunch: Between 11:30 and 13:15</li>
+	<li>Afternoon: Between 13:15 and 14:00</li>
+	<li>Close: Between 14:00 and 15:15</li>
+	<li>Post-Market: Between 15:15 and 18:00</li>
+	<li>Night: Between 18:00 and 05:30</li>
+</ul>
+
+<p>
+	One can exploit these sessions by making a round-trip trade on a single E-Mini S&P500 futures contract by entering at the beginning of a session and exiting at the end of a session. The article argues that, in a Bullish Season (November to May, as defined in the first article), the Night Session (18:00 - 05:30) provides the most significant opportunity for profit, followed by the Afternoon Session and the Close Session. However, in a Bearish Season, only the Night Session and the Afternoon Session proved consistently profitable.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html
index c47adc2..f96c2e3 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html	
@@ -1,3 +1,3 @@
 <p>
-	The logic of this strategy is to buy at the open of the session and close that trade at the conclusion of the session. In this tutorial, we will show the implementation of the algorithm on QuantConnect, and study the performance of Combined Strategy, and the performance of Night Session Strategy on Bearish Season and Bullish Season. 
+	The underlying logic of this strategy is to buy at the open of a session and close our position at the conclusion of the session. Given that there are multiple profitable sessions, it is natural to come up with a combined strategy which aggregates the top three profitable bullish and the top two profitable bearish strategies to see if we can maximize the returns. In this tutorial, we will show the implementation of the algorithm on QuantConnect, and study the performance of Combined Strategy, and the performance of Night Session Strategy on Bearish Season and Bullish Season.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
index 5b56aeb..4b38b43 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
@@ -1,53 +1,40 @@
 <h3>Step 1:  Initialization</h3>
 
 <p>
-	We set up an 8-year long backtest period from 2008-01-01 to 2015-12-31. Then, we choose the E-mini S&P 500 futures, sort them in ascending order according to days to expiration and pick the nearest expiration contract. After that, we set the Bullish Season and the Bearish Season based on months and set the Night Session based on time of a day. We trigger the open and the close of trades with self.Schedule.On() function.
+	We set up an 8-year long backtest period from 2008-01-01 to 2015-12-31. Then, we choose the E-mini S&P 500 futures, sort them in ascending order according to their time to expiration and pick the nearest expiration contract. After that, we set the Bullish Season and the Bearish Season based on months and set the Night Session based on time of a day. We trigger the open and the close of trades with self.Schedule.On() function.
 </p>
 
 <div class="section-example-container">
 
 <pre class="python">
-	self.SetStartDate(2008, 1, 1)  # Set Start Date
-    self.SetEndDate(2015, 12, 31) # Set End Date
-    self.SetCash(100000)  # Set Strategy Cash
-        
-    future = self.AddFuture(Futures.Indices.SP500EMini) # S&P E-mini future contracts
-    future.SetFilter(timedelta(0), timedelta(180)) # Filter according to Expiry date
-    
-    # Set benchmark
-    benchmark = self.AddEquity("SPY")
-    self.SetBenchmark(benchmark.Symbol)
-    
-    # Set season
-    # Bullish Season: Nov - May; 
-    # Bearish Season: Jun - Oct
-    
-    # self.bullSeason = {11, 12, 1, 2, 3, 4, 5} # Bullish Season
-    
-    self.bearSeason = {6, 7, 8, 9, 10} # Bearish Season
-    
-    # Overnight trading strategy
-    
-    
-    # Bullish Season: buy at 18:00, sell at 5:30; buy at 13:15, sell at 15:15
-    # Bearlish Season: buy at 18:00, sell at 5:30; buy at 13:15, sell at 14:00
-    
-    
-    # Night: 1800 - 530
-    # Afternoon: 1315 - 1400
-    # Close: 1400 - 1515
     
-    # Night session
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(18, 0), Action(self.Enter))
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(5, 30), Action(self.Exit))
-    
-    # Afternoon session
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 15), Action(self.Enter))
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.ExitException))
-    
-    # Close session
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.EnterException))
-    self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(15, 15), Action(self.Exit))
+    def Initialize(self):
+        self.SetStartDate(2008, 1, 1)  # Set Start Date
+        self.SetEndDate(2015, 12, 31) # Set End Date
+        self.SetCash(100000)  # Set Strategy Cash
+        
+        future = self.AddFuture(Futures.Indices.SP500EMini) # S&P E-mini future contracts
+        future.SetFilter(timedelta(0), timedelta(180)) # Filter according to Expiry date
+        
+        # Set benchmark
+        benchmark = self.AddEquity("SPY")
+        self.SetBenchmark(benchmark.Symbol)
+        
+        self.bullSeason = {11, 12, 1, 2, 3, 4, 5}
+        
+        self.bearSeason = {6, 7, 8, 9, 10}
+        
+        # Night session
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(18, 0), Action(self.Enter))
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(5, 30), Action(self.Exit))
+        
+        # Afternoon session
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 15), Action(self.Enter))
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.ExitException))
+        
+        # Close session
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.EnterException))
+        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(15, 15), Action(self.Exit))
 
 </pre>
 
@@ -56,11 +43,11 @@ <h3>Step 1:  Initialization</h3>
 <h3>Step 2:  Set up the Trading Signals</h3>
 
 <p>
-	For Combined Strategy, in Bullish Season, we buy the contract at 13:15, sell at 15:15 ( Afternoon Session + Close Session) and then buy again at 18:00 and sell it at 5:30 the next day (Night Session); in Bearish Season, we buy the contract at 13:15, sell at 14:00 ( Afternoon Session only) and then buy again at 18:00 and sell it at 5:30 the next day (Night Session). So there are two trades for every trading day. To realize this, we just need to check the month of the trading time and judge when to close the first trade (at 15:15 or at 14:00). 
+	For Combined Strategy, in Bullish Season, we buy the contract at 13:15, sell at 15:15 (Afternoon Session + Close Session) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session); in Bearish Season, we buy the contract at 13:15, sell at 14:00 (Afternoon Session only) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session). So there are two trades for every trading day. To realize this, we just need to check the month of the trading time and judge when to close the first trade (at 15:15 or at 14:00). 
 </p>
 
 <p>
-	For Night Session Strategy in Bullish Season or Bearish Season, we only buy at 18:00 and sell at 5:30 the next day, so there is only one trade for every trading day. This can also be realized by similar ideas (See code snippet for details).
+	For Night Session Strategy in Bullish Season or Bearish Season, we only buy at 18:00 and sell at 05:30 the next day, so there is only one trade for every trading day. This can also be realized by similar ideas (See code snippet for details).
 </p>
 
 <div class="section-example-container">
@@ -71,9 +58,7 @@ <h3>Step 2:  Set up the Trading Signals</h3>
             Arguments:
                 data: Slice object keyed by symbol containing the stock data
         '''
-        # self.Debug(f'Month: {self.Time.date().month}')
         self.data = data
-        pass
     
     def Enter(self):
         if not self.Portfolio.Invested:
@@ -81,8 +66,6 @@ <h3>Step 2:  Set up the Trading Signals</h3>
             for chain in self.data.FutureChains:
                 contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
                 
-                # self.Debug(f'Num of Contracts {self.Time}: {len(contracts)}')
-                
                 if len(contracts) == 0:
                     continue
                 
@@ -93,7 +76,7 @@ <h3>Step 2:  Set up the Trading Signals</h3>
     
     def Exit(self):
         if self.Portfolio.Invested:
-            # short
+            # liquidate
             self.Liquidate()
             
     def EnterException(self):
@@ -116,7 +99,7 @@ <h3>Step 2:  Set up the Trading Signals</h3>
         isBear = self.Time.date().month in self.bearSeason
         
         if self.Portfolio.Invested and isBear:
-            # short
+            # liquidate
             self.Liquidate()	   
 </pre>
 
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
index 0a8cd20..9399e5e 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
@@ -1,7 +1,7 @@
 <p>
-For Night Session Strategy, both in the Bullish Season and the Bearish Season, the Night Session effect (well trading performance if buy at its open and sell at its end) for E-mini S&P 500 futures exists based on an 8-year long backtest. Particularly, on the bearish Season, we gain a 13%  return only with 5% maximum drawdown. 
+	As can be seen in the backtest results, trading just during the Night Session, in both market seasons, performs well over an extended period. This strong performance is particularly true during a Bearish Season, where we can achieve 13% returns with a small drawdown of only 5%.
 
-For the Combined Strategy, its performance is not good, which is not consistent with the two articles on Seasonality and Market Session Effect. The reason is in the Afternoon Session and Close Session, the return is not as high as expected, and hence the Combined Strategy underperforms compared to the Night Session-only strategy.
+	Despite our initial intuition, the combined strategy performs poorly after running a backtest. Despite trading during the top-performing sessions of both seasons, we don't see nearly the level of performance we would have expected, and it underperforms trading just the Night Session. The likely reason is that the Afternoon Session and Close Session underperform their theoretical results and thus drag down the combined performance.
 
-For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day.
+	For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day and consider implementing other risk-management strategies.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html
index d1f73bc..06b4640 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html	
@@ -1,11 +1,8 @@
 <ol>
   <li>
-    <a href="http://easylanguagemastery.com/market-seasonality-study/">Market Seasonality Study
-
-</a>
+    <a href="http://easylanguagemastery.com/market-seasonality-study/">Market Seasonality Study</a>
   </li>
   <li>
-    <a href="http://easylanguagemastery.com/seasonality-sp-market-session/">Seasonality S&P Market Session
-</a>
+    <a href="http://easylanguagemastery.com/seasonality-sp-market-session/">Seasonality S&P Market Session</a>
   </li>
 </ol>

From ffa46ce044cdb479154362e718a1d39ad15cfe69 Mon Sep 17 00:00:00 2001
From: Daniel <735673488@qq.com>
Date: Fri, 28 Jun 2019 11:18:29 -0700
Subject: [PATCH 536/753] Minor Modification

minor modification
---
 .../03 Method.html                                            | 4 ----
 .../04 Conclusion.html                                        | 2 +-
 2 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
index 4b38b43..defd407 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
@@ -54,10 +54,6 @@ <h3>Step 2:  Set up the Trading Signals</h3>
 
 <pre class="python">
     def OnData(self, data):
-        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
-            Arguments:
-                data: Slice object keyed by symbol containing the stock data
-        '''
         self.data = data
     
     def Enter(self):
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
index 9399e5e..b3499c3 100644
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
+++ b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
@@ -1,7 +1,7 @@
 <p>
 	As can be seen in the backtest results, trading just during the Night Session, in both market seasons, performs well over an extended period. This strong performance is particularly true during a Bearish Season, where we can achieve 13% returns with a small drawdown of only 5%.
 
-	Despite our initial intuition, the combined strategy performs poorly after running a backtest. Despite trading during the top-performing sessions of both seasons, we don't see nearly the level of performance we would have expected, and it underperforms trading just the Night Session. The likely reason is that the Afternoon Session and Close Session underperform their theoretical results and thus drag down the combined performance.
+	Contrary our initial intuition, the combined strategy performs poorly after running a backtest. Despite trading during the top-performing sessions of both seasons, we don't see nearly the level of performance we would have expected, and it underperforms trading just the Night Session. The likely reason is that the Afternoon Session and Close Session underperform their theoretical results and thus drag down the combined performance.
 
 	For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day and consider implementing other risk-management strategies.
 </p>
\ No newline at end of file

From e957fb997f963b5d90fd6730f3be1e52558daab3 Mon Sep 17 00:00:00 2001
From: linkliang <linckyliang@gmail.com>
Date: Fri, 28 Jun 2019 17:39:20 -0700
Subject: [PATCH 537/753] add Skylight documentation

---
 .../08 Skylight/00 What is Skylight.html      |  1 +
 .../08 Skylight/01 Introduction.html          |  6 ++++
 .../08 Skylight/01 Skylight Roadmap.html      |  6 ++++
 .../08 Skylight/02 Installation.html          | 35 +++++++++++++++++++
 03 Open Source/08 Skylight/03 Usage.html      |  3 ++
 5 files changed, 51 insertions(+)
 create mode 100644 03 Open Source/08 Skylight/00 What is Skylight.html
 create mode 100644 03 Open Source/08 Skylight/01 Introduction.html
 create mode 100644 03 Open Source/08 Skylight/01 Skylight Roadmap.html
 create mode 100644 03 Open Source/08 Skylight/02 Installation.html
 create mode 100644 03 Open Source/08 Skylight/03 Usage.html

diff --git a/03 Open Source/08 Skylight/00 What is Skylight.html b/03 Open Source/08 Skylight/00 What is Skylight.html
new file mode 100644
index 0000000..b5ab664
--- /dev/null
+++ b/03 Open Source/08 Skylight/00 What is Skylight.html	
@@ -0,0 +1 @@
+<p>Skylight is a desktop application to enhance the user experience of LEAN. It is built on Electron and runs as an individual application. We are continually implementing new features to bring those cool features we are offering on QuantConnect.com to your desktop.</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/01 Introduction.html b/03 Open Source/08 Skylight/01 Introduction.html
new file mode 100644
index 0000000..ed42d1d
--- /dev/null
+++ b/03 Open Source/08 Skylight/01 Introduction.html	
@@ -0,0 +1,6 @@
+<h4>V0.1: File Synchronize</h4>
+<p>
+After logging into your account, Skylight synchronizes projects between the cloud and your local machine every minute. Every modification on either end, including creating, modifying, and deleting would sync automatically to the other end. Skylight is intended to be the foundation step for QuantConnect to deploy additional features such as desktop charting and remote algorithm monitoring. 
+
+Skylight updates automatically, checking our servers for the latest features and fixes. Once it finds a new update, Skylight downloads it silently in the background and installs it after you close the application.
+</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/01 Skylight Roadmap.html b/03 Open Source/08 Skylight/01 Skylight Roadmap.html
new file mode 100644
index 0000000..6b3606c
--- /dev/null
+++ b/03 Open Source/08 Skylight/01 Skylight Roadmap.html	
@@ -0,0 +1,6 @@
+<h4>Skylight V0.1: File Synchronize</h4>
+<p>
+After logging into your account, Skylight synchronizes projects between the cloud and your local machine every minute. Every modification on either end, including creating, modifying, and deleting would sync automatically to the other end. Skylight is intended to be the foundation for QuantConnect to deploy additional features such as desktop charting and remote algorithm monitoring. 
+
+Skylight updates automatically, checking our servers for the latest features and fixes. Once it finds a new update, Skylight downloads it silently in the background and installs it after you close the application.
+</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/02 Installation.html b/03 Open Source/08 Skylight/02 Installation.html
new file mode 100644
index 0000000..563fd0c
--- /dev/null
+++ b/03 Open Source/08 Skylight/02 Installation.html	
@@ -0,0 +1,35 @@
+
+<h4>Windows</h4>
+<ol>
+    <li>Download windows installer</li>
+    <li>Double-click on the downloaded file</li>
+</ol>
+<p>
+Skylight should install and launch automatically. To open Skylight when it’s not running, you may double-click the Skylight icon on your desktop.
+</p>
+
+<h4>MacOS</h4>
+<ol>
+    <li>Download mac installer</li>
+    <li>Double click on the downloaded file</li>
+    <li>Drag and drop Skylight over to your Applications folder</li>
+</ol>
+<p>
+To open Skylight, you may double click Skylight icon in your Applications folder, or click the Skylight icon in your Launchpad.
+</p>
+
+<p>
+Because Skylight launches at user login by default, a prompt of “Skylight wants access to control System Events” is expected on macOS 10.14 or newer. You can change this setting later in the Skylight preference settings.
+</p>
+
+<h4>Linux</h4>
+<ol>
+    <li>Download Skylight AppImage</li>
+    <li>Right click on the downloaded AppImage file</li>
+    <li>Select “Properties” in the pop-up menu</li>
+    <li>In “Permissions” tab, check “Allow executing file as program”</li>
+    <li>Double click on the AppImage file and then click “OK”</li>
+</ol>
+<p>
+The process of making the AppImage file executable is required only for the first launch. After that, you could open Skylight by simply double click on Skylight.AppImage.
+</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/03 Usage.html b/03 Open Source/08 Skylight/03 Usage.html
new file mode 100644
index 0000000..e86fa6e
--- /dev/null
+++ b/03 Open Source/08 Skylight/03 Usage.html	
@@ -0,0 +1,3 @@
+<p>
+When Skylight is running, you will find the Skylight tray icon in the system tray. Right click on that, and you may log in/log out and change preference settings.
+</p>
\ No newline at end of file

From 1fc73c8359736c844062a093927eb5d6fdd3dcc3 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Mon, 1 Jul 2019 13:22:56 -0700
Subject: [PATCH 538/753] Revert "add Skylight documentation"

---
 .../08 Skylight/00 What is Skylight.html      |  1 -
 .../08 Skylight/01 Introduction.html          |  6 ----
 .../08 Skylight/01 Skylight Roadmap.html      |  6 ----
 .../08 Skylight/02 Installation.html          | 35 -------------------
 03 Open Source/08 Skylight/03 Usage.html      |  3 --
 5 files changed, 51 deletions(-)
 delete mode 100644 03 Open Source/08 Skylight/00 What is Skylight.html
 delete mode 100644 03 Open Source/08 Skylight/01 Introduction.html
 delete mode 100644 03 Open Source/08 Skylight/01 Skylight Roadmap.html
 delete mode 100644 03 Open Source/08 Skylight/02 Installation.html
 delete mode 100644 03 Open Source/08 Skylight/03 Usage.html

diff --git a/03 Open Source/08 Skylight/00 What is Skylight.html b/03 Open Source/08 Skylight/00 What is Skylight.html
deleted file mode 100644
index b5ab664..0000000
--- a/03 Open Source/08 Skylight/00 What is Skylight.html	
+++ /dev/null
@@ -1 +0,0 @@
-<p>Skylight is a desktop application to enhance the user experience of LEAN. It is built on Electron and runs as an individual application. We are continually implementing new features to bring those cool features we are offering on QuantConnect.com to your desktop.</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/01 Introduction.html b/03 Open Source/08 Skylight/01 Introduction.html
deleted file mode 100644
index ed42d1d..0000000
--- a/03 Open Source/08 Skylight/01 Introduction.html	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h4>V0.1: File Synchronize</h4>
-<p>
-After logging into your account, Skylight synchronizes projects between the cloud and your local machine every minute. Every modification on either end, including creating, modifying, and deleting would sync automatically to the other end. Skylight is intended to be the foundation step for QuantConnect to deploy additional features such as desktop charting and remote algorithm monitoring. 
-
-Skylight updates automatically, checking our servers for the latest features and fixes. Once it finds a new update, Skylight downloads it silently in the background and installs it after you close the application.
-</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/01 Skylight Roadmap.html b/03 Open Source/08 Skylight/01 Skylight Roadmap.html
deleted file mode 100644
index 6b3606c..0000000
--- a/03 Open Source/08 Skylight/01 Skylight Roadmap.html	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h4>Skylight V0.1: File Synchronize</h4>
-<p>
-After logging into your account, Skylight synchronizes projects between the cloud and your local machine every minute. Every modification on either end, including creating, modifying, and deleting would sync automatically to the other end. Skylight is intended to be the foundation for QuantConnect to deploy additional features such as desktop charting and remote algorithm monitoring. 
-
-Skylight updates automatically, checking our servers for the latest features and fixes. Once it finds a new update, Skylight downloads it silently in the background and installs it after you close the application.
-</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/02 Installation.html b/03 Open Source/08 Skylight/02 Installation.html
deleted file mode 100644
index 563fd0c..0000000
--- a/03 Open Source/08 Skylight/02 Installation.html	
+++ /dev/null
@@ -1,35 +0,0 @@
-
-<h4>Windows</h4>
-<ol>
-    <li>Download windows installer</li>
-    <li>Double-click on the downloaded file</li>
-</ol>
-<p>
-Skylight should install and launch automatically. To open Skylight when it’s not running, you may double-click the Skylight icon on your desktop.
-</p>
-
-<h4>MacOS</h4>
-<ol>
-    <li>Download mac installer</li>
-    <li>Double click on the downloaded file</li>
-    <li>Drag and drop Skylight over to your Applications folder</li>
-</ol>
-<p>
-To open Skylight, you may double click Skylight icon in your Applications folder, or click the Skylight icon in your Launchpad.
-</p>
-
-<p>
-Because Skylight launches at user login by default, a prompt of “Skylight wants access to control System Events” is expected on macOS 10.14 or newer. You can change this setting later in the Skylight preference settings.
-</p>
-
-<h4>Linux</h4>
-<ol>
-    <li>Download Skylight AppImage</li>
-    <li>Right click on the downloaded AppImage file</li>
-    <li>Select “Properties” in the pop-up menu</li>
-    <li>In “Permissions” tab, check “Allow executing file as program”</li>
-    <li>Double click on the AppImage file and then click “OK”</li>
-</ol>
-<p>
-The process of making the AppImage file executable is required only for the first launch. After that, you could open Skylight by simply double click on Skylight.AppImage.
-</p>
\ No newline at end of file
diff --git a/03 Open Source/08 Skylight/03 Usage.html b/03 Open Source/08 Skylight/03 Usage.html
deleted file mode 100644
index e86fa6e..0000000
--- a/03 Open Source/08 Skylight/03 Usage.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>
-When Skylight is running, you will find the Skylight tray icon in the system tray. Right click on that, and you may log in/log out and change preference settings.
-</p>
\ No newline at end of file

From 06fe2750b216389e5fa9c167f8480ffb5ad4f14d Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Wed, 3 Jul 2019 15:45:38 -0700
Subject: [PATCH 539/753] Delete Strategy per Request

---
 .../01 Introduction.html                      |  22 ----
 .../02 Strategy Theory.html                   |   3 -
 .../03 Method.html                            | 103 ------------------
 .../04 Conclusion.html                        |   7 --
 .../05 Algorithm.html                         |  29 -----
 .../06 References.html                        |   8 --
 6 files changed, 172 deletions(-)
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html
 delete mode 100644 04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html

diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html
deleted file mode 100644
index ec204bf..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/01 Introduction.html	
+++ /dev/null
@@ -1,22 +0,0 @@
-<p>
-	In this research, we investigate the Seasonality and Market Session effect in the E-Mini S&P 500 futures. A recent article, Market Seasonality Study, argues that there is a seasonality effect in the S&P 500. The study argues that it's trading strategy implemented in the period from November to May lends itself to a bull market (Bullish Season), while the trading days between June and October (Bearish Season) hold far less profit. 
-</p>
-
-<p>
-	As for Session effect, another article, Seasonality S&P Market Session, argues that a trading day can be broken up into eight distinct sessions:
-</p>
-
-<ul>
-	<li>Pre-Market: Between 05:30 and 08:30</li>
-	<li>Open: Between 08:30 and 09:00</li>
-	<li>Morning: Between 09:00 and 11:30</li>
-	<li>Lunch: Between 11:30 and 13:15</li>
-	<li>Afternoon: Between 13:15 and 14:00</li>
-	<li>Close: Between 14:00 and 15:15</li>
-	<li>Post-Market: Between 15:15 and 18:00</li>
-	<li>Night: Between 18:00 and 05:30</li>
-</ul>
-
-<p>
-	One can exploit these sessions by making a round-trip trade on a single E-Mini S&P500 futures contract by entering at the beginning of a session and exiting at the end of a session. The article argues that, in a Bullish Season (November to May, as defined in the first article), the Night Session (18:00 - 05:30) provides the most significant opportunity for profit, followed by the Afternoon Session and the Close Session. However, in a Bearish Season, only the Night Session and the Afternoon Session proved consistently profitable.
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html
deleted file mode 100644
index f96c2e3..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/02 Strategy Theory.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>
-	The underlying logic of this strategy is to buy at the open of a session and close our position at the conclusion of the session. Given that there are multiple profitable sessions, it is natural to come up with a combined strategy which aggregates the top three profitable bullish and the top two profitable bearish strategies to see if we can maximize the returns. In this tutorial, we will show the implementation of the algorithm on QuantConnect, and study the performance of Combined Strategy, and the performance of Night Session Strategy on Bearish Season and Bullish Season.
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html
deleted file mode 100644
index defd407..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/03 Method.html	
+++ /dev/null
@@ -1,103 +0,0 @@
-<h3>Step 1:  Initialization</h3>
-
-<p>
-	We set up an 8-year long backtest period from 2008-01-01 to 2015-12-31. Then, we choose the E-mini S&P 500 futures, sort them in ascending order according to their time to expiration and pick the nearest expiration contract. After that, we set the Bullish Season and the Bearish Season based on months and set the Night Session based on time of a day. We trigger the open and the close of trades with self.Schedule.On() function.
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">
-    
-    def Initialize(self):
-        self.SetStartDate(2008, 1, 1)  # Set Start Date
-        self.SetEndDate(2015, 12, 31) # Set End Date
-        self.SetCash(100000)  # Set Strategy Cash
-        
-        future = self.AddFuture(Futures.Indices.SP500EMini) # S&P E-mini future contracts
-        future.SetFilter(timedelta(0), timedelta(180)) # Filter according to Expiry date
-        
-        # Set benchmark
-        benchmark = self.AddEquity("SPY")
-        self.SetBenchmark(benchmark.Symbol)
-        
-        self.bullSeason = {11, 12, 1, 2, 3, 4, 5}
-        
-        self.bearSeason = {6, 7, 8, 9, 10}
-        
-        # Night session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(18, 0), Action(self.Enter))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(5, 30), Action(self.Exit))
-        
-        # Afternoon session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(13, 15), Action(self.Enter))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.ExitException))
-        
-        # Close session
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(14, 0), Action(self.EnterException))
-        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.At(15, 15), Action(self.Exit))
-
-</pre>
-
-</div>
-
-<h3>Step 2:  Set up the Trading Signals</h3>
-
-<p>
-	For Combined Strategy, in Bullish Season, we buy the contract at 13:15, sell at 15:15 (Afternoon Session + Close Session) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session); in Bearish Season, we buy the contract at 13:15, sell at 14:00 (Afternoon Session only) and then buy again at 18:00 and sell it at 05:30 the next day (Night Session). So there are two trades for every trading day. To realize this, we just need to check the month of the trading time and judge when to close the first trade (at 15:15 or at 14:00). 
-</p>
-
-<p>
-	For Night Session Strategy in Bullish Season or Bearish Season, we only buy at 18:00 and sell at 05:30 the next day, so there is only one trade for every trading day. This can also be realized by similar ideas (See code snippet for details).
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">
-    def OnData(self, data):
-        self.data = data
-    
-    def Enter(self):
-        if not self.Portfolio.Invested:
-            
-            for chain in self.data.FutureChains:
-                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
-                
-                if len(contracts) == 0:
-                    continue
-                
-                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
-
-                # long
-                self.MarketOrder(front.Symbol, 1)
-    
-    def Exit(self):
-        if self.Portfolio.Invested:
-            # liquidate
-            self.Liquidate()
-            
-    def EnterException(self):
-        isBear = self.Time.date().month in self.bearSeason
-        
-        if (not self.Portfolio.Invested) and isBear:
-            
-            for chain in self.data.FutureChains:
-                contracts = list(filter(lambda x: x.Expiry < self.Time + timedelta(90), chain.Value))
-
-                if len(contracts) == 0:
-                    continue
-                
-                front = sorted(contracts, key = lambda x: x.Expiry, reverse = False)[0]
-
-                # long
-                self.MarketOrder(front.Symbol, 1)
-    
-    def ExitException(self):
-        isBear = self.Time.date().month in self.bearSeason
-        
-        if self.Portfolio.Invested and isBear:
-            # liquidate
-            self.Liquidate()	   
-</pre>
-
-</div>
-
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html
deleted file mode 100644
index b3499c3..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/04 Conclusion.html	
+++ /dev/null
@@ -1,7 +0,0 @@
-<p>
-	As can be seen in the backtest results, trading just during the Night Session, in both market seasons, performs well over an extended period. This strong performance is particularly true during a Bearish Season, where we can achieve 13% returns with a small drawdown of only 5%.
-
-	Contrary our initial intuition, the combined strategy performs poorly after running a backtest. Despite trading during the top-performing sessions of both seasons, we don't see nearly the level of performance we would have expected, and it underperforms trading just the Night Session. The likely reason is that the Afternoon Session and Close Session underperform their theoretical results and thus drag down the combined performance.
-
-	For further study, we could consider a more delicate rule for stop loss/profit instead of just trading at a specific time on every trading day and consider implementing other risk-management strategies.
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html
deleted file mode 100644
index 10d9687..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/05 Algorithm.html	
+++ /dev/null
@@ -1,29 +0,0 @@
-<p>
-	Backtest for Combined Strategy
-</p>
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fcc074b7939a0fcb6ba9fc82d0935b06.html"></iframe>
-    </div>
-</div>
-
-<p>
-	Backtest for Night Session Strategy in Bearish Season
-</p>
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2e2fec3a3fb010d260e342cca7a71f7e.html"></iframe>
-    </div>
-</div>
-
-<p>
-	Backtest for Night Session Strategy in Bullish Season
-</p>
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b141a4532f76c5d638aa17110763e4b6.html"></iframe>
-    </div>
-</div>
\ No newline at end of file
diff --git a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html b/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html
deleted file mode 100644
index 06b4640..0000000
--- a/04 Strategy Library/999 Seasonality and Session Effect in Index Futures/06 References.html	
+++ /dev/null
@@ -1,8 +0,0 @@
-<ol>
-  <li>
-    <a href="http://easylanguagemastery.com/market-seasonality-study/">Market Seasonality Study</a>
-  </li>
-  <li>
-    <a href="http://easylanguagemastery.com/seasonality-sp-market-session/">Seasonality S&P Market Session</a>
-  </li>
-</ol>

From 7b0092aa78bcc3dc9095c0d16eaa6c10137e59f1 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 7 Jul 2019 10:38:14 -0700
Subject: [PATCH 540/753] Update 00.html

---
 .../01 Creating BootCamp Tutorials/00.html    | 31 +++++++++++++++++++
 1 file changed, 31 insertions(+)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
index 28f560b..37470cf 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/00.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/00.html	
@@ -1 +1,32 @@
 <img src="https://cdn.quantconnect.com/tutorials/i/bootcamp-header.png" class="img-responsive"/>
+
+<style>
+.work-in-progress {
+    width: 100%;
+    border: 1px solid #f5ae29;
+    border-radius: 5px;
+    padding: 15px;
+    color: #f5ae29;
+}
+.tip {
+    width: 100%;
+    border: 1px solid #f5ae29;
+    border-radius: 5px;
+    padding: 15px;
+    margin-bottom: 20px;
+    margin-top: 20px;
+}
+.tip i {
+    color: #f5ae29;
+}
+.tip-title { 
+ font-weight: bold;
+color: #f5ae29;
+margin-left: 5px;
+margin-right: 5px;
+}
+.tip p { display: inline; } 
+</style>
+<div class="work-in-progress"> 
+    <i class="fa fa-warning"></i> Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress.
+</div>

From 472aee4f24bfbb8ed65493919cb1e778f117aa7d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 7 Jul 2019 10:43:40 -0700
Subject: [PATCH 541/753] Update 00.html

---
 08 Meta/01 Creating BootCamp Tutorials/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
index 37470cf..7d60d9d 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/00.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/00.html	
@@ -28,5 +28,5 @@
 .tip p { display: inline; } 
 </style>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Updated July 14th, 2018: The Brokerage Development Guide is an active work in progress.
+    <i class="fa fa-warning"></i> Updated July 7th, 2019: The create BootCamp tutorial is a work in progress.
 </div>

From 128d01d738e7518ce6b3589a4bbc3ccef409b131 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 7 Jul 2019 10:44:02 -0700
Subject: [PATCH 542/753] Update 00.html

---
 08 Meta/01 Creating BootCamp Tutorials/00.html | 1 +
 1 file changed, 1 insertion(+)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
index 7d60d9d..403498a 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/00.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/00.html	
@@ -27,6 +27,7 @@
 }
 .tip p { display: inline; } 
 </style>
+<br/>
 <div class="work-in-progress"> 
     <i class="fa fa-warning"></i> Updated July 7th, 2019: The create BootCamp tutorial is a work in progress.
 </div>

From a4a4a59daebad163f3e6c79fc3d34881e0763049 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 7 Jul 2019 18:52:54 +0000
Subject: [PATCH 543/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html
index 15ff07b..f7f62c6 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/02 Requirements.html	
@@ -7,5 +7,5 @@
 <li>Principles of Algorithmic Trading: Domain expertise in finance is important to be able to build thoughtful and educational content.</li>
 </ol> 
 <p>
-We highly recommended working closely with the QuantConnect team as you design and code the BootCamp lessons to get continuous feedback to ensure its deployed.
+The combination of all these talents is fairly rare so we're reaching out to the community to help us create this content. If you can cover all three of these categories let us know and we'll enable BootCamp editing permissions on your account. In exchange for a completed BootCamp lesson we're offering compensation which we'll cover in the section below.
 </p>

From 90d3c470cb2285e3ad30364b27ef5e91989def9f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 7 Jul 2019 20:55:52 +0000
Subject: [PATCH 544/753] Automated push from server made by Jared Broad

---
 .../04 Content Structure.html                        | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html
index ba95097..dc4c918 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/04 Content Structure.html	
@@ -1,5 +1,5 @@
 <p>
-BootCamp is divided into <i>Courses</i>, <i>Lessons</i>, and <i>Tasks</i>.  A <i>Course</i> groups together a super-category and is currently organized by asset-class. A <i>Lesson</i> focuses on the implementation of a single algorithm.   A <i>Task</i> breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design. 
+BootCamp is divided into <i>Lessons</i>, and <i>Tasks</i>. A <i>Lesson</i> focuses on the implementation of a single algorithm. A <i>Task</i> breaks up the algorithm implementation into tiny steps which can each be easily coded, with the aim of guiding the user through each layer of the algorithm design. 
 </p>
 
 
@@ -10,17 +10,15 @@ <h4>Core Principle</h4>
 </style>
 
 <ul class="nested-list">
-   <li>Course: Algorithmic Trading with Equities
-      <ul><li>Lesson: Buy and Hold Algorithm 
+   <li>Lesson: Buy and Hold Algorithm 
          <ul><li>Task: Initializing Your Algorithm -> Set Cash</il></ul> 
       </li></ul>
-   </li>
 </ul>
 
-<h4>Course</h4>
+<!--<h4>Course</h4>
 <p>
 A course is a collection of <i>Lessons</i> at a similar difficulty level, relating to a similar strategy topic. If you have a Boot Camp submission which requires another <i>Course</i> please let us know and we'll create one for you.
-</p>
+</p>-->
 
 <h4>Lessons</h4>
 <p>
@@ -29,5 +27,5 @@ <h4>Lessons</h4>
 
 <h4>Tasks</h4>
 <p>
-A Task is the atomic unit of the Boot Camp system. It is a single step required to be completed in the pathway to an algorithm. Tasks are grouped with a subheading according to algorithm implementation concept.
+A Task is the atomic unit of the Boot Camp system. It is a single step required to be completed in the pathway to an algorithm. Tasks are grouped with a subheading according to algorithm implementation concept. A task should aim to implement just few lines of code so that each task can be easily achieved. 
 </p>
\ No newline at end of file

From d9ae9e3c44013438e00d6d5a44ce96a7608b09be Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 7 Jul 2019 21:18:07 +0000
Subject: [PATCH 545/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 29 +++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index ad0dc1e..9790f06 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -2,13 +2,38 @@
 Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step.
 </p>
 
-<h4>1. Strategy Selection and Implementation</h4>
+<h4>1. Strategy Selection</h4>
 <p>
 Every BootCamp lesson is focused on an algorithmic strategy's implementation. The first step to planning a lesson is choosing a strategy which does not overlap with any of the existing BootCamp topics. This can be incrementally more difficult but should introduce new concepts.
 </p>
+<p>
+QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level.
+</p>
+<table class="table">
+<thead>
+<tr>
+<th style="width: 60%;">BootCamp Lesson</th>
+<th style="width: 15%;">Difficulty</th> 
+<th style="width: 15%;">Status</th> 
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><p>Buy and Hold (Equities/Forex) <br/> <small style="font-size: 0.9em">Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.</small></p></td>
+<td><p>Beginner</p></td>
+<td><p>Completed</p></td>
+</tr>
+<tr>
+<td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p></td>
+<td><p>Beginner</p></td>
+<td><p>Assigned</p></td>
+</tr>
+</tbody>
+</table>
 
+<h4>2. Strategy Implementation</h4>
 <p>
 After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
 </p>
 
-<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price.</p>
\ No newline at end of file
+<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.</p>
\ No newline at end of file

From 6005ba5e24bd3b0188f76206d6ee5c908f575d95 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 7 Jul 2019 21:43:14 +0000
Subject: [PATCH 546/753] Automated push from server made by Jared Broad

---
 .../01 Creating BootCamp Tutorials/05 Planning Your Lesson.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 9790f06..5324e9e 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -1,5 +1,5 @@
 <p>
-Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step.
+Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing.
 </p>
 
 <h4>1. Strategy Selection</h4>

From 7b2fd02a9927882853e24cd794fdc7fd8b2147e7 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 7 Jul 2019 23:57:12 +0000
Subject: [PATCH 547/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 42 +++++++++++++++----
 1 file changed, 35 insertions(+), 7 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 5324e9e..0fe3f5c 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -1,5 +1,5 @@
 <p>
-Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing.
+Carefully planning and structuring your lesson is critical to ensuring its success. In the next section, we'll create this plan step by step. Boot Camp lessons typically fall into one of the following algorithm types: <em>Macro Economics, Technical Indicators, Scaling, Market Making, Market Microstructure, Sentimental, or Value-Fundamental Investing.</em>
 </p>
 
 <h4>1. Strategy Selection</h4>
@@ -9,24 +9,52 @@ <h4>1. Strategy Selection</h4>
 <p>
 QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level.
 </p>
+<style>
+.status p { text-align: right; }
+</style>
+
 <table class="table">
 <thead>
 <tr>
-<th style="width: 60%;">BootCamp Lesson</th>
-<th style="width: 15%;">Difficulty</th> 
+<th style="width: 75%;">Beginner BootCamp Lessons</th>
 <th style="width: 15%;">Status</th> 
 </tr>
 </thead>
 <tbody>
+
 <tr>
 <td><p>Buy and Hold (Equities/Forex) <br/> <small style="font-size: 0.9em">Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.</small></p></td>
-<td><p>Beginner</p></td>
-<td><p>Completed</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
+
 <tr>
 <td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p></td>
-<td><p>Beginner</p></td>
-<td><p>Assigned</p></td>
+<td class="status"><p>Assigned</p></td>
+</tr>
+
+<tr>
+<td><p>Momentum-Based Tactical Allocation <br/> <small style="font-size: 0.9em">Using a momentum indicator to shift investment between the S&amp;P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.</small></p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
+</tr>
+
+<tr>
+<td><p>Liquid Universe Selection<br/> <small style="font-size: 0.9em">Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.</small></p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
+</tr>
+
+<tr>
+<td><p>200-50 EMA Momentum Universe<br/> <small style="font-size: 0.9em">Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.</small></p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
+</tr>
+
+<tr>
+<td><p>Fading The Gap<br/> <small style="font-size: 0.9em">Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.</small></p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p></td>
+</tr>
+
+<tr>
+<td><p>Open Range Breakout<br/> <small style="font-size: 0.9em">Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.</small></p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
 </tr>
 </tbody>
 </table>

From f2f24ce5618928b6da32e9a8cc7d9a1c2a401b8c Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 7 Jul 2019 16:58:02 -0700
Subject: [PATCH 548/753] Update 05 Planning Your Lesson.html

---
 .../05 Planning Your Lesson.html                              | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 0fe3f5c..3548fcd 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -17,7 +17,7 @@ <h4>1. Strategy Selection</h4>
 <thead>
 <tr>
 <th style="width: 75%;">Beginner BootCamp Lessons</th>
-<th style="width: 15%;">Status</th> 
+<th style="width: 15%; text-align: right;">Status</th> 
 </tr>
 </thead>
 <tbody>
@@ -64,4 +64,4 @@ <h4>2. Strategy Implementation</h4>
 After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
 </p>
 
-<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.</p>
\ No newline at end of file
+<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.</p>

From a97390cd0b4b860dcccdf9364ba7e4f5042772d6 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 8 Jul 2019 00:56:41 +0000
Subject: [PATCH 549/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 40 +++++++++++++------
 1 file changed, 28 insertions(+), 12 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 3548fcd..5f2ce66 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -11,9 +11,14 @@ <h4>1. Strategy Selection</h4>
 </p>
 <style>
 .status p { text-align: right; }
+.bootcamp-lessons p { margin-bottom: 0px;}
+.badge { background-color: #ccc; margin-right: 5px; }
+.badge::before {
+  content: "\25CF \00A0";
+}
 </style>
 
-<table class="table">
+<table class="table bootcamp-lessons">
 <thead>
 <tr>
 <th style="width: 75%;">Beginner BootCamp Lessons</th>
@@ -23,39 +28,50 @@ <h4>1. Strategy Selection</h4>
 <tbody>
 
 <tr>
-<td><p>Buy and Hold (Equities/Forex) <br/> <small style="font-size: 0.9em">Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.</small></p></td>
+<td><p>Buy and Hold (Equities/Forex) <br/> <small style="font-size: 0.9em">Strategy purchasing assets and holding them for the duration of the algorithm. Seeking to demonstrate how to initialize an algorithm and access price data.</small></p>
+<span class="badge">Accessing Data</span><span class="badge">Initializing Algorithms</span>
+</td>
 <td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
-<td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p></td>
+<td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p>
+<span class="badge">Order Management</span><span class="badge">Basic Charting</span></td>
 <td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
-<td><p>Momentum-Based Tactical Allocation <br/> <small style="font-size: 0.9em">Using a momentum indicator to shift investment between the S&amp;P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.</small></p></td>
+<td><p>Momentum-Based Tactical Allocation <br/> <small style="font-size: 0.9em">Using a momentum indicator to shift investment between the S&amp;P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.</small></p>
+<span class="badge">Multi-Asset Portfolio</span><span class="badge">Using Indicators</span>
+</td>
 <td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>Liquid Universe Selection<br/> <small style="font-size: 0.9em">Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.</small></p></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
+<td><p>Open Range Breakout <br/> <small style="font-size: 0.9em">Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.</small></p>
+<span class="badge">Consolidators</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>200-50 EMA Momentum Universe<br/> <small style="font-size: 0.9em">Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.</small></p></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
+<td><p>Liquid Universe Selection<br/> <small style="font-size: 0.9em">Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.</small></p>
+<span class="badge">Universes</span><span class="badge">Price Volume Filtering</span></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>Fading The Gap<br/> <small style="font-size: 0.9em">Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.</small></p></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p></td>
+<td><p>200-50 EMA Momentum Universe<br/> <small style="font-size: 0.9em">Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.</small></p><span class="badge">Universes</span><span class="badge">SymbolData Pattern</span><span class="badge">History</span></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>Open Range Breakout<br/> <small style="font-size: 0.9em">Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.</small></p></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
+<td><p>Fading The Gap<br/> <small style="font-size: 0.9em">Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.</small></p><span class="badge">Scheduled Events</span><span class="badge">STD Indicator</span><span class="badge">Parameter Minimization</span></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p>
+</td>
 </tr>
+
+
 </tbody>
 </table>
 

From 2dd5d01463b911728fe8c4b78c05aebba20bd983 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 8 Jul 2019 14:45:53 +0000
Subject: [PATCH 550/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 76 ++++++++++++++++++-
 1 file changed, 72 insertions(+), 4 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 5f2ce66..d18140c 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -42,7 +42,9 @@ <h4>1. Strategy Selection</h4>
 
 <tr>
 <td><p>Momentum-Based Tactical Allocation <br/> <small style="font-size: 0.9em">Using a momentum indicator to shift investment between the S&amp;P500 to a Bond ETF. Introducing multiple asset portfolios and the use of an indicator.</small></p>
-<span class="badge">Multi-Asset Portfolio</span><span class="badge">Using Indicators</span>
+<span class="badge">Multi-Asset Portfolio</span>
+<span class="badge">Using Indicators</span>
+<span class="badge">Equities</span>
 </td>
 <td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
 </tr>
@@ -50,31 +52,97 @@ <h4>1. Strategy Selection</h4>
 <tr>
 <td><p>Open Range Breakout <br/> <small style="font-size: 0.9em">Uses consolidators to aggregate the first 20 minutes of a day and trades when the price moves beyond that range. Introduces custom period consolidated price bars.</small></p>
 <span class="badge">Consolidators</span>
+<span class="badge">Equities</span>
 </td>
 <td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
 </tr>
 
 <tr>
 <td><p>Liquid Universe Selection<br/> <small style="font-size: 0.9em">Using a universe selection filter, invest in the top 10 stocks which are liquid and cost more than $10 per share. Introduces universe selection features.</small></p>
-<span class="badge">Universes</span><span class="badge">Price Volume Filtering</span></td>
+<span class="badge">Universes</span>
+<span class="badge">Price Volume Filtering</span>
+<span class="badge">Equities</span>
+</td>
 <td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>200-50 EMA Momentum Universe<br/> <small style="font-size: 0.9em">Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.</small></p><span class="badge">Universes</span><span class="badge">SymbolData Pattern</span><span class="badge">History</span></td>
+<td><p>200-50 EMA Momentum Universe<br/> <small style="font-size: 0.9em">Select assets where the 50-EMA is greater than the 200 EMA. Seeking to introduce creating structures to contain symbol specific data, and using the history API to warm them up.</small></p>
+<span class="badge">Universes</span>
+<span class="badge">SymbolData Pattern</span>
+<span class="badge">History</span>
+<span class="badge">Equities</span></td>
 <td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
 </tr>
 
 <tr>
-<td><p>Fading The Gap<br/> <small style="font-size: 0.9em">Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.</small></p><span class="badge">Scheduled Events</span><span class="badge">STD Indicator</span><span class="badge">Parameter Minimization</span></td>
+<td><p>Fading The Gap<br/> <small style="font-size: 0.9em">Using scheduled events to monitor for overnight price gaps in the market and shorting abnormal activity. Introduces scheduled events, and elimination of a parameter with STD indicator.</small></p>
+<span class="badge">Scheduled Events</span>
+<span class="badge">STD Indicator</span>
+<span class="badge">Parameter Minimization</span>
+<span class="badge">Equities</span></td>
 <td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p>
 </td>
 </tr>
+</tbody>
+</table>
+
+<table class="table bootcamp-lessons">
+<thead>
+<tr>
+<th style="width: 75%;">Intermediate BootCamp Lessons</th>
+<th style="width: 15%; text-align: right;">Status</th> 
+</tr>
+</thead>
+<tbody>
+
+<tr>
+<td><p>Pairs Trading with SMA <br/> <small style="font-size: 0.9em">Simple pairs trading strategy monitoring for divergence in correlation of two hand picked assets. Invests in a market neutral manner, using position sizing to calculate the right holding of each asset.</small></p>
+<span class="badge">Pairs Trading</span>
+<span class="badge">Market Neutrality</span>
+<span class="badge">Position Sizing</span>
+<span class="badge">Equities</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-pairs-trading">Available</a></p>
+</tr>
+
+<tr>
+<td><p>Pairs Trading with Cointegration Test<br/> <small style="font-size: 0.9em">Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test, and </small></p>
+<span class="badge">Pairs Trading</span>
+<span class="badge">Cointegration Test</span>
+<span class="badge">Scheduled Events</span>
+<span class="badge">Equities</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-cointegration">Available</a></p>
+</tr>
+
+<tr>
+<td><p>Liquid Value Stocks<br/> <small style="font-size: 0.9em">Selecting a universe of the 100 most liquid assets and rank by their PE-Ratio to get the best value stocks. Each month buy the 10 best value stocks, and short the worst value stocks.</small></p>
+<span class="badge">Universe Selection</span>
+<span class="badge">Fine Universe Selection</span>
+<span class="badge">Long-Short Hedge</span>
+<span class="badge">Equities</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-longshort-value-universe">Available</a></p>
+</tr>
+
+<tr>
+<td><p>Sector Balanced Universe Selection<br/> <small style="font-size: 0.9em">Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.</small></p>
+<span class="badge">Universe Selection</span>
+<span class="badge">Advanced Universe Selection</span>
+<span class="badge">Sector Exposure</span>
+<span class="badge">Equities</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-sector-exposure">Available</a></p>
+</tr>
+
+
 
 
 </tbody>
 </table>
 
+
 <h4>2. Strategy Implementation</h4>
 <p>
 After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.

From 6ff0132a26037610361944c3e461f0bb804c0595 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 8 Jul 2019 16:06:34 +0000
Subject: [PATCH 551/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 37 +++++++++++++++++--
 1 file changed, 34 insertions(+), 3 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index d18140c..8a20177 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -125,7 +125,6 @@ <h4>1. Strategy Selection</h4>
 </td>
 <td class="status"><p><a href="/contact?subject=bootcamp-longshort-value-universe">Available</a></p>
 </tr>
-
 <tr>
 <td><p>Sector Balanced Universe Selection<br/> <small style="font-size: 0.9em">Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.</small></p>
 <span class="badge">Universe Selection</span>
@@ -135,10 +134,42 @@ <h4>1. Strategy Selection</h4>
 </td>
 <td class="status"><p><a href="/contact?subject=bootcamp-sector-exposure">Available</a></p>
 </tr>
+<tr>
+<td><p>Hedging FX Books with Interest Rate<br/> <small style="font-size: 0.9em">Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.</small></p>
+<span class="badge">Trading Economics</span>
+<span class="badge">Alternative Data</span>
+<span class="badge">Global Macro</span>
+<span class="badge">Forex</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-trading-economics">Available</a></p>
+</tr>
+<tr>
+<td><p>Sentiment Analysis on Stocks<br/> <small style="font-size: 0.9em">Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most postive sentiment.</small></p>
+<span class="badge">Psychsignal</span>
+<span class="badge">NLP</span>
+<span class="badge">Alternative Data</span>
+<span class="badge">Sentiment Analysis</span>
+<span class="badge">Equities</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-trading-economics">Available</a></p>
+</tr>
 
+</tbody>
+</table>
 
-
-
+<table class="table bootcamp-lessons">
+<thead>
+<tr>
+<th style="width: 75%;">Advanced BootCamp Lessons</th>
+<th style="width: 15%; text-align: right;">Status</th> 
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><p>Coming Soon<br/> <small style="font-size: 0.9em"></small></p>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-advanced"></a></p>
+</tr>
 </tbody>
 </table>
 

From 141352655919f75223bb737d2fbe967e1427361d Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 8 Jul 2019 16:24:16 +0000
Subject: [PATCH 552/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html              | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 8a20177..a839fca 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -6,9 +6,18 @@ <h4>1. Strategy Selection</h4>
 <p>
 Every BootCamp lesson is focused on an algorithmic strategy's implementation. The first step to planning a lesson is choosing a strategy which does not overlap with any of the existing BootCamp topics. This can be incrementally more difficult but should introduce new concepts.
 </p>
+
+<h4>2. Strategy Implementation</h4>
+<p>
+After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
+</p>
+
+<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.</p>
+
 <p>
 QuantConnect has worked with the community to create a list of lessons to be created which would be eligible for compensation. The table below describes these strategies and their associated difficulty level.
 </p>
+
 <style>
 .status p { text-align: right; }
 .bootcamp-lessons p { margin-bottom: 0px;}
@@ -171,12 +180,4 @@ <h4>1. Strategy Selection</h4>
 <td class="status"><p><a href="/contact?subject=bootcamp-advanced"></a></p>
 </tr>
 </tbody>
-</table>
-
-
-<h4>2. Strategy Implementation</h4>
-<p>
-After selecting your strategy you need to fully implement the algorithm, writing the code in C# and Python as simply as possible. Users new to coding have a hard time deciphering large blocks of code so strategies should be kept very simple.
-</p>
-
-<p>In writing the strategy remain aware of the conceptual layers you put into the algorithm's codebase. These layers of concepts are where you can separate out the lesson tasks. For example: in writing a lesson <i>"Buy and Hold, with Trailing Stop"</i> you might start by coding up the buy and hold logic, followed by placing a "trailing stop" (Stop Market Order), then finally you can make the stop move by updating its trigger price. These conceptual layers form the basis for how tasks are grouped together.</p>
+</table>
\ No newline at end of file

From 87fce67f92bf72696355c320ce9e2c2cce5010b5 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Thu, 11 Jul 2019 17:19:58 +0000
Subject: [PATCH 553/753] Automated push from server made by Jared Broad

---
 .../02 Backtesting from Visual Studio/01 Introduction.html      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
index 8d29485..4005af9 100644
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
@@ -8,6 +8,6 @@
 
 <p>
    You can download the plugin here: 
-    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_20190429.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin_20190429.vsix</a> *<br/>
+    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_20190711.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin_20190711.vsix</a> *<br/>
 <small style="font-size: 0.8em">* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.</small>
 </p>

From 87e083fdc1feabc4cc32eae183dded7c147054e1 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Fri, 12 Jul 2019 09:21:12 -0700
Subject: [PATCH 554/753] Update PE Anomaly in Strategy Library

---
 .../92 Price Earnings Anomaly/02 Method.html  | 32 +++++++++----------
 .../03 Algorithm.html                         |  2 +-
 .../92 Price Earnings Anomaly/04 Summary.html |  2 +-
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html b/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html
index 91ab301..cd87637 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html	
@@ -9,16 +9,15 @@
 </p>
  
 <div class="section-example-container">
-<pre class="python">
+    <pre class="python">
     def CoarseSelectionFunction(self, coarse):
-        if self.yearly_rebalance:
-            # drop stocks which have no fundamental data and have low price
-            CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
-            sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
-            top = sortedByDollarVolume[:self._NumCoarseStocks]
-            return [i.Symbol for i in top]
-        else: 
-            return [] 
+        if self.Time.year == self.year: return []
+        
+        # drop stocks which have no fundamental data or have low price
+        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
+        sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
+        top = sortedByDollarVolume[:self._NumCoarseStocks]
+        return [i.Symbol for i in top] 
 </pre>
 </div>
  
@@ -27,19 +26,20 @@
 </p>
  
 <div class="section-example-container">
-<pre class="python">
+    <pre class="python">
  
     def FineSelectionFunction(self, fine):
-        if self.yearly_rebalance:
+        if self.Time.year == self.year:
+            self.filtered_fine = []
+        else:
             fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)]
             for i in fine:
                 i.PERatio = float(i.ValuationRatios.PERatio)
-            sortedPERatio = sorted(fine, key=lambda x: x.PERatio,reverse=True)
+            sortedPERatio = sorted(fine, key=lambda x: x.PERatio)
+            
             self.filtered_fine = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
-            self.yearly_rebalance = False
-            return self.filtered_fine
-        else:
-            return []
+            
+        return self.filtered_fine
 </pre>
 </div>
 
diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
index 820b4ff..32fcbdf 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_28c47727b202b88fd00d9b0b52dadedf.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c1c62e5e8782b05cc465c5a503463616.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html b/04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html
index e3fdaad..7140b66 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/04 Summary.html	
@@ -1,5 +1,5 @@
 <p>
-The portfolio significantly outperforms the benchmark, S&P 500, during the two years backtest period, from the beginning of the year 2016 to the end of the year 2017.
+The portfolio significantly outperforms the benchmark, S&P 500, during the three and half years backtest period, from the beginning of the year 2016 to July of the year 2019.
 </p>
 <p>
 The universe of stocks is filtered using dollar volume and price criteria. Most of the stocks chosen by the algorithm during the backtest period are small market capitalization stocks.

From 39fddac1a09ff7004849517c200566f92148715d Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Fri, 12 Jul 2019 12:06:43 -0700
Subject: [PATCH 555/753] Updated new code snipets that addresses the year
 signal and unchanged universe

---
 .../92 Price Earnings Anomaly/02 Method.html  | 29 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 2 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html b/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html
index cd87637..def8def 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/02 Method.html	
@@ -11,13 +11,15 @@
 <div class="section-example-container">
     <pre class="python">
     def CoarseSelectionFunction(self, coarse):
-        if self.Time.year == self.year: return []
+        
+        if self.Time.year == self.year:
+            return self.symbols
         
         # drop stocks which have no fundamental data or have low price
         CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
         sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False) 
-        top = sortedByDollarVolume[:self._NumCoarseStocks]
-        return [i.Symbol for i in top] 
+        
+        return [i.Symbol for i in sortedByDollarVolume[:self._NumCoarseStocks]] 
 </pre>
 </div>
  
@@ -29,17 +31,18 @@
     <pre class="python">
  
     def FineSelectionFunction(self, fine):
+        
         if self.Time.year == self.year:
-            self.filtered_fine = []
-        else:
-            fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)]
-            for i in fine:
-                i.PERatio = float(i.ValuationRatios.PERatio)
-            sortedPERatio = sorted(fine, key=lambda x: x.PERatio)
-            
-            self.filtered_fine = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
-            
-        return self.filtered_fine
+            return self.symbols
+        
+        self.year = self.Time.year
+        
+        fine = [x for x in fine if x.ValuationRatios.PERatio > 0]
+        sortedPERatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio)
+
+        self.symbols = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
+        
+        return self.symbols
 </pre>
 </div>
 
diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
index 32fcbdf..000cded 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c1c62e5e8782b05cc465c5a503463616.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fea3faa781ba616473611d50a6ef444a.html"></iframe>
     </div>
 </div>

From d6c9d462d32eecac832f2a2ca796f2594842573a Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Fri, 12 Jul 2019 13:53:34 -0700
Subject: [PATCH 556/753] Initialize self.symbols = [ ] instead of None

---
 04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
index 000cded..dce77dd 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fea3faa781ba616473611d50a6ef444a.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc4e18fb2db3e4a4ef61732dec927fe5.html"></iframe>
     </div>
 </div>

From e0e476f13ffad15be775e5e2cefe9124ea21ab8e Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 14 Jul 2019 18:25:32 +0000
Subject: [PATCH 557/753] Automated push from server made by Jared Broad

---
 .../05 Planning Your Lesson.html                       | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index a839fca..d44db62 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -105,6 +105,16 @@ <h4>2. Strategy Implementation</h4>
 </thead>
 <tbody>
 
+<tr>
+<td><p>Separation of Concerns with the Algorithm Framework<br/> <small style="font-size: 0.9em">A simple strategy to buy SPY each morning on market open using the algorithm framework - a scaffolding for powerful strategy design.</small></p>
+<span class="badge">Algorithm Framework</span>
+<span class="badge">Execution Model</span>
+<span class="badge">Portfolio Model</span>
+<span class="badge">Universe Selection Model</span>
+</td>
+<td class="status"><p><a href="/contact?subject=bootcamp-algorithm-framework">Available</a></p>
+</tr>
+
 <tr>
 <td><p>Pairs Trading with SMA <br/> <small style="font-size: 0.9em">Simple pairs trading strategy monitoring for divergence in correlation of two hand picked assets. Invests in a market neutral manner, using position sizing to calculate the right holding of each asset.</small></p>
 <span class="badge">Pairs Trading</span>

From fe133f0f27d333d4cb0b80c6bba2f144450e507c Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 14 Jul 2019 20:08:55 +0000
Subject: [PATCH 558/753] Automated push from server made by Jared Broad

---
 .../06 Writing a Lesson.html                  | 90 +++++++++++++++++--
 1 file changed, 85 insertions(+), 5 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
index 658cc88..a919041 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
@@ -1,7 +1,87 @@
 <p>
-   Task structure,
-  bulleting out the concepts and tasks,
-  translating to HTML and full tasks.
-  
-  
+Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy, after this you can break it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task.
 </p>
+
+<style> 
+.arrow-steps {
+margin: 20px 0px;
+}
+.arrow-steps .step {
+	font-size: 14px;
+	text-align: center;
+	color: #666;
+	cursor: default;
+	margin: 0 3px;
+	padding: 6px 10px 6px 20px;
+	min-width: 145px;
+	float: left;
+	position: relative;
+	background-color: #d9e3f7;
+	-webkit-user-select: none;
+	-moz-user-select: none;
+	-ms-user-select: none;
+	user-select: none;  
+}
+
+.arrow-steps .step:after,
+.arrow-steps .step:before {
+	content: " ";
+	position: absolute;
+	top: 0;
+	right: -17px;
+	width: 0;
+	height: 0;
+	border-top: 17px solid transparent;
+	border-bottom: 17px solid transparent;
+	border-left: 17px solid #d9e3f7;	
+	z-index: 2; 
+}
+
+.arrow-steps .step:before {
+	right: auto;
+	left: 0;
+	border-left: 17px solid #fff;	
+	z-index: 0;
+}
+
+.arrow-steps .step:first-child:before {
+	border: none;
+}
+
+.arrow-steps .step:first-child {
+	border-top-left-radius: 4px;
+	border-bottom-left-radius: 4px;
+}
+</style>	
+<div class="arrow-steps clearfix">
+  <div class="step"><span>Lesson Algorithm</span></div>
+  <div class="step"><span>Task Guides</span></div>
+  <div class="step"><span>Validators</span></div>
+  <div class="step"><span>Hints, Solutions</span></div>
+  <div class="step"><span>Submit</span></div>
+</div>
+
+<h4>1. Write Lesson Algorithm</h4>
+<p>
+Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy. This should be drafted as simply as possible to ensure each task the student needs to complete will only be 2-5 lines of code. 
+<p>
+Readability is critical and the code should be well commented with descriptive variable names. Depending on the complexity of the algorithm sometimes its more readable to use string tickers instead of class variables.
+</p>
+<p>
+Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in tasks by the student. 
+</p>
+
+<table style="table">
+<thead>
+<tr><th>x</th><th> dx</th></tr>
+</thead>
+<tbody>
+<tr><td>x</td></tr><tr><td>x</td></tr><tr><td>x</td></tr>
+</tbody>
+</table>
+
+<ul style="border: 1px solid green">
+<li>Be Concise.</li>
+<li>Ensure Highly Readable.</li>
+<li>Keep Task Divisions in Mind.</li>
+</ul>

From 777914be38b00ef24ab94dfa08a06afb392909a2 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Sun, 14 Jul 2019 23:30:16 +0000
Subject: [PATCH 559/753] Automated push from server made by Jared Broad

---
 .../06 Writing a Lesson.html                  | 31 ++++++++++++-------
 1 file changed, 19 insertions(+), 12 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
index a919041..a968212 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
@@ -68,20 +68,27 @@ <h4>1. Write Lesson Algorithm</h4>
 Readability is critical and the code should be well commented with descriptive variable names. Depending on the complexity of the algorithm sometimes its more readable to use string tickers instead of class variables.
 </p>
 <p>
-Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in tasks by the student. 
+Carefully write code in a way which neatly separates the algorithm concepts as much as possible. Keep in mind the algorithm will be implemented in separate tasks by the student. 
 </p>
-
-<table style="table">
+<div class="tip">
+  <i class="fa fa-bolt"></i><span class="tip-title">Action:</span>
+  <p>Write highly readable, concise C# and Python versions of the algorithm and request review by QuantConnect Education team. Plan ahead for divisions of the code into tasks.</p>
+</div> 
+ 
+<h4>
+2. Write Task Guides
+</h4>
+<p>
+Each task has a short write up to explain the features need to complete next step of the BootCamp task. This guide should assume the student has no prior knowledge and include representitive code snippets demonstrating the key API code needed.
+</p>
+<table class="table">
 <thead>
-<tr><th>x</th><th> dx</th></tr>
+<tr><th style="width: 20%">Style</th><th>Code Tag</th></tr>
 </thead>
 <tbody>
-<tr><td>x</td></tr><tr><td>x</td></tr><tr><td>x</td></tr>
+<tr>
+<td><p>Headings</p></td>
+<td><pre class="prettyprint">&lt;h4&gt;Initializaing Algorithms&lt;/h4&gt;</pre> </td>
+</tr>
 </tbody>
-</table>
-
-<ul style="border: 1px solid green">
-<li>Be Concise.</li>
-<li>Ensure Highly Readable.</li>
-<li>Keep Task Divisions in Mind.</li>
-</ul>
+</table>
\ No newline at end of file

From 072e3ce64ac96de19a2c29b60ec1892cff4bf03f Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 15 Jul 2019 01:03:19 +0000
Subject: [PATCH 560/753] Automated push from server made by Jared Broad

---
 .../06 Writing a Lesson.html                  | 35 ++++++++++++++++---
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
index a968212..b56511a 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
@@ -1,5 +1,5 @@
 <p>
-Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy, after this you can break it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task.
+Writing a BootCamp lesson starts by carefully writing out the complete code for the strategy, breaking it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task.
 </p>
 
 <style> 
@@ -63,7 +63,7 @@
 
 <h4>1. Write Lesson Algorithm</h4>
 <p>
-Writing a bootcamp lesson starts by carefully writing out the complete code for the strategy. This should be drafted as simply as possible to ensure each task the student needs to complete will only be 2-5 lines of code. 
+Writing a BootCamp lesson starts by carefully writing out the complete code for the strategy. This should be drafted as simply as possible to ensure each task the student needs to complete will only be 2-5 lines of code. 
 <p>
 Readability is critical and the code should be well commented with descriptive variable names. Depending on the complexity of the algorithm sometimes its more readable to use string tickers instead of class variables.
 </p>
@@ -79,16 +79,41 @@ <h4>
 2. Write Task Guides
 </h4>
 <p>
-Each task has a short write up to explain the features need to complete next step of the BootCamp task. This guide should assume the student has no prior knowledge and include representitive code snippets demonstrating the key API code needed.
+Each task has a short write-up to explain the features needed to complete the next task of BootCamp. This write-up should assume the student has no prior knowledge and include representative code snippets demonstrating the key API code needed.
 </p>
 <table class="table">
 <thead>
-<tr><th style="width: 20%">Style</th><th>Code Tag</th></tr>
+<tr><th style="width: 16%">Style</th><th>Example Code Tag</th></tr>
 </thead>
 <tbody>
 <tr>
 <td><p>Headings</p></td>
 <td><pre class="prettyprint">&lt;h4&gt;Initializaing Algorithms&lt;/h4&gt;</pre> </td>
 </tr>
+<tr>
+<td><p>Paragraphs</p></td>
+<td><pre class="prettyprint">&lt;p&gt;Setting cash is done with &lt;span class="python"&gt;self.SetCash().&lt;/span&gt;&lt;/p&gt;</pre> </td>
+</tr>
+<tr>
+<td><p>Code Snippets</p></td>
+<td><pre class="prettyprint">&lt;pre class="prettyprint python"&gt;self.SetCash()&lt;/pre&gt;</pre> </td>
+</tr>
 </tbody>
-</table>
\ No newline at end of file
+</table>
+<p>
+For a fluent experience between lessons, all content should follow the same structure as the sections laid out below. 
+</p>
+<p>
+<img style="width: 33%; float: left; margin-right: 40px;" src="https://cdn.quantconnect.com/bootcamp/i/20190714-bootcamp-task-content-structure.png"/>
+
+Tasks start their contents divided by subtitle using the <code>h4</code> tag. Use a short title about the specific API or content you're trying to summarize. <br/><br/>
+
+After each heading, write a short paragraph concisely summarizing the content in as few words as possible. Use inline <code>&lt;code&gt;</code> blocks to highlight API syntax, and links to new tabs referencing any documentation required. <br/><br/>
+
+Any content which is specific to one programming language should be wrapped in a span tag with the language set in the class, for example: <code>&lt;span class="python"&gt;</code>.
+
+Each section should include a code snippet of documentation using a <code>&lt;pre class="prettyprint"&gt;</code> code block. <br/><br/>
+
+Finally, each task should define 2-4 objectives to achieve in the task. These tasks should guide the user on the required steps to implement the strategy step. At the end of the task, there should be a measurable output that the system can use to judge if the task is a success. This can be a state change of the algorithm, a debug/log statement, or a trade. This will be covered in the next section, <em>Code Validators</em>.
+</p>
+

From f4043d81f1183aeace68f9c70c499e92b4d65fff Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Mon, 15 Jul 2019 01:13:15 +0000
Subject: [PATCH 561/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html
index b56511a..56fadfc 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/06 Writing a Lesson.html	
@@ -1,5 +1,5 @@
 <p>
-Writing a BootCamp lesson starts by carefully writing out the complete code for the strategy, breaking it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task.
+Writing a BootCamp lesson starts by carefully writing out the complete code for the strategy, breaking it into tasks, and write small text summaries for each task with the documentation required to teach the reader how to complete the task. Finally, JavaScript is used to read the exhaust output of the algorithm to validate it achieved the required objective.
 </p>
 
 <style> 

From 32fd8ab6457f529a30c4c710a017bc07f785f765 Mon Sep 17 00:00:00 2001
From: www-data <www-data@qc-web.c.continual-bay-418.internal>
Date: Fri, 19 Jul 2019 17:31:55 +0000
Subject: [PATCH 562/753] Automated push from server made by Jared Broad

---
 08 Meta/01 Creating BootCamp Tutorials/00.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/00.html b/08 Meta/01 Creating BootCamp Tutorials/00.html
index 403498a..cda40df 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/00.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/00.html	
@@ -29,5 +29,5 @@
 </style>
 <br/>
 <div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Updated July 7th, 2019: The create BootCamp tutorial is a work in progress.
+    <i class="fa fa-warning"></i> Updated July 14th, 2019: The create BootCamp tutorial is a work in progress.
 </div>

From 4652d20f2c93868f209b9ec03a4b05f8bc5fe03d Mon Sep 17 00:00:00 2001
From: Rohan Talip <RohanTalip@users.noreply.github.com>
Date: Sun, 21 Jul 2019 18:26:01 -0700
Subject: [PATCH 563/753] Updated some links in README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 371ab1a..da878ac 100644
--- a/README.md
+++ b/README.md
@@ -6,12 +6,12 @@
 
 This repository is a collection of WordPress and Jupyter notebook tutorials for LEAN demonstrating financial concepts and LEAN. Tutorials are categorized into folders with each Category and Tutorial Series. The Jupyter notebook and the associated HTML webpage must use matching file names. The HTML is generated and displayed with wordpress.
 
-Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean Engine Repository][4].
+Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean][4] Engine repository.
 
 
 ## New Tutorial Requests and Edits ##
 
-Please submit new tutorial requests as an issue to the [Tutorial Repository][5]. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed! 
+Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
 
 ## Mailing List ##
 
@@ -24,7 +24,7 @@ Contributions are warmly very welcomed but we ask you read the existing code to
 [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer"
 [2]: https://www.quantconnect.com/lean/docs "Lean Documentation"
 [3]: https://github.com/QuantConnect/Lean/archive/master.zip
-[4]: https://github.com/QuantConnect/Lean/archive/master.zip
+[4]: https://github.com/QuantConnect/Lean
 [5]: https://github.com/QuantConnect/Tutorials/issues
 [6]: https://groups.google.com/forum/#!forum/lean-engine
 [7]: https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md

From 8e6ce223f903417e419272322de929ed714e6c89 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Mon, 22 Jul 2019 22:13:18 +0100
Subject: [PATCH 564/753] Fixes Small Capitalization Stocks Premium Anomaly
 Algorithm

Fixes the issue on the Universe Selection functions returning empty list
of Symbol that unsubscribes them.
Refactors the algorithm to use a pattern that does not depend on Schedule
Events and rely on OnSecuritiesChanged to liquidate securities.
---
 .../02 Method.html                            | 27 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
index 148d56e..f8b5aba 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
@@ -2,25 +2,26 @@
   The first step is coarse universe selection. We create an investment universe with stocks that have fundmental data and with a price greater than $5.
 </p>
 <div class="section-example-container">
-<pre class="python">self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData) and (float(x.AdjustedPrice) > 5)]
+<pre class="python">return [x.Symbol for x in coarse if x.HasFundamentalData and x.AdjustedPrice > 5]
 </pre>
 </div>
 <p>In fine universe selection, we sort the stocks in the universe by the market capitalization and choose 10 stocks with the lowest market cap.
 </p>
 <div class="section-example-container">
 <pre class="python">def FineSelectionFunction(self, fine):
-    if self.yearly_rebalance:
-        fine = [x for x in fine if (x.ValuationRatios.PERatio > 0)
-                                and (x.EarningReports.BasicAverageShares.ThreeMonths > 0)
-                                and (x.EarningReports.BasicEPS.TwelveMonths > 0)]
-        for i in fine:
-            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
-        sorted_market_cap = sorted(fine, key=lambda x: x.MarketCap)
-        self.filtered_fine = [i.Symbol for i in sorted_market_cap[:20]]
-        self.yearly_rebalance = False
-        return self.filtered_fine
-    else:
-        return []
+    if self.year == self.Time.year:
+        return self.symbols
+
+    # Calculate the market cap and add the "MarketCap" property to fine universe object
+    for i in fine:
+        i.MarketCap = (i.EarningReports.BasicAverageShares.ThreeMonths * 
+                       i.EarningReports.BasicEPS.TwelveMonths *
+                       i.ValuationRatios.PERatio)
+
+    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0], key=lambda x: x.MarketCap)
+
+    self.symbols = [i.Symbol for i in sorted_market_cap[:10]]
+    return self.symbols
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
index 5be3efc..8bab69d 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_da3bf5ee608fb8b7dc952372dd925542.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc510b17cc0f58224282d37fa49643e8.html"></iframe>
     </div>
 </div>

From 5eb304bdfb725391331db0582a2fe04771f8c208 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Mon, 22 Jul 2019 23:19:48 +0100
Subject: [PATCH 565/753] Use Price instead of AdjustedPrice to Filter Coarse
 Data

---
 .../02 Method.html                                              | 2 +-
 .../03 Algorithm.html                                           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
index f8b5aba..2894c75 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
@@ -2,7 +2,7 @@
   The first step is coarse universe selection. We create an investment universe with stocks that have fundmental data and with a price greater than $5.
 </p>
 <div class="section-example-container">
-<pre class="python">return [x.Symbol for x in coarse if x.HasFundamentalData and x.AdjustedPrice > 5]
+<pre class="python">return [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 5]
 </pre>
 </div>
 <p>In fine universe selection, we sort the stocks in the universe by the market capitalization and choose 10 stocks with the lowest market cap.
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
index 8bab69d..f14f61e 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc510b17cc0f58224282d37fa49643e8.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ed4394d8d45f7f2e6d7e807824508ef0.html"></iframe>
     </div>
 </div>

From ca37faaeac4db7f49f7abcea089ad86f470aa7fe Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Tue, 30 Jul 2019 18:54:25 +0100
Subject: [PATCH 566/753] Fixes Monthly Universe Selection Update

- Fixes the issue on the Universe Selection function returning different
  lists of symbols. We need to keep a single list that reflects the final
  selection at Fine Fundamental.
- Refactors the algorithm to use a dictionary of `Momentum` instead of a
  `SymbolData` and use an integer to track the month change instead of Schedule Events.
- Fixes the securities selection based on momentum: instead of buying
  those with the hightest momentum, the algorithm was buying those with
  the lowest.
---
 .../02 Method.html                            | 23 +++----------------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 5 insertions(+), 22 deletions(-)

diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html
index 2f6518a..1253e08 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
@@ -6,25 +6,9 @@ <h4>Momentum Calculation</h4>
 <p>
   Momentum is the absolute difference in stocks.
   \[Momentum = Close_{today}-Close_{N-days-ago}\]
-  LEAN has the Momentum indicator. We create a class to save the momentum value and warm up the indictor for each symbol.
-  <div class="section-example-container">
-  <pre class="python">
-class SymbolData:
-    def __init__(self, symbol, lookback):
-        self.symbol = symbol
-        self.MOM = Momentum(lookback)
-
-    def WarmUpIndicator(self, history):
-        # warm up the Momentum indicator with the history request
-        for tuple in history.itertuples():
-            item = IndicatorDataPoint(self.symbol, tuple.Index, float(tuple.close))
-            self.MOM.Update(item)
-  </pre>
-  </div>
 <p>
-  Dictionary <code>self.symbolDataDict</code> is used to save the momentum class instance <code>SymbolData</code> for each symbol.
-  In <code>OnSecuritiesChanged</code> event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary. Each day in <code>OnData</code>, the 
-  Momentum indicator for all symbols in the dictionary will be updated with the latest closing price.
+  Dictionary <code>self.mom</code> is used to save the LEAN Momentum class instance <code>Momentum</code> for each symbol.
+  In <code>OnSecuritiesChanged</code> event method, we add the newly selected symbol to the dictionary and initialize the momentum indicator with the history request. For symbols removed from the universe, we remove it from the dictionary and liquidate its positions. Each day in <code>OnData</code>, the Momentum indicator for all symbols in the dictionary will be updated with the latest closing price.
 </p>
 <p>
   We choose a period of 12 months for the momentum indicator. Stocks with the best 12-month momentum (12-month performance) are then added to our portfolio and are weighted equally.
@@ -32,6 +16,5 @@ <h4>Momentum Calculation</h4>
 
 <h4> Monthly Rebalance</h4>
 <p>
-  The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the bool variable
-  <code>self.monthly_rebalance</code> to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator.
+  The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable <code>self.month</code> that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols <code>self.symbols</code>. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator.
 </p>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html
index a8ca0da..f57e9ce 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_ec30318cc245fe200ca1261b7f26f17b.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6e3182577984f2c6950b7aacfc17301d.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index cd0aa80..ee208ec 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -8,7 +8,7 @@
      8 : "336bbb8e9e4a55a6adfa00c0d9f904bb",
      12: "72532ccafeaa844c81aa75c5696b4b24",
      13: "05d0edd633b1438852d1d641af0224ae",
-     14: "ec30318cc245fe200ca1261b7f26f17b",
+     14: "6e3182577984f2c6950b7aacfc17301d",
      15: "bd83aa417032f8407382a1c065aa7511",
      16: "afd2c9d67fd51d602bf8eac2ef28d712",
      18: "ba1bb35b26896e2e710a510f62230a24",

From 0ff978f6cdb10271ce1b0279079abb9b43c0d7e5 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Thu, 1 Aug 2019 17:31:26 +0100
Subject: [PATCH 567/753] Addresses Peer-Review

Updates the algorithm and the text to mention `Universe.Unchanged` usage.
---
 04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html | 2 +-
 .../21 Momentum Effect in Stocks/03 Algorithm.html              | 2 +-
 quantpedia.json                                                 | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html
index 1253e08..2dc76cf 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/02 Method.html	
@@ -16,5 +16,5 @@ <h4>Momentum Calculation</h4>
 
 <h4> Monthly Rebalance</h4>
 <p>
-  The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable <code>self.month</code> that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return the same symbols <code>self.symbols</code>. In contrast to returning an empty list, returning the same symbols as before is a better way for monthly rebalance universe selection. Since if there are no open positions for certain symbol, returning empty list will stop the data subscription of that symbol halt updates of the indicator.
+  The portfolio is rebalanced once a month. The coarse and fine universe selection is set to default to run at midnight once a day. To make the universe selection run at the first trading day each month, we use the int variable <code>self.month</code> that tracks the current month to manage the universe selection. At the start of each month, the universe selection will filter new stocks. On all other days, the universe selection function will return <code>Universe.Unchanged</code>. In contrast to returning an empty list or a list of previously selected symbols, returning <code>Universe.Unchanged</code> is the best way for monthly rebalance universe selection. If there are no open positions for certain symbol, returning an empty list will stop the data subscription of that symbol and halt updates of the indicator.
 </p>
diff --git a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html
index f57e9ce..b92aa3f 100644
--- a/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html	
+++ b/04 Strategy Library/21 Momentum Effect in Stocks/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6e3182577984f2c6950b7aacfc17301d.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e7b55d46fa8677ed8ec91dd1a0159a6e.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index ee208ec..567f8b4 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -8,7 +8,7 @@
      8 : "336bbb8e9e4a55a6adfa00c0d9f904bb",
      12: "72532ccafeaa844c81aa75c5696b4b24",
      13: "05d0edd633b1438852d1d641af0224ae",
-     14: "6e3182577984f2c6950b7aacfc17301d",
+     14: "e7b55d46fa8677ed8ec91dd1a0159a6e",
      15: "bd83aa417032f8407382a1c065aa7511",
      16: "afd2c9d67fd51d602bf8eac2ef28d712",
      18: "ba1bb35b26896e2e710a510f62230a24",

From b15b0b08b1223eed7d9016d75d0769e548f4ec97 Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Tue, 6 Aug 2019 17:36:12 -0700
Subject: [PATCH 568/753] Update 05 Planning Your Lesson.html

Marking as assigned since they're being done internally
---
 .../05 Planning Your Lesson.html              | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index d44db62..caeec73 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -46,7 +46,7 @@ <h4>2. Strategy Implementation</h4>
 <tr>
 <td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p>
 <span class="badge">Order Management</span><span class="badge">Basic Charting</span></td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -55,7 +55,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Using Indicators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -63,7 +63,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Consolidators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -72,7 +72,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Price Volume Filtering</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -81,7 +81,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">SymbolData Pattern</span>
 <span class="badge">History</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -90,7 +90,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">STD Indicator</span>
 <span class="badge">Parameter Minimization</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </td>
 </tr>
 </tbody>
@@ -112,7 +112,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Portfolio Model</span>
 <span class="badge">Universe Selection Model</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-algorithm-framework">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -122,7 +122,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Position Sizing</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-pairs-trading">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -132,7 +132,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Scheduled Events</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-cointegration">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -142,7 +142,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Long-Short Hedge</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-longshort-value-universe">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 <tr>
 <td><p>Sector Balanced Universe Selection<br/> <small style="font-size: 0.9em">Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.</small></p>
@@ -151,7 +151,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Sector Exposure</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-sector-exposure">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 <tr>
 <td><p>Hedging FX Books with Interest Rate<br/> <small style="font-size: 0.9em">Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.</small></p>

From 560f40ecac5887a85edde121bc0cd3aad433c49d Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 6 Aug 2019 17:45:50 -0700
Subject: [PATCH 569/753] Revert "Update 05 Planning Your Lesson.html"

---
 .../05 Planning Your Lesson.html              | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index caeec73..d44db62 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -46,7 +46,7 @@ <h4>2. Strategy Implementation</h4>
 <tr>
 <td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p>
 <span class="badge">Order Management</span><span class="badge">Basic Charting</span></td>
-<td class="status"><p>Completed</p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -55,7 +55,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Using Indicators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Completed</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
 </tr>
 
 <tr>
@@ -63,7 +63,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Consolidators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
 </tr>
 
 <tr>
@@ -72,7 +72,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Price Volume Filtering</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
 </tr>
 
 <tr>
@@ -81,7 +81,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">SymbolData Pattern</span>
 <span class="badge">History</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
 </tr>
 
 <tr>
@@ -90,7 +90,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">STD Indicator</span>
 <span class="badge">Parameter Minimization</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p>
 </td>
 </tr>
 </tbody>
@@ -112,7 +112,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Portfolio Model</span>
 <span class="badge">Universe Selection Model</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-algorithm-framework">Available</a></p>
 </tr>
 
 <tr>
@@ -122,7 +122,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Position Sizing</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-pairs-trading">Available</a></p>
 </tr>
 
 <tr>
@@ -132,7 +132,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Scheduled Events</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-cointegration">Available</a></p>
 </tr>
 
 <tr>
@@ -142,7 +142,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Long-Short Hedge</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-longshort-value-universe">Available</a></p>
 </tr>
 <tr>
 <td><p>Sector Balanced Universe Selection<br/> <small style="font-size: 0.9em">Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.</small></p>
@@ -151,7 +151,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Sector Exposure</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p><a href="/contact?subject=bootcamp-sector-exposure">Available</a></p>
 </tr>
 <tr>
 <td><p>Hedging FX Books with Interest Rate<br/> <small style="font-size: 0.9em">Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.</small></p>

From c85d30a36439dad79ae5dec269fd8ad6c81145db Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 6 Aug 2019 17:53:18 -0700
Subject: [PATCH 570/753] Update 05 Planning Your Lesson.html

---
 .../05 Planning Your Lesson.html                   | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index d44db62..daea9e9 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -46,7 +46,7 @@ <h4>2. Strategy Implementation</h4>
 <tr>
 <td><p>Buy and Hold with Trailing Stop <br/> <small style="font-size: 0.9em">Placing and updating a stop-market order combined with basic charting to visualize the stop price movement.</small></p>
 <span class="badge">Order Management</span><span class="badge">Basic Charting</span></td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -55,7 +55,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Using Indicators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-tactical-allocation">Available</a></p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -63,7 +63,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Consolidators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-open-range">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -72,7 +72,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Price Volume Filtering</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-liquid-universe">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -81,7 +81,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">SymbolData Pattern</span>
 <span class="badge">History</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-momentum-universe">Available</a></p></td>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -90,7 +90,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">STD Indicator</span>
 <span class="badge">Parameter Minimization</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p><a href="/contact?subject=bootcamp-scheduling">Available</a></p>
+<td class="status"><p>Assigned</p>
 </td>
 </tr>
 </tbody>
@@ -190,4 +190,4 @@ <h4>2. Strategy Implementation</h4>
 <td class="status"><p><a href="/contact?subject=bootcamp-advanced"></a></p>
 </tr>
 </tbody>
-</table>
\ No newline at end of file
+</table>

From e913b2837fbc4024a95c6cb2be61ad0abb5228dd Mon Sep 17 00:00:00 2001
From: Jack Simonson <jack.simonson@quantconnect.com>
Date: Tue, 6 Aug 2019 17:57:12 -0700
Subject: [PATCH 571/753] Update 05 Planning Your Lesson.html

---
 .../05 Planning Your Lesson.html                       | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index daea9e9..672b9be 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -112,7 +112,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Portfolio Model</span>
 <span class="badge">Universe Selection Model</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-algorithm-framework">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -122,7 +122,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Position Sizing</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-pairs-trading">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -132,7 +132,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Scheduled Events</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-cointegration">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 
 <tr>
@@ -142,7 +142,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Long-Short Hedge</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-longshort-value-universe">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 <tr>
 <td><p>Sector Balanced Universe Selection<br/> <small style="font-size: 0.9em">Selecting an equally weighted universe of assets covering 33% technology stocks, 33% finance and 33% consumer goods.</small></p>
@@ -151,7 +151,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Sector Exposure</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p><a href="/contact?subject=bootcamp-sector-exposure">Available</a></p>
+<td class="status"><p>Assigned</p></td>
 </tr>
 <tr>
 <td><p>Hedging FX Books with Interest Rate<br/> <small style="font-size: 0.9em">Harnessing an alternative data source (Trading Economics) to invest proportionately with interest rate changes in the underlying economies.</small></p>

From 1f345566bb6f03af4b502d57128aaf5cf0f6f9a8 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Wed, 14 Aug 2019 21:32:19 +0100
Subject: [PATCH 572/753] Removes Tutorial on Deprecated Desktop Charting

---
 .../07 Desktop Charting with LEAN/00.html     | 43 -------------------
 .../01 Desktop Charting with LEAN.html        | 27 ------------
 2 files changed, 70 deletions(-)
 delete mode 100644 03 Open Source/07 Desktop Charting with LEAN/00.html
 delete mode 100755 03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html

diff --git a/03 Open Source/07 Desktop Charting with LEAN/00.html b/03 Open Source/07 Desktop Charting with LEAN/00.html
deleted file mode 100644
index 9a35478..0000000
--- a/03 Open Source/07 Desktop Charting with LEAN/00.html	
+++ /dev/null
@@ -1,43 +0,0 @@
-<style>
-.work-in-progress {
-    width: 100%;
-    border: 1px solid #f5ae29;
-    border-radius: 5px;
-    padding: 15px;
-    color: #f5ae29;
-}
-.tip {
-    width: 100%;
-    border: 1px solid #f5ae29;
-    border-radius: 5px;
-    padding: 15px;
-    margin-bottom: 20px;
-    margin-top: 20px;
-}
-.tip i {
-    color: #f5ae29;
-}
-.tip-title { 
- font-weight: bold;
-color: #f5ae29;
-margin-left: 5px;
-margin-right: 5px;
-}
-.tip p { display: inline; }
-table th i {
-    color: #f5ae29;
-}
-th.summary {
-   font-family: "Courier New"; 
-   font-weight: normal;
-}
-.table.qc-table tbody tr td {
-   text-align: left;
-}
-.implementation {
-    font-family: "Courier New";
-}
-</style>
-<div class="work-in-progress"> 
-    <i class="fa fa-warning"></i> Note: 2019-01-20: Desktop Charting v1 works in Windows but it is no longer supported. Version 2 (Electron) is a work in progress and will be published in 2019. 
-</div>
diff --git a/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html b/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html
deleted file mode 100755
index 91f56e4..0000000
--- a/03 Open Source/07 Desktop Charting with LEAN/01 Desktop Charting with LEAN.html	
+++ /dev/null
@@ -1,27 +0,0 @@
-<p>
-With a few configuration changes you can get desktop charting in LEAN with a HTML5 interface very similar to the one you see in QuantConnect.com. This gives you better visual feedback on your strategy and allows you to improve faster. This tutorial guides you through configuring a desktop charting environment with LEAN.
-</p>
-<p>
-  Local charting (and all local backtesting) requires you to have your own source of data. We provide a way to download FX and CFD data through our <a href="https://www.quantconnect.com/blog/use-api-file-provider/">API</a>. To get started make sure you have your data in your data folder. By default this is the /Data/ directory relative to your LEAN installation.
-</p>
-<p>
-  Two configuration changes are required for desktop charting to work:
-</p>
-<ul>
- 	<li>Change the "environment" field to use "backtesting-desktop". This instructs LEAN to use the configuration settings block at the bottom of the config file.
-<pre class="prettyprint">  "environment": "backtesting-desktop",</pre>
-</li>
- 	<li>Insert your API token and user id into configuration file. This is required for chart streaming.
-<pre class="prettyprint">  // To get your api access token go to quantconnect.com/account
-  "job-user-id": "....",
-  "api-access-token": "...........",</pre>
-</li>
-</ul>
-<p>
-  With those changes in place you can simply run the project and your backtesting chart will appear in a few seconds. For live trading; use the"live-desktop" configuration environment.
-  If you get the run-time exception<em> "The port configured in config.json is either being used or blocked by a firewall"- </em>This normally means you've left the user interface open (you should close it between each run). It can also be because another program is sharing that port. You can fix this by changing the port LEAN transmits the data with the <code>"desktop-http-port"</code> setting.
-</p>
-<p>
-  In the tutorial video below we demonstrate this feature on LEAN:
-</p>
-<iframe width="100%" height="425" src="https://www.youtube.com/embed/m6llfznP4d4" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

From b7d105a2bc22eabd0006105a8209ac00f03371ff Mon Sep 17 00:00:00 2001
From: Martin Molinero <martin.molinero1@gmail.com>
Date: Wed, 14 Aug 2019 22:08:13 -0300
Subject: [PATCH 573/753] Update python debugging tutorial

---
 .../01 Introduction.html                      | 13 ----------
 .../02 Prerequisites.html                     |  3 ---
 .../05 Limitations.html                       |  3 ---
 .../00.html                                   |  0
 .../01 Debugging Python/01 Introduction.html  |  7 ++++++
 .../02 Installing LEAN with Python.html}      |  0
 .../03 Method 1 - PTVSD.html}                 |  5 ++++
 .../04 Method 2 - PDB.html                    | 24 ++++++++++++++++++
 .../05 Method 3 - VisualStudio Debugger.html  | 25 +++++++++++++++++++
 .../01 Debugging Python/06 Limitations.html   |  3 +++
 .../07 Summary.html}                          |  0
 11 files changed, 64 insertions(+), 19 deletions(-)
 delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html
 delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html
 delete mode 100644 03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
 rename 03 Open Source/{01 Debugging Python in Visual Studio => 01 Debugging Python}/00.html (100%)
 create mode 100644 03 Open Source/01 Debugging Python/01 Introduction.html
 rename 03 Open Source/{01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html => 01 Debugging Python/02 Installing LEAN with Python.html} (100%)
 rename 03 Open Source/{01 Debugging Python in Visual Studio/04 Attaching the Debugger.html => 01 Debugging Python/03 Method 1 - PTVSD.html} (88%)
 create mode 100644 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html
 create mode 100644 03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html
 create mode 100644 03 Open Source/01 Debugging Python/06 Limitations.html
 rename 03 Open Source/{01 Debugging Python in Visual Studio/06 Summary.html => 01 Debugging Python/07 Summary.html} (100%)

diff --git a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html b/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html
deleted file mode 100644
index c854c52..0000000
--- a/03 Open Source/01 Debugging Python in Visual Studio/01 Introduction.html	
+++ /dev/null
@@ -1,13 +0,0 @@
-<style>
-.backdrop { background-color: #ddd; padding: 15px; border: 1px solid #ccc; border-radius: 5px;}
-</style>
-
-<p>
-    We often want to use an IDE for algorithm development because it provides comprehensive facilities such as a source code editor and a debugger. QuantConnect delivers a robust online source code editor, but not a debugger. For some algorithm developers, a debugger is an essential tool; therefore they opt to work offline with full-featured IDE.
-</p>
-<p>
-    Visual Studio is a full-featured IDE that makes debugging<span style="color: rgb(36, 41, 46); font-family: -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;, &quot;Segoe UI Symbol&quot;; font-size: 14px; text-align: start;">&nbsp;</span>easy. Unlike other solutions that only allows debugging a single process/language, we can use it to debug a python algorithm in Lean (C# engine).
-</p>
-<p>
-    In this tutorial, we'll show you how to debug your algorithm in LEAN from Visual Studio.
-</p>
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html b/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html
deleted file mode 100644
index 5f525e7..0000000
--- a/03 Open Source/01 Debugging Python in Visual Studio/02 Prerequisites.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>Python Tools for Visual Studio debug (<b><a href="https://github.com/Microsoft/ptvsd">ptvsd</a></b>) server python library. It can be easily installed using <b>pip</b>:</p>
-<div class="section-example-container"><pre class="python">pip install ptvsd</pre></div>
-
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html b/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html
deleted file mode 100644
index 566ac60..0000000
--- a/03 Open Source/01 Debugging Python in Visual Studio/05 Limitations.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>Unfortunately, this debugging scenario entails some limitations.</p>
-<p>It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).</p>
-<p>The algorithm will always stop after <mark>ptvsd.break_into_debugger()</mark> call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks. In order to resume the algorithm execution without breakpoints, we need to detach the process (Debug -&gt; Detach All).</p>
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/00.html b/03 Open Source/01 Debugging Python/00.html
similarity index 100%
rename from 03 Open Source/01 Debugging Python in Visual Studio/00.html
rename to 03 Open Source/01 Debugging Python/00.html
diff --git a/03 Open Source/01 Debugging Python/01 Introduction.html b/03 Open Source/01 Debugging Python/01 Introduction.html
new file mode 100644
index 0000000..ea57f4a
--- /dev/null
+++ b/03 Open Source/01 Debugging Python/01 Introduction.html	
@@ -0,0 +1,7 @@
+<style>
+.backdrop { background-color: #ddd; padding: 15px; border: 1px solid #ccc; border-radius: 5px;}
+</style>
+
+<p>
+    In this tutorial, for those algorithm developers who opt to work offline, we will explore the different methods to debug a python algorithm.
+</p>
\ No newline at end of file
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html b/03 Open Source/01 Debugging Python/02 Installing LEAN with Python.html
similarity index 100%
rename from 03 Open Source/01 Debugging Python in Visual Studio/03 Installing LEAN with Python.html
rename to 03 Open Source/01 Debugging Python/02 Installing LEAN with Python.html
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html b/03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html
similarity index 88%
rename from 03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html
rename to 03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html
index aaf8b00..4407bf1 100644
--- a/03 Open Source/01 Debugging Python in Visual Studio/04 Attaching the Debugger.html	
+++ b/03 Open Source/01 Debugging Python/03 Method 1 - PTVSD.html	
@@ -1,3 +1,8 @@
+<h4>Prerequisites</h4>
+<p>Python Tools for Visual Studio debug (<b><a href="https://github.com/Microsoft/ptvsd">ptvsd</a></b>) server python library. It can be easily installed using <b>pip</b>:</p>
+<div class="section-example-container"><pre class="python">pip install ptvsd</pre></div>
+
+<h4>Attaching the Debugger</h4>
 <p>The process that we will use to attach the python debugger requires that we run Lean <b>without</b> debugging and attach the process.</p>
 
 <p>First, we will add the <b>ptvsd</b> library, and the following statements:</p>
diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html
new file mode 100644
index 0000000..026b73a
--- /dev/null
+++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html	
@@ -0,0 +1,24 @@
+<p>This method uses the built in, <mark>cross-platform</mark>, command line python debugger (<b><a href="https://docs.python.org/3.6/library/pdb.html">pdb</a></b>).
+
+<p>
+<ul style="list-style-type:disc;">
+  <li>Edit config.json with the following paramenters:</li>
+  <div class="section-example-container">
+  <pre>
+      "debugging": true,
+  </pre>
+  </div>
+  <li>Run Lean without debugging by clicking on Debug -> Start Without Debugging</li>
+  <li>Lean will hit a first break outside algorithm code</li>
+  <li>Start debugging! Example commands:</li>
+  <div class="section-example-container">
+  <pre>
+      break add ../../../Algorithm.Python/BasicTemplateAlgorithm.py:37
+      continue
+      print(self.Portfolio.Invested)
+  </pre>
+  </div>
+</ul>
+</p>
+
+<img class="img-responsive" src="http://cdn.quantconnect.com/i/tu/python-debugging-command-line.png" />
\ No newline at end of file
diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html
new file mode 100644
index 0000000..16bec79
--- /dev/null
+++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html	
@@ -0,0 +1,25 @@
+<p>This method uses the (<b><a href="https://docs.microsoft.com/en-us/visualstudio/python/debugging-python-in-visual-studio?view=vs-2019">VisualStudio python debugger</a></b>).
+
+<h4>Prerequisites</h4>
+<p>Visual Studio <mark>Python development</mark> feature. It can be easily installed at Tools -> Get Tools and Features...</p>
+
+<p>
+<ul style="list-style-type:disc;">
+  <li>Edit config.json with the following paramenters:</li>
+  <div class="section-example-container">
+  <pre>
+      "debugging": true,
+      "debugging-method": "VisualStudio",
+  </pre>
+  </div>
+  <li>Run Lean without debugging by clicking on Debug -> Start Without Debugging</li>
+  <li>Lean will stop and wait for the debugger to attach</li>
+  <li>Set breakpoints</li>
+  <li>In Visual Studio, select Debug -> Attach to Process (or press Ctrl+Alt+P) to open the Attach to Process dialog box.</li>
+  <li>For Connection type, select Default</li>
+  <li>For Attach to, select Python</li>
+  <li>Search for <mark>QuantConnect.Lean.Launcher.exe</mark> process and attach</li>
+</ul>
+</p>
+
+<img class="img-responsive" src="http://cdn.quantconnect.com/i/tu/visual-studio-python-debugger.gif">
\ No newline at end of file
diff --git a/03 Open Source/01 Debugging Python/06 Limitations.html b/03 Open Source/01 Debugging Python/06 Limitations.html
new file mode 100644
index 0000000..f126eb5
--- /dev/null
+++ b/03 Open Source/01 Debugging Python/06 Limitations.html	
@@ -0,0 +1,3 @@
+<p>Unfortunately, this debugging scenario entails some limitations.</p>
+<p>It is not possible to concurrently debug the python algorithm and Lean (C# code). That means that the debugger will not stop at breakpoints in Lean nor we can step into methods defined in Lean (e.g., SetHoldings).</p>
+<p><b>Method 1 ptvsd</b> will always stop after <mark>ptvsd.break_into_debugger()</mark> call and it not possible to untoggled it. However, we can use conditional statements to prevent its call and avoid unnecessary breaks. In order to resume the algorithm execution without breakpoints, we need to detach the process (Debug -&gt; Detach All).</p>
diff --git a/03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html b/03 Open Source/01 Debugging Python/07 Summary.html
similarity index 100%
rename from 03 Open Source/01 Debugging Python in Visual Studio/06 Summary.html
rename to 03 Open Source/01 Debugging Python/07 Summary.html

From 827e272da339991879efbea5d7254fdc74580135 Mon Sep 17 00:00:00 2001
From: Martin Molinero <martin.molinero1@gmail.com>
Date: Wed, 14 Aug 2019 22:15:46 -0300
Subject: [PATCH 574/753] Small fixes

---
 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html      | 3 ++-
 .../05 Method 3 - VisualStudio Debugger.html                   | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html
index 026b73a..93c5b88 100644
--- a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html	
+++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html	
@@ -1,4 +1,4 @@
-<p>This method uses the built in, <mark>cross-platform</mark>, command line python debugger (<b><a href="https://docs.python.org/3.6/library/pdb.html">pdb</a></b>).
+<p>This method uses the built in, <mark>cross-platform</mark>, command line python debugger <b><a href="https://docs.python.org/3.6/library/pdb.html">pdb</a></b>.
 
 <p>
 <ul style="list-style-type:disc;">
@@ -6,6 +6,7 @@
   <div class="section-example-container">
   <pre>
       "debugging": true,
+      "debugging-method": "CommandLine",
   </pre>
   </div>
   <li>Run Lean without debugging by clicking on Debug -> Start Without Debugging</li>
diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html
index 16bec79..6f744e0 100644
--- a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html	
+++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html	
@@ -1,4 +1,4 @@
-<p>This method uses the (<b><a href="https://docs.microsoft.com/en-us/visualstudio/python/debugging-python-in-visual-studio?view=vs-2019">VisualStudio python debugger</a></b>).
+<p>This method uses the <b><a href="https://visualstudio.microsoft.com/vs/features/python/">Visual Studio Python Development Tools</a></b>.
 
 <h4>Prerequisites</h4>
 <p>Visual Studio <mark>Python development</mark> feature. It can be easily installed at Tools -> Get Tools and Features...</p>

From 58fdb388df72157c8cafb5e6a5246122abd3f0d9 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Wed, 21 Aug 2019 15:01:22 -0700
Subject: [PATCH 575/753] Fama-French Five-Factor Strategy

---
 .../01 Introduction.html                      |  3 +
 .../02 Fama French Five-Factor Model.html     | 17 +++++
 .../03 Algorithm.html                         | 66 +++++++++++++++++++
 .../04 References.html                        |  5 ++
 4 files changed, 91 insertions(+)
 create mode 100644 04 Strategy Library/230 Fama French Five Factors/01 Introduction.html
 create mode 100644 04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html
 create mode 100644 04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html
 create mode 100644 04 Strategy Library/230 Fama French Five Factors/04 References.html

diff --git a/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html
new file mode 100644
index 0000000..12ef392
--- /dev/null
+++ b/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+    At QuantConnect, we seek to make financial models ever more accessible to our community. This article will walk through the implementation of a stock selection strategy based on the popular Fama French five-factor financial model.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html
new file mode 100644
index 0000000..fd20400
--- /dev/null
+++ b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html	
@@ -0,0 +1,17 @@
+<p>
+    The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends.
+    Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return.
+    The model is as follows
+</p>
+\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\]
+
+<p>
+where
+</p>
+<ul>
+    <li>MKT is the excess return of the market. It is the return on the value-weight market portfolio.</li>
+    <li>SMB is the return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stocks.</li>
+    <li>HML is the difference between the returns on diversified portfolios of high and low B/M stocks.</li>
+    <li>RMW is the difference between the returns on diversified portfolios of stocks with robust and weak profitability.</li>
+    <li>CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive.</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html
new file mode 100644
index 0000000..f552038
--- /dev/null
+++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html	
@@ -0,0 +1,66 @@
+<p>
+    Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. 
+</p>
+<p>
+    In the following backtest,  we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will long the five stocks with the highest scores and short the five stocks with the lowest scores.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    '''Select securities with highest score on Fama French 5 factors'''
+    
+    # select stocks with these 5 factors
+    
+    # Operation profit margin: Quality
+    # Book value per share: Value
+    # ROE: Profitability
+    # TotalEquity: Size
+    # TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.OperationRatios.OperationMargin.Value
+                                and x.ValuationRatios.BookValuePerShare
+                                and x.OperationRatios.ROE
+                                and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.TotalAssetsGrowth]
+                                
+    
+    # sort by factors
+    sortedByFactor1 = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByFactor2 = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedByFactor3 = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByFactor4 = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByFactor5 = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+    
+    stockBySymbol = {}
+    
+    # get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByFactor1):
+        rank1 = index
+        rank2 = sortedByFactor2.index(stock)
+        rank3 = sortedByFactor3.index(stock)
+        rank4 = sortedByFactor4.index(stock)
+        rank5 = sortedByFactor5.index(stock)
+        avgRank = np.mean([rank1,rank2,rank3,rank4,rank5])
+        stockBySymbol[stock.Symbol] = avgRank
+        
+    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
+    symbols = [x[0] for x in sorted_dict]
+    
+    # pick the stocks with the highest scores to long
+    self.longSymbols= symbols[:self.num_long]
+    # pick the stocks with the lowest scores to short
+    self.shortSymbols = symbols[-self.num_short:]
+    
+    return self.longSymbols + self.shortSymbols   
+</pre>
+</div>
+
+<p>
+    In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency.
+</p>
+
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fd8b4225046a2e9b46d5234a430b9bcd.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/230 Fama French Five Factors/04 References.html b/04 Strategy Library/230 Fama French Five Factors/04 References.html
new file mode 100644
index 0000000..a660292
--- /dev/null
+++ b/04 Strategy Library/230 Fama French Five Factors/04 References.html	
@@ -0,0 +1,5 @@
+<ul>
+    <li>
+      <a href="https://tevgeniou.github.io/EquityRiskFactors/bibliography/FiveFactor.pdf" rel="nofollow">A five-factor asset pricing model</a>
+    </li>
+</ul>
\ No newline at end of file

From fcbfcea5a6613e5249d258dc53bcdf0b9d74ebf7 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Wed, 21 Aug 2019 17:26:38 -0700
Subject: [PATCH 576/753] Requested Changes

---
 .../02 Fama French Five-Factor Model.html     | 10 +--
 .../03 Algorithm.html                         | 75 ++++++++++---------
 2 files changed, 44 insertions(+), 41 deletions(-)

diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html
index fd20400..0cd9ad3 100644
--- a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html	
+++ b/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html	
@@ -9,9 +9,9 @@
 where
 </p>
 <ul>
-    <li>MKT is the excess return of the market. It is the return on the value-weight market portfolio.</li>
-    <li>SMB is the return on a diversified portfolio of small stocks minus the return on a diversified portfolio of big stocks.</li>
-    <li>HML is the difference between the returns on diversified portfolios of high and low B/M stocks.</li>
-    <li>RMW is the difference between the returns on diversified portfolios of stocks with robust and weak profitability.</li>
-    <li>CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive.</li>
+    <li>MKT is the excess return of the market. It is the return on the value-weighted market portfolio.</li>
+    <li>SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.</li>
+    <li>HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.</li>
+    <li>RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.</li>
+    <li>CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.</li>
 </ul>
\ No newline at end of file
diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html
index f552038..7f2f419 100644
--- a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html	
+++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html	
@@ -2,54 +2,52 @@
     Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. 
 </p>
 <p>
-    In the following backtest,  we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will long the five stocks with the highest scores and short the five stocks with the lowest scores.
+    In the following backtest,  we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores.
 </p>
 <div class="section-example-container">
 <pre class="python">
 def FineSelectionFunction(self, fine):
     '''Select securities with highest score on Fama French 5 factors'''
-    
-    # select stocks with these 5 factors
-    
-    # Operation profit margin: Quality
-    # Book value per share: Value
-    # ROE: Profitability
-    # TotalEquity: Size
-    # TotalAssetsGrowth: Investment Pattern
-    filtered = [x for x in fine if x.OperationRatios.OperationMargin.Value
-                                and x.ValuationRatios.BookValuePerShare
-                                and x.OperationRatios.ROE
+
+    # Select stocks with these 5 factors:
+    # MKT -- Book value per share: Value
+    # SMB -- TotalEquity: Size
+    # HML -- Operation profit margin: Quality
+    # RMW -- ROE: Profitability
+    # CMA -- TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
                                 and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.OperationMargin.Value
+                                and x.OperationRatios.ROE
                                 and x.OperationRatios.TotalAssetsGrowth]
-                                
-    
-    # sort by factors
-    sortedByFactor1 = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-    sortedByFactor2 = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-    sortedByFactor3 = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
-    sortedByFactor4 = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
-    sortedByFactor5 = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
-    
+
+    # Sort by factors
+    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+
     stockBySymbol = {}
-    
-    # get the rank based on 5 factors for every stock
-    for index, stock in enumerate(sortedByFactor1):
-        rank1 = index
-        rank2 = sortedByFactor2.index(stock)
-        rank3 = sortedByFactor3.index(stock)
-        rank4 = sortedByFactor4.index(stock)
-        rank5 = sortedByFactor5.index(stock)
-        avgRank = np.mean([rank1,rank2,rank3,rank4,rank5])
+
+    # Get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByMkt):
+        mktRank = self.beta_m * index
+        smbRank = self.beta_s * sortedBySmb.index(stock)
+        hmlRank = self.beta_h * sortedByHml.index(stock)
+        rmwRank = self.beta_r * sortedByRmw.index(stock)
+        cmaRank = self.beta_c * sortedByCma.index(stock)
+        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
         stockBySymbol[stock.Symbol] = avgRank
-        
+
     sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
     symbols = [x[0] for x in sorted_dict]
-    
-    # pick the stocks with the highest scores to long
+
+    # Pick the stocks with the highest scores to long
     self.longSymbols= symbols[:self.num_long]
-    # pick the stocks with the lowest scores to short
+    # Pick the stocks with the lowest scores to short
     self.shortSymbols = symbols[-self.num_short:]
-    
+
     return self.longSymbols + self.shortSymbols   
 </pre>
 </div>
@@ -61,6 +59,11 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_fd8b4225046a2e9b46d5234a430b9bcd.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_78601a5bd785ca32803c13525c688046.html"></iframe>
     </div>
 </div>
+
+<p>
+    The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest and it performed well.
+    However, there are still many aspects need to be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!
+</p>
\ No newline at end of file

From ee6ca904f2c343dc6055b406eeb41e609dcbeb23 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Wed, 21 Aug 2019 18:04:36 -0700
Subject: [PATCH 577/753] requested changes

---
 .../230 Fama French Five Factors/03 Algorithm.html            | 4 ++--
 quantpedia.json                                               | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html
index 7f2f419..7f04f12 100644
--- a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html	
+++ b/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html	
@@ -64,6 +64,6 @@
 </div>
 
 <p>
-    The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest and it performed well.
-    However, there are still many aspects need to be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!
+    The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years.
+    These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!
 </p>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index cd0aa80..807c061 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -47,4 +47,5 @@
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      207: "9bcf7ac117397af393ca59f795c4abdd",
      229: "5544552803512ca667342d5011dedd1d",
+     230: "78601a5bd785ca32803c13525c688046",
 }

From 2a0f7c1308930ecc90e89c86d34b7cef4f902d3b Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 22 Aug 2019 08:42:43 -0700
Subject: [PATCH 578/753] requested changes

---
 .../01 Introduction.html                                         | 0
 .../02 Fama French Five-Factor Model.html                        | 0
 .../03 Algorithm.html                                            | 0
 .../04 References.html                                           | 0
 quantpedia.json                                                  | 1 -
 5 files changed, 1 deletion(-)
 rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/01 Introduction.html (100%)
 rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/02 Fama French Five-Factor Model.html (100%)
 rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/03 Algorithm.html (100%)
 rename 04 Strategy Library/{230 Fama French Five Factors => Fama French Five Factors}/04 References.html (100%)

diff --git a/04 Strategy Library/230 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/Fama French Five Factors/01 Introduction.html
similarity index 100%
rename from 04 Strategy Library/230 Fama French Five Factors/01 Introduction.html
rename to 04 Strategy Library/Fama French Five Factors/01 Introduction.html
diff --git a/04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html
similarity index 100%
rename from 04 Strategy Library/230 Fama French Five Factors/02 Fama French Five-Factor Model.html
rename to 04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html
diff --git a/04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/Fama French Five Factors/03 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/230 Fama French Five Factors/03 Algorithm.html
rename to 04 Strategy Library/Fama French Five Factors/03 Algorithm.html
diff --git a/04 Strategy Library/230 Fama French Five Factors/04 References.html b/04 Strategy Library/Fama French Five Factors/04 References.html
similarity index 100%
rename from 04 Strategy Library/230 Fama French Five Factors/04 References.html
rename to 04 Strategy Library/Fama French Five Factors/04 References.html
diff --git a/quantpedia.json b/quantpedia.json
index 807c061..cd0aa80 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -47,5 +47,4 @@
      199: "2deff750ba4eff5bf2f2138ecffb4a7c",
      207: "9bcf7ac117397af393ca59f795c4abdd",
      229: "5544552803512ca667342d5011dedd1d",
-     230: "78601a5bd785ca32803c13525c688046",
 }

From 3675f27e197eccdd51b7ab770a0f594573e88842 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 22 Aug 2019 13:33:06 -0700
Subject: [PATCH 579/753] requested changes

---
 .../01 Introduction.html                      |  3 +
 .../02 Method.html                            | 93 +++++++++++++++++++
 .../03 Results.html                           |  8 ++
 .../04 Algorithm.html                         |  6 ++
 .../05 References.html                        |  5 +
 .../01 Introduction.html                      |  3 -
 .../02 Fama French Five-Factor Model.html     | 17 ----
 .../03 Algorithm.html                         | 69 --------------
 .../04 References.html                        |  5 -
 9 files changed, 115 insertions(+), 94 deletions(-)
 create mode 100644 04 Strategy Library/353 Fama French Five Factors/01 Introduction.html
 create mode 100644 04 Strategy Library/353 Fama French Five Factors/02 Method.html
 create mode 100644 04 Strategy Library/353 Fama French Five Factors/03 Results.html
 create mode 100644 04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html
 create mode 100644 04 Strategy Library/353 Fama French Five Factors/05 References.html
 delete mode 100644 04 Strategy Library/Fama French Five Factors/01 Introduction.html
 delete mode 100644 04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html
 delete mode 100644 04 Strategy Library/Fama French Five Factors/03 Algorithm.html
 delete mode 100644 04 Strategy Library/Fama French Five Factors/04 References.html

diff --git a/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html b/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html
new file mode 100644
index 0000000..c5889c0
--- /dev/null
+++ b/04 Strategy Library/353 Fama French Five Factors/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+    The relationship between return and risk has long been a popular topic for research. Investors have been seeking financial models that quantify risk and use it to estimate the expected return on equity. The Fama French five-factor model, improved from the Fama French three-factor model, is one of the most classic models (Fama and French, 2015). In this post, we will discuss this model and develop a stock-picking strategy based on it.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/353 Fama French Five Factors/02 Method.html b/04 Strategy Library/353 Fama French Five Factors/02 Method.html
new file mode 100644
index 0000000..1fd2f52
--- /dev/null
+++ b/04 Strategy Library/353 Fama French Five Factors/02 Method.html	
@@ -0,0 +1,93 @@
+<p>
+    The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends.
+    Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return.
+    The model is as follows
+</p>
+\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\]
+
+<p>
+where
+</p>
+<ul>
+    <li>MKT is the excess return of the market. It is the return on the value-weighted market portfolio.</li>
+    <li>SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.</li>
+    <li>HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.</li>
+    <li>RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.</li>
+    <li>CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.</li>
+</ul>
+
+<p>
+    Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. 
+</p>
+
+<p>
+    First, we run a Coarse Selection to drop equities which have no fundamental data or have too low prices. Then we select those with the highest dollar volume.
+    Note that a useful technique is used here: we can use Universe.Unchanged to remain the same universe when there is no necessary change, which greatly speeds up the backtest.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    '''Drop securities which have no fundamental data or have too low prices.
+    Select those with highest by dollar volume'''
+
+    if self.Time < self.nextLiquidate:
+        return Universe.Unchanged
+
+    selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5],
+                        key=lambda x: x.DollarVolume, reverse=True)
+
+    return [x.Symbol for x in selected[:self.num_coarse]]   
+</pre>
+</div>
+
+<p>
+    Secondly, in Fine Selection, we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    '''Select securities with highest score on Fama French 5 factors'''
+
+    # Select stocks with these 5 factors:
+    # MKT -- Book value per share: Value
+    # SMB -- TotalEquity: Size
+    # HML -- Operation profit margin: Quality
+    # RMW -- ROE: Profitability
+    # CMA -- TotalAssetsGrowth: Investment Pattern
+    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
+                                and x.FinancialStatements.BalanceSheet.TotalEquity
+                                and x.OperationRatios.OperationMargin.Value
+                                and x.OperationRatios.ROE
+                                and x.OperationRatios.TotalAssetsGrowth]
+
+    # Sort by factors
+    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
+    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
+    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
+    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
+    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
+
+    stockBySymbol = {}
+
+    # Get the rank based on 5 factors for every stock
+    for index, stock in enumerate(sortedByMkt):
+        mktRank = self.beta_m * index
+        smbRank = self.beta_s * sortedBySmb.index(stock)
+        hmlRank = self.beta_h * sortedByHml.index(stock)
+        rmwRank = self.beta_r * sortedByRmw.index(stock)
+        cmaRank = self.beta_c * sortedByCma.index(stock)
+        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
+        stockBySymbol[stock.Symbol] = avgRank
+
+    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
+    symbols = [x[0] for x in sorted_dict]
+
+    # Pick the stocks with the highest scores to long
+    self.longSymbols= symbols[:self.num_long]
+    # Pick the stocks with the lowest scores to short
+    self.shortSymbols = symbols[-self.num_short:]
+
+    return self.longSymbols + self.shortSymbols   
+</pre>
+</div>
diff --git a/04 Strategy Library/353 Fama French Five Factors/03 Results.html b/04 Strategy Library/353 Fama French Five Factors/03 Results.html
new file mode 100644
index 0000000..4d05305
--- /dev/null
+++ b/04 Strategy Library/353 Fama French Five Factors/03 Results.html	
@@ -0,0 +1,8 @@
+<p>
+    In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency.
+</p>
+
+<p>
+    The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years.
+    These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html b/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html
new file mode 100644
index 0000000..f3cdbee
--- /dev/null
+++ b/04 Strategy Library/353 Fama French Five Factors/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_78601a5bd785ca32803c13525c688046.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/353 Fama French Five Factors/05 References.html b/04 Strategy Library/353 Fama French Five Factors/05 References.html
new file mode 100644
index 0000000..4a09697
--- /dev/null
+++ b/04 Strategy Library/353 Fama French Five Factors/05 References.html	
@@ -0,0 +1,5 @@
+<ul>
+    <li>
+      <a href="https://www.sciencedirect.com/science/article/pii/S0304405X14002323" rel="nofollow">A five-factor asset pricing model</a>
+    </li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/Fama French Five Factors/01 Introduction.html b/04 Strategy Library/Fama French Five Factors/01 Introduction.html
deleted file mode 100644
index 12ef392..0000000
--- a/04 Strategy Library/Fama French Five Factors/01 Introduction.html	
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>
-    At QuantConnect, we seek to make financial models ever more accessible to our community. This article will walk through the implementation of a stock selection strategy based on the popular Fama French five-factor financial model.
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html b/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html
deleted file mode 100644
index 0cd9ad3..0000000
--- a/04 Strategy Library/Fama French Five Factors/02 Fama French Five-Factor Model.html	
+++ /dev/null
@@ -1,17 +0,0 @@
-<p>
-    The Fama French five-factor model was proposed in 2014 and is adapted from the Fama French three-factor model (Fama and French, 2015). It builds upon the dividend discount model which states that the value of stocks today is dependent upon future dividends.
-    Fama and French add two factors, investment and profitability, to the dividend discount model to better capture the relationship between risk and return.
-    The model is as follows
-</p>
-\[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML + \beta_r RMW + \beta_c CMA\]
-
-<p>
-where
-</p>
-<ul>
-    <li>MKT is the excess return of the market. It is the return on the value-weighted market portfolio.</li>
-    <li>SMB is the return on a diversified portfolio of small-cap stocks minus the return on a diversified portfolio of big-cap stocks.</li>
-    <li>HML is the difference between the returns on diversified portfolios of stocks with high and low Book-to-Market ratios.</li>
-    <li>RMW is the difference between the returns on diversified portfolios of stocks with robust (high and steady) and weak (low) profitability.</li>
-    <li>CMA is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which we call conservative and aggressive. Here, low/high investment means reinvestment ratio is low/high.</li>
-</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/Fama French Five Factors/03 Algorithm.html b/04 Strategy Library/Fama French Five Factors/03 Algorithm.html
deleted file mode 100644
index 7f04f12..0000000
--- a/04 Strategy Library/Fama French Five Factors/03 Algorithm.html	
+++ /dev/null
@@ -1,69 +0,0 @@
-<p>
-    Taking inspiration from the Fama French five-factor model, we can develop a multi-factor stock selection strategy that focuses on five factors: size, value, quality, profitability, and investment pattern. 
-</p>
-<p>
-    In the following backtest,  we use the terms TotalEquity, BookValuePerShare, OperationProfitMargin, ROE, and TotalAssetsGrowth to account for the five factors, respectively. We then calculate a custom ranking metric for each stock using these five terms. Our algorithm will go long in the five stocks with the highest scores and short the five stocks with the lowest scores.
-</p>
-<div class="section-example-container">
-<pre class="python">
-def FineSelectionFunction(self, fine):
-    '''Select securities with highest score on Fama French 5 factors'''
-
-    # Select stocks with these 5 factors:
-    # MKT -- Book value per share: Value
-    # SMB -- TotalEquity: Size
-    # HML -- Operation profit margin: Quality
-    # RMW -- ROE: Profitability
-    # CMA -- TotalAssetsGrowth: Investment Pattern
-    filtered = [x for x in fine if x.ValuationRatios.BookValuePerShare
-                                and x.FinancialStatements.BalanceSheet.TotalEquity
-                                and x.OperationRatios.OperationMargin.Value
-                                and x.OperationRatios.ROE
-                                and x.OperationRatios.TotalAssetsGrowth]
-
-    # Sort by factors
-    sortedByMkt = sorted(filtered, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
-    sortedBySmb = sorted(filtered, key=lambda x: x.FinancialStatements.BalanceSheet.TotalEquity.Value, reverse=True)
-    sortedByHml = sorted(filtered, key=lambda x: x.OperationRatios.OperationMargin.Value, reverse=True)
-    sortedByRmw = sorted(filtered, key=lambda x: x.OperationRatios.ROE.Value, reverse=True)
-    sortedByCma = sorted(filtered, key=lambda x: x.OperationRatios.TotalAssetsGrowth.Value, reverse=False)
-
-    stockBySymbol = {}
-
-    # Get the rank based on 5 factors for every stock
-    for index, stock in enumerate(sortedByMkt):
-        mktRank = self.beta_m * index
-        smbRank = self.beta_s * sortedBySmb.index(stock)
-        hmlRank = self.beta_h * sortedByHml.index(stock)
-        rmwRank = self.beta_r * sortedByRmw.index(stock)
-        cmaRank = self.beta_c * sortedByCma.index(stock)
-        avgRank = np.mean([mktRank,smbRank,hmlRank,rmwRank,cmaRank])
-        stockBySymbol[stock.Symbol] = avgRank
-
-    sorted_dict = sorted(stockBySymbol.items(), key = lambda x: x[1], reverse = True)
-    symbols = [x[0] for x in sorted_dict]
-
-    # Pick the stocks with the highest scores to long
-    self.longSymbols= symbols[:self.num_long]
-    # Pick the stocks with the lowest scores to short
-    self.shortSymbols = symbols[-self.num_short:]
-
-    return self.longSymbols + self.shortSymbols   
-</pre>
-</div>
-
-<p>
-    In this example, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. You can improve upon this strategy by changing the fundamental factors, the weight of each factor and the rebalance frequency.
-</p>
-
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_78601a5bd785ca32803c13525c688046.html"></iframe>
-    </div>
-</div>
-
-<p>
-    The Fama French five-factor model provides a scientific way to measure asset pricing. For the five aspects that Fama and French mentioned, we used one possible combination in our backtest. We can see from the results that it achieves an annual rate of return around 5% with a max drawdown of 30% over 8 years.
-    These factors perhaps cannot capture a sufficient amount of information on the assets' pricing, and therefore, there are still many aspects can be improved (e.g. the weights of factors, a different set of factors for different kinds of equities,etc.) We encourage you to explore and create better algorithms upon this tutorial!
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/Fama French Five Factors/04 References.html b/04 Strategy Library/Fama French Five Factors/04 References.html
deleted file mode 100644
index a660292..0000000
--- a/04 Strategy Library/Fama French Five Factors/04 References.html	
+++ /dev/null
@@ -1,5 +0,0 @@
-<ul>
-    <li>
-      <a href="https://tevgeniou.github.io/EquityRiskFactors/bibliography/FiveFactor.pdf" rel="nofollow">A five-factor asset pricing model</a>
-    </li>
-</ul>
\ No newline at end of file

From 4af0db1856b075832c5487e363167cd247de6942 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Thu, 22 Aug 2019 21:46:56 +0100
Subject: [PATCH 580/753] Fixes Logic in Momentum and Style Rotation Effect

Instead of the algorithm goes long the top performing ETF and short the
ETF at the bottom as desired, the algorithm was doing the opposite.
Refactors and adds more comments.
---
 .../02 Method.html                            | 47 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 28 insertions(+), 23 deletions(-)

diff --git a/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html
index 2146e81..2efbe7b 100644
--- a/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html	
+++ b/04 Strategy Library/91 Momentum and Style Rotation Effect/02 Method.html	
@@ -6,23 +6,25 @@
 <div class="section-example-container">
 <pre class="python">
 def Initialize(self):
+
     self.SetStartDate(2001, 1, 1)
     self.SetEndDate(2018, 8, 1)
     self.SetCash(100000)
-    self.tickers = [
-                    "IJJ", # iShares S&P MidCap 400 Value Index ETF
-                    "IJS", # iShares S&P SmallCap 600 Value ETF
-                    "IVE", # iShares S&P 500 Value Index ETF
-                    "IVW", # iShares S&P 500 Growth ETF
-                    "IJK", # iShares S&P Mid-Cap 400 Growth ETF
-                    "IJT", # iShares S&P Small-Cap 600 Growth ETF
-                    ]
-    self.symbols = []
-    for ticker in self.tickers:
-        self.symbols.append(self.AddEquity(ticker, Resolution.Daily).Symbol)
-    self.SetWarmUp(timedelta(days=12*20))
-    # save all momentum indicator in the dictionary
-    self.mom = {i:self.MOM(i, 12*20, Resolution.Daily) for i in self.symbols}
+
+    tickers = ["IJJ",   # iShares S&P Mid-Cap 400 Value Index ETF
+               "IJK",   # iShares S&P Mid-Cap 400 Growth ETF
+               "IJS",   # iShares S&P Small-Cap 600 Value ETF
+               "IJT",   # iShares S&P Small-Cap 600 Growth ETF
+               "IVE",   # iShares S&P 500 Value Index ETF
+               "IVW"]   # iShares S&P 500 Growth ETF
+
+    lookback = 12*20
+
+    # Save all momentum indicator into the dictionary
+    self.mom = dict()
+    for ticker in tickers:
+        symbol = self.AddEquity(ticker, Resolution.Daily).Symbol
+        self.mom[symbol] = self.MOM(symbol, lookback)
 </pre>
 </div>
 <p>
@@ -33,12 +35,15 @@
 <div class="section-example-container">
 <pre class="python">
 def Rebalance(self):
+    # Order the MOM dictionary by value
     sorted_mom = sorted(self.mom, key = lambda x: self.mom[x].Current.Value)
-    invested = [x.Key for x in self.Portfolio if x.Value.Invested]
-    for i in invested:
-        if i not in [sorted_mom[0], sorted_mom[1]]:
-            self.Liquidate(i)
-    self.SetHoldings(sorted_mom[0], -0.5)
-    self.SetHoldings(sorted_mom[-1], 0.5)
+
+    # Liquidate the ETFs that are no longer selected
+    for symbol in sorted_mom[1:-1]:
+        if self.Portfolio[symbol].Invested:
+            self.Liquidate(symbol, 'No longer selected')
+
+    self.SetHoldings(sorted_mom[-1], -0.5)   # Short the ETF with lowest MOM
+    self.SetHoldings(sorted_mom[0], 0.5)     # Long the ETF with highest MOM
 </pre>
-</div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html b/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html
index d0bf935..ae7ab5e 100644
--- a/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html	
+++ b/04 Strategy Library/91 Momentum and Style Rotation Effect/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d0b1d400ad21477d83ae2b4f85616318.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_95cffbeec0d003da873b791d3a10f60f.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index 567f8b4..e92eac3 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,7 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
-     91: "d0b1d400ad21477d83ae2b4f85616318",
+     91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",
      113: "9b1291a5f08dcc07df86363e46144084",

From 9bbbe164cd72c202e05e2f5b3d1d3e8d5d0b2fc0 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Mon, 26 Aug 2019 13:44:06 -0700
Subject: [PATCH 581/753] Add Statistical Arbitrage Strategy of Avellaneda &
 Lee, 2008

---
 .../01 Introduction.html                      |  3 +
 .../02 Method.html                            | 62 +++++++++++++++++++
 .../03 Results.html                           |  8 +++
 .../04 Algorithm.html                         |  6 ++
 .../05 Reference.html                         |  5 ++
 5 files changed, 84 insertions(+)
 create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
 create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
 create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
 create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
 create mode 100644 04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
new file mode 100644
index 0000000..490405c
--- /dev/null
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  In this tutorial, we will study the model-driven statistical arbitrage strategies in U.S. stocks market. We employ a combination of Principal Components Analysis (PCA) and Linear Regression to implement this strategy. First, by applying PCA, we get a generalized statistical arbitrage strategy that minimizes exposure to market factors and the asset universe is projected onto its first n (n=3 in our algorithm) orthogonal principal components. Secondly, we model the mean-reverting residuals of the cluster of assets and get their weights based on the level of deviation using linear regression.
+</P>
diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
new file mode 100644
index 0000000..759a3ca
--- /dev/null
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -0,0 +1,62 @@
+<p>
+  There are three elements of the strategy that will promote mean-reversion and the opportunity for statistical arbitrage: (1) Utilize Coarse selection to obtain the initial universe, (2) Employ PCA to historical prices to get the first 3 principal components for dimension reduction, (3) Apply Linear Regression to get the residuals for measuring the price deviation of each stock in the universe.
+  We will show how we apply Coarse selection first.
+</P>
+
+<div class="section-example-container">
+<pre class="python">
+  # Sort the equities in DollarVolume decendingly
+  selected = sorted([x for x in coarse if x.Price > 5],
+                    key=lambda x: x.DollarVolume, reverse=True)
+  symbols = [x.Symbol for x in selected[:self.num_equities]]
+</pre>
+</div>
+
+<p>
+  We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume.
+  Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us
+  reduce the dimension of the feature space and exclude the noise at the same time.
+</P>
+
+<div class="section-example-container">
+  <pre class="python">
+  # Sample data for PCA (smooth it using np.log function)
+  sample = np.log(history.dropna(axis=1))
+  sample -= sample.mean() # Center it column-wise
+
+  # Fit the PCA model for sample data
+  model = PCA().fit(sample)
+
+  # Get the first n_components factors
+  factors = np.dot(sample, model.components_.T)[:,:self.num_components]
+  </pre>
+</div>
+
+<p>
+  Finally, we get to the linear regression part. This part helps us get the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
+  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
+  based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
+  So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
+  All details can be found in the following code snippet.
+</P>
+
+<div class="section-example-container">
+  <pre class="python">
+    # Train Ordinary Least Squares linear model for each stock
+    OLSmodels = {ticker: sm.OLS(sample[ticker], factors).fit() for ticker in sample.columns}
+
+    # Get the residuals from the linear regression after PCA for each stock
+    resids = pd.DataFrame({ticker: model.resid for ticker, model in OLSmodels.items()})
+
+    # Get the Z scores by standarize the given pandas dataframe X
+    zscores = ((resids - resids.mean()) / resids.std()).iloc[-1] # residuals of the most recent day
+
+    # Get the stocks far from mean (for mean reversion)
+    selected = zscores[zscores < -1.5]
+
+    # Return the weights for each selected stock
+    weights = selected * (1 / selected.abs().sum())
+  </pre>
+</div>
+
diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
new file mode 100644
index 0000000..ceddace
--- /dev/null
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -0,0 +1,8 @@
+<p>
+    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
+    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is reasonable. However, there are still many aspects can be improved.
+    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the algorithm only find one or even no candidate, which might be not enough.
+    You might increase the number of universe. Besides, we can develop this strategy to a long & short one (now it is only a long strategy). 
+    You could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more amazing algorithms upon this tutorial.  
+</p>
+
diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
new file mode 100644
index 0000000..78e62f1
--- /dev/null
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cdeb6ddf06f77f02c3a55b8d7568d64b.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html
new file mode 100644
index 0000000..218aff7
--- /dev/null
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/05 Reference.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf" rel="nofollow">Statistical Arbitrage in the U.S. Equities Market</a>
+</li>
+</ul>

From 046e6301923ab0d9013dad6636fb332e2bde1383 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 12:16:31 -0700
Subject: [PATCH 582/753] Update 01 Introduction.html

Made initial edits.
---
 .../01 Introduction.html                               | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 490405c..c3e8b0a 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -1,3 +1,11 @@
 <p>
-  In this tutorial, we will study the model-driven statistical arbitrage strategies in U.S. stocks market. We employ a combination of Principal Components Analysis (PCA) and Linear Regression to implement this strategy. First, by applying PCA, we get a generalized statistical arbitrage strategy that minimizes exposure to market factors and the asset universe is projected onto its first n (n=3 in our algorithm) orthogonal principal components. Secondly, we model the mean-reverting residuals of the cluster of assets and get their weights based on the level of deviation using linear regression.
+  In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
+  based on the paper <a target=_BLANK href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf">
+  Statistical Arbitrage in teh U.S. Equities Market</a>.
+</p>
+<p> 
+  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project the first n (n=3 in our algorithm) 
+  orthogonal principal components on our asset universe. Then we will model the mean-reverting residuals of our assets. 
+  Next we will create our model using linear regression. Each factor will have a weight or coefficient equal to the residuals' level 
+  of deviation from the mean. 
 </P>

From e32237d82353d929f93e115fab5cde854660201d Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 13:35:31 -0700
Subject: [PATCH 583/753] Update 02 Method.html

Submit initial edits.
---
 .../02 Method.html                                   | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 759a3ca..7118a9f 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,6 +1,8 @@
+
+<h3>Method</h3>
+<h3>Step 1: Select our universe</h3> 
 <p>
-  There are three elements of the strategy that will promote mean-reversion and the opportunity for statistical arbitrage: (1) Utilize Coarse selection to obtain the initial universe, (2) Employ PCA to historical prices to get the first 3 principal components for dimension reduction, (3) Apply Linear Regression to get the residuals for measuring the price deviation of each stock in the universe.
-  We will show how we apply Coarse selection first.
+  ...mean-reversion and opportunity for statistical arbitrage... 
 </P>
 
 <div class="section-example-container">
@@ -12,6 +14,7 @@
 </pre>
 </div>
 
+<h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
   We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume.
   Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us
@@ -32,14 +35,15 @@
   </pre>
 </div>
 
+<h3>Step 3: Measure price deviation</h3>  
 <p>
-  Finally, we get to the linear regression part. This part helps us get the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
   If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
   it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
   based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
   So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
   All details can be found in the following code snippet.
-</P>
+</p>
 
 <div class="section-example-container">
   <pre class="python">

From 5d9d3a96d14563fda3b56f4b61c460111b1b48c2 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 13:36:18 -0700
Subject: [PATCH 584/753] Update 01 Introduction.html

Submit word change.
---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index c3e8b0a..0021b43 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -1,6 +1,6 @@
 <p>
   In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
-  based on the paper <a target=_BLANK href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf">
+  derived from the paper <a target=_BLANK href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf">
   Statistical Arbitrage in teh U.S. Equities Market</a>.
 </p>
 <p> 

From 520a8fcb48c8266e2212b171f7796f9c7349c97f Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 13:40:56 -0700
Subject: [PATCH 585/753] Update 03 Results.html

Submit initial changes.
---
 .../03 Results.html                             | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index ceddace..8734b4f 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,8 +1,15 @@
+<h3>Results</h3>
 <p>
-    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is reasonable. However, there are still many aspects can be improved.
-    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the algorithm only find one or even no candidate, which might be not enough.
-    You might increase the number of universe. Besides, we can develop this strategy to a long & short one (now it is only a long strategy). 
-    You could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more amazing algorithms upon this tutorial.  
+    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
+    We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
+    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is 
+    reasonable. However, there are still many aspects can be improved.
+</p>
+<p>
+    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the 
+    algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
+    develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
+    level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 
+    amazing algorithms upon this tutorial.  
 </p>
 

From 91d15af3f084a0e19821f8a5e13741097dcb4a6e Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 13:41:58 -0700
Subject: [PATCH 586/753] Update 04 Algorithm.html

Submit initial edits.
---
 .../04 Algorithm.html                                            | 1 +
 1 file changed, 1 insertion(+)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
index 78e62f1..f404e0b 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
@@ -1,3 +1,4 @@
+<h3>Algorithm</h3>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">

From 360338b448a4f877bf731e572a5832f62a9585b2 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 15:56:41 -0700
Subject: [PATCH 587/753] Update 01 Introduction.html

Add background to strategy.
---
 .../01 Introduction.html                      | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 0021b43..626bd4a 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -1,11 +1,14 @@
 <p>
-  In this tutorial, we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
+  In this tutorial we will take a close look at a principal component analysis (PCA)-based statistical arbitrage strategy
   derived from the paper <a target=_BLANK href="https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf">
-  Statistical Arbitrage in teh U.S. Equities Market</a>.
+  Statistical Arbitrage in the U.S. Equities Market</a>.
 </p>
-<p> 
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project the first n (n=3 in our algorithm) 
-  orthogonal principal components on our asset universe. Then we will model the mean-reverting residuals of our assets. 
-  Next we will create our model using linear regression. Each factor will have a weight or coefficient equal to the residuals' level 
-  of deviation from the mean. 
-</P>
+<p>
+  Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
+  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often referenced with 
+  <a traget="_BLANK" href="https://www.quantconnect.com/tutorials/strategy-library/pairs-trading-with-stocks">Pairs Trading</a>. 
+  In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
+  Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 
+  from ETF-based strategies. 
+</p>
+

From cf44cdbf7972d0b7fcb2350b72dccd7df1b0f15a Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 15:57:35 -0700
Subject: [PATCH 588/753] Update 01 Introduction.html

Add grammatical change.
---
 .../01 Introduction.html                                        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 626bd4a..4b469ed 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -5,7 +5,7 @@
 </p>
 <p>
   Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
-  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often referenced with 
+  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often discussed in conjunction with 
   <a traget="_BLANK" href="https://www.quantconnect.com/tutorials/strategy-library/pairs-trading-with-stocks">Pairs Trading</a>. 
   In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
   Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 

From 353d0d2b839e5083386902fbfc459f8595d6f935 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 15:59:38 -0700
Subject: [PATCH 589/753] Update 02 Method.html

Update steps.
---
 .../02 Method.html                                           | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 7118a9f..29c85cf 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,8 +1,7 @@
-
-<h3>Method</h3>
 <h3>Step 1: Select our universe</h3> 
 <p>
-  ...mean-reversion and opportunity for statistical arbitrage... 
+  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project some orthogonal principal components on 
+  our asset universe. Then we will model the mean-reverting residuals of our assets from our regression line.   
 </P>
 
 <div class="section-example-container">

From 29f49177d586526762b81b178c9a2e3df68afbcc Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 16:01:20 -0700
Subject: [PATCH 590/753] Update 03 Results.html

Remove redundancies.
---
 .../03 Results.html                                      | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index 8734b4f..d556c32 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,12 +1,11 @@
-<h3>Results</h3>
 <p>
-    In this tutorial, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
+    In our alorithm, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
     We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with Linear regression to measure the deviation level is 
-    reasonable. However, there are still many aspects can be improved.
+    The performance is generally good, which indicates using PCA combined with linear regression to measure the deviation level is 
+    reasonable. 
 </p>
 <p>
-    For example, we could expand the original coarse-selected universe. Now we only used 20 equities in this example, and sometimes the 
+    To tune the model, we could expand the original coarse-selected universe. The current universe contains 20 equities, and sometimes the 
     algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
     develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
     level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 

From d8ab3e770a796e91f67647f54c7c86238dcad0d0 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 16:01:39 -0700
Subject: [PATCH 591/753] Update 04 Algorithm.html

Remove redundancies.
---
 .../04 Algorithm.html                                            | 1 -
 1 file changed, 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html
index f404e0b..78e62f1 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/04 Algorithm.html	
@@ -1,4 +1,3 @@
-<h3>Algorithm</h3>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">

From 94e32534461ee079fdd319b5e87e13d0d19aefe3 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 16:08:53 -0700
Subject: [PATCH 592/753] Update 02 Method.html

Add description changes.
---
 .../02 Method.html                                    | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 29c85cf..d0295b8 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,7 +1,6 @@
 <h3>Step 1: Select our universe</h3> 
 <p>
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and project some orthogonal principal components on 
-  our asset universe. Then we will model the mean-reverting residuals of our assets from our regression line.   
+  We will select our universe of stocks by dropping those with prices lower than $5 and pick the ones with the highest dollar volume.   
 </P>
 
 <div class="section-example-container">
@@ -15,9 +14,9 @@ <h3>Step 1: Select our universe</h3>
 
 <h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
-  We see that in Coarse selection we drop stocks with prices lower than $5 and pick the ones with the highest dollar volume.
-  Then, we go to the PCA part. In this part, based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the historical close values). This helps us
-  reduce the dimension of the feature space and exclude the noise at the same time.
+  First, we will apply PCA to minimizes our algorithm's exposure to market factors and select k principal components on 
+  our asset universe. Based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the 
+  historical close values). This helps us reduce the dimension of the feature space and exclude the noise at the same time.
 </P>
 
 <div class="section-example-container">
@@ -36,7 +35,7 @@ <h3>Step 2: Reduce dimensions to three principal components</h3>
 
 <h3>Step 3: Measure price deviation</h3>  
 <p>
-  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  Then we will model the mean-reverting residuals of our assets from our regression line. We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
   If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
   it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
   based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 

From 301888ca3c6ff637aae934b45143ead18ea0d84e Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 16:23:48 -0700
Subject: [PATCH 593/753] Update 03 Results.html

Add changes.
---
 .../03 Results.html                                 | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index d556c32..681b990 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -1,14 +1,11 @@
 <p>
     In our alorithm, the portfolio is rebalanced every 30 days and the backtest period runs from Jan 2010 to Aug 2019. 
-    We can see from the results that it achieves an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years.
-    The performance is generally good, which indicates using PCA combined with linear regression to measure the deviation level is 
-    reasonable. 
+    Our result is an annual rate of return over 7% with a max drawdown of around 40% for nearly 10 years. Our performance indicates 
+    using PCA combined with linear regression to measure the deviation level is reasonable. 
 </p>
 <p>
-    To tune the model, we could expand the original coarse-selected universe. The current universe contains 20 equities, and sometimes the 
-    algorithm only find one or even no candidate, which might be not enough. You might increase the number of universe. Besides, we can 
-    develop this strategy to a long & short one (now it is only a long strategy). You could also come up with another way to measure the 
-    level of deviation or change the rebalance frequency of the algorithm(30 days in this example). We sincerely hope you create more 
-    amazing algorithms upon this tutorial.  
+    To tune the model, we could expand our universe of stocks beyond the current 20 equities. 
+    We could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm 
+    (30 days in this example). 
 </p>
 

From 448faa9aacb9de14feda2d74b9d01051ac04d2dc Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 16:30:53 -0700
Subject: [PATCH 594/753] Update 02 Method.html

Add changes.
---
 .../02 Method.html                             | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index d0295b8..f3a7449 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -14,9 +14,9 @@ <h3>Step 1: Select our universe</h3>
 
 <h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
-  First, we will apply PCA to minimizes our algorithm's exposure to market factors and select k principal components on 
-  our asset universe. Based on historical close values, we perform PCA to get the first 3 principal components of the feature space (formed by the 
-  historical close values). This helps us reduce the dimension of the feature space and exclude the noise at the same time.
+  We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  The number of principal components or eigenvectors we use is variable. In our algorithm use 3 principal components of the feature space formed by the 
+  historical close values. This helps us reduce the dimension of the feature space and exclude the noise at the same time.
 </P>
 
 <div class="section-example-container">
@@ -35,12 +35,14 @@ <h3>Step 2: Reduce dimensions to three principal components</h3>
 
 <h3>Step 3: Measure price deviation</h3>  
 <p>
-  Then we will model the mean-reverting residuals of our assets from our regression line. We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
-  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it more weight in the portfolio. Similarly, if the absolute value of the residual is small,
-  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get their z scores. Then, 
-  based on the z scores, it is easy to detect the level of price deviation. Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
+  We will model the mean-reverting residuals of our assets from our regression line. 
+  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
+  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it 
+  more weight in the portfolio. Similarly, if the absolute value of the residual is small,
+  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get 
+  their z scores. Then, based on the z scores, it is easy to detect the level of price deviation. 
+  Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
   So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
-  All details can be found in the following code snippet.
 </p>
 
 <div class="section-example-container">

From 071658ed3251d858cba65d574509cfa542157203 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Wed, 28 Aug 2019 17:10:10 -0700
Subject: [PATCH 595/753] Update 02 Method.html

Add changes.
---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index f3a7449..01ba081 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -15,8 +15,8 @@ <h3>Step 1: Select our universe</h3>
 <h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
   We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
-  The number of principal components or eigenvectors we use is variable. In our algorithm use 3 principal components of the feature space formed by the 
-  historical close values. This helps us reduce the dimension of the feature space and exclude the noise at the same time.
+  The number of principal components or eigenvectors we use is variable. For the sake of demonstration we chose 3 components to account for the bulk of the variance.
+  In our algorithm use 3 principal components of the feature space formed by the historical close values. 
 </P>
 
 <div class="section-example-container">

From 687fcfd7a4add970af24c42a4e9d9ba8bf1edade Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:26:59 -0700
Subject: [PATCH 596/753] Update 01 Introduction.html

---
 .../01 Introduction.html                                    | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html
index 4b469ed..42d452d 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/01 Introduction.html	
@@ -4,11 +4,11 @@
   Statistical Arbitrage in the U.S. Equities Market</a>.
 </p>
 <p>
-  Statistical arbitrage strategies uses mean version models to take advantage of pricing inefficiencies between groups of correlated 
-  securities. This class of short-term financial trading strategies produce moves contrarian with the market and are often discussed in conjunction with 
+  Statistical arbitrage strategies uses mean-reversion models to take advantage of pricing inefficiencies between groups of correlated 
+  securities. This class of short-term financial trading strategies produce moves that can contrarian to the broader market movement and are often discussed in conjunction with 
   <a traget="_BLANK" href="https://www.quantconnect.com/tutorials/strategy-library/pairs-trading-with-stocks">Pairs Trading</a>. 
   In our algorithm, we will be using a PCA-based approach as opposed to an ETF-based approach to limit our universe of stocks. 
-  Backtests from the period of 1997-2007 support our strategy by showing PCA-based strategies have Sharpe ratios that outperform ratios 
+  Backtests from the period 1997-2007 support our strategy by showing that PCA-based strategies have Sharpe ratios that outperform Sharpe ratios 
   from ETF-based strategies. 
 </p>
 

From d89d6c2b8e173efb40b80e5d72a790a7da54f5f3 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:35:53 -0700
Subject: [PATCH 597/753] Update 02 Method.html

---
 .../02 Method.html                            | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 01ba081..535a282 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -1,6 +1,6 @@
 <h3>Step 1: Select our universe</h3> 
 <p>
-  We will select our universe of stocks by dropping those with prices lower than $5 and pick the ones with the highest dollar volume.   
+  We will select our universe of stocks by dropping securities with prices lower than $5 and pick the ones with the highest dollar traded volume.   
 </P>
 
 <div class="section-example-container">
@@ -14,9 +14,9 @@ <h3>Step 1: Select our universe</h3>
 
 <h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
-  We want to minimize our algorithm's exposure to market factors. PCA enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
-  The number of principal components or eigenvectors we use is variable. For the sake of demonstration we chose 3 components to account for the bulk of the variance.
-  In our algorithm use 3 principal components of the feature space formed by the historical close values. 
+  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  Based on the results found in the cited paper, and for the sake of demonstration, we chose 3 components to account for the bulk of the variance.
+  In our algorithm, the 3 principal components of the feature space are formed by the historical close values. 
 </P>
 
 <div class="section-example-container">
@@ -35,14 +35,14 @@ <h3>Step 2: Reduce dimensions to three principal components</h3>
 
 <h3>Step 3: Measure price deviation</h3>  
 <p>
-  We will model the mean-reverting residuals of our assets from our regression line. 
-  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation measured by the residual. 
-  If the absolute value of the residual is large, it means that the level of price deviation is high and hence we should give it 
+  We will model the mean-reverting residuals of our assets from a regression line. 
+  We use linear regression to derive the weight of each stock in the portfolio based on its price deviation, which is measured by the residual. 
+  If the absolute value of a stock's residual is large, it means that the level of price deviation is high and we should give it 
   more weight in the portfolio. Similarly, if the absolute value of the residual is small,
-  it is reasonable to give the stock less weight in the portfolio. Therefore, we could first standardize the residuals to get 
-  their z scores. Then, based on the z scores, it is easy to detect the level of price deviation. 
-  Specifically, the level of deviation is higher when the absolute values of the z scores are large. 
-  So it is natural to use the inverse of the absolute values of the z scores as a measurement of the weights of the portfolio. 
+  it is reasonable to give the stock less weight in the portfolio. To facilitate this, we can first standardize the residuals to get 
+  their z-scores. Then, based on the z-scores, it is easy to detect the level of price deviation. 
+  Specifically, the level of deviation is higher when the absolute values of the z-scores are large. 
+  From this it is natural to use the inverse of the absolute values of the z-scores as a measurement of the weights of the portfolio. 
 </p>
 
 <div class="section-example-container">

From f80a4677ec1fa6eb04aa515f293c351115298a85 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:37:01 -0700
Subject: [PATCH 598/753] Update 03 Results.html

---
 .../03 Results.html                                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html
index 681b990..5c3f0f7 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/03 Results.html	
@@ -4,7 +4,7 @@
     using PCA combined with linear regression to measure the deviation level is reasonable. 
 </p>
 <p>
-    To tune the model, we could expand our universe of stocks beyond the current 20 equities. 
+    To tune the model, we could expand our universe of stocks beyond the current 20 equities or incorporate more PCA components.
     We could also come up with another way to measure the level of deviation or change the rebalance frequency of the algorithm 
     (30 days in this example). 
 </p>

From 901bf8cdc0f4ed4ca5bdbff7cace5c489be8a759 Mon Sep 17 00:00:00 2001
From: Jack Simonson <46326528+simonsonjack@users.noreply.github.com>
Date: Wed, 28 Aug 2019 17:38:00 -0700
Subject: [PATCH 599/753] Update 02 Method.html

---
 .../02 Method.html                                              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html
index 535a282..bdbfa32 100644
--- a/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/211 Mean-Reversion Statistical Arbitrage Strategy in Stocks/02 Method.html	
@@ -14,7 +14,7 @@ <h3>Step 1: Select our universe</h3>
 
 <h3>Step 2: Reduce dimensions to three principal components</h3>
 <p>
-  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
+  We want to minimize our algorithm's exposure to market factors. PCA is a procedure that extracts uncorrelated components of a possibly-correlated set of observations to reveal the factors that contribute most to a the variance of the observations as a whole. Applying PCA to the data above enables us to reduce dimensionality and select the most relevant market factors to shape our asset universe.
   Based on the results found in the cited paper, and for the sake of demonstration, we chose 3 components to account for the bulk of the variance.
   In our algorithm, the 3 principal components of the feature space are formed by the historical close values. 
 </P>

From 5842ba6028be9ce82b3dacf8e7f180d40cf60f52 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Thu, 29 Aug 2019 15:18:34 -0700
Subject: [PATCH 600/753] Adds Fama & French Factor File to Upcoming Tutorial

---
 .../F-F_Research_Data_Factors_daily.CSV       | 24522 ++++++++++++++++
 1 file changed, 24522 insertions(+)
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV b/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV
new file mode 100644
index 0000000..ac04c74
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV	
@@ -0,0 +1,24522 @@
+This file was created by CMPT_ME_BEME_RETS_DAILY using the 201906 CRSP database.
+The Tbill return is the simple daily rate that, over the number of trading days
+in the month, compounds to 1-month TBill rate from Ibbotson and Associates Inc.
+
+,Mkt-RF,SMB,HML,RF
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+20190628,    0.68,    0.64,    0.60,   0.009
+
+Copyright 2019 Kenneth R. French

From 5a40511ba4be28644fff102c34991a5e6ba5ab73 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Thu, 29 Aug 2019 15:59:06 -0700
Subject: [PATCH 601/753] Changed to a shorter folder name

test if the long folder name caused error in downloading
---
 .../F-F_Research_Data_Factors_daily.CSV                           | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 04 Strategy Library/{354 Expected Idiosyncratic Skewness Using Fama and French Factors => 354 Expected Idiosyncratic Skewness}/F-F_Research_Data_Factors_daily.CSV (100%)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV b/04 Strategy Library/354 Expected Idiosyncratic Skewness/F-F_Research_Data_Factors_daily.CSV
similarity index 100%
rename from 04 Strategy Library/354 Expected Idiosyncratic Skewness Using Fama and French Factors/F-F_Research_Data_Factors_daily.CSV
rename to 04 Strategy Library/354 Expected Idiosyncratic Skewness/F-F_Research_Data_Factors_daily.CSV

From f3e34d1878c8f3fc98fc7da36bf6b48d4969afe9 Mon Sep 17 00:00:00 2001
From: Ethan Lee <ethan.lee@quantconnect.com>
Date: Fri, 30 Aug 2019 13:33:20 -0700
Subject: [PATCH 602/753] Update introduction link for the new section

---
 03 Open Source/00 Introduction/Introduction.html | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index d895560..2fef38d 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -6,10 +6,10 @@
 i.tutorial-link-icon { position: absolute; top: 15px; right: 15px } 
 </style>
 
-<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/desktop-charting-with-lean">
+<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/debugging-python">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-list-alt"></i> 100 Desktop Charting with LEAN</h4>    
-    <p>Guide to using the desktop charting environment that comes with LEAN (UX v1.0).</p>
+    <h4><i class="fa fa-list-alt"></i> Debugging Python</h4>
+    <p>Explore different methods to debug python algorithms.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>
@@ -17,7 +17,7 @@ <h4><i class="fa fa-list-alt"></i> 100 Desktop Charting with LEAN</h4>
 <a class="tutorial-link docs-internal-link" href="/tutorials/open-source/backtesting-from-visual-studio">
   <div class="tutorial-box">    
     <h4><i class="fa fa-windows"></i> 101 Backtesting from Visual Studio</h4>    
-    <p>VIsual Studio plugin integrated with the QuantConnect API.</p>
+    <p>Visual Studio plugin integrated with the QuantConnect API.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
 </a>

From 257541687fca48242aadbac5886065e915c4b15d Mon Sep 17 00:00:00 2001
From: Ethan Lee <ethan.lee@quantconnect.com>
Date: Fri, 30 Aug 2019 13:43:11 -0700
Subject: [PATCH 603/753] Add course code

---
 03 Open Source/00 Introduction/Introduction.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 2fef38d..744480f 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -8,7 +8,7 @@
 
 <a class="tutorial-link docs-internal-link" href="/tutorials/open-source/debugging-python">
   <div class="tutorial-box">    
-    <h4><i class="fa fa-list-alt"></i> Debugging Python</h4>
+    <h4><i class="fa fa-list-alt"></i> 100 Debugging Python</h4>
     <p>Explore different methods to debug python algorithms.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>

From 735009dd82bce5d69ef84d023d7cdaf9afa08d56 Mon Sep 17 00:00:00 2001
From: Ethan Lee <ethan.lee@quantconnect.com>
Date: Fri, 6 Sep 2019 11:13:46 -0700
Subject: [PATCH 604/753] Add new strategy mapping for search table

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e855425..6f45a59 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -513,6 +513,15 @@
         'description' => "Invests in stocks with low P/E ratio.",
         'tags'=>'Beginner,Fundamental Factors,Equities'
     ],
+    [
+        'name' => 'Mean-Reversion Statistical Arbitrage Strategy in Stocks',
+        'link' => 'strategy-library/mean-reversion-statistical-arbitrage-strategy-in-stocks',
+        'sources' => [
+            'NYU' => 'https://www.math.nyu.edu/faculty/avellane/AvellanedaLeeStatArb071108.pdf'
+        ],
+        'description' => "Apply statistical arbitrage to take advantage of pricing inefficiencies in stocks.",
+        'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
+    ],
 ];
 
 ?>

From e461046b7fc884b5a7afad293388c79761fdcecb Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 6 Sep 2019 11:41:05 -0700
Subject: [PATCH 605/753] Add startegy mapping for search table

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e855425..0e5cf5a 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -513,6 +513,15 @@
         'description' => "Invests in stocks with low P/E ratio.",
         'tags'=>'Beginner,Fundamental Factors,Equities'
     ],
+    [
+        'name' => 'Fama French Five Factors',
+        'link' => 'strategy-library/fama-french-five-factors',
+        'sources' => [
+            'NYU' => 'https://www.quantpedia.com/Screener/Details/78'
+        ],
+        'description' => "Stock selecting strategy based on Fama-French Five Factors Model.",
+        'tags'=>'Long Short,Fundamental Factors,Equities'
+    ],
 ];
 
 ?>

From f4a3ee8a75773789aadc3c9740479ce53ec3b79b Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 6 Sep 2019 11:42:26 -0700
Subject: [PATCH 606/753] Add strategy mapping for search table

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 0e5cf5a..bbce99e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -517,7 +517,7 @@
         'name' => 'Fama French Five Factors',
         'link' => 'strategy-library/fama-french-five-factors',
         'sources' => [
-            'NYU' => 'https://www.quantpedia.com/Screener/Details/78'
+            'NYU' => 'https://www.sciencedirect.com/science/article/pii/S0304405X14002323'
         ],
         'description' => "Stock selecting strategy based on Fama-French Five Factors Model.",
         'tags'=>'Long Short,Fundamental Factors,Equities'

From e0d30dbd929b2e47444f86e86b7eb9535aa4ea85 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Fri, 6 Sep 2019 17:16:24 -0700
Subject: [PATCH 607/753] Tutorial - Expected Idiosyncratic Skewness

NYU Strategy -- Expected Idiosyncratic Skewness
---
 .../01 Abstract.html                          |  3 ++
 .../02 Theory.html                            |  7 +++
 .../03 Data Description.html                  |  3 ++
 .../04 Method.html                            | 50 +++++++++++++++++++
 .../05 Conclusion and Future Work.html        | 13 +++++
 .../06 Algorithm.html                         |  6 +++
 .../07 References.html                        | 11 ++++
 7 files changed, 93 insertions(+)
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
 create mode 100644 04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
new file mode 100644
index 0000000..9b7d744
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The annual return during the period of July 1, 2009 to July 1, 2019 is 21.0%.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
new file mode 100644
index 0000000..b1ae556
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
@@ -0,0 +1,7 @@
+<p>
+	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors (“lotto investors”) have a preference for positive skewness while others (“traditional investors”) are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
+</p>
+
+<p>
+	Despite the theoretical basis for the pricing effects of skewness preference, empirically testing the relation is not straightforward as expected skewness is difficult to measure. BMV accounts for the phenomenon that lagged skewness alone does not adequately forecast skewness by presenting a cross-sectional model of expected skewness using additional predictive variables. Using their model, they reaffirm the existing theory that expected idiosyncratic skewness and returns are negatively correlated. Notably, they find the Fama-French alpha of a low-expected-skewness quintile exceeds the alpha of a high-expected-skewness quintile by 1.00% per month. Furthermore, the Fama-MacBeth cross-sectional regressions have statistically significant, negative coefficients. Besides, BMV finds that the expected skewness helps explain how stocks with low idiosyncratic volatility have high expected returns.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
new file mode 100644
index 0000000..3aa2885
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -0,0 +1,3 @@
+<p>
+	To execute our algorithm, we will use daily data from Kenneth French’s <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html">Data Library</a> that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to our Github cloud. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect’s Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this <a href="http://boyer.byu.edu/Research/skew/skewmethodology.pdf">online technical appendix</a> for descriptions of these variables.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
new file mode 100644
index 0000000..b864049
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
@@ -0,0 +1,50 @@
+<p>
+	We can develop a model of estimated expected idiosyncratic skewness using Fama-French three factors. Lower expected idiosyncratic skewness will predict a higher alpha. We will let the investment horizon over which investors are hoping to experience an extreme positive outcome be 1 month. And, let S(t) denote the set of trading days in the current month, and let N(t) denote the number of days in this set.
+</p>
+
+<h3>Step 1: Getting Fama-French three-factor regression residuals</h3>
+
+<p>
+	Let \(\epsilon_{i,d}\) be the regression residual using the Fama and French (1993) three-factor model on day d for firm i, where the regression coefficients that define this residual are estimated using daily data for days in S(t) as the time-series regression below.
+</p>
+
+\[R_{i,d} - R_{f,d} = \alpha_i + \beta_i [R_{M,d} - R_{f,d}] + s_i SMB_{d} + h_i HML_{d} + \epsilon_{i,d}\]
+
+<p>
+	for all day \(d \in S(t)\) and each \(i = 1,2,\dots,N\).
+</p>
+
+<h3>Step 2: Estimating historical idiosyncratic moments</h3>
+
+<p>
+	Let \(iv_{i,t}\) and \(is_{i,t}\) denote historical estimates of idiosyncratic volatility and skewness (respectively) for firm i using daily data for all days in S(t). We can then define \(iv_{i,t}\) and \(is_{i,t}\) as:
+</p>
+
+\[iv_{i,t} = \left( \frac{1}{N(t) - 1} \sum_{d\in S(t)} \epsilon_{i,d}^2 \right)^{1/2}\]
+
+\[is_{i,t} = \frac{1}{N(t) - 2} \frac{ \sum_{d\in S(t)} \epsilon_{i,d}^3 } { iv_{i,t}^{3/2} }\]
+
+<h3>Step 3: Estimating expected idiosyncratic skewness</h3>
+
+<p>
+	We need measures of expected skewness over a horizon of 1 months for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
+</p>
+
+\[is_{i,t} = \beta_0^t + \beta_1^t is_{i,t-1} + \beta_2^t iv_{i,t-1} + \varepsilon_{i,t}\]
+
+<p>
+	Superscripts on regression parameters are included to emphasize that we estimate these parameters using information observable at the end of month t. We then use the regression parameters from equation above, along with information observable at the end of each month t, to estimate expected skewness for each firm,
+</p>
+
+\[ E_t[is_{i,t+1}] = \beta_0^t + \beta_1^t is_{i,t} + \beta_2^t iv_{i,t} \]
+
+<p>
+	This approach provides feasible estimates of each month’s expected skewness and accounts for variation between historical moments and expected skewness across time.
+</p>
+
+<h3>Step 4: Generating trading signals</h3>
+
+<p>
+	At the end of each month, we use the results of equation above to sort stocks by expected idiosyncratic skewness. We construct our universe using the lowest 5% of expected skewness, and long our assets to construct a value-weighted portfolio.
+</p>
+
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html
new file mode 100644
index 0000000..0252ef9
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/05 Conclusion and Future Work.html	
@@ -0,0 +1,13 @@
+<p>
+	Before the BMV paper was published in 2009, a number of theories on the pricing premium for stocks with idiosyncratic skewness existed, but lacked supporting empirical evidence of the relationship between idiosyncratic skewness and returns. BMV fills this void by estimating a model of predicted skewness and using predicted skewness to explain the cross-section of returns. The paper finds that lagged idiosyncratic volatility is a stronger predictor of skewness than lagged idiosyncratic skewness.
+</p>
+
+<p>
+	In this implementation, we rely on idiosyncratic volatility and skewness to predict idiosyncratic skewness. Interested users can build from this implementation by trying the following extensions:
+</p>
+
+<ol>
+	<li> Including a number of firm-specific variables to improve predictive power for expected idiosyncratic skewness;
+	<li> Using different investment horizons such as 3 months, 6 months, 1 year;
+	<li> Adding more lags in the time-series regression for both expected and historical idiosyncratic skewness.
+</ol>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
new file mode 100644
index 0000000..af8d286
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0e098feaa8500e8dd07c0e43093b0326.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html
new file mode 100644
index 0000000..337cf0a
--- /dev/null
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/07 References.html	
@@ -0,0 +1,11 @@
+<ol>
+  <li>
+    Boyer B, Mitton T, Vorkink K. Expected idiosyncratic skewness. The Review of Financial Studies. 2009 Jun 3;23(1):169-202. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.825.592&rep=rep1&type=pdf">Online Copy</a>
+  </li>
+  <li>
+	Fama EF, French KR. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 1993 Feb 1;33(1):3-56. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.139.5892&rep=rep1&type=pdf">Online Copy</a>
+  </li>
+  <li>
+	Mitton T, Vorkink K. Equilibrium underdiversification and the preference for skewness. The Review of Financial Studies. 2007 Jan 29;20(4):1255-88. <a href="https://www.researchgate.net/profile/Keith_Vorkink/publication/5217123_Equilibrium_Underdiversification_and_the_Preference_for_Skewness/links/0deec521f4b5803dd1000000/Equilibrium-Underdiversification-and-the-Preference-for-Skewness.pdf">Online Copy</a>
+  </li>
+<ol>

From 5b318eb5751eb70778b82ae4be6795d1359c025d Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Mon, 9 Sep 2019 15:53:54 -0700
Subject: [PATCH 608/753] Add Strategy of Keloharju, T. Linnainmaa & Nyberg,
 2014

---
 .../01 Strategy Library.php                   | 10 +++
 .../01 Introduction.html                      | 19 +++++
 .../02 Method.html                            | 70 +++++++++++++++++++
 .../03 Results.html                           |  9 +++
 .../04 Algorithm.html                         |  6 ++
 .../05 Reference.html                         |  5 ++
 6 files changed, 119 insertions(+)
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html
 create mode 100644 04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..eca1bd5 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,16 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+    [
+        'name' => 'Seasonality Effect based on Same-Calendar Month Returns',
+        'link' => 'strategy-library/seasonality-effect-based-on-same-calendar-month-returns',
+        'sources' => [
+            'NYU' => 'https://www.nber.org/papers/w20815.pdf'
+        ],
+        'description' => "A strategy that takes long and short positions based on historical same-calendar month returns",
+        'tags'=>'Seasonality,Stocks,Universe Selection'
+
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html
new file mode 100644
index 0000000..17958c4
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/01 Introduction.html	
@@ -0,0 +1,19 @@
+<p>
+  This tutorial implements a seasonality strategy that trades based on historical same-calendar-month returns. The strategy is
+  derived from the paper <a target=_BLANK href="https://www.nber.org/papers/w20815.pdf">Common Factors in Return Seasonalities</a>. 
+</p>
+<p>
+  A great deal of research on seasonality effects in algorithmic trading exists. Seasonality patterns are well documented in stock returns across numerous
+  countries and in commodity and country portfolios. The phenomenon’s occurrence is not isolated to specific stocks or monthly time intervals, for example, seasonality is observed at the daily frequency as well. Our implementation reflects the existing research. 
+</p>
+<p>
+  In our algorithm, we will first use a coarse selection filter function to narrow down our universe to the top 100 liquid securities with a price greater than $5.
+</p>
+<p>
+  Next, for each security in the universe, we will calculate the monthly return for the same-calendar month of the previous year. For example, if we implement this strategy on a backtest for the period of August 2019, we would base our long and short positions on monthly returns from August 2018. We will long the securities with top monthly returns and short those with the bottom monthly returns. 
+</p>
+<p>
+  At the end of each month we will rebalance and repeat the strategy. The following section offers further explanation of how to implement each step of the strategy.
+</p>
+  
+  
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
new file mode 100644
index 0000000..4bcd1ee
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
@@ -0,0 +1,70 @@
+<h3>Step 1: Select our universe</h3>
+<p>
+    We first select liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from “Common Factors” suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the <a target=”_blank” href=”https://www.quantconnect.com/tutorials/strategy-library”>library</a> do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
+</p>  
+
+<div class="section-example-container">
+<pre class="python">
+    # Sort the securities with prices > 5 in DollarVolume decendingly
+    selected = sorted([x for x in coarse if x.Price > 5],
+                        key=lambda x: x.DollarVolume, reverse=True)
+
+    # Get securities after coarse selection
+    symbols = [x.Symbol for x in selected[:self.num_coarse]]
+</pre>
+</div>
+
+<h3>Step 2: Calculate the same-calendar month returns of the previous year</h3>
+<p>
+    “Common Factors” indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    # Get historical close data for coarse-selected symbols of the same calendar month
+    start = self.Time.replace(day = 1, year = self.Time.year-1)
+    end = Expiry.EndOfMonth(start) - timedelta(1)
+    history = self.History(symbols, start, end, Resolution.Daily).close.unstack(level=0)
+
+    # Get the same calendar month returns for the symbols
+    MonthlyReturn = {ticker: prices.iloc[-1]/prices.iloc[0] for ticker, prices in history.iteritems()}
+
+    # Sorted the values of monthly return
+    sortedReturn = sorted(MonthlyReturn.items(), key=lambda x:x[1], reverse=True)
+
+    # Get the symbols to long / short
+    self.longSymbols = [x[0] for x in sortedReturn[:self.num_long]]
+    self.shortSymbols = [x[0] for x in sortedReturn[-self.num_short:]]
+
+    # Note that self.longSymbols/self.shortSymbols contains strings instead of symbols
+    return [x for x in symbols if str(x) in self.longSymbols + self.shortSymbols]
+  </pre>
+</div>
+
+<h3>Step 3: Rebalance monthly</h3>
+<p>
+  At the end of each month, we rebalance our portfolio, liquidate the securities that are not part of the new month’s universe, and repeat step 1 and 2. Keep in mind we use equal weights for the long and short positions of securities in our portfolio.
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    '''
+    Rebalance every month based on same-calendar month returns effect
+    '''
+    # Before next rebalance, do nothing
+    if self.Time < self.nextRebalance:
+        return
+
+    count = len(self.longSymbols + self.shortSymbols)
+    # Open long positions
+    for symbol in self.longSymbols:
+        self.SetHoldings(symbol, 1/count)
+
+    # Open short positions
+    for symbol in self.shortSymbols:
+        self.SetHoldings(symbol, -1/count)
+
+    # Rebalance at the end of every month
+    self.nextRebalance = Expiry.EndOfMonth(self.Time) - timedelta(1)
+  </pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
new file mode 100644
index 0000000..d502249
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
@@ -0,0 +1,9 @@
+<p>
+    In backtesting our algorithm achieves an annual rate of return over 5% with a max drawdown of approximately 30% for the past 10 years.
+    The performance indicates using the idea of same-calendar month returns makes sense. Interested users can build upon this implementation by trying the following extensions: 
+</p>
+<ol>
+    <li>Using the same-calendar months of multiple years (e.g. the last 5 years), instead of using the previous year as we did in this tutorial, to get more stable monthly returns.</li>
+    <li>Using discounting to capture time effects in the returns.</li>
+    <li>Creating the initial universe using different criteria such as quarterly, rather than monthly, returns.</li>
+</ol>
\ No newline at end of file
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html
new file mode 100644
index 0000000..03c24b0
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e4d3da5d7a9147affe8a6ea75f2f0a0f.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html
new file mode 100644
index 0000000..a859e6e
--- /dev/null
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/05 Reference.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://www.nber.org/papers/w20815.pdf" rel="nofollow">Common Factors in Return Seasonalities</a>
+</li>
+</ul>

From d0fa07ef18618b07f0d18b7f6028825eca53d114 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Tue, 10 Sep 2019 10:53:32 -0700
Subject: [PATCH 609/753] Add Strategy of Lemperiere, Deremble & Nguyen, 2015

---
 .../01 Strategy Library.php                   | 10 +++
 .../01 Introduction.html                      |  7 ++
 .../02 Method.html                            | 73 +++++++++++++++++++
 .../03 Results.html                           |  9 +++
 .../04 Algorithm.html                         |  6 ++
 .../05 Reference.html                         |  5 ++
 6 files changed, 110 insertions(+)
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html
 create mode 100644 04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..3ca35d0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,16 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+    [
+        'name' => 'Risk Premia in Forex Markets',
+        'link' => 'strategy-library/risk-premia-in-forex-markets',
+        'sources' => [
+            'NYU' => 'https://arxiv.org/pdf/1409.7720.pdf'
+        ],
+        'description' => "A strategy based on asymmetric tail risks and excess returns in forex markets.",
+        'tags'=>'Forex,Skewness,Risk Premium'
+
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html b/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html
new file mode 100644
index 0000000..2c85d7e
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/01 Introduction.html	
@@ -0,0 +1,7 @@
+<p>
+  This tutorial implements a risk premia strategy that enters long-short positions in the forex market based on signals from a skewness indicator. The strategy is derived from the paper <a target=_BLANK href="https://arxiv.org/pdf/1409.7720.pdf"> “Risk Premia: Asymmetric Tail Risks and Excess Returns”</a> by Lemperiere, Deremble, Nguyen, Seager, Potters, and Bouchaud.
+</p>
+
+<p>
+  One of the pillars in modern finance theory is the concept of risk premium, which states the riskier an investment is today the more profitable it should be in the long run. Risk premia strategies aim to profit from risk premiums. Lemperiere et al. describe a positive linear relationship between the Sharpe ratio of risk premia strategies and their negative skewness. It provides extensive evidence that risk premium is indeed strongly correlated with the skewness of a strategy, not only in the equity world but also in currencies, options, credit, etc.
+</p>
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html b/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html
new file mode 100644
index 0000000..fd1d64a
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/02 Method.html	
@@ -0,0 +1,73 @@
+<h3>Step 1: Select our forex universe</h3> 
+<p>
+  In this algorithm, we use a fixed forex universe which contains four symbols: "EURUSD", "AUDUSD", "USDCAD" and "USDJPY". By using self.AddForex(), we add the requested forex data into the data feed. 
+</P>
+
+<div class="section-example-container">
+<pre class="python">
+  # Add forex data of the following symbols
+  for pair in ['EURUSD', 'AUDUSD', 'USDCAD', 'USDJPY']:
+      self.AddForex(pair, Resolution.Hour, Market.FXCM)    
+</pre>
+</div>
+
+<h3>Step 2: Calculate the skewness indicator</h3>
+<p>
+  In statistics, <a href="https://en.wikipedia.org/wiki/Skewness" rel="nofollow">skewness</a> is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. Lemperiere et al. suggest there is a positive relationship between risk premia strategies and their negative skewness. We will use this relationship in our trading logic. Our implementation goes long for a forex pair when the skewness indicator is lower than a minimum threshold (-0.6) and short the pair when the indicator exceeds a maximum threshold (0.6). For each forex pair in the universe, we will calculate the skewness indicator with historical close prices and select the symbols as follows:
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    ### In OnData()
+    # Get historical close data for the symbols
+    history = self.History(self.Securities.Keys, self.lookback, Resolution.Daily)
+    history = history.drop_duplicates().close.unstack(level=0)
+
+    # Get the skewness of the historical data
+    skewness = self.GetSkewness(history)
+
+    longSymbols = [k for k,v in skewness.items() if v < self.longSkewLevel]
+    shortSymbols = [k for k,v in skewness.items() if v > self.shortSkewLevel]
+
+  def GetSkewness(self, values):
+    '''
+    Get the skewness for all forex symbols based on its historical data
+    Ref: https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
+    '''
+    # Get the numerator of the skewness
+    numer = ((values - values.mean()) ** 3).sum()
+
+    # Get the denominator of the skewness
+    denom = self.lookback * values.std() ** 3
+
+    # Return the skewness
+    return (numer/denom).to_dict()
+  </pre>
+</div>
+
+<h3>Step 3: Rebalance weekly</h3>  
+<p>
+  We rebalance every week, liquidate the forex pairs not on the trading list, then repeat steps 1-2. We use equal weights for the long and short positions of securities in our portfolio.
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+  # Liquidate the holdings for pairs that will not trade
+  for holding in self.Portfolio.Values:
+      symbol = holding.Symbol
+      if holding.Invested and symbol.Value not in longSymbols + shortSymbols:
+          self.Liquidate(symbol, 'Not selected pair')
+
+  # Open positions for the symbols with equal weights
+  count = len(longSymbols) + len(shortSymbols)
+
+  for pair in longSymbols:
+      self.SetHoldings(pair, 1/count)
+
+  for pair in shortSymbols:
+      self.SetHoldings(pair, -1/count)
+
+  # Set next rebalance time
+  self.nextRebalance += timedelta(self.rebalanceDays)    
+  </pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html b/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html
new file mode 100644
index 0000000..3e38c14
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/03 Results.html	
@@ -0,0 +1,9 @@
+<p>
+    In this case our backtest results in a low annual return of approximately -0.7% over a decade. The poor performance may be due to several reasons:
+    <ol>
+        <li>The fixed forex universe chosen is not large enough to properly diversify market risk</li>
+        <li>The thresholds for entering long and short positions (0.6, -0.6) may need adjustment</li>
+        <li>The length of historical data might be not large enough for this weekly-rebalanced strategy.</li>
+    </ol>
+    We encourage the community to further develop this strategy by testing out different symbols, thresholds, and historical data lengths.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html b/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html
new file mode 100644
index 0000000..f3764f8
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_8dfbaa1cbb1ebacdb3a9d8b3d640f4ec.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html b/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html
new file mode 100644
index 0000000..dfffb15
--- /dev/null
+++ b/04 Strategy Library/270 Risk Premia in Forex Markets/05 Reference.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://arxiv.org/pdf/1409.7720.pdf" rel="nofollow">Risk Premia: Asymmetric Tail Risks and Excess Returns</a>
+</li>
+</ul>

From 332860875f137d6c2cf3d085e0145fce2421ee7b Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Tue, 10 Sep 2019 11:27:32 -0700
Subject: [PATCH 610/753] Add mapping of strategy 354 for search table

Strategy 354 PR: https://github.com/QuantConnect/Tutorials/pull/235
---
 .../00 Strategy Library/01 Strategy Library.php        | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..f65e379 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -530,8 +530,16 @@
         ],
         'description' => "Apply statistical arbitrage to take advantage of pricing inefficiencies in stocks.",
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
-
     ],
+	[
+		'name' => 'Expected Idiosyncratic Skewness',
+		'link' => 'strategy-library/expected-idiosyncratic-skewness',
+		'sources' => [
+			'NYU' => 'http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.825.592&rep=rep1&type=pdf'
+		],
+		'description' => "Stock selection strategy that calculates expected idiosyncratic skewness using Fama-French three-factor model, sorts stocks based on the calculated skewness, and longs the bottom 5%.",
+		'tags' => 'Equities, Skewness, Fundamental Factors, Statistical Arbitrage'
+	],
 ];
 
 ?>

From d1dca0ae71b352ec9484ae3d849429ec81cb6007 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Tue, 10 Sep 2019 14:31:02 -0700
Subject: [PATCH 611/753] Add Strategy Standardized Unexpected Earnings

Based on paper: https://www.jstor.org/stable/pdf/247321.pdf
---
 .../01 Strategy Library.php                   |   9 ++
 .../01 Abstract.html                          |   3 +
 .../02 Theory.html                            |   3 +
 .../03 Method.html                            | 124 ++++++++++++++++++
 .../04 Conclusion and Future Work.html        |  10 ++
 .../05 Algorithm.html                         |   6 +
 .../06 References.html                        |   8 ++
 7 files changed, 163 insertions(+)
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html
 create mode 100644 04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e1f954c..6c8af87 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -532,6 +532,15 @@
         'tags'=>'PCA,Mean Reversion,Stocks,Arbitrage'
 
     ],
+	[
+		'name' => 'Standardized Unexpected Earnings',
+		'link' => 'strategy-library/standardized-unexpected-earnings',
+		'sources' => [
+			'NYU' => 'https://www.jstor.org/stable/pdf/247321.pdf'
+		],
+		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly."
+		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
+	]
 ];
 
 ?>
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
new file mode 100644
index 0000000..07de981
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed an 11.8% annual return using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html b/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html
new file mode 100644
index 0000000..0179e8b
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/02 Theory.html	
@@ -0,0 +1,3 @@
+<p>
+	In market efficiency literature, one frequently discussed topic is the anomalous behavior of stock returns following earnings announcements. The market does not adjust to news from earning announcements instantaneously. Instead, many studies report evidence that the direction and magnitude of returns in the post-earnings announcement period are positively correlated with the direction and magnitude of the unexpected component in the earnings releases. This observed phenomenon is consistent with suggestions that the capital market is inefficient.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
new file mode 100644
index 0000000..130dcfd
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
@@ -0,0 +1,124 @@
+<p>
+	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE’s numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
+</p>
+
+\[ 
+	SUE_q = \frac{ EPS_q - EPS_{q-4} }{ \sigma( EPS_q - EPS_{q-4} ) }
+\]
+
+<p>
+	where \(\sigma(X)\) is the standard deviation of X, EPS a firm’s quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE’s stock ranking changes month to month because each company’s earnings announcement release date for the quarter differs (i.e., firm A’s Q3 announcement may come out in August while firm B’s Q3 announcement comes out in September).
+</p>
+
+
+<h3>Step 1: Narrow down the universe with a coarse selection filter function</h3>
+
+<p>
+	We use a coarse selection filter to narrow down the universe to 1000 stocks at the beginning of each month according to dollar volume, price and whether the stock has fundamental data in our data library. 
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def CoarseSelectionFunction(self, coarse):
+    '''Get dynamic coarse universe to be further selected in fine selection
+    '''
+    # Before next rebalance time, just remain the current universe
+    if self.Time < self.next_rebalance:
+        return Universe.Unchanged
+        
+    ### Run the coarse selection to narrow down the universe
+    # filter by fundamental data and price & Sort descendingly by daily dollar volume
+    sorted_by_volume = sorted([ x for x in coarse if x.HasFundamentalData and x.Price > 5 ],
+                                key = lambda x: x.DollarVolume, reverse = True)
+    self.new_fine = [ x.Symbol for x in sorted_by_volume[:self.num_coarse] ]
+        
+    # Return all symbols that have appeared in Coarse Selection
+    return list( set(self.new_fine).union( set(self.eps_by_symbol.keys()) ) )
+	</pre>
+	</div>
+
+<h3>Step 2: Sort the universe by SUE and select the top 5%</h3>
+
+<p>
+	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don’t trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
+</p>
+
+
+<div class="section-example-container">
+
+<pre class="python">def FineSelectionAndSueSorting(self, fine):
+	'''Select symbols to trade based on sorting of SUE'''
+	
+	sue_by_symbol = dict()
+	
+	for stock in fine:
+		
+		### Save (symbol, rolling window of EPS) pair in dictionary
+		if not stock.Symbol in self.eps_by_symbol:
+			self.eps_by_symbol[stock.Symbol] = RollingWindow[float](self.months_count)
+		# update rolling window for each stock
+		self.eps_by_symbol[stock.Symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths)
+	
+		### Calculate SUE
+	
+		if stock.Symbol in self.new_fine and self.eps_by_symbol[stock.Symbol].IsReady:
+	
+			# Calculate the EPS change from four quarters ago
+			rw = self.eps_by_symbol[stock.Symbol]
+			eps_change = rw[0] - rw[self.months_eps_change]
+			
+			# Calculate the st dev of EPS change for the prior eight quarters
+			new_eps_list = list(rw)[:self.months_count - self.months_eps_change:3]
+			old_eps_list = list(rw)[self.months_eps_change::3]
+			eps_std = np.std( [ new_eps - old_eps for new_eps, old_eps in 
+								zip( new_eps_list, old_eps_list )
+							] )
+			
+			# Get Standardized Unexpected Earnings (SUE)
+			sue_by_symbol[stock.Symbol] = eps_change / eps_std
+	
+	# Sort and return the top quantile
+	sorted_dict = sorted(sue_by_symbol.items(), key = lambda x: x[1], reverse = True)
+	
+	self.long = [ x[0] for x in sorted_dict[:math.ceil( self.top_percent * len(sorted_dict) )] ]
+	# If universe is empty, OnData will not be triggered, then update next rebalance time here
+	if not self.long:
+		self.next_rebalance = Expiry.EndOfMonth(self.Time)
+	
+	return self.long
+	</pre>
+	</div>
+
+<h3>Step 3: Form an equal-weighted portfolio and place orders</h3>
+
+<p>
+	Once the symbols are selected, we form an equal-weighted portfolio and place orders. Finally, we update the next rebalance time to the beginning of the next calendar month. The portfolio will be held until liquidated at next rebalance time.
+</p>
+
+
+<div class="section-example-container">
+
+<pre class="python">def OnSecuritiesChanged(self, changes):
+	'''Liquidate symbols that are removed from the dynamic universe
+	'''
+	for security in changes.RemovedSecurities:
+		if security.Invested:
+			self.Liquidate(security.Symbol, 'Removed from universe')        
+	
+
+def OnData(self, data):
+	'''Monthly rebalance at the beginning of each month. Form portfolio with equal weights.
+	'''
+	# Before next rebalance, do nothing
+	if self.Time < self.next_rebalance or not self.long:
+		return
+	
+	# Placing orders (with equal weights)
+	equal_weight = 1 / len(self.long)
+	for stock in self.long:
+		self.SetHoldings(stock, equal_weight)
+	
+	# Rebalance at the beginning of every month
+	self.next_rebalance = Expiry.EndOfMonth(self.Time)
+	</pre>
+	</div>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
new file mode 100644
index 0000000..3ac670b
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
@@ -0,0 +1,10 @@
+<p>
+	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generations a non-trivial 11.8% annual return. Interested users can build from this implementation by trying the following extensions:
+</p>
+
+<ol>
+	<li> Using a more complicated measure for expected earnings to replace the historical EPS from four quarters ago.
+	<li> Using different investment horizons such as 3 months, 6 months, 1 year. In a longer investment horizon of n months, each month’s decile will have n subdeciles, each of which is initiated in a different month in the prior n-month period. An example is a horizon of 6 months with each month having 6 subdeciles, each initiated in a different month in the prior 6-month period.
+	<li> Importing custom data of analysts’ forecasts of firms’ earnings to replace the expected earnings based on historical EPS.
+	<li> Selecting small-size companies and then trade based on SUE ranking, since studies suggest that post-earnings announcement is more significant for small-size companies than larger ones.
+</ol>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html b/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html
new file mode 100644
index 0000000..26f8e7e
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/05 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_3154cdef40e24f01d8283752476a2f74.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html b/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html
new file mode 100644
index 0000000..b138986
--- /dev/null
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/06 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    Foster G, Olsen C, Shevlin T. Earnings releases, anomalies, and the behavior of security returns. Accounting Review. 1984 Oct 1:574-603 <a href="https://www.jstor.org/stable/pdf/247321.pdf">Online Copy</a>
+  </li>
+  <li>
+	Hou K, Xue C, Zhang L. Replicating Anomalies. The Review of Financial Studies <a href="http://theinvestmentcapm.com/HouXueZhang2019RFS.pdf">Online Copy</a>
+  </li>
+<ol>

From 59bcdf0671fb43027f399c6f6094a6b717080fee Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Tue, 10 Sep 2019 17:20:31 -0700
Subject: [PATCH 612/753] Requested Changes to Strategy 354

---
 .../354 Expected Idiosyncratic Skewness/02 Theory.html        | 2 +-
 .../03 Data Description.html                                  | 2 +-
 .../354 Expected Idiosyncratic Skewness/04 Method.html        | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html
index b1ae556..b49f79e 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/02 Theory.html	
@@ -1,5 +1,5 @@
 <p>
-	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors (“lotto investors”) have a preference for positive skewness while others (“traditional investors”) are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
+	BMV tests recent theories that stocks with low idiosyncratic skewness should have high expected returns. For example, Mitton and Vorkink (2007) develop a model that some investors ("lotto investors") have a preference for positive skewness while others ("traditional investors") are mean-variance optimizers seeking to maximize the Sharpe ratio of their portfolios. Lotto investors accept lower average returns on stocks with high idiosyncratic skewness because they have a preference for stocks with lottery-like return properties. In equilibrium, markets clear at prices such that stocks with high idiosyncratic skewness have low expected returns, due to the different portfolio preferences of the two groups of investors.
 </p>
 
 <p>
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
index 3aa2885..cfe92cd 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -1,3 +1,3 @@
 <p>
-	To execute our algorithm, we will use daily data from Kenneth French’s <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html">Data Library</a> that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to our Github cloud. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect’s Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this <a href="http://boyer.byu.edu/Research/skew/skewmethodology.pdf">online technical appendix</a> for descriptions of these variables.
+	To execute our algorithm, we will use daily data from Kenneth French's <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html">Data Library</a> that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this <a href="http://boyer.byu.edu/Research/skew/skewmethodology.pdf">online technical appendix</a> for descriptions of these variables.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html
index b864049..fa80957 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/04 Method.html	
@@ -27,7 +27,7 @@ <h3>Step 2: Estimating historical idiosyncratic moments</h3>
 <h3>Step 3: Estimating expected idiosyncratic skewness</h3>
 
 <p>
-	We need measures of expected skewness over a horizon of 1 months for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
+	We need measures of expected skewness over a horizon of 1 month for firm i at the end of month t, \(E_t[is_{i,t+1}]\), rather than measures of historical skewness as defined in equation above. To model investor perceptions of expected skewness in a feasible manner, we first estimate cross-sectional regression separately at the end of each month t in our sample,
 </p>
 
 \[is_{i,t} = \beta_0^t + \beta_1^t is_{i,t-1} + \beta_2^t iv_{i,t-1} + \varepsilon_{i,t}\]
@@ -39,7 +39,7 @@ <h3>Step 3: Estimating expected idiosyncratic skewness</h3>
 \[ E_t[is_{i,t+1}] = \beta_0^t + \beta_1^t is_{i,t} + \beta_2^t iv_{i,t} \]
 
 <p>
-	This approach provides feasible estimates of each month’s expected skewness and accounts for variation between historical moments and expected skewness across time.
+	This approach provides feasible estimates of each month's expected skewness and accounts for variation between historical moments and expected skewness across time.
 </p>
 
 <h3>Step 4: Generating trading signals</h3>

From fc331e2a2b9c1303f9a1a1039aff4c25729c832d Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Tue, 10 Sep 2019 18:07:29 -0700
Subject: [PATCH 613/753] Requested Changes to Strategy 355

---
 .../01 Abstract.html                                  |  2 +-
 .../03 Method.html                                    | 11 ++++++-----
 .../04 Conclusion and Future Work.html                |  2 +-
 3 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html
index 07de981..9972cb0 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed an 11.8% annual return using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
+	This tutorial implements a strategy that standardizes the unexpected earnings of stocks and trades the top 5% of those standardized stocks. It is written based on a paper published in The Accounting Review by Foster, Olsen, and Shevlin (1984). Our implementation narrows down our universe to 1000 liquid assets based on daily trading volume and price, and the availability of fundamental data on the stocks in our data library. We calculate the unexpected earnings at the beginning of each month, standardize the unexpected earnings, go long on the top 5%, and rebalance the portfolio monthly. We observed a Sharpe ratio of 0.83 relative to SPY Sharpe of 0.88 using this implementation during the period of December 1, 2009 to September 1, 2019 in backtesting.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html
index 130dcfd..a1b3ba6 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/03 Method.html	
@@ -1,5 +1,5 @@
 <p>
-	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE’s numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
+	Unexpected earnings, or earnings surprise, is the difference between reported earnings and the expected earnings of a firm. Expected earnings is calculated using a combination of analyst forecasts and mathematical models based on earnings of previous periods. In this tutorial, we use standardized unexpected earnings (SUE) to measure earnings surprise. SUE's numerator is the change in quarterly earnings per share (EPS) from EPS four quarters ago. Its denominator is the standard deviation of a series of deltas each calculated by subtracting EPS at quarter q-4 from EPS at quarter q. It can be formulated as
 </p>
 
 \[ 
@@ -7,7 +7,7 @@
 \]
 
 <p>
-	where \(\sigma(X)\) is the standard deviation of X, EPS a firm’s quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE’s stock ranking changes month to month because each company’s earnings announcement release date for the quarter differs (i.e., firm A’s Q3 announcement may come out in August while firm B’s Q3 announcement comes out in September).
+	where \(\sigma(X)\) is the standard deviation of X, EPS a firm's quarterly earnings per share, q the current quarter, and q-4 four quarters ago. Keep in mind that although we use quarterly EPS data, the portfolio rebalances monthly. Additionally, note that SUE's stock ranking changes month to month because each company's earnings announcement release date for the quarter differs (i.e., firm A's Q3 announcement may come out in August while firm B's Q3 announcement comes out in September).
 </p>
 
 
@@ -22,12 +22,13 @@ <h3>Step 1: Narrow down the universe with a coarse selection filter function</h3
 <pre class="python">def CoarseSelectionFunction(self, coarse):
     '''Get dynamic coarse universe to be further selected in fine selection
     '''
-    # Before next rebalance time, just remain the current universe
+    # Before next rebalance time, keep the current universe unchanged
     if self.Time < self.next_rebalance:
         return Universe.Unchanged
         
     ### Run the coarse selection to narrow down the universe
-    # filter by fundamental data and price & Sort descendingly by daily dollar volume
+    # Filter stocks by price and whether they have fundamental data
+	# Then, sort descendingly by daily dollar volume
     sorted_by_volume = sorted([ x for x in coarse if x.HasFundamentalData and x.Price > 5 ],
                                 key = lambda x: x.DollarVolume, reverse = True)
     self.new_fine = [ x.Symbol for x in sorted_by_volume[:self.num_coarse] ]
@@ -40,7 +41,7 @@ <h3>Step 1: Narrow down the universe with a coarse selection filter function</h3
 <h3>Step 2: Sort the universe by SUE and select the top 5%</h3>
 
 <p>
-	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don’t trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
+	Next we use a fine universe selection filter to extract quarterly EPS data and save it in a rolling window for each stock. We don't trade during the first 36-month warm-up period because the window is not ready yet. After the warm-up period, we can calculate quarterly EPS change from four quarters ago and the standard deviation of the change over the prior eight quarters using historical EPS data saved in the rolling windows. Then we sort the universe and assign the top 5% of symbols to self.long.
 </p>
 
 
diff --git a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html
index 3ac670b..291c81a 100644
--- a/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
+++ b/04 Strategy Library/355 Standardized Unexpected Earnings/04 Conclusion and Future Work.html	
@@ -1,5 +1,5 @@
 <p>
-	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generations a non-trivial 11.8% annual return. Interested users can build from this implementation by trying the following extensions:
+	This tutorial shows that SEU is a valid indicator for earnings surprise, which can be used as a trading signal to follow post-earning announcement drifts. Our implementation generates a Sharpe ratio of 0.83 relative to SPY Sharpe ratio of 0.88. Interested users can build from this implementation by trying the following extensions:
 </p>
 
 <ol>

From 2335b97261680ce702d701beb6789421cdeced23 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Tue, 10 Sep 2019 18:14:19 -0700
Subject: [PATCH 614/753] Replaced Annual Return with Sharpe

---
 .../354 Expected Idiosyncratic Skewness/01 Abstract.html        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
index 9b7d744..5d4ea33 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The annual return during the period of July 1, 2009 to July 1, 2019 is 21.0%.
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.1 relative to SPY Sharpe ratio of 0.99 during the period of July 1, 2009 to July 1, 2019.
 </p>
\ No newline at end of file

From 0d5b1ad63d0492e5ee235b99351e789faac07f30 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Wed, 11 Sep 2019 16:22:32 -0700
Subject: [PATCH 615/753] Resolve Conflict & A Few Extra Minor Changes

Minor changes include:
(1) Fixed a bug related to month-end rebalance in code.
(2) Changed texts accordingly.
---
 .../354 Expected Idiosyncratic Skewness/01 Abstract.html        | 2 +-
 .../03 Data Description.html                                    | 2 +-
 .../354 Expected Idiosyncratic Skewness/06 Algorithm.html       | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html
index 5d4ea33..ffa6192 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.1 relative to SPY Sharpe ratio of 0.99 during the period of July 1, 2009 to July 1, 2019.
+	This tutorial implements a strategy that trades stocks with low expected idiosyncratic skewness based on a paper by Boyer, Mitton and Vorkink (2009, hereafter BMV) published in The Review of Financial Studies. Our implementation narrows down our initial universe to liquid assets by selecting 200 stocks based on daily trading volume, price and whether the stock has fundamental data in our data library. We calculate the expected idiosyncratic skewness at the end of each month and sort our universe based on the calculated skewness. This implementation will long the bottom 5%, hold for the next month, and rebalance the portfolio monthly. The Sharpe ratio is 1.03 relative to S&P 500 (SPY) Sharpe ratio of 1.00 during the period of July 1, 2009 to July 30, 2019.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html
index cfe92cd..31483b2 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/03 Data Description.html	
@@ -1,3 +1,3 @@
 <p>
-	To execute our algorithm, we will use daily data from Kenneth French's <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html">Data Library</a> that captures the Fama-French three factors for the period July 1, 2009 to July 1, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this <a href="http://boyer.byu.edu/Research/skew/skewmethodology.pdf">online technical appendix</a> for descriptions of these variables.
+	To execute our algorithm, we will use daily data from Kenneth French's <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html">Data Library</a> that captures the Fama-French three factors for the period July 1, 2009 to June 30, 2019. The raw data is delivered in a zip file which is not directly importable into LEAN. We need to unzip the file and upload the CSV to a Github repository. All other data used for this algorithm, including stock price, volume, and market capitalization, are from QuantConnect's Data Library. In the original paper, BSV also includes firm-specific variables like momentum, turnover, and dummies of properties including Nasdaq-listed stocks, small-size, medium-size, industries. We can refer to this <a href="http://boyer.byu.edu/Research/skew/skewmethodology.pdf">online technical appendix</a> for descriptions of these variables.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html
index af8d286..20f54ed 100644
--- a/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
+++ b/04 Strategy Library/354 Expected Idiosyncratic Skewness/06 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0e098feaa8500e8dd07c0e43093b0326.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_03c1d5025863e1e29f9060369a3b218e.html"></iframe>
     </div>
 </div>
\ No newline at end of file

From 7e101aff6883324720fec86c87942fb0cd23a781 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Thu, 12 Sep 2019 10:28:11 -0700
Subject: [PATCH 616/753] Fix strategy library map

Address missing comma, that was preventing the tutorials repository from being generated.
---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 8316643..c2eb29a 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -556,7 +556,7 @@
 		'sources' => [
 			'NYU' => 'https://www.jstor.org/stable/pdf/247321.pdf'
 		],
-		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly."
+		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
 	]
 ];

From 410079a33dff785310f3b1bd46ce2f4a55439063 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 12 Sep 2019 16:51:57 -0700
Subject: [PATCH 617/753] Changes requested

---
 .../02 Method.html                                            | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html
index 4bcd1ee..5b5da95 100644
--- a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/02 Method.html	
@@ -1,6 +1,6 @@
 <h3>Step 1: Select our universe</h3>
 <p>
-    We first select liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from “Common Factors” suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the <a target=”_blank” href=”https://www.quantconnect.com/tutorials/strategy-library”>library</a> do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
+    We first select the top 100 liquid securities and ETFs with prices greater than $5 based on Dollar Volume for our universe. Research from "Common Factors" suggests that the U.S. equity, commodity, and index markets are all affected by seasonality patterns. Therefore, we can include any assets in our universe. Note that while this strategy does not require fundamental data for implementation, other strategies in the <a target="_blank" href="https://www.quantconnect.com/tutorials/strategy-library">library</a> do. In those cases we would need to remove ETFs from the universe because we don’t have fundamental data for ETFs.
 </p>  
 
 <div class="section-example-container">
@@ -16,7 +16,7 @@ <h3>Step 1: Select our universe</h3>
 
 <h3>Step 2: Calculate the same-calendar month returns of the previous year</h3>
 <p>
-    “Common Factors” indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
+    "Common Factors" indicates that taking long and short positions based on historical same-calendar month returns earns an average monthly return of 1.88%. Our implementation also selects securities to long and short based on their same-calendar month returns. For each security in the universe, we calculate the monthly return for the same-calendar month of the previous year and choose the symbols as follows:
 </p>
 
 <div class="section-example-container">

From aaf91ce77c44446bee6912867d049d958d9397e9 Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Thu, 12 Sep 2019 18:03:05 -0700
Subject: [PATCH 618/753] Update 03 Results

---
 .../03 Results.html                                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html
index d502249..e4e319f 100644
--- a/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
+++ b/04 Strategy Library/269 Seasonality Effect based on Same-Calendar Month Returns/03 Results.html	
@@ -1,5 +1,5 @@
 <p>
-    In backtesting our algorithm achieves an annual rate of return over 5% with a max drawdown of approximately 30% for the past 10 years.
+    In backtesting our algorithm achieves a Sharpe ratio of 0.332 relative to S&P 500 (SPY) Sharpe ratio of 0.893 for the past 10 years.
     The performance indicates using the idea of same-calendar month returns makes sense. Interested users can build upon this implementation by trying the following extensions: 
 </p>
 <ol>

From 2d92e4e421644e93c0c187d2d84bb5758b638569 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Tue, 17 Sep 2019 10:44:40 -0700
Subject: [PATCH 619/753] Add Improved Momentum Strategy on Commodities Futures

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   3 +
 .../02 Introduction.html                      |  16 ++
 .../03 TSMOM-CF Theory.html                   |  99 +++++++++
 .../04 Method.html                            | 207 ++++++++++++++++++
 .../05 Summary.html                           |   3 +
 .../06 Algorithm.html                         |   6 +
 .../07 References.html                        |   8 +
 8 files changed, 351 insertions(+)
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html
 create mode 100644 04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1aef751..76fe5bf 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -568,6 +568,15 @@
 		],
 		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
+	],
+	[
+		'name' => 'Improved Momentum Strategy on Commodities Futures',
+		'link' => 'strategy-library/improved-momentum-strategy-on-commodities-futures',
+		'sources' => [
+			'NYU' => 'https://pdfs.semanticscholar.org/a2e9/df201d4b4774fda84a961cc804f2450988c5.pdf'
+		],
+		'description' => "An advanced momentum strategy that modifies the basic momentum strategies by introducing Baltas and Kosowski weights and rebalances the portfolio monthly. The new weighing scheme incorporates trend strength into the trading signal, uses an efficient volatility estimator, and adds a dynamic leverage mechanism.",
+		'tags' => 'Momentum, Futures, Commodities'
 	]
 ];
 
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
new file mode 100644
index 0000000..2c4d2da
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper “Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations” by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">Momentum Effect in Commodities Futures</a>.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
new file mode 100644
index 0000000..7dae98b
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
@@ -0,0 +1,16 @@
+<p>
+	Baltas and Kosowski modify the basic momentum strategy by incorporating trend strength into the trading signal, using an efficient volatility estimator, and adding a dynamic leverage mechanism. The modifications overcome these three weaknesses:
+</p>
+
+<ol>
+	<li>
+	<strong>An Oversimplified Trading Signal:</strong> The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy’s extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
+	</li>
+	<li>
+	<strong>An Inefficient Volatility Estimator:</strong> The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy’s volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
+	</li>
+	<li>
+	<strong>A Fixed Portfolio Allocation Mechanism:</strong> The TSMOM does not consider the correlation between assets during portfolio construction. It simply allocates funds to each asset based on the properties of the individual assets. Strategies based on TSMOM significantly underperform in the post-2008 global financial crisis (GFC) period due to the increased level of asset co-movement at the time. As a remedy, Baltas and Kosowski introduce a dynamic leverage adjustment for the overall portfolio by adding a correlation factor to the weighting scheme.
+	</li>
+</ol>
+
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
new file mode 100644
index 0000000..2c51e91
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -0,0 +1,99 @@
+<p>
+Baltas and Kosowski’s modifications to the basic time-series momentum strategy can be summarized in the formula below:
+</p>
+
+\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
+
+<p>
+	where:
+</p>
+\[r_{t,t+1}^{TSMOM-CF} = \text{TSMOM-CF portfolio return from time t to time t+1}\]
+\[N_t = \text{Number of portfolio constituents at time t}\]
+\[X_t^i = \text{Trading signal value of asset i at time t}\]
+\[\sigma_{P,tgt} = \text{Target level of volatility for the overall portfolio}\]
+\[\sigma_t^i = \text{Estimated volatility of asset i at time t}\]
+\[CF(\bar{\rho}_t) = \text{Correlation factor that adjusts the level of leverage applied to each portfolio constituents at time t}\] 
+\[r_{t,t+1}^i = \text{return of asset i from time t to time t+1}\]
+
+<p>
+	The formula shows that the weights for each portfolio constituent are dependent on three parts:
+</p>
+
+
+<h3>Part I: Trading Rule Adjustment (\(X_t^i\))</h3>
+
+<p>
+The TREND trading rule determines the trading signal based on the statistical strength of the realized return:
+</p>
+
+<script src=
+http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML
+></script>
+\[
+\text{TREND}_i^{12M} \quad
+\begin{cases} +1, \text{   if } t(r_{t-12,t})>+1 \\
+t(r_{t-12,t}), \text{   otherwise} \\
+-1, \text{   if } t(r_{t-12,t})<-1 \\
+\end{cases}
+\]
+
+<p>
+where t() is the t-statistic of the daily futures log-returns over the past 12 months to scale the gross exposure to each portfolio constituents.
+</p>
+
+<p>
+When the absolute value of our t-statistic is greater than 1, the trend is highly statistically significant, so the strategy puts 100% exposure to the asset. When the t-statistic is between -1 and 1, the strength of the trend is not as significant, so the strategy scales its exposure to less than 100%.
+</p>
+
+
+<h3>Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))</h3>
+<p>
+	Instead of estimating each asset’s volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
+</p>
+
+\[\sigma_{YZ}^2(t) = \sigma_{OJ}^2(t) + k \sigma_{SD}^2(t) + (1-k) \sigma_{RS}^2(t)\]
+
+<p>
+	where:
+</p>
+
+\[\sigma_{OJ} = \text{Overnight jump estimator (standard deviation of close-to-open daily logarithmic returns)}\]
+\[\sigma_{SD} = \text{Standard volatility estimator (standard deviation of close-to-close daily logarithmic returns)}\]
+\[\sigma_{RS} = \text{Rogers and Satchell (1991) range estimator}\]
+\[k = \text{parameter that minimizes YZ estimator variance, which is a function of the numbers of days in the estimation}\]
+
+<p>
+The formula for parameter k is below:
+</p>
+\[k = \frac{0.34}{1.34+\frac{N_D+1}{N_D-1}}\]
+
+<p>
+The Rogers and Satchell range estimator calculation is based on the following formula:
+</p>
+
+\[\sigma_{RS}^2(\tau) = h(\tau)[h(\tau)-c(\tau)]+l(\tau)[l(\tau)-c(\tau)]\]
+
+<p>
+	where \(h(\tau)\), \(l(\tau)\) and \(c(\tau)\) denote the logarithmic difference between the high, low and closing prices respectively with the opening price. The RS volatility of an asset at the end of month t, assuming a certain estimation period, is equal to the average daily RS volatility over this period.
+</p>
+
+<p>
+The estimation period is chosen to be 1 month, or 21 trading days, based on Baltas and Kosowski's suggestions.
+</p>
+
+
+<h3>Part III: Correlation Factor (CF)</h3>
+Baltas and Kosowski’s correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
+
+\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
+\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
+
+<p>
+	where:
+</p>
+
+\[N = \text{number of assets in the portfolio}\]
+\[\rho_{i,j} = \text{correlation between asset i, j}\]
+\[X_i = \text{trade signal of asset i}\]
+\[\bar{\rho} = \text{average pairwise signed correlation for the entire portfolio}\]
+
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html
new file mode 100644
index 0000000..2014718
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/04 Method.html	
@@ -0,0 +1,207 @@
+<p>
+	The strategy requires the continuous futures contract, so we import the custom data from Quandl. We manually create a universe of tradable commodity futures from all available commodity futures traded on CME and ICE. They are all liquid and active continuous contracts #1. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations. The data resolution is daily.
+</p>
+
+
+<h3>Step 1: Import the data</h3>
+
+<div class="section-example-container">
+<pre class="python">
+from QuantConnect.Python import PythonQuandl
+class ImprovedCommodityMomentumTrading(QCAlgorithm):
+	def Initialize(self):
+		for ticker in tickers:
+			data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+			data.SetLeverage(3) # Leverage was set to 3 for each of the futures contract
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "Settle"
+</pre>
+</div>
+
+
+
+<h3>Step 2: Set the portfolio target volatility and decide rebalance schedule</h3>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):
+	# Last trading date tracker to achieve rebalancing the portfolio every month
+    self.nextRebalance = self.Time
+
+	# Set portfolio target level of volatility, set to 12% 
+	self.portfolio_target_sigma = 0.12
+</pre>
+</div>
+
+<h3>Step 3: Implement functions to calculate the three components of Baltas and Kosowski weights</h3>
+<h4>1. TREND Trade Signal</h4>
+<div class="section-example-container">
+<pre class="python">
+def GetTradingSignal(self, history):
+	'''
+	TREND Trading Signal
+	- Uses the t-statistics of historical daily log-returns to reflect the strength of price movement trend
+	- TREND Signal Conditions:
+		t-stat > 1 => TREND Signal = 1
+		t-stat < 1 => TREND Signal = -1
+		-1 < t-stat < 1 => TREND Signal = t-stat
+	'''
+	settle = history.settle.unstack(level = 0)
+
+	# daily futures log-returns based on close-to-close
+	log_returns = np.log(settle/settle.shift(1)).dropna()
+
+	# Calculate the t-statistics as
+	# (mean-0)/(stdev/sqrt(n)), where n is sample size
+	mean = np.mean(log_returns)
+	std = np.std(log_returns)
+	n = len(log_returns)
+	t_stat = mean/(std/np.sqrt(n))
+
+	# cap holding at 1 and -1
+	return np.clip(t_stat, a_max=1, a_min=-1)
+</pre>
+</div>
+
+<h4>2. Yang and Zhang Volatility Estimator</h4>
+<div class="section-example-container">
+<pre class="python">
+def GetYZVolatility(self, history, available_symbols):
+	'''
+	Yang and Zhang 'Drift-Independent Volatility Estimation'
+	
+	Formula: sigma_YZ^2 = sigma_OJ^2 + self.k * sigma_SD^2 + (1-self.k)*sigma_RS^2 (Equation 20 in [1])
+		where,  sigma_OJ - (Overnight Jump Volitility estimator)
+				sigma_SD - (Standard Volitility estimator)
+				sigma_RS - (Rogers and Satchell Range Volatility estimator)'''
+	YZ_volatility = []
+
+	time_index = history.loc[available_symbols[0]].index
+	today = time_index[-1]
+
+	#Calculate YZ volatility for each security and append to list
+	for ticker in available_symbols:
+	    past_month_ohlc = history.loc[ticker].loc[today-timedelta(self.OneMonth):today]
+	    open, high, low, close = past_month_ohlc.open, past_month_ohlc.high, past_month_ohlc.low, past_month_ohlc.settle
+	    estimation_period = past_month_ohlc.shape[0]
+
+	    # Calculate constant parameter k for Yang and Zhang volatility estimator
+	    # using the formula found in Yang and Zhang (2000)
+	    k = 0.34 / (1.34 + (estimation_period + 1) / (estimation_period - 1))
+
+	    # sigma_OJ (overnight jump => stdev of close-to-open log returns)
+	    open_to_close_log_returns = np.log(open/close.shift(1))
+	    open_to_close_log_returns = open_to_close_log_returns[np.isfinite(open_to_close_log_returns)] 
+	    sigma_OJ = np.std(open_to_close_log_returns) 
+
+	    # sigma_SD (standard deviation of close-to-close log returns)
+	    close_to_close_log_returns = np.log(close/close.shift(1))
+	    close_to_close_log_returns = close_to_close_log_returns[np.isfinite(close_to_close_log_returns)]
+	    sigma_SD = np.std(close_to_close_log_returns) 
+
+	    # sigma_RS (Rogers and Satchell (1991))
+	    h = np.log(high/open)
+	    l = np.log(low/open)
+	    c = np.log(close/open)
+	    sigma_RS_daily = (h * (h - c) + l * (l - c))**0.5
+	    sigma_RS_daily = sigma_RS_daily[np.isfinite(sigma_RS_daily)] 
+	    sigma_RS = np.mean(sigma_RS_daily) 
+		
+	    # daily Yang and Zhang volatility
+	    sigma_YZ = np.sqrt(sigma_OJ**2 + k * sigma_SD**2 + (1 - k) * sigma_RS**2) 
+
+	    # append annualized volatility to the list
+	    YZ_volatility.append(sigma_YZ*np.sqrt(252)) 
+
+	return YZ_volatility
+</pre>
+</div>
+
+<h4>3. Correlation Factor (CF)</h4>
+<div class="section-example-container">
+<pre class="python">
+def GetCorrelationFactor(self, history, trade_signals, available_symbols):
+	'''
+	Calculate the Correlation Factor, which is a function of the average pairwise correlation of all portfolio contituents
+	- the calculation is based on past three month pairwise correlation
+	- Notations:
+	    rho_bar - average pairwise correlation of all portfolio constituents
+	    CF_rho_bar - the correlation factor as a function of rho_bar'''
+
+	# Get the past three month simple daily returns for all securities
+	settle = history.settle.unstack(level = 0)
+	past_three_month_returns = settle.pct_change().loc[settle.index[-1]-timedelta(self.ThreeMonths):]
+
+	# Get number of assets 
+	N_assets = len(available_symbols)
+	
+	# Get the pairwise signed correlation matrix for all assets
+	correlation_matrix = past_three_month_returns.corr() 
+
+	# Calculate rho_bar
+	summation = 0
+	for i in range(N_assets-1):
+	    for temp in range(N_assets - 1 - i):
+		    j = i + temp + 1
+		    x_i = trade_signals[i]
+		    x_j = trade_signals[j]
+		    rho_i_j = correlation_matrix.iloc[i,j]
+		    summation += x_i * x_j * rho_i_j
+			
+	# Equation 14 in [1]
+	rho_bar = (2 * summation) / (N_assets * (N_assets - 1)) 
+
+	# Calculate the correlation factor (CF_rho_bar)
+	# Equation 18 in [1]
+	return np.sqrt(N_assets / (1 + (N_assets - 1) * rho_bar)) 
+</pre>
+</div>
+
+<h3>Step 4: Construct/Rebalance the Portfolio</h3>
+<p>
+For efficiency purposes, a History() request is called once on each rebalance date to get all the data from the past year for all securities. We retrieve our trade signal, Yang and Zhang volatility, and correlation factor by passing the history data frame to each respective function. 
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+	'''
+	Monthly rebalance at the beginning of each month.
+	Portfolio weights for each constituents are calculated based on Baltas and Kosowski weights.
+	'''
+
+	# skip if less than 30 days passed since the last trading date
+	if self.Time < self.nextRebalance:
+		return
+
+	'''Monthly Rebalance Execution'''
+	# dataframe that contains the historical data for all securities
+	history = self.History(self.Securities.Keys, self.OneYear, Resolution.Daily)
+	history.replace(0, np.nan, inplace = True)
+
+	# Get the security symbols are are in the history dataframe
+	available_symbols = list(set(history.index.get_level_values(level = 0)))
+
+	# Liquidate symbols that are not in the history dataframe anymore
+	for security in self.Securities.Keys:
+		if security.Value not in available_symbols:
+			self.Liquidate(security, 'Not found in history request')
+
+	# Get the trade signals and YZ volatility for all securities
+	trade_signals = self.GetTradingSignal(history) 
+	volatility = self.GetYZVolatility(history, available_symbols) 
+	
+	# Get the correlation factor
+	CF_rho_bar = self.GetCorrelationFactor(history, trade_signals, available_symbols)
+
+	#Rebalance the portfolio according to Baltas and Kosowski suggested weights
+	N_assets = len(available_symbols)
+	for symbol, signal, vol in zip(available_symbols, trade_signals, volatility):
+		# Baltas and Kosowski weights (Equation 19 in [1])
+		weight = (signal*self.portfolio_target_sigma*CF_rho_bar)/(N_assets*vol)
+		self.SetHoldings(symbol, weight)
+
+	# Set next rebalance time
+	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+</pre>
+</div>
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
new file mode 100644
index 0000000..80f9ba3
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
@@ -0,0 +1,3 @@
+<p>
+The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM’s Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">strategy library</a>. 
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html
new file mode 100644
index 0000000..6eebbb0
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/06 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6def9f88c8595dfc56ba056fdf75573b.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html
new file mode 100644
index 0000000..5a494dd
--- /dev/null
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/07 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>		
+	Baltas, Nick & Kosowski, Robert. (2017). Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations. SSRN Electronic Journal. 10.2139/ssrn.2140091. <a href="https://pdfs.semanticscholar.org/a2e9/df201d4b4774fda84a961cc804f2450988c5.pdf">Online Copy</a>
+  </li>
+  <li>
+	Yang, Dennis & Zhang, Qiang. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. The Journal of Business, 73(3), 477-492. doi:10.1086/209650. <a href="www.jstor.org/stable/10.1086/209650">Online Copy</a>
+  </li>
+ <ol>
\ No newline at end of file

From 409eeea47e4aacc4cb9f6cf848569ac3ad4de932 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Tue, 17 Sep 2019 10:55:32 -0700
Subject: [PATCH 620/753] Apostrophes edited

---
 .../01 Abstract.html                                        | 2 +-
 .../02 Introduction.html                                    | 4 ++--
 .../03 TSMOM-CF Theory.html                                 | 6 +++---
 .../05 Summary.html                                         | 2 +-
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html
index 2c4d2da..a095d84 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper “Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations” by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">Momentum Effect in Commodities Futures</a>.
+	In this tutorial we implement a correlation-adjusted time-series momentum strategy (TSMOM-CF) that addresses three weaknesses typically found in traditional time-series momentum strategies (TSMOM). Our implementation is based on the paper "Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations" by Nick Baltas and Robert Kosowski. We will also compare TSMOM-CF to the basic momentum strategy implemented in our strategy library - <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">Momentum Effect in Commodities Futures</a>.
 </p>
\ No newline at end of file
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html
index 7dae98b..2130e92 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/02 Introduction.html	
@@ -4,10 +4,10 @@
 
 <ol>
 	<li>
-	<strong>An Oversimplified Trading Signal:</strong> The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy’s extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
+	<strong>An Oversimplified Trading Signal:</strong> The traditional time-series momentum strategy (TSMOM) results in high portfolio turnover which, after accounting for transaction costs, leads to diminished performance. Baltas and Kosowski attribute the traditional strategy's extreme long/short positions to an oversimplified trading signal whose values are a discrete +1 or -1. The traditional trading signal is based on the sign of the past 12-month average simple return. Baltas and Kosowski propose a trading signal with a continuous value between +1 and -1. Their signal is a statistical measure that reflects the strength of the price trend.
 	</li>
 	<li>
-	<strong>An Inefficient Volatility Estimator:</strong> The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy’s volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
+	<strong>An Inefficient Volatility Estimator:</strong> The TSMOM generally scales asset positions using the estimated volatility of portfolio constituents. The traditional strategy's volatility estimator is the standard deviation of past daily close-to-close returns, which is subject to large estimation errors. Baltas and Kosowski demonstrate that a more efficient volatility estimator can significantly reduce portfolio turnover which, after taking into account transaction costs, boosts the portfolio performance. They present the Yang and Zhang volatility estimator, a range-based estimator that considers the open, high, low, and close prices of assets. The next section will discuss this estimator in greater detail.
 	</li>
 	<li>
 	<strong>A Fixed Portfolio Allocation Mechanism:</strong> The TSMOM does not consider the correlation between assets during portfolio construction. It simply allocates funds to each asset based on the properties of the individual assets. Strategies based on TSMOM significantly underperform in the post-2008 global financial crisis (GFC) period due to the increased level of asset co-movement at the time. As a remedy, Baltas and Kosowski introduce a dynamic leverage adjustment for the overall portfolio by adding a correlation factor to the weighting scheme.
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 2c51e91..8057d58 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -1,5 +1,5 @@
 <p>
-Baltas and Kosowski’s modifications to the basic time-series momentum strategy can be summarized in the formula below:
+Baltas and Kosowski's modifications to the basic time-series momentum strategy can be summarized in the formula below:
 </p>
 
 \[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
@@ -48,7 +48,7 @@ <h3>Part I: Trading Rule Adjustment (\(X_t^i\))</h3>
 
 <h3>Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))</h3>
 <p>
-	Instead of estimating each asset’s volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
+	Instead of estimating each asset's volatility as the standard deviation of past close-to-close daily logarithmic returns, Baltas and Kosowski adopt a more efficient volatility estimator proposed by Yang and Zhang (2000). The formula for the Yang and Zhang volatility estimator (\(\sigma_{YZ}\)) is shown below:
 </p>
 
 \[\sigma_{YZ}^2(t) = \sigma_{OJ}^2(t) + k \sigma_{SD}^2(t) + (1-k) \sigma_{RS}^2(t)\]
@@ -83,7 +83,7 @@ <h3>Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))</h3>
 
 
 <h3>Part III: Correlation Factor (CF)</h3>
-Baltas and Kosowski’s correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
+Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
 \[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
 \[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html
index 80f9ba3..572eb95 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/05 Summary.html	
@@ -1,3 +1,3 @@
 <p>
-The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM’s Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">strategy library</a>. 
+The implementation of TSMOM-CF in the post-GFC period, January 2018 to September 2019, shows significant performance improvement over the basic TSMOM. The backtest of TSMOM-CF produces Sharpe ratio of 0.321, compared to TSMOM's Sharpe ratio of -0.746 and SPY Sharpe ratio of 0.46. The exact TSMOM algorithm can be found in the <a href="https://www.quantconnect.com/tutorials/strategy-library/momentum-effect-in-commodities-futures">strategy library</a>. 
 </p>
\ No newline at end of file

From 8e513ede895cab4cff44a6f29725a2aa296c61cc Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Wed, 18 Sep 2019 17:58:28 -0700
Subject: [PATCH 621/753] Fixes Formula

---
 .../03 TSMOM-CF Theory.html                                   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 8057d58..9792a24 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -2,7 +2,7 @@
 Baltas and Kosowski's modifications to the basic time-series momentum strategy can be summarized in the formula below:
 </p>
 
-\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
+\[r_{t,t+1}^{TSMOM-CF} = \frac{1}{N_t} \sum_{i=1}^{N_t} X_t^i \frac{\sigma_{P,tgt}}{\sigma_t^i} CF(\bar{\rho}_t)r_{t,t+1}^i\]
 
 <p>
 	where:
@@ -86,7 +86,7 @@ <h3>Part III: Correlation Factor (CF)</h3>
 Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
 \[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
-\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}{N(N-1)}\]
+\[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}}{N(N-1)}\]
 
 <p>
 	where:

From 0c788deb2d33f1f9182f65ff1ecb79edb4caeff6 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Thu, 19 Sep 2019 08:39:27 -0700
Subject: [PATCH 622/753] Update 03 TSMOM-CF Theory.html

---
 .../03 TSMOM-CF Theory.html                                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html
index 9792a24..cff2f81 100644
--- a/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
+++ b/04 Strategy Library/356 Improved Momentum Strategy on Commodities Futures/03 TSMOM-CF Theory.html	
@@ -85,7 +85,7 @@ <h3>Part II: Yang and Zhang Volatility Estimato(\(\sigma_{YZ}\))</h3>
 <h3>Part III: Correlation Factor (CF)</h3>
 Baltas and Kosowski's correlation factor (CF) is a function of \(\bar{\rho}\), which is the average pairwise signed correlation of all portfolio constituents. The calculations are shown below:
 
-\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}\]
+\[CF(\bar{\rho}) = \sqrt{\frac{N}{1+(N-1)\bar{\rho}}}\]
 \[\bar{\rho} = 2 \frac{\sum_{i=1}^N \sum_{j=i+1}^N X_i X_j \rho_{i,j}}{N(N-1)}\]
 
 <p>

From 363b28750eaf96910b5464ef8342f5e767f2eb7b Mon Sep 17 00:00:00 2001
From: Daniel Chen <44457690+QilongChan@users.noreply.github.com>
Date: Fri, 20 Sep 2019 15:12:54 -0700
Subject: [PATCH 623/753] Add Strategy of Price and Earnings Momentum

---
 .../01 Strategy Library.php                   |  12 +-
 .../01 Introduction.html                      |  11 ++
 .../02 Method.html                            | 166 ++++++++++++++++++
 .../03 Results.html                           |   9 +
 .../04 Algorithm.html                         |   6 +
 .../05 Reference.html                         |   5 +
 6 files changed, 208 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/02 Method.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/03 Results.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html
 create mode 100644 04 Strategy Library/271 Price and Earning Momentum/05 Reference.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 1aef751..22cad2e 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -568,7 +568,17 @@
 		],
 		'description' => "Stock selection strategy that calculates the unexpected earnings, standardizes the unexpected earnings, goes long on the top 5%, and rebalances the portfolio monthly.",
 		'tags' => 'Equities, Fundamental Factors, Earnings, Anomaly'
-	]
+	],
+    [
+        'name' => 'Price and Earnings Momentum',
+        'link' => 'strategy-library/price-and-earnings-momentum',
+        'sources' => [
+            'NYU' => 'http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107'
+        ],
+        'description' => "A momentum strategy based on quarterly returns and earnings growth",
+        'tags'=>'Momentum, Stocks, Universe Selection, Historical Data, Rolling Window'
+
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html b/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html
new file mode 100644
index 0000000..aa3ce63
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/01 Introduction.html	
@@ -0,0 +1,11 @@
+<p>
+  In this tutorial, we will develop a strategy based on the price and earnings momentum effect of stocks. This strategy is derived from the paper <a target=_BLANK href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107"> "Momentum"</a> by N.Jegadeesh and S.Titman.
+</p>
+
+<p>
+  N.Jegadeesh et al. describe price/return momentum as a tendency for stocks that perform well over a three to twelve month period to continue to perform well over a subsequent three to twelve month period. Similarly, stocks that perform poorly over a three to twelve month period have a tendency to continue to perform poorly. They describe earnings momentum as the tendency for stocks with high earnings per share (EPS) to continue to outperform stocks with low EPS.
+</p>
+
+<p>
+  Below, we will implement a quarterly-rebalanced stock strategy based on the price and earnings momentum.
+</p>
diff --git a/04 Strategy Library/271 Price and Earning Momentum/02 Method.html b/04 Strategy Library/271 Price and Earning Momentum/02 Method.html
new file mode 100644
index 0000000..755688e
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/02 Method.html	
@@ -0,0 +1,166 @@
+<h3>Step 1: Select the coarse universe</h3> 
+<p>
+  We will use both a coarse selection filter and in a later step, a fine universe filter, to narrow down our universe of assets. Our coarse universe filter creates a set of stocks based on volume, price, and whether fundamental data on the stock exists. In this step we filter for the top 100 liquid equities with prices greater than $5. We also exclude the equities missing fundamental data because EPS is needed in the fine selection step.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelection(self, coarse):
+  '''
+  Pick the top 100 liquid equities as the coarse-selected universe
+  '''
+  # Before next rebalance time, just remain the current universe
+  if self.Time < self.nextRebalance:
+      return Universe.Unchanged
+
+  # Sort the equities (prices > 5) by Dollar Volume descendingly
+  selectedByDollarVolume = sorted([x for x in coarse if x.Price > 5 and x.HasFundamentalData], 
+                                  key = lambda x: x.DollarVolume, reverse = True)
+
+  # Pick the top 100 liquid equities as the coarse-selected universe
+  return [x.Symbol for x in selectedByDollarVolume[:self.numOfCoarse]]  
+</pre>
+</div>
+
+<h3>Step 2: Calculate quarterly return and earnings growth</h3>
+<p>
+  N.Jegadeesh et al. state price momentum and earnings momentum can be used as two indicators for trading. We will calculate for price momentum with the "GetQuarterlyReturn" method and for earnings momentum with the "GetEarningGrowth" method.
+</p>
+<p>
+  "GetQuarterlyReturn" calculates price momentum for each symbol in our coarse universe and ranks each stock based on its quarterly return. First we request last quarter’s close price for all stocks. Then we calculate quarterly return by taking the delta of the first day’s close price and the last day’s close price. Finally, we store the symbols and their corresponding rankings by quarterly return in a dictionary in preparation for fine selection.
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    def GetQuarterlyReturn(self, history):
+        '''
+        Get the rank of securities based on their quarterly return from historical close prices
+        Return: dictionary
+        '''
+        # Get quarterly returns for all symbols
+        # (The first row divided by the last row)
+        returns = history.iloc[0] / history.iloc[-1]
+
+        # Transform them to dictionary structure
+        returns = returns.to_dict()
+
+        # Get the rank of the returns (key: symbol; value: rank)
+        # (The symbol with the 1st quarterly return ranks the 1st, etc.)
+        ranked = sorted(returns, key = returns.get, reverse = True)
+        return {symbol: rank for rank, symbol in enumerate(ranked, 1)}
+  </pre>
+</div>
+<p>
+  "GetEarningGrowth" calculates earnings momentum for each symbol in our coarse universe and ranks each stock based on its earnings growth. First we use a RollingWindow to store and update the BasicEPS to reflect quarterly earnings reports. A RollingWindow holds a set of the most recent entries of data. As we move from time t=0 forward, our rolling window will shuffle data further along to a different index until it leaves the window completely. The object in the window with index[0] refers to the most recent item. The length-1 in the window is the oldest object.
+</p>
+
+<p>
+  Our rolling window has a length of 2 so index[0] is the current EPS and index[1] is last quarter's EPS. We calculate earnings growth for each stock by taking the delta of this quarter’s EPS and last quarter’s EPS divided by last quarter’s EPS. Finally we rank each asset based on earnings growth.
+</p>
+<div class="section-example-container">
+  <pre class="python">
+    def GetEarningGrowth(self, fine):
+        '''
+        Get the rank of securities based on their EPS growth
+        Return: dictionary
+        '''
+
+        # Earning Growth by symbol
+        egBySymbol = {}
+        for stock in fine:
+
+            # Select the securities with EPS (> 0)
+            if stock.EarningReports.BasicEPS.ThreeMonths == 0:
+                continue
+
+            # Add the symbol in the dict if not exist
+            if not stock.Symbol in self.epsBySymbol:
+                self.epsBySymbol[stock.Symbol] = RollingWindow[float](2)
+
+            # Update the rolling window for each stock
+            self.epsBySymbol[stock.Symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths)
+
+            # If the rolling window is ready
+            if self.epsBySymbol[stock.Symbol].IsReady:
+                rw = self.epsBySymbol[stock.Symbol]
+                # Caculate the Earning Growth
+                egBySymbol[stock.Symbol] = (rw[0] - rw[1]) / rw[1]
+
+        # Get the rank of the Earning Growth
+        ranked = sorted(egBySymbol, key = egBySymbol.get, reverse = True)
+        return {symbol: rank for rank, symbol in enumerate(ranked, 1)}
+  </pre>
+</div>
+
+<h3>Step 3: Select the fine universe</h3>  
+<p>
+  We use a fine selection filter in addition to a coarse selection filter to refine our asset selection based on corporate fundamental data. We can use both quarterly return and earnings growth from our two indicators to generate an average rank for each stock. Then we can go long on the top 10 and short the bottom 10.
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    def FineSelection(self, fine):
+        '''
+        Select securities based on their quarterly return and their earnings growth 
+        '''
+        symbols = [x.Symbol for x in fine]
+
+        # Get the quarterly returns for each symbol
+        history = self.History(symbols, self.rebalanceDays, Resolution.Daily)
+        history = history.drop_duplicates().close.unstack(level = 0)
+        rankByQuarterReturn = self.GetQuarterlyReturn(history)
+
+        # Get the earning growth for each symbol
+        rankByEarningGrowth = self.GetEarningGrowth(fine) 
+
+        # Get the sum of rank for each symbol and pick the top ones to long and the bottom ones to short
+        rankSumBySymbol = {key: rankByQuarterReturn.get(key, 0) + rankByEarningGrowth.get(key, 0) 
+                                for key in set(rankByQuarterReturn) | set(rankByEarningGrowth)}
+
+        # Get 10 symbols to long and short respectively
+        sortedDict = sorted(rankSumBySymbol.items(), key = lambda x: x[1], reverse = True)
+        self.longSymbols = [x[0] for x in sortedDict[:10]]
+        self.shortSymbols = [x[0] for x in sortedDict[-10:]]
+
+        return [x for x in symbols if str(x) in self.longSymbols + self.shortSymbols]    
+  </pre>
+</div>
+
+<h3>Step 4: Rebalance quarterly</h3>  
+<p>
+  We choose to rebalance every quarter and use equal weights for the long and short positions of securities in our portfolio.
+</p>
+
+<div class="section-example-container">
+  <pre class="python">
+    def OnData(self, data):
+        '''
+        Rebalance quarterly
+        '''
+        # Do nothing until next rebalance
+        if self.Time < self.nextRebalance:
+            return
+
+        # Liquidate the holdings if necessary
+        for holding in self.Portfolio.Values:
+            symbol = holding.Symbol
+            if holding.Invested and symbol.Value not in self.longSymbols + self.shortSymbols:
+                self.Liquidate(symbol, "Not Selected")
+
+        # Open positions for the symbols with equal weights
+        count = len(self.longSymbols + self.shortSymbols)
+        if count == 0:
+            return
+
+        # Enter long positions
+        for symbol in self.longSymbols:
+            self.SetHoldings(symbol, 1 / count)
+
+        # Enter short positions
+        for symbol in self.shortSymbols:
+            self.SetHoldings(symbol, -1 / count)
+
+        # Set next rebalance time
+        self.nextRebalance += timedelta(self.rebalanceDays)   
+  </pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/271 Price and Earning Momentum/03 Results.html b/04 Strategy Library/271 Price and Earning Momentum/03 Results.html
new file mode 100644
index 0000000..e0cab48
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/03 Results.html	
@@ -0,0 +1,9 @@
+<p>
+    Our backtest results in a Sharpe ratio of 0.59 while the SP500 Sharpe ratio is 0.8 during the same decade. This performance may be due to several factors:
+    <ol>
+        <li>The number of stocks in our portfolio, 100, could be too low.</li>
+        <li>Equal weighting for all stocks may not fully capture the strength of higher ranking stocks.</li>
+        <li>Rebalancing quarterly may be too frequent for a momentum strategy in equities.</li>
+    </ol>
+    This tutorial shows us how to take advantage of the techniques of requesting historical data and using a RollingWindow. We hope the community can further develop strategies based on these techniques.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html b/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html
new file mode 100644
index 0000000..65639b2
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_a8094e5fd7fb3fb39afabd91ac6876bb.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html b/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html
new file mode 100644
index 0000000..8ce9247
--- /dev/null
+++ b/04 Strategy Library/271 Price and Earning Momentum/05 Reference.html	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=299107" rel="nofollow">Momentum,  N. Jegadeesh, S.Titman</a>
+</li>
+</ul>

From dd315de127685c40d99555d8c59f05e3c746c445 Mon Sep 17 00:00:00 2001
From: Xin Wei <wei24@indiana.edu>
Date: Fri, 20 Sep 2019 17:12:18 -0700
Subject: [PATCH 624/753] Fixed bug in copula pairs trading

03 - Part I - Copula Method
06 - Algorithm
05 - Summary (need to be changed after cointegration code is updated)
---
 .../03 Part I - Copula Method.html            | 275 +++++++++++-------
 .../06 Algorithm.html                         |   2 +-
 2 files changed, 166 insertions(+), 111 deletions(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html
index 9c26993..a16ea55 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 Part I - Copula Method.html	
@@ -3,7 +3,7 @@
 </p>
 
 <p>
-  We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of 5 years from 2011 to 2017 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
+  We use the first 3 years of data to choose the best fitting copula and asset pair ("training formation period"). Next, we use a period of more than 9 years from January 2010 to September 2019 ("the trading period"), to execute the strategy. During the trading period we use a rolling 12 month window of data to get the copula parameters ("rolling formation period").
 </p>
 
 <h3>Step 1: Selecting the Paired Stocks</h3>
@@ -59,28 +59,42 @@ <h4><strong>2) Filter the trading pair with statistical correlation</strong></h4
 
 <div class="section-example-container">
 
-<pre class="python">def _pair_selection(self):
-
-    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
-                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
-    logreturn={}
-    for i in range(2):
-        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
-        history = self.History(syl, self.lookbackdays,Resolution.Daily)
-        # generate the log return series of paired stocks
-        close = history['close'].unstack(level=0)
-        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-        for j in tick_syl[i]:
-            logreturn[j] = df_logreturn[j]
-    # estimate coefficients of different correlation measures
-    tau_coef,pr_coef,sr_coef= [],[],[]
-    for i in range(len(tick_syl[i])):
-        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
-        tau_coef.append(kendalltau(tik_x, tik_y)[0])
-        pr_coef.append(pearsonr(tik_x, tik_y)[0])
-        sr_coef.append(spearmanr(tik_x, tik_y)[0])
-    index_max = tau_coef.index(max(tau_coef))
-    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+<pre class="python">def PairSelection(self, date):
+	'''Selects the pair of stocks with the maximum Kendall tau value.
+	It's called on first day of each month'''
+	
+	if date.month == self.month:
+		return Universe.Unchanged
+	
+	symbols = [ Symbol.Create(x, SecurityType.Equity, Market.USA) 
+				for x in [  
+							"QQQ", "XLK",
+							"XME", "EWG", 
+							"TNA", "TLT",
+							"FAS", "FAZ",
+							"XLF", "XLU",
+							"EWC", "EWA",
+							"QLD", "QID"
+						] ]
+
+	logreturns = self._get_historical_returns(symbols, self.lookbackdays)
+	
+	tau = 0
+	for i in range(0, len(symbols), 2):
+		
+		x = logreturns[str(symbols[i])]
+		y = logreturns[str(symbols[i+1])]
+		
+		# Estimate Kendall rank correlation for each pair
+		tau_ = kendalltau(x, y)[0]
+		
+		if tau > tau_:
+			continue
+
+		tau = tau_
+		self.pair = symbols[i:i+2]
+	
+	return [x.Value for x in self.pair]
 </pre>
 </div>
 
@@ -126,17 +140,29 @@ <h3>Step 3: Estimating Copula Parameters</h3>
 <div class="section-example-container">
 
 <pre class="python">def _parameter(self, family, tau):
-    if  family == 'clayton':
-        return 2*tau/(1-tau)
-    elif family == 'frank':
-        # debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
-    	# frank_fun is the squared difference
-    	# Minimize the frank_fun would give the parameter theta for the frank copula
-      integrand = lambda t: t/(np.exp(t)-1)
-    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
-      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
-    elif family == 'gumbel':
-        return 1/(1-tau)</pre>
+	''' Estimate the parameters for three kinds of Archimedean copulas
+	according to association between Archimedean copulas and the Kendall rank correlation measure
+	'''
+	
+	if  family == 'clayton':
+		return 2 * tau / (1 - tau)
+	
+	elif family == 'frank':
+		
+		'''
+		debye = quad(integrand, sys.float_info.epsilon, theta)[0]/theta  is first order Debye function
+		frank_fun is the squared difference
+		Minimize the frank_fun would give the parameter theta for the frank copula 
+		''' 
+		
+		integrand = lambda t: t / (np.exp(t) - 1)  # generate the integrand
+		frank_fun = lambda theta: ((tau - 1) / 4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0] / theta - 1) / theta) ** 2
+		
+		return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x 
+	
+	elif family == 'gumbel':
+		return 1 / (1 - tau)
+</pre>
 </div>
 
 <h3>Step 4: Selecting the Best Fitting Copula</h3>
@@ -183,18 +209,23 @@ <h3>Step 4: Selecting the Best Fitting Copula</h3>
 <div class="section-example-container">
 
 <pre class="python">def _lpdf_copula(self, family, theta, u, v):
-    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
-    if  family == 'clayton':
-        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
-    elif family == 'frank':
-        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
-        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
-        pdf = num/denom
-    elif family == 'gumbel':
-        A = (-np.log(u))**theta + (-np.log(v))**theta
-        c = np.exp(-A**(1/theta))
-        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
-    return np.log(pdf)
+	'''Estimate the log probability density function of three kinds of Archimedean copulas
+	'''
+	
+	if  family == 'clayton':
+		pdf = (theta + 1) * ((u ** (-theta) + v ** (-theta) - 1) ** (-2 - 1 / theta)) * (u ** (-theta - 1) * v ** (-theta - 1))
+		
+	elif family == 'frank':
+		num = -theta * (np.exp(-theta) - 1) * (np.exp(-theta * (u + v)))
+		denom = ((np.exp(-theta * u) - 1) * (np.exp(-theta * v) - 1) + (np.exp(-theta) - 1)) ** 2
+		pdf = num / denom
+		
+	elif family == 'gumbel':
+		A = (-np.log(u)) ** theta + (-np.log(v)) ** theta
+		c = np.exp(-A ** (1 / theta))
+		pdf = c * (u * v) ** (-1) * (A ** (-2 + 2 / theta)) * ((np.log(u) * np.log(v)) ** (theta - 1)) * (1 + (theta - 1) * A ** (-1 / theta))
+		
+	return np.log(pdf)
 </pre>
 </div>
 
@@ -202,22 +233,6 @@ <h3>Step 4: Selecting the Best Fitting Copula</h3>
   The copula that provides the best fit is the one that corresponds to the lowest value of AIC criterion. The chosen pair is "QQQ" &amp; "XLK".
 </p>
 
-<div class="section-example-container">
-
-<pre class="python">self.family = ['clayton', 'frank', 'gumbel']
-tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
-AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
-for i in self.family:
-    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
-    # Replace nan with zero and inf with finite numbers in lpdf list
-    lpdf = np.nan_to_num(lpdf)
-    loglikelihood = sum(lpdf)
-    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
-    # choose the copula with the minimum AIC
-    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
-</pre>
-</div>
-
 
 <h3>Step 5: Generating the Trading Signals</h3>
 <p>
@@ -271,20 +286,57 @@ <h3>Step 5: Generating the Trading Signals</h3>
 
 <div class="section-example-container">
 
-<pre class="python">def _set_signal(self):
-    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
-    # generate the log return series of paired stocks
-    close = history['close'].unstack(level=0)
-    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
-    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
-    # estimate Kendall'rank correlation each trading day
-    tau = kendalltau(x, y)[0]
-    # etstimate the copula parameter: theta
-    self.theta = self._parameter(self.copula, tau)
-    # simulate the empirical distribution function for returns of two paired stocks
-    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
-    # run linear regression over the two history return series
-    self.coef = stats.linregress(x,y).slope
+<pre class="python">def SetSignal(self, slice):
+	'''Computes the mispricing indices to generate the trading signals.
+	It's called on first day of each month'''
+
+	if self.Time.month == self.month:
+		return
+	
+	## Compute the best copula
+	
+	# Pull historical log returns used to determine copula
+	logreturns = self._get_historical_returns(self.pair, self.numdays)
+	x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]
+
+	# Convert the two returns series to two uniform values u and v using the empirical distribution functions
+	ecdf_x, ecdf_y  = ECDF(x), ECDF(y)
+	u, v = [ecdf_x(a) for a in x], [ecdf_y(a) for a in y]
+	
+	# Compute the Akaike Information Criterion (AIC) for different copulas and choose copula with minimum AIC
+	tau = kendalltau(x, y)[0]  # estimate Kendall'rank correlation
+	AIC ={}  # generate a dict with key being the copula family, value = [theta, AIC]
+	
+	for i in ['clayton', 'frank', 'gumbel']:
+		param = self._parameter(i, tau)
+		lpdf = [self._lpdf_copula(i, param, x, y) for (x, y) in zip(u, v)]
+		# Replace nan with zero and inf with finite numbers in lpdf list
+		lpdf = np.nan_to_num(lpdf) 
+		loglikelihood = sum(lpdf)
+		AIC[i] = [param, -2 * loglikelihood + 2]
+		
+	# Choose the copula with the minimum AIC
+	self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+	
+	## Compute the signals
+	
+	# Generate the log return series of the selected trading pair
+	logreturns = logreturns.tail(self.lookbackdays)
+	x, y = logreturns[str(self.pair[0])], logreturns[str(self.pair[1])]
+	
+	# Estimate Kendall'rank correlation
+	tau = kendalltau(x, y)[0] 
+	
+	# Estimate the copula parameter: theta
+	self.theta = self._parameter(self.copula, tau)
+	
+	# Simulate the empirical distribution function for returns of selected trading pair
+	self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y) 
+	
+	# Run linear regression over the two history return series and return the desired trading size ratio
+	self.coef = stats.linregress(x,y).slope
+	
+	self.month = self.Time.month
 </pre>
 </div>
 
@@ -294,39 +346,42 @@ <h3>Step 5: Generating the Trading Signals</h3>
 
 <div class="section-example-container">
 
-<pre class="python">def OnData(self,data):
-    for i in self.syl:
-        self.price_list[i].append(self.Portfolio[i].Price)
-    # compute today's log return of 2 stocks
-    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
-    else:
-        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
-        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
-    # Convert the two returns to uniform values u and v using the empirical distribution functions
-    u_value = self.ecdf_x(return_x)
-    v_value = self.ecdf_y(return_y)
-    # Compute the mispricing indices for u and v by using estimated copula
-    self._misprice_index(self.copula, self.theta, u_value, v_value)
-    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
-        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-        else:
-            self.Sell(self.syl[1], 1 * quantity )
-            self.Buy(self.syl[0], self.coef * quantity)
-    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
-        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
-            self.Liquidate(self.syl[0])
-            self.Liquidate(self.syl[1])
-            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
-        else:
-            self.Buy(self.syl[1], 1 * quantity )
-            self.Sell(self.syl[0], self.coef * quantity)
+<pre class="python">def OnData(self, slice):
+	'''Main event handler. Implement trading logic.'''
+
+	self.SetSignal(slice)     # only executed at first day of each month
+
+	# Daily rebalance
+	if self.Time.day == self.day:
+		return
+	
+	long, short = self.pair[0], self.pair[1]
+
+	# Update current price to trading pair's historical price series
+	for kvp in self.Securities:
+		symbol = kvp.Key
+		if symbol in self.pair:
+			price = kvp.Value.Price
+			self.window[symbol].append(price)
+
+	if len(self.window[long]) < 2 or len(self.window[short]) < 2:
+		return
+	
+	# Compute the mispricing indices for u and v by using estimated copula
+	MI_u_v, MI_v_u = self._misprice_index()
+
+	# Placing orders: if long is relatively underpriced, buy the pair
+	if MI_u_v < self.floor_CL and MI_v_u > self.cap_CL:
+		
+		self.SetHoldings(short, -self.weight_v, False, f'Coef: {self.coef}')
+		self.SetHoldings(long, self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)
+
+	# Placing orders: if short is relatively underpriced, sell the pair
+	elif MI_u_v > self.cap_CL and MI_v_u < self.floor_CL:
+
+		self.SetHoldings(short, self.weight_v, False, f'Coef: {self.coef}')
+		self.SetHoldings(long, -self.weight_v * self.coef * self.Portfolio[long].Price / self.Portfolio[short].Price)
+	
+	self.day = self.Time.day
 </pre>
 </div>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 038e110..21099b5 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_59d99772a1736de98db5f14e2e548ce8.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1affb5ffa1ff019c9db6b9eda1e9aecb.html"></iframe>
     </div>
 </div>
 

From d8340fa8d6308be805b02b7715c8f1eb584dbb33 Mon Sep 17 00:00:00 2001
From: wyiyun95 <wyiyun1995@outlook.com>
Date: Wed, 25 Sep 2019 17:27:12 -0700
Subject: [PATCH 625/753] Mandarin Translation (Strategy Library)

03, 100 full
07 partial section 01-03
---
 .../01 \346\221\230\350\246\201.cn.html"      |   7 +
 ...7\263\273\346\241\206\346\236\266.cn.html" |  71 ++++
 ...0\201\224\347\263\273\346\263\225.cn.html" | 332 ++++++++++++++++++
 ...5\215\217\346\225\264\346\263\225.cn.html" |  47 +++
 .../05 \346\200\273\347\273\223.cn.html"      |  35 ++
 .../06 \347\256\227\346\263\225.cn.html"      |  19 +
 ...0\200\203\346\226\207\347\214\256.cn.html" |  32 ++
 .../01 \346\221\230\350\246\201.cn.html"      |  11 +
 .../02 \347\256\200\344\273\213.cn.html"      | 115 ++++++
 .../03 \346\226\271\346\263\225.cn.html"      | 252 +++++++++++++
 .../01 \347\256\200\344\273\213.cn.html"      |   3 +
 .../02 \346\226\271\346\263\225.cn.html"      | 117 ++++++
 .../03 \347\256\227\346\263\225.cn.html"      |   6 +
 .../04 \346\235\245\346\272\220.cn.html"      |   5 +
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 create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html"
 create mode 100644 "04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html"
 create mode 100644 "04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html"
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diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html"
new file mode 100644
index 0000000..e06b3ac
--- /dev/null
+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/01 \346\221\230\350\246\201.cn.html"	
@@ -0,0 +1,7 @@
+<p>
+  在本研究中，我们研究了两种配对交易方法，并对结果进行了比较。配对交易涉及到对两种高度相关资产之间依附结构的研究。在假定会出现平均值回复的情况下，当出现价格差异时，多头或空头头寸将反向入市。一般情况下，资产价格分布是采用收益率序列高斯分布来建模的，但联合正态分布可能无法捕捉到股票对价格依赖性的一些关键特征（如尾部相关性）。我们的研究使用了联系理论来识别这些交易机会。
+</p>
+
+<p>
+  我们将从数学的角度讨论联系的基本框架，并解释如何在配对交易中应用这一方法。算法的实现基于Stander Y, Marais D, BothaI(2013)的论文<em><a href="https://journals.co.za/content/jefs/6/1/EJC135921">带有联系函数的交易策略</a></em>。根据Hanson T A，Hall J R.(2012)的论文<em><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012">统计套利交易策略和高频交易</a></em>，我们将联系配对交易策略的性能与协整配对交易方法进行了比较。协整法假设配对股票之间存在协整关系，可以识别有利可图的交易机会。实证结果表明，与传统的配对交易策略相比，基于联系的策略更容易获益。
+</p>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html"
new file mode 100644
index 0000000..5fdb07a
--- /dev/null
+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/02 \350\201\224\347\263\273\346\241\206\346\236\266.cn.html"	
@@ -0,0 +1,71 @@
+<h3>1.定义</h3>
+<p>
+  给定一个随机向量\(X_1,X_2,...,X_p\)，其边际累积分布函数(CDF)为\(F_i(x) = P[X_i \leq x]\)。通过对每个分量进行概率积分变换，\((U_1,U_2,...,U_p) = (F_1(X_1),F_2(X_2),...,F_p(X_p))\)的边际分布是一致的(from <a href="https://en.wikipedia.org/wiki/Copula_(probability_theory)">Wikipedia</a>)。然后将\(X_1,X_2,...,X_p\)的联系定义为\(U_1,U_2,...,U_p\)的联合累积分布函数，其中每个变量U的边际分布均为\(U(0,1)\)。
+</p>
+
+\[C(u_1,u_2,...,u_p) = P[U_1\leq u_1,U_2\leq u_2,..., U_1\leq u_1]\]
+
+<p>
+  联系函数包含了所有边际分布的依赖性特征，能够更好地利用概率描述变量之间的线性和非线性关系。它们使边际分布能够彼此独立地建模，并且不需要对边际分布的联合行为进行假设。
+</p>
+
+<h3>2.二元联系</h3>
+<p>
+  由于本研究的重点是二元联系 (对于配对交易，我们有2个随机变量)，因此指定了一些概率性质。
+  设X和Y为两个随机变量，累积概率函数为\(F_1(X)\)和\(F_2(Y)\)。\(U=F_1(X), V=F_2(Y)\)是均匀分布的。则联系函数为\(C(u,v)=P(U\leq u,V\leq v)\)。取U和V联系函数的偏导数，所得到条件分布函数如下：
+</p>
+
+\[P(U\leq u\mid V= v)=\frac{\partial C(u,v)}{\partial v}\]
+\[P(V\leq v\mid U= u)=\frac{\partial C(u,v)}{\partial u}\]
+
+<h3>3.阿基米德联系</h3>
+<p>
+  除了高斯假设之外，还有许多联系函数可以用来描述变量之间的依附结构。这里我们将重点讨论其中的三种，它们分别是来自阿基米德分类中的<em>Clayton</em>，<em>Gumbel</em>和<em>Frank</em>。
+  阿基米德联系是基于单变量分布函数的Laplace变换参数φ。它们是由特定的生成器函数\(\phi\)创建的。
+</p>
+
+\[C(u,v)=\phi^{-1}( \phi(u),\phi(v) )\]
+
+<p>
+  概率密度函数为：
+</p>
+
+\[c(u,v)=\phi_{(2)}^{-1}(\phi(u)+\phi(v))\phi^{'}(u)\phi^{'}(v)\]
+
+<p>
+  其中，\(\phi_{(2)}^{-1}\)是生成器函数二次导数的倒数。
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: left;">联系</th>
+<th style="text-align: center;">联系函数C(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton联系公式</td>
+<td style="text-align: left;">\[(u^{-\theta}+v^{-\theta}-1)^{-1/\theta}\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Gumbel联系公式</td>
+<td style="text-align: left;">\[exp(-[(-\ln u)^{\theta}＋(-\ln v)^{\theta}]^{1/\theta})\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank联系公式</td>
+<td>\[-\theta^{-1}\ln\left[1+\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)}{exp(-\theta)-1}\right]\]</td>
+</tr>
+</tbody>
+</table>
+
+
+<p>
+  Genest和MacKay证明了在双变量情况下联系生成器函数与Kendall秩相关tau之间的关系为：
+</p>
+
+\[\tau=1+4\int_{0}^{1} \frac{\partial \phi (v)}{\partial \phi^{'}(v)}dv\]
+
+<p>
+  因此，只要知道Kendall的tau秩测量值和生成器函数，就可以很容易地估算出阿基米德联系函数中的参数。请参照步骤3查看公式。
+</p>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html"
new file mode 100644
index 0000000..920e147
--- /dev/null
+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/03 \347\254\254I\351\203\250\345\210\206 \342\200\223 \350\201\224\347\263\273\346\263\225.cn.html"	
@@ -0,0 +1,332 @@
+<p>
+  交易所交易基金（ETF）有许多不同的股票板块和资产类别，为我们提供了各种不同的配对交易选择。我们的数据集包括在纳斯达克或纽约证券交易所进行基金买卖交易的每日数据。
+</p>
+
+<p>
+  我们使用前3年的数据来选择联系和资产配对的最佳组合（“训练形成期”）。接下来，我们使用2011年至2017年的5年时间（“交易期”）来执行策略。在交易期间，我们使用12个月的滚动窗口数据来获取联系参数（“滚动形成期”）。
+</p>
+
+<h3>步骤1：选择配对股票</h3>
+<p>
+  配对选择的一般方法基于基础分析和统计分析。
+</p>
+
+<h4><strong>1)收集可能相关的配对</strong></h4>
+<p>
+  任何随机对都可以相互关联。这些变量之间可能并没有因果关系，而是由于巧合或存在某种第三种不可见因素而产生了虚假关系。因此，对于我们来说重要的是从一些有共同点的证券开始。为了说明这一点，我们选择了一些在纳斯达克或纽交所交易中流动性最强的基金。这些潜在相关配对之间的关系可能是由于指数、行业或资产类别重叠所造成的。例如，QQQ和XLK就是市场领先指数的两支基金。
+</p>
+
+<h4><strong>2)采用统计相关性来筛选交易配对</strong></h4>
+
+<p>
+  为了确定分析中所包含的股票配对，将要对预先选择基金对之间的相关性进行分析。以下是我们在统计中常用的三种相关指标：
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th colspan="3">相关测量技术</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td width="33%">Pearson相关系数</td>
+<td>\[r = \frac{\sum (x_i- \bar{x})(y_i- \bar{y})}{\sqrt{\sum (x_i- \bar{x})^2)\sum (y_i- \bar{y})^2)} }\]</td>
+</tr>
+<tr>
+<td>Kendall秩相关系数</td>
+<td>\[\tau=\frac{n_c-n_d}{\frac{1}{2}n(n-1)}\]</td>
+</tr>
+<tr>
+<td>Spearman秩相关系数</td>
+<td>\[\rho=1-\frac{6\sum d_i^2}{n(n^2-1)}\]</td>
+</tr>
+<tr>
+<td colspan="2"> \(n\) = 每个数据集中的值数
+\(n_c\) = 一致数
+\(n_d\) = 不一致数
+\(d_i\) = \(x_i\)和\(y_i\)对应值秩之间的差异。</td>
+</tr>
+</tbody>
+</table>
+
+
+<p>
+  我们可以在Python中使用SciPy统计值程序库中的函数来获得这些系数。利用训练形成期间的每日股价收益率来计算相关系数。我们可以发现这三种相关性技术对配对基金给出了相同的相关系数排名。Pearson相关性假设两个变量都是正态分布的。因此，本文采用Kendall秩作为相关性测量法，并选择具有最高Kendall秩相关性的配对来进行配对交易。通过使用历史函数并将价格转换为对数收益序列，我们可以得出基金对的每日历史收盘价。设Px和Py为股票x和股票y的历史股价序列，则基金配对的对数收益为：
+</p>
+
+\[R_x = ln(\frac{P_{x,t}}{P_{x,t-1}}),   R_y = ln(\frac{P_{y,t}}{P_{y,t-1}})\]   t = 1,2,...,n，其中n是价格数据的数量 
+
+<div class="section-example-container">
+
+<pre class="python">def _pair_selection(self):
+
+    tick_syl =  [["QQQ","XME","TNA","FAS","XLF","EWC","QLD"],
+                 ["XLK","EWG","TLT","FAZ","XLU","EWA","QID"]]
+    logreturn={}
+    for i in range(2):
+        syl = [self.AddSecurity(SecurityType.Equity, x, Resolution.Daily).Symbol.Value for x in tick_syl[i]]
+        history = self.History(syl, self.lookbackdays,Resolution.Daily)
+        # 生成配对股票的对数收益序列
+        close = history['close'].unstack(level=0)
+        df_logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+        for j in tick_syl[i]:
+            logreturn[j] = df_logreturn[j]
+    # 不同相关性测量法的估计系数
+    tau_coef,pr_coef,sr_coef= [],[],[]
+    for i in range(len(tick_syl[i])):
+        tik_x, tik_y= logreturn[tick_syl[0][i]], logreturn[tick_syl[1][i]]
+        tau_coef.append(kendalltau(tik_x, tik_y)[0])
+        pr_coef.append(pearsonr(tik_x, tik_y)[0])
+        sr_coef.append(spearmanr(tik_x, tik_y)[0])
+    index_max = tau_coef.index(max(tau_coef))
+    self.ticker = [tick_syl[0][index_max],tick_syl[1][index_max]]
+</pre>
+</div>
+
+<h3>步骤2：估算对数收益的边缘分布</h3>
+<p>
+  为了构造联系，我们需要将对数收益序列Rx和Ry转化为两个均匀分布的值u和v。这可以通过估计Rx和Ry的边际分布函数，并将回归值代入一个分布函数来实现。由于我们对两个对数收益序列的分布没有做任何假设，所以在这里我们会使用经验分布函数来接近边际分布F1(Rx)F1(Rx)和F2(Ry)F2(Ry)。统计模型库中的Python 经验累积分布（ECDF）函数将经验性的累积分布函数（CDF）作为阶梯函数提供给我们。
+</p>
+<h3>步骤3：估算联系参数</h3>
+<p>
+  正如上文所讨论的，对于每个阿基米德联系来说，我们通过联系与依附测度Kendall tau之间的关系来估算联系参数。
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th>联系</th>
+<th style="text-align: center;">Kendall's tau</th>
+<th style="text-align: center;">参数θ</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Clayton联系</td>
+<td>\[\frac{\theta}{\theta +2}\]</td>
+<td>\[\theta=2\tau(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel联系</td>
+<td>\[1-\theta^{-1}\]</td>
+<td>\[\theta=(1-\tau)^{-1}\]</td>
+</tr>
+<tr>
+<td>Frank联系</td>
+<td>\[1+4[D_1(\theta)-1]/\theta\]</td>
+<td>\[arg min\left(\frac{\tau-1}{4}-\frac{D_1(\theta)-1}{\theta}\right)^2\]</td>
+</tr>
+<tr>
+<td colspan="3">\[D_1(\theta)=\frac{1}{\theta}\int_{0}^{\theta}\frac{t}{exp(t)-1}dt \]</td>
+</tr>
+</tbody>
+</table>
+
+<div class="section-example-container">
+
+<pre class="python">def _parameter(self, family, tau):
+    if  family == 'clayton':
+        return 2*tau/(1-tau)
+    elif family == 'frank':
+        # debye = quad(被积函数，sys.float_info.epsilon, theta)[0]/theta是一阶Debye函数
+    	# frank_fun是平方差
+    	# 将frank_fun最小化可以得出直接联系的参数theta
+      integrand = lambda t: t/(np.exp(t)-1)
+    	frank_fun = lambda theta: ((tau - 1)/4.0  - (quad(integrand, sys.float_info.epsilon, theta)[0]/theta - 1)/theta)**2
+      return minimize(frank_fun, 4, method='BFGS', tol=1e-5).x
+    elif family == 'gumbel':
+        return 1/(1-tau)</pre>
+</div>
+
+<h3>步骤4：选择最适合的联系</h3>
+<p>
+  得到联系函数的参数估计值后，我们使用AIC准则来选择最适合算法初始化的联系。
+</p>
+
+\[AIC=-2L(\theta)+2k\]
+
+<p>
+  其中\(L(\theta)=\sum_{t=1}^T\log c(u_t,v_t;\theta)\)是对数似然函数，k是参数的数量，在这里k=1。
+</p>
+
+<p>
+  各联系函数的密度函数如下：
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">联系</th>
+<th style="text-align: center;">密度函数c(u,v;θ)</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;">Clayton联系</td>
+<td style="text-align: left;">\[(\theta+1)(u^{-\theta}+v^{-\theta}-1)^{-2-1/\theta}u^{-\theta-1}v^{-\theta-1}\]</td>
+</tr>
+<tr>
+<td>Gumbel联系</td>
+<td>\[C(u,v;\theta)(uv)^{-1}A^{-2+2/\theta}[(\ln u)(\ln v)]^{\theta -1}[1+(\theta-1)A^{-1/\theta}]\]</td>
+</tr>
+<tr>
+<td style="text-align: left;">Frank联系</td>
+<td style="text-align: left;">\[\frac{-\theta(exp(-\theta)-1)(exp(-\theta(u+v)))}{((exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1))^2}\]</td>
+</tr>
+<tr>
+<td colspan="2">\[A=(-\ln u)^{\theta}+(-\ln v)^{\theta}\]</td>
+</tr>
+</tbody>
+</table>
+
+<div class="section-example-container">
+
+<pre class="python">def _lpdf_copula(self, family, theta, u, v):
+    ''' estimate the log probability density function of three kinds of Archimedean copulas '''
+    if  family == 'clayton':
+        pdf = (theta+1) * ((u**(-theta)+v**(-theta)-1)**(-2-1/theta)) * (u**(-theta-1)*v**(-theta-1))
+    elif family == 'frank':
+        num = -theta * (np.exp(-theta)-1) * (np.exp(-theta*(u+v)))
+        denom = ((np.exp(-theta*u)-1) * (np.exp(-theta*v)-1) + (np.exp(-theta)-1))**2
+        pdf = num/denom
+    elif family == 'gumbel':
+        A = (-np.log(u))**theta + (-np.log(v))**theta
+        c = np.exp(-A**(1/theta))
+        pdf = c * (u*v)**(-1) * (A**(-2+2/theta)) * ((np.log(u)*np.log(v))**(theta-1)) * (1+(theta-1)*A**(-1/theta))
+    return np.log(pdf)
+</pre>
+</div>
+
+<p>
+  所提供最适合的联系应对应于AIC准则的最低值。所选择的配对是“QQQ”和“XLK”。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">self.family = ['clayton', 'frank', 'gumbel']
+tau = kendalltau(x, y)[0]  # 估算Kendall秩相关
+AIC ={}  # 生成一个关键字为联系系列的字典，值 = [theta, AIC]
+for i in self.family:
+    lpdf = [self._lpdf_copula(i, self._parameter(i,tau), x, y) for (x, y) in zip(u, v)]
+    # 在lpdf列表中用零替换非数字，用有限数目替换无穷大
+    lpdf = np.nan_to_num(lpdf)
+    loglikelihood = sum(lpdf)
+    AIC[i] = [self._parameter(i,tau), -2*loglikelihood + 2]
+    # 选择AIC最小的联系
+    self.copula = min(AIC.items(), key = lambda x: x[1][1])[0]
+</pre>
+</div>
+
+
+<h3>步骤5：生成交易信号</h3>
+<p>
+  联系函数中包含了两个收益序列依附结构的所有信息。根据Stander Y、Marais D和Botha I，在有联系的交易策略中，使用适合联系推导出\(C(v\mid u)\)和\(C(u\mid v)\)条件边际分布函数的置信区间，即错误定价指数。当市场观察值处于置信区间之外时，表明配对交易机会是可用的。在这里，我们选取95%作为置信区间上边际，5%作为置信区间下边际。置信水平的选择是基于本文的反测试分析，该分析表明，使用95%的置信水平应该可以确定适当的交易机会。
+</p>
+
+<p>
+  将股票X、股票Y的当前收益设为\(R_x, R_y\)，我们可以将“错误定价指数”定义为：
+</p>
+
+\[MI_{X|Y}=P(U\leq u\mid V\leq v)=\frac{\partial C(u,v)}{\partial v}\]
+
+\[MI_{Y|X}=P(V\leq v\mid U\leq u)=\frac{\partial C(u,v)}{\partial u}\]
+
+<p>
+  关于进一步的数学证明，请参考Xie W和Wu Y的《以联系为基础的配对交易策略》。通过表1中联系函数的偏导数，可以推导出二元联系的条件概率公式。结果如下：
+</p>
+<p>
+Gumbel联系
+</p>
+\[C(v\mid u)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln u)^{\theta-1}\frac{1}{u}\]
+
+\[C(u\mid v)=C(u,v;\theta)[(-\ln u)^\theta+(-\ln v)^\theta]^{\frac{1-\theta}{\theta}}(-\ln v)^{\theta-1}\frac{1}{v}\]
+
+<p>
+  Clayton联系
+</p>
+
+\[C(v\mid u)=u^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+\[C(u\mid v)=v^{-\theta-1}(u^{-\theta}+v^{-\theta}-1)^{-\frac{1}{\theta}-1}\]
+
+<p>
+  Frank联系
+</p>
+
+\[C(v\mid u)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta v)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)}  \]
+
+\[C(u\mid v)=\frac{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta u)-1)}{(exp(-\theta u)-1)(exp(-\theta v)-1)+(exp(-\theta)-1)} \]
+
+<p>
+  在选择交易配对和最合适的联系后，我们采取下列步骤进行交易。请注意，我们在每个月的第一天使用过去12个月的每日数据来实施步骤1、2、3、4，这意味着我们的经验分布函数和联系参数theta估值会每月更新一次。在每月总结中：
+</p>
+
+<ul>
+ 	<li>在12个月的滚动形成期内，使用日收盘价计算这对基金的每日对数收益，然后计算Kendall秩的相关性。</li>
+ 	<li>估算X和Y对数收益的边际分布函数，分别为ecdf_x和ecdf_y。</li>
+ 	<li>将Kendall tau代入联系参数估计函数，得到theta的值。</li>
+ 	<li>对两个价格系列进行线性回归。这个系数用来决定股票X和股票Y的买卖数量。例如，如果系数是2，每买入或卖出一股X，就买入或卖出2股Y。</li>
+</ul>
+
+<div class="section-example-container">
+
+<pre class="python">def _set_signal(self):
+    history = self.History(self.ticker, self.lookbackdays,Resolution.Daily)
+    # 生成配对股票的对数收益序列
+    close = history['close'].unstack(level=0)
+    logreturn = (np.log(close) - np.log(close.shift(1))).dropna()
+    x, y = logreturn[self.ticker[0]], logreturn[self.ticker[1]]
+    # 估算每个交易日的Kendall秩相关
+    tau = kendalltau(x, y)[0]
+    # 估算联系参数：theta
+    self.theta = self._parameter(self.copula, tau)
+    # 模拟两对股票收益的经验分布函数
+    self.ecdf_x, self.ecdf_y  = ECDF(x), ECDF(y)
+    # 对两个历史收益序列进行线性回归
+    self.coef = stats.linregress(x,y).slope
+</pre>
+</div>
+
+<p>
+  最后，在交易期间，我们每天使用经验分布函数ecdf_x和ecdf_y将当天的收益转换为u和v。然后，利用估算的联系C，在每个交易日计算两个错误定价指标。\(MI_{Y|X}&lt;0.05\)和\(MI_{X|Y}&gt;0.95\)的日子，算法构建了X的空头头寸和Y的多头头寸。\(MI_{Y|X}&gt;0.95\)且\(MI_{X|Y}&lt;0.05\)时，构建Y的空头头寸和X的多头头寸。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def OnData(self,data):
+    for i in self.syl:
+        self.price_list[i].append(self.Portfolio[i].Price)
+    # 计算两支股票的当天对数收益
+    if len(self.price_list[self.syl[0]]) &lt; 2 or len(self.price_list[self.syl[1]]) &lt; 2: return
+    else:
+        return_x = np.log(float(self.price_list[self.syl[0]][-1]/self.price_list[self.syl[0]][-2]))
+        return_y = np.log(float(self.price_list[self.syl[1]][-1]/self.price_list[self.syl[1]][-2]))
+    # 使用经验分布函数将两种收益转换为统一值u和v
+    u_value = self.ecdf_x(return_x)
+    v_value = self.ecdf_y(return_y)
+    # 用估算联系来计算u和v的错误定价指数
+    self._misprice_index(self.copula, self.theta, u_value, v_value)
+    quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+    if self.MI_u_v &lt; self.floor_CL and self.MI_v_u &gt; self.cap_CL:
+        if self.Portfolio[self.syl[0]].Quantity &lt; 0 and self.Portfolio[self.syl[1]].Quantity &gt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+        else:
+            self.Sell(self.syl[1], 1 * quantity )
+            self.Buy(self.syl[0], self.coef * quantity)
+    elif self.MI_u_v &gt; self.cap_CL and self.MI_v_u &lt; self.floor_CL:
+        if self.Portfolio[self.syl[0]].Quantity &gt; 0 and self.Portfolio[self.syl[1]].Quantity &lt; 0:
+            self.Liquidate(self.syl[0])
+            self.Liquidate(self.syl[1])
+            quantity = self.CalculateOrderQuantity(self.syl[1],0.4)
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+        else:
+            self.Buy(self.syl[1], 1 * quantity )
+            self.Sell(self.syl[0], self.coef * quantity)
+</pre>
+</div>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 \347\254\254II\351\203\250\345\210\206 \342\200\223 \345\215\217\346\225\264\346\263\225.cn.html"
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+<p>
+  对于协整配对交易法，我们选择了相同的基金对“GLD”和“DGL”。不需要选择联系函数，因此只有12个月的滚动形成周期。交易期为5年，从2011年1月到2017年5月。
+</p>
+
+<h3>步骤1：生成价差序列</h3>
+<p>
+  在每个月月初，我们使用每日收盘价生成两支基金的对数价格序列。然后基于对数价格序列数据，采用回归分析法对价差序列进行估算。对于股票X和Y，我们对对数价格序列进行线性回归，得到系数β。
+</p>
+
+\[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
+
+<h3>步骤2：计算阈值</h3>
+
+<p>
+  在本文中，利用滚动形成期价差的标准差，为交易策略设定了两个标准差的阈值。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
+                     index = [i.Time for i in self.symbols[0].hist_window])
+
+price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
+                     index = [i.Time for i in self.symbols[1].hist_window])
+if len(price_x) < 250: return
+spread = self.regr(np.log(price_x), np.log(price_y))
+mean = np.mean(spread)
+std = np.std(spread)
+ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
+if spread[-1] > mean + self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
+        self.Sell(self.symbols[1], 100)
+        self.Buy(self.symbols[0],  ratio * 100)
+
+elif spread[-1] < mean - self.threshold * std:
+    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
+        self.Sell(self.symbols[0], 100)
+        self.Buy(self.symbols[1], ratio * 100)
+else:
+    self.Liquidate()
+</pre>
+</div>
+
+<h3>步骤3：设置交易信号</h3>
+<p>
+  在每个交易日，只要价差偏离平均值超过两个标准差，我们就进入交易。换句话说，我们在价差平均值+2*std的当天构建X的空多头寸和Y的多头头寸。当价差&lt;平均值-2*std时，我们在当天构建Y的空多头寸和X的多头头寸。如果价差恢复到均衡水平(定义为偏离零价差小于标准差的一半)，就会退出交易。平均值和标准偏差的值从滚动形成周期开始计算，每月更新一次。
+</p>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html"
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+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 \346\200\273\347\273\223.cn.html"	
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+<p>
+  配对交易的最终目的是通过平均值回归来捕捉两种相关资产的价格差异。我们的结果表明，配对交易的联系方法要优于传统的协整方法，因为联系法基于依附结构的概率，而协整法则依赖于正常定价的简单线性回归方差。通过对联系法性能的测试，发现联系法对启动参数的敏感性较低。由于协整方法依赖于标准分布，基金对的波动性较小，交易机会也较少。
+</p>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;">方法</th>
+<th style="text-align: center;">交易</th>
+<th style="text-align: center;">利润</th>
+<th style="text-align: center;">夏普指数</th>
+<th style="text-align: center;">动用资金</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: center;">联系法</td>
+<td style="text-align: center;">346</td>
+<td style="text-align: center;">274.293%</td>
+<td style="text-align: center;">1.022</td>
+<td style="text-align: center;">19.4%</td>
+</tr>
+<tr>
+<td style="text-align: center;">协整法</td>
+<td style="text-align: center;">91</td>
+<td style="text-align: center;">26.358%</td>
+<td style="text-align: center;">0.298</td>
+<td style="text-align: center;">23.7%</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+  一般情况下，基金的波动性并不大，因此均值回归并不会提供太多交易机会。在5年的时间里，只有39笔交易采用了协整法。根据Liew R Q和Wu Y的《配对交易联系法》，我们发现在配对交易中使用联系法可以提供更多的交易机会，因为它不需要任何严格的假设。
+</p>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"
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+++ "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,19 @@
+<p>
+  联系法的回溯测试
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_59d99772a1736de98db5f14e2e548ce8.html"></iframe>
+    </div>
+</div>
+
+<p>
+  协整法的回溯测试
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d4443ef5bd0d45da3d003fe8e49a966e.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
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@@ -0,0 +1,32 @@
+<ol>
+  <li>
+    Stander Y, Marais D, Botha I. Trading strategies with copulas[J]. Journal of Economic and Financial Sciences, 2013, 6(1): 83-107. <a href="https://journals.co.za/content/jefs/6/1/EJC135921">Online Copy</a>
+  </li>
+  <li>
+    Hanson T A, Hall J R. Statistical arbitrage trading strategies and high-frequency trading[J]. 2012. <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147012"> Online Copy</a>
+  </li>
+  <li>
+    Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>
+  </li>
+  <li>
+    Rad H, Low R K Y, Faff R. The profitability of pairs trading strategies: distance, cointegration and copula methods[J]. Quantitative Finance, 2016, 16(10): 1541-1558.<a href="http://www.tandfonline.com/doi/full/10.1080/14697688.2016.1164337?needAccess=true">online copy</a>
+  </li>
+  <li>
+    Mahfoud M, Michael M. Bivariate Archimedean copulas: an application to two stock market indices[J]. BMI Paper, 2012.  <a href="https://www.few.vu.nl/nl/Images/werkstuk-mahfoud_tcm243-277460.pdf">Online Copy</a>
+  </li>
+  <li>
+    LANDGRAF N, SCHOLTUS K, DIRIS D R B. High-Frequency copula-based pairs trading on US Goldmine Stocks[J]. 2016.
+  </li>
+  <li>
+    Genest, C. and MacKay, J., 1986, The Joy of Copulas: Bivariate Distributions with Uniform Marginals, The American Statistician, 40, 280-283
+  </li>
+  <li>
+    Jean Folger, Pairs Trading Example <a href="http://www.investopedia.com/university/guide-pairs-trading/pairs-trade-example.asp"> Online Copy</a>
+  </li>
+  <li>
+    Xie W, Wu Y. Copula-based pairs trading strategy[C] Asian Finance Association (AsFA) 2013 Conference. doi. 2013, 10.
+  </li>
+  <li>
+    Liew R Q, Wu Y. Pairs trading: A copula approach[J]. Journal of Derivatives &amp; Hedge Funds, 2013, 19(1): 12-30.
+  </li>
+ </ol>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/01 \346\221\230\350\246\201.cn.html"
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+<p>
+  在本教程中，我们使用两阶段关联法和协整法，基于市场中性统计套利策略实施了高频动态配对交易策略。这种策略基于George J. Miao的研究。我们将这一交易策略应用于美国银行板块股票，使用2012年至2013年的10分钟股票数据对这一策略进行了回溯测试。我们交易策略所产生的复合年回报率高达29.4%，夏普指数为0.968。
+</p>
+
+<p>
+  当市场表现不佳时，这种策略尤其有利可图。利润来自于错误定价，当市场下跌或波动性增加时，很可能会发生错误定价。
+</p>
+
+<p>
+  为了进一步探讨这种策略，我们将策略设计得更为灵活。我们可以通过简单地更改参数，将数据分辨率更改为5分钟、10分钟甚至30分钟。选择最优的进入、结束和止损阈值也是非常重要的。每个人都可以有自己的策略。
+</p>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html"
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+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/02 \347\256\200\344\273\213.cn.html"	
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+<p>
+  高频交易（HFT）是一种定量交易，其特点是持有期短，利用复杂的计算机方法迅速交易证券。它的目标是在每笔短期交易中都获得小额利润(Cartea &amp; Penalva, 2012)。
+  统计套利是根据一项或多项资产的预期价值对该资产进行统计错误定价的情况。当证券之间因定价效率低下而出现盈利情况时，交易者可以通过数学模型识别统计套利情况。统计套利在很大程度上取决于市场价格回归历史或预测均值的能力。<strong>单一价格法则（LOP）</strong>为这一假设奠定了基础。LOP指出，如果两支股票在各种自然状态下都具有相同的收益，则它们必定具有相同的现值(Gatev, Goetzmann, &amp; Rouwenhorst, 2006)。因此，两支股票近似替代资产之间的价差应在一段时间内具有稳定的长期均衡价格。
+</p>
+
+<h3>数据描述</h3>
+<p>
+  为了获得更多高度相关的配对，我们选择了特定行业的股票。从经济角度看，我们更喜欢传统行业，因为这些行业的公司更有可能成为近似替代品。。如果我们选择N只股票，可以使用\(\textrm{C}_{n}^{2} = \frac{n*(n-1)}{2}\)来计算配对的数目。在演示的策略中，我们使用了80只股票，所以总共得到了3160对。我们使用分钟数据并将其聚合为较低的分辨率，因此1分钟是该策略的最高分辨率。
+</p>
+
+<h3>关联法</h3>
+<p>
+  关联性衡量的是具有价格趋势两只股票之间的关系。由于这两支股票相互关联，因此倾向于一起移动。关联性过滤器是对备选配对进行筛选的第一步。以A股票和B股票为例，股票之间的关联系数是一个统计数值，用于衡量股票A和股票B之间的关联程度。股票A和股票B的相关系数\(\rho\)可以通过下式获得：
+</p>
+
+\[\rho = \frac{\sum_{i}^{N}(A_i - \bar{A})(B_i - \bar{B}))}{[\sum_{i}^{N}(A - \bar{A})^2\sum_{i}^{N}(B_i - \bar{B})^2]^\frac{1}{2}}\]
+
+<p>
+  其中\(\bar{A}\)和\(\bar{B}\)分别是股票A和股票B的平均价格，N表示交易数据范围。\(\rho\)在[-1,1]范围内。正值的\(\rho\)越多，股票A和股票B之间的关联性就越积极。
+</p>
+
+<p>
+  然而，随着时间的推移，仅仅基于关联法的配对交易将存在不稳定的缺点。关联系数并不一定意味着两支配对股票价格之间的均值回归。为了克服上述问题，将进一步采用协整法作为配对选择过程的第二步。
+</p>
+
+<h3>协整法</h3>
+<p>
+  协整概念是由诺贝尔奖得主Engle和Granger开发的一种创新经济学数学模型。协整法指出，在某些情况下，尽管给定两个非平稳时间序列，但两个时间序列的特定线性组合实际上是平稳的。换句话说，这两个时间序列以步调一致的模式一起移动。
+</p>
+
+<p>
+  协整的定义如下：假设\(x_t\)和\(y_t\)是两个非平稳的时间序列。如果存在参数\(\gamma\)，则可以得出以下方程：
+</p>
+
+\[z_t = y_t - \gamma x_t\]
+
+<p>
+  这是一个平稳的过程，然后xt和yt将会进行协整。这一过程是研究多元时间序列中共同资产趋势的强大工具。
+</p>
+<p>
+  在我们的例子中，\(p_t^A\)和\(p_t^B\)分别为A股和B股的价格。如果假设{\({p_t^A, p_t^B}\)}都分别不平稳，则存在参数\(\gamma\)，以下方程是一个平稳过程：
+</p>
+
+\[P_t^A - \gamma P_t^B = \mu + \epsilon_t\]
+
+<p>
+  其中\(\mu\)是协整模型的平均值。\(\epsilon_t\)是平稳的均值回复过程，被称为<em>协整残差</em>。参数\(\gamma\)被称为<em>协整系数</em>。上面的公式表示股票A和股票B的协整对模型。
+</p>
+
+<p>
+  了解协整残差和协整系数如何决定交易方向是非常重要的。如果\(\epsilon\)为正值，处于给定的置信区间内，这是股票 A定价相对较高而股票B定价相对较低的信号，我们将买入股票B，并抛出股票A。如果\(\epsilon\)为负值，则买入股票A并抛出股票B。
+</p>
+
+<h3>协整验证（可选阅读部分）</h3>
+<p>
+  在Engle-Granger法中(Engle &amp; Granger, 1987)，如上文方程式所述，我们首先在股票A和股票B之间建立协整回归，然后使用普通最小二乘法（OLS）估算回归参数\(\mu\)和\(\gamma\)。我们对回归残差\(epsilon_t\)进行了测试，以确定其是否平稳。
+</p>
+
+<p>
+  在协整领域中，最受欢迎的平稳性测试是Augmented Dickey Fuller （ADF）测试，此测试用于确定回归残差\(\epsilon\)是否具有单位根。
+</p>
+<p>
+  利用ADF测试来检验回归残差是否存在单位根：
+</p>
+
+\[\Delta Z_t = \alpha + \beta t + \gamma Z_{t-1} + \sum_{i = 1}^{p -1}\delta_i \Delta Z_{t-i} + \mu_t\]
+
+<p>
+  其中\(\alpha\)是常数，\(\beta\)是时间趋势系数，p是自回归过程的滞后阶，\(\mu_t\)是误差项，与连续性无关。
+</p>
+
+<p>
+  方程中滞后阶p的数量通常是未知的，因此需要进行估算。为了确定滞后阶p的数量，采用了滞后阶选择的信息准则。这里我们选择贝叶斯信息准则(BIC)。
+</p>
+
+\[BIC = (T-p)\ln\frac{T\hat{\sigma}_p^2}{T-p} + T[1+ln(\sqrt{2\pi})] + p\ln[\frac{\sum_{t=1}^{T}(\Delta Z_t)^2 -T\hat{\sigma}_p^2}{p}]\]
+
+<p>
+  其中T为样本量。
+</p>
+<p>
+  然后在零假设\(H_0 : \gamma = 0\)与替代假设\(H_1 : \gamma &lt; 0\)的情况下，使用ADF测试对回归残差\(\epsilon\)进行单位根检验。通过下式得出ADF测试的统计值
+</p>
+
+\[ADF  test = \frac{\hat{\gamma }}{SE(\hat{\gamma })}\]
+
+<p>
+  将上述方程的测试结果与ADF测试的临界值进行比较。如果测试结果小于临界值，则否定零假设。这意味着回归残差\(\epsilon\)是平稳的。因此，两支股票价格{\({p_t^A, p_t^B}\)}是协整的。
+</p>
+<h3>配对交易策略</h3>
+<p>
+  配对交易策略使用基于回归残差\(\epsilon\)的交易信号，并被建模为均值回复过程。
+</p>
+
+<p>
+  为了选择合适的股票进行配对交易，采用了两阶段的关联协整法。第一步是识别来自同一行业的潜在股票配对，使用关联方法选择关联系数至少达到0.9的股票配对。第二步是检验通过关联性测试股票配对的协整性。如果协整性的测试值等于或小于-3.34（这也是95%置信水平的阈值），则否定零假设\(H_0 : \gamma = 0\)，因此残差\(\epsilon\)是平稳的，股票对通过了协整测试。第三步是根据协整测试值对所有通过两阶段测试的股票对进行排序。协整测试值越小，股票对的排名越高。排名靠前的金融股票配对将用于配对交易。
+</p>
+<p>
+  策略的最后一步是定义交易规则。打开一个配对交易，回归残差\(\epsilon_t\)必须超过高于标准值的正\(\sigma\)标准差，或是低于标准值的负\(\sigma\)标准差。若残差为正，则抛出股票B，买入股票A；如果残差为负，则抛出股票A，买入股票B。当回归残差(\epsilon_t\)返回到一定水平时，配对交易结束。此外，为了防止在单一配对交易中损失过多，当残差达到\(4\epsilon\)正负标准差时，采用止损关闭配对交易。
+</p>
+
+<p>
+  在训练期间，每个培训数据包含3个月的周期，这是一个动态滚动窗口规模。在训练期结束后，我们立即开始为期一个月的交易周期，动态滚动窗口自动向前移动，记录每对股票的新价格。在第一个交易期之后，我们使用更新后的股票价格再次选择交易配对，并开始另一个交易周期。
+</p>
+
+<h3>参数调整</h3>
+<p>
+  该策略的性能对参数非常敏感。主要有四个参数需要调整：开始阀值、结束阀值、止损阀值和数据分辨率。
+</p>
+<p>
+  开始阈值代表有多少残差\(\epsilon\)超过标准差，可通过\(\frac{\epsilon - \bar{\epsilon}}{\sigma}\)进行计算。默认情况下，我们将其设置为2.32和-2.32，如果假设残差服从正态分布，则这是99%置信区间的阈值。结束阈值的计算方法与开始阈值相同，我们将其默认设置为0.5，可以尽早结束以防止进一步的发散。
+</p>
+<p>
+  止损阀值设置为4.5。这取决于我们能承受的错误定价水平。我们对风险的容忍度越高，我们对这一参数的设置就可以越高。但是，如果我们将这个数字设置得太低，我们可能会在止损反转前结束过多的配对。
+</p>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html"
new file mode 100644
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+<p>
+  在此类交易策略中，我们将定义一个名为“配对”的类。我们不直接对股票进行管理，而是对股票配对进行管理，这可以使我们更方便地计算关联性和协整性，更新配对股票的价格，并交易选定的配对。
+</p>
+
+<h3>步骤1：配对分类定义</h3>
+<p>
+  配对由两支股票组成，即股票A和股票B。这一分类有几种属性。基本属性包括股票A和股票B的代码、包含这两支股票时间和价格的数据分析框架、当前误差、最后一个数据点的误差，以及记录股票价格以便进行更新的列表。我们不会每5分钟更新一次数据框架，而是将价格记录在列表中，每月更新一次数据框架。由于操作数据框架非常耗时，这可以使算法的速度至少提高10倍。每个月使用cor_update方法更新配对股票中两支股票之间的关联性。采用cointegration_test每月进行最小二乘法（OLS）回归，进行ADF测试，计算残差的均值和标准差。这种方法还会将这些计算值作为属性分配给配对对象。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">class pairs(object):
+    def __init__(self, a, b):
+        self.a = a
+        self.b = b
+        self.name = str(a) + ':' + str(b)
+        self.df = pd.concat([a.df,b.df],axis = 1).dropna()
+    # The number of bars in the rolling window would be determined by the resolution, so we get this
+      information from the shape of the DataFrame here.
+        self.num_bar = self.df.shape[0]
+        self.cor = self.df.corr().ix[0][1]
+    # Set the initial signals to be 0
+        self.error = 0
+        self.last_error = 0
+        self.a_price = []
+        self.a_date = []
+        self.b_price = []
+        self.b_date = []
+
+    def cor_update(self):
+        self.cor = self.df.corr().ix[0][1]
+
+    def cointegration_test(self):
+        self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
+    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
+      the tuple is the test value.
+        self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
+        self.mean_error = np.mean(self.model.resid)
+        self.sd = np.std(self.model.resid)
+
+    def price_record(self,data_a,data_b):
+        self.a_price.append(float(data_a.Close))
+        self.a_date.append(data_a.EndTime)
+        self.b_price.append(float(data_b.Close))
+        self.b_date.append(data_b.EndTime)
+
+    def df_update(self):
+        new_df = pd.DataFrame({str(self.a):self.a_price,str(self.b):self.b_price},index =
+                 [self.a_date]).dropna()
+        self.df = pd.concat([self.df,new_df])
+        self.df = self.df.tail(self.num_bar)
+    # after updating the DataFrame, we empty the lists for the incoming data
+        for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
+            list = []
+</pre>
+</div>
+<h3>Step 2: Generate and Clean Pairs</h3>
+<p>
+  The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
+  The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def generate_pairs(self):
+    for i in range(len(self.symbols)):
+        for j in range(i+1,len(self.symbols)):
+            self.pair_list.append(pairs(self.symbols[i],self.symbols[j]))
+
+    self.pair_list = [x for x in self.pair_list if x.cor &gt; self.pair_threshold]
+
+    self.pair_list.sort(key = lambda x: x.cor, reverse = True)
+
+    if len(self.pair_list) &gt; self.pair_num:
+        	self.pair_list = self.pair_list[:self.pair_num]
+
+def pair_clean(self,list):
+    l = []
+    l.append(list[0])
+    for i in list:
+        symbols = [x.a for x in l] + [x.b for x in l]
+        if i.a not in symbols and i.b not in symbols:
+            l.append(i)
+        else:
+            pass
+    return l
+</pre>
+</div>
+
+<h3>Step 3: Warming up Period</h3>
+<p>
+  This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">if len(self.symbols[0].prices) &lt; self.num_bar:
+    for symbol in self.symbols:
+        if data.ContainsKey(i) is True:
+    	    symbol.prices.append(float(data[symbol].Close))
+            symbol.dates.append(data[symbol].EndTime)
+        else:
+            self.Log('%s is missing'%str(symbol))
+            self.symbols.remove(symbol)
+    self.data_count = 0
+    return</pre>
+</div>
+<h3>Step 4: Pairs Selection</h3>
+<p>
+  This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">if self.count == 0 and len(self.symbols[0].prices) == self.num_bar:
+    if self.generate_count == 0:
+        for symbol in self.symbols:
+        symbol.df = pd.DataFrame(symbol.prices, index = symbol.dates, columns = ['%s'%str(symbol)])
+
+        self.generate_pairs()
+        self.generate_count +=1
+        self.Log('pair list length:'+str(len(self.pair_list)))
+
+        for pair in self.pair_list:
+            pair.cor_update()
+    # Update the DataFrame and correlation selection
+    if len(self.pair_list[0].a_price) != 0:
+        for pair in self.pair_list:
+    	    pair.df_update()
+            pair.cor_update()
+
+    self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
+    # Cointegration test
+    for pair in self.selected_pair:
+        pair.cointegration_test()
+
+    self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
+    self.selected_pair.sort(key = lambda x: x.adf)
+    # If no pair passed the two-stage test, return.
+    if len(self.selected_pair) == 0:
+        self.Log('no selected pair')
+        self.count += 1
+        return
+    # clean the pair to avoid overlapping stocks.
+    self.selected_pair = self.pair_clean(self.selected_pair)
+    # assign a property to the selected pair, this is a signal that would be used for trading.
+    for pair in self.selected_pair:
+        pair.touch = 0
+        self.Log(str(pair.adf) + pair.name)
+    # limit the number of selected pairs.
+    if len(self.selected_pair) &gt; self.selected_num:
+        self.selected_pair = self.selected_pair[:self.selected_num]
+
+    self.count +=1
+    self.data_count = 0
+    return
+</pre>
+</div>
+
+<h3>Step 5: Trade Period</h3>
+<p>
+  It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
+</p>
+
+<h4>Updating Pairs</h4>
+<p>
+  This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">num_select = len(self.selected_pair)
+for pair in self.pair_list:
+    if data.ContainsKey(pair.a) is True and data.ContainsKey(pair.b) is True:
+        i.price_record(data[i.a],data[i.b])
+    else:
+        self.Log('%s has no data'%str(pair.name))
+        self.pair_list.remove(pair)
+
+for pair in self.selected_pair:
+    pair.last_error = pair.error
+
+for pair in self.trading_pairs:
+    pair.last_error = pair.error</pre>
+</div>
+<h4>Opening Pairs Trading</h4>
+<p>
+  For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
+  When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">for i in self.selected_pair:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.model.params[0] + i.model.params[1]*price_b)
+    if (self.Portfolio[i.a].Quantity == 0 and self.Portfolio[i.b].Quantity == 0) and i not in
+    self.trading_pairs:
+        if i.touch == 0:
+            if i.error &lt; i.mean_error - self.open_size*i.sd and i.last_error &gt; i.mean_error -
+            self.open_size*i.sd:
+                i.touch += -1
+            elif i.error &gt; i.mean_error + self.open_size*i.sd and i.last_error &lt; i.mean_error + self.open_size*i.sd: i.touch += 1 else: pass elif i.touch == -1: if i.error &gt; i.mean_error - self.open_size*i.sd and i.last_error &lt; i.mean_error -
+            self.open_size*i.sd:
+                self.Log('long %s and short %s'%(str(i.a),str(i.b)))
+                i.record_model = i.model
+                i.record_mean_error = i.mean_error
+                i.record_sd = i.sd
+                self.trading_pairs.append(i)
+                self.SetHoldings(i.a, 5.0/(len(self.selected_pair)))
+                self.SetHoldings(i.b, -5.0/(len(self.selected_pair)))
+                i.touch = 0
+         elif i.touch == 1:
+             if i.error &lt; i.mean_error + self.open_size*i.sd and i.last_error &gt; i.mean_error +
+             self.open_size*i.sd:
+             self.Log('long %s and short %s'%(str(i.b),str(i.a)))
+             i.record_model = i.model
+             i.record_mean_error = i.mean_error
+             i.record_sd = i.sd
+             self.trading_pairs.append(i)
+             self.SetHoldings(i.b, 5.0/(len(self.selected_pair)))
+             self.SetHoldings(i.a, -5.0/(len(self.selected_pair)))
+             i.touch = 0
+         else:
+             pass
+    else:
+        pass
+</pre>
+</div>
+
+<h4>Closing Pairs Trading</h4>
+<p>
+  This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">for i in self.trading_pairs:
+    price_a = float(data[i.a].Close)
+    price_b = float(data[i.b].Close)
+    i.error = price_a - (i.record_model.params[0] + i.record_model.params[1]*price_b)
+    if ((i.error &lt; i.record_mean_error + self.close_size*i.record_sd and i.last_error &gt;i.record_mean_error + self.close_size*i.record_sd) or (i.error &gt; i.record_mean_error -
+    self.close_size*i.record_sd and i.last_error  i.record_mean_error +
+    self.stop_loss*i.record_sd:
+        self.Log('close %s to stop loss'%str(i.name))
+        self.Liquidate(i.a)
+        self.Liquidate(i.b)
+        self.trading_pairs.remove(i)
+    else:
+        pass</pre>
+</div>
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..1645559
--- /dev/null
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  WTI-Brent价差指的是两种原油价格之间的差异：即多头的西德克萨斯中质原油(WTI)和空头布伦特原油(Brent)。多年来，两者之间的价格差距平均只有几美元。由于这两种原油非常相似，它们的价差显示出很强的可预测性，通常在某个平均值附近波动。因此，有可能使用偏离公允价差的值来押注回复到公允价值。本文提出了一种基于价差价格差异的交易策略。
+</P>
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..979fd31
--- /dev/null
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,117 @@
+<p>
+  我们从<a href="www.macrotrends.net" rel="nofollow">Macrotrends</a>下载WTI和Brent原油价格，并使用自定义数据下载器导入csv文件。单位为美元/桶。
+</p>
+<div class="section-example-container">
+<pre class="python">
+  class WTI(PythonData):
+      "Class to import WTI Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/jpie3z6j0stp97d/wti-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = WTI()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+
+  class BRENT(PythonData):
+      "Class to import BRENT Spot Price(Dollars per Barrel) data from Dropbox"
+
+      def GetSource(self, config, date, isLiveMode):
+          return SubscriptionDataSource("https://www.dropbox.com/s/w380c4n7xjmdqxl/brent-crude-oil-prices-10-year-daily.csv?dl=1", SubscriptionTransportMedium.RemoteFile)
+
+      def Reader(self, config, line, date, isLiveMode):
+          if not (line.strip() and line[1].isdigit()): return None
+          index = BRENT()
+          index.Symbol = config.Symbol
+          try:
+              data = line.split(',')
+              index.Time = datetime.strptime(data[0], "%Y-%m-%d")
+              index.Value = Decimal(data[1])
+          except:
+              return None
+          return index
+</pre>
+</div>
+<p>
+  价差定义为WTI价格与Brent价格之间的差。接下来，我们需要使用指标<code>SimpleMovingAverage</code>来计算价差系列的移动平均值。由于指标使用的是价格差异而不是价格系列，因此需要使用历史请求手动初始化指标。
+</p>
+<div class="section-example-container">
+<pre class="python">
+self.SpreadSMA = SimpleMovingAverage(20)
+hist = self.History(["WTI", "BRENT"], 400, Resolution.Daily)["value"].unstack(level=0).dropna()
+hist_20days = hist[-20:]
+spread = (hist_20days["WTI"] - hist_20days["BRENT"]).dropna()
+for index, value in spread.items():
+    self.SpreadSMA.Update(index, value)
+</pre>
+</div>
+<p>
+  为了得到价差的公允价值，我们对WTI和Brent价格在过去一年的历史价格进行了线性回归。
+</P>
+ \[P_{Brent}=\beta \cdot P_{WTI}+\alpha\]
+<p>
+   那么差价的公允价值是
+</P>
+\[Fair \ Spread =(1-\beta)\cdot CurrentPrice_{WTI}-\alpha\]
+</P>
+<div class="section-example-container">
+<pre class="python">
+hist_one_year = hist[-252:]
+X = hist_one_year["WTI"][:, np.newaxis]
+y = hist_one_year["BRENT"]
+self.regr = linear_model.LinearRegression()
+self.regr.fit(X, y)
+</pre>
+</div>
+<p>
+  公允价值每天计算一次。如果当前价差值高于SMA 20，那么我们将在收盘时进入价差空头头寸(押注价差将降至SMA 20所代表的公允价值)。当价差低于公允价值时，交易在交易日收盘时结束。如果当前价差低于SMA 20，那么我们就进入多头头寸，押注价差将会增加，当价差超过公允价值时，交易将在交易日收盘时结束。
+</P>
+
+<div class="section-example-container">
+<pre class="python">
+def OnData(self, data):
+    if not (data.ContainsKey("WTI") and data.ContainsKey("BRENT")): return
+    self.Plot("Spread Plot", "Spread", data["WTI"].Price - data["BRENT"].Price)
+
+    self.SpreadSMA.Update(self.Time, data["WTI"].Price - data["BRENT"].Price)
+    if not self.SpreadSMA.IsReady: return
+    spread = self.Securities["WTI"].Price - self.Securities["BRENT"].Price
+    fair_value =self.Securities["WTI"].Price - Decimal(self.regr.predict([[self.Securities["WTI"].Price]])[0])
+
+    if spread > self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong):
+        self.SetHoldings("WTI", -0.5)
+        self.SetHoldings("BRENT", 0.5) 
+        self.Plot("Spread Plot", "Long Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    elif spread < self.SpreadSMA.Current.Value and not (self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort):
+        self.SetHoldings("WTI", 0.5)
+        self.SetHoldings("BRENT", -0.5)
+        self.Plot("Spread Plot", "Short Spread Trade", data["WTI"].Price - data["BRENT"].Price)
+
+    if self.Portfolio["WTI"].IsShort and self.Portfolio["BRENT"].IsLong and spread < fair_value:
+        self.Liquidate()
+
+    if self.Portfolio["WTI"].IsLong and self.Portfolio["BRENT"].IsShort and spread > fair_value:
+        self.Liquidate()
+</pre>
+</div>
+<p>
+  为了显示价差序列的趋势，我们添加了价差图，并在价差曲线上标记了价差的多头/空头点。
+</P>
+<div class="section-example-container">
+<pre class="python">
+spreadPlot = Chart("Spread Plot")
+spreadPlot.AddSeries(Series("Spread", SeriesType.Line, 0))
+spreadPlot.AddSeries(Series("Long Spread Trade", SeriesType.Scatter, 0))
+spreadPlot.AddSeries(Series("Short Spread Trade", SeriesType.Scatter, 0))
+self.AddChart(spreadPlot)
+</pre>
+</div>
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..15151d8
--- /dev/null
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/03 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5ee5507fef6bf190ae533ce05ccaa785.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..5cbb24e
--- /dev/null
+++ "b/04 Strategy Library/100 Trading with WTI BRENT Spread/04 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/100" rel="nofollow">Quantpedia - Trading WTI/BRENT Spread</a>
+</li>
+</ul>

From 4cd938580117501807f3d659da69146a81321000 Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Thu, 26 Sep 2019 10:36:54 -0700
Subject: [PATCH 626/753] Add Commodities Futures Trend Following

---
 .../01 Abstract.html                          |   3 +
 .../02 Introduction.html                      |  21 +++
 .../03 Method.html                            | 126 ++++++++++++++++++
 .../04 Summary.html                           |   7 +
 .../05 Algorithm.html                         |   6 +
 .../06 References.html                        |   5 +
 6 files changed, 168 insertions(+)
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html
 create mode 100644 04 Strategy Library/357 Commodities Futures Trend Following/06 References.html

diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html b/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html
new file mode 100644
index 0000000..46860f0
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+	In this tutorial we implement a trend following strategy on commodities futures based on a 2014 paper <a href="https://arxiv.org/pdf/1404.3274.pdf">"Two Centuries Of Trend Following"</a> by Y. Lempérière, C. Deremble, P. Seager, M. Potters, and J. P. Bouchaud.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html b/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html
new file mode 100644
index 0000000..21ba8d1
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/02 Introduction.html	
@@ -0,0 +1,21 @@
+<p>
+The paper  highlights the existence of trends as an anomaly that contradicts the efficient market hypothesis. If financial markets are completely efficient as the hypothesis suggests, then asset price changes should be totally unpredictable. In other words, no systematic excess return based on public information should exist since asset prices ought to reflect all public information available. However observationally, trend existence in the market do exist. They make it possible to use the simple trend following strategy which states, buy when prices goes up and sell when prices goes down. Numerous academic studies have demonstrated that trend following strategies generate persistent returns over long periods of time.
+</p>
+
+<p>
+The paper extends the backtest period of trend following strategies to two centuries and demonstrates statistically significant systematic excess returns on four asset classes (commodities, currencies, stock indices, and bonds). It implements a risk managed strategy that buys or sells a quantity of \(\sigma_n^{-1}\) of the underlying contract depending on the sign of \(s_n\). 
+</p>
+
+<p>
+The signal \(s_n(t)\) at the beginning of month t is:
+</p>
+
+\[s_n(t) = \frac{p(t-1)-\text{&lt;}p\text{&gt;}_{n,t-1}}{\sigma_n(t-1)}\]
+
+<p>
+where \(\text{&lt;}p\text{&gt;}_{n,t-1}\) is last month's exponential moving average of past prices with a decay rate equal to n months,\(p(t-1)\) is the price of last month, and \(\sigma_n(t-1)\) is last month's volatility, estimated as the exponential moving average of the absolute monthly price changes, with a decay rate equal to n months. The decay rate was set to 5 months.
+</p>
+
+<p>
+Below, we will implement the above monthly-rebalanced trend following strategy on commodities futures.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html
new file mode 100644
index 0000000..783e8bf
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html	
@@ -0,0 +1,126 @@
+<p>
+	The strategy requires the continuous futures contract, so we import the custom data from Quandl. We manually create a universe of tradable commodity futures. They are all liquid and active continuous contracts #1. The data from Quandl are non-adjusted price based on spot-month continuous contract calculations. The data resolution is daily.
+</p>
+
+
+<h3>Step 1: Import futures data from Quandl</h3>
+
+<p>
+The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper.  We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+from QuantConnect.Python import PythonQuandl
+class ImprovedCommodityMomentumTrading(QCAlgorithm):
+	def Initialize(self):
+		tickers = ["CHRIS/CME_W1",  # Wheat Futures, Continuous Contract #1
+                   "CHRIS/CME_C1",  # Corn Futures, Continuous Contract #1
+                   "CHRIS/CME_LC1", # Live Cattle Futures, Continuous Contract #1 
+                   "CHRIS/CME_CL1",  # Crude Oil Futures, Continuous Contract #1
+                   "CHRIS/CME_NG1",  # Natural Gas (Henry Hub) Physical Futures, Continuous Contract #1
+                   "CHRIS/LIFFE_W1", # White Sugar Future, Continuous Contract #1
+                   "CHRIS/CME_HG1"] # Copper Futures, Continuous Contract #1
+		for ticker in tickers:
+			data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+			data.SetLeverage(3) 
+class QuandlFutures(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = "Settle"
+</pre>
+</div>
+
+
+
+<h3>Step 2: Create a SymbolData class to store and update the number of contracts to trade for each security</h3>
+
+<p>
+In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a MonthlyConsolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly produced monthly data.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Initialize(self):                   
+	# Container to store the SymbolData object for each security
+	self.Data = {}
+	
+	for ticker in tickers:
+		# Add Quandl data and set desired leverage
+		data = self.AddData(QuandlFutures, ticker, Resolution.Daily)
+		data.SetLeverage(3) 
+		
+		# Create a monthly consolidator for each security
+		MonthlyConsolidator = self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
+		
+		# Create a SymbolData object for each security to store relevant indicators and calculated quantity of contracts to Buy/Sell
+		self.Data[data.Symbol] = SymbolData()
+def CalendarHandler(self, bar):
+	'''
+	Event Handler that updates the SymbolData object for each security when a new monthly bar becomes available
+	'''
+	self.Data[bar.Symbol].Update(bar)
+</pre>
+</div>
+
+<p>
+The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    '''
+    Contains the relevant indicators used to calculate number of contracts to Buy/Sell
+    '''
+    def __init__(self):
+        self.ema = ExponentialMovingAverage("MonthEMA", 5)
+		
+		# Volatility estimation is defined as the EMA of absolute monthly price changes
+		# Use Momentum indicator to get absolute monthly price changes. Then use the IndicatorExtensions.Of and pass the momentum indicator values to get the volatility
+        self.mom = Momentum("MonthMOM", 1) 
+        self.vol = IndicatorExtensions.Of(ExponentialMovingAverage("Vol", 5), self.mom)
+        self.Quantity = 0
+        
+    def Update(self, bar):
+        self.ema.Update(bar.Time, bar.Value)
+        self.mom.Update(bar.Time, bar.Value)
+        self.vol.Update(bar.Time, self.mom.Current.Value)
+        if self.ema.IsReady and self.vol.IsReady:
+            # Equation 1 in [1]
+            signal = ( bar.Value - self.ema.Current.Value )/ self.vol.Current.Value
+            # Equation 2 in [1]
+            self.Quantity = np.sign(signal)/abs(self.vol.Current.Value)
+        
+        return self.Quantity != 0
+</pre>
+</div>
+
+<h3>Step 3: Buy and Sell at the beginning of each month</h3>
+<p>
+Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we have set a warm up period of 5 months which prepares data to allow the algorithm to execute trades on the start date.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+
+def Initialize(self):
+	# Set decay rate equal to 5 months and warm up period
+	period = 150
+	self.SetWarmUp(period)
+	
+	# Set monthly rebalance
+	self.nextRebalance = self.Time
+def OnData(self, data):
+	'''
+	Buy/Sell security every month
+	'''
+	if self.IsWarmingUp: return
+	if self.Time < self.nextRebalance: return
+	
+	for symbol in data.Keys:
+		symbolData = self.Data[symbol]
+		if symbolData.Quantity != 0:
+			self.MarketOrder(symbol, symbolData.Quantity)
+	
+	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+</pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html
new file mode 100644
index 0000000..db5ce31
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html	
@@ -0,0 +1,7 @@
+<p>
+For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.273, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds). 
+</p>
+
+<p>
+This tutorial demonstrates trends as one of the most powerful sources of anomalous excess returns in financial markets. We hope to inspire the community to develop more trend-based strategies and encourage you to test out this strategy on other asset classes from the original paper. 
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html
new file mode 100644
index 0000000..d21ff42
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_316c9ee1606830461765ddd875ab188c.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html b/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html
new file mode 100644
index 0000000..cb5636b
--- /dev/null
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/06 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>		
+	Y. Lempérière, C. Deremble, P. Seager, M. Potters, J. P. Bouchaud (2014). Two centuries of trend following. <a href="https://arxiv.org/pdf/1404.3274.pdf">Online Copy</a>
+  </li>
+ <ol>
\ No newline at end of file

From bbf9e5e7f5a0a8ec8deaf53cfcc78fca3a8c332c Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Thu, 26 Sep 2019 10:43:18 -0700
Subject: [PATCH 627/753] Add Commodities Futures Trend Following

---
 .../00 Strategy Library/01 Strategy Library.php          | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 76fe5bf..ec88ded 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -577,6 +577,15 @@
 		],
 		'description' => "An advanced momentum strategy that modifies the basic momentum strategies by introducing Baltas and Kosowski weights and rebalances the portfolio monthly. The new weighing scheme incorporates trend strength into the trading signal, uses an efficient volatility estimator, and adds a dynamic leverage mechanism.",
 		'tags' => 'Momentum, Futures, Commodities'
+	],
+	[
+		'name' => 'Commodities Futures Trend Following',
+		'link' => 'strategy-library/commodities-futures-trend-following',
+		'sources' => [
+			'NYU' => 'https://arxiv.org/pdf/1404.3274.pdf'
+		],
+		'description' => "A simple trend following strategy on commodities futures.",
+		'tags' => 'Momentum, Futures, Commodities'
 	]
 ];
 

From 499d462b0fee3348c5344efd40bf0742763f003c Mon Sep 17 00:00:00 2001
From: Alethea <31491434+AlinXlin@users.noreply.github.com>
Date: Thu, 26 Sep 2019 14:11:05 -0700
Subject: [PATCH 628/753] Edit: Add Commodities Futures Trend Following

Edited text and attached new edited algorithm
---
 .../03 Method.html                            | 67 +++++++++----------
 .../04 Summary.html                           |  2 +-
 .../05 Algorithm.html                         |  2 +-
 3 files changed, 34 insertions(+), 37 deletions(-)

diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html
index 783e8bf..6e8e094 100644
--- a/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html	
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/03 Method.html	
@@ -6,7 +6,7 @@
 <h3>Step 1: Import futures data from Quandl</h3>
 
 <p>
-The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper.  We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY.
+The paper selected a well-balanced commodities pool to include 7 representative contracts: Crude oil, Henry Hub Natural Gas, Corn, Wheat, Super, Live Cattle and Copper. We will add continuous futures data of these contracts from Quandl. This implementation performs a backtest on 20 years as opposed to 200 years of data for the purpose of comparing to benchmark SPY.
 </p>
 
 <div class="section-example-container">
@@ -35,7 +35,7 @@ <h3>Step 1: Import futures data from Quandl</h3>
 <h3>Step 2: Create a SymbolData class to store and update the number of contracts to trade for each security</h3>
 
 <p>
-In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a MonthlyConsolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly produced monthly data.
+In Initialize(), we create a dictionary to store the SymbolData object for each security. The strategy is designed to trade monthly, so we will create a monthly consolidator for each security as well. When a new monthly data becomes available, the consolidator calls an event handler CalendarHandler. Within this event handler, we will update the SymbolData object with the freshly received monthly data.
 </p>
 <div class="section-example-container">
 <pre class="python">
@@ -49,9 +49,10 @@ <h3>Step 2: Create a SymbolData class to store and update the number of contract
 		data.SetLeverage(3) 
 		
 		# Create a monthly consolidator for each security
-		MonthlyConsolidator = self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
+		self.Consolidate(ticker, CalendarType.Monthly, self.CalendarHandler)
 		
-		# Create a SymbolData object for each security to store relevant indicators and calculated quantity of contracts to Buy/Sell
+		# Create a SymbolData object for each security to store relevant indicators 
+		# and calculate quantity of contracts to Buy/Sell
 		self.Data[data.Symbol] = SymbolData()
 def CalendarHandler(self, bar):
 	'''
@@ -62,7 +63,7 @@ <h3>Step 2: Create a SymbolData class to store and update the number of contract
 </div>
 
 <p>
-The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum.
+The SymbolData class is designed to contain everything we need for calculating how many contracts to Buy/Sell at the beginning of each month. LEAN provides helpful indicators to get the exponential moving average and momentum indicators. The Introduction section above detailed the formula for calculating the number of contracts to Buy/Sell. We implement the formula in the Update function.
 </p>
 
 <div class="section-example-container">
@@ -73,54 +74,50 @@ <h3>Step 2: Create a SymbolData class to store and update the number of contract
     '''
     def __init__(self):
         self.ema = ExponentialMovingAverage("MonthEMA", 5)
-		
-		# Volatility estimation is defined as the EMA of absolute monthly price changes
-		# Use Momentum indicator to get absolute monthly price changes. Then use the IndicatorExtensions.Of and pass the momentum indicator values to get the volatility
-        self.mom = Momentum("MonthMOM", 1) 
-        self.vol = IndicatorExtensions.Of(ExponentialMovingAverage("Vol", 5), self.mom)
+
+        # Volatility estimation is defined as the EMA of absolute monthly price changes
+        # Use Momentum indicator to get absolute monthly price changes. 
+        # Then use the IndicatorExtensions.EMA and pass the momentum indicator values to get the volatility
+        self.mom = Momentum("MonthMOM", 1)
+        # Note: self.vol will automatically be updated with self.mom
+        self.vol = IndicatorExtensions.EMA(self.mom, 5)
+
         self.Quantity = 0
-        
+
+
     def Update(self, bar):
         self.ema.Update(bar.Time, bar.Value)
         self.mom.Update(bar.Time, bar.Value)
-        self.vol.Update(bar.Time, self.mom.Current.Value)
+        
         if self.ema.IsReady and self.vol.IsReady:
             # Equation 1 in [1]
-            signal = ( bar.Value - self.ema.Current.Value )/ self.vol.Current.Value
+            signal = (bar.Value - self.ema.Current.Value) / self.vol.Current.Value
             # Equation 2 in [1]
             self.Quantity = np.sign(signal)/abs(self.vol.Current.Value)
-        
+
         return self.Quantity != 0
 </pre>
 </div>
 
 <h3>Step 3: Buy and Sell at the beginning of each month</h3>
 <p>
-Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we have set a warm up period of 5 months which prepares data to allow the algorithm to execute trades on the start date.
+Now we’ll place orders based on the quantity of contracts calculated from previous month stored in the SymbolData object. Note that we warm up the algorithm with 150 days of data to allow the algorithm to execute trades on the start date.
 </p>
 
 <div class="section-example-container">
 <pre class="python">
-
-def Initialize(self):
-	# Set decay rate equal to 5 months and warm up period
-	period = 150
-	self.SetWarmUp(period)
-	
-	# Set monthly rebalance
-	self.nextRebalance = self.Time
 def OnData(self, data):
-	'''
-	Buy/Sell security every month
-	'''
-	if self.IsWarmingUp: return
-	if self.Time < self.nextRebalance: return
-	
-	for symbol in data.Keys:
-		symbolData = self.Data[symbol]
-		if symbolData.Quantity != 0:
-			self.MarketOrder(symbol, symbolData.Quantity)
-	
-	self.nextRebalance = Expiry.EndOfMonth(self.Time)
+        '''
+        Buy/Sell security every month
+        '''
+        if self.Time < self.nextRebalance or self.IsWarmingUp:
+            return
+
+        for symbol in data.Keys:
+            symbolData = self.Data[symbol]
+            if symbolData.Quantity != 0:
+                self.MarketOrder(symbol, symbolData.Quantity)
+
+        self.nextRebalance = Expiry.EndOfMonth(self.Time)
 </pre>
 </div>
\ No newline at end of file
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html
index db5ce31..363d402 100644
--- a/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html	
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/04 Summary.html	
@@ -1,5 +1,5 @@
 <p>
-For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.273, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds). 
+For the backtest period (January 1998 to September 2019), the trend following strategy produced a Sharpe ratio of 0.266, compared to SPY’s Sharpe ratio of 0.459. The positive performance of the trend-following strategy over the approximately 20-year time horizon indeed suggests the existence of statistically significant, anomalous, systematic, excess returns from trends. Furthermore, the paper suggests the anomaly is universal across 3 other asset classes (currencies, stock indices and bonds). 
 </p>
 
 <p>
diff --git a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html
index d21ff42..c56a62c 100644
--- a/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html	
+++ b/04 Strategy Library/357 Commodities Futures Trend Following/05 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_316c9ee1606830461765ddd875ab188c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4ac9910558af65c0e33451993ccef017.html"></iframe>
     </div>
 </div>

From e4ecb10f76813386b6582543e8a0decdcec6df1c Mon Sep 17 00:00:00 2001
From: wyiyun95 <wyiyun1995@outlook.com>
Date: Thu, 26 Sep 2019 17:36:56 -0700
Subject: [PATCH 629/753] Mandarin Translation (Strategy Library)

01, 07, 09, 162, 20, 22, 36, 39 full
---
 .../01 \347\256\200\344\273\213.cn.html"      |  14 ++
 ...7\274\211\347\220\206\350\256\272.cn.html" |  44 ++++
 .../03 \346\226\271\346\263\225.cn.html"      |  95 +++++++
 .../04 \346\200\273\347\273\223.cn.html"      |  14 ++
 .../05 \347\256\227\346\263\225.cn.html"      |   9 +
 ...0\200\203\346\226\207\347\214\256.cn.html" |   8 +
 .../03 \346\226\271\346\263\225.cn.html"      |  52 ++--
 .../04 \347\273\223\346\236\234.cn.html"      |  20 ++
 .../05 \346\200\273\347\273\223.cn.html"      |   3 +
 .../06 \347\256\227\346\263\225.cn.html"      |   6 +
 ...0\200\203\346\226\207\347\214\256.cn.html" |  16 ++
 .../01 \346\221\230\350\246\201.cn.html"      |   3 +
 .../02 Factor Selection.html                  |   2 +-
 ...7\264\240\351\200\211\346\213\251.cn.html" | 236 ++++++++++++++++++
 ...7\245\250\351\200\211\346\213\251.cn.html" |  17 ++
 .../04 \347\256\227\346\263\225.cn.html"      |   6 +
 ...0\200\203\346\226\207\347\214\256.cn.html" |   5 +
 .../01 \347\256\200\344\273\213.cn.html"      |   3 +
 .../02 \346\226\271\346\263\225.cn.html"      |  63 +++++
 .../03 \347\256\227\346\263\225.cn.html"      |   6 +
 .../04 \346\235\245\346\272\220.cn.html"      |   5 +
 .../01 \347\256\200\344\273\213.cn.html"      |   3 +
 .../02 \346\226\271\346\263\225.cn.html"      |  19 ++
 .../03 \347\256\227\346\263\225.cn.html"      |   6 +
 .../04 \346\235\245\346\272\220.cn.html"      |   5 +
 .../01 \347\256\200\344\273\213.cn.html"      |   3 +
 .../02 \346\226\271\346\263\225.cn.html"      |   6 +
 .../03 \347\273\223\350\256\272.cn.html"      |   3 +
 .../04 \347\256\227\346\263\225.cn.html"      |  14 ++
 .../05 \346\235\245\346\272\220.cn.html"      |   5 +
 .../01 \347\256\200\344\273\213.cn.html"      |   3 +
 .../02 \346\226\271\346\263\225.cn.html"      | 120 +++++++++
 .../03 \347\256\227\346\263\225.cn.html"      |   6 +
 .../04 \346\235\245\346\272\220.cn.html"      |   5 +
 .../01 \347\256\200\344\273\213.cn.html"      |   6 +
 .../02 \346\226\271\346\263\225.cn.html"      |  57 +++++
 .../03 \347\256\227\346\263\225.cn.html"      |   6 +
 .../04 \346\235\245\346\272\220.cn.html"      |   5 +
 38 files changed, 870 insertions(+), 29 deletions(-)
 create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html"
 create mode 100644 "04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html"
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 create mode 100644 "04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"
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 create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html"
 create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html"
 create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"
 create mode 100644 "04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html"
 create mode 100644 "04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html"
 create mode 100644 "04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html"
 create mode 100644 "04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html"
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diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..9ccb0d0
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,14 @@
+<p>
+	本教程执行一个简单的线性回归来构建资本资产定价模型(CAPM)，这是由William F. Sharpe和Harry Markowitz开发的一个经典模型。该模型对各资产都会产生alpha和beta值，并通过做多alpha值最高的股票进行交易。本教程将演示以下内容：
+</p>
+
+<ul>
+	<li>如何使用历史数据</li>
+	<li>设置事件处理程序</li>
+	<li>进行线性回归</li>
+	<li>在QuantConnect算法实验室中构建自己的函数</li>
+</ul>
+
+<p>
+	该策略的实施表明，在上个月跑赢大盘的股票很可能在接下来的一个月里再次跑赢大盘。该算法在市场平稳时性能良好。然而，当市场波动性增加时，模型未能捕捉到alpha值，且表现不佳。我们从中了解到，市场波动降低了线性回归系数的显著性水平，特别是当我们使用日收益率来拟合模型时。
+</p>
diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html"
new file mode 100644
index 0000000..7add139
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/02 \350\265\204\346\234\254\350\265\204\344\272\247\345\256\232\344\273\267\346\250\241\345\236\213\357\274\210CAPM\357\274\211\347\220\206\350\256\272.cn.html"	
@@ -0,0 +1,44 @@
+<p>
+	资本资产定价模型(CAPM)描述了系统性风险与资产(通常是股票)预期收益之间的关系。给定风险的资产预期收益计算公式如下：
+</p>
+
+\[r_a = r_f + \beta_a*(r_m - r_f) + \epsilon \]
+
+<p>
+	其中：
+</p>
+
+\[r_f = Risk Free Rate\]
+\[\beta = Beta of the security\]
+
+\[r_m = Expected market return\]
+
+\[\epsilon = Tracking error\]
+
+<p>
+	将公式重构如下，可以更好地理解这个公式：
+</p>
+
+\[(r_a - r_f ) = \beta_a*(r_m - r_f) + \epsilon \]
+
+<p>
+	方程的左边给出了资产收益和无风险利率之间的差额，即<strong>"超额收益"</strong>。如果我们将<strong>市场超额收益</strong>与<strong>资产超额收益</strong>进行对比，斜率代表资产的<strong>"beta"</strong>。因此，beta也可以通过公式计算：
+</p>
+
+\[\beta = \frac{Cov(r_a,r_b)}{var(r_b)}\]
+
+<p>
+	因此beta可以描述为：
+</p>
+
+\[\beta = \rho _a,_b*\frac{\sigma _a}{\sigma_b}\]
+
+<p>
+	由上式可知，beta可以解释为<strong>“关联相对波动”</strong>。为了更加简化，可以通过简单的线性回归来计算beta，线性回归可以看作是解释收僧的一个因素，跟踪误差可以表示alpha。为了使这个理论对我们的算法更加便利，我们将上面的公式改为如下形式：
+</p>
+
+\[r_a = \beta*r_m + r_f*(1-\beta) + \epsilon\]
+
+<p>
+	等式右边的<strong>r*(1-β) </strong>是一个非常小的项目，在道琼斯前30强企业的背景下可以忽略不计。如果我们使用基准收益对股票收益进行回归，斜率和截距将分别为beta和alpha。
+</p>
diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..b64df7a
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,95 @@
+<p>
+	我们的投资逻辑简单明了。我们认为上个月跑赢大盘的股票将继续跑赢大盘。我们根据alpha值对股票进行排名，每个月我们都会对排名前两支的股票“做多”。要使这一战略发挥作用，我们需要在每月月初做下列工作：
+</p>
+
+<ul>
+ 	<li>计算道琼斯前30强股票在过去21个交易日的历史价格，并计算它们的日收益。</li>
+ 	<li>根据基准(S&amp;P 500指数，SPY)对每支股票的收益进行简单的线性回归。</li>
+ 	<li>根据截距对股票进行排名。</li>
+ 	<li>平掉所有仓位，并购买排序列表中的排名前两支股票。</li>
+</ul>
+<p>
+	道琼斯指数成份股很少变动，最近一次变动发生在2015年3月19日。为了使实施更加容易，我们在这个算法中简单地列出了当前的道琼期成份股。这意味着该算法最早的开始日期是2015年3月19日。
+</p>
+
+<h3>步骤1：设置事件处理程序</h3>
+<p>
+	在初始化方法中，我们定义了日程事件来触发投资组合的每月重新平衡。有关如何使用日程事件的更多细节，可以阅读<a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a>或查看示例<a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>。
+</p>
+<div class="section-example-container">
+
+<pre class="python">def Initialize(self):
+    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
+	</pre>
+	</div>
+
+<h3>步骤2：线性回归函数</h3>
+<p>
+	为了进行线性回归，我们需要编写一个函数来获取价格数据并输出回归结果。函数获得“资产价格”列表(x)和一个“基准价格”列表(y)，然后计算变化百分比并进行线性回归。输出是一个包含截距和斜率的元组。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def regression(self,x,y):
+    x = np.array(x)
+    x = np.diff(x)/x[:-1]
+    y = np.array(y)
+    y = np.diff(y)/y[:-1]
+    A = np.vstack([x, np.ones(len(x))]).T
+    result = np.linalg.lstsq(A, y)[0]
+    beta = result[0]
+    alpha = result[1]
+    return(alpha,beta)
+</pre>
+</div>
+<h3>步骤3：历史功能</h3>
+<p>
+	每个月我们都会使用<a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API获得道琼斯30强成份股的历史价格。数据作为复杂的<em>Slice</em>对象从API返回。为了使其在算法中可以使用，我们将资产价格和基准价格提取到一个列表中。
+</p>
+<div class="section-example-container">
+
+<pre class="python">def get_regression_data(self,symbol,history):
+    symbol_price = []
+    benchmark_price = []
+    for i in history:
+        bar = i[symbol]
+        benchmark = i[self.benchmark]
+        symbol_price.append(bar.Close)
+        benchmark_price.append(benchmark.Close)
+
+    result = self.regression(symbol_price,benchmark_price)
+    return result
+</pre>
+</div>
+<h3>步骤4：再平衡功能</h3>
+<p>
+	此功能是所有动作发生的地方，将作为预定事件在每个月的第一个交易日执行。SetHoldings的第二个参数是小数，将其设置为“1”表示算法将投资组合设置为“100%多头”而不使用杠杆。有关这一功能的更多信息可以在链接<a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>上阅读。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def rebalance(self):
+    # 获得历史股票代码和价格，然后放入元组中
+    history = self.History(self.regression_dates, Resolution.Daily)
+    filter = []
+    for i in self.symbols:
+        filter.append((i,self.get_regression_data(i, history)[0]))
+    # 根据alpha排序筛选
+    filter.sort(key = lambda x : x[1],reverse = True)
+    sorted_symbols = []
+    for i in range(2):
+    	sorted_symbols.append(filter[i][0])
+    # 获得所持有股票的代码
+    holding_list = []
+    for i in self.Portfolio:
+    	if i.Value.Invested:
+    		holding_list.append(i.Value.Symbol)
+    # 如果我们不打算继续持有现有的股份，则将其出售
+    if holding_list:
+    	for i in holding_list:
+    		if i not in sorted_symbols:
+    			self.Liquidate(i)
+    # 做多列表中的两支股票
+    for i in sorted_symbols:
+    	self.SetHoldings(i,1)
+		</pre>
diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html"
new file mode 100644
index 0000000..c68387d
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/04 \346\200\273\347\273\223.cn.html"	
@@ -0,0 +1,14 @@
+<p>
+	我们已经证明，在一个平稳的市场中，上个月跑赢大盘的股票很可能在接下来的一个月里再次跑赢大盘。当市场波动时，线性回归的显著性水平降低，模型性能下降。我们可以通过观察资产(x)和基准(y)的协方差来理解这一点。当协方差减小到零时，beta将会减小。
+</p>
+
+\[\hat{\beta} = \frac{Cov[x,y]}{\sum (x_i - \beta{x})^2}\]
+
+<p>
+	作为实验，我们根据2015年的市场数据对算法进行了测试。对市场来说，这是一个极不稳定的时期，波动回到了接近于零的平均值，并在当年8月18日至8月25日期间下跌了近10%。该算法在今年的表现很差，收益率为-11.58%。与这一策略相关的风险包括大幅削减、缺乏对冲和止损。由于我们使用杠杆，风险增加了，因此在1月份有追加保证金的通知。我们可以通过应用以下技术来提高性能：
+</p>
+
+<ul>
+	<li>进行优化：我们可以实施均值方差分析来确定每月的资产配置，选择更多的股票进行交易。这将降低我们的风险，更科学地管理投资组合。</li>
+	<li>考虑beta：如果我们想要更加积极，我们可以结合alpha和beta来选择目标。这意味着我们选择的股票具有比市场波动更大的高alpha值。然而，如果我们是保守的投资者，我们可以使策略保持市场中立，这意味着投资组合不会受到市场表现的影响。例如，如果我们做多两只beta值为1和-1的股票，并分别持有相同的头寸大小，那么我们的投资组合就会变得市场中性。</li>
+</ul>
diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..1f296ed
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,9 @@
+<p>
+	使用OptionChainProvider进行回溯测试。
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2f525825fa6fb72ee4b23b9e63ecf025.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
new file mode 100644
index 0000000..f6f75d0
--- /dev/null
+++ "b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/06 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    <a href="https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average">https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average</a>
+  </li>
+  <li>
+    <a href="http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf">Sharpe, William, 1990 http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1990/sharpe-lecture.pdf</a>
+  </li>
+</ol>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html"
index 2be6e77..a69dc45 100644
--- "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html"	
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/03 \346\226\271\346\263\225.cn.html"	
@@ -15,11 +15,10 @@ <h3>步骤1：配对分类定义</h3>
         self.b = b
         self.name = str(a) + ':' + str(b)
         self.df = pd.concat([a.df,b.df],axis = 1).dropna()
-    # The number of bars in the rolling window would be determined by the resolution, so we get this
-      information from the shape of the DataFrame here.
+    # 滚动窗口中的条数应由分辨率确定，因此我们可以得到
         self.num_bar = self.df.shape[0]
         self.cor = self.df.corr().ix[0][1]
-    # Set the initial signals to be 0
+    # 将初始信号设置为0
         self.error = 0
         self.last_error = 0
         self.a_price = []
@@ -32,8 +31,7 @@ <h3>步骤1：配对分类定义</h3>
 
     def cointegration_test(self):
         self.model = sm.ols(formula = '%s ~ %s'%(str(self.a),str(self.b)), data = self.df).fit()
-    # This line conduct ADF test on the residual. ts.adfuller() returns a tuple and the first element in
-      the tuple is the test value.
+    # 此行对残差进行ADF测试，ts.adfuller() 返回元组，第一元素为
         self.adf = ts.adfuller(self.model.resid,autolag = 'BIC')[0]
         self.mean_error = np.mean(self.model.resid)
         self.sd = np.std(self.model.resid)
@@ -49,15 +47,14 @@ <h3>步骤1：配对分类定义</h3>
                  [self.a_date]).dropna()
         self.df = pd.concat([self.df,new_df])
         self.df = self.df.tail(self.num_bar)
-    # after updating the DataFrame, we empty the lists for the incoming data
+    # 更新数据框架后，我们清空输入数据列表
         for list in [self.a_price,self.a_date,self.b_price,self.b_date]:
             list = []
 </pre>
 </div>
-<h3>Step 2: Generate and Clean Pairs</h3>
+<h3>步骤2：生成并整理配对</h3>
 <p>
-  The function generate_pairs generates pairs using the stock symbols. self.pair_threshold and self.pair_num are pre-determined to control the number of candidate pairs. The pairs in self.pair_list would be kept and updated throughout our backtesting period. we set self.pair_threshold to 0.88 and self.pair_num to 120 to limit the number of pairs in the list. If we put too many pairs in the list, the backtesting would be too time consuming.
-  The function pair_clean is called after the two-stage screen. If the first pair contains stock A and stock B, and the second pair contains stock B and stock C, we would remove the second pair because the overlapped signal would disturb the balance of our portfolio.
+  函数generate_pair使用股票代码生成配对。self.pair_threshold和self.pair_num是预先确定的，用于控制候选配对的数量。在我们的回溯测试期间，self.pair_list中的配对将保留并更新。我们将self.pair_threshold设置为0.88，将self.pair_num设置为120，以限制列表中配对的数量。如果我们列表中的配对过多，那么回溯测试将会非常耗时。在两阶段筛选后会调用函数pair_clean。如果第一对包含股票A和股票B，第二对包含股票B和股票C，我们将删除第二对，因为重叠的信号会干扰投资组合的平衡。
 </p>
 
 <div class="section-example-container">
@@ -87,9 +84,9 @@ <h3>Step 2: Generate and Clean Pairs</h3>
 </pre>
 </div>
 
-<h3>Step 3: Warming up Period</h3>
+<h3>步骤3：预热期</h3>
 <p>
-  This part is under the OnData step. We set self.num_bar equals to the number of TradeBar in three months, which is determined by the resolution. During this period we fill the stock prices in lists, and assign each stock's price list to the symbol as a property. We would also remove the symbol from the symbol list if it has no data.
+  此部分属于OnData步骤。我们将self.num_bar设置为三个月内TradeBar的数量，这将由决议决定。在此期间，我们将股票价格填入列表，并将每支股票的价格列表作为属性分配给代码。如果代码列表中没有数据，我们还将从代码列表中删除此代码。
 </p>
 
 <div class="section-example-container">
@@ -105,9 +102,9 @@ <h3>Step 3: Warming up Period</h3>
     self.data_count = 0
     return</pre>
 </div>
-<h3>Step 4: Pairs Selection</h3>
+<h3>步骤4：配对选择</h3>
 <p>
-  This process is also under the OnData step. This step would generate pairs if it is the first trading period of this algorithm. If it's not, it will update the DataFrame and correlation coefficient of each pair in self.pair_list. After that the pairs have a correlation coefficient higher than 0.9 would be selected into self.selected_pair. Then all the pairs in self.selected_pair would be tested on their cointegration, and the pairs with a test value less than -3.34 would be selected to the final list. This step will also limit the number of stocks in the final list, by default we set self.selected_num to 10. self.count is a flag to count the number of datapoint we received. Once it reach 1-month amount, that means one trading period is passed and it would be set to 0.
+  这一过程同样属于OnData步骤。如果这是此算法的第一个交易周期，则此步骤将生成配对。如果不是，它将更新self.pair_list中每对股票的数据框架和关联系数。然后将关联系数大于0.9的配对选择到self.selected_pair中。然后对self.selected_pair中的所有配对进行协整，测试值小于-3.34的配对将被选择到最终列表中。此步骤还会限制最终列表中的股票数量，在默认情况下我们将self.selected_num设置为10。self.count是一个标志，用于计算我们所接收到数据点的数量。一旦达到1个月的数量，这意味着一个交易周期已经过去，它将被设置为0。
 </p>
 
 <div class="section-example-container">
@@ -123,31 +120,31 @@ <h3>Step 4: Pairs Selection</h3>
 
         for pair in self.pair_list:
             pair.cor_update()
-    # Update the DataFrame and correlation selection
+    # 更新数据框架和关联性选择
     if len(self.pair_list[0].a_price) != 0:
         for pair in self.pair_list:
     	    pair.df_update()
             pair.cor_update()
 
     self.selected_pair = [x for x in self.pair_list if x.cor &gt; 0.9]
-    # Cointegration test
+    # 协整测试
     for pair in self.selected_pair:
         pair.cointegration_test()
 
     self.selected_pair = [x for x in self.selected_pair if x.adf &lt; self.BIC]
     self.selected_pair.sort(key = lambda x: x.adf)
-    # If no pair passed the two-stage test, return.
+    # 如果没有配对通过两阶段测试，则返回
     if len(self.selected_pair) == 0:
         self.Log('no selected pair')
         self.count += 1
         return
-    # clean the pair to avoid overlapping stocks.
+    # 整理配对避免出现重叠股票
     self.selected_pair = self.pair_clean(self.selected_pair)
-    # assign a property to the selected pair, this is a signal that would be used for trading.
+    # 为所选配对分析属性，这是用于交易的信号
     for pair in self.selected_pair:
         pair.touch = 0
         self.Log(str(pair.adf) + pair.name)
-    # limit the number of selected pairs.
+    # 限制选择配对的数量
     if len(self.selected_pair) &gt; self.selected_num:
         self.selected_pair = self.selected_pair[:self.selected_num]
 
@@ -157,14 +154,14 @@ <h3>Step 4: Pairs Selection</h3>
 </pre>
 </div>
 
-<h3>Step 5: Trade Period</h3>
+<h3>步骤5：交易期</h3>
 <p>
-  It would be too long to read if we paste all the code in trading period together. Thus we would separate the code into three part: updating pairs, opening pairs trading and closing pairs trading. But all those lines are under OnData step and are under the condition: if self.count != 0 and self.count &lt; self.one_month. This means it's in the trading period.
+  如果我们把交易期间的所有代码都粘贴在一起，清单将会过长。因此，我们将代码分为三个部分： 更新配对、开始配对交易和结束配对交易。 但是所有这些行都属于OnData步骤，并且都在此条件下：即self.count != 0且self.count &lt; self.one_month。这意味着它在交易期内。
 </p>
 
-<h4>Updating Pairs</h4>
+<h4>更新配对</h4>
 <p>
-  This step would update the stock prices in each pair. It would also update the signal called 'last_error' and immediately after this the pairs would receive new signals.
+  这一步将更新每对股票的价格。它还会更新名为“last_error”的信号，在此之后，配对会立即接收到新的信号。
 </p>
 
 <div class="section-example-container">
@@ -183,10 +180,9 @@ <h4>Updating Pairs</h4>
 for pair in self.trading_pairs:
     pair.last_error = pair.error</pre>
 </div>
-<h4>Opening Pairs Trading</h4>
+<h4>开始配对交易</h4>
 <p>
-  For each pair in self.selected_pair, we receive the current prices of the stocks, and then use the cointegration model to calculate the residual \(\epsilon\), which is assigned to the pair as a property named 'error'. self.trading.pairs is a list to store the trading pairs. Once a pairs trading is open, this pair would be add to the list, and it would be removed when the trading is closed. The property 'touch' is signal. If the residual \(\epsilon\) cross over the positive threshold standard deviation(we set \(\ 2.23*sigma\) here), the signal would become +1; while if it cross down the negative threshold deviation(\(\ -2.23*sigma\), the signal would become -1. For those pairs with +1 signal, if the error cross down positive threshold, there is a signal to open a trade. We long stock B and short stock A. For those pairs with -1 signal, if the error cross over negative threshold, we long Stock A and short stock B.
-  When we opening a trade, we need to record the current model, current mean and standard deviation of the residual. This is necessary because if we enter a new trading period and the trade has not been closed yet, the cointegration model, mean and standard deviation of the pairs would be changed. We need to use the original thresholds to close the trades. while adding the pairs into self.trading_pairs, we also need to set the signal 'touch' to 0 for further use.
+  对于self.selected_pair中的每对股票，我们接受当前的股票价格，然后使用协整模型计算残差\(\epsilon\)，这是分配给配对的属性，命名为“误差”。 self.trading.pairs是存储交易配对的列表。一旦配对交易开始，配对就会被添加到列表中，当交易结束时将被删除。属性“touch”是一个信号。如果残差\(\epsilon\)超出正阈值标准偏差（我们在此设置为\(\ 2.23*sigma\))，信号会+ 1；如果低于负阈值偏差(\(\ -2.23*sigma\)，则信号会-1。对于那些获得+1信号的配对，如果误差超过正阈值，则会有开始交易的信号。我们买入股票B并抛出股票A。对于那些获得-1信号的配对，如果误差低于负阈值，我们买入股票A并抛出股票B。这是必要的，当我们开始交易时，我们需要记录残差的当前模型、当前平均值和标准。因为如果我们进入一个新的交易周期，而交易尚未结束，配对的协整模型、平均值和标准差将发生变化。我们需要使用最初的阈值来结束交易。同时把配对添加到self.trading_pairs中。我们还需要将信号“touch”设置为0，以便进一步使用。
 </p>
 
 <div class="section-example-container">
@@ -229,9 +225,9 @@ <h4>Opening Pairs Trading</h4>
 </pre>
 </div>
 
-<h4>Closing Pairs Trading</h4>
+<h4>结束配对交易</h4>
 <p>
-  This part controls pairs trading exit. It works similar to the opening part. It uses the recorded original model and thresholds to determine whether or not we should close the position. If the residual \(\epsilon\) reaches our closing threshold, we liquidate stock A and stock B to close. If the residual continue to deviate from the mean and goes too far, we would also close the position to stop loss. When we close a pairs trading, we also remove the pairs from self.trading_pairs.
+  此部分控制退出交易。它的工作原理与开始部分相似。它使用记录原始模型和阈值来决定我们是否应该结束仓位。如果残差\(\epsilon\)达到结束阈值，我们将平仓股票A和股票B结束交易。如果残差继续偏离平均值，并且偏离太远，我们也会平仓止损。当我们结束配对交易时，我们也从self.trading_pair中删除了这些配对。
 </p>
 
 <div class="section-example-container">
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html"
new file mode 100644
index 0000000..9361eda
--- /dev/null
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/04 \347\273\223\346\236\234.cn.html"	
@@ -0,0 +1,20 @@
+<p>
+  我们使用了2013年1月至2016年12月的10分钟分辨率数据对该策略进行了回溯测试。为了证明样本的培训结果，我们随机选取了2016年9月7日至2013年11月30日的训练期。
+</p>
+<p>
+  下表展示了上述训练期间所选择的前10组配对。可以看出，关联系数最高的配对不一定具有最好的ADF测试值。我们用ADF测试值来进行排序，因为它更加稳健。
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-1.png" alt="Tutorial07-pairs-trading-1" />
+
+<p>
+  下表的上半部分是ING和TCB配对的股票价格。下半部分表示残差偏离平均值的次数。如果我们将开始阈值设为2.32，则有5个交易机会。
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-2.png" alt="Tutorial07-pairs-trading-2" />
+
+<p>
+  下图是残差的密度图。从图中可以看出，误差近似正常分布。
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-pairs-trading-3.png" alt="Tutorial07-pairs-trading-3" />
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html"
new file mode 100644
index 0000000..fa65597
--- /dev/null
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/05 \346\200\273\347\273\223.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  该策略被认为是市场中性策略，因为它是一种对价格趋同进行押注的多/空策略。回溯测试beta为-0.112，在我们的预期范围内。从理论上讲，我们使用的分辨率越高，获胜的几率就越高，因为一方面，更高的分辨率会增加我们训练期间的数据点数量，这将使我们更难通过两阶段测试；另一方面，高分辨率的数据可以让我们更准确地捕捉微小的利润。然而，在性能和回测时间之间存在权衡。较高的分辨率将导致回溯测试时间急剧增加。初始化步骤中股票的数量也会影响我们的性能。理论上，我们拥有的股票越多，我们可能选择的股票配对就越好。但过多的股票也会耗费时间，值得一提的是，每个行业的优化参数是不同的。这取决于特定行业价格模式的特点。绘制出配对价格和观察残差是调整阈值的较好选择。
+</p>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..69b6365
--- /dev/null
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/06 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5d0576da03c50667c3af8aefa86f4f44.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
new file mode 100644
index 0000000..b50463a
--- /dev/null
+++ "b/04 Strategy Library/07 Intraday Dynamic Pairs Trading using Correlation and Cointegration Approach/07 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -0,0 +1,16 @@
+<ol>
+  
+  <li>
+    George J. Miao High Frequency and Dynamic Pairs Trading Based on Statistical Arbitrage Using a Two-Stage Correlation and Cointegration Approach <a href="https://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;ved=0ahUKEwiX9qHlqt7UAhVBOj4KHX-IAU4QFggkMAA&amp;url=http%3A%2F%2Fwww.ccsenet.org%2Fjournal%2Findex.php%2Fijef%2Farticle%2Fdownload%2F33007%2F19708&amp;usg=AFQjCNFbUXJRGML636l1kKu_sEGoI4SZcQ"><strong>Online Copy</strong></a>
+  </li>
+  <li>
+    Cartea &amp; Penalva, 2012, Where is the value in high frequency trading? <a href="http://dx.doi.org/10.1142/S2010139212500140"> Online Copy</a>
+  </li>
+  <li>
+    Gatev, Goetzmann, &amp; Rouwenhorst, 2006, Pairs trading: Performance of a relative-value arbitrage rule. The Review of Financial Studies, 19(3), 797–827. <a href="http://dx.doi.org/10.1093/rfs/hhj020"><strong>Online Copy</strong></a>
+  </li>
+  <li>
+    Engle and Granger, Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. <strong><a href="http://dx.doi.org/10.2307/1913236"><strong>Online Copy</strong></a>
+  </li>
+
+</ol>
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html"
new file mode 100644
index 0000000..64620eb
--- /dev/null
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/01 \346\221\230\350\246\201.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+	近年来，投资要素在全球机构投资者中非常受欢迎。在本教程中，我们首先开发了一个要素选择模型，用于测试要素是否具有区分股市中潜在赢家和输家的能力。然后，根据<em>2015年基于要素的土耳其股票选择模型</em>，我们利用这些预先选择要素，实现了要素排序选股算法，Ayhan Yüksel。
+</p>
diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html
index eb8ac07..663d5e1 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 Factor Selection.html	
@@ -51,7 +51,7 @@ <h3>Step 1: Ranking the stocks by factor values</h3>
 	2. We extract the factor values of candidate stocks at the beginning of each month and sort the stocks in ascending 
 	order of their factor values. Here we use 12-months' total risk-based capital data 
 	<code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code> as an example.
-	It is t<span class="s1">he sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> 
+	It is the sum of Tier 1 and Tier 2 Capital. <code>x.Symbol.Value</code> 
 	can give the string symbol of selected stock x. Then we save those sorted symbols as <code>self.symbol</code>.
 </p>
 
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html"
new file mode 100644
index 0000000..b8cefb8
--- /dev/null
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/02 \350\246\201\347\264\240\351\200\211\346\213\251.cn.html"	
@@ -0,0 +1,236 @@
+<p>
+	QuantConnect为美国股市提供了晨星基本面数据。估值比率是每日数据。对于其他项目，如营业比率和财务报表，可以根据不同的属性获得不同时期的数据。详细的可用要素请参阅<a href="https://www.quantconnect.com/data#fundamentals/usa/morningstar">Data Library</a>。
+</p>
+<p>
+	该算法的设计是为了每次测试一个要素的重要性。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def Initialize(self):
+	self.SetStartDate(2005,01,01)  # 设置开始日期
+	self.SetEndDate(2012,03,01)    # 设置结束日期
+	self.SetCash(50000)            # 设置策略现金
+	self.UniverseSettings.Resolution = Resolution.Daily
+	self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
+	self.AddEquity("SPY") # add benchmark
+	self.numOfCourseSymbols = 200
+	self.numOfPortfolio = 5
+	self._changes = None
+	self.flag1 = 1  # 控制粗选和精选函数每月重新平衡的变量
+	self.flag2 = 0  # 控制OnData函数每月重新平衡的变量
+	self.flag3 = 0  # 记录重新平衡次数的变量
+        # 将不同投资组合的月收益存储在一个数据框架内
+	self.df_return = pd.DataFrame(index = range(self.numOfPortfolio+1))
+        # 预定事件在SPY的第一个交易日解除
+	self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY"), Action(self.Rebalancing))
+</pre>
+</div>
+<h3>步骤1：按要素值对股票进行排序</h3>
+<p>
+	1. 首先，我们按每日美元交易量对股票进行排序，并将美元交易量最高的股票作为候选股票。有一种方便的方法使用我们的集合选择API。在默认情况下，集合每天都会刷新，但也可以根据需要经常刷新。这是由可变<code>UniverseSettings.Resolution</code>控制的。您可以参考<a href="https://www.quantconnect.com/docs#Universes">documentation</a>了解更多详细信息。在这里，我们使用<a href="https://www.quantconnect.com/docs#Scheduled-Events">Scheduled events API</a>来触发在每个月第一个交易日运行的代码，并使用三个标志变量来控制<code>CoarseSelection</code>，<code>FineSelection</code>和<code>Ondata</code>函数的重新平衡。
+</p>
+<p>
+	粗略集合选择是由QuantConnect提供的内置集合数据，它允许你对超过16,000个符号的集合进行筛选，在你的算法之前进行粗略的筛选。由于粗选功能考虑了包括基金在内的所有无基础数据股票，所以我们需要属性<code>x.HasFundamentalData</code>将这些股票排除在我们的候选股票池之外。
+</p>
+<div class="section-example-container">
+
+<pre class="python"># 按照每日美元交易量对数据进行排序，并取排名最上面的条目
+def CoarseSelectionFunction(self, coarse):
+    if self.flag1:
+        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
+        sortedByVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=True)
+        top = sortedByVolume[:self.numOfCourseSymbols]
+				return [i.Symbol for i in top]
+    else:
+        return []
+</pre>
+</div>
+
+<p>
+	2. 我们在每个月初提取候选股票的要素值，并按其要素值的升序对股票进行排序。例如，这里我们使用了12个月的总风险基础资本数据<code>x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths</code>。它是一级资本和二级资本的总和。<code>x.Symbol.Value</code>可以给出所选股票x的串符号，然后将这些排序后的符号保存为<code>self.symbol</code>。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">def FineSelectionFunction(self, fine):
+	if self.flag1:
+		self.flag1 = 0
+		self.flag2 = 1
+		# 通过删除要素值为零的股票来进行精细筛选
+		filtered_fine = [x for x in fine if x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths != 0 ]
+		# 按照要素值倒序排序
+		sorted_fine = sorted(filtered_fine, key=lambda x: x.FinancialStatements.TotalRiskBasedCapital.TwelveMonths, reverse=True)
+		self.symbol = [str(x.Symbol.Value) for x in sorted_fine]
+		# 要素值 = [x.ValuationRatios.PERatio for x in sorted_fine]
+		self.flag3 = self.flag3 + 1
+ 	 	return []
+	else:
+		return []
+</pre>
+</div>
+
+<h3>步骤2：计算投资组合的月收益</h3>
+<p>
+	1. 在每个月底，我们提取每支股票的一个月历史收盘价，并计算月收益。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">sorted_symbol = self.symbol
+self.AddEquity("SPY") # 添加基准
+for x in sorted_symbol:
+	self.AddEquity(x)
+history = self.History(20,Resolution.Daily)
+monthly_return =[]
+new_symbol_list =[]
+for j in range(len(sorted_symbol)):
+	try:
+		daily_price = []
+		for slice in history:
+			bar = slice[sorted_symbol[j]]
+			daily_price.append(float(bar.Close))
+		new_symbol_list.append(sorted_symbol[j])
+ 		monthly_return.append(daily_price[-1] / daily_price[0] - 1)
+	except:
+		self.Log("No history data for " + str(sorted_symbol[j]))
+		del daily_price
+# 月收益列表的长度应能够被投资组合的数量整除
+monthly_return = monthly_return[:int(math.floor(len(monthly_return) / self.numOfPortfolio) * self.numOfPortfolio)]
+</pre>
+</div>
+
+
+<p>
+	2. 我们将股票分为5个投资组合，计算每个投资组合的平均月收益。然后在数据框架<code>df_return</code>的最后一行添加基准“SPY”的月收益。
+</p>
+
+<div class="section-example-container">
+
+<pre class="python">reshape_return = np.reshape(monthly_return, (self.numOfPortfolio, len(monthly_return)/self.numOfPortfolio))
+# 计算不同投资组合的平均收益
+port_avg_return = np.mean(reshape_return,axis=1).tolist()
+# 将"SPY"的收益作为基准添加到收益列表的末尾
+benchmark_syl = self.AddEquity("SPY").Symbol
+history_benchmark = self.History(20,Resolution.Daily)
+benchmark_daily_price = [float(slice[benchmark_syl].Close) for slice in history_benchmark]
+benchmark_monthly_return = (benchmark_daily_price[-1]/benchmark_daily_price[0]) - 1
+port_avg_return.append(benchmark_monthly_return)
+self.df_return[str(self.flag3)] = port_avg_return
+</pre>
+</div>
+<h3>步骤3：生成度量来测试要素重要性</h3>
+<p>
+	在得到投资组合的月收益和基准后，我们计算了整个回溯测试期间各投资组合的年平均收益和基准之上的超额收益。然后我们生成三个度量来判断每个因素的重要性。
+</p>
+
+<ul>
+ 	<li>第一个度量是投资组合收益与其排名之间的相关性。关联系数绝对值应大于0.8。</li>
+ 	<li>如果排名第一投资组合的收益大于排名垫底的投资组合，我们将其定义为盈利投资组合和亏损投资组合，反之亦然。盈利概率是盈利投资组合收益超过基准收益的概率。亏损概率是指亏损组合收益低于基准的概率。如果要素显著，则损益概率均应大于0.4。</li>
+ 	<li>盈利投资组合的超额收益应大于0.25，亏损投资组合的超额收益应小于0.05。</li>
+</ul>
+
+<div class="section-example-container">
+
+<pre class="python">def calculate_criteria(self,df_port_return):
+	total_return = (df_port_return + 1).T.cumprod().iloc[-1,:] - 1
+	annual_return = (total_return+1)**(1./6)-1
+	excess_return = annual_return - np.array(annual_return)[-1]
+	correlation = annual_return[0:5].corr(pd.Series([5,4,3,2,1],index = annual_return[0:5].index))
+	# 高收益的高要素
+	if np.array(total_return)[0] &gt; np.array(total_return)[-2]:
+		loss_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[0]
+		loss_port_excess_return = np.array(excess_return)[-2]
+	# 低收益的高要素
+	else:
+		loss_excess = df_port_return.iloc[0,:] - df_port_return.iloc[-1,:]
+		win_excess = df_port_return.iloc[-2,:] - df_port_return.iloc[-1,:]
+		loss_prob = loss_excess[loss_excess&lt;0].count()/float(len(loss_excess)) win_prob = win_excess[win_excess&gt;0].count()/float(len(win_excess))
+		win_port_excess_return = np.array(excess_return)[-2]
+		loss_port_excess_return = np.array(excess_return)[0]
+	test_result = {}
+	test_result["correelation"]=correlation
+	test_result["win probality"]=win_prob
+	test_result["loss probality"]=loss_prob
+	test_result["win portfolio excess return"]=win_port_excess_return
+	test_result["loss portfolio excess return"]=loss_port_excess_return
+
+	return test_result</pre>
+</div>
+
+<table class="table qc-table">
+<thead>
+<tr>
+<th style="text-align: center;" colspan="8">要素显著性测试结果</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>要素</td>
+<td> FCFYield</td>
+<td> BuyBackYield</td>
+<td> PriceChange1M</td>
+<td>TrailingDividendYield</td>
+<td> EVToEBITDA</td>
+<td> RevenueGrowth</td>
+<td>BookValuePerShare</td>
+</tr>
+<tr>
+<td>关联性</td>
+<td> -0.936</td>
+<td> -0.987</td>
+<td> 0.918</td>
+<td> -0.981</td>
+<td> 0.939</td>
+<td>0.89</td>
+<td>-0.92</td>
+</tr>
+<tr>
+<td>盈利概率</td>
+<td>0.630</td>
+<td>0.639</td>
+<td> 1</td>
+<td> 0.667</td>
+<td> 0.722</td>
+<td> 0.69</td>
+<td>0.69</td>
+</tr>
+<tr>
+<td>亏损概率</td>
+<td> 0.426</td>
+<td>0.472</td>
+<td> 1</td>
+<td> 0.518</td>
+<td> 0.472</td>
+<td> 0.42</td>
+<td>0.40</td>
+</tr>
+<tr>
+<td>超额收益（盈利）</td>
+<td> 0.324</td>
+<td> 0.212</td>
+<td>0.303</td>
+<td> 0.225</td>
+<td> 0.414</td>
+<td> 0.23</td>
+<td> 0.27</td>
+</tr>
+<tr>
+<td>超额收益（亏损）</td>
+<td> 0.060</td>
+<td> 0.037</td>
+<td> -1.67</td>
+<td> 0.043</td>
+<td> 0.042</td>
+<td> 0.07</td>
+<td> 0.06</td>
+</tr>
+</tbody>
+</table>
+
+<p>
+	我们选择了4个要素：FCFYield、PriceChange1M、BookValuePerShare和RevenueGrowth。
+</p>
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html"
new file mode 100644
index 0000000..00bbed2
--- /dev/null
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/03 \350\202\241\347\245\250\351\200\211\346\213\251.cn.html"	
@@ -0,0 +1,17 @@
+接下来我们将选择股票。
+<h3>步骤1：按照要素值对股票排序</h3>
+<p>
+	首先，我们删除没有基础数据或要素值为零的股票。对于每种预先选择的因素，我们根据这些因素值对股票进行排序。当要素相关性为负时，顺序递减；要素相关性为正时，顺序递增。
+</p>
+
+<h3>步骤2：计算平均加权综合要素得分</h3>
+<p>
+	第二步是使用不同的选择要素变量来计算每支股票的平均加权综合要素得分。
+</p>
+
+<ul>
+ 	<li>首先，根据要素顺序，我们将股票集合分成不同的五分位投资组合，分别为P1、P2、P3、P4和P5。投资组合的排序列出了要素模型的偏好，即第一投资组合（P1）对应“最优”的股票，第五投资组合（P5）对应“最不喜爱”的股票。假设总共有n支股票。然后进入第一投资组合的股票得分为p，进入第二级投资组合的股票得分为p-1，以此类推。然后我们可以得到每支股票的分数。我们对每个要素都进行了相同的计算。</li>
+ 	<li>其次，我们将6个因素的得分结合起来，使用平均加权方案来计算“综合要素得分”。然后可以得到每支股票的综合要素得分。</li>
+ 	<li>第三，然后我们根据综合要素得分对我们集合中的股票进行排名，并在每个月初选择排名最高的20支股票来构建我们的投资组合。</li>
+ 	<li>每个月月底，我们都会重复上述步骤来构建新的投资组合，调整持有的股票。</li>
+</ul>
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..bcf0bc2
--- /dev/null
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4f8946295bb7358518b35ecbd3d46f01.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
new file mode 100644
index 0000000..145c7bf
--- /dev/null
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>
+  </li>
+</ol>
diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..e81a4a4
--- /dev/null
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  动量异常的主要原因是投资者的行为偏差，例如反应不足和确认偏差。动量策略通常会使用由成千上万只股票组成的投资组合来计算动量要素收益。这对于持有小型投资组合的小型散户投资者来说是不可能的。与大型对冲基金相比，它们受到限制，无法进行多样化投资。在本教程中，我们将构建一个由50支股票组成的小型投资组合，以检查动量所产生影响。
+</p>
diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..61298fc
--- /dev/null
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,63 @@
+<p>
+  投资集合包括所有美国上市公司。没有基本数据的股票被排除在集合之外。
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+    if self.yearly_rebalance:
+        # 放弃没有基本数据的股票
+        self.filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)]
+        return self.filtered_coarse
+    else:
+        return []
+</pre>
+</div>
+<p>
+  在<code>FineSelectionFunction</code>中，由于流动性较低，市值最低的股票（占集合的25%）被排除在外。动量被定义为过去12个月的股市收益。动量利润是根据排名公司的年度收益进行计算的。排名周期为一年。
+</p>
+<div class="section-example-container">
+<pre class="python">
+  def FineSelectionFunction(self, fine):
+      if self.yearly_rebalance:
+          # 计算年收益和市值
+          for i in fine:
+              i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+          top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len(fine)*0.75)]
+          has_return = []
+          for i in top_market_cap:
+              history = self.History([i.Symbol], timedelta(days=365), Resolution.Daily)
+              if not history.empty:
+                  close = history.loc[str(i.Symbol)]['close']
+                  i.returns = (close[0]-close[-1])/close[-1]
+                  has_return.append(i)
+          sorted_by_return = sorted(has_return, key = lambda x: x.returns)
+          self.long = [i.Symbol for i in sorted_by_return[-10:]]
+          self.short = [i.Symbol for i in sorted_by_return[:10]]
+
+          return self.long+self.short
+      else:
+          return []
+</pre>
+</div>
+<p>
+  投资者做多表现最好的10支股票，做空表现最差的10支股票。投资组合每年都进行平均加权和重新平衡。
+</p>
+<div class="section-example-container">
+<pre class="python">
+  def OnData(self, data):
+      if not self.yearly_rebalance: return
+      if self.long and self.short:
+          stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+          # 平仓未进入交易清单的股票
+          for i in stocks_invested:
+              if i not in self.long+self.short:
+                  self.Liquidate(i)
+          for i in self.short:
+              self.SetHoldings(i, -0.5/len(self.short))
+          for i in self.long:
+              self.SetHoldings(i, 0.5/len(self.long))
+          self.long = None
+          self.short = None
+          self.yearly_rebalance = False
+</pre>
+</div>
diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..6bbcd8e
--- /dev/null
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/03 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5761c1817a9dcb805be487f9e32adfa1.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..0cfa5d6
--- /dev/null
+++ "b/04 Strategy Library/162 Momentum Effect in Stocks in Small Portfolios/04 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/162" rel="nofollow">Quantpedia - Momentum Effect in Stocks in Small Portfolios</a>
+</li>
+</ul>
diff --git "a/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..f770bb0
--- /dev/null
+++ "b/04 Strategy Library/20 Forex Carry Trade/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  套利交易在外汇市场上很常见。此策略系统性地出售低利率货币，买入高利率货币。资产的“套利”是持有该资产的机会成本。套利交易策略持有相对于其他货币的某一种货币，以获取利率之间的价差。我们可以把这种策略看作是从一个利率较低的国家借钱，然后将钱投资到另一个利率较高的国家。 
+</p>
diff --git "a/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..2fc26c1
--- /dev/null
+++ "b/04 Strategy Library/20 Forex Carry Trade/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,19 @@
+<h4>导入自定义数据</h4>
+<p>
+  央行利率数据来自Quandl。对于交易集合来说，我们选择了央行利率数据在Quandl中可用的9种货币。导入自定义数据的方法是<code>AddData(type, symbol, resoltuion, timeZone, fillDataForward)</code>。由于自定义文件具有唯一的colume名称，因此我们需要创建一个类别来指定利率的colume名称。
+</p>
+<div class="section-example-container">
+<pre class="python">
+from QuantConnect.Python import PythonQuandl
+class QuandlRate(PythonQuandl):
+    def __init__(self):
+        self.ValueColumnName = 'Value'
+</pre>
+</div>
+<p>
+  我们将利率符号和对应的外汇资产符号保存到字典中。
+</p>
+<h4>每月调整交易</h4>
+<p>
+  下一步，我们根据利率的值对外汇符号进行排序。这种算法会做多利率最高的货币，做空利率最低的货币。该战略每月都会重新调整。采用日程事件法在每个月的第一个交易日触发重新调整事件。
+</p>
diff --git "a/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..1bb774b
--- /dev/null
+++ "b/04 Strategy Library/20 Forex Carry Trade/03 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_798687a2edab75475f0c9095ecd71c1f.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..18f54dd
--- /dev/null
+++ "b/04 Strategy Library/20 Forex Carry Trade/04 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/5" rel="nofollow">Quantpedia - FX Carry Trade</a>
+</li>
+</ul>
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..6b02628
--- /dev/null
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  此算法检验了国家指数交易所交易基金的动量效应。
+</p>
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..7b69538
--- /dev/null
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<p>
+此算法选取35个国家的指数基金作为交易集合。由于集合中的符号不会随时间发生变化，我们使用动量指示器辅助方法<code>self.MOM(symbol, period, resolution)</code>。这种辅助方法会创建一个新的动量指示器，并计算证券在绝对n周期中的变化。与指示器构造函数<code>Momentum(period)</code>相反，辅助方法指示器将根据给定的分辨率自动更新。
+</p>
+<p>
+  在<code>Initialize()</code>中，我们将预热周期设置为动量周期并创建字典<code>self.data</code>，以保存每个符号的指示符。每个月，将选择6个月势头最好的前5支指数基金进行多头仓位。没有列在榜首的基金将被斩仓。采用日程事件API制定投资组合，在每个月月初时重新平衡。
+</p>
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html"
new file mode 100644
index 0000000..b3aecfd
--- /dev/null
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/03 \347\273\223\350\256\272.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  算法结果表明，国家指数存在动量效应。在2002年至2018年的16年时间里，持有一个月的策略（即在过去6个月中表现最好的5个国家指数基金投资组合）的表现每年要超出平均加权投资组合约40%。
+</p>
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..cd34f0c
--- /dev/null
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/04 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,14 @@
+<h4>动量效应</h4>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_bd83aa417032f8407382a1c065aa7511.html"></iframe>
+    </div>
+</div>
+<h4>平均加权基准</h4>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_32bbadd991642fc989e1857afb12003d.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..2334ff4
--- /dev/null
+++ "b/04 Strategy Library/22 Momentum Effect in Country Equity Indexes/05 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/15" rel="nofollow">Quantpedia - Momentum Effect in Country Equity Indexes</a>
+</li>
+</ul>
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..c26b591
--- /dev/null
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,3 @@
+<p>
+  成长型股票指的是高质量、成功的公司，它们的盈利预期将继续以高于市场平均水平的速度增长。成长型股票通常具有高市盈率(P/E)和高市净率(P/B)。有时，成长型股票被认为价格过高。价值型股票是指股息率高或财务比率（如市盈率和市净率）低的股票。价值型股票通常会被市场低估。此算法将基于投资者情绪和价值型股票相对于成长型股票表现之间的关系来建立多空头寸。
+</p>
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..3104b1b
--- /dev/null
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,120 @@
+<h4>投资情绪的测量</h4>
+<p>
+  要衡量投资者的情绪，我们使用的指标是：芝加哥期权交易所(CBOE)的股票看跌/看涨比率和市场波动率(VIX)指数。VIX指数是利用标准普尔500指数期权的隐含波动率构建的，它显示了市场对30天波动率的预期。芝加哥期权交易所(CBOE) 股票看跌/看涨比率的计算方法是将芝加哥期权交易所股票看跌期权的交易量除以芝加哥期权交易所股票看涨期权的交易量。看跌/看涨期权比率上升，意味着股票交易员买入的看跌期权多于看涨期权，表明市场人气看跌，而看跌/看涨比率下降则被视为市场人气上涨。
+</p>
+<p>
+  我们从Quandl导入每日波动率数据。CBOE提供了从2006年11月1日到现在的成交量看跌/看涨比率数据，因此我们从<a href="http://www.cboe.com/data/historical-options-data/volume-put-call-ratios">CBOE</a>导入自定义数据。
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SentimentAndStyleRotationAlgorithm(QCAlgorithm):
+  def Initialize(self):
+      self.SetStartDate(2010, 1, 1)
+      self.SetEndDate(2018, 7, 1)
+      self.SetCash(100000)
+      self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily)
+      self.AddData(CBOE, "PutCallRatio", Resolution.Daily)
+
+class QuandlVix(PythonQuandl):
+    '''Quandl VIX data class'''
+    def __init__(self):
+        self.ValueColumnName = "VIX Close"
+
+class CBOE(PythonData):
+    '''Cboe Equity Volume Put/Call Ratios (11-01-2006 to present) Custom Data Class'''
+    def GetSource(self, config, date, isLiveMode):
+        return SubscriptionDataSource("http://www.cboe.com/publish/scheduledtask/mktdata/datahouse/equitypc.csv", SubscriptionTransportMedium.RemoteFile)
+
+    def Reader(self, config, line, date, isLiveMode):
+        if not (line.strip() and line[0].isdigit()): return None
+        index = CBOE()
+        index.Symbol = config.Symbol
+
+        try:
+            # 示例文件格式：
+            # 日期        看涨      看跌       合计      看跌/看涨比率
+            # 11/1/06    976510    623929    1600439    0.64
+            data = line.split(',')
+            index.Time = datetime.strptime(data[0], "%m/%d/%Y").strftime("%Y-%m-%d")
+            index.Value = Decimal(data[4])
+
+        except ValueError:
+                return None
+
+        return index
+</pre>
+</div>
+<h4>股票增长和价值的衡量</h4>
+<p>
+  纽交所和纳斯达克的所有股票都被用作投资集合。在<code>CoarseSelectionFunction</code>中，我们剔除了没有基本数据的基金。在<code>FineSelectionFunction</code>中，股票根据市值大小被分成十等分。我们的算法只使用前百分之三十来避免小型非流动性股票的潜在问题。
+</p>
+<div class="section-example-container">
+<pre class="python">
+def FineSelectionFunction(self, fine):
+    if self.month_start:
+        self.selection = True
+
+        fine = [i for i in fine if i.EarningReports.BasicAverageShares.ThreeMonths>0
+                                and i.EarningReports.BasicEPS.TwelveMonths>0
+                                and i.ValuationRatios.PERatio>0
+                                and i.ValuationRatios.PBRatio>0]
+        # 计算市场价值并将“市值”属性添加到精细集合对象中
+        for i in fine:
+            i.MarketCap = float(i.EarningReports.BasicAverageShares.ThreeMonths * (i.EarningReports.BasicEPS.TwelveMonths*i.ValuationRatios.PERatio))
+        # 根据市值对精细对象分类
+        sotrted_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)
+        decile_top1 = sotrted_market_cap[:floor(len(sotrted_market_cap)/10)]
+        decile_top2 = sotrted_market_cap[floor(len(sotrted_market_cap)/10):floor(len(sotrted_market_cap)*2/10)]
+        decile_top3 = sotrted_market_cap[floor(len(sotrted_market_cap)*2/10):floor(len(sotrted_market_cap)*3/10)]
+</pre>
+</div>
+<p>
+  在接下来的步骤中，我们根据市净率将十等分中的每一份再细分为五个投资组合。对于前百分之三十的股票，价值型投资组合由市净率最低的五分之一公司组成，而成长型投资组合则由市净率最高的股票组成。
+</p>
+<div class="section-example-container">
+<pre class="python">
+sorted_PB1 = sorted(decile_top1, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB2 = sorted(decile_top2, key = lambda x: x.ValuationRatios.PBRatio)
+sorted_PB3 = sorted(decile_top3, key = lambda x: x.ValuationRatios.PBRatio)
+# 价值型投资组合由市净率最低的五分之一公司组成
+PB_bottom1 = sorted_PB1[:floor(len(decile_top1)/5)]
+PB_bottom2 = sorted_PB2[:floor(len(decile_top2)/5)]
+PB_bottom3 = sorted_PB3[:floor(len(decile_top3)/5)]
+self.value_portfolio = [i.Symbol for i in PB_bottom1 + PB_bottom2 + PB_bottom3]
+# 成长型投资组合由市净率最高的五分之一公司组成
+PB_top1 = sorted_PB1[-floor(len(decile_top1)/5):]
+PB_top2 = sorted_PB2[-floor(len(decile_top2)/5):]
+PB_top3 = sorted_PB3[-floor(len(decile_top3)/5):]
+self.growth_portfolio = [i.Symbol for i in PB_top1 + PB_top2 + PB_top3]
+</pre>
+</div>
+
+<h4>投资者情绪和股票风格之间的关系</h4>
+<p>
+  根据Lee和Song的研究论文《<a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=410185">When Do Value Stocks Outperform Growth Stocks?: Investor Sentiment and Equity Style Rotation Strategies</a>》，当芝加哥期权交易所股票看跌/看涨比率相对较低，并且波动率指数相对较高时，价值型股票的表现往往优于成长型股票。当看跌/看涨比率和波动率指数均较高时，价值组合的表现明显要逊于成长型投资组合。要将每日看跌/看涨比率和波动率数据转换为月度数值，我们取最近一个月和前六个月的平均值。
+</p>
+<p>
+  如果最近芝加哥期权交易所的月平均看跌/看涨比率低于6个月平均水平，而波动率指数(VIX)的一个月平均水平高于6个月平均水平，那么该算法就会做多由排名前百分之三十价值股(市净率最低的五分之一)组成的平均加权投资组合。如果最近芝加哥期权交易所的月平均看跌/看涨比率和VIX指数均高于6个月平均水平，该算法就会做空价值股。否则，该算法既做多价值型股票，也做多成长型股票。持仓期为三个月，投资组合每三个月重新平衡一次。
+</P>
+<div class="section-example-container">
+<pre class="python">
+stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
+for i in stocks_invested:
+    if i not in self.value_portfolio+self.growth_portfolio:
+        self.Liquidate(i)
+
+if self.vix_SMA_1.Current.Value > self.vix_SMA_6.Current.Value:
+    if self.PCRatio_SMA_1.Current.Value < self.PCRatio_SMA_6.Current.Value:
+        long_weight = 1/len(self.value_portfolio)
+        for long in self.value_portfolio:
+            self.SetHoldings(long, long_weight)
+    elif self.PCRatio_SMA_1.Current.Value > self.PCRatio_SMA_6.Current.Value:
+        short_weight = 1/len(self.value_portfolio)
+        for short in self.value_portfolio:
+            self.SetHoldings(short, -short_weight)
+else:
+    long_weight = 1/len(self.value_portfolio+self.growth_portfolio)
+    for long in self.value_portfolio+self.growth_portfolio:
+        self.SetHoldings(long, long_weight)
+</pre>
+</div>
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..9af8464
--- /dev/null
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/03 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e8a75429c6a43e969e4a9243290e05f4.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..d965226
--- /dev/null
+++ "b/04 Strategy Library/36 Sentiment and Style Rotation Effect in Stocks/04 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/53" rel="nofollow">Quantpedia - Sentiment and Style Rotation Effect in Stocks</a>
+</li>
+</ul>
diff --git "a/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html"
new file mode 100644
index 0000000..69d32c7
--- /dev/null
+++ "b/04 Strategy Library/39 Asset Growth Effect/01 \347\256\200\344\273\213.cn.html"	
@@ -0,0 +1,6 @@
+<p>
+ 资产增长效基于高资产增长股票的表现弱于低资产增长股票。一些研究认为，低资产成长型股票的收益溢价与风险补偿是一致的。有两种流行的观点支持这种回报溢价。一种观点是，随着公司的成长，公司的资产组合风险会降低，因为现有资产取代了公司未来预期投资的资产价值。第二种观点是，由于将过去的收益推断为高资产成长型企业的增长，导致了对成长型企业的系统性市场定价错误。 
+ </p>
+ <p>
+ 这一策略将在低资产增长型公司做多，同时在高资产增长的公司做空。值得注意的是，这一策略需要根据当前和过去一年的基本数据来进行分析。因此，这种策略需要一年的实时交易时间来确定信号。
+</p>
\ No newline at end of file
diff --git "a/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html"
new file mode 100644
index 0000000..983e337
--- /dev/null
+++ "b/04 Strategy Library/39 Asset Growth Effect/02 \346\226\271\346\263\225.cn.html"	
@@ -0,0 +1,57 @@
+<p>
+ 第一步是粗略和精细集合选择。通过粗选，我们创建了一个包含所有在纽交所、美国证券交易所和纳斯达克上市的非金融类美国股票的投资领域，这些股票都包含基本数据。这一集合将被保存下来，这样我们就可以对下一年中总资产的年度变化进行分析。
+</p>
+<div class="section-example-container">
+<pre class="python">
+def CoarseSelectionFunction(self, coarse):
+        if self.yearly_rebalance:
+            filtered_coarse = [x.Symbol for x in coarse if (x.HasFundamentalData)
+                                                            and (x.Market == "usa")]
+            return filtered_coarse
+        else: 
+            return []
+
+def FineSelectionFunction(self, fine):
+    if self.yearly_rebalance:
+        fine = [x for x in fine if x.FinancialStatements.BalanceSheet.TotalAssets.Value > 0
+                and ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))
+                and (x.CompanyReference.IndustryTemplateCode!="B")
+                and (x.CompanyReference.IndustryTemplateCode!="I")]
+        if not self.previous_fine:
+            self.previous_fine = fine
+            self.yearly_rebalance = False
+            return []
+        else:
+            self.filtered_fine = self.Calculate(fine,self.previous_fine)
+            sorted_filter = sorted(self.filtered_fine, key=lambda x: x.delta_assets)
+            self.filtered_fine = [i.Symbol for i in sorted_filter]
+            self.previous_fine = fine
+            return self.filtered_fine
+    else:
+        return []
+</pre>
+</div>
+<p>
+  在精细集合选择中，我们从上一年和当年的股票中计算出它们的总资产增长。然后根据采用的计算按照升序对股票进行排序。要注意的是，我们通过去年总资产的增长比例来计算样本公司之间的规模差异。
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Calculate(self, current, previous):
+        growth = []
+        for stock_data in current:
+            try:
+                prev_data = None
+                for x in previous:
+                    if x.Symbol == stock_data.Symbol:
+                        prev_data = x
+                        break
+                stock_data.delta_assets = (float(stock_data.FinancialStatements.BalanceSheet.TotalAssets.Value)-float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value))/float(prev_data.FinancialStatements.BalanceSheet.TotalAssets.Value) 
+                growth.append(stock_data)
+            except:
+                pass
+        return growth
+</pre>
+</div>
+<p>
+   在<code>OnData()</code>中，我们做空排序列表中前百分之十的股票，做多后百分之十的股票。投资组合每年会在6月底重新调整。
+</p>
diff --git "a/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html"
new file mode 100644
index 0000000..04cf1c1
--- /dev/null
+++ "b/04 Strategy Library/39 Asset Growth Effect/03 \347\256\227\346\263\225.cn.html"	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_813285e2c02ffca1afa114e668301ea8.html"></iframe>
+    </div>
+</div>
diff --git "a/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html" "b/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html"
new file mode 100644
index 0000000..a9d5834
--- /dev/null
+++ "b/04 Strategy Library/39 Asset Growth Effect/04 \346\235\245\346\272\220.cn.html"	
@@ -0,0 +1,5 @@
+<ul>
+  <li>
+    <a href="https://quantpedia.com/Screener/Details/52"  rel="nofollow">Quantpedia - Asset Growth Effect</a>
+</li>
+</ul>
\ No newline at end of file

From 9af6160d72f423673858345735b2e212bb7c28a5 Mon Sep 17 00:00:00 2001
From: Kamuela Franco <kamuela.franco@gmail.com>
Date: Wed, 2 Oct 2019 00:09:50 +0100
Subject: [PATCH 630/753] Fix typographical error

---
 .../02 Introduction to Options/01 What Will I learn %3F.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html
index 2e6c07b..f05701e 100644
--- a/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html	
+++ b/02 Tutorial Series/02 Introduction to Options/01 What Will I learn %3F.html	
@@ -1,7 +1,7 @@
 <div class="row tutorials-learn">
 <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>General Features of Options</div>
 <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>QuantConnect Options API</div>
-<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Options Pricing: Black-Sholes-Merton Model</div>
+<div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Options Pricing: Black-Scholes-Merton Model</div>
 <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Stochastic Process</div>
 <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>Monte Carlo Method</div>
 <div class="col-xs-12 col-sm-6"><i class="fa fa-check"></i>The Greek Letters</div>

From 5b30b123ecf13e09e05e6be8ac1b5a2f7a9db2a7 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 8 Oct 2019 15:04:29 -0700
Subject: [PATCH 631/753] Update 05 Planning Your Lesson.html

---
 .../05 Planning Your Lesson.html                          | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 672b9be..6174895 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -63,7 +63,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Consolidators</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -72,7 +72,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">Price Volume Filtering</span>
 <span class="badge">Equities</span>
 </td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -81,7 +81,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">SymbolData Pattern</span>
 <span class="badge">History</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p>Assigned</p></td>
+<td class="status"><p>Completed</p></td>
 </tr>
 
 <tr>
@@ -90,7 +90,7 @@ <h4>2. Strategy Implementation</h4>
 <span class="badge">STD Indicator</span>
 <span class="badge">Parameter Minimization</span>
 <span class="badge">Equities</span></td>
-<td class="status"><p>Assigned</p>
+<td class="status"><p>Completed</p>
 </td>
 </tr>
 </tbody>

From 9e1c8a80dc1cc064b037d0abdeae5864f4098b63 Mon Sep 17 00:00:00 2001
From: Sherry Yang <sherry.yang33@gmail.com>
Date: Tue, 12 Nov 2019 09:11:03 -0800
Subject: [PATCH 632/753] Update 05 Planning Your Lesson.html

Fixed some typos.
---
 .../05 Planning Your Lesson.html                            | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html
index 6174895..67986a8 100644
--- a/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
+++ b/08 Meta/01 Creating BootCamp Tutorials/05 Planning Your Lesson.html	
@@ -106,7 +106,7 @@ <h4>2. Strategy Implementation</h4>
 <tbody>
 
 <tr>
-<td><p>Separation of Concerns with the Algorithm Framework<br/> <small style="font-size: 0.9em">A simple strategy to buy SPY each morning on market open using the algorithm framework - a scaffolding for powerful strategy design.</small></p>
+<td><p>The Algorithm Framework<br/> <small style="font-size: 0.9em">A simple strategy to buy SPY each morning on market open using the algorithm framework - a scaffolding for powerful strategy design.</small></p>
 <span class="badge">Algorithm Framework</span>
 <span class="badge">Execution Model</span>
 <span class="badge">Portfolio Model</span>
@@ -126,7 +126,7 @@ <h4>2. Strategy Implementation</h4>
 </tr>
 
 <tr>
-<td><p>Pairs Trading with Cointegration Test<br/> <small style="font-size: 0.9em">Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test, and </small></p>
+<td><p>Pairs Trading with Cointegration Test<br/> <small style="font-size: 0.9em">Scanning a basket of assets monthly for potential cointegration and making a pairs trade when detect a divergent pair. Using scheduled events for the cointegration test.</small></p>
 <span class="badge">Pairs Trading</span>
 <span class="badge">Cointegration Test</span>
 <span class="badge">Scheduled Events</span>
@@ -163,7 +163,7 @@ <h4>2. Strategy Implementation</h4>
 <td class="status"><p><a href="/contact?subject=bootcamp-trading-economics">Available</a></p>
 </tr>
 <tr>
-<td><p>Sentiment Analysis on Stocks<br/> <small style="font-size: 0.9em">Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most postive sentiment.</small></p>
+<td><p>Sentiment Analysis on Stocks<br/> <small style="font-size: 0.9em">Harness Psychsignal data to rank the sentiment of a basket of US Equity stocks and invest in those with the most positive sentiment.</small></p>
 <span class="badge">Psychsignal</span>
 <span class="badge">NLP</span>
 <span class="badge">Alternative Data</span>

From 2f00ffcbf4662b62cd27ffe61ce83134c94f8e4c Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Sat, 23 Nov 2019 01:02:24 +0000
Subject: [PATCH 633/753] Refactors Strategy 01

---
 .../03 Method.html                            | 114 ++++++++----------
 .../05 Algorithm.html                         |   7 +-
 2 files changed, 54 insertions(+), 67 deletions(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index b6d9f96..3675eee 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -1,7 +1,6 @@
 <p>
 	Our investment logic is simple and straightforward. We assume that stocks which beat the market last month will continue to beat the market. We rank stocks according to their alpha, and each month we "long" the top two stocks. For this strategy to work, we need to do the following at the start of each month:
 </p>
-
 <ul>
  	<li>Get the historical price of Dow 30 stocks in the past 21 trading days and calculate their daily rates of return.</li>
  	<li>Conduct simple linear regression on the return of each stock against a benchmark (S&amp;P 500 index, SPY).</li>
@@ -11,85 +10,76 @@
 <p>
 	Dow Jones components change very infrequently, with the last change being on March 19th, 2015. To make the implementation easier we have simply listed the current Dow components in this algorithm. This means that the earliest start date of this algorithm is March 19th, 2015.
 </p>
-
 <h3>Step 1: Setup Event Handler</h3>
 <p>
 	In the initialize method we define a Scheduled Event to trigger a monthly re-balancing of the portfolio. For more details about how to use Scheduled Events, you can read the <a href="https://www.quantconnect.com/docs#Scheduled-Events">Documentation</a> or see the example <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/ScheduledEventsAlgorithm.py">ScheduledEventsAlgorithm</a>.
 </p>
 <div class="section-example-container">
-
 <pre class="python">def Initialize(self):
-    self.Schedule.On(self.DateRules.MonthStart(self.benchmark), self.TimeRules.AfterMarketOpen(self.benchmark), Action(self.rebalance))
-	</pre>
-	</div>
-
-<h3>Step 2: Linear Regression Function</h3>
+    self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]),
+                     self.TimeRules.AfterMarketOpen(self.symbols[0]),
+                     self.Rebalance)</pre>
+</div>
+<h3>Step 2: History Function</h3>
 <p>
-	In order to conduct linear regression, we need to write a function to take the price data and output the regression results. The function takes a list of the "asset prices" (x) and a list of the "benchmark prices" (y). It then calculates the percentage change and conducts a linear regression. The output is a tuple which contains the intercept and slope.
+	Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/docs/algorithm-reference/historical-data">History</a> API. The data is returned from the API as a pandas.DataFrame indexed by <em>Symbol</em> objects. The close data is selected and the data frame is unstack to create columns of <em>Symbol</em> objects.
 </p>
-
 <div class="section-example-container">
-
-<pre class="python">def regression(self,x,y):
-    x = np.array(x)
-    x = np.diff(x)/x[:-1]
-    y = np.array(y)
-    y = np.diff(y)/y[:-1]
-    A = np.vstack([x, np.ones(len(x))]).T
-    result = np.linalg.lstsq(A, y)[0]
-    beta = result[0]
-    alpha = result[1]
-    return(alpha,beta)
-</pre>
+<pre class="python"># Fetch the historical data to perform the linear regression
+history = self.History(
+    self.symbols + [self.benchmark], 
+    self.lookback,
+    Resolution.Daily).close.unstack(level=0)</pre>
 </div>
-<h3>Step 3: History Function</h3>
+<h3>Step 3: Symbol Selection Function</h3>
 <p>
-	Each month we get the historical prices of the DOW30 components using the <a href="https://www.quantconnect.com/lean/docs#topic182.html">History</a> API. The data is returned from the API as complex <em>Slice</em> objects. To make this useful in the algorithm we extract the asset prices, and benchmark prices to a list.
+	We aim to trade the two assets with the highest alpha to the benchmark. In order to conduct linear regression to find the alpha (linear regression intercept), we need to compute returns (percentage change of closing price) benchmark and the asset then conduct a linear regression.
 </p>
 <div class="section-example-container">
+<pre class="python">def SelectSymbols(self, history):
+    '''Select symbols with the highest intercept/alpha to the benchmark
+    '''
+    alphas = dict()
 
-<pre class="python">def get_regression_data(self,symbol,history):
-    symbol_price = []
-    benchmark_price = []
-    for i in history:
-        bar = i[symbol]
-        benchmark = i[self.benchmark]
-        symbol_price.append(bar.Close)
-        benchmark_price.append(benchmark.Close)
+    # Get the benchmark returns
+    benchmark = history[self.benchmark].pct_change().dropna()
 
-    result = self.regression(symbol_price,benchmark_price)
-    return result
-</pre>
+    # Conducts linear regression for each symbol and save the intercept/alpha
+    for symbol in self.symbols:
+
+        # Get the security returns
+        returns = history[symbol].pct_change().dropna()
+        returns = np.vstack([returns, np.ones(len(returns))]).T
+
+        # Simple linear regression function in Numpy
+        result = np.linalg.lstsq(returns, benchmark)
+        alphas[symbol] = result[0][1]
+
+    # Select symbols with the highest intercept/alpha to the benchmark
+    selected = sorted(alphas.items(), key=lambda x: x[1], reverse=True)[:2]
+    return [x[0] for x in selected]</pre>
 </div>
 <h3>Step 4: Rebalance Function:</h3>
 <p>
-	This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The second argument of SetHoldings is a decimal, setting this to "1" tells the algorithm to set the portfolio as "long 100%" with no leverage. More information on the function can be read on this link: <a href="https://www.quantconnect.com/docs#Trading-and-Orders">SetHoldings</a>.
+	This function is where all the action happens, it will be executed on the first trading day of each month as a scheduled event. The algorithm closes all positions of securities that were not selected using <a href="https://www.quantconnect.com/docs/algorithm-reference/trading-and-orders#Trading-and-Orders-Liquidating-Portfolio">Liquidate</a> and go 100% long for both of the selected symbols using <a href="https://www.quantconnect.com/docs/algorithm-reference/trading-and-orders#Trading-and-Orders-Automatic-Position-Sizing-SetHoldings">SetHoldings</a>.
 </p>
-
 <div class="section-example-container">
+<pre class="python">def Rebalance(self):
+
+    # Fetch the historical data to perform the linear regression
+    history = self.History(
+        self.symbols + [self.benchmark], 
+        self.lookback,
+        Resolution.Daily).close.unstack(level=0)
+
+    symbols = self.SelectSymbols(history)
+
+    # Liquidate positions that are not held by selected symbols
+    for holdings in self.Portfolio.Values:
+        symbol = holdings.Symbol
+        if symbol not in symbols and holdings.Invested:
+            self.Liquidate(symbol)
 
-<pre class="python">def rebalance(self):
-    # get historical stock symbols and prices, then put them in tuples
-    history = self.History(self.regression_dates, Resolution.Daily)
-    filter = []
-    for i in self.symbols:
-        filter.append((i,self.get_regression_data(i, history)[0]))
-    # sort the filter by alpha
-    filter.sort(key = lambda x : x[1],reverse = True)
-    sorted_symbols = []
-    for i in range(2):
-    	sorted_symbols.append(filter[i][0])
-    # get the symbols of our holding stocks
-    holding_list = []
-    for i in self.Portfolio:
-    	if i.Value.Invested:
-    		holding_list.append(i.Value.Symbol)
-    # if we have holdings and we are not going to hold them anymore, sell them
-    if holding_list:
-    	for i in holding_list:
-    		if i not in sorted_symbols:
-    			self.Liquidate(i)
-    # Long the 2 stock in our list.
-    for i in sorted_symbols:
-    	self.SetHoldings(i,1)
-		</pre>
+    # Invest 100% in the each of the selected symbols
+    for symbol in symbols:
+        self.SetHoldings(symbol, 1)</pre>
\ No newline at end of file
diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index 3d63cc8..f47a119 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1,9 +1,6 @@
-<p>
-	Backtest using OptionChainProvider
-</p>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_2f525825fa6fb72ee4b23b9e63ecf025.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7794e68c615e9be3c7144821923fcd7a.html"></iframe>
     </div>
-</div>
+</div>
\ No newline at end of file

From ead032b94e57fc7c5c6cc0de7086d0800d8b6076 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Mon, 25 Nov 2019 15:43:58 -0800
Subject: [PATCH 634/753] Fix bug in the strategy listing.

Missing comma to separate two elements.
---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index b3ff4a6..5909ba0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -578,7 +578,7 @@
         'description' => "A momentum strategy based on quarterly returns and earnings growth",
         'tags'=>'Momentum, Stocks, Universe Selection, Historical Data, Rolling Window'
 
-    ]
+    ],
 	[
 		'name' => 'Improved Momentum Strategy on Commodities Futures',
 		'link' => 'strategy-library/improved-momentum-strategy-on-commodities-futures',

From 9e319ea54ff013ae6d365f3d6b5eef65c178f3ba Mon Sep 17 00:00:00 2001
From: marii <mariiapopovych@gmail.com>
Date: Tue, 21 Jan 2020 17:21:31 -0800
Subject: [PATCH 635/753] initial commit

---
 00 Home/01 Home/01 Home.html | 170 ++++++++++++++++++++++-------------
 1 file changed, 110 insertions(+), 60 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index 170ca8b..a5161a8 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -7,72 +7,122 @@ <h2 style="margin-top: 8px;"><span style="font-size: 22px;">Learn to use QuantCo
 		</h2>
 	</div>
 </div>
-<div>
-	<div class="home-buttons-backdrop"></div>
-	<div class="row home-buttons" style="padding-top: 18px;"><a class="docs-internal-link" href="/tutorials/api-tutorials/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/api_tutorials_2x.png">
-            API Tutorials
-        </a><a class="docs-internal-link" href="/tutorials/tutorial-series/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/tutorial_series_2x.png">
-            Tutorial Series
-        </a>
+
+
+<div class="row" >
+	<div class="column">
+		<div class="card">
+			<div class="image">
+				<img src="http://cdn.quantconnect.com/i/tu/api tutorials@2x.png" width="60px">
+			</div>
+			<div class="content">
+				<a class="" href="/tutorials/api-tutorials/introduction">
+					<h4 style="margin-bottom: 10px;">
+						API Tutorials
+					</h4>
+				</a>
+				Learn how to build algorithms in QuantConnect
+			</div>
+		</div>
 	</div>
-	<div class="row home-buttons" style="padding-bottom: 18px;"><a class="docs-internal-link" href="/tutorials/open-source/introduction"><img src="https://cdn.quantconnect.com/tutorials/i/open_source_2x.png">
-            Open Source
-        </a><a class="docs-internal-link" href="/tutorials/strategy-library/strategy-library"><img src="https://cdn.quantconnect.com/tutorials/i/strategy_library_2x.png">
-            Strategy Library
-        </a>
+	<div class="column" >
+		<div class="card">
+			<div class="image">
+				<img src="http://cdn.quantconnect.com/i/tu/tutorial series@2x.png" width="65px">
+			</div>
+			<div class="content">
+				<a class="" href="/tutorials/tutorial-series/introduction">
+					<h4>
+						Tutorial Series
+					</h4>
+				</a>
+				Step by step introductions to the basics of python and finance
+			</div>
+		</div>
+	</div>
+</div>
+<div class="row" >
+	<div class="column">
+		<div class="card">
+			<div class="image">
+				<img src="http://cdn.quantconnect.com/i/tu/open source@2x.png" width="60px">
+			</div>
+			<div class="content">
+				<a class="" href="/tutorials/open-source/introduction">
+					<h4>
+						Open Source
+					</h4>
+				</a>
+				Deploy and harness the LEAN algorithmic trading engine
+			</div>
+		</div>
+	</div>
+	<div class="column" >
+		<div class="card">
+			<div class="image">
+				<img src="http://cdn.quantconnect.com/i/tu/strategy library@2x.png" width="60px">
+			</div>
+			<div class="content">
+				<a class="" href="/tutorials/strategy-library/strategy-library">
+					<h4>
+						Strategy Library
+					</h4>
+				</a>
+				Hundreds of academic papers implemented as LEAN trading strategies
+			</div>
+		</div>
 	</div>
 </div>
-<style>
-    .page-sub-heading,
-    .article-body h1.automated {
-        display: none;
-    }
 
-    .home-buttons-backdrop {
-        height: 570px;
-        background: url('https://cdn.quantconnect.com/web/i/docs/docs_background_rev0.png');
-        position: absolute;
-        left: -55px;
-        min-width: 910px;
-        width: calc(100vw - 317px);
-        z-index: -1;
-    }
 
-    .home-buttons > a {
-        width: 233px;
-        display: inline-block;
-        text-align: center;
-        background-color: #fff;
-        border-radius: 5px;
-        height: 233px;
-        margin: 17px;
-        font-family: Norpeth-DemiBold, Helvetica, Arial, sans-serif;
-        color: #26262a;
-        font-size: 20px;
-        float: left;
-    }
+<style>
+	* {
+		box-sizing: border-box;
+	}
+	.column {
+		float: left;
+		width: 50%;
+		padding: 10px;
+	}
+	.row:after {
+		content: "";
+		display: table;
+		clear: both;
+	}
+	.card {
+		background: #fff;
+		display: table;
+	}
+	.image {
+		width: 120px;
+		display:table-cell;
+		vertical-align:middle;
+		text-align:center;
+	}
+	.content {
+		width: 80%;
+		overflow: hidden;
+		padding: 5px;
+		color: #8f9ca3;
+		display:table-cell;
+		vertical-align:middle;
+		padding-left: 25px;
+		line-height: 1.8rem;
+	}
+	.content h4 {
+		margin: 2px 0;
+		margin-bottom: 5px;
+		color: #0072bc;
+		font-size: 18px;
+		line-height: 1.4rem;
+	}
 
-    .home-buttons > a:focus,
-    .home-buttons > a:active,
-    .home-buttons > a:hover {
-        text-decoration: none;
-        color: #93a5b6;
-    }
+	.documentation #doc-content h2.page-sub-heading {
+		display:none !important;
+	}
 
-    .home-buttons > a:last-child {
-        margin-right: 0;
-    }
+	h1.automated {
+		display: none;
+	}
 
-    .home-buttons > a > img {
-        display: table-row;
-        height: 80px;
-        margin: 50px auto 36px auto;
-    }
-    
-    .documentation #doc-content .pre-title-area > span {
-        display: none;
-    }
-    .documentation #doc-content .pre-title-area > span:first-child{
-        display: inline;
-    }
 </style>

From 248eb8f2d3f6fb918186a7e7768d830e036c87c2 Mon Sep 17 00:00:00 2001
From: marii <mariiapopovych@gmail.com>
Date: Tue, 21 Jan 2020 23:54:27 -0800
Subject: [PATCH 636/753] fix floating issue

---
 00 Home/01 Home/01 Home.html | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index a5161a8..df7c9f0 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -1,5 +1,5 @@
-<div style="border-bottom: solid 1px  #e4e6ea;">
-	<div style="float: left; padding-right: 30px"><img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
+<div style="border-bottom: solid 1px  #e4e6ea; display: table;">
+	<div style="display: table-cell; vertical-align:middle; padding-right: 30px"><img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
 	</div>
 	<div>
 		<h1>Tutorials</h1>
@@ -116,13 +116,9 @@ <h4>
 		font-size: 18px;
 		line-height: 1.4rem;
 	}
-
-	.documentation #doc-content h2.page-sub-heading {
-		display:none !important;
+	.documentation #doc-content .article-body {
+	 flex: 0 10000px;
 	}
 
-	h1.automated {
-		display: none;
-	}
 
-</style>
+</style>
\ No newline at end of file

From 9ef74f26206826486122d3f170d971e4d5f27716 Mon Sep 17 00:00:00 2001
From: marii <mariiapopovych@gmail.com>
Date: Tue, 21 Jan 2020 23:58:53 -0800
Subject: [PATCH 637/753] fix floating issue

---
 00 Home/01 Home/01 Home.html | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index df7c9f0..753cefd 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -120,5 +120,13 @@ <h4>
 	 flex: 0 10000px;
 	}
 
+	.documentation #doc-content h2.page-sub-heading {
+		display:none !important;
+	}
+
+	h1.automated {
+		display: none;
+	}
+
 
 </style>
\ No newline at end of file

From f2125016c2b2c29c730a7d9063c6363db4e0d5ea Mon Sep 17 00:00:00 2001
From: marii <mariiapopovych@gmail.com>
Date: Wed, 22 Jan 2020 13:42:11 -0800
Subject: [PATCH 638/753] Change some fonts and change container width

---
 00 Home/01 Home/01 Home.html | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index 753cefd..d28f89b 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -1,4 +1,4 @@
-<div style="border-bottom: solid 1px  #e4e6ea; display: table;">
+<div style="border-bottom: solid 1px  #e4e6ea; display: table; width: 100%;">
 	<div style="display: table-cell; vertical-align:middle; padding-right: 30px"><img src="https://cdn.quantconnect.com/web/i/docs/docs_home_icon.rev0.png"/>
 	</div>
 	<div>
@@ -9,7 +9,7 @@ <h2 style="margin-top: 8px;"><span style="font-size: 22px;">Learn to use QuantCo
 </div>
 
 
-<div class="row" >
+<div class="row" style="margin-top: 50px;">
 	<div class="column">
 		<div class="card">
 			<div class="image">
@@ -108,6 +108,7 @@ <h4>
 		vertical-align:middle;
 		padding-left: 25px;
 		line-height: 1.8rem;
+		font-weight: bold;
 	}
 	.content h4 {
 		margin: 2px 0;
@@ -116,9 +117,7 @@ <h4>
 		font-size: 18px;
 		line-height: 1.4rem;
 	}
-	.documentation #doc-content .article-body {
-	 flex: 0 10000px;
-	}
+
 
 	.documentation #doc-content h2.page-sub-heading {
 		display:none !important;

From 8a210cb193d36625ed25f19c4ca0adad37b55ddc Mon Sep 17 00:00:00 2001
From: Mariia <mariiapopovych@gmail.com>
Date: Sun, 26 Jan 2020 12:23:27 -0800
Subject: [PATCH 639/753] Add margin && made links secure

---
 00 Home/01 Home/01 Home.html | 23 +++++++++--------------
 1 file changed, 9 insertions(+), 14 deletions(-)

diff --git a/00 Home/01 Home/01 Home.html b/00 Home/01 Home/01 Home.html
index d28f89b..ff41ca3 100644
--- a/00 Home/01 Home/01 Home.html	
+++ b/00 Home/01 Home/01 Home.html	
@@ -9,11 +9,11 @@ <h2 style="margin-top: 8px;"><span style="font-size: 22px;">Learn to use QuantCo
 </div>
 
 
-<div class="row" style="margin-top: 50px;">
-	<div class="column">
+<div class="row" style="margin-top: 50px; margin-bottom: 10px;">
+	<div class="col-md-6 col-sm-12">
 		<div class="card">
 			<div class="image">
-				<img src="http://cdn.quantconnect.com/i/tu/api tutorials@2x.png" width="60px">
+				<img src="https://cdn.quantconnect.com/i/tu/api tutorials@2x.png" width="60px">
 			</div>
 			<div class="content">
 				<a class="" href="/tutorials/api-tutorials/introduction">
@@ -25,10 +25,10 @@ <h4 style="margin-bottom: 10px;">
 			</div>
 		</div>
 	</div>
-	<div class="column" >
+	<div class="col-md-6 col-sm-12">
 		<div class="card">
 			<div class="image">
-				<img src="http://cdn.quantconnect.com/i/tu/tutorial series@2x.png" width="65px">
+				<img src="https://cdn.quantconnect.com/i/tu/tutorial series@2x.png" width="65px">
 			</div>
 			<div class="content">
 				<a class="" href="/tutorials/tutorial-series/introduction">
@@ -42,10 +42,10 @@ <h4>
 	</div>
 </div>
 <div class="row" >
-	<div class="column">
+	<div class="col-md-6 col-sm-12">
 		<div class="card">
 			<div class="image">
-				<img src="http://cdn.quantconnect.com/i/tu/open source@2x.png" width="60px">
+				<img src="https://cdn.quantconnect.com/i/tu/open source@2x.png" width="60px">
 			</div>
 			<div class="content">
 				<a class="" href="/tutorials/open-source/introduction">
@@ -57,10 +57,10 @@ <h4>
 			</div>
 		</div>
 	</div>
-	<div class="column" >
+	<div class="col-md-6 col-sm-12">
 		<div class="card">
 			<div class="image">
-				<img src="http://cdn.quantconnect.com/i/tu/strategy library@2x.png" width="60px">
+				<img src="https://cdn.quantconnect.com/i/tu/strategy library@2x.png" width="60px">
 			</div>
 			<div class="content">
 				<a class="" href="/tutorials/strategy-library/strategy-library">
@@ -79,11 +79,6 @@ <h4>
 	* {
 		box-sizing: border-box;
 	}
-	.column {
-		float: left;
-		width: 50%;
-		padding: 10px;
-	}
 	.row:after {
 		content: "";
 		display: table;

From 74ecdf2f441c6f54d40c1a34de11a793a1a512af Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Tue, 28 Jan 2020 08:55:00 -0800
Subject: [PATCH 640/753] Update 02 Rate of Return.html

---
 .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 9204847..5ae0cc5 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -75,7 +75,7 @@ <h4>Logarithm Return</h4>
 aapl = aapl_table.loc['2017-3',['Open','Close']]
 #take log return
 aapl['log_price'] = np.log(aapl.Close)
-aapl['log_return'] = np.log_price.diff()
+aapl['log_return'] = aapl['log_price'].diff()
 print aapl
 </pre>
 </div>

From 0f739251155332300e5dc0af0bb730b97f3cd6d6 Mon Sep 17 00:00:00 2001
From: Gerardo Salazar <gsalaz9800@gmail.com>
Date: Mon, 10 Feb 2020 17:43:26 -0800
Subject: [PATCH 641/753] Updates Lean Report Creator tutorial to use new
 Report creator

---
 03 Open Source/04 Lean Report Creator/00.html |  2 +-
 .../01 Introduction.html                      | 12 +++--
 .../02 Creating a Report.html                 | 28 +++++++---
 .../03 Source Backtest.html                   | 31 +++++------
 .../04 Customizing Report Contents.html       | 28 +++++++---
 .../05 Interpreting Report Charts.html        | 54 ++++++-------------
 6 files changed, 78 insertions(+), 77 deletions(-)

diff --git a/03 Open Source/04 Lean Report Creator/00.html b/03 Open Source/04 Lean Report Creator/00.html
index f5722bc..02cabcd 100644
--- a/03 Open Source/04 Lean Report Creator/00.html	
+++ b/03 Open Source/04 Lean Report Creator/00.html	
@@ -1 +1 @@
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/20180615-drawdown-1024x543.png" />
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-example-rev0.png" />
\ No newline at end of file
diff --git a/03 Open Source/04 Lean Report Creator/01 Introduction.html b/03 Open Source/04 Lean Report Creator/01 Introduction.html
index 74cffbc..f5f8f98 100644
--- a/03 Open Source/04 Lean Report Creator/01 Introduction.html	
+++ b/03 Open Source/04 Lean Report Creator/01 Introduction.html	
@@ -1,8 +1,14 @@
 <p>
-The LEAN Report Creator is a report generated from backtesting-result objects and allows you to quickly create polished, professional-grade reports for each backtest (see a full <a href="https://cdn.quantconnect.com/blog/20180615-backtest-report.html">example report</a> generated by LRC). Our hope is that you can use these reports to share your strategy performance with prospective investors.
+The LEAN Report Creator (LRC) is a report generated from backtesting-result objects and allows you to quickly create polished, professional-grade reports for each backtest (see a full <a href="https://cdn.quantconnect.com/blog/tutorial-lrc-report-rev1.html">example report</a> generated by LRC). We hope that you can use these reports to share your strategy performance with prospective investors. 
+The report was generated using the examples provided in this tutorial.
 </p>
 <p>
-Users working through the QuantConnect.com IDE can have these reports generated automatically at the end of a backtest. To generate one, look at the “Report” tab below your backtest result charts.
+The algorithm used to generate the example report is provided below.
 </p>
-<img src="https://cdn.quantconnect.com/tutorials/i/20180615-terminal-report-1024x380.png" class="img-responsive">
+<iframe style="border: solid 1px #ebecee; width: 100%; height: 330px" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_1f3702a2d8d7e3abc9a40eeecaf49afd.html"></iframe>
+
+<p>
+Users working through the QuantConnect.com IDE can have these reports generated automatically at the end of a backtest. To generate one, look at the “Report” tab below your backtest result charts and click "Request Report".
+</p>
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-terminal-report-rev0.png" class="img-responsive">
 
diff --git a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html
index d34f496..a2ea27a 100644
--- a/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
+++ b/03 Open Source/04 Lean Report Creator/02 Creating a Report.html	
@@ -1,13 +1,14 @@
 <p>
-The LEAN Report Creator is a python script located in the <a href="https://github.com/QuantConnect/Lean/tree/master/PythonToolbox">PythonToolbox</a>. It is a command line tool which takes the json backtest result object as its input. You can run it like this:
+The Lean Report Creator is a project located in the <a href="https://github.com/QuantConnect/Lean/tree/master/Report">Report folder</a>. It is a command line tool that takes the JSON backtest result object as its input. 
+You can run it using command line arguments, or by filling in the <code>config.json</code> file.
 </p>
 <div class="section-example-container">
 <pre class="all">
-python CreateLeanReport.py--backtest=./sample.json --output=./report.html --user=data.json
+./QuantConnect.Report.exe --backtest-data-source-file sample.json --report-destination Example.html
 </pre>
 </div>
 <p>
-The program has three key options you should configure to your requirements:
+The program has a few options you should configure to your requirements:
 </p>
 <style> table.text-left > tbody > tr > td { text-align: left; } </style>
 <table class="table table-striped qc-table text-left">
@@ -19,16 +20,27 @@
 </thead>
 <tbody>
 <tr>
-<td><code>--backtest</code></td>
-<td>Location of the source backtest json file.</td>
+<td><code>--backtest-data-source-file</code></td>
+<td>Location of the source backtest JSON file.</td> </tr>
+<tr>
+<td><code>--live-data-source-file</code></td>
+<td>Location of the source live JSON file.</td>
 </tr>
 <tr>
-<td><code>--output</code></td>
+<td><code>--report-destination</code></td>
 <td>Resulting output HTML file location.</td>
 </tr>
 <tr>
-<td><code>--user</code></td>
-<td>Json data object for the user data of the report (see <a href="https://github.com/QuantConnect/Lean/blob/master/PythonToolbox/user_data.json" target="_BLANK">example</a>).</td>
+<td><code>--strategy-name</code></td>
+<td>Name of the strategy. This will appear at the top-right corner of each page in the report.</td>
+</tr>
+<tr>
+<td><code>--strategy-version</code></td>
+<td>Version number of the strategy. This will appear at the top-right corner next to the strategy name.</td>
+</tr>
+<tr>
+<td><code>--strategy-description</code></td>
+<td>Description that describes what the strategy does. This will appear under the "Strategy Description" section of the report.</td>
 </tr>
 </tbody>
 </table>
diff --git a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html
index 8a4bc3a..f7fa836 100644
--- a/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
+++ b/03 Open Source/04 Lean Report Creator/03 Source Backtest.html	
@@ -1,32 +1,21 @@
 <p>
-The <code>--backtest</code> switch refers to a JSON file created by the LEAN Engine. This holds all of the backtest data which is required for generating the report html. This data can be sourced for desktop or cloud backtests.
+The <code>--backtest-data-source-file</code> switch refers to a JSON file created by the LEAN Engine. This holds all of the backtest data which is required for generating the report HTML. This data can be sourced from desktop or cloud backtests.
 </p>
 
 <h4>Desktop Backtesting Results</h4>
 <p>
-By default LEAN saves the backtest result data to disk inside of your LEAN launcher folder: <code>Lean/Launcher/bin/Debug/BasicTemplateAlgorithm.json</code>. It is saved there by the <a href="https://github.com/QuantConnect/Lean/blob/master/Engine/Results/BaseResultsHandler.cs#L35">BaseResultHandler.SaveResult</a> method. You can access it like this from the Python Toolbox:
+By default, LEAN saves the backtest result data to disk inside of your LEAN launcher folder: <code>Lean/Launcher/bin/Debug/BasicTemplateAlgorithm.json</code>. You can access it like this from the Report executable:
 </p>
 <div class="section-example-container">
-<pre class="all">python CreateLeanReport.py --backtest=../Launcher/bin/Debug/BasicTemplateAlgorithm.json ....</pre>
+<pre class="all">./QuantConnect.Report.exe --backtest-data-source-file ../../../Launcher/bin/Debug/BasicTemplateAlgorithm.json ...</pre>
 </div>
 
 <h4>Cloud Backtesting Results</h4>
 <p>
-Backtest processed reports can also be downloaded via API. The following example imports the QuantConnect API class; and uses it to download the final HTML report. To access the API you will need to know your API User Id and Token, which can be located on your <a href="/account">Account</a> page.
+Backtest processed reports can be downloaded via API. The following example imports the QuantConnect API class and uses it to download the final HTML report. To access the API you will need to know your API User ID and Token, which can be obtained in the <a href="/account">Account</a> page.
 </p>
 
-<em>Option 1: Easy: Downloading Cloud Generated Reports:</em>
-<div class="section-example-container">
-<pre class="all">
->>> from IPython.core.display import display, HTML
->>> from quantconnect.api import Api
->>> api = Api(your-user-id, your-token)
->>> lean_report = api.read_backtest_report(project-id, backtest-id)
->>> display(HTML(lean_report['report']))
-</pre>
-</div>
-
-<em>Option 2: Harder: Downloading Cloud Generated Backtest Json Files:</em>
+<em>Downloading Cloud Generated Backtest JSON Files:</em>
 <div class="section-example-container">
 <pre class="all">
 >>> from quantconnect.api import Api
@@ -38,7 +27,13 @@ <h4>Cloud Backtesting Results</h4>
 </pre>
 </div>
 
+<em>Downloading Cloud Generated Backtest JSON Files (Manually):</em>
+<p>
+You can manually download the backtest result file and place the file in the location configured in the <code>config.json</code> file.
+</p>
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-download-results-rev0.png" class="img-responsive" />
+
 <p>
-You can update your report description by clicking on the Project Description button in your project panel. The report automatically uses your profile picture for the report image.
+You can update your report description by clicking on the Project Description button in your project panel.
 </p>
-<img src="https://cdn.quantconnect.com/tutorials/i/project-description.png" class="img-responsive" />
\ No newline at end of file
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-project-description-rev0.png" class="img-responsive" />
\ No newline at end of file
diff --git a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html
index c9a997f..7c86acc 100644
--- a/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html	
+++ b/03 Open Source/04 Lean Report Creator/04 Customizing Report Contents.html	
@@ -1,12 +1,24 @@
 <p>
-The report imports a profile image and description of the strategy. This is stored inside a JSON file <code>user_data.json</code>. You can customize the contents of this file to automatically generate future reports with the new data:
+The report can contain a description of the strategy. This is set either in the <code>config.json</code> file or via the command line argument <code>--strategy-description</code>.
+An example <code>config.json</code> configuration is provided below.
 </p>
 <div class="section-example-container">
 <pre class="all">{
-    "authorName": "Joe Blogs",
-    "authorPicture": "AuthorProfile.png",
-    "authorBiography": "Put your biography here.",
-    "projectName": " -- Page title / project name --",
-    "projectDescription": " -- Longer description text area -- "
-}</pre>
-</div>
\ No newline at end of file
+  "data-folder": "../../../Data",
+  "strategy-name": "Example Algo (FX + Equity) Strategy",
+  "strategy-version": "v1.2.0",
+  "strategy-description": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.",
+  "live-data-source-file": "",
+  "backtest-data-source-file": "example_algo_fx.json",
+  "report-destination": "Report.html",
+
+  ...
+}
+</pre>
+</div>
+
+<p>
+We recommend that you use the existing <a href="https://github.com/QuantConnect/Lean/blob/master/Report/config.example.json" target="_blank" rel="noopener noreferer">config.example.json</a> 
+and rename it to <code>config.json</code> once you have finished editing the file. Please rebuild the project on rename or if you make any changes to the configuration  
+so that the file is copied into the binary output path.
+</p>
\ No newline at end of file
diff --git a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html
index a5d8f47..e7a0510 100644
--- a/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html	
+++ b/03 Open Source/04 Lean Report Creator/05 Interpreting Report Charts.html	
@@ -1,63 +1,39 @@
 <h4>Cumulative Return</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/cumulative-return.png" class="img-responsive">
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-cumulative-return-rev0.png" class="img-responsive">
 <p>
-This chart shows the cumulative returns for your strategy in orange and the benchmark in gray. 
+This chart shows the cumulative returns for your strategy in blue for backtesting, orange for live trading, and the benchmark in gray.
 </p>
 
-<h4>Daily Return</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/daily-returns.png" class="img-responsive">
+<h4>Returns per Trade</h4>
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-returns-per-trade-rev0.png" class="img-responsive" />
 <p>
-This chart shows the daily returns for your strategy. When the return is positive, a orange bar will show above the horizontal line; when the return is negative, a gray bar will show below the horizontal line.
+This chart shows a histogram showing the distribution of returns per trade over the algorithm's backtesting or live trading period.
 </p>
 
 <h4>Top 5 Drawdown Periods</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/drawdowns.png" class="img-responsive">
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-drawdowns-rev0.png" class="img-responsive">
 <p>
-This chart shows the drawdown of each day. A certain day's drawdown is defined as the percentage of loss compared to the maximum value prior to this day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors.
+This chart shows the drawdown of each day. A certain day's drawdown is defined as the percentage of loss compared to the maximum value before this day. The drawdowns are calculated based on daily data. The top 5 drawdown periods are marked in the chart with different colors.
 </p>
 
 <h4>Monthly Returns</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/monthly-returns.png" class="img-responsive">
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-monthly-returns-rev0.png" class="img-responsive">
 <p>
-This chart shows the return of each month. We convert original price series into monthly series, and calculate the returns of each month. The green color indicates positive return, the red color indicates negative return, and the greater the loss is, the darker the color is; the yellow color means the gain or loss is rather small; the white color means the month is not included in the backtest period. The values in the cells are in percentage.
+This chart shows the return of each month. We convert original price series into monthly series and calculate the returns of each month. The green color indicates positive return, the red color indicates negative return, and the greater the loss is, the darker the color is; the yellow color means the gain or loss is rather small; the white color means the month is not included in the backtest period. The values in the cells are in percentage.
 </p>
 
-
 <h4>Annual Returns</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/annual-returns.png" class="img-responsive">
-<p>This chart shows the return of each year. We calculate the total return within each year, shown by the blue bars. The red dotted line represents the average of the annual returns. If the backtest covers less than 12 months of a certain year, then the value in the chart is the actual return which is not annualized.
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-annual-returns-rev0.png" class="img-responsive">
+<p>This chart shows the return of each year. We calculate the total return within each year, shown by the blue bars. The red dotted line represents the average of the annual returns.
 </p>
 
 <h4>Crisis Events</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/crisis-lehman-brothers.png" class="img-responsive">
-<p>This group of charts shows the behaviors of both your strategy and the benchmark during a certain historical period. We set the value of your strategy the same as the benchmark at the beginning of each crisis event, and the lines represent the cumulative returns of your strategy and benchmark from the beginning of this crisis event. The report only draws the crisis event charts whose time periods are covered by your strategy.
-</p>
-
-<h4>Rolling Portfolio Beta to Equity</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/rolling-portfolio-beta-to-equity.png" class="img-responsive">
-<p>
-This chart shows the rolling portfolio beta to the benchmark. This chart is drawn based on daily data. Every day, we calculate the beta of your portfolio to the benchmark over the past 6 months (gray line) or 12 months (blue line).</p> 
-<p>
-A beta close to 1 means the strategy has a risk exposure similar to the benchmark. A beta higher than 1 means the strategy has more risk than the benchmark, a beta close to 0 means the strategy is "market neutral", which isn't much affected by market situation. Beta can also be negative - in this situation the the strategy has opposite risk exposure to the benchmark. We won't draw this chart when your backtest period is less than 12 months.
-</p>
-
-<h4>Rolling Sharpe Ratio</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/rolling-sharpe-ratio%286-month%29.png" class="img-responsive">
-<p>This chart shows the rolling sharpe ratio of your strategy. The rolling sharpe ratio is calculated on daily data, and annualized. Every day, we calculate the sharpe ratio of your portfolio over the past 6 months, and connect the sharpe ratios into a line. The red dotted line represents the mean value of the total sharpe ratios. We won't draw this chart when your backtest period is less than 6 months.
-</p>
-
-<h4>Net Holdings</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/net-holdings.png" class="img-responsive">
-<p>This chart shows the net holdings of your portfolio. The net holding is the aggregated weight of risky assets in your portfolio. It could be either positive (when your total position is long), negative (when your total position is short) or 0 (when you only hold cash). The net holding changes only if new order is fired. The chart is drawn based on minute data, which means we aggregate all the positions of a minute together.
-</p>
-
-<h4>Leverage</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/leverage.png" class="img-responsive">
-<p>This chart shows the leverage of your portfolio. The value of the leverage is always non-negative. When you only hold cash, the leverage is 0; a leverage smaller than 1 means you either long assets with money less than your portfolio value or short assets with total value less than your portfolio value; a leverage larger than 1 means you either borrow money to buy assets or short assets whose value is larger than your portfolio value. The leverage changes only if new order is fired.
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-crisis-gfc-rev0.png" class="img-responsive">
+<p>This group of charts shows the behaviors of both your strategy and the benchmark during a certain historical period. We set the value of your strategy the same as the benchmark at the beginning of each crisis event, and the lines represent the cumulative returns of your strategy and benchmark from the beginning of this crisis event. The report only draws the crisis event charts whose periods are covered by your strategy.
 </p>
 
 <h4>Asset Allocations</h4>
-<img src="https://cdn.quantconnect.com/tutorials/i/asset-allocation-equity.png" class="img-responsive">
+<img src="https://cdn.quantconnect.com/i/tu/tutorial-lrc-asset-allocations-rev0.png" class="img-responsive">
 <p>
-The asset allocation charts show a time-weighted average of each class of asset to your portfolio. When a certain asset has very small percentage and is too small to be shown in the pie chart, it will be incorporated into an "Others" category. The value of the percentage could be either positive or negative. 
+The asset allocation charts show a time-weighted average of each asset to your portfolio. When a certain asset has a very small percentage and is too small to be shown in the pie chart, it will be incorporated into an "Others" category. The value of the percentage could be either positive or negative. 
 </p>

From 1f8ffff6cc34a4ce1ad1e6b4e48feaf3bc91d50e Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Sat, 22 Feb 2020 00:04:30 +0000
Subject: [PATCH 642/753] Refactors Small Capitalization Stocks Premium

Uses FineFundamental.MarketCap. Current version raises exception.
---
 .../02 Method.html                               | 16 ++++------------
 .../03 Algorithm.html                            |  2 +-
 quantpedia.json                                  |  2 +-
 3 files changed, 6 insertions(+), 14 deletions(-)

diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html
index 2894c75..98c8577 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/02 Method.html	
@@ -9,19 +9,11 @@
 </p>
 <div class="section-example-container">
 <pre class="python">def FineSelectionFunction(self, fine):
-    if self.year == self.Time.year:
-        return self.symbols
+    ''' Selects the stocks by lowest market cap '''
+    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0],
+        key=lambda x: x.MarketCap)
 
-    # Calculate the market cap and add the "MarketCap" property to fine universe object
-    for i in fine:
-        i.MarketCap = (i.EarningReports.BasicAverageShares.ThreeMonths * 
-                       i.EarningReports.BasicEPS.TwelveMonths *
-                       i.ValuationRatios.PERatio)
-
-    sorted_market_cap = sorted([x for x in fine if x.MarketCap > 0], key=lambda x: x.MarketCap)
-
-    self.symbols = [i.Symbol for i in sorted_market_cap[:10]]
-    return self.symbols
+    return [x.Symbol for x in sorted_market_cap[:self.count]]
 </pre>
 </div>
 <p>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
index f14f61e..cc3a665 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
@@ -1,6 +1,6 @@
 div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ed4394d8d45f7f2e6d7e807824508ef0.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ae6b2ce97f01f10af90c8823828ae5c9.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index e92eac3..0d3bd2a 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -16,7 +16,7 @@
      21: "203a6729604c80a71b5c3b2baa2b3f69",
      22: "d7285b5353d51cd5bd033f205e5faf44",
      23: "12a417567b5706fdff6cf6265506b98c",
-     25: "da3bf5ee608fb8b7dc952372dd925542",
+     25: "ae6b2ce97f01f10af90c8823828ae5c9",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
      38: "55081a6fa81c88fd75579c2f838a1180",
      41: "ac6336b5911ac0d4848bae6546ea1dde",

From d5f9742be6c3fb98570fc10678d807dc020c3ca8 Mon Sep 17 00:00:00 2001
From: AlexCatarino <alexandre.catarino@gmail.com>
Date: Sat, 22 Feb 2020 00:53:27 +0000
Subject: [PATCH 643/753] Refactors Turn of the Month in Equity Indexes

Uses Portfolio.Invested to control the liquidation logic. Remove
unnecessary schedules event.
---
 .../02 Method.html                            | 35 +++++++++----------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 3 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html
index a055225..2f57e97 100644
--- a/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html	
+++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes/02 Method.html	
@@ -1,29 +1,28 @@
 <p>
-  We start by creating scheduled events. The event at <code>MonthEnd()</code> will trigger the algorithm to buy SPY and the event at <code>MonthStart()</code> will start the process to sell SPY.  
+  We start by creating a scheduled event, <code>MonthEnd()</code>, that will trigger the algorithm to buy SPY.
 </p>
 <div class="section-example-container">
-<pre class="python">
-self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.rebalance)
-self.Schedule.On(self.DateRules.MonthEnd("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), self.purchase)
+<pre class="python">self.Schedule.On(
+    self.DateRules.MonthEnd(self.spy),
+    self.TimeRules.AfterMarketOpen(self.spy, 1),
+    self.Purchase)
 </pre>
 </div>
 <p>
-  We will purchase the SPY immediately, and we will wait 3 trading days, as suggested, before liquidating our portfolio. Assigning <code>self.sell_flag</code> in the scheduled event handler will help us wait 3 days before executing the liquidate order in <code>OnData()</code>. The equity index is bought and sold every month.
+  We will purchase the SPY immediately, and we will wait 3 trading days, as suggested, before liquidating our portfolio. The boolean <code>self.Portfolio.Invested</code> will help us wait 3 days before executing the liquidate order in <code>OnData()</code>. The equity index is bought and sold every month.
 </p>
 <div class="section-example-container">
-<pre class="python">
-def purchase(self):
-        self.SetHoldings("SPY", 1)
-    
-def rebalance(self):
-    self.sell_flag = True
-    
+<pre class="python">def Purchase(self):
+    ''' Immediately purchases the ETF at market opening '''
+    self.SetHoldings(self.spy, 1)
+    self.days = 0
+
 def OnData(self, data):
-    if self.sell_flag:
+    if self.Portfolio.Invested:
         self.days += 1
-        if self.days == 3:
-            self.Liquidate()
-            self.sell_flag = False
-            self.days = 0
+
+        # Liquidates after 3 days
+        if self.days > 3:
+            self.Liquidate(self.spy, 'Liquidate after 3 days')
 </pre>
-</div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html b/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html
index a56b8e9..ddaf1fb 100644
--- a/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html	
+++ b/04 Strategy Library/35 Turn of the Month in Equity Indexes/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ac6336b5911ac0d4848bae6546ea1dde.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_d91ba9f0676ddc619a573b4d9135d988.html"></iframe>
     </div>
 </div>
diff --git a/quantpedia.json b/quantpedia.json
index e92eac3..a73d0e5 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -19,7 +19,7 @@
      25: "da3bf5ee608fb8b7dc952372dd925542",
      26: "d8cd05d85b47eea4381d2ca20abf2f74",
      38: "55081a6fa81c88fd75579c2f838a1180",
-     41: "ac6336b5911ac0d4848bae6546ea1dde",
+     41: "d91ba9f0676ddc619a573b4d9135d988",
      43: "707c5bb33fede32269a986370bf8a0a9",
      44: "6fe04ea9049f2cf36d3c64ee672254ea",
      51: "b6d06b2074e4efe807c29135507fa2ab",

From 05c657672b02c10ce5458b4006eec717ac188bf5 Mon Sep 17 00:00:00 2001
From: Ayushman Chhabra <ayushmxn@protonmail.com>
Date: Wed, 11 Mar 2020 02:56:34 -0400
Subject: [PATCH 644/753] Fix typographical error

---
 .../04 Select Contracts.html                  | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index 8f31d28..6a16b78 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -160,18 +160,18 @@
 
 <pre class="python">for i in slice.OptionChains:
     if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    optionchain = i.Value
+    # differentiate the call and put options
+    call = [x for x in optionchain if x.Right == 0]
+    put = [x for x in optionchain if x.Right == 1]
+    # choose ITM contracts
+    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
+    # or choose ATM contracts
+    contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+    # or choose OTM contracts
+    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
+    # sort the contracts by their expiration dates
+    contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
 </pre>
 </div>
 <p>

From 5b97f854d81bee500e268464788542de5aefdbee Mon Sep 17 00:00:00 2001
From: Ayushman Chhabra <ayushmxn@protonmail.com>
Date: Wed, 11 Mar 2020 06:33:43 -0400
Subject: [PATCH 645/753] Fix code indentation

---
 .../04 Select Contracts.html                           | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index 6a16b78..fa6bde3 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -65,11 +65,11 @@
 <pre class="python">def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+	      optionchain = i.Value
+	      self.Log("underlying price:" + str(optionchain.Underlying.Price))
+        df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+        index=[x.Symbol.Value for x in optionchain],
+        columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
         self.Log(str(df))
 </pre>
 </div>

From f08072dd121f4ffb9364da98dbe74949eba617dd Mon Sep 17 00:00:00 2001
From: Ayushman Chhabra <ayushmxn@protonmail.com>
Date: Fri, 13 Mar 2020 23:09:09 -0400
Subject: [PATCH 646/753] Made the requested changes and then another

---
 .../04 Select Contracts.html                  | 30 ++++++++++---------
 1 file changed, 16 insertions(+), 14 deletions(-)

diff --git a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html
index fa6bde3..99d89ed 100755
--- a/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
+++ b/01 API Tutorials/04 Using Options in QuantConnect/04 Select Contracts.html	
@@ -63,14 +63,14 @@
 <div class="section-example-container">
 
 <pre class="python">def OnData(self,slice):
-    for i in slice.OptionChains:
-        if i.Key != self.symbol: continue
-	      optionchain = i.Value
-	      self.Log("underlying price:" + str(optionchain.Underlying.Price))
+    for kvp in slice.OptionChains:
+        if kvp.Key != self.symbol:
+            continue
+	      optionchain = kvp.Value
         df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
         index=[x.Symbol.Value for x in optionchain],
         columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
+        self.Log(f"Underlying price: {optionchain.Underlying.Price}\n{df}")
 </pre>
 </div>
 <table class="table qc-table">
@@ -158,20 +158,22 @@
 
 <div class="section-example-container">
 
-<pre class="python">for i in slice.OptionChains:
-    if i.Key != self.symbol: continue
-    optionchain = i.Value
+<pre class="python">for kvp in slice.OptionChains:
+    if kvp.Key != self.symbol:
+        continue
+    optionchain = kvp.Value
     # differentiate the call and put options
-    call = [x for x in optionchain if x.Right == 0]
-    put = [x for x in optionchain if x.Right == 1]
+    call = [x for x in optionchain if x.Right == OptionRight.Call]
+    put = [x for x in optionchain if x.Right == OptionRight.Put]
+    price = optionchain.Underlying.Price
     # choose ITM contracts
-    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
+    contracts = [x for x in call if price - x.Strike > 0]
     # or choose ATM contracts
-    contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
+    contracts = [x for x in call if price - x.Strike == 0]
     # or choose OTM contracts
-    contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
+    contracts = [x for x in call if price - x.Strike < 0]
     # sort the contracts by their expiration dates
-    contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    contracts = sorted(contracts, key = lambda x: x.Expiry, reverse = True)
 </pre>
 </div>
 <p>

From 7f31149a37fdbed4fbee691ee32b392bad6ad2e3 Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 10:25:24 -0700
Subject: [PATCH 647/753] Distinguished notations for interest rates

Nominal and effective interest rates were denoted by the same notation r. The change is to clearly distinguish them.
---
 .../02 Rate of Return.html                                  | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 5ae0cc5..4ad9640 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -55,11 +55,11 @@ <h4>Logarithm Return</h4>
 <p>
   From the above limitation equation, we know that if we assume continuous compounding:
 </p>
-\[e^r = 1 + r = \frac{p_t}{p_0}\]
+\[e^{r_{nominal}} = 1 + r_{effective} = \frac{p_t}{p_0}\]
 <p>
   Then we take \(ln\) on both side of the equation:
 </p>
-\[r = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
+\[r_{nominal} = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
 <p>
   Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>. This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
 </p>
@@ -127,7 +127,7 @@ <h4>Logarithm Return</h4>
 <p>
   It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
 </p>
-\[1+r = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
+\[1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
 <p>
   According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
 </p>

From 9c4c3ffbeaa1528aced846eb2f56a861761354fc Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 11:17:53 -0700
Subject: [PATCH 648/753] Explained why effective rate is close to nominal

Explained why effective rate is close to nominal rate with compounding every millisecond.
---
 .../02 Rate of Return.html                                 | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 4ad9640..04f1154 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -61,7 +61,12 @@ <h4>Logarithm Return</h4>
 </p>
 \[r_{nominal} = ln\frac{p_t}{p_0} = lnp_t - lnp_0\]
 <p>
-  Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>. This is frequently used when calculating returns, because once we take logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as a example:
+  Here we got the<strong> logarithmic return, </strong>or <strong>continuously compounded return</strong>. This return is the nominal return with the interest compounding every millisecond. To see how it is close to effective interest rate, recall the equation above:
+</p>
+\[e^{r_{nominal}} = 1 + r_{effective}\]
+<p>then we have</p>
+\[r_{effective} = e^{r_{nominal}} - 1 \approx r_{nominal}\]
+<p>where the second equality holds due to Taylor Expansion and the interest rate being small. This is frequently used when calculating returns, because once we take the logarithm of asset prices, we can calculate the logarithm return by simply doing a subtraction. Here we use Apple stock prices as an example:
 </p>
 
 <div class="section-example-container">

From afebd8582225d6c851f139b2ef72c0dee724e285 Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 11:24:13 -0700
Subject: [PATCH 649/753] Adjusted the goal being finding effective rate

---
 .../06 Rate of Return, Mean and Variance/02 Rate of Return.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html
index 04f1154..aab985e 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/02 Rate of Return.html	
@@ -132,7 +132,7 @@ <h4>Logarithm Return</h4>
 <p>
   It may sounds incorrect to sum up the daily returns, but we can prove that it's mathematically correct. Let's assume the stock prices in a period of time are represented by \([p_0, p_1, p_2, p_3.....p_n]\). Then the cumulative rate of return is given by:
 </p>
-\[1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
+\[1 + r_{effective} \approx 1+r_{nominal} = ln\frac{p_t}{p_0} = ln\frac{p_t}{p_{t-1}} + ln\frac{p_{t-1}}{p_{t-2}}+......+ln\frac{p_1}{p_0}\]
 <p>
   According to the equation above, we can simple sum up each logarithmic return in a period to get the cumulative return. The convenience of this method is also one of the reasons why we use logarithmic return in quantitative finance.
 </p>

From a9346ad8c406cbe336e87465f07ec16adee7b734 Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 12:06:45 -0700
Subject: [PATCH 650/753] Fixed a few typos

---
 .../06 Rate of Return, Mean and Variance/03 Mean.html       | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html
index 9d51b9c..72b9534 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/03 Mean.html	
@@ -24,12 +24,12 @@ <h4>Geometric Mean</h4>
 <p>
   Let's calculate the geometric mean of a series of single-period return:
 </p>
-\[1+\bar{r} = \sqrt[n]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}}\]
-\[(1+\bar{r}) = \sqrt[n]{\frac{p_t}{p_0}}\]
+\[1+\bar{r} = \sqrt[t]{\frac{p_t}{p_{t-1}}*\frac{p_{t-1}}{p_{t-2}}*...*\frac{p_2}{p_1}*\frac{p_1}{p_0}}\]
+\[(1+\bar{r}) = \sqrt[t]{\frac{p_t}{p_0}}\]
 <p>
   Now the equation becomes the form which we are familiar with:
 </p>
-\[(1+\bar{r})^n = \frac{p_t}{p_0}\]
+\[(1+\bar{r})^t = \frac{p_t}{p_0}\]
 <p>
   This is why we said it make sense when applied to growth rates.
 </p>

From 3cb69147929d476cf585fca14d8122ae49b17516 Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 12:09:17 -0700
Subject: [PATCH 651/753] Fixed the symbol for variance

---
 .../04 Variance and Standard Deviation.html                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html
index 5161451..f11f818 100755
--- a/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html	
+++ b/05 Introduction to Financial Python[]/06 Rate of Return, Mean and Variance/04 Variance and Standard Deviation.html	
@@ -1,6 +1,6 @@
 <h4>Variance</h4>
 <p>
-  <strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma\), and it's calculated by
+  <strong>Variance</strong> is a measure of dispersion. In finance, most of the time variance is a synonym for risk. The higher the variance of an asset price is, the higher risk the asset bears. Variance is usually represented by \(\sigma^2\), and it's calculated by
 </p>
 \[\sigma^2 = \frac{\sum_{i = 1}^{n}(x_i- \mu)^2}{n}\]
 <p>

From 8cc8293cbb9a9b4c9d67071eabe5a4701bc01b37 Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 12:17:14 -0700
Subject: [PATCH 652/753] Typo fixed

---
 .../02 Random Variables.html                                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html
index 30c1882..82718e8 100755
--- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html	
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/02 Random Variables.html	
@@ -1,3 +1,3 @@
 <p>
-  First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imaging rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from{1,2,3,4,5,6}. The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
+  First let's start with the concept of <strong>random variable</strong>. A random variable can be thought of as a drawing from a distribution whose outcome prior to the draw is uncertain. Imagine rolling a dice, you know that your chance of getting each is 1/6, but you don't know what's the number of your next roll is. If we roll the dice N times and record the number of each roll, a collection of those numbers is called <strong>discrete random variable</strong>. A discrete variable can take on a finite number of values. For our example, we can only take numbers from {1,2,3,4,5,6}. The other kind of variable is <strong>continuous random variable</strong>. A continuous variable can take on any value in a given range. You can think the rate of return as a continuous variable, it theoretically can take any value from \((-\infty, +\infty)\).
 </p>

From 6e028a2f72be076f20c74fd1d2011a547cebac9a Mon Sep 17 00:00:00 2001
From: metricwang <metric.wang@gmail.com>
Date: Sat, 25 Apr 2020 12:34:39 -0700
Subject: [PATCH 653/753] Fixed multiple typos

---
 .../03 Distributions.html                              | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html
index d80c6dc..933b38c 100755
--- a/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html	
+++ b/05 Introduction to Financial Python[]/07 Random Variables and Distributions/03 Distributions.html	
@@ -1,10 +1,10 @@
 <p>
-  Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represents probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\). However, we can't use this for a continuous distribution, because the the probability that draw a specific number from a continuous variable is 0, due to the infinity possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We we cover this later. For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It defines as \(P(X&lt;x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
+  Each random variable follows a <strong>probability distribution</strong>, which is a function that can be thought of as providing the probabilities of occurrence of different possible outcomes in an experiment. In our dice example, the probability distribution of each number is 1/6. We usually use \(P(X)\) to represent a probability distribution function, where X is the outcome value. In our example, \(P(1) = P(2) = p(3) = 1/6\). However, we can't use this for a continuous distribution, because the probability of drawing a specific number from a continuous variable is 0, due to the infinite possible outcomes we have. Instead, we use <strong>probability density function</strong>(PDF) function to describe the probability that a value is in a specific range. We cover this later. For each probability distribution function, we have a <strong>cumulative distribution function</strong>(CDF). It is defined as \(P(X\leq x)\), which models the probability that the random variable X will take a value less than or equal to x. For discrete random variables, we just sum up the values less than or equal to x and then divide it with number of observations.
 </p>
 
 <h4>Uniform Distribution</h4>
 <p>
-  Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and toss a fare coin are classical uniform distributions. Here we use python to simulate rolling a dice 10000 times.
+  Uniform distribution is the simplest type of probability distribution. A discrete uniform distribution has equal weight assigned to all outcomes. Both rolling a dice and tossing a fare coin are classical uniform distributions. Here we use python to simulate rolling a dice 10000 times.
 </p>
 
 <div class="section-example-container">
@@ -24,7 +24,7 @@ <h4>Uniform Distribution</h4>
 </div>
 
 <p>
-  We create a series of random variable here. We can plot the values on the x-axis and put their number of occurrences on the y-axis to have a direct view of the distribution:
+  We create a series of random variables here. We can plot the values on the x-axis and put their numbers of occurrences on the y-axis to have a direct view of the distribution:
 </p>
 
 <div class="section-example-container">
@@ -39,7 +39,7 @@ <h4>Uniform Distribution</h4>
 </div>
 <img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial07-plot1.png" alt="plot1" />
 <p>
-  Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X&lt;3)\).
+  Let's say we want to know the frequency that the observations are less than or equal to 3. In other words we want to seek the value of \(P(X\leq 3)\).
 </p>
 <div class="section-example-container">
 
@@ -50,7 +50,7 @@ <h4>Uniform Distribution</h4>
 </pre>
 </div>
 <p>
-  \(P(X&lt;3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate it's mean: it's the center of the distribution because everyone outcome is equal weight. For our dice example, we can think it as
+  \(P(X\leq 3)\) is very close to 0.5. This is not surprising because we rolled the dice 1000 times, and the frequency that the observations are less than or equal to 3 should be close to the real probability, which is 0.5. For a given uniform distribution, it's straightforward to calculate its mean: it's the center of the distribution because every outcome is of equal weight. For our dice example, we can think of it as
 </p>
 \[\mu = (1+2+3+4+5+6)/6 = 3.5\]
 <p>

From b481cf1c10e048bafcb95129dc9cf443cc4e9835 Mon Sep 17 00:00:00 2001
From: Rahul <haxdds@gmail.com>
Date: Mon, 4 May 2020 10:21:59 -0700
Subject: [PATCH 654/753] Updates SPY data source from Quandl to QC

---
 .../02 Confidence Interval.html                 | 17 +++++++++++------
 1 file changed, 11 insertions(+), 6 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
index 82a7170..f38faaf 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -8,13 +8,18 @@ <h4>Sample Error</h4>
 <pre class="python">import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
+
+qb = QuantBook()
+spy = qb.AddEquity("SPY").Symbol
+
+#get SPY data from August 2010 to the present
+start_date = datetime(2010, 8, 1, 0, 0, 0)
+end_date = qb.Time
+spy_table = qb.History(spy, start_date, end_date, Resolution.Daily)
+
+spy_total = spy_table[['open','close']]
 #calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
+spy_log_return = np.log(spy_total.close).diff().dropna()
 print 'Population mean:', np.mean(spy_log_return)
 [out]: Population mean: 0.000443353825615
 print 'Population standard deviation:',np.std(spy_log_return)

From 798365481ed6cbaef79960301363c0bacf0a5004 Mon Sep 17 00:00:00 2001
From: Rahul <haxdds@gmail.com>
Date: Mon, 4 May 2020 10:28:53 -0700
Subject: [PATCH 655/753] Updates print syntax

---
 .../02 Confidence Interval.html                  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html
index f38faaf..2d4404b 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/02 Confidence Interval.html	
@@ -20,9 +20,9 @@ <h4>Sample Error</h4>
 spy_total = spy_table[['open','close']]
 #calculate log returns
 spy_log_return = np.log(spy_total.close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
+print('Population mean:', np.mean(spy_log_return))
 [out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
+print('Population standard deviation:',np.std(spy_log_return))
 [out]: Population standard deviation: 0.00784267293815
 </pre>
 </div>
@@ -34,13 +34,13 @@ <h4>Sample Error</h4>
 <div class="section-example-container">
 
 <pre class="python">
-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
+print('10 days sample returns:', np.mean(spy_log_return.tail(10)))
 [out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
+print('10 days sample standard deviation:', np.std(spy_log_return.tail(10)))
 [out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
+print('1000 days sample returns:', np.mean(spy_log_return.tail(1000)))
 [out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
+print('1000 days sample standard deviation:', np.std(spy_log_return.tail(1000)))
 [out]: 1000 days sample standard deviation: 0.00766589174299
 </pre>
 </div>
@@ -75,9 +75,9 @@ <h4>Confidence Interval</h4>
 bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
 upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
 #print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
+print('10 days 95% confidence inverval:', (bottom_1,upper_1))
 [out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
+print('1000 days 95% confidence inverval:', (bottom_2,upper_2))
 [out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
 </pre>
 </div>

From f2152f9f12c76f5fb536ccddafac8c5fc990df6c Mon Sep 17 00:00:00 2001
From: Rahul <haxdds@gmail.com>
Date: Mon, 4 May 2020 10:34:56 -0700
Subject: [PATCH 656/753] Update to print syntax

---
 .../03 Hypothesis testing.html                     | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html
index acd6912..e25e886 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/03 Hypothesis testing.html	
@@ -9,7 +9,7 @@
 mean_10 = np.mean(spy_log_return.tail(10))
 std_10 = np.std(spy_log_return.tail(10))
 s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
+print(s)
 
 [out]: mean_10      0.000845
        std_10       0.003136
@@ -25,7 +25,7 @@
 
 <pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
 upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00039756352254768874, 0.00039756352254768874)
 </pre>
 </div>
@@ -37,7 +37,7 @@
 
 <pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
 upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
+print((bottom, upper))
 [out]: (-0.00047513689280089639, 0.00047513689280089639)
 </pre>
 </div>
@@ -55,7 +55,7 @@
 </p>
 <div class="section-example-container">
 
-<pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
+<pre class="python">print(np.sqrt(1000)*(mean_1000 - 0)/std_1000)
 [out]: 1.90922032428
 </pre>
 </div>
@@ -66,7 +66,7 @@
 <div class="section-example-container">
 
 <pre class="python">import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
+print((1 - st.norm.cdf(1.9488)))
 [out]: 0.025659656888
 </pre>
 </div>
@@ -82,10 +82,10 @@
 <pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
 std_1200 = np.std(spy_log_return.tail(1200))
 z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
+print('z-score = ',z_score)
 [out]: z-score =  2.19793023185
 p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
+print('p_value = ',p_value)
 [out]: p_value =  0.0139770390655
 </pre>
 </div>

From d5513f57c7dfdebcf6fd97709b501911ae18ab26 Mon Sep 17 00:00:00 2001
From: Rahul <haxdds@gmail.com>
Date: Mon, 4 May 2020 10:38:06 -0700
Subject: [PATCH 657/753] Removed duplicate of tutorial

---
 .../04 Summary.html                           | 213 ------------------
 1 file changed, 213 deletions(-)

diff --git a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html
index 3cc20c3..724eb85 100755
--- a/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html	
+++ b/05 Introduction to Financial Python[]/08 Confidence Interval and Hypothesis Testing/04 Summary.html	
@@ -1,216 +1,3 @@
-<h2>Introduction</h2>
-<p>
-  In the last chapter we discussed random variables and random distributions. Now we are going to use the distributions we learned to test our hypothesis and also to model the financial data. When building a trading strategy, it's essential to do some research. However, you won't be able to test your idea using all the data, because it's infinity. You can only use a <strong>sample</strong> to do your experiment. That's why we need to understand the difference between population and sample, and then use confidence interval to test our hypothesis.
-</p>
-<p>
-  As we mentioned before, both mean and standard deviation are <strong>point estimation</strong>, and they can be deceiving because sample means are different from population means. Financial data is generated every day now and in the future, thus even though we can use all the data available, it's still just a sample. This is why we need to use confidence interval to attempt to determine how accurate our sample mean estimation is.
-</p>
-
-<h2>Confidence Interval</h2>
-<h3>Sample Error</h3>
-<p>
-  Let's use the daily return on S&amp;P 500 index from Aug 2010 to present is our population. If we take the recent 10 daily returns to calculate the mean, will it be the same as the population mean? How about increasing the sample size to 1000?
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import quandl
-quandl.ApiConfig.api_key = 'tAyfv1zpWnyhmDsp91yv'
-#get data from quandl
-spy_table = quandl.get('BCIW/_SPXT')
-spy_total = spy_table[['Open','Close']]
-#calculate log returns
-spy_log_return = np.log(spy_total.Close).diff().dropna()
-print 'Population mean:', np.mean(spy_log_return)
-[out]: Population mean: 0.000443353825615
-print 'Population standard deviation:',np.std(spy_log_return)
-[out]: Population standard deviation: 0.00784267293815
-</pre>
-</div>
-
-<p>
-  Now let's check the recent 10 days sample and recent 1000 days sample:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">
-print '10 days sample returns:', np.mean(spy_log_return.tail(10))
-[out]: 10 days sample returns: 0.000845189915474
-print '10 days sample standard deviation:', np.std(spy_log_return.tail(10))
-[out]: 10 days sample standard deviation: 0.00313558001122
-print '1000 days sample returns:', np.mean(spy_log_return.tail(1000))
-[out]: 1000 days sample returns: 0.000462827047221
-print '1000 days sample standard deviation:', np.std(spy_log_return.tail(1000))
-[out]: 1000 days sample standard deviation: 0.00766589174299
-</pre>
-</div>
-<p>
-  As we expected, the two samples has different means and variances.
-</p>
-
-<h3>Confidence Interval</h3>
-<p>
-  In order to estimate the range of population mean, we define <strong>standard error of the mean</strong> as follows:
-</p>
-\[SE = \frac{\sigma}{\sqrt{n}}\]
-<p>
-  Where \(\sigma \) is the sample standard deviation and \(n\) is the sample size.
-</p>
-<p>
-  Generally, if we want to estimate an interval of the population so that 95% of the time the interval will contain the population mean, the interval is calculated as:
-</p>
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-<p>
-  Where \(\mu\) is the sample mean and SE is the standard error.
-</p>
-<p>
-  This interval is called <strong>confidence interval</strong>. We usually use 1.96 to calculate a 95% confidence interval because we assume that the sample mean follows normal distribution. We will cover this in detail later. Let's try to calculate the confidence interval using the samples above:
-</p>
-<div class="section-example-container">
-
-<pre class="python">
-#apply the formula above to calculate confidence interval
-bottom_1 = np.mean(spy_log_return.tail(10))-1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-upper_1 = np.mean(spy_log_return.tail(10))+1.96*np.std(spy_log_return.tail(10))/(np.sqrt(len((spy_log_return.tail(10)))))
-bottom_2 = np.mean(spy_log_return.tail(1000))-1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-upper_2 = np.mean(spy_log_return.tail(1000))+1.96*np.std(spy_log_return.tail(1000))/(np.sqrt(len((spy_log_return.tail(1000)))))
-#print the outcomes
-print '10 days 95% confidence inverval:', (bottom_1,upper_1)
-[out]: 10 days 95% confidence inverval: (-0.0010982627102681939, 0.002788642541217079)
-print '1000 days 95% confidence inverval:', (bottom_2,upper_2)
-[out]: 1000 days 95% confidence inverval: (-1.230984558013321e-05, 0.00093796394002165957)
-</pre>
-</div>
-
-<p>
-  As we can see, the 95% confidence interval became much narrower if we increase the sample size from 10 to 1000. Imagine that if N goes positive infinite, then we have \(\lim_{n\rightarrow \infty}\frac{\sigma}{\sqrt{n}} = 0\). The confidence interval would become a certain value, which is the sample mean!
-</p>
-
-<h3>Confidence Interval of Normal Distribution</h3>
-<p>
-  Normal Distribution is so commonly used that we should be able to remember some critical values of it. Specifically, we usually use 90%, 95% and 99% as the confidence level of a confidence interval. The critical values for these three confidence levels are 1.64, 1.96, and 2.32 respectively. in other words:
-</p>
-\[\%90 upperabnd = \mu + 1.64*SE\]
-\[\%90 lowerband = \mu + 1.64*SE\]
-<p>
-  The same for other confidence intervals. It's also important to remember the famous 'Three sigma rule' or '68-95-99.7' rule associated with normal distribution. This is used to remember the confidence level of the intervals with a width of two, four and six standard deviation. Mathematically:
-</p>
-\[P(\mu - \sigma \leq X \leq \mu+\sigma)\approx 0.6827\]
-\[P(\mu - 2\sigma \leq X \leq \mu+2\sigma)\approx 0.9545\]
-\[P(\mu - 3\sigma \leq X \leq \mu+3\sigma)\approx 0.9973\]
-<p>
-  This can also be remembered by using the chart:
-</p>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-empirical-rule.png" alt="empirical rule" />
-<h3>Central Limit Theory</h3>
-<p>
-  As we mentioned, if we use the sample to estimate the confidence interval of the population, the 95% confidence interval is:
-</p>
-\[(\mu - 1.96*SE, \mu + 1.96*SE)\]
-<p>
-  Now you may have some sense to the number 1.96. It's the 95% critical value of a normal distribution. Does this means we assume the mean of sample follows a normal distribution? The answer is yes. This assumption is supported by <strong>central limit theorem</strong>. This theorem tells us that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population, and the means of the samples will be approximately normal distributed. This is the foundation of population mean confidence interval estimation.
-</p>
-
-<h2>Hypothesis testing</h2>
-<p>
-  Now we can talk about <strong>hypothesis testing</strong>. Hypothesis test is essentially test your inference based on a sample. Let's use our dataset, the daily return of S&amp;P 500 us our population. Assume that we don't know the mean of this population. I guess that the mean of this population is 0. Is my guess correct? I need to test this hypothesis with my sample. Let's start from observing our sample:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">mean_1000 = np.mean(spy_log_return.tail(1000))
-std_1000 = np.std(spy_log_return.tail(1000))
-mean_10 = np.mean(spy_log_return.tail(10))
-std_10 = np.std(spy_log_return.tail(10))
-s = pd.Series([mean_10,std_10,mean_1000,std_1000],index = ['mean_10', 'std_10','mean_1000','std_1000'])
-print s
-
-[out]: mean_10      0.000845
-       std_10       0.003136
-       mean_1000    0.000463
-       std_1000     0.007666
-</pre>
-</div>
-<p>
-  We know how to calculate the confidence interval now. If I were right, i.e. the population mean is 0, then the 90% confidence interval of the sample with 1000 observations should be:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">bottom = 0 - 1.64*std_1000/np.sqrt(1000)
-upper = 0 + 1.64*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00039756352254768874, 0.00039756352254768874)
-</pre>
-</div>
-<p>
-  Our mean of the sample is out of the 90% confidence interval. This means on a 90% confidence level, we can claim that the mean of our population is not 0. In other word, we <strong>rejected</strong> the hypothesis that the daily return on S&amp;P500 from aug 2010 is zero. Can we claim that with 95% confidence level?
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">bottom = 0 - 1.96*std_1000/np.sqrt(1000)
-upper = 0 + 1.96*std_1000/np.sqrt(1000)
-print (bottom, upper)
-[out]: (-0.00047513689280089639, 0.00047513689280089639)
-</pre>
-</div>
-<p>
-  This time the sample mean is within the confidence interval. Thus we can't reject my hypothesis. In other words, we can't claim with 95% confidence level that the mean return is positive. Even though we can claim it with 90% confidence level. We have actually already finished a hypothesis testing above! In general, we have <strong>null hypothesis</strong> \(H_0\) and <strong>alternative hypothesis</strong>. They are usually in the following forms:
-</p>
-\[H_0:\bar{\mu} = 0\]
-\[H_0:\bar{\mu} \neq 0\]
-<p>
-  If the tested value is outside the confidence interval, we reject the null hypothesis, or accept the alternative hypothesis; If the tested value is within the confidence interval, we can't reject the null hypothesis. Although the hypothesis testing method we used above is straightforward, it's not so convenient to implement. Instead, we reverse the process to calculate the critical value, or <strong>Z-score</strong>. Z-score is defined as:
-</p>
-\[Z = \frac{X - \mu}{\frac{\sigma}{\sqrt{n}}}\]
-<p>
-  Let's calculate the Z score from our sample:
-</p>
-<div class="section-example-container">
-
-<pre class="python">print np.sqrt(1000)*(mean_1000 - 0)/std_1000
-[out]: 1.90922032428
-</pre>
-</div>
-<p>
-  We know that the critical value for the 90% confidence level is 1.64 and that for the 95% confidence level is 95%. The higher the Z score is, the further the tested value is from the hypothesized value(which is 0 in this example). Thus with 90% confidence level, we are far away enough from zero and we reject the null hypothesis. However with 95% confidence level, we are not far away enough from zero, so we can't reject the null hypothesis. One reason of doing in this way is that we can know how wide our confidence interval is. In our example, the z-score is 1.8488. We can know the width is the confidence interval referring to a normal distribution table. Of course we can do this in Python:
-</p>
-
-<div class="section-example-container">
-
-<pre class="python">import scipy.stats as st
-print (1 - st.norm.cdf(1.9488))
-[out]: 0.025659656888
-</pre>
-</div>
-<p>
-  It's worth noting that st.norm.cdf will return the probability that a value take from the distribution is less than our tested value. In other words, 1 - st.norm.cdf(1.9488) will return the probability that the value is greater than our tested value, which is 0.025659 in this example. This calculated number is called <strong>p-value</strong>. If our confidence level our confidence interval is 95%, then we have 2.5% on the left side and 2.5% on the right side. This is called <strong>two-tail test</strong>. If our null hypothesis is \(\mu = 0\), we are conducting two-tail test because the tested sample mean can be either positive enough or negative enough to reject the null hypothesis. We can see it from the chart:
-</p>
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/Tutorial08-confidence-interval.jpg" alt="confidence interval" />
-<p>
-  If we use 95% confidence interval, we need a p-value less than 0.025 to reject the null hypothesis. However, now our p-value is 0.025659, which is greater than 0.025, thus we can't reject the null hypothesis. It's obviously less than 0.05, so we can still reject the null hypothesis with 90% confidence level. Now let's test the hypothesis that population mean = 0 again with a large sample, which has 1200 observations:
-</p>
-<div class="section-example-container">
-
-<pre class="python">mean_1200 = np.mean(spy_log_return.tail(1200))
-std_1200 = np.std(spy_log_return.tail(1200))
-z_score = np.sqrt(1200)*(mean_1200 - 0)/std_1200
-print 'z-score = ',z_score
-[out]: z-score =  2.19793023185
-p_value = (1 - st.norm.cdf(z_score))
-print 'p_value = ',p_value
-[out]: p_value =  0.0139770390655
-</pre>
-</div>
-<p>
-  Using the a larger sample, now we can reject the null hypothesis with a higher confidence interval! our p-value is 0.0105, and it's a two-tail test, so our confidence level of the interval is 1-(0.0105*2) = 0.979. We can say at most with 97.9% confidence interval, we can claim that the population mean is not zero. We already know that the population mean is not 0. As our sample size increasing, the accurate rate of our hypothesis goes up.
-</p>
-<h2>Summary</h2>
 <p>
   In this chapter we introduced confidence interval, especially that for the normal distribution, and hypothesis test. Now we know how to test our idea rigorously. Normal distribution and it's confidence interval can be applied to many quantitative finance theories, we will see it frequently in our following tutorials.
 </p>

From 72b385ec06e54ce397a76ddfd156fb6114e64682 Mon Sep 17 00:00:00 2001
From: Weiqi Zhang <zhangweiqi357@gmail.com>
Date: Mon, 4 May 2020 23:42:12 -0700
Subject: [PATCH 658/753] Update 05 Options Moneyness.html

Fix typo.
---
 .../01 General Features of Options/05 Options Moneyness.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html
index 5f7a43a..ae5cbc4 100755
--- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html	
@@ -26,5 +26,5 @@
   But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.
 </p>
 <p>
-  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
 </p>

From 02ca9566222cde5c4d25f3bd333bdc79661525ce Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 15 May 2020 11:23:50 -0600
Subject: [PATCH 659/753] Add residual momentum strategy writeup

---
 .../01 Introduction.html                      |  3 ++
 .../136 Residual Momentum/02 Method.html      | 31 +++++++++++++++++++
 .../136 Residual Momentum/03 Algorithm.html   |  6 ++++
 .../136 Residual Momentum/04 References.html  |  8 +++++
 4 files changed, 48 insertions(+)
 create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html

diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html
new file mode 100644
index 0000000..28518a1
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html
new file mode 100644
index 0000000..5b927d1
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/02 Method.html	
@@ -0,0 +1,31 @@
+<h4>Importing Custom Data</h4>
+<p>
+  The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the <a href="https://github.com/QuantConnect/Tutorials/raw/feature-data-directory/Data/F-F_Research_Data_Factors.csv">Fama-French data source URL</a> from our <a href="https://github.com/QuantConnect/Tutorials">Tutorials repo</a>. We save the loaded data into a DataFrame in the alpha model.
+</p>
+
+<h4>Universe Selection</h4>
+<p>
+  In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices.
+</p>
+
+<h4>Calculate Residual Momentum Score</h4>
+<p>
+  During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable.
+</p>
+
+\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \]
+
+\begin{p}
+  where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
+\end{p}
+
+\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \]
+
+<p>
+  If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance.
+</p>
+
+<h4>Rebalancing</h4>
+<p>
+  We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html
new file mode 100644
index 0000000..90f2cf2
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_10e626ccfe29e78f0eaead76158e788c.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html
new file mode 100644
index 0000000..bd3b043
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/04 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    <a href="https://ssrn.com/abstract=2319861">Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009)</a>
+  </li>
+  <li>
+    <a href="https://ssrn.com/abstract=2929306">Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017)</a>
+  </li>
+</ol>
\ No newline at end of file

From 66a5e71f586cd9cf6d5ddfadd8bbf5263c0b00c8 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Fri, 15 May 2020 10:35:20 -0700
Subject: [PATCH 660/753] Update 05 Options Moneyness.html

---
 .../01 General Features of Options/05 Options Moneyness.html    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html
index ae5cbc4..a089ece 100755
--- a/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/05 Options Moneyness.html	
@@ -26,5 +26,5 @@
   But why are they in the money or out of the money? For a call option as an example, it is ITM because it already has an intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $150, then this call option is in the money 150-143=$7. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $150 and secure $7 profit.
 </p>
 <p>
-  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 143-130=$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
+  Likewise, for a call option, it is OTM because it doesn't have any intrinsic value. If you buy the call option and own the right to buy AAPL at strike price $143 and the current market price is $130, then this call option is out of the money 130-143=-$13. If you need to exercise it, you can buy shares of AAPL at $143 and sell them immediately for $130 and then you will lose $13. In this case, you would not do that. But why this kind of OTM options still traded in the market and you need to pay for them? That because they still have time values and have time to expiration. There is probability or there is the expectation from the OTM option holders that the apple price will go up and above the strike price before the expiration.
 </p>

From dac2cfe5f606a9f82e1d7ec0f790479deb8aafa1 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 15 May 2020 11:23:50 -0600
Subject: [PATCH 661/753] Add residual momentum strategy writeup

---
 .../01 Introduction.html                      |  3 ++
 .../136 Residual Momentum/02 Method.html      | 31 +++++++++++++++++++
 .../136 Residual Momentum/03 Algorithm.html   |  6 ++++
 .../136 Residual Momentum/04 References.html  |  8 +++++
 4 files changed, 48 insertions(+)
 create mode 100644 04 Strategy Library/136 Residual Momentum/01 Introduction.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/02 Method.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/03 Algorithm.html
 create mode 100644 04 Strategy Library/136 Residual Momentum/04 References.html

diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html
new file mode 100644
index 0000000..28518a1
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
@@ -0,0 +1,3 @@
+<p>
+  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html
new file mode 100644
index 0000000..5b927d1
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/02 Method.html	
@@ -0,0 +1,31 @@
+<h4>Importing Custom Data</h4>
+<p>
+  The first step of algorithm is to load 3 years of trailing values of the Fama-French factors. We call the AddData method and provide the <a href="https://github.com/QuantConnect/Tutorials/raw/feature-data-directory/Data/F-F_Research_Data_Factors.csv">Fama-French data source URL</a> from our <a href="https://github.com/QuantConnect/Tutorials">Tutorials repo</a>. We save the loaded data into a DataFrame in the alpha model.
+</p>
+
+<h4>Universe Selection</h4>
+<p>
+  In coarse universe selection, we return the first 400 symbols that have fundamental data. In fine universe selection, we rank the stocks by market cap and return the symbols that are in the top 10%. We only create ResidualMomentum objects for stocks added to the universe that have atleast 3 years of historical prices.
+</p>
+
+<h4>Calculate Residual Momentum Score</h4>
+<p>
+  During construction of ResidualMomentum objects, we manually warmup the trailing 3 years of monthly returns for the security. We also set up a monthly consolidator to update the trailing returns each month and to recalculate the score. The score is calculated by fitting a linear regression model to the trailing 36 months of data, using the Fama-French factors as independent variables and the monthly returns of each stock as the dependent variable.
+</p>
+
+\[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \]
+
+\begin{p}
+  where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
+\end{p}
+
+\[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \]
+
+<p>
+  If a stock's price is below $1 when calculating the score, it's score is set to None, excluding it from trading during the rebalance.
+</p>
+
+<h4>Rebalancing</h4>
+<p>
+  We rebalance the portfolio at the beginning of each month. After calculating a score for each security, we long the 10% of stocks with the greatest scores and short the 10% of stocks with the lowest scores, holding until the next rebalance.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html
new file mode 100644
index 0000000..90f2cf2
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_10e626ccfe29e78f0eaead76158e788c.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/136 Residual Momentum/04 References.html b/04 Strategy Library/136 Residual Momentum/04 References.html
new file mode 100644
index 0000000..bd3b043
--- /dev/null
+++ b/04 Strategy Library/136 Residual Momentum/04 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    <a href="https://ssrn.com/abstract=2319861">Blitz, David and Huij, Joop and Martens, Martin P.E., Residual Momentum (August 1, 2009)</a>
+  </li>
+  <li>
+    <a href="https://ssrn.com/abstract=2929306">Huij, Joop and Lansdorp, Simon, Residual Momentum and Reversal Strategies Revisited (March 8, 2017)</a>
+  </li>
+</ol>
\ No newline at end of file

From 642a1e337b988ae6bff49802f84ed774986d01f7 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 15 May 2020 11:41:30 -0600
Subject: [PATCH 662/753] Add strategy to quantpedia json and strategy library
 files

---
 .../00 Strategy Library/01 Strategy Library.php        | 10 ++++++++++
 quantpedia.json                                        |  1 +
 2 files changed, 11 insertions(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 5909ba0..6470de6 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -91,6 +91,16 @@
         'description' => 'Selects ETFs in different asset classes with the highest momentum and assigns an equally weighted allocation.',
         'tags'=>'Beginner,Momentum,ETFs'
         
+    ],
+    [
+        'name' => 'Residual Momentum',
+        'link' => 'strategy-library/residual-momentum',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/136'
+        ],
+        'description' => 'Constructs a long/short portfolio based on trailing residual momentum normalized by its standard deviation',
+        'tags'=>'Intermediate,Equities,Momentum'
+        
     ],
     [
         'name' => 'Sector Momentum',
diff --git a/quantpedia.json b/quantpedia.json
index 13a6d8d..ffdd5f6 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -40,6 +40,7 @@
      113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",
      125: "1f9f254c932d8e7ae85e803affd52a7b",
+     136: "10e626ccfe29e78f0eaead76158e788c",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      162: "5761c1817a9dcb805be487f9e32adfa1",

From 6fe5dc6fd34f3068cb82c7433af8233cc5ac8a42 Mon Sep 17 00:00:00 2001
From: dhantisha <44590120+dhantisha@users.noreply.github.com>
Date: Sat, 23 May 2020 08:14:52 +0200
Subject: [PATCH 663/753] added var ticket =

---
 .../03 Tracking and Managing Orders/01 Overview.html            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
index ca80763..445f481 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -16,7 +16,7 @@
 <tbody>
 <tr>
 <td width="25%">Market Order</td>
-<td><code>MarketOrder("SPY", 100);</code></td>
+<td><code>var ticket = MarketOrder("SPY", 100);</code></td>
 </tr>
 <tr>
 <td>Limit Order</td>

From 105b688a4d46a16b46c25485b0121fe750114376 Mon Sep 17 00:00:00 2001
From: metricquant <44590120+metricquant@users.noreply.github.com>
Date: Sat, 23 May 2020 22:29:07 +0200
Subject: [PATCH 664/753] removed reference to a C# example

The example includes both C# and Python code, yet the text refers to only C#.
---
 .../03 Tracking and Managing Orders/02 Updating Orders.html     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html
index a923569..668d4a1 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/02 Updating Orders.html	
@@ -1,5 +1,5 @@
 <p>
-Once you have an order ticket you can use it to get order fields. In <b>C#</b> it looks like:
+Once you have an order ticket you can use it to get order fields.
 </p>
 <div class="section-example-container">
 <pre class="csharp">var currentStopPrice = _ticket.Get(OrderField.StopPrice);

From 0afa0cefd413c0f27187685e5399277994fceba0 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 24 May 2020 07:47:18 -0700
Subject: [PATCH 665/753] Update 01 Overview.html

---
 .../03 Tracking and Managing Orders/01 Overview.html | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html
index 445f481..9c9b242 100755
--- a/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
+++ b/01 API Tutorials/03 Tracking and Managing Orders/01 Overview.html	
@@ -16,27 +16,27 @@
 <tbody>
 <tr>
 <td width="25%">Market Order</td>
-<td><code>var ticket = MarketOrder("SPY", 100);</code></td>
+<td><code class="csharp">var ticket = MarketOrder("SPY", 100);</code><code class="python">self.ticket = self.MarketOrder("SPY", 100)</code></td>
 </tr>
 <tr>
 <td>Limit Order</td>
-<td><code>var ticket = LimitOrder("SPY", 100, 100.10m);</code></td>
+<td><code class="csharp">var ticket = LimitOrder("SPY", 100, 100.10m);</code><code class="python">self.ticket = self.LimitOrder("SPY", 100, 100.1)</code></td>
 </tr>
 <tr>
 <td>Stop Market Order</td>
-<td><code>var ticket = StopMarketOrder("SPY", 100, 100.10m);</code></td>
+<td><code class="csharp">var ticket = StopMarketOrder("SPY", 100, 100.10m);</code><code class="python">self.ticket = self.StopMarketOrder("SPY", 100, 100.1)</code></td>
 </tr>
 <tr>
 <td>Stop Limit Order</td>
-<td><code>var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code></td>
+<td><code class="csharp">var ticket = StopLimitOrder("SPY", 100, 100.12m, 99.5m);</code><code class="python">self.ticket = self.StopLimitOrder("SPY", 100, 100.12, 99.5)</code></td>
 </tr>
 <tr>
 <td>Market On Open Order</td>
-<td><code>var ticket = MarketOnOpen("SPY", 100);</code></td>
+<td><code class="csharp">var ticket = MarketOnOpen("SPY", 100);</code><code class="python">self.ticket = self.MarketOnOpen("SPY", 100)</code></td>
 </tr>
 <tr>
 <td>Market On Close Order</td>
-<td><code>var ticket = MarketOnClose("SPY", 100);</code></td>
+<td><code class="csharp">var ticket = MarketOnClose("SPY", 100);</code><code class="python">self.ticket = self.MarketOnClose("SPY", 100)</code></td>
 </tr>
 </tbody>
 </table>

From cbab941fe0af715e9596e33b4ae28779c04b0199 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 24 May 2020 08:19:13 -0700
Subject: [PATCH 666/753] Update 01 Introduction.html

---
 .../136 Residual Momentum/01 Introduction.html             | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/136 Residual Momentum/01 Introduction.html b/04 Strategy Library/136 Residual Momentum/01 Introduction.html
index 28518a1..57d67e1 100644
--- a/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
+++ b/04 Strategy Library/136 Residual Momentum/01 Introduction.html	
@@ -1,3 +1,6 @@
 <p>
-  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. This strategy is more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
-</p>
\ No newline at end of file
+  Residual momentum is the phenomenon that stocks with greater monthly residual returns (normalized by the volatility of the 
+  residual returns) tend to outperform those with less. Research has shown the strategy experiences less exposure to the dynamic 
+  Fama-French factors, produces greater sharpe ratios, and is more robust out-of-sample than a total return momentum strategy. 
+  This strategy is claimed to be more stable throughout the business cycle than a total return momentum strategy. It tends to underperform during trending regimes and outperform during reverting regimes. Additionally, this strategy is less concentrated is small-cap stocks than a total return strategy can sometimes be, leading to less trading costs and reducing the effect of tax-loss selling.
+</p>

From 87290fff62a1af50edee0ed334a91ef12e9940a1 Mon Sep 17 00:00:00 2001
From: David Jacobson <DavidJacobson@users.noreply.github.com>
Date: Tue, 26 May 2020 04:25:57 -0400
Subject: [PATCH 667/753] Update 04 Select Contracts.html

Fix documentation to be consistent about 0 1 being call / put
---
 .../02 QuantConnect Options API/04 Select Contracts.html      | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index cbf5dbc..54084fb 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -42,8 +42,8 @@
 <tr>
 <td><code>x.Right</code></td>
 <td>Get the right being purchased
-x.Right ＝ 1  call option[right to buy]
-x.Right ＝ 0  put option[right to sell]</td>
+x.Right ＝ 0  call option[right to buy]
+x.Right ＝ 1  put option[right to sell]</td>
 </tr>
 <tr>
 <td><code>x.UnderlyingLastPrice</code></td>

From a6514b4ea1285e994f88aa7d31309f3aa55bd3d2 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Tue, 26 May 2020 15:18:27 -0600
Subject: [PATCH 668/753] Replace residual momentum strategy backtest with code
 reviewed one

---
 04 Strategy Library/136 Residual Momentum/02 Method.html    | 6 +++---
 04 Strategy Library/136 Residual Momentum/03 Algorithm.html | 2 +-
 quantpedia.json                                             | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/136 Residual Momentum/02 Method.html b/04 Strategy Library/136 Residual Momentum/02 Method.html
index 5b927d1..7ed4ada 100644
--- a/04 Strategy Library/136 Residual Momentum/02 Method.html	
+++ b/04 Strategy Library/136 Residual Momentum/02 Method.html	
@@ -15,9 +15,9 @@ <h4>Calculate Residual Momentum Score</h4>
 
 \[r_t = \alpha + \beta_1 * Mkt_t + \beta_2 * SMB_t + \beta_3 * HML_t + \epsilon_t \]
 
-\begin{p}
-  where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
-\end{p}
+<p>
+  Where \(r_t\) is the monthly return of the stock in month \(t\); \(Mkt_t\), \(SMB_t\), and \(HML_t\) are the Fama-French factor values in month \(t\); and \(epsilon_t\) is the residual return in month \(t\). After fitting, we test the model on the trailing 12 months of data (excluding the most recent month) to calculate the score. We simply sum the residuals and divide by the standard deviation of the residuals to get the score.
+</p>
 
 \[score = \frac{\sum{} \epsilon}{\sigma_\epsilon} \]
 
diff --git a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html
index 90f2cf2..a989254 100644
--- a/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
+++ b/04 Strategy Library/136 Residual Momentum/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_10e626ccfe29e78f0eaead76158e788c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_d8932a9697f7e98e9a00761d37e627dd.html"></iframe>
     </div>
 </div>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index ffdd5f6..87ee747 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -40,7 +40,7 @@
      113: "9b1291a5f08dcc07df86363e46144084",
      114: "7a404f43e9191ac5bce9a9446572cb98",
      125: "1f9f254c932d8e7ae85e803affd52a7b",
-     136: "10e626ccfe29e78f0eaead76158e788c",
+     136: "d8932a9697f7e98e9a00761d37e627dd",
      152: "b35034509d95c173fed1c0adcecc5be0",
      155: "886a9f73bc29142f14fcb2c2d01404a9",
      162: "5761c1817a9dcb805be487f9e32adfa1",

From 96fd9d1760026754d7c3ab397f581bafc0a5c7cb Mon Sep 17 00:00:00 2001
From: Jared Broad <jaredbroad@gmail.com>
Date: Sun, 7 Jun 2020 08:27:04 -0700
Subject: [PATCH 669/753] Delete visual studio tutorial

---
 .../02 Backtesting from Visual Studio/00.html |  1 -
 .../01 Introduction.html                      | 13 ------
 .../02 Installation.html                      | 33 -------------
 .../03 Plugin Features.html                   | 46 -------------------
 .../04 Backtest Tool Window.html              | 31 -------------
 .../05 Troubleshooting.html                   |  7 ---
 6 files changed, 131 deletions(-)
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/00.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
 delete mode 100644 03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html

diff --git a/03 Open Source/02 Backtesting from Visual Studio/00.html b/03 Open Source/02 Backtesting from Visual Studio/00.html
deleted file mode 100644
index 4b86dc6..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/00.html	
+++ /dev/null
@@ -1 +0,0 @@
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/visual-studio.png" />
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html b/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html
deleted file mode 100644
index 4005af9..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/01 Introduction.html	
+++ /dev/null
@@ -1,13 +0,0 @@
-<style>
-.backdrop { background-color: #ddd; padding: 15px; border: 1px solid #ccc; border-radius: 5px;}
-</style>
-
-<p>
-The Visual Studio plugin is a tool which allows you to code locally; harnessing all the power of Visual Studio's autocomplete and code analysis; while also backtesting in the QuantConnect Cloud. It aims to facilitate your strategy development. The plugin supports <b>Visual Studio 2015, 2017 and 2019</b>.
-</p>
-
-<p>
-   You can download the plugin here: 
-    <a target="_BLANK" href="https://cdn.quantconnect.com/tutorials/QuantConnect.VisualStudioPlugin_20190711.vsix"><i class="fa fa-cloud-download"></i> QuantConnect.VisualStudioPlugin_20190711.vsix</a> *<br/>
-<small style="font-size: 0.8em">* Some browsers rename the VSIX file link above to ZIP once its downloaded. Please make sure its a VSIX file before executing.</small>
-</p>
diff --git a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html b/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html
deleted file mode 100644
index e97282f..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/02 Installation.html	
+++ /dev/null
@@ -1,33 +0,0 @@
-
-<p>
-To install the plugin simply execute ("double click") the binary supplied download above, or you can rebuild it from scratch from the LEAN project. To build it from scratch please follow the instructions below.
-</p> 
-
-<h4>Build</h4>
-<ol>
-    <li>Open LEAN Solution in Visual Studio. Select ‘Release’ solution configuration.</li>
-    <li>Right click project ‘QuantConnect.VisualStudioPlugin’ and select ‘Rebuild’.</li>
-    <li>Verify in the Visual Studio ‘Output’ panel the compilation succeeded with no errors.</li>
-</ol>
-
-<h4>Installation</h4>
-<p>
-If you have intstalled a previous version of the plugin you first need to remove this first. You do this from the ‘Tools’ Menu → ‘Extensions and Updates...’ → ‘Installed’. Then search for ‘QuantConnect.VisualStudioPlugin’ and click uninstall. <em>Once uninstalled; remember to restart Visual Studio.</em>
-</p>
-<p>
-After building navigate to ‘..\Lean\VisualStudioPlugin\bin\Release’. If you built sucessfully you should be able to execute ‘QuantConnect.VisualStudioPlugin.vsix’.
-</p>
-
-<h4>Setting Up Your Plugin</h4>
-<ol>
-    <li>Login to QuantConnect and navigate to your <a href="/account">My Account</a> page.</li>
-    <li>In the API panel, you will find your a ‘unique access token’ and your ‘user id’ which are required to connect to the API. Copy these to your clipboard.</li>
-    <li>From the Visual Studio IDE go to ‘Tools’ menu → ‘QuantConnect’ → ‘Login’. Enter your API credentials and click Login.</li>
-</ol>
-<figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/loginBox.png"/>
-</figcaption>
-
-<p>
-Manually logging in is only required the first time. After the first login, the plugin will automatically log in using previously saved credentials.
-</p>
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html b/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html
deleted file mode 100644
index 8a4607c..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/03 Plugin Features.html	
+++ /dev/null
@@ -1,46 +0,0 @@
-<p>
-The Visual Studio plugin can currently save files to a project, compile the project, and backtest it in the cloud. Through the accompanying "tool window" it can also rename, open or add a note to a backtest, and create a new project.
-</p>
-
-<h4>Saving Files to QuantConnect</h4>
-<p>
-Save files from your local project to a QuantConnect project. You can save many files at a time.
-</p>
-<ol>
- <li>Select the desired files with your mouse from the Solution Explorer tool window.</li>
- <li>Right-click selection → ‘Save to QuantConnect’.</li>
- <li>Select target project name:
-   <figcaption class="backdrop"><img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/selectProjectBox.png"/></figcaption>
-</li>
-<li>
-  Result:
-   <ul>
-    <li>If the operation succeeds you will be informed through the VS Status Bar.</li>
-    <li>If any error occurs you will be informed through VS Status Bar and with a popup box.</li>
-   </ul>
-   <figcaption class="backdrop"><img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/uploadFailedBox.png"/></figcaption>
-</li>
-</ol>
-
-<h4>Send for Backtesting</h4>
-<p>
-This feature allows you to upload one or more files to a target project, compile it and backtest it in the QuantConnect cloud.
-</p>
-<ol>
- <li>Select the desired files you'd like to backtest.</li>
- <li>Right-click → ‘Send for Backtesting’</li>
- <li>Select the target project with the project list view.</li>
- <li>Result:
- <ul>
-    <li>If the operation succeeds you will be informed through the VS Status Bar and the target QC project will be opened using the default browser pointing at your new backtest.</li>
-    <li>If any error occurs you will be informed through VS Status Bar and with a popup box.</li>
-   </ul>
-   <figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/uploadFailedBox.png"/></figcaption>
-If the error is due to a compilation error, the pop-up box will contain the first compilation error.
-<br/><br/>
-<figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/backtestFailedBox.png"/></figcaption>
-If the error is due to a run-time issue during the backtesting process, the popup box will contain error details and the corresponding stack trace.
-
-</li>
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html b/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html
deleted file mode 100644
index 8066c56..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/04 Backtest Tool Window.html	
+++ /dev/null
@@ -1,31 +0,0 @@
-<p>
-The backtesting tool window utility allows you to monitor and control ongoing backtests, along with editing various properties of existing completed backtests.
-</p>
-<figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/toolWindow.png"/>
-</figcaption>
-<p>
-If you select a project ‘BuyTheDip_007’ using the tool windows combo box and launch a backtest using ‘Send For Backtesting’ for ‘BuyTheDip_007’ project, it will display the backtests progress in the tool window.
-</p>
-
-<h4>How do I get the Backtest Window?</h4>
-<p>
-From the Visual Studio IDE go to ‘View’ menu → ‘Other Windows’ → ‘QuantConnect’. If there are previous valid credentials, the tool window will auto login when open or when the user performs an action.
-</p>
-
-<h4>Available Backtest Window Features</h4>
-<ul>
- <li><i class="fa fa-bar-chart"></i> Open Backtest: Open backtest viewer on QuantConnect.</li>
- <li><i class="fa fa-refresh"></i> Refresh/Reload Project List</li> 
- <li><i class="fa fa-trash"></i> Delete Backtest</li>
- <li><i class="fa fa-pencil"></i> Edit Backtest: Rename or add notes to backtest results.
-  <figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/editBacktest.png"/>
-</figcaption>
- </li>
- <li><i class="fa fa-plus"></i> Create a New Project
-  <figcaption class="backdrop">
-<img class="img-responsive" src="https://cdn.quantconnect.com/tutorials/i/newProject.png"/>
-</figcaption>
- </li>
-</ul>
\ No newline at end of file
diff --git a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html b/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html
deleted file mode 100644
index 9312fcf..0000000
--- a/03 Open Source/02 Backtesting from Visual Studio/05 Troubleshooting.html	
+++ /dev/null
@@ -1,7 +0,0 @@
-<p>
-VisualStudio plugin can write log data to the VisualStudio activity log, but only if VisualStudio is started with the /log parameter switch. To debug the QuantConnect plugin start VisualStudio with the following command:
-</p>
-<pre>devenv /log &lt;path-to-log&gt;</pre>
-<p>
-See <a target="_BLANK" href="https://docs.microsoft.com/en-us/visualstudio/ide/reference/log-devenv-exe">Visual Studio Documentation</a> for more information.
-</p>
\ No newline at end of file

From 58429f39bf70e37c5815e195397fd7239b5f6379 Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Sun, 7 Jun 2020 08:33:38 -0700
Subject: [PATCH 670/753] Update Introduction.html

---
 03 Open Source/00 Introduction/Introduction.html | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/03 Open Source/00 Introduction/Introduction.html b/03 Open Source/00 Introduction/Introduction.html
index 744480f..dff5ed2 100644
--- a/03 Open Source/00 Introduction/Introduction.html	
+++ b/03 Open Source/00 Introduction/Introduction.html	
@@ -14,14 +14,6 @@ <h4><i class="fa fa-list-alt"></i> 100 Debugging Python</h4>
   </div>
 </a>
 
-<a class="tutorial-link docs-internal-link" href="/tutorials/open-source/backtesting-from-visual-studio">
-  <div class="tutorial-box">    
-    <h4><i class="fa fa-windows"></i> 101 Backtesting from Visual Studio</h4>    
-    <p>Visual Studio plugin integrated with the QuantConnect API.</p>
-    <i class="fa fa-external-link tutorial-link-icon"></i>
-  </div>
-</a>
-
 <a class="tutorial-link docs-internal-link" href="/tutorials/open-source/how-do-i-use-the-api-file-provider">
   <div class="tutorial-box">    
     <h4><i class="fa fa-list-alt"></i> 102 Using the API File Provider</h4>    
@@ -52,4 +44,4 @@ <h4><i class="fa fa-briefcase"></i> 301 Brokerage Development Guide</h4>
     <p>Guide to implementing your own brokerage in LEAN.</p>
     <i class="fa fa-external-link tutorial-link-icon"></i>
   </div>
-</a>
\ No newline at end of file
+</a>

From 0afb0eaab71b9c54a55fcc6f1e7d76d1a5c02c0e Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Fri, 12 Jun 2020 09:38:50 -0700
Subject: [PATCH 671/753] Update image protocol

---
 03 Open Source/01 Debugging Python/04 Method 2 - PDB.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html
index 93c5b88..a0d48d6 100644
--- a/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html	
+++ b/03 Open Source/01 Debugging Python/04 Method 2 - PDB.html	
@@ -22,4 +22,4 @@
 </ul>
 </p>
 
-<img class="img-responsive" src="http://cdn.quantconnect.com/i/tu/python-debugging-command-line.png" />
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/python-debugging-command-line.png" />

From 1d76f6ce967fd92e68435b9b4374e33a84962aac Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Fri, 12 Jun 2020 09:39:27 -0700
Subject: [PATCH 672/753] Update Gif protocol.

---
 .../05 Method 3 - VisualStudio Debugger.html                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html
index 6f744e0..7767d5e 100644
--- a/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html	
+++ b/03 Open Source/01 Debugging Python/05 Method 3 - VisualStudio Debugger.html	
@@ -22,4 +22,4 @@ <h4>Prerequisites</h4>
 </ul>
 </p>
 
-<img class="img-responsive" src="http://cdn.quantconnect.com/i/tu/visual-studio-python-debugger.gif">
\ No newline at end of file
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/visual-studio-python-debugger.gif">

From 291389c69ca15cc7094b3c84476731a0232dde7f Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 15 Jun 2020 18:37:05 -0600
Subject: [PATCH 673/753] Adds strategy 85 - Momentum in Mutual Fund Returns

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Introduction.html                      |   8 +
 .../02 Method.html                            | 179 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 References.html                        |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 208 insertions(+)
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6470de6..0f120a9 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -337,6 +337,15 @@
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.',
         'tags'=>'Intermediate,Long Short,Equities'
     ],
+    [
+        'name' => 'Momentum in Mutual Fund Returns',
+        'link' => 'strategy-library/momentum-in-mutual-fund-returns',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85'
+        ],
+        'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.',
+        'tags'=>'Intermediate,Long Short,Equities'
+    ],
     [
         'name' => 'Momentum and Style Rotation Effect',
         'link' => 'strategy-library/momentum-and-style-rotation-effect',
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html
new file mode 100644
index 0000000..3b57a7e
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+  Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide 
+  significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the 
+  persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds 
+  with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in 
+  individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use 
+  their respective share price as a proxy for fund performance and NAV.
+</p>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
new file mode 100644
index 0000000..119b3e7
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
@@ -0,0 +1,179 @@
+<h4>Universe Selection</h4>
+<p>
+  In coarse universe selection, we return symbols that have fundamental data and are traded in the US market.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+</pre>
+</div>
+
+<p>
+  In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+</pre>
+</div>
+
+<h4>Alpha Construction</h4>
+<p>
+  When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to 
+  long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to 
+  calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+</pre>
+</div>
+
+<p>
+  For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers 
+  a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+</pre>
+</div>
+
+<h4>Alpha Update</h4>
+<p>
+  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+</pre>
+</div>
+
+<h4>Alpha Ranking</h4>
+<p>
+  We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with 
+  the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. 
+  The symbols with a larger final sum have a greater index in the list we return.
+</p>
+<div class="section-example-container">
+<pre class="python">
+@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+</pre>
+</div>
+
+<p>
+  We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and 
+  then computing the respective values.
+</p>
+<div class="section-example-container">
+<pre class="python">
+@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+</pre>
+</div>
+
+<h4>Alpha Insights</h4>
+<p>
+  We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the 
+  universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest 
+  rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+</pre>
+</div>
+
+<h4>Portfolio Construction</h4>
+<p>
+  We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of 
+  insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol 
+  with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate 
+  the net direction of the symbols with the following helper method.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+</pre>
+</div>
+
+
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
new file mode 100644
index 0000000..646988c
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cdcfb760e59da292a56e08c04a219179.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html
new file mode 100644
index 0000000..29e36f1
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    <a href="https://ssrn.com/abstract=1462408">Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011.</a>
+  </li>
+</ol>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 87ee747..255ed4f 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,6 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
+     85: "cdcfb760e59da292a56e08c04a219179",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From a4ead7960f6c38dcaa90cba9548822fbd7621125 Mon Sep 17 00:00:00 2001
From: hsm207 <hsm207@users.noreply.github.com>
Date: Sun, 28 Jun 2020 15:44:12 +0800
Subject: [PATCH 674/753] Fix typo

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6470de6..e5ab9e0 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -50,7 +50,7 @@
         'tags'=>'Expert,Pairs Trading,Equities'
     ],
     [
-        'name' => 'The Momentum Strategy Based on the Low Frequency Compoment of Forex Market',
+        'name' => 'The Momentum Strategy Based on the Low Frequency Component of Forex Market',
         'link' => 'strategy-library/the-momentum-strategy-based-on-the-low-frequency-Component-of-forex-market',
         'description' => 'Applies high frequency filter to the momentum strategy.',
         'tags'=>'Intermediate,Momentum,Forex'

From 0316975d2d33489b9da8852a23265032a787b52f Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 13 Jul 2020 09:31:45 -0600
Subject: [PATCH 675/753] Add strategy library workflow

---
 README.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/README.md b/README.md
index da878ac..5e2fd23 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,22 @@ The mailing list for the project can be found on [Google Groups][6]
 
 Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7].
 
+## Strategy Library Development Workflow ##
+To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps:
+1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process).
+2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png).
+3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py).
+4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)).
+5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project.
+6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples).
+7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)).
+8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)):
+    - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload).
+    - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023.
+    - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json).
+    - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID).
+9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)).
+
 [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer"
 [2]: https://www.quantconnect.com/lean/docs "Lean Documentation"
 [3]: https://github.com/QuantConnect/Lean/archive/master.zip

From a927c4c081002b6031009e6d8edbc4a03f7a2a79 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 15 Jun 2020 18:37:05 -0600
Subject: [PATCH 676/753] Adds strategy 85 - Momentum in Mutual Fund Returns

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Introduction.html                      |   8 +
 .../02 Method.html                            | 179 ++++++++++++++++++
 .../03 Algorithm.html                         |   6 +
 .../04 References.html                        |   5 +
 quantpedia.json                               |   1 +
 6 files changed, 208 insertions(+)
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..28ae3dc 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -337,6 +337,15 @@
         'description' => 'Decile portfolios are formed based on balance sheet based accruals and highest decile is shorted while lowest decile is bought for a year.',
         'tags'=>'Intermediate,Long Short,Equities'
     ],
+    [
+        'name' => 'Momentum in Mutual Fund Returns',
+        'link' => 'strategy-library/momentum-in-mutual-fund-returns',
+        'sources' => [
+            'Quantpedia' => 'https://www.quantpedia.com/Screener/Details/85'
+        ],
+        'description' => 'Forms a long-short portfolio of asset management firms based on trailing rate of change and nearness to trailing high.',
+        'tags'=>'Intermediate,Long Short,Equities'
+    ],
     [
         'name' => 'Momentum and Style Rotation Effect',
         'link' => 'strategy-library/momentum-and-style-rotation-effect',
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html
new file mode 100644
index 0000000..3b57a7e
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+  Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide 
+  significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the 
+  persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds 
+  with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in 
+  individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use 
+  their respective share price as a proxy for fund performance and NAV.
+</p>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
new file mode 100644
index 0000000..119b3e7
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
@@ -0,0 +1,179 @@
+<h4>Universe Selection</h4>
+<p>
+  In coarse universe selection, we return symbols that have fundamental data and are traded in the US market.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def SelectCoarse(self, algorithm, coarse):
+    if self.month == algorithm.Time.month:
+        return Universe.Unchanged
+    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
+    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+</pre>
+</div>
+
+<p>
+  In fine universe selection, we return symbols that Morningstar has classified as being in the asset management industry.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
+    return [ x.Symbol for x in filtered[:self.fine_size] ]
+</pre>
+</div>
+
+<h4>Alpha Construction</h4>
+<p>
+  When constructing the alpha model, we can provide parameters for the lookback windows and the percentage of the universe to 
+  long/short. Both of these arguments are validated in the constructor. By default, this alpha model uses the trailing 6 months to 
+  calculate the rate of change factor and the trailing 12 months to calculate the nearness to historical highs.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
+    if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
+        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        algorithm.Quit()
+        return
+
+    if pct_long_short <= 0 or pct_long_short > 50:
+        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
+        algorithm.Quit()
+        return
+
+    self.roc_lookback_months = roc_lookback_months
+    self.nearness_lookback_months = nearness_lookback_months
+    self.holding_months = holding_months
+    self.pct_long_short = pct_long_short
+</pre>
+</div>
+
+<p>
+  For each security added to the universe, we construct a ROCAndNearness indicator which warm up the lookback windows and registers 
+  a data consolidator. When a security is removed from the universe, we unsubscribe the associated consolidator.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for added in changes.AddedSecurities:
+        roc_and_nearness = ROCAndNearness(added.Symbol, algorithm, self.roc_lookback_months, self.nearness_lookback_months)
+        self.symbol_data_by_symbol[added.Symbol] = roc_and_nearness
+
+    for removed in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(removed.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+</pre>
+</div>
+
+<h4>Alpha Update</h4>
+<p>
+  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    # Emit insights on a monthly basis
+    time = algorithm.Time
+    if self.month == time.month:
+        return []
+    self.month = time.month
+
+    return self.generate_insights(self.ranked_symbols, time)
+</pre>
+</div>
+
+<h4>Alpha Ranking</h4>
+<p>
+  We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with 
+  the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. 
+  The symbols with a larger final sum have a greater index in the list we return.
+</p>
+<div class="section-example-container">
+<pre class="python">
+@property
+def ranked_symbols(self):
+    ranking_df = pd.DataFrame()
+
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if symbol_data.IsReady:
+            row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
+            ranking_df = ranking_df.append(row)
+
+    return ranking_df.rank().sum(axis=1).sort_values().index
+</pre>
+</div>
+
+<p>
+  We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and 
+  then computing the respective values.
+</p>
+<div class="section-example-container">
+<pre class="python">
+@property
+def roc(self):
+    lookback = self.get_lookback(self.roc_lookback_months)
+    start_price = lookback.iloc[0].open
+    end_price = lookback.iloc[-1].close
+    return (end_price - start_price) / start_price 
+
+@property
+def nearness(self):
+    lookback = self.get_lookback(self.nearness_lookback_months)
+    return lookback.iloc[-1].close / lookback.high.max()
+</pre>
+</div>
+
+<h4>Alpha Insights</h4>
+<p>
+  We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the 
+  universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest 
+  rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def generate_insights(self, ranked_symbols, time):
+    insights = []
+
+    num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
+    if num_long_short > 0:
+        hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
+        for symbol in ranked_symbols[-num_long_short:]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
+        for symbol in ranked_symbols[:num_long_short]:
+            insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
+
+    return insights
+</pre>
+</div>
+
+<h4>Portfolio Construction</h4>
+<p>
+  We utilize a custom portfolio construction model that rebalances monthly and performs allocations based on the net direction of 
+  insights for each symbol. A symbol that has two active insights with an up direction will have twice the allocation than a symbol 
+  with only one. Furthermore, a symbol that has an up active insight and a down active insight will have no position. We calculate 
+  the net direction of the symbols with the following helper method.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def get_net_direction(self, insights):
+    net_direction_by_symbol = {}
+    num_directional_insights = 0
+
+    for insight in insights:
+        symbol = insight.Symbol
+        direction = insight.Direction
+        if symbol in net_direction_by_symbol:
+            net_direction_by_symbol[symbol] += direction
+        else:
+            net_direction_by_symbol[symbol] = direction
+
+        num_directional_insights += abs(direction)
+
+    return net_direction_by_symbol, num_directional_insights
+</pre>
+</div>
+
+
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
new file mode 100644
index 0000000..646988c
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cdcfb760e59da292a56e08c04a219179.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html
new file mode 100644
index 0000000..29e36f1
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    <a href="https://ssrn.com/abstract=1462408">Sapp, Travis, The 52-Week High, Momentum, and Predicting Mutual Fund Returns (April 1, 2010). Review of Quantitative Finance and Accounting, Vol. 37, pp. 149-179, 2011.</a>
+  </li>
+</ol>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 87ee747..255ed4f 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,6 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
+     85: "cdcfb760e59da292a56e08c04a219179",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From bfc770811be63b9c81ef66222702a165fe907672 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 17 Jul 2020 13:42:10 -0600
Subject: [PATCH 677/753] define NAV, refactor code, fix lookback bug

---
 .../01 Introduction.html                      | 13 ++--
 .../02 Method.html                            | 65 ++++++++++---------
 .../03 Algorithm.html                         |  2 +-
 quantpedia.json                               |  2 +-
 4 files changed, 45 insertions(+), 37 deletions(-)

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html
index 3b57a7e..ec8c6d8 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/01 Introduction.html	
@@ -1,8 +1,9 @@
 <p>
-  Researchers have shown that the historical returns of a mutual fund and the nearness of its NAV to a previous high can provide 
-  significant predictive power about the fund's future returns. In respect to the historical returns, some have attributed the 
-  persistence to investor herding and macroeconomic variables. When it comes to the NAV, some suggest the outperformance of funds 
-  with a NAV near its trailing high is a result of anchoring bias in investors' psychology. As we do not have access to invest in 
-  individual mutual funds on the QC platform or access to NAV metrics, in this tutorial, we trade asset management firms and use 
-  their respective share price as a proxy for fund performance and NAV.
+  Researchers have shown that the historical returns of a mutual fund and the nearness of its net asset value (NAV) to 
+  a previous high can provide significant predictive power about the fund's future returns. In respect to the 
+  historical returns, some have attributed the persistence to investor herding and macroeconomic variables. When it 
+  comes to the NAV, some suggest the outperformance of funds with a NAV near its trailing high is a result of 
+  anchoring bias in investors' psychology. As we do not have access to invest in individual mutual funds on the QC 
+  platform or access to NAV metrics, in this tutorial, we trade asset management firms and use their respective share 
+  price as a proxy for fund performance and NAV.
 </p>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
index 119b3e7..cd4436e 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
@@ -1,14 +1,14 @@
 <h4>Universe Selection</h4>
 <p>
-  In coarse universe selection, we return symbols that have fundamental data and are traded in the US market.
+  In coarse universe selection, we return symbols that have fundamental data.
 </p>
 <div class="section-example-container">
 <pre class="python">
 def SelectCoarse(self, algorithm, coarse):
     if self.month == algorithm.Time.month:
-        return Universe.Unchanged
-    filtered = [x for x in coarse if (x.HasFundamentalData) and (x.Market == "usa")]
-    return [ x.Symbol for x in filtered[:self.coarse_size] ]
+            return Universe.Unchanged
+            
+    return [x.Symbol for x in coarse if x.HasFundamentalData]
 </pre>
 </div>
 
@@ -19,8 +19,8 @@ <h4>Universe Selection</h4>
 <pre class="python">
 def SelectFine(self, algorithm, fine):
     self.month = algorithm.Time.month
-    filtered = [f for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
-    return [ x.Symbol for x in filtered[:self.fine_size] ]
+        
+    return [f.Symbol for f in fine if f.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.AssetManagement]
 </pre>
 </div>
 
@@ -34,14 +34,10 @@ <h4>Alpha Construction</h4>
 <pre class="python">
 def __init__(self, roc_lookback_months=6, nearness_lookback_months=12, holding_months=6, pct_long_short=10):
     if roc_lookback_months <= 0 or nearness_lookback_months <= 0 or holding_months <= 0:
-        algorithm.Error(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
-        algorithm.Quit()
-        return
-
+        algorithm.Quit(f"Requirement violated:  roc_lookback_months > 0 and nearness_lookback_months > 0 and holding_months > 0")
+        
     if pct_long_short <= 0 or pct_long_short > 50:
-        algorithm.Error(f"Requirement violated: 0 < pct_long_short <= 50")
-        algorithm.Quit()
-        return
+        algorithm.Quit(f"Requirement violated: 0 < pct_long_short <= 50")
 
     self.roc_lookback_months = roc_lookback_months
     self.nearness_lookback_months = nearness_lookback_months
@@ -70,7 +66,8 @@ <h4>Alpha Construction</h4>
 
 <h4>Alpha Update</h4>
 <p>
-  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction model.
+  On the first trading day of each month, we rank the symbols in the universe and emit insights for the portfolio construction 
+  model. We instruct the alpha model to emit insights on a monthly basis by adding the following guard to the Update method. 
 </p>
 <div class="section-example-container">
 <pre class="python">
@@ -81,34 +78,45 @@ <h4>Alpha Update</h4>
         return []
     self.month = time.month
 
-    return self.generate_insights(self.ranked_symbols, time)
+    ...
 </pre>
 </div>
 
 <h4>Alpha Ranking</h4>
 <p>
-  We only rank symbols that have enough history to fill the rate of change lookback window. We start by filling a DataFrame with 
-  the rate of change and nearness to trailing high values for each symbol. We rank the symbols by both metrics and sum the ranks. 
-  The symbols with a larger final sum have a greater index in the list we return.
+  We only rank symbols that have enough history to fill the rate of change lookback window. Therefore, we define the 
+  IsReady method of the ROCAndNearness as
 </p>
 <div class="section-example-container">
 <pre class="python">
 @property
-def ranked_symbols(self):
-    ranking_df = pd.DataFrame()
+def IsReady(self):
+    return self.get_lookback(self.roc_lookback_months).shape[0] > 1
+</pre>
+</div>
 
+<p>
+  To rank the symbols, we start by filling a DataFrame with the rate of change and nearness to trailing high values for each 
+  symbol. When the DataFrame is full, we rank the symbols by both metrics and sum the ranks. The symbols with a larger final 
+  sum have a greater index in the `ranked_symbols` list.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    ...
+    ranking_df = pd.DataFrame()
     for symbol, symbol_data in self.symbol_data_by_symbol.items():
         if symbol_data.IsReady:
             row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
             ranking_df = ranking_df.append(row)
-
-    return ranking_df.rank().sum(axis=1).sort_values().index
+    ranked_symbols =  ranking_df.rank().sum(axis=1).sort_values().index
+   ...
 </pre>
 </div>
 
 <p>
-  We calculate the rate of change and nearness factors by slicing the historical data into the approriate lookback window size, and 
-  then computing the respective values.
+  Calculating the rate of change and nearness factors is done by slicing the historical data into the approriate lookback window 
+  size, and then computing the respective values.
 </p>
 <div class="section-example-container">
 <pre class="python">
@@ -129,14 +137,14 @@ <h4>Alpha Ranking</h4>
 <h4>Alpha Insights</h4>
 <p>
   We return insights that instruct the portfolio construction model to form a balance long-short portfolio. The percentage of the 
-  universe we long and short is customizable in the alpha model constructor. Here, we long the 10% of symbols with the highest 
-  rank, short the 10% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
+  universe we long and short is customizable in the alpha model constructor. Here, we long the 25% of symbols with the highest 
+  rank, short the 25% of symbols with the lowest ranks, and instruct the portfolio construction model to hold positions for 6 months.
 </p>
 <div class="section-example-container">
 <pre class="python">
-def generate_insights(self, ranked_symbols, time):
+def Update(self, algorithm, data):
+    ...
     insights = []
-
     num_long_short = int(len(ranked_symbols) * (self.pct_long_short / 100))
     if num_long_short > 0:
         hold_duration = Expiry.EndOfMonth(time) + relativedelta(months=self.holding_months-1, seconds=-1)
@@ -144,7 +152,6 @@ <h4>Alpha Insights</h4>
             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Up))
         for symbol in ranked_symbols[:num_long_short]:
             insights.append(Insight.Price(symbol, hold_duration, InsightDirection.Down))
-
     return insights
 </pre>
 </div>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
index 646988c..ade7d3b 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cdcfb760e59da292a56e08c04a219179.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_ba030edb022016f967a6296a556d717e.html"></iframe>
     </div>
 </div>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index 255ed4f..cbf4970 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,7 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
-     85: "cdcfb760e59da292a56e08c04a219179",
+     85: "ba030edb022016f967a6296a556d717e",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From ccbf44f6741687e50985494f4a6baa3cb3d90ef0 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Wed, 29 Jul 2020 18:07:56 -0600
Subject: [PATCH 678/753] Adds intraday etf momentum strategy

---
 .../01 Strategy Library.php                   |  11 +-
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  19 +++
 .../1026 Intraday ETF Momentum/03 Method.html | 143 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Conclusion.html                        |  23 +++
 .../06 References.html                        |   6 +
 7 files changed, 214 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
 create mode 100644 04 Strategy Library/1026 Intraday ETF Momentum/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..30af0f7 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+    [
+        'name' => 'Intraday ETF Momentum',
+        'link' => 'strategy-library/intraday-etf-momentum',
+        'sources' => [
+            'NYU' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2440866'
+        ],
+        'description' => "A momentum strategy based on returns of the market open",
+        'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+    ]
 ];
 
 ?>
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html
new file mode 100644
index 0000000..c9aef2c
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/01 Abstract.html	
@@ -0,0 +1,7 @@
+<p>
+  In this tutorial, we implement an intraday momentum strategy that trades some of the most actively traded ETFs. 
+  Specifically, we observe the return generated from the first half-hour of the trading day to predict the sign of 
+  the trading day's last half-hour return. Researchers have shown that this momentum pattern is statistically and 
+  economically significant, even after accounting for trading fees. The algorithm we design here is a recreation of 
+  the research completed by Gao, Han, Li, and Zhou (2017).
+</p>
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html
new file mode 100644
index 0000000..c06af0a
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/02 Background.html	
@@ -0,0 +1,19 @@
+<p>
+  News items are usually released before the opening bell. As it takes time for traders to digest and interpret the 
+  news, the first half-hour of trading typically has relatively higher levels of volume and volatility. Additionally, 
+  as traders attempt to mitigate overnight risk by unloading positions near the close, the last half-hour of trading 
+  also sees these higher levels of volume and volatility. These characteristics can be observed from the image below, 
+  which is reproducible in the attached research notebook.
+</p>
+
+<img class="img-responsive" 
+     src="https://cdn.quantconnect.com/i/tu/avg-volume-volatility.png" 
+     alt="Tutorial1026-intraday-etf-momentum-1" /> 
+
+<p>
+  Bogousslavsky (2016) points out that some investors are late-informed or simply prefer to delay their trading until 
+  the market close. As a result, a positive correlation exists between the direction of the opening and closing 
+  periods. Gao et al (2017) find that when trading this momentum strategy, the average annual return over their 
+  sample period was 6.67% for SPY, 11.72% for IWM, and 24.22% for IYR. Equal-weighting these returns leads to a 
+  combined average annual return of 14.2%.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html
new file mode 100644
index 0000000..29481ab
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/03 Method.html	
@@ -0,0 +1,143 @@
+<h4>Universe Selection</h4>
+<p>
+  We implement a manual universe selection model that supplies a subset of the proposed ETFs in the attached research 
+  paper. Gao et al (2017) select the following tickers: DIA, QQQ, IWM, EEM, FXI, EFA, VWO, XLF, IYR, and TLT. In an 
+  effort to increase the backtest performance, we narrow our universe to SPY, IWM, and IYR.
+</p>
+<div class="section-example-container">
+<pre class="python">
+tickers = ['SPY',  # S&P 500
+           'IWM',  # Russell 2000
+           'IYR'   # Real Estate ETF
+]
+symbols = [ Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+</pre>
+</div>
+
+<h4>Alpha Construction</h4>
+<p>
+  The IntradayMomentumAlphaModel emits insights to take positions for the last `return_bar_count` minutes of the day 
+  in the direction of the return for the first `return_bar_count` minutes of the day. During construction, we create 
+  a dictionary to store IntradayMomentum data for each symbol, define a method to determine the sign of returns, and 
+  specify the value of `return_bar_count`. In this tutorial, we follow Gao et al (2017) in setting `return_bar_count`
+  to 30 by default.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class IntradayMomentumAlphaModel(AlphaModel):
+    intraday_momentum_by_symbol = {}
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+
+    def __init__(self, algorithm, return_bar_count = 30):
+        self.return_bar_count = return_bar_count
+</pre>
+</div>
+
+<h4>Alpha Securities Management</h4>
+<p>
+  When a new security is added to the universe, we create an IntradayMomentum object for it to store information 
+  needed to calculate morning returns. The management of the IntradayMomentum objects occurs in the alpha model's 
+  OnSecuritiesChanged method.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.intraday_momentum_by_symbol[security.Symbol] = IntradayMomentum(security, algorithm)
+
+    for security in changes.RemovedSecurities:
+        self.intraday_momentum_by_symbol.pop(security.Symbol, None)
+</pre>
+</div>
+
+<p>
+  The definition of the IntradayMomentum class is shown below. We save a reference to the security's exchange so we 
+  can access the market hours of the exchange when generating insights.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class IntradayMomentum:
+    def __init__(self, security, algorithm):
+        self.symbol = security.Symbol
+        self.exchange = security.Exchange
+
+        self.bars_seen_today = 0
+        self.yesterdays_close = algorithm.History(self.symbol, 1, Resolution.Daily).loc[self.symbol].close[0]
+        self.morning_return = 0
+</pre>
+</div>
+
+<h4>Alpha Update</h4>
+<p>
+  With each call to the alpha model's Update method, we count the number of bars the algorithm has received for each 
+  symbol. If we've reached the end of the morning window, we calculate the morning return. If we are at the 
+  beginning of the close window, we emit an insight in the direction of the morning window's return. If we are at 
+  the end of the day, we save the closing price and reset the counter for the number of bars seen today.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, slice):
+    insights = []
+
+    for symbol, intraday_momentum in self.intraday_momentum_by_symbol.items():
+        if slice.ContainsKey(symbol) and slice[symbol] is not None:
+            intraday_momentum.bars_seen_today += 1
+            
+            # End of the morning return
+            if intraday_momentum.bars_seen_today == self.return_bar_count:
+                intraday_momentum.morning_return = (slice[symbol].Close - intraday_momentum.yesterdays_close) / intraday_momentum.yesterdays_close
+            
+            ## Beginning of the close
+            next_close_time = intraday_momentum.exchange.Hours.GetNextMarketClose(slice.Time, False)
+            mins_to_close = int((next_close_time - slice.Time).total_seconds() / 60)
+            
+            if mins_to_close == self.return_bar_count + 1:
+                insight = Insight.Price(intraday_momentum.symbol, 
+                                        next_close_time, 
+                                        self.sign(intraday_momentum.morning_return))
+                insights.append(insight)
+                continue
+            
+            # End of the day
+            if not intraday_momentum.exchange.DateTimeIsOpen(slice.Time):
+                intraday_momentum.yesterdays_close = slice[symbol].Close
+                intraday_momentum.bars_seen_today = 0
+            
+    return insights
+</pre>
+</div>
+
+<h4>Trade Execution</h4>
+<p>
+  The attached research paper holds positions for the last 30 minutes of the trading day, exiting at the market close.
+  In order to accomplish this, we create a custom execution model. The model defined below submits a market order for 
+  the entry while also submitting a market on close order in the same time step.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class CloseOnCloseExecutionModel(ExecutionModel):
+    def __init__(self):
+        self.targetsCollection = PortfolioTargetCollection()
+        self.invested_symbols = []
+
+    def Execute(self, algorithm, targets):
+        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        self.targetsCollection.AddRange(targets)
+        if self.targetsCollection.Count > 0:
+            for target in self.targetsCollection.OrderByMarginImpact(algorithm):
+                # calculate remaining quantity to be ordered
+                quantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
+                if quantity == 0:
+                    continue
+
+                algorithm.MarketOrder(target.Symbol, quantity)
+                algorithm.MarketOnCloseOrder(target.Symbol, -quantity)
+
+            self.targetsCollection.ClearFulfilled(algorithm)
+</pre>
+</div>
+
+
+
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
new file mode 100644
index 0000000..c7304ae
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_47fcfa784d1e69265b86d7be5bc82cb9.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
new file mode 100644
index 0000000..7623dda
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -0,0 +1,23 @@
+<p>
+  We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period.
+  In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the 
+  inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their 
+  research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum 
+  pattern. These areas of future research include:
+</p>
+
+<ul>
+  <li>
+    Trading only on days with economic news events by utilizing the 
+    <a href="https://www.quantconnect.com/docs/alternative-data/trading-economics">TradingEconomics</a> data set. 
+    Gae et al (2017) suggest using the Michigan Consumer Sentiment Index, and news released on gross domestic 
+    product or the consumer price index.
+  </li>
+  <li>Restricting trading to times of greater volatility or during financial crises.</li>
+  <li>Incorporating a volume threshold the morning session must pass to signal a trade for the close.</li>
+  <li>Increasing diversification by extending the universe to include ETFs from other sectors.</li>
+  <li>
+    Considering the return from multiple n-minute periods throughout the day to predict the return of the closing 
+    period.
+  </li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html
new file mode 100644
index 0000000..a3127a1
--- /dev/null
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/06 References.html	
@@ -0,0 +1,6 @@
+<ol>
+  <li>
+    Gao, Lei and Han, Yufeng and Li, Sophia Zhengzi and Zhou, Guofu, Market Intraday Momentum (June 19, 2017). 
+    <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2440866">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 75a44e5d0877fd4c51bcafa9b911aaab1727879f Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Mon, 3 Aug 2020 08:48:05 -0700
Subject: [PATCH 679/753] Added 279 temporal cnn strategy

---
 .../01 Abstract.html                          |   6 +
 .../02 Introduction.html                      |   8 +
 .../03 Method.html                            | 280 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 References.html                        |   7 +
 5 files changed, 307 insertions(+)
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
new file mode 100644
index 0000000..a6cf634
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html	
@@ -0,0 +1,6 @@
+<p>
+    In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices.
+</p>
+<p>
+    <em>Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading</em>
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
new file mode 100644
index 0000000..1fa0f4e
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html	
@@ -0,0 +1,8 @@
+<p>
+    Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements.
+    More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from
+    medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price
+    movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in
+    image classification, and now, researchers are applying CNNs to extract patterns, also known as <em>features</em>, from times-series
+    data to forecast future stock prices.
+</p>
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
new file mode 100644
index 0000000..1a4765a
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html	
@@ -0,0 +1,280 @@
+<h3>Overview</h3>
+<p>
+    Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data
+    to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and
+    bet on the direction of the price movement based on our model prediction. We will walk through the code required
+    for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand.
+</p>
+
+<h3>Inputs/Outputs</h3>
+
+<p>
+    Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model.
+    The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a
+    direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps).
+    The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will
+    form the labels for which our model will try to classify, thus we have a <em>classification</em> problem.
+</p>
+
+
+<h3>Neural Network Model Architecture</h3>
+
+<p>
+    Now, we will need to build our Neural Network Architecture, which we will build using <a href="https://keras.io/">Keras</a>,
+    a high-level Python Deep Learning API. To begin, we will need a few import statements:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    import tensorflow as tf
+    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
+    from tensorflow.keras import Model
+    from tensorflow.keras import metrics
+    from tensorflow.keras.losses import CategoricalCrossentropy
+    from tensorflow.keras import utils
+    from sklearn.preprocessing import StandardScaler
+    import numpy as np
+    import math
+</pre>
+</div>
+
+
+<p>
+    We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input
+    variables (OHLCV), our input shape will be 15 x 5.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    inputs = Input(shape=(15, 5))
+</pre>
+</div>
+
+
+<p>
+    We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as
+    the Neural Network's method of extracting patterns from the time-series data.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
+</pre>
+</div>
+
+
+<div class="section-example-container">
+<pre class="python">
+    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
+    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
+    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
+
+    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
+    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
+    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
+</pre>
+</div>
+
+
+<p>
+    These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten
+    our layer.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
+    flattened = Flatten()(combined)
+</pre>
+</div>
+
+
+<p>
+    Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down),
+    this layer will have three nodes.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    outputs = Dense(3, activation='softmax')(flattened)
+</pre>
+</div>
+
+<p>
+    The resulting Neural Network Architecture is shown in the following:
+</p>
+
+<img src="https://cdn.quantconnect.com/i/tu/temporal-cnn-architecture.png" alt="temporal cnn model architecture">
+
+
+<h3>Preparing the Data for Our Model</h3>
+
+<p>
+    First, we need to define a class and a few variables:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    input_vars = ['open', 'high', 'low', 'close', 'volume']
+
+    class Direction:
+        UP = 0
+        DOWN = 1
+        STATIONARY = 2
+
+    rolling_avg_window_size = 5
+
+    shift = -(rolling_avg_window_size-1)
+
+    stationary_threshold = .0001
+
+    scaler = StandardScaler()
+</pre>
+</div>
+
+<p>
+    <em>input_vars</em> define the variables we want to use to make our predictions. The class <em>Direction</em>
+    defines a few integers that we will label our data with (labels are needed for classification problems). The reason
+    we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on,
+    <em>rolling_avg_window_size</em> is the number of time steps used for the calculate the average of future closing prices,
+    described earlier in
+    <b>Inputs/Outputs</b> (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5).
+    The constant <em>stationary_threshold</em> defines the threshold for a change in price to be considered
+    stationary, and this change also described in <b>Inputs/Outputs</b>. The <em>shift</em> is the shift needed to align
+    the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces
+    manageable for our Neural Network model. The <em>scaler</em> object will be used later to scale our data.
+    The purpose of the variables will become clearer in use.
+<p>
+    Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate
+    the percent change from the close at t, we use the following lines of code:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
+    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
+</pre>
+</div>
+
+<p>
+    The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize
+    this is a more advanced resource).
+    Here, <em>.shift(shift)</em> aligns the five time step rolling average <em>'close_avg'</em> column to the end of the last
+    time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input
+    and labeled data for our model much easier.
+</p>
+
+<p>
+    To label our data, we need to first define a function that we will use with the DataFrame's <em>apply()</em> method.
+    Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    def label_data(row):
+        if row['close_avg_change_pct'] > stationary_threshold:
+            return Direction.UP
+        elif row['close_avg_change_pct'] < -stationary_threshold:
+            return Direction.DOWN
+        else:
+            return Direction.STATIONARY
+</pre>
+</div>
+
+<p>
+    Now, we apply the above function to our DataFrame to get a column of labels:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    df['movement_labels'] = df.apply(label_data, axis=1)
+</pre>
+</div>
+
+<p>
+    With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into
+    lists:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    data = []
+    labels = []
+
+    for i in range(len(df)-self.n_tsteps+1+shift):
+        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
+        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
+        labels.append(label)
+
+    data = np.array(data)
+</pre>
+</div>
+
+<p>
+    Here, we iterate numerically through the DataFrame, with a carefully calculated value in our <em>range()</em>
+    function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because
+    Keras works best with numpy arrays.
+</p>
+
+<p>
+    Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the
+    range of values is normalized across the features.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    dim1, dim2, dim3 = data.shape
+    data = data.reshape(dim1*dim2, dim3)
+    data = scaler.fit_transform(data)
+    data = data.reshape(dim1, dim2, dim3)
+</pre>
+</div>
+
+<p>
+    The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after,
+    we can return the data to the original shape with another reshaping.
+</p>
+
+<p>
+    Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of
+    1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following:
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    labels = utils.to_categorical(labels, num_classes=3)
+</pre>
+</div>
+
+<p>
+    Specifying <em>num_classes</em> to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary).
+</p>
+
+<p>
+    We have now finished the walk through of the difficult parts of the code.
+</p>
+
+<h3>Trading</h3>
+
+<p>
+    After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be
+    found in <b>Algorithm</b>) we can
+    use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make
+    a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit
+    an Price Insight with direction <em>InsightDirection.Up</em> (resp. <em>InsightDirection.Down</em>). Since we are
+    betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights
+    in the respective direction for <em>timedeltas</em> of one through five. However, we choose to only emit an Insight
+    with a <em>timedelta</em> with a random integer between one and five to constrain the number of insights we emit.
+</p>
+
+
+<h3>The Rest</h3>
+
+<p>
+    We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the
+    necessary code to execute the strategy can be found in <b>Algorithm</b>.
+</p>
+
+
+
+
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
new file mode 100644
index 0000000..9e98ce9
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4f9621a910142db83b6b756177a5b83c.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html
new file mode 100644
index 0000000..dedbeb6
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html	
@@ -0,0 +1,7 @@
+<ol>
+  <li>
+      Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis:
+      "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019;
+      <a href="https://arxiv.org/pdf/1901.08280.pdf">https://arxiv.org/pdf/1901.08280.pdf.</a>
+  </li>
+</ol>
\ No newline at end of file

From a7c9fbec4ab53b8a8334d2acde9d6b87dec13848 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Mon, 3 Aug 2020 08:50:08 -0700
Subject: [PATCH 680/753] 279 temporal cnn update 00 SL

---
 .../00 Strategy Library/01 Strategy Library.php       | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..2efe9eb 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+	[
+        'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model',
+        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn',
+        'sources' => [
+            'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf'
+        ],
+        'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.",
+        'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning'
+    ]
 ];
 
 ?>

From cb872175e7804070859b0b63661189572f8ad161 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 3 Aug 2020 17:45:37 -0600
Subject: [PATCH 681/753] Fix missing div tags

---
 .../03 Method.html                                            | 3 ++-
 .../03 Algorithm.html                                         | 2 +-
 .../03 Algorithm.html                                         | 2 +-
 .../29 Term Structure Effect in Commodities/03 Algorithm.html | 4 ++--
 .../92 Price Earnings Anomaly/03 Algorithm.html               | 2 +-
 5 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html
index 3675eee..ffff064 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/03 Method.html	
@@ -82,4 +82,5 @@ <h3>Step 4: Rebalance Function:</h3>
 
     # Invest 100% in the each of the selected symbols
     for symbol in symbols:
-        self.SetHoldings(symbol, 1)</pre>
\ No newline at end of file
+        self.SetHoldings(symbol, 1)</pre>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html
index 635c7b2..ad5e9f8 100644
--- a/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html	
+++ b/04 Strategy Library/27 Momentum Effect in Commodities Futures/03 Algorithm.html	
@@ -1,4 +1,4 @@
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0414f4f614c36b28af3a0883bad4b018.html"></iframe>
diff --git a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html
index cc3a665..b50f912 100644
--- a/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/28 Small Capitalization Stocks Premium Anomaly/03 Algorithm.html	
@@ -1,4 +1,4 @@
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ae6b2ce97f01f10af90c8823828ae5c9.html"></iframe>
diff --git a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html
index 1707718..ada8627 100644
--- a/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
+++ b/04 Strategy Library/29 Term Structure Effect in Commodities/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <p>Algorithm</p>
 <span class="python">
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d7285b5353d51cd5bd033f205e5faf44.html"></iframe>
@@ -8,7 +8,7 @@
 </div>
 </span>
 <span class="csharp">
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a14a3ca9e9de51e5670f10cb7ec14a95.html"></iframe>
diff --git a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html
index dce77dd..35a5232 100644
--- a/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
+++ b/04 Strategy Library/92 Price Earnings Anomaly/03 Algorithm.html	
@@ -1,4 +1,4 @@
-div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
         <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc4e18fb2db3e4a4ef61732dec927fe5.html"></iframe>

From 53e3114c169f284aba74960ff1b61d9ad3837982 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Tue, 11 Aug 2020 17:03:22 -0600
Subject: [PATCH 682/753] Adds strategy 1028 - Ichimoku Clouds in the Energy
 Sector

---
 .../01 Strategy Library.php                   |  11 +-
 .../01 Abstract.html                          |   8 +
 .../02 Introduction.html                      |  35 ++++
 .../03 Method.html                            | 150 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Benchmark Performance.html             |  10 ++
 ...06 Market & Competition Qualification.html |  13 ++
 .../07 Conclusion.html                        |  21 +++
 .../08 References.html                        |   6 +
 9 files changed, 259 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index e5ab9e0..e4eb7df 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -606,7 +606,16 @@
 		],
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
-	]
+	],
+    [
+        'name' => 'Ichimoku Clouds in the Energy Sector',
+        'link' => 'strategy-library/ichimoku-clouds-in-the-energy-sector',
+        'sources' => [
+            'SSRN' => 'https://ssrn.com/abstract=3520582'
+        ],
+        'description' => 'A techincal indicator crossover strategy trading the largest energy companies.',
+        'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities'
+    ],
 ];
 
 ?>
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html
new file mode 100644
index 0000000..3dd4983
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html	
@@ -0,0 +1,8 @@
+<p>
+  Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the 
+  largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the 
+  effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an 
+  impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib 
+  (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular 
+  energy sector ETF.
+</p>
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html
new file mode 100644
index 0000000..c5efef1
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/02 Introduction.html	
@@ -0,0 +1,35 @@
+<p>
+  A vast amount of research studies have been published which document mixed results when utilizing technical 
+  analysis to forecast future prices of securities. The Ichimoku Cloud, one of the most widely-used technical 
+  indicators in Japan, was first publicized by Goichi Hosoda. In 1996, Hidenobu Sasaki reworked the framework to 
+  form the current charting analysis tool. This indicator is composed of 5 lines in a time series, each of which 
+  are described mathematically in 
+  <a href="https://www.investopedia.com/terms/i/ichimoku-cloud.asp">online resources</a>.
+</p>
+
+<p>
+  Gurrib (2020) finds that applying a simple trading strategy using the time series of the Ichimoku Cloud can 
+  increase the mean return of a basket containing the top energy stocks from 21.5% (buy-and-hold) to 194% over a 7 
+  year period. The components of the Ichimoku Cloud that Gurrib (2020) utilizes in this strategy are the Chikou 
+  Span, Senkou Span A, and Senkou Span B. The Senkou Span lines form the top and bottom of the Ichimoku Cloud. The 
+  strategy that we trade off of these lines is defined as follows:
+</p>
+
+<ul>
+  <li>Long when the Chikou line crosses the top of the cloud from below.</li>
+  <li>Short when the Chikou line crosses the bottom of the cloud from above.</li>
+</ul>
+
+<p>
+  For better understanding, here is a visualization of the price of XOM, it's Ichimoku Cloud time series, and the 
+  resulting buy/sell signals.
+</p>
+
+<img class="img-responsive" 
+     src="https://cdn.quantconnect.com/i/tu/xom-trading-signals.png" 
+     alt="Tutorial1028-ichimoku-cloud-1" /> 
+
+<p>
+  Note: all of the plots throughout this tutorial are reproducible in the attached research notebook, along with 
+  some descriptive statistics for securities in the universe.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html
new file mode 100644
index 0000000..8669ce3
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/03 Method.html	
@@ -0,0 +1,150 @@
+<h4>Universe Selection</h4>
+<p>
+  Gurrib (2020) selects a universe of the 10 largest-weighed constituents of the S&P Composite 1500 Energy Index 
+  over the testing period. This inherently incorporates lookahead-bias into the study as the security weights are 
+  sourced over the period the trading simulation occurs. Furthermore, since the publication of Gurrib (2020), some 
+  of the securities have even been delisted. Thus, to eliminate lookahead-bias and avoid delistings, we implement a
+  universe selection model that provides the trading system with the 10 largest companies in the energy sector as of
+  the current date in the backtest. Since the largest companies change infrequently, we only refresh the universe on
+  a monthly basis.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def SelectCoarse(self, algorithm, coarse):
+    if algorithm.Time.month == self.month:
+        return Universe.Unchanged
+    return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+
+def SelectFine(self, algorithm, fine):
+    self.month = algorithm.Time.month
+    
+    energy_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy ]
+    sorted_by_market_cap = sorted(energy_stocks, key=lambda x: x.MarketCap, reverse=True)
+    return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+</pre>
+</div>
+
+
+<h4>Alpha Construction</h4>
+<p>
+  The IchimokuCloudCrossOverAlphaModel emits insights to hold a long position after the Chikou Span crosses over the
+  top of the cloud for a given security. Additionally, the strategy is made symmetrical by entering short positions 
+  after the Chikou Span crosses below the bottom of the cloud. During construction of this alpha model, we simply 
+  set up a dictionary to hold a SymbolData object for each symbol in the universe.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class IchimokuCloudCrossOverAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+</pre>
+</div>
+
+<p>
+  The SymbolData class constructor is shown below. We first set up two class variables, `previous_location` and 
+  `direction`. The former enables the algorithm to signal when the Chikou Span crosses over the boundaries of the 
+  Ichimoku Cloud. The latter is added to ensure we continue to emit daily insights in the proper direction. Inside 
+  the `__init__` method is where we create the 
+  <a href="https://www.quantconnect.com/lean/documentation/topic28736.html">IchimokuKinkoHyo</a> indicator and warm 
+  it up.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    previous_location = None
+    direction = None
+    
+    def __init__(self, symbol, algorithm):
+        # Create Ichimoku indicator
+        self.ichimoku = IchimokuKinkoHyo()
+        
+        # Warm up indicator
+        history = algorithm.History(symbol, self.ichimoku.WarmUpPeriod + 1, Resolution.Daily).loc[symbol]
+        for idx, row in history.iterrows():
+            if self.ichimoku.IsReady:
+                self.previous_location = self.get_location()
+            
+            tradebar = TradeBar(idx, symbol, row.open, row.high, row.low, row.close, row.volume)
+            self.ichimoku.Update(tradebar)
+</pre>
+</div>
+
+
+<h4>Determining the Indicator Location</h4>
+<p>
+  We define the following helper method to return the location of the Chikou Span with respect to the cloud. The 
+  alpha model utilizes this helper method to determine when the Chikou Span is exiting the Ichimoku Cloud.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def get_location(self):
+    chikou = self.ichimoku.Chikou.Current.Value
+    
+    senkou_span_a = self.ichimoku.SenkouA.Current.Value
+    senkou_span_b = self.ichimoku.SenkouB.Current.Value
+    cloud_top = max(senkou_span_a, senkou_span_b)
+    cloud_bottom = min(senkou_span_a, senkou_span_b)
+    
+    if chikou > cloud_top:
+        return 1    # Above cloud
+    if chikou < cloud_bottom:
+        return -1   # Below cloud
+        
+    return 0        # Inside cloud
+</pre>
+</div>
+
+
+<h4>Alpha Update</h4>
+<p>
+  As new <a href="https://www.quantconnect.com/lean/documentation/topic24427.html">TradeBar</a>s are provided to the
+  alpha model's Update method, we update the Ichimoku indicator of each symbol. We then emit insights for the 
+  symbols that have their Chikou Span breaking out of their respective Ichimoku Cloud in a new direction. To 
+  maintain positions while we wait for another crossover in the Ichimoku Cloud, we emit insights on a daily basis 
+  with 1-day duration.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    insights = []
+    
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        if not data.ContainsKey(symbol) or data[symbol] is None:
+            continue
+        
+        # Update indicator with the latest TradeBar
+        symbol_data.ichimoku.Update(data[symbol])
+        
+        # Determine insight direction
+        current_location = symbol_data.get_location()
+        if symbol_data.previous_location is not None: # Indicator is ready
+            if symbol_data.previous_location != 1 and current_location == 1:
+                symbol_data.direction = InsightDirection.Up
+            if symbol_data.previous_location != -1 and current_location == -1:
+                symbol_data.direction = InsightDirection.Down
+        
+        symbol_data.previous_location = current_location
+        
+        # Emit insight
+        if symbol_data.direction:
+            insight = Insight.Price(symbol, timedelta(days=1), symbol_data.direction)
+            insights.append(insight)
+    
+    return insights
+</pre>
+</div>
+
+
+<h4>Portfolio Construction & Trade Execution</h4>
+<p>
+  Following the guidelines of <a href="https://www.quantconnect.com/docs/alpha-streams/overview">Alpha Streams</a> 
+  and the <a href="https://www.quantconnect.com/competitions/quant-league-7">Quant League</a> competition, we 
+  utilize the <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Portfolio/EqualWeightingPortfolioConstructionModel.py">
+  EqualWeightingPortfolioConstructionModel</a> and the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm/Execution/ImmediateExecutionModel.py">
+  ImmediateExecutionModel</a>.
+</p>
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html
new file mode 100644
index 0000000..487c85d
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1ba5ba885d63246989cf0c2ed32d2d97.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html
new file mode 100644
index 0000000..f3592aa
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html	
@@ -0,0 +1,10 @@
+<p>
+  To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a 
+  popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the 
+  benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over
+  the backtest period.
+</p>
+
+<img class="img-responsive" 
+     src="https://cdn.quantconnect.com/i/tu/benchmark-performance.png" 
+     alt="Tutorial1028-ichimoku-cloud-2" />
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..e63b0d3
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/06 Market & Competition Qualification.html	
@@ -0,0 +1,13 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-7#rules">metrics</a> required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+</p>
+
+<ul>
+  <li>Profitable</li>
+  <li>PSR >= 80%</li>
+  <li>Max drawdown duration <= 6 months</li>
+  <li>Insights contain the following properties: Symbol, Duration, Direction, and Weight</li>
+  <li>Minute or second data resolution</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html
new file mode 100644
index 0000000..4631b74
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html	
@@ -0,0 +1,21 @@
+<p>
+  While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash,
+  we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The 
+  strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of 
+  0.92. To continue the development of this strategy, future areas of research include:
+</p>
+
+<ul>
+  <li>
+    Only trading when a bullish or bearish trend are confirmed. Gurrib (2020) provides conditions for evaluating 
+    the trend.
+  </li>
+  <li>Adjusting the data resolution used within the indicator.</li>
+  <li>Considering fundamental or alternative data points before placing trades.</li>
+  <li>Adjusting the parameters of the Ichimoku indicator.</li>
+  <li>
+    Replacing the portfolio construction model with one that only allocates 10% of the capital base to each security.
+    This will violate the requirements of the Alpha Streams and Quant League competition, but it will reduce the 
+    transaction costs and provide a more accurate equity curve of the underlying strategies performance.
+  </li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html
new file mode 100644
index 0000000..37c3e11
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/08 References.html	
@@ -0,0 +1,6 @@
+<ol>
+  <li>
+    Gurrib, Ikhlaas, Can the Leading Us Energy Stock Prices Be Predicted Using Ichimoku Clouds? (January 16, 2020). 
+    <a href="https://ssrn.com/abstract=3520582">Online Copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 6142dbf3ae57c8d8500e5722e8ff4b84d1106e97 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 09:29:42 -0700
Subject: [PATCH 683/753] fixed 01 strategy library information

---
 .../00 Strategy Library/01 Strategy Library.php               | 4 ++--
 .../01 Abstract.html                                          | 0
 .../02 Introduction.html                                      | 0
 .../03 Method.html                                            | 0
 .../04 Algorithm.html                                         | 0
 .../05 References.html                                        | 0
 6 files changed, 2 insertions(+), 2 deletions(-)
 rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/01 Abstract.html (100%)
 rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/02 Introduction.html (100%)
 rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/03 Method.html (100%)
 rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/04 Algorithm.html (100%)
 rename 04 Strategy Library/{1024 Forecasting Stock Prices using a Temporal CNN => 1024 Forecasting Stock Prices using a Temporal CNN Model}/05 References.html (100%)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2efe9eb..1c02f03 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -608,8 +608,8 @@
 		'tags' => 'Momentum, Futures, Commodities'
 	],
 	[
-        'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model',
-        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn',
+        'name' => 'Forecasting Stock Prices using a Temporal CNN Model',
+        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn-model',
         'sources' => [
             'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf'
         ],
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/01 Abstract.html
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/02 Introduction.html
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html

From 0328c543a7d0cf791ffa8d7e561ca875029ae620 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 09:35:47 -0700
Subject: [PATCH 684/753] Adds 292 Leveraged ETFS

---
 .../00 Strategy Library/01 Strategy Library.php      |  9 +++++++++
 .../01 Abstract.html                                 |  3 +++
 .../02 Introduction.html                             |  9 +++++++++
 .../03 Method.html                                   | 12 ++++++++++++
 .../04 Algorithm.html                                |  6 ++++++
 .../05 Results.html                                  |  5 +++++
 .../06 References.html                               |  7 +++++++
 7 files changed, 51 insertions(+)
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html
 create mode 100644 04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2efe9eb..4ddbbdf 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -615,6 +615,15 @@
         ],
         'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.",
         'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning'
+    ],
+    [
+        'name' => 'Leveraged ETFs with Systematic Risk Management',
+        'link' => 'leveraged-etfs-with-systematic-risk-management',
+        'sources' => [
+            'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701'
+        ],
+        'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500',
+        'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF'
     ]
 ];
 
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html
new file mode 100644
index 0000000..6e0ee07
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+    In this tutorial, we attempt to beat the returns of the S&P500 Index using leverage and systematic risk management.
+</p>
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html
new file mode 100644
index 0000000..0b1b370
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/02 Introduction.html	
@@ -0,0 +1,9 @@
+<p>
+    When measuring the profitability of a strategy, it is usually not enough to be profitable, as it should also beat
+    the benchmark, for which the SP500 Index is most commonly used. If not, what would be the reason to not just invest
+    in a low-cost SP500 index instead (not accounting for risk)? However, what if we could beat the SP500 with the SP500?
+    This is what Gayed et al. proposed with their strategy of using Leveraged SP500 indices with Systematic Risk Management.
+    They propose that Moving Averages are a good method to assess volatility in the market, and they use it to manage risk,
+    which is especially important since the effects of price swings in a leveraged ETF are magnified due to the leverage.
+    With this method, we hope to outperform the SP500 while at the same time, attempt to reduce drawdown.
+</p>
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html
new file mode 100644
index 0000000..a43a70e
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/03 Method.html	
@@ -0,0 +1,12 @@
+<p>
+    We develop hold/liquidate positions based on the 200-day Simple Moving Average (SMA) of our ETF, for which we use SSO,
+    a 2x leveraged SP500 index ETF. With 200 days instead of using fewer days, say 50, we reduce the number of trades per year,
+    thereby reducing transaction costs and the effects of slippage. Moving on, if the current price of SOO is above the 200-day SMA,
+    we hold SSO, and if SSO dips below our 200-day SMA, we sell liquidate our position and rotate our position into short-term
+    treasuries, which is done through SHY, an ETF that tracks 1-3 year U.S. Treasury Bonds. If we are holding SHY, and the
+    current price of SSO moves above the 200-day SMA, then we rotate back into SSO and liquidate SHY.
+</p>
+
+
+
+
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html
new file mode 100644
index 0000000..3b0ead5
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_24e24709bb5688f6e3098c1caeec93d6.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html
new file mode 100644
index 0000000..dfa86cf
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html	
@@ -0,0 +1,5 @@
+<p>
+    We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years,
+    our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY
+    in final ROI. The algorithm produced a CAGR of 11.49% during the five years.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html
new file mode 100644
index 0000000..c1ce885
--- /dev/null
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/06 References.html	
@@ -0,0 +1,7 @@
+<ol>
+    <li>
+        Gayed, Michael and Bilello, Charles, Leverage for the Long Run - A Systematic Approach to Managing Risk and
+        Magnifying Returns in Stocks (March 3, 2016). 2016 Charles H. Dow Award.
+        <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701">Online Copy</a>.
+    </li>
+</ol>
\ No newline at end of file

From ba096a3db4291a475c71e7356b4c4b0f24aff9f6 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 12:03:40 -0700
Subject: [PATCH 685/753] updated algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index 9e98ce9..ef8f26c 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4f9621a910142db83b6b756177a5b83c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7e213b53b058e7fde577ad8cc92f1c2e.html"></iframe>
     </div>
 </div>

From ac9c4113615c14c7feb66311d29081f9cdbf3b47 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 13:14:33 -0700
Subject: [PATCH 686/753] removed feature-292 from PR

---
 .../01 Strategy Library.php                   |   9 -
 .../01 Abstract.html                          |   6 -
 .../02 Introduction.html                      |   8 -
 .../03 Method.html                            | 280 ------------------
 .../04 Algorithm.html                         |   6 -
 .../05 References.html                        |   7 -
 6 files changed, 316 deletions(-)
 delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
 delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
 delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
 delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
 delete mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 4ddbbdf..cb9f4b9 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -607,15 +607,6 @@
 		'description' => "A simple trend following strategy on commodities futures.",
 		'tags' => 'Momentum, Futures, Commodities'
 	],
-	[
-        'name' => 'Price Forecasting using a Temporal Convolutional Neural Network Model',
-        'link' => 'strategy-library/forecasting-stock-prices-using-a-temporal-cnn',
-        'sources' => [
-            'Tampere University' => 'https://arxiv.org/pdf/1901.08280.pdf'
-        ],
-        'description' => "Applying a Temporal Convolutional Neural Network to forecasting future stock prices.",
-        'tags' => 'Convolutional Neural Network, Equities, Forecasting, Deep Learning'
-    ],
     [
         'name' => 'Leveraged ETFs with Systematic Risk Management',
         'link' => 'leveraged-etfs-with-systematic-risk-management',
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html
deleted file mode 100644
index a6cf634..0000000
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/01 Abstract.html	
+++ /dev/null
@@ -1,6 +0,0 @@
-<p>
-    In this tutorial, we apply Deep Learning Classification in an attempt to forecast the movement of future stock prices.
-</p>
-<p>
-    <em>Key Concepts: Convolutional Neural Network, Deep Learning, Time-series Forecasting, Classification, Trading</em>
-</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html
deleted file mode 100644
index 1fa0f4e..0000000
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/02 Introduction.html	
+++ /dev/null
@@ -1,8 +0,0 @@
-<p>
-    Various time series forecasting models (SMA, EMA, etc.) have been applied to stocks to forecast price movements.
-    More recently, with the advent of Neural Networks, which have seen applications in several fields, ranging from
-    medicine to fraud detection, researchers have tried to apply Neural Networks to the markets in an attempt to forecast price
-    movements. Convolutional Neural Networks (CNNs) are a class of Neural Networks most widely known for their use in
-    image classification, and now, researchers are applying CNNs to extract patterns, also known as <em>features</em>, from times-series
-    data to forecast future stock prices.
-</p>
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html
deleted file mode 100644
index 1a4765a..0000000
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/03 Method.html	
+++ /dev/null
@@ -1,280 +0,0 @@
-<h3>Overview</h3>
-<p>
-    Our strategy is to develop a Temporal Convolutional Neural Network model and train our model on historical OHLCV data
-    to predict the movement of future prices. Then, when trading, we take the most recent data, feed it into our model, and
-    bet on the direction of the price movement based on our model prediction. We will walk through the code required
-    for building the Neural Network Architecture and for preparing the data for our model, as this part is the harder part to understand.
-</p>
-
-<h3>Inputs/Outputs</h3>
-
-<p>
-    Before we build our Neural Network Architecture, we need to understand the inputs and outputs to our model.
-    The input to the model will be the OHLC+Volume data for t-14 to t time steps (past 15 time steps). The output is a
-    direction (Up, Down, Stationary) of the movement of the average close of the t+1 to t+5 time steps (5 future timestamps).
-    The movement is considered stationary if the abs(% change 5-step average close) < .01%. These three directions will
-    form the labels for which our model will try to classify, thus we have a <em>classification</em> problem.
-</p>
-
-
-<h3>Neural Network Model Architecture</h3>
-
-<p>
-    Now, we will need to build our Neural Network Architecture, which we will build using <a href="https://keras.io/">Keras</a>,
-    a high-level Python Deep Learning API. To begin, we will need a few import statements:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    import tensorflow as tf
-    from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate
-    from tensorflow.keras import Model
-    from tensorflow.keras import metrics
-    from tensorflow.keras.losses import CategoricalCrossentropy
-    from tensorflow.keras import utils
-    from sklearn.preprocessing import StandardScaler
-    import numpy as np
-    import math
-</pre>
-</div>
-
-
-<p>
-    We start with an Input Layer, where training and testing data are initially accepted. With 15 time steps and 5 input
-    variables (OHLCV), our input shape will be 15 x 5.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    inputs = Input(shape=(15, 5))
-</pre>
-</div>
-
-
-<p>
-    We then feed this Layer into our Convolutional Layer, where we extract features, which will serve as
-    the Neural Network's method of extracting patterns from the time-series data.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    feature_extraction = Conv1D(30, 4, activation='relu')(inputs)
-</pre>
-</div>
-
-
-<div class="section-example-container">
-<pre class="python">
-    long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction)
-    mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction)
-    short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction)
-
-    long_term_conv = Conv1D(1, 1, activation='relu')(long_term)
-    mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term)
-    short_term_conv = Conv1D(1, 1, activation='relu')(short_term)
-</pre>
-</div>
-
-
-<p>
-    These three layers are then combined, and since we will be working with 2D input matrices, we will then need to flatten
-    our layer.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv])
-    flattened = Flatten()(combined)
-</pre>
-</div>
-
-
-<p>
-    Our final layer will be our output layer, and since we have three outputs (Up, Stationary, Down),
-    this layer will have three nodes.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    outputs = Dense(3, activation='softmax')(flattened)
-</pre>
-</div>
-
-<p>
-    The resulting Neural Network Architecture is shown in the following:
-</p>
-
-<img src="https://cdn.quantconnect.com/i/tu/temporal-cnn-architecture.png" alt="temporal cnn model architecture">
-
-
-<h3>Preparing the Data for Our Model</h3>
-
-<p>
-    First, we need to define a class and a few variables:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    input_vars = ['open', 'high', 'low', 'close', 'volume']
-
-    class Direction:
-        UP = 0
-        DOWN = 1
-        STATIONARY = 2
-
-    rolling_avg_window_size = 5
-
-    shift = -(rolling_avg_window_size-1)
-
-    stationary_threshold = .0001
-
-    scaler = StandardScaler()
-</pre>
-</div>
-
-<p>
-    <em>input_vars</em> define the variables we want to use to make our predictions. The class <em>Direction</em>
-    defines a few integers that we will label our data with (labels are needed for classification problems). The reason
-    we use integers instead of strings is because Keras, like most ML libraries, only work with numerical data. Moving on,
-    <em>rolling_avg_window_size</em> is the number of time steps used for the calculate the average of future closing prices,
-    described earlier in
-    <b>Inputs/Outputs</b> (t+1 to t+5 is 5 time steps, thus this value accordingly is set to 5).
-    The constant <em>stationary_threshold</em> defines the threshold for a change in price to be considered
-    stationary, and this change also described in <b>Inputs/Outputs</b>. The <em>shift</em> is the shift needed to align
-    the average value (mentioned earlier), in our pandas DataFrame to make it easier for us to slice our DataFrame into pieces
-    manageable for our Neural Network model. The <em>scaler</em> object will be used later to scale our data.
-    The purpose of the variables will become clearer in use.
-<p>
-    Next, say we are at time t in the pandas DataFrame, to calculate the average closing prices of t+1 to t+5, and calculate
-    the percent change from the close at t, we use the following lines of code:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift)
-    df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close']
-</pre>
-</div>
-
-<p>
-    The rolling mean should be self explanatory for those familiar with pandas (if not, I hope by now readers realize
-    this is a more advanced resource).
-    Here, <em>.shift(shift)</em> aligns the five time step rolling average <em>'close_avg'</em> column to the end of the last
-    time step we want to use as an input for prediction, and this action will make slicing up the DataFrame into input
-    and labeled data for our model much easier.
-</p>
-
-<p>
-    To label our data, we need to first define a function that we will use with the DataFrame's <em>apply()</em> method.
-    Usually, lambda functions are used for this purpose, however, our function's logic will not fit inside a lambda.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    def label_data(row):
-        if row['close_avg_change_pct'] > stationary_threshold:
-            return Direction.UP
-        elif row['close_avg_change_pct'] < -stationary_threshold:
-            return Direction.DOWN
-        else:
-            return Direction.STATIONARY
-</pre>
-</div>
-
-<p>
-    Now, we apply the above function to our DataFrame to get a column of labels:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    df['movement_labels'] = df.apply(label_data, axis=1)
-</pre>
-</div>
-
-<p>
-    With our labels in place, we can now slice up our DataFrame into pieces manageable for our model and collect them into
-    lists:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    data = []
-    labels = []
-
-    for i in range(len(df)-self.n_tsteps+1+shift):
-        label = df['movement_labels'].iloc[i+self.n_tsteps-1]
-        data.append(df[input_vars].iloc[i:i+self.n_tsteps].values)
-        labels.append(label)
-
-    data = np.array(data)
-</pre>
-</div>
-
-<p>
-    Here, we iterate numerically through the DataFrame, with a carefully calculated value in our <em>range()</em>
-    function to make sure we do access an out-of-bounds index. We cast the list of numpy arrays to a numpy array because
-    Keras works best with numpy arrays.
-</p>
-
-<p>
-    Now, we need to scale our data. It is good practice to scale data when using Machine Learning models so that the
-    range of values is normalized across the features.
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    dim1, dim2, dim3 = data.shape
-    data = data.reshape(dim1*dim2, dim3)
-    data = scaler.fit_transform(data)
-    data = data.reshape(dim1, dim2, dim3)
-</pre>
-</div>
-
-<p>
-    The reason we reshape the data before the scaling is because sklearn is only able to handle 2D data, but right after,
-    we can return the data to the original shape with another reshaping.
-</p>
-
-<p>
-    Finally, since Keras requires the labels to be dummified (which essentially turns a list of labels into a matrix of
-    1s and 0s, where the index of the 1 is equal to the value of the integer label), we use the following:
-</p>
-
-<div class="section-example-container">
-<pre class="python">
-    labels = utils.to_categorical(labels, num_classes=3)
-</pre>
-</div>
-
-<p>
-    Specifying <em>num_classes</em> to 3 ensures our matrix will have three columns, one for each label (Up, Down, Stationary).
-</p>
-
-<p>
-    We have now finished the walk through of the difficult parts of the code.
-</p>
-
-<h3>Trading</h3>
-
-<p>
-    After we feed in the prepared data into the model (the corresponding code, as well as the rest of the code, can be
-    found in <b>Algorithm</b>) we can
-    use our model to make predictions. We take the most recent 15 bars of OHLCV data and apply our model on it to make
-    a prediction. If the model predicts with above 55% confidence that the future direction is up (resp. down), we emit
-    an Price Insight with direction <em>InsightDirection.Up</em> (resp. <em>InsightDirection.Down</em>). Since we are
-    betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights
-    in the respective direction for <em>timedeltas</em> of one through five. However, we choose to only emit an Insight
-    with a <em>timedelta</em> with a random integer between one and five to constrain the number of insights we emit.
-</p>
-
-
-<h3>The Rest</h3>
-
-<p>
-    We have covered the difficult aspects of the code, as well as give an overview of our strategy. The rest of the
-    necessary code to execute the strategy can be found in <b>Algorithm</b>.
-</p>
-
-
-
-
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html
deleted file mode 100644
index 9e98ce9..0000000
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/04 Algorithm.html	
+++ /dev/null
@@ -1,6 +0,0 @@
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4f9621a910142db83b6b756177a5b83c.html"></iframe>
-    </div>
-</div>
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html
deleted file mode 100644
index dedbeb6..0000000
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN/05 References.html	
+++ /dev/null
@@ -1,7 +0,0 @@
-<ol>
-  <li>
-      Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis:
-      "Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data", 2019;
-      <a href="https://arxiv.org/pdf/1901.08280.pdf">https://arxiv.org/pdf/1901.08280.pdf.</a>
-  </li>
-</ol>
\ No newline at end of file

From b922d8c1ac92684470ce8020782b9d8f1260a1de Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 14:00:50 -0700
Subject: [PATCH 687/753] updated algorithm and methodology

---
 .../03 Method.html                                             | 3 +++
 .../04 Algorithm.html                                          | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html
index 1a4765a..e56515f 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/03 Method.html	
@@ -265,6 +265,9 @@ <h3>Trading</h3>
     betting on the direction of the average of the future five closing prices, it would be intuitive to emit an Insights
     in the respective direction for <em>timedeltas</em> of one through five. However, we choose to only emit an Insight
     with a <em>timedelta</em> with a random integer between one and five to constrain the number of insights we emit.
+    <br>
+    As an additional note, we decided to set the trading fees to zero (the default fee
+    is one dollar per trade), or else our algorithm will only lose money due to the frequency at which we trade.
 </p>
 
 
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index ef8f26c..bf5b897 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7e213b53b058e7fde577ad8cc92f1c2e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7323c11d51dadb2b909bd8923a06c136.html"></iframe>
     </div>
 </div>

From 6a8345a2d3720170ea4bc4eb4e8fe075c04e82fa Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 14:30:29 -0700
Subject: [PATCH 688/753] fixed error with certain starting dates

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index bf5b897..b2d6569 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7323c11d51dadb2b909bd8923a06c136.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b1120c9cfc82ed4e97c90733777c8a34.html"></iframe>
     </div>
 </div>

From 8c7f56c4223d4545afb61b56b92b6193240c2d55 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 12 Aug 2020 16:53:08 -0700
Subject: [PATCH 689/753] Leveraged ETFs with Systematic Risk Management

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index b2d6569..f3cdced 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_b1120c9cfc82ed4e97c90733777c8a34.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6eecde997dfde6c33ced61f74092d036.html"></iframe>
     </div>
 </div>

From be1fa00392688a3af30a3d05c04aa74e8ac0b662 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gaviles@users.noreply.github.com>
Date: Thu, 13 Aug 2020 10:02:50 -0700
Subject: [PATCH 690/753] Fix missing opening bracket.

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 1 +
 1 file changed, 1 insertion(+)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index c1c2ad7..ce7332d 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -634,6 +634,7 @@
      'description' => 'A techincal indicator crossover strategy trading the largest energy companies.',
      'tags'=>'Intermediate, Technical Indicator, Ichimoku Cloud, Crossover, Equities'
   ],
+  [
      'name' => 'Intraday ETF Momentum',
      'link' => 'strategy-library/intraday-etf-momentum',
      'sources' => [

From d5da7a245726b7c2e51f83141f1c215ae6bca507 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 13 Aug 2020 13:20:33 -0700
Subject: [PATCH 691/753] Leveraged ETFs with Systematic Risk Management

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index f3cdced..ec0cfdf 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6eecde997dfde6c33ced61f74092d036.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7a9fe30a6e5af195d1edfecb3d024ecd.html"></iframe>
     </div>
 </div>

From 72f735277380dcb79f6a046b06a48a108583132b Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 13 Aug 2020 16:25:28 -0700
Subject: [PATCH 692/753] fixed typo and link

---
 .../00 Strategy Library/01 Strategy Library.php               | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index cb9f4b9..5516ba3 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -609,11 +609,11 @@
 	],
     [
         'name' => 'Leveraged ETFs with Systematic Risk Management',
-        'link' => 'leveraged-etfs-with-systematic-risk-management',
+        'link' => 'strategy-library/leveraged-etfs-with-systematic-risk-management',
         'sources' => [
             'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701'
         ],
-        'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to by the S&P500',
+        'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500',
         'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF'
     ]
 ];

From c5c4e8c8ce2dc46d6282824a07549dd836dd2373 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 13 Aug 2020 16:34:10 -0700
Subject: [PATCH 693/753] updated Results section

---
 .../05 Results.html                                          | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html
index dfa86cf..34fa59b 100644
--- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html	
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html	
@@ -1,5 +1,4 @@
 <p>
-    We compare our results to the SP500 Benchmark, for which we use SPY, over a five year period. For some years,
-    our algorithm outperforms, but for other years, the algorithm is beaten by SPY, and the returns just slightly beat SPY
-    in final ROI. The algorithm produced a CAGR of 11.49% during the five years.
+    Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period
+    yielded a Sharpe Ratio of .572.
 </p>
\ No newline at end of file

From dd18c61a059301e86e84cfb07c0f49b99ce9ed87 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 13 Aug 2020 16:36:32 -0700
Subject: [PATCH 694/753] improved wording of description

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 5516ba3..1f9fe06 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -613,7 +613,7 @@
         'sources' => [
             'The Lead-Lag Report' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701'
         ],
-        'description' => 'We apply Simple Moving Averages to manage risk in holding leveraged ETFs in an attempt to beat the S&P500',
+        'description' => 'We apply Simple Moving Averages to manage the risk of holding leveraged ETFs in an attempt to beat the S&P500',
         'tags' => 'Simple Moving Average, Risk Management, S&P500, ETF'
     ]
 ];

From b7865614fd803ca14d376453528125c21caca440 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Mon, 17 Aug 2020 11:10:56 -0700
Subject: [PATCH 695/753] updated description

---
 .../05 Results.html                                           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html
index 34fa59b..ade26d7 100644
--- a/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html	
+++ b/04 Strategy Library/1025 Leveraged ETFs with Systematic Risk Management/05 Results.html	
@@ -1,4 +1,4 @@
 <p>
-    Our strategy yielded a Sharpe Ratio of .732 over the five years, while buying and holding SPY for the same period
-    yielded a Sharpe Ratio of .572.
+    We use the S&P 500 as our benchmark, which we track by using the SPY ETF. Our strategy yielded a Sharpe Ratio of .732
+    over the past five years, while buying and holding SPY for the same period yielded a Sharpe Ratio of .572.
 </p>
\ No newline at end of file

From 3aa608104eae8d3c059c8ecbcc753ecd201e21b3 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 17 Aug 2020 19:14:13 -0600
Subject: [PATCH 696/753] Add relative metrics to strategy 1026

---
 .../04 Algorithm.html                         |  2 +-
 .../05 Conclusion.html                        | 89 ++++++++++++++++++-
 2 files changed, 86 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html
index c7304ae..1379e93 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_47fcfa784d1e69265b86d7be5bc82cb9.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_417d176d06ca1509194e3ade608190b6.html"></iframe>
     </div>
 </div>
\ No newline at end of file
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
index 7623dda..c03c6ca 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -1,9 +1,90 @@
 <p>
   We conclude that the momentum pattern documented by Gao et al (2017) produces lower returns over our testing period.
-  In the algorithm attached above, we find a compounding annual return of -3.8%. This may be attributed to the 
-  inclusion of transaction costs in our analysis while Gao et al (2017) decide to ignore them. Throughout their 
-  research paper, Gao et al (2017) provide several suggestions to increase the return generated by this momentum 
-  pattern. These areas of future research include:
+  Comparing the strategy to the S&P 500 benchmark, the strategy has a lower Sharpe ratio during the backtesting 
+  period and during the recovery from the 2020 stock market crash. However, the strategy greatly outperforms the 
+  benchmark during the downfall of the 2020 crash, achieving a 4.8 Sharpe ratio. Throughout all of the time periods 
+  we tested, the strategy had a lower annual standard deviation than the benchmark, meaning more consistent returns. 
+  A breakdown of the results from all of the testing periods can be seen in the table below.
+</p>
+
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>ASD</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">Backtest</td>
+            <td rowspan="2">1/1/2015</td>
+            <td rowspan="2">8/16/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_417d176d06ca1509194e3ade608190b6.html">Strategy</a></td>
+            <td>-0.764</td>
+            <td style="background-color: lightgrey;">0.05</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_34ab9a25fa033b66e9465e5e25d2e446.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.709</td>
+            <td>0.185</td>
+        </tr>
+        <tr>
+            <td rowspan="2">Fall 2015</td>
+            <td rowspan="2">8/10/2015</td>
+            <td rowspan="2">10/10/2015</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ab959f993d324a3ab3c5f9f3c1a41e2f.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">-0.696</td>
+            <td style="background-color: lightgrey;">0.058</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_19229d5f18c2cc47163a5ff910fb7870.html">Benchmark</a></td>
+            <td>-1.243</td>
+            <td>0.793</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_5bc289c0c4f2fc1a0bc08ad8f96f9595.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">4.818</td>
+            <td style="background-color: lightgrey;">0.266</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc2afe14cbcdef1c74d0f3718cac49fc.html">Benchmark</a></td>
+            <td>-1.243</td>
+            <td>0.793</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_47953c00c8cce7442882e7c64bb9ac23.html">Strategy</a></td>
+            <td>0.602</td>
+            <td style="background-color: lightgrey;">0.103</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9f37bccb0732468f3f9ed2c09136bee4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">13.761</td>
+            <td>0.386</td>
+        </tr>
+    </tbody>
+</table>  
+
+
+<p>
+  We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs 
+  in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs,
+  and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the 
+  original research paper. Refer to the   <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2ff2604a2ebad4a3df94ca20ae7cfadc.html">backtest results</a>.
+</p>
+
+<p>
+  Throughout their research paper, Gao et al (2017) provide several suggestions to increase the return generated by 
+  this momentum pattern. These areas of future research include:
 </p>
 
 <ul>

From 5611f91976a151d6c1d0f46a3470516433701a57 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 17 Aug 2020 19:58:35 -0600
Subject: [PATCH 697/753] Fix typo

---
 .../1026 Intraday ETF Momentum/05 Conclusion.html               | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
index c03c6ca..ff087ea 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -78,7 +78,7 @@
 <p>
   We find the lack of performance for this strategy is not largely attributed to the inclusion of transaction costs 
   in our analysis while Gao et al (2017) decide to ignore them. Even with ignoring the transaction fees, spread costs,
-  and slippage, the strategy still has a lower Sharpe ratio the S&P 500 and doesn't match the results found in the 
+  and slippage, the strategy still has a lower Sharpe ratio than the S&P 500 and doesn't match the results found in the 
   original research paper. Refer to the   <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2ff2604a2ebad4a3df94ca20ae7cfadc.html">backtest results</a>.
 </p>
 

From fd71669aff0599ec339f3b3f28aae2b789f65815 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Mon, 17 Aug 2020 20:15:37 -0600
Subject: [PATCH 698/753] Add relative metrics to strat 1028

---
 .../01 Abstract.html                          |  6 +-
 .../04 Algorithm.html                         |  2 +-
 .../05 Benchmark Performance.html             | 10 --
 .../05 Relative Performance.html              | 92 +++++++++++++++++++
 .../07 Conclusion.html                        |  8 +-
 5 files changed, 100 insertions(+), 18 deletions(-)
 delete mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html
 create mode 100644 04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html

diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html
index 3dd4983..587ce33 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/01 Abstract.html	
@@ -2,7 +2,7 @@
   Gurrib (2020) is the first published research paper to analyze the predictive power of Ichimoku Clouds for the 
   largest 10 stocks in the US energy sector. In this tutorial, we implement a similar strategy while reducing the 
   effect of look-ahead bias integrated into the original study. Our findings show that while the strategy has an 
-  impressive 79% return during the 2020 oil price war, the strategy has worse performance than found by Gurrib 
-  (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of a popular 
-  energy sector ETF.
+  impressive 176 Sharpe ratio during the downfall of the 2020 oil price war, the strategy has worse performance than 
+  found by Gurrib (2020). We discover that throughout a 5 year backtest, the strategy fails to beat the benchmark of 
+  a popular energy sector ETF.
 </p>
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html
index 487c85d..56ab2db 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1ba5ba885d63246989cf0c2ed32d2d97.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_93f0df937ca928a3aae0da169142b0b5.html"></iframe>
     </div>
 </div>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html
deleted file mode 100644
index f3592aa..0000000
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Benchmark Performance.html	
+++ /dev/null
@@ -1,10 +0,0 @@
-<p>
-  To analyze the performance of this trading strategy, we compare its return to the return of buying and holding a 
-  popular ETF tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the 
-  benchmark. We can see from the plot below that holding the benchmark ETF would have produced a return of -32% over
-  the backtest period.
-</p>
-
-<img class="img-responsive" 
-     src="https://cdn.quantconnect.com/i/tu/benchmark-performance.png" 
-     alt="Tutorial1028-ichimoku-cloud-2" />
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html
new file mode 100644
index 0000000..a1d1610
--- /dev/null
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
@@ -0,0 +1,92 @@
+<p>
+  To analyze the value of this trading strategy, we compare its performance to buying and holding a popular ETF 
+  tracking the energy sector. In this study, we use XLE, the Energy Select Sector SPDR® Fund, as the benchmark. We 
+  can see from the plot below how the portfolio would have performed just had we had just invested in the benchmark.
+</p>
+
+<img class="img-responsive" 
+     src="https://cdn.quantconnect.com/i/tu/benchmark-performance.png" 
+     alt="Tutorial1028-ichimoku-cloud-2" />
+
+<p>
+  We now analyze the Sharpe ratio and annual standard deviation of returns for both the strategy and the benchmark. 
+  From the table below, we can see the results of the strategy and the benchmark over the entire backtest period, the 
+  Fall 2015 crisis, and the 2020 oil price war. The strategy has a lower Sharpe ratio than the benchmark across all 
+  of the time periods we tested, except for the crash during the 2020 oil price war, where it generated an impressive
+  176 Sharpe ratio. We can also see the strategy has a lower annual standard deviation accross all of the time 
+  frames, implying that the strategy has more consistent returns than the benchmark.
+</p>
+
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>ASD</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">Backtest</td>
+            <td rowspan="2">1/1/2015</td>
+            <td rowspan="2">8/16/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_93f0df937ca928a3aae0da169142b0b5.html">Strategy</a></td>
+            <td>-0.31</td>
+            <td style="background-color: lightgrey;">0.223</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_383b6e278d66af10a9b90461b61ccbc4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">-0.083</td>
+            <td>0.312</td>
+        </tr>
+        <tr>
+            <td rowspan="2">Fall 2015</td>
+            <td rowspan="2">8/10/2015</td>
+            <td rowspan="2">10/10/2015</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_138ea84253c4df2688a512e21917c55d.html">Strategy</a></td>
+            <td>-0.31</td>
+            <td style="background-color: lightgrey;">0.294</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_383b6e278d66af10a9b90461b61ccbc4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.242</td>
+            <td>0.351</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_5c735b89a65e453c9f0e40c41f69a314.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">176.524</td>
+            <td style="background-color: lightgrey;">0.949</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0288d3b1c7b2af85c5884c86d64ed148.html">Benchmark</a></td>
+            <td>-0.902</td>
+            <td>1.108</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_92bbed7c7ede0861c82d76e36d34d781.html">Strategy</a></td>
+            <td>-1.556</td>
+            <td style="background-color: lightgrey;">0.447</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_022fe9ca07b14eab2f045ea1e8551d8e.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">46.068</td>
+            <td>0.703</td>
+        </tr>
+    </tbody>
+</table>
+
+<p>
+  We find the lack of performance for this strategy is not largely attributed to the transaction costs. After 
+  ignoring the transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the 
+  benchmark and doesn't match the results found in the original research paper. See the backtest results 
+  <a href="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_c3deb95af7014cfe2e51ffe1678b2939.html">here</a>.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html
index 4631b74..452f0d8 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/07 Conclusion.html	
@@ -1,8 +1,8 @@
 <p>
-  While the strategy examined herein produces a 79% return throughout the 2020 oil price war and stock market crash,
-  we conclude the strategy does not currently provide as profitable of results as documented by Gurrib (2020). The 
-  strategy experiences a -39% return, a 48% drawdown, about a 5 year drawdown duration, and a profit-loss ratio of 
-  0.92. To continue the development of this strategy, future areas of research include:
+  While the strategy examined herein produces a 176 Sharpe ratio throughout the downfall of the 2020 oil price war 
+  and stock market crash, we conclude the strategy does not currently provide as profitable of results as documented 
+  by Gurrib (2020). The strategy experiences a -0.234 Sharpe ratio over the entire backtest period. To continue the 
+  development of this strategy, future areas of research include: 
 </p>
 
 <ul>

From 273015c388b224938520ba2f6957dd0f7852c4d1 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Tue, 18 Aug 2020 13:56:53 -0700
Subject: [PATCH 699/753] updated algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index ec0cfdf..9d83532 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7a9fe30a6e5af195d1edfecb3d024ecd.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_53764d2c98bc7166533343c3473067d5.html"></iframe>
     </div>
 </div>

From 5e2cdad4412f2a862cd2d4829a4dd6b98525e51a Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Tue, 18 Aug 2020 15:37:07 -0600
Subject: [PATCH 700/753] Add relative metrics to strat 85 and fix PCM bug

---
 .../02 Method.html                            |  2 +-
 .../04 Relative Performance.html              | 81 +++++++++++++++++++
 ...05 Market & Competition Qualification.html | 15 ++++
 .../06 Conclusion.html                        | 17 ++++
 ...{04 References.html => 07 References.html} |  0
 5 files changed, 114 insertions(+), 1 deletion(-)
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html
 rename 04 Strategy Library/85 Momentum in Mutual Fund Returns/{04 References.html => 07 References.html} (100%)

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html
index cd4436e..a43b163 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/02 Method.html	
@@ -106,7 +106,7 @@ <h4>Alpha Ranking</h4>
     ...
     ranking_df = pd.DataFrame()
     for symbol, symbol_data in self.symbol_data_by_symbol.items():
-        if symbol_data.IsReady:
+        if data.ContainsKey(symbol) and symbol_data.IsReady:
             row = pd.DataFrame({'ROC': symbol_data.roc, 'Nearness': symbol_data.nearness}, index=[symbol])
             ranking_df = ranking_df.append(row)
     ranked_symbols =  ranking_df.rank().sum(axis=1).sort_values().index
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html
new file mode 100644
index 0000000..26cb3ef
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 Relative Performance.html	
@@ -0,0 +1,81 @@
+<p>
+  To analyze the value of this trading strategy, we compare its performance to buying-and-holding the S&P 500 index 
+  ETF, SPY. We can see the results from the table below. The strategy has a lower Sharpe ratio than the SPY for all of 
+  the time frames we tested, except for the downfall of the 2020 stock market crash. During this time it greatly 
+  outperformed the SPY, achieving a 10.4 Sharpe ratio. We also notice that the strategy generates more consistent 
+  returns than the benchmark, documented by the lower annual standard deviation of returns throughout all the testing 
+  periods. 
+</p>
+
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>ASD</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">Backtest</td>
+            <td rowspan="2">1/1/2015</td>
+            <td rowspan="2">8/16/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_03e550a891c8af7c23f484acd851c04b.html">Strategy</a></td>
+            <td>0.192</td>
+            <td style="background-color: lightgrey;">0.046</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_19229d5f18c2cc47163a5ff910fb7870.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.709</td>
+            <td>0.186</td>
+        </tr>
+        <tr>
+            <td rowspan="2">Fall 2015</td>
+            <td rowspan="2">8/10/2015</td>
+            <td rowspan="2">10/10/2015</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_03e550a891c8af7c23f484acd851c04b.html">Strategy</a></td>
+            <td>-1.448</td>
+            <td style="background-color: lightgrey;">0.052</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_19229d5f18c2cc47163a5ff910fb7870.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">-0.724</td>
+            <td>0.251</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e18eada3fc749e2e6caeb2b270006ea5.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">10.386</td>
+            <td style="background-color: lightgrey;">0.104</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc2afe14cbcdef1c74d0f3718cac49fc.html">Benchmark</a></td>
+            <td>-1.243</td>
+            <td>0.793</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1160727b35d074687972b3f3799c3158.html">Strategy</a></td>
+            <td>-2.942</td>
+            <td style="background-color: lightgrey;">0.177</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9f37bccb0732468f3f9ed2c09136bee4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">13.761</td>
+            <td>0.386</td>
+        </tr>
+    </tbody>
+</table>
+
+<p>
+  We find the lack of performance for this strategy is not largely attributed to the trading fees. After ignoring the 
+  transaction fees, spread costs, and slippage, the strategy still has a lower Sharpe ratio than the benchmark. See 
+  the backtest results <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_954ccea020e02c6b15ae2f564fdd5da3.html">here</a>.
+</p>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html
new file mode 100644
index 0000000..fbc487d
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/05 Market & Competition Qualification.html	
@@ -0,0 +1,15 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-7#rules">metrics</a> required for Alpha Streams and 
+  the Quant League competition, it requires further work to meet the following requirements:
+</p>
+
+
+<ul>
+  <li>Profitable</li>
+  <li>PSR >= 80%</li>
+  <li>Max drawdown duration <= 6 months </li>
+  <li>Insights contain the following properties: Symbol, Duration, Direction, and Weight</li>
+  <li>Minute or second data resolution</li>
+  <li>Insight Weighting or Equal Weighting Portfolio Construction model</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html
new file mode 100644
index 0000000..763902d
--- /dev/null
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/06 Conclusion.html	
@@ -0,0 +1,17 @@
+<p>
+  The momentum pattern examined throughout this tutorial has a greater Sharpe ratio than the SPY during the downfall 
+  of the 2020 stock market crash and has more a lower annual standard deviation of returns than the SPY over all the 
+  periods we tested. However, we conclude the strategy, which is loosely based on the research of Sapp (2010), does 
+  not consistently outperform our benchmark. To continue the development of this strategy, future areas of research 
+  include:
+</p>
+
+
+<ul>
+  <li>
+    Incorporating historical returns and NAV of mutual funds to better-reflect the strategy documented by Saap (2010).
+  </li>
+  <li>Adjusting the parameters in the ROCAndNearnessAlphaModel.</li>
+  <li>Performing more filtering and sorting in the AssetManagementUniverseSelection model.</li>
+  <li>Testing other portfolio construction techniques.</li>
+</ul>
diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html
similarity index 100%
rename from 04 Strategy Library/85 Momentum in Mutual Fund Returns/04 References.html
rename to 04 Strategy Library/85 Momentum in Mutual Fund Returns/07 References.html

From a398733997925cfd76a15c44332d3176dfc56dc2 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Tue, 18 Aug 2020 16:05:58 -0600
Subject: [PATCH 701/753] Update backtest with PCM fix

---
 .../85 Momentum in Mutual Fund Returns/03 Algorithm.html        | 2 +-
 quantpedia.json                                                 | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html
index ade7d3b..93109c0 100644
--- a/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
+++ b/04 Strategy Library/85 Momentum in Mutual Fund Returns/03 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_ba030edb022016f967a6296a556d717e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_a86b19f12b40d8a676bf7f885742631d.html"></iframe>
     </div>
 </div>
\ No newline at end of file
diff --git a/quantpedia.json b/quantpedia.json
index cbf4970..22f8d06 100644
--- a/quantpedia.json
+++ b/quantpedia.json
@@ -34,7 +34,7 @@
      77: "0483e5a7094604254ab37eda8b5141b8",
      78: "27fb5f05b0e48f488f0994d8d83ddc77",
      83: "fdfcddd132eaf55039d867c03efe3012",
-     85: "ba030edb022016f967a6296a556d717e",
+     85: "a86b19f12b40d8a676bf7f885742631d",
      91: "95cffbeec0d003da873b791d3a10f60f",
      100: "5ee5507fef6bf190ae533ce05ccaa785",
      102: "cd2d187e44a00c7b19f64aee8b0895d9",

From 53c3c878864a735c9e826d4208cd63e733a7ff52 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Wed, 19 Aug 2020 13:09:15 -0600
Subject: [PATCH 702/753] Add relative metrics & fix execution model bug

---
 .../01 Strategy Library.php                   |   9 ++
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |   9 ++
 .../03 Method.html                            | 129 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  91 ++++++++++++
 ...06 Market & Competition Qualification.html |  19 +++
 .../07 Conclusion.html                        |  10 ++
 .../08 References.html                        |   8 ++
 9 files changed, 288 insertions(+)
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html
 create mode 100644 04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index ce7332d..426d089 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -642,6 +642,15 @@
      ],
      'description' => "A momentum strategy based on returns of the market open",
      'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+  ],
+  [
+     'name' => 'Intraday Arbitrage Between Index ETFs',
+     'link' => 'strategy-library/intraday-arbitrage-between-index-etfs',
+     'sources' => [
+       'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1709599'
+     ],
+     'description' => "A strategy that tracks the price paths of two correlated ETFs and takes advantage of mis-pricings that arise when the price paths diverge",
+     'tags'=>'Intermediate, Equities, Arbitrage, Intraday'
   ]
 ];
 
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html
new file mode 100644
index 0000000..7d69c19
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/01 Abstract.html	
@@ -0,0 +1,7 @@
+<p>
+  In this tutorial, we implement an intraday arbitrage strategy that capitalizes on deviations between two closely correlated 
+  index ETFs. Even though at times both ETFs may hold different constituents and different weights of securities while tracking 
+  the index, they are both highly correlated and extremely liquid. Researchers have shown these two properties are essential to 
+  an arbitrage system's success. The algorithm we implement here is inspired by the work of Kakushadze and Serur (2018) and 
+  Marshall, Nguyen, and Visaltanachoti (2010).
+</p>
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html
new file mode 100644
index 0000000..8c03fe6
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/02 Background.html	
@@ -0,0 +1,9 @@
+<p>
+  Marshall et al (2010) define an arbitrage opportunity as when the bid price of ETF A (B) diverts high enough away from the ask 
+  price of ETF B (A) such that their quotient reaches a threshold. In their paper, an arbitrage opportunity is only acted upon 
+  when the threshold is satisfied for 15 seconds. When these criteria are met, the algorithm enters the arbitrage trade by going 
+  long ETF B (A) and short ETF A (B). When the spread reverts back to where the bid of ETF B (A) >= the ask of ETF A (B) for 15 
+  seconds, the positions are liquidated. An overview of the trade process is illustrated in the image below.
+</p>
+
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/arbitrage-trade-process.jpg" alt="Tutorial1023-intraday-arbitrage-1" />
\ No newline at end of file
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html
new file mode 100644
index 0000000..ecac338
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/03 Method.html	
@@ -0,0 +1,129 @@
+<h4>Universe Selection</h4>
+<p>
+  We implement a manual universe selection model that includes our two ETFs, SPY and IVV. The attached research notebook finds 
+  the correlation of daily returns to be >0.99.
+</p>
+<div class="section-example-container">
+<pre class="python">
+tickers = ['IVV', 'SPY']
+symbols = [ Symbol.Create(t, SecurityType.Equity, Market.USA) for t in tickers ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+</pre>
+</div>
+
+<h4>Spread Adjustments</h4>
+<p>
+  Plotting the ratio of the security prices shows its trending behavior.
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/trending-spread.jpg" alt="Tutorial1023-intraday-arbitrage-2" />
+<p>
+  Without adjusting this ratio over time, an arbitrage system would be stuck in a single trade for majority of the backtest. To 
+  resolve this, we subtract a trailing mean from each data point.
+</p>
+<img class="img-responsive" src="https://cdn.quantconnect.com/i/tu/adjusted-ratio.jpg" alt="Tutorial1023-intraday-arbitrage-3" />
+<p>
+  Both of the above plots can be reproduced in the attached research notebook. During backtesting, this adjustment is done 
+  during trading by setting up a 
+  <a href="https://www.quantconnect.com/lean/documentation/topic9114.html">QuoteBarConsolidator</a> for each security in our 
+  universe. On each new consolidated <a href="https://www.quantconnect.com/lean/documentation/topic24265.html">QuoteBar</a>, we 
+  update the trailing window of L1 data, then calculate the latest spread adjustment values.
+</p>
+<div class="section-example-container">
+<pre class="python">
+# In OnSecuritiesChanged
+for symbol in self.symbols:
+    self.consolidators[symbol] = QuoteBarConsolidator(1)
+    self.consolidators[symbol].DataConsolidated += self.CustomDailyHandler
+    algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidators[symbol])
+
+def CustomDailyHandler(self, sender, consolidated):
+    # Add new data point to history while removing expired history
+    self.history[consolidated.Symbol]['bids'] = np.append(self.history[consolidated.Symbol]['bids'][1:], consolidated.Bid.Close)
+    self.history[consolidated.Symbol]['asks'] = np.append(self.history[consolidated.Symbol]['asks'][1:], consolidated.Ask.Close)
+    
+    self.update_spread_adjusters()
+
+def update_spread_adjusters(self):
+    for i in range(2):
+        numerator_history = self.history[self.symbols[i]]['bids']
+        denominator_history = self.history[self.symbols[abs(i-1)]]['asks']
+        self.spread_adjusters[i] = (numerator_history / denominator_history).mean()
+</pre>
+</div>
+
+
+<h4>Alpha Construction</h4>
+<p>
+  The ArbitrageAlphaModel monitors the intraday bid and ask prices of the securities in the universe. In the constructor, we 
+  can specify the model parameters. In this tutorial, we select a shorter window an arbitrage opportunity must be active before 
+  we act on it by setting `order_delay` to 3.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class ArbitrageAlphaModel(AlphaModel):
+    symbols = [] # IVV, SPY
+    entry_timer = [0, 0]
+    exit_timer = [0, 0]
+    spread_adjusters = [0, 0]
+    long_side = -1
+    consolidators = {}
+    history = {}
+
+    def __init__(self, order_delay = 3, profit_pct_threshold = 0.02, window_size = 400):
+        self.order_delay = order_delay
+        self.pct_threshold = profit_pct_threshold / 100
+        self.window_size = window_size
+</pre>
+</div>
+
+
+<h4>Trade Signals</h4>
+<p>
+  To emit insights, we check if either side of the arbitrage strategy warrants an entry. If no new entries are to be made, the 
+  algorithm then looks to exit any current positions. With this design, we can flip our long/short bias without first 
+  flattening our position. We use a practically-infinite insight durations as we do not know how long the algorithm will be in 
+  an arbitrage trade.
+</p>
+<div class="section-example-container">
+<pre class="python">
+# Search for entries
+for i in range(2):
+    if quotebars[abs(i-1)].Bid.Close / quotebars[i].Ask.Close - self.spread_adjusters[abs(i-1)] >= self.pct_threshold:
+        self.entry_timer[i] += 1
+        if self.entry_timer[i] == self.order_delay:
+            self.exit_timer = [0, 0]
+            if self.long_side == i:
+                return []
+            self.long_side = i
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Up),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Down)]
+        else:
+            return []
+    self.entry_timer[i] = 0
+
+# Search for an exit
+if self.long_side >= 0: # In a position
+    if quotebars[self.long_side].Bid.Close / quotebars[abs(self.long_side-1)].Ask.Close - self.spread_adjusters[self.long_side] >= 0: # Exit signal
+        self.exit_timer[self.long_side] += 1
+        if self.exit_timer[self.long_side] == self.order_delay: # Exit signal lasted long enough
+            self.exit_timer[self.long_side] = 0
+            i = self.long_side
+            self.long_side = -1
+            return [Insight.Price(self.symbols[i], timedelta(days=9999), InsightDirection.Flat),
+                    Insight.Price(self.symbols[abs(i-1)], timedelta(days=9999), InsightDirection.Flat)]
+        else:
+            return []
+return []
+</pre>
+</div>
+
+
+<h4>Portfolio Construction & Trade Execution</h4>
+<p>
+  Following the guidelines of <a href="https://www.quantconnect.com/docs/alpha-streams/overview">Alpha Streams</a> 
+  and the <a href="https://www.quantconnect.com/competitions/quant-league-7">Quant League</a> competition, we 
+  utilize the <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Portfolio/EqualWeightingPortfolioConstructionModel.py">
+  EqualWeightingPortfolioConstructionModel</a> and the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm/Execution/ImmediateExecutionModel.py">
+  ImmediateExecutionModel</a>.
+</p>
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html
new file mode 100644
index 0000000..c59088b
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ea37adf7f839f5c10070072a70e12323.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html
new file mode 100644
index 0000000..fd71509
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
@@ -0,0 +1,91 @@
+<p>
+  We analyze the performance of this strategy by comparing it to the S&P 500 ETF benchmark, SPY. We notice that the 
+  strategy has a lower Sharpe ratio over all of our testing periods than the benchmark, except for the Fall 2015 
+  crisis where it achieved a 2.8 Sharpe ratio. The strategy also has a lower annual standard deviation of returns 
+  when compared to the SPY, implying more consistent returns over time. A breakdown of the strategy's performance 
+  across all our testing periods is displayed in the table below.
+</p>
+
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>ASD</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">Backtest</td>
+            <td rowspan="2">8/11/2015</td>
+            <td rowspan="2">8/11/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ea37adf7f839f5c10070072a70e12323.html">Strategy</a></td>
+            <td>-0.447</td>
+            <td style="background-color: lightgrey;">0.053</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f2c41f11332bbc9d81f5e3f2bfaa7975.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.732</td>
+            <td>0.192</td>
+        </tr>
+        <tr>
+            <td rowspan="2">Fall 2015</td>
+            <td rowspan="2">8/10/2015</td>
+            <td rowspan="2">10/10/2015</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_04dc51a34b8a4b04c243cbb712d95bc6.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">2.837</td>
+            <td style="background-color: lightgrey;">0.225</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_19229d5f18c2cc47163a5ff910fb7870.html">Benchmark</a></td>
+            <td>-0.724</td>
+            <td>0.251</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4657f7dc1dfb9ceed86fed494e2d894d.html">Strategy</a></td>
+            <td>-4.196</td>
+            <td style="background-color: lightgrey;">0.209</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc2afe14cbcdef1c74d0f3718cac49fc.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">-1.243</td>
+            <td>0.793</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_0e1889d7a885d4ac567296a645246c97.html">Strategy</a></td>
+            <td>-3.443</td>
+            <td style="background-color: lightgrey;">0.013</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9f37bccb0732468f3f9ed2c09136bee4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">13.761</td>
+            <td>0.386</td>
+        </tr>
+    </tbody>
+</table>
+
+
+<p>
+  The lack of performance for this arbitrage strategy is mostly attributed to the fees it incurs while trading. This 
+  is common for an intraday arbitrage strategy, but we discuss ways to reduces these fees in the conclusion of this 
+  tutorial. After removing the costs of commissions, crossing the spread, and slippage, the strategy outperforms the 
+  SPY over the entire backtesting period. Without these costs, the strategy generates a 1.09 Share ratio while the 
+  SPY generates a 0.732 Sharpe ratio. See the backtest performance without fees below.
+</p>
+
+
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_4e5c71ecaae8819d56fde07ba9d721b2.html"></iframe>
+    </div>
+</div> 
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..c76ecb6
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/06 Market & Competition Qualification.html	
@@ -0,0 +1,19 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-7#rules">metrics</a> required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+</p>
+
+
+<ul>
+  <li>Profitable</li>
+  <li>PSR >= 80%</li>
+  <li>Max drawdown duration <= 6 months</li>
+  <li>Insights contain the following properties: Symbol, Duration, Direction, and Weight</li>
+  <li>Alphas need to place at least 10 trades per month for the majority of the backtest</li>
+</ul>
+
+<p>
+  The algorithm currently places trades during 12 unique months throughout the backtest. Since the backtest spans 
+  across 61 months, it places trades through a minority of the backtest.
+</p>
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html
new file mode 100644
index 0000000..3df342d
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/07 Conclusion.html	
@@ -0,0 +1,10 @@
+<p>
+  The intraday arbitrage strategy we built and tested throughout this tutorial underperforms the SPY benchmark in 
+  terms of Sharpe ratio when including trading costs. Without these costs, we found the strategy outperforms the SPY 
+  in terms of Sharpe ratio. In our implementation, we specified the alpha model to initiate trading when atleast a 
+  0.02% profit threshold is reached for 3 seconds. Both of these parameters are set lower than the strategy examined 
+  in Marshall et al (2010) for demonstration purposes. Increasing the profit threshold will lead to more profitable, 
+  but fewer, trades that may overcome the cost of trading. We leave this area of study for future research. 
+  Additional areas of future research include increasing the resolution of data from second to tick and incorportating 
+  an execution model that utilizes limit orders to reduce fees.
+</p>
diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html
new file mode 100644
index 0000000..a684227
--- /dev/null
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/08 References.html	
@@ -0,0 +1,8 @@
+<ol>
+  <li>
+    Marshall, Ben R. and Nguyen, Nhut (Nick) Hoang and Visaltanachoti, Nuttawat, ETF Arbitrage: Intraday Evidence (November 16, 2010). <a href="https://ssrn.com/abstract=1709599">Online copy</a>
+  </li>
+  <li>
+    Kakushadze, Zura and Serur, Juan Andrés, 151 Trading Strategies (August 17, 2018). Z. Kakushadze and J.A. Serur. 151 Trading Strategies. Cham, Switzerland: Palgrave Macmillan, an imprint of Springer Nature, 1st Edition (2018), XX, 480 pp; ISBN 978-3-030-02791-9. <a href="https://ssrn.com/abstract=3247865">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 85f17f582596286a29138f65bd752b32d942b9d3 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 10:39:19 -0700
Subject: [PATCH 703/753] updated algorithm

---
 .../05 Results.html                                          | 5 +++++
 .../{05 References.html => 06 References.html}               | 0
 2 files changed, 5 insertions(+)
 create mode 100644 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html
 rename 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/{05 References.html => 06 References.html} (100%)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html
new file mode 100644
index 0000000..2b6a817
--- /dev/null
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html	
@@ -0,0 +1,5 @@
+<p>
+    Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over
+    two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the
+    algorithm is non-deterministic, users may see different results from repeated backtests.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html
similarity index 100%
rename from 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 References.html
rename to 04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/06 References.html

From 5f6d72305f567da1d1544b3ed45ebb4798514d65 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 12:02:33 -0700
Subject: [PATCH 704/753] updated algorithm

---
 .../04 Algorithm.html                                      | 2 +-
 .../05 Results.html                                        | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html
index 9d83532..0a1b0bd 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_53764d2c98bc7166533343c3473067d5.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9791a5615cd7ab78c4c70c12e8157014.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html
index 2b6a817..a9d5b42 100644
--- a/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html	
+++ b/04 Strategy Library/1024 Forecasting Stock Prices using a Temporal CNN Model/05 Results.html	
@@ -1,5 +1,6 @@
 <p>
-    Since we trade three technology stocks, we decided to use QQQ as our benchmark. We achieve a Sharpe Ratio of 1.028 over
-    two years, while QQQ achieves a Sharpe Ratio of 0.931 over the same period. However, it should be noted that since the
-    algorithm is non-deterministic, users may see different results from repeated backtests.
+    Since our algorithm is non-deterministic, users should expect to see different results in repeated backtests. From
+    running our algorithm ten times, we achieved Sharpe Ratios with an average of .211, a maximum of 0.917, and minimum
+    of -0.312, and a standard deviation of 0.327. As we traded three technology stocks, we compare our results to QQQ.
+    Comparing our algorithm to QQQ, our average Sharpe of .211 is significantly lower than the 0.967 Sharpe of QQQ.
 </p>
\ No newline at end of file

From 2b686253c3d088fb9bf3008bbc2b36a16cc11d08 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 14:32:57 -0700
Subject: [PATCH 705/753] updated algorithm

---
 .../01 Strategy Library.php                   |  9 ++++
 .../01 Abstract.html                          |  4 ++
 .../02 Introduction.html                      |  9 ++++
 .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 +++
 .../05 Video Walkthrough.html                 |  5 ++
 .../06 Results.html                           |  8 +++
 .../07 References.html                        |  7 +++
 8 files changed, 98 insertions(+)
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index ce7332d..e4d357f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -642,6 +642,15 @@
      ],
      'description' => "A momentum strategy based on returns of the market open",
      'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+  ],
+  [
+     'name' => 'Optimal Pairs Trading',
+     'link' => 'strategy-library/optimal-pairs-trading',
+     'sources' => [
+       'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf'
+     ],
+     'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
+     'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
   ]
 ];
 
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html
new file mode 100644
index 0000000..f913c9e
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html	
@@ -0,0 +1,4 @@
+<p>
+    In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation
+    levels.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html
new file mode 100644
index 0000000..d0a7cd5
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html	
@@ -0,0 +1,9 @@
+<p>
+    Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit
+    from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter,
+    which we have implemented in this <a href="https://www.quantconnect.com/forum/discussion/6826/from-research-to-production-kalman-filters-and-pairs-trading/p1">post</a>.
+    However, today, we will model the portfolio values of holding positions in
+    a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate
+    the position in the pair of stocks. Let (<em>number</em>) refer to the corresponding equation in the given paper, which can
+    be found under the References section. For example, (2.1) refers to equation 2.1 in the paper.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
new file mode 100644
index 0000000..594383e
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
@@ -0,0 +1,50 @@
+<h3>Computing the OU Coefficients</h3>
+<p>Say there are two arbitrary stocks A and B. For each value <em>&beta;</em> in .01, .02, .03, &hellip;, 1.00:</p>
+<ol>
+    <li>We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $<em>&beta;</em> B (we short B) on each of the 252 days&nbsp;</li>
+    <li>Then we compute the OU coefficients (&theta;, &micro;, &sigma;) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:</li>
+</ol>
+$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$
+<p>where</p>
+<p><em>x</em><em>j</em><em> is the value at the jth index of the portfolio values</em></p>
+<p>and&nbsp;</p>
+$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$
+<p>and <em>n = the number of portfolio values</em></p>
+<p>and <em>&Delta;t = 1 &divide; (days between the start and end dates of the portfolio values),</em>&nbsp;</p>
+<p>We then select the <em>&beta;</em>, which we differentiate as <em>&beta;*, </em>that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding <em>&theta;*, &micro;*, &sigma;* </em>values. The implementation of finding <em>&theta;*, &micro;*, </em>and<em> &sigma;* </em>can be found in <strong>ou_mle.py</strong> in the <strong>Algorithm</strong> section.</p>
+
+
+<h3>Deriving the Optimal Entry and Liquidation Levels</h3>
+<p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
+$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$
+$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$
+$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$
+<p>Where constants <em>c = the cost of transaction </em>and <em>r = investor&rsquo;s subject discount rate</em>. We choose to set <em>c = r = .05</em> as these were the values given in the paper.&nbsp;</p>
+<p>With these equations, we can now solve for the optimal liquidation level, which we denote as <em>b*</em>, as well as the optimal entry level, which we denote at <em>d*</em>. Note: the reason we derive the optimal liquidation level first is because we use <em>b*</em> in the computation of <em>d*</em>.</p>
+<p>As deriving <em>b* </em>and <em>d* </em>require the derivative of the functions given above, given an arbitrary function input <em>x</em> and an arbitrary function <em>f(x)</em>, we approximate <em>f&rsquo;(x) </em>with the following equation equation:</p>
+$f'(x) = \frac{f(x+h)-f(x)}{h}$
+<p>where <em>h</em> is some arbitrarily small value (we set <em>h = 1 x 10<sup>-4</sup></em> in our implementation).</p>
+<p>To find <em>b*</em>, we solve for <em>b </em>in the following equation:</p>
+$F(b)=(b-c)F'(b)\quad(4.3)$
+<p>We solve this equation by getting all terms to one side:</p>
+$F(b)-(b-c)F'(b)=0$
+<p>Finding the root of the above equation results in <em>b*</em>.&nbsp;</p>
+<p>Now to derive <em>d*</em>, we solve for <em>d</em> in the following equation:</p>
+$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$
+<p>Again, we will move all terms to one side:</p>
+$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$
+<p>And finding the root of this equation results in <em>d*</em>.</p>
+<p><br />The full method of finding <em>b*</em> and <em>d*</em> can be found in the <strong>OptimalStopping.py</strong>
+    file in the <strong>Algorithm </strong>section.</p>
+
+<h3>Trading</h3>
+<p>
+    Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices
+    for stocks A and B. We then train our model on these data points, and we get the
+    <em>b*</em>, <em>d*</em>, and <em>&beta;* </em>values. We then keep track of a hypothetical portfolio of holding $1
+    of A and -$<em>&beta;*</em> of B. Once the value of our hypothetical portfolio is less than or equal to <em>b*</em>,
+    we allocate 1.0 of our capital to long of A and short B using (<em>&beta;*</em> x capital used for A) worth of stock.
+    Once the hypothetical portfolio value we tracking reaches <em>d*</em>, we liquidate our positions. We repeat these
+    trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to
+    update our <em>b*</em>, <em>d*</em>, and <em>&beta;*</em> values.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
new file mode 100644
index 0000000..40bda21
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2564b078469f4e323372ea02af036ea4.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html
new file mode 100644
index 0000000..1e7e3ae
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html	
@@ -0,0 +1,5 @@
+<iframe width="560" height="315"
+        src="https://www.youtube.com/embed/RFj8SPuZ43Y"
+        frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+        allowfullscreen>
+</iframe>
\ No newline at end of file
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html
new file mode 100644
index 0000000..3eb8a0f
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html	
@@ -0,0 +1,8 @@
+<p>
+    Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period
+    yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods
+    of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the
+    entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage
+    our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters
+    of the algorithm.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html
new file mode 100644
index 0000000..87de37d
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html	
@@ -0,0 +1,7 @@
+<ol>
+    <li>
+        Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015).
+        International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015.
+        <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701">Online Copy</a>.
+    </li>
+</ol>
\ No newline at end of file

From f02f7eaccecf43df2079c10bb6b97791ff50bf63 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 14:43:40 -0700
Subject: [PATCH 706/753] updated algorithm

---
 .../1029 Optimal Pairs Trading/04 Algorithm.html                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
index 40bda21..04066ff 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2564b078469f4e323372ea02af036ea4.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_14b6c6614134b8af76818387e9e74232.html"></iframe>
     </div>
 </div>

From fc66d22377675ad67b0fa1090611938fce46f0b2 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 14:32:57 -0700
Subject: [PATCH 707/753] updated algorithm

---
 .../01 Strategy Library.php                   |  9 ++++
 .../01 Abstract.html                          |  4 ++
 .../02 Introduction.html                      |  9 ++++
 .../1029 Optimal Pairs Trading/03 Method.html | 50 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 +++
 .../05 Video Walkthrough.html                 |  5 ++
 .../06 Results.html                           |  8 +++
 .../07 References.html                        |  7 +++
 8 files changed, 98 insertions(+)
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html
 create mode 100644 04 Strategy Library/1029 Optimal Pairs Trading/07 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 5671080..c65c7b8 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -651,6 +651,15 @@
      ],
      'description' => "A momentum strategy based on returns of the market open",
      'tags'=>'Momentum, Stocks, Universe Selection, Equities, Anomaly'
+  ],
+  [
+     'name' => 'Optimal Pairs Trading',
+     'link' => 'strategy-library/optimal-pairs-trading',
+     'sources' => [
+       'arXiv' => 'https://arxiv.org/pdf/1411.5062.pdf'
+     ],
+     'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
+     'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
   ]
 ];
 
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html
new file mode 100644
index 0000000..f913c9e
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/01 Abstract.html	
@@ -0,0 +1,4 @@
+<p>
+    In this tutorial, we apply Ornstein-Uhlenbeck model to a Pairs Trading process and derive the Optimal Entry and Liquidation
+    levels.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html
new file mode 100644
index 0000000..d0a7cd5
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/02 Introduction.html	
@@ -0,0 +1,9 @@
+<p>
+    Pairs trading is holding one stock while simultaneously shorting another stock, typically in an attempt to profit
+    from the convergence of the spread between these two stocks. One method of execution is to apply a Kalman Filter,
+    which we have implemented in this <a href="https://www.quantconnect.com/forum/discussion/6826/from-research-to-production-kalman-filters-and-pairs-trading/p1">post</a>.
+    However, today, we will model the portfolio values of holding positions in
+    a pair of stocks as an Ornstein-Uhlenbeck (OU) process in order to derive the optimal values to enter and liquidate
+    the position in the pair of stocks. Let (<em>number</em>) refer to the corresponding equation in the given paper, which can
+    be found under the References section. For example, (2.1) refers to equation 2.1 in the paper.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
new file mode 100644
index 0000000..594383e
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
@@ -0,0 +1,50 @@
+<h3>Computing the OU Coefficients</h3>
+<p>Say there are two arbitrary stocks A and B. For each value <em>&beta;</em> in .01, .02, .03, &hellip;, 1.00:</p>
+<ol>
+    <li>We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $<em>&beta;</em> B (we short B) on each of the 252 days&nbsp;</li>
+    <li>Then we compute the OU coefficients (&theta;, &micro;, &sigma;) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:</li>
+</ol>
+$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$
+<p>where</p>
+<p><em>x</em><em>j</em><em> is the value at the jth index of the portfolio values</em></p>
+<p>and&nbsp;</p>
+$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$
+<p>and <em>n = the number of portfolio values</em></p>
+<p>and <em>&Delta;t = 1 &divide; (days between the start and end dates of the portfolio values),</em>&nbsp;</p>
+<p>We then select the <em>&beta;</em>, which we differentiate as <em>&beta;*, </em>that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding <em>&theta;*, &micro;*, &sigma;* </em>values. The implementation of finding <em>&theta;*, &micro;*, </em>and<em> &sigma;* </em>can be found in <strong>ou_mle.py</strong> in the <strong>Algorithm</strong> section.</p>
+
+
+<h3>Deriving the Optimal Entry and Liquidation Levels</h3>
+<p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
+$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$
+$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$
+$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$
+<p>Where constants <em>c = the cost of transaction </em>and <em>r = investor&rsquo;s subject discount rate</em>. We choose to set <em>c = r = .05</em> as these were the values given in the paper.&nbsp;</p>
+<p>With these equations, we can now solve for the optimal liquidation level, which we denote as <em>b*</em>, as well as the optimal entry level, which we denote at <em>d*</em>. Note: the reason we derive the optimal liquidation level first is because we use <em>b*</em> in the computation of <em>d*</em>.</p>
+<p>As deriving <em>b* </em>and <em>d* </em>require the derivative of the functions given above, given an arbitrary function input <em>x</em> and an arbitrary function <em>f(x)</em>, we approximate <em>f&rsquo;(x) </em>with the following equation equation:</p>
+$f'(x) = \frac{f(x+h)-f(x)}{h}$
+<p>where <em>h</em> is some arbitrarily small value (we set <em>h = 1 x 10<sup>-4</sup></em> in our implementation).</p>
+<p>To find <em>b*</em>, we solve for <em>b </em>in the following equation:</p>
+$F(b)=(b-c)F'(b)\quad(4.3)$
+<p>We solve this equation by getting all terms to one side:</p>
+$F(b)-(b-c)F'(b)=0$
+<p>Finding the root of the above equation results in <em>b*</em>.&nbsp;</p>
+<p>Now to derive <em>d*</em>, we solve for <em>d</em> in the following equation:</p>
+$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$
+<p>Again, we will move all terms to one side:</p>
+$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$
+<p>And finding the root of this equation results in <em>d*</em>.</p>
+<p><br />The full method of finding <em>b*</em> and <em>d*</em> can be found in the <strong>OptimalStopping.py</strong>
+    file in the <strong>Algorithm </strong>section.</p>
+
+<h3>Trading</h3>
+<p>
+    Our trading logic is very simple. First, we feed in 252 points of the most recent data for the daily closing prices
+    for stocks A and B. We then train our model on these data points, and we get the
+    <em>b*</em>, <em>d*</em>, and <em>&beta;* </em>values. We then keep track of a hypothetical portfolio of holding $1
+    of A and -$<em>&beta;*</em> of B. Once the value of our hypothetical portfolio is less than or equal to <em>b*</em>,
+    we allocate 1.0 of our capital to long of A and short B using (<em>&beta;*</em> x capital used for A) worth of stock.
+    Once the hypothetical portfolio value we tracking reaches <em>d*</em>, we liquidate our positions. We repeat these
+    trading rules when possible. On the first day of every quarter, we retrain our model with the most recent 252 points of data to
+    update our <em>b*</em>, <em>d*</em>, and <em>&beta;*</em> values.
+</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
new file mode 100644
index 0000000..40bda21
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2564b078469f4e323372ea02af036ea4.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html
new file mode 100644
index 0000000..1e7e3ae
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/05 Video Walkthrough.html	
@@ -0,0 +1,5 @@
+<iframe width="560" height="315"
+        src="https://www.youtube.com/embed/RFj8SPuZ43Y"
+        frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+        allowfullscreen>
+</iframe>
\ No newline at end of file
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html
new file mode 100644
index 0000000..3eb8a0f
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/06 Results.html	
@@ -0,0 +1,8 @@
+<p>
+    Our algorithm yielded a Sharpe ratio of 0.898 over a five year period, while holding SPY over the same period
+    yielded a Sharpe ratio of 0.667. However it should be noted, due to the fact the algorithm had to wait periods
+    of time before our optimal entry and liquidation levels were reached, our algorithm only made twelve trades over the
+    entire backtest duration. To increase the number of trades, we can add additional pairs, such as GLD-GDX. We encourage
+    our users to clone this algorithm and and to experiment with different ideas for pairs and to play with the parameters
+    of the algorithm.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html
new file mode 100644
index 0000000..87de37d
--- /dev/null
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html	
@@ -0,0 +1,7 @@
+<ol>
+    <li>
+        Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015).
+        International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015.
+        <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701">Online Copy</a>.
+    </li>
+</ol>
\ No newline at end of file

From 81c8ab32405ffd6cf6acf51635fe97dd8a05791f Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 14:43:40 -0700
Subject: [PATCH 708/753] updated algorithm

---
 .../1029 Optimal Pairs Trading/04 Algorithm.html                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html
index 40bda21..04066ff 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_2564b078469f4e323372ea02af036ea4.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_14b6c6614134b8af76818387e9e74232.html"></iframe>
     </div>
 </div>

From bc5492740e6f0ebfa6df6dbb095de46839466c9f Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 16:34:22 -0700
Subject: [PATCH 709/753] fixed typos

---
 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
index 594383e..fd3f849 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
@@ -15,7 +15,7 @@ <h3>Computing the OU Coefficients</h3>
 
 
 <h3>Deriving the Optimal Entry and Liquidation Levels</h3>
-<p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
+<p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
 $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$
 $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$
 $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$

From 700abdbfd2115364f0d091bfbb4edfe69e5bd0f9 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 17 Sep 2020 16:37:47 -0700
Subject: [PATCH 710/753] fixed typos

---
 04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
index 8ab9f29..fd3f849 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
@@ -15,11 +15,7 @@ <h3>Computing the OU Coefficients</h3>
 
 
 <h3>Deriving the Optimal Entry and Liquidation Levels</h3>
-<<<<<<< HEAD
 <p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
-=======
-<p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level, before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reach, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
->>>>>>> origin/feature-310-optimal-pairs-trading
 $F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$
 $G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$
 $V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$

From fdf740bdf7815509d61ac17e968b7d10ef1155dd Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 18 Sep 2020 10:25:18 -0700
Subject: [PATCH 711/753] changed LaTex format as $$ don't render properly

---
 .../1029 Optimal Pairs Trading/03 Method.html | 20 +++++++++----------
 .../07 References.html                        |  2 +-
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html
index fd3f849..83986fa 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/03 Method.html	
@@ -4,11 +4,11 @@ <h3>Computing the OU Coefficients</h3>
     <li>We compute the portfolio values over 252 days of the pairs trade by computing the value of holding $1 of A minus the value of holding $<em>&beta;</em> B (we short B) on each of the 252 days&nbsp;</li>
     <li>Then we compute the OU coefficients (&theta;, &micro;, &sigma;) of the 252 portfolio values using the Maximum Likelihood Estimation method, maximizing the average log-likelihood defined by this function:</li>
 </ol>
-$-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)$
+\[-\frac{1}{2}ln(2\pi)-ln(\widetilde{\sigma})-\frac{1}{2n{\widetilde{\sigma}}^2}\sum_{i=1}^{n}[x_i-x_{i-1}e^{-\mu\Delta t}-\theta(1-e^{-\mu\Delta t})]^2\quad(2.2)\]
 <p>where</p>
 <p><em>x</em><em>j</em><em> is the value at the jth index of the portfolio values</em></p>
 <p>and&nbsp;</p>
-$\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}$
+\[\widetilde{\sigma}^2 = \sigma ^2\frac{1-e^{-2\mu\Delta t}}{2\mu}\]
 <p>and <em>n = the number of portfolio values</em></p>
 <p>and <em>&Delta;t = 1 &divide; (days between the start and end dates of the portfolio values),</em>&nbsp;</p>
 <p>We then select the <em>&beta;</em>, which we differentiate as <em>&beta;*, </em>that maximizes the average log-likelihood defined in the equation in step 2, while keeping the corresponding <em>&theta;*, &micro;*, &sigma;* </em>values. The implementation of finding <em>&theta;*, &micro;*, </em>and<em> &sigma;* </em>can be found in <strong>ou_mle.py</strong> in the <strong>Algorithm</strong> section.</p>
@@ -16,23 +16,23 @@ <h3>Computing the OU Coefficients</h3>
 
 <h3>Deriving the Optimal Entry and Liquidation Levels</h3>
 <p>With the OU coefficients in hand, we can now calculate the optimal entry level, the portfolio value at which we buy, and liquidation level, the portfolio value at which we sell. We don&rsquo;t buy the portfolio of $1 of A and -$<em>&beta;*</em> of B immediately (note: to scale up a position, for each additional dollar we invest in A, we short an additional -$<em>&beta;*</em> of B). Instead, we wait until our portfolio reaches the computed entry level, before we buy the portfolio of the pair of stocks. Once we bought this portfolio, we wait until the portfolio value reaches the computed liquidation level before we liquidate our portfolio. By waiting for our optimal entry and liquidation values to be reached, we can mathematically maximize our expected gain. To compute the optimal entry and liquidation levels, we first need to define a few equations:</p>
-$F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)$
-$G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)$
-$V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)$
+\[F(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(x-\theta)u-\frac{u^2}{2}}du\quad(3.3)\]
+\[G(x):=\int_{0}^{\infty}u^{\frac{r}{\mu}-1}e^{\sqrt{\frac{2\mu}{\sigma^2}}(\theta-x)u-\frac{u^2}{2}}du\quad(3.4)\]
+\[V(x)=(x\in (-\infty, b^*)\ ?\ (b^*-c) \frac{F(x)}{F(b^*)}:x-c)\quad(4.2)\]
 <p>Where constants <em>c = the cost of transaction </em>and <em>r = investor&rsquo;s subject discount rate</em>. We choose to set <em>c = r = .05</em> as these were the values given in the paper.&nbsp;</p>
 <p>With these equations, we can now solve for the optimal liquidation level, which we denote as <em>b*</em>, as well as the optimal entry level, which we denote at <em>d*</em>. Note: the reason we derive the optimal liquidation level first is because we use <em>b*</em> in the computation of <em>d*</em>.</p>
 <p>As deriving <em>b* </em>and <em>d* </em>require the derivative of the functions given above, given an arbitrary function input <em>x</em> and an arbitrary function <em>f(x)</em>, we approximate <em>f&rsquo;(x) </em>with the following equation equation:</p>
-$f'(x) = \frac{f(x+h)-f(x)}{h}$
+\[f'(x) = \frac{f(x+h)-f(x)}{h}\]
 <p>where <em>h</em> is some arbitrarily small value (we set <em>h = 1 x 10<sup>-4</sup></em> in our implementation).</p>
 <p>To find <em>b*</em>, we solve for <em>b </em>in the following equation:</p>
-$F(b)=(b-c)F'(b)\quad(4.3)$
+\[F(b)=(b-c)F'(b)\quad(4.3)\]
 <p>We solve this equation by getting all terms to one side:</p>
-$F(b)-(b-c)F'(b)=0$
+\[F(b)-(b-c)F'(b)=0\]
 <p>Finding the root of the above equation results in <em>b*</em>.&nbsp;</p>
 <p>Now to derive <em>d*</em>, we solve for <em>d</em> in the following equation:</p>
-$G(d)(V'(d)-1)=G'(d)(V(d)-d-c)$
+\[G(d)(V'(d)-1)=G'(d)(V(d)-d-c)\]
 <p>Again, we will move all terms to one side:</p>
-$G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0$
+\[G(d)(V'(d)-1)-G'(d)(V(d)-d-c)=0\]
 <p>And finding the root of this equation results in <em>d*</em>.</p>
 <p><br />The full method of finding <em>b*</em> and <em>d*</em> can be found in the <strong>OptimalStopping.py</strong>
     file in the <strong>Algorithm </strong>section.</p>
diff --git a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html
index 87de37d..10b2dac 100644
--- a/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html	
+++ b/04 Strategy Library/1029 Optimal Pairs Trading/07 References.html	
@@ -2,6 +2,6 @@
     <li>
         Leung, Tim and Li, Xin, Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit (April 26, 2015).
         International Journal of Theoretical and Applied Finance, Vol. 18, No. 3, 2015.
-        <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2741701">Online Copy</a>.
+        <a href="https://arxiv.org/pdf/1411.5062.pdf">Online Copy</a>.
     </li>
 </ol>
\ No newline at end of file

From 7fb1a0c3b7c573ca18d19ef20beb614a34219a5f Mon Sep 17 00:00:00 2001
From: Aaron Janeiro Stone <60862650+aarjaneiro@users.noreply.github.com>
Date: Fri, 18 Sep 2020 14:26:12 -0400
Subject: [PATCH 712/753] Correction of a typo
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

\text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) →\text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}})
---
 .../12 Modern Portfolio Theory/04 Mean-Variance Analysis.html   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html
index 51a1bf9..e60dcab 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/04 Mean-Variance Analysis.html	
@@ -26,7 +26,7 @@ <h4>Capital Market Line</h4>
   Since there is only <em>n</em> = 1 risky asset, the variance of the CML portfolio return is
 </p>
 
-\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}}) \]
+\[ \text{Var} (R_P) = w^2 \text{Var} (R_{\text{market}}) \]
 
 <p>
   Taking square roots, we deduce that a CML portfolio's risk is proportional to the market portfolio's weight:

From 0fe294f7414b458dded00ddcad919e1a7269dd54 Mon Sep 17 00:00:00 2001
From: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
Date: Fri, 18 Sep 2020 19:34:28 +0100
Subject: [PATCH 713/753] Add Data Directory and Fama-French Data Factors

Latest files from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
---
 Data/F-F_Research_Data_5_Factors_2x3.CSV      |   748 +
 .../F-F_Research_Data_5_Factors_2x3_daily.CSV | 14374 +++++++++
 Data/F-F_Research_Data_Factors.CSV            |  1231 +
 Data/F-F_Research_Data_Factors_daily.CSV      | 24797 ++++++++++++++++
 Data/F-F_Research_Data_Factors_weekly.CSV     |  4916 +++
 5 files changed, 46066 insertions(+)
 create mode 100644 Data/F-F_Research_Data_5_Factors_2x3.CSV
 create mode 100644 Data/F-F_Research_Data_5_Factors_2x3_daily.CSV
 create mode 100644 Data/F-F_Research_Data_Factors.CSV
 create mode 100644 Data/F-F_Research_Data_Factors_daily.CSV
 create mode 100644 Data/F-F_Research_Data_Factors_weekly.CSV

diff --git a/Data/F-F_Research_Data_5_Factors_2x3.CSV b/Data/F-F_Research_Data_5_Factors_2x3.CSV
new file mode 100644
index 0000000..8adb14e
--- /dev/null
+++ b/Data/F-F_Research_Data_5_Factors_2x3.CSV
@@ -0,0 +1,748 @@
+This file was created by CMPT_ME_BEME_OP_INV_RETS using the 202007 CRSP database.
+The 1-month TBill return is from Ibbotson and Associates Inc.
+
+,Mkt-RF,SMB,HML,RMW,CMA,RF
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+200805,    1.86,    3.22,   -1.38,    0.81,    0.04,    0.18
+200806,   -8.44,    1.15,   -2.41,    4.30,   -0.42,    0.17
+200807,   -0.77,    3.64,    5.93,   -1.00,    1.06,    0.15
+200808,    1.53,    3.42,    1.55,    1.84,    0.81,    0.13
+200809,   -9.24,    0.35,    6.32,    2.95,    1.78,    0.15
+200810,  -17.23,   -3.21,   -2.88,    3.58,    1.92,    0.08
+200811,   -7.86,   -3.88,   -6.03,    4.79,    2.72,    0.03
+200812,    1.74,    3.38,   -0.28,   -0.08,   -1.41,    0.00
+200901,   -8.12,   -1.99,  -11.23,    0.23,   -1.16,    0.00
+200902,  -10.10,   -1.17,   -7.30,    1.71,   -1.11,    0.01
+200903,    8.95,    0.66,    3.64,   -2.33,   -2.22,    0.02
+200904,   10.19,    6.70,    5.53,    0.29,    0.14,    0.01
+200905,    5.21,   -2.33,   -0.21,   -0.98,   -2.16,    0.00
+200906,    0.43,    2.31,   -2.66,   -1.06,   -0.18,    0.01
+200907,    7.72,    2.45,    5.31,   -0.59,    3.21,    0.01
+200908,    3.33,   -0.05,    7.76,   -2.99,    3.23,    0.01
+200909,    4.08,    2.74,    0.91,    1.44,    0.38,    0.01
+200910,   -2.59,   -4.78,   -4.17,    4.18,   -1.67,    0.00
+200911,    5.56,   -2.80,   -0.19,    0.82,    0.16,    0.00
+200912,    2.75,    6.24,   -0.01,    0.65,   -0.08,    0.01
+201001,   -3.36,    0.28,    0.31,   -1.41,    0.44,    0.00
+201002,    3.40,    1.47,    3.13,   -0.28,    1.43,    0.00
+201003,    6.31,    1.79,    2.13,   -0.62,    1.69,    0.01
+201004,    2.00,    5.05,    2.82,    1.12,    1.69,    0.01
+201005,   -7.89,    0.06,   -2.39,    1.29,   -0.19,    0.01
+201006,   -5.56,   -2.49,   -4.48,   -0.27,   -1.50,    0.01
+201007,    6.93,    0.09,   -0.26,    0.24,    2.01,    0.01
+201008,   -4.77,   -3.11,   -1.96,    0.72,   -1.69,    0.01
+201009,    9.54,    3.72,   -3.13,   -0.21,    0.47,    0.01
+201010,    3.88,    0.78,   -2.61,    1.29,   -0.23,    0.01
+201011,    0.60,    3.61,   -0.90,    0.24,    1.63,    0.01
+201012,    6.82,    0.95,    3.81,   -3.65,    3.23,    0.01
+201101,    1.99,   -2.42,    0.81,   -0.89,    0.69,    0.01
+201102,    3.49,    1.64,    1.09,   -1.93,    0.95,    0.01
+201103,    0.45,    2.67,   -1.58,    1.65,   -0.03,    0.01
+201104,    2.90,   -0.51,   -2.52,    1.10,   -0.84,    0.00
+201105,   -1.27,   -0.68,   -2.07,    2.02,   -1.50,    0.00
+201106,   -1.75,    0.11,   -0.31,    2.38,   -1.45,    0.00
+201107,   -2.36,   -1.35,   -1.21,    2.43,   -1.69,    0.00
+201108,   -5.99,   -3.21,   -2.45,    3.19,   -0.20,    0.01
+201109,   -7.59,   -3.77,   -1.39,    1.89,    0.28,    0.00
+201110,   11.35,    3.56,   -0.18,   -1.94,   -0.85,    0.00
+201111,   -0.28,   -0.26,   -0.34,    1.77,    1.50,    0.00
+201112,    0.74,   -0.44,    1.78,    0.76,    2.36,    0.00
+201201,    5.05,    2.16,   -1.14,   -1.80,   -1.41,    0.00
+201202,    4.42,   -1.63,    0.09,   -0.19,   -0.06,    0.00
+201203,    3.11,   -0.46,    0.91,   -0.41,    0.81,    0.00
+201204,   -0.85,   -0.59,   -0.49,    1.12,    0.67,    0.00
+201205,   -6.19,   -0.10,   -0.58,    2.25,    2.41,    0.01
+201206,    3.89,    0.96,    0.44,   -1.23,    0.32,    0.00
+201207,    0.79,   -2.68,   -0.26,    1.13,    0.13,    0.00
+201208,    2.55,    0.44,    1.28,   -1.27,   -0.73,    0.01
+201209,    2.73,    0.66,    1.52,   -1.38,    1.59,    0.01
+201210,   -1.76,   -0.81,    3.79,   -1.39,    2.28,    0.01
+201211,    0.78,    0.36,   -0.98,    0.67,    0.91,    0.01
+201212,    1.18,    1.88,    3.58,   -1.99,    0.86,    0.01
+201301,    5.57,    0.56,    0.91,   -1.64,    1.47,    0.00
+201302,    1.29,   -0.38,    0.01,   -0.68,    0.48,    0.00
+201303,    4.03,    0.83,   -0.28,    0.17,    1.31,    0.00
+201304,    1.55,   -2.30,    0.59,    0.08,    0.47,    0.00
+201305,    2.80,    2.07,    2.56,   -1.68,   -0.78,    0.00
+201306,   -1.20,    1.35,   -0.17,   -0.45,   -0.01,    0.00
+201307,    5.65,    1.79,    0.55,   -1.45,    0.56,    0.00
+201308,   -2.71,   -0.02,   -2.76,    0.62,   -2.18,    0.00
+201309,    3.77,    2.67,   -1.20,   -0.77,   -1.30,    0.00
+201310,    4.18,   -1.51,    1.10,    2.66,    0.88,    0.00
+201311,    3.12,    1.35,    0.27,    0.13,    0.08,    0.00
+201312,    2.81,   -0.53,   -0.30,   -0.49,    0.08,    0.00
+201401,   -3.32,    0.59,   -2.10,   -3.97,   -1.36,    0.00
+201402,    4.65,    0.13,   -0.46,   -0.25,   -0.43,    0.00
+201403,    0.43,   -1.17,    5.06,    2.14,    1.89,    0.00
+201404,   -0.19,   -4.17,    1.07,    3.48,    1.04,    0.00
+201405,    2.06,   -1.88,   -0.19,    0.09,   -1.06,    0.00
+201406,    2.61,    3.08,   -0.76,   -1.95,   -1.94,    0.00
+201407,   -2.04,   -4.25,    0.01,    0.94,    0.47,    0.00
+201408,    4.24,    0.28,   -0.58,   -0.63,   -0.69,    0.00
+201409,   -1.97,   -3.79,   -1.23,    1.12,   -0.52,    0.00
+201410,    2.52,    3.78,   -1.68,   -0.47,   -0.17,    0.00
+201411,    2.55,   -2.30,   -2.99,    1.40,    0.19,    0.00
+201412,   -0.06,    2.87,    2.06,   -1.16,    0.88,    0.00
+201501,   -3.11,   -0.89,   -3.47,    1.67,   -1.66,    0.00
+201502,    6.13,    0.22,   -1.83,   -1.06,   -1.76,    0.00
+201503,   -1.12,    3.04,   -0.46,    0.10,   -0.51,    0.00
+201504,    0.59,   -3.02,    1.85,   -0.14,   -0.49,    0.00
+201505,    1.36,    0.80,   -1.36,   -1.73,   -0.73,    0.00
+201506,   -1.53,    2.85,   -0.79,    0.50,   -1.48,    0.00
+201507,    1.54,   -4.55,   -4.14,    0.04,   -2.54,    0.00
+201508,   -6.04,    0.41,    2.66,    0.76,    1.25,    0.00
+201509,   -3.08,   -2.79,    0.53,    1.83,   -0.47,    0.00
+201510,    7.75,   -2.17,   -0.06,    0.81,    0.45,    0.00
+201511,    0.56,    3.36,   -0.50,   -2.59,   -1.11,    0.00
+201512,   -2.17,   -2.99,   -2.60,    0.32,    0.10,    0.01
+201601,   -5.77,   -3.42,    2.08,    2.60,    3.04,    0.01
+201602,   -0.07,    0.95,   -0.49,    3.27,    2.06,    0.02
+201603,    6.96,    1.10,    1.15,    0.82,   -0.06,    0.02
+201604,    0.92,    1.18,    3.26,   -2.88,    1.97,    0.01
+201605,    1.78,   -0.72,   -1.82,   -1.01,   -2.54,    0.01
+201606,   -0.05,    0.49,   -1.46,    1.19,    1.96,    0.02
+201607,    3.95,    2.64,   -1.09,    1.32,   -1.24,    0.02
+201608,    0.50,    1.73,    3.37,   -1.31,   -0.35,    0.02
+201609,    0.25,    1.76,   -1.47,   -2.31,   -0.06,    0.02
+201610,   -2.02,   -4.04,    4.14,    1.19,    0.21,    0.02
+201611,    4.86,    6.80,    8.32,   -0.06,    3.78,    0.01
+201612,    1.81,    0.42,    3.60,    0.98,   -0.29,    0.03
+201701,    1.94,   -1.26,   -2.77,   -0.01,   -0.94,    0.04
+201702,    3.57,   -2.14,   -1.82,    0.79,   -1.74,    0.04
+201703,    0.17,    0.78,   -3.17,    0.66,   -1.04,    0.03
+201704,    1.09,    0.48,   -1.87,    1.99,   -1.58,    0.05
+201705,    1.06,   -3.07,   -3.81,    1.21,   -1.93,    0.06
+201706,    0.78,    2.49,    1.35,   -1.99,   -0.05,    0.06
+201707,    1.87,   -1.57,   -0.26,   -0.58,   -0.15,    0.07
+201708,    0.16,   -1.82,   -2.22,    0.24,   -2.43,    0.09
+201709,    2.51,    4.80,    3.02,   -1.35,    1.62,    0.09
+201710,    2.25,   -1.94,   -0.08,    0.96,   -3.33,    0.09
+201711,    3.12,   -0.43,   -0.04,    3.12,    0.01,    0.08
+201712,    1.06,   -1.04,    0.11,    0.68,    1.65,    0.09
+201801,    5.58,   -3.11,   -1.55,   -0.41,   -0.89,    0.11
+201802,   -3.65,    0.34,   -1.09,    0.53,   -2.25,    0.11
+201803,   -2.35,    3.50,    0.00,   -0.60,   -0.01,    0.12
+201804,    0.29,    0.93,    0.56,   -2.36,    1.18,    0.14
+201805,    2.65,    4.75,   -3.21,   -2.04,   -1.43,    0.14
+201806,    0.48,    0.88,   -2.40,    0.73,    0.34,    0.14
+201807,    3.19,   -1.95,    0.47,    1.55,    0.40,    0.16
+201808,    3.44,    0.64,   -4.13,   -0.26,   -2.47,    0.16
+201809,    0.06,   -2.52,   -1.32,    0.60,    1.26,    0.15
+201810,   -7.68,   -4.53,    3.47,    0.82,    3.42,    0.19
+201811,    1.69,   -0.84,    0.27,   -0.63,    0.33,    0.18
+201812,   -9.55,   -3.05,   -1.49,   -0.14,    0.15,    0.19
+201901,    8.41,    3.08,   -0.60,   -0.70,   -1.36,    0.21
+201902,    3.40,    1.78,   -2.83,    0.23,   -1.47,    0.18
+201903,    1.10,   -3.58,   -4.04,    0.87,   -1.03,    0.19
+201904,    3.96,   -1.15,    1.96,    1.72,   -2.18,    0.21
+201905,   -6.94,   -1.51,   -2.35,   -0.43,    1.78,    0.21
+201906,    6.93,    0.36,   -1.08,    0.94,   -0.38,    0.18
+201907,    1.19,   -1.89,    0.17,   -0.04,    0.34,    0.19
+201908,   -2.58,   -3.30,   -5.05,    0.41,   -0.88,    0.16
+201909,    1.43,    0.27,    6.81,    1.98,    3.50,    0.18
+201910,    2.06,    0.22,   -2.04,    0.39,   -0.99,    0.15
+201911,    3.87,    0.52,   -1.80,   -1.52,   -1.24,    0.12
+201912,    2.77,    0.96,    1.93,    0.19,    1.29,    0.14
+202001,   -0.11,   -4.40,   -6.37,   -1.24,   -2.34,    0.13
+202002,   -8.13,   -0.05,   -4.05,   -1.67,   -2.49,    0.12
+202003,  -13.39,   -8.42,  -14.17,   -1.33,    1.21,    0.12
+202004,   13.65,    2.81,   -1.18,    2.65,   -1.03,    0.00
+202005,    5.58,    1.92,   -4.92,    0.66,   -3.28,    0.01
+202006,    2.45,    1.93,   -2.19,   -0.02,    0.34,    0.01
+202007,    5.77,   -3.11,   -1.27,    0.61,    1.07,    0.01
+
+ Annual Factors: January-December 
+,Mkt-RF,SMB,HML,RMW,CMA,RF
+  1964,   12.54,    0.33,    9.86,   -2.99,    6.80,    3.54
+  1965,   10.52,   24.41,    7.36,   -0.79,   -3.17,    3.93
+  1966,  -13.51,    2.15,   -0.68,   -0.12,   -0.34,    4.76
+  1967,   24.49,   50.40,   -8.58,    7.53,  -15.04,    4.21
+  1968,    8.79,   26.32,   18.49,  -12.84,   16.25,    5.21
+  1969,  -17.54,  -14.06,   -9.81,   11.77,   -4.14,    6.58
+  1970,   -6.49,  -12.36,   22.34,   -2.65,   24.45,    6.52
+  1971,   11.78,    5.58,  -11.29,   10.16,   -5.86,    4.39
+  1972,   13.05,  -11.43,    1.75,    7.99,   -3.05,    3.84
+  1973,  -26.19,  -20.00,   18.08,   -9.03,    6.66,    6.93
+  1974,  -35.75,    0.67,    9.67,   -4.04,   22.88,    8.00
+  1975,   32.44,   19.05,    9.49,    0.53,    0.48,    5.80
+  1976,   21.91,   19.90,   24.50,   -6.67,    7.47,    5.08
+  1977,   -8.26,   24.64,    7.51,    2.16,   -0.39,    5.12
+  1978,    1.03,   13.68,    0.37,    5.07,    4.15,    7.18
+  1979,   13.09,   21.05,   -2.12,   -2.80,   -0.99,   10.38
+  1980,   22.13,    4.91,  -25.06,   14.08,  -11.32,   11.24
+  1981,  -18.13,    7.48,   25.01,   -1.60,   10.36,   14.71
+  1982,   10.66,    7.42,   13.59,   -4.46,   18.01,   10.54
+  1983,   13.74,   11.84,   20.85,    0.67,   16.43,    8.80
+  1984,   -6.05,   -8.45,   19.63,   14.88,    3.84,    9.85
+  1985,   24.91,   -0.62,    1.35,   11.89,   -2.49,    7.72
+  1986,   10.12,  -10.37,    9.58,    7.13,    9.84,    6.16
+  1987,   -3.87,  -11.28,   -1.64,    6.20,    6.90,    5.47
+  1988,   11.55,    6.46,   14.77,    3.08,    9.74,    6.35
+  1989,   20.49,  -13.02,   -4.29,    2.74,    8.21,    8.37
+  1990,  -13.95,  -14.30,   -9.72,    7.44,    0.60,    7.81
+  1991,   29.18,   15.77,  -14.41,   12.06,  -14.91,    5.60
+  1992,    6.23,    9.07,   24.28,    7.54,    6.96,    3.51
+  1993,    8.21,    6.01,   18.91,   -8.55,   11.99,    2.90
+  1994,   -4.10,   -2.51,   -0.69,    6.35,    3.90,    3.90
+  1995,   31.22,   -8.17,    5.30,    1.97,    2.81,    5.60
+  1996,   15.96,   -2.80,    6.16,   15.53,    0.52,    5.21
+  1997,   25.96,   -6.17,   17.46,   10.51,    5.19,    5.26
+  1998,   19.46,  -28.67,   -8.89,    0.63,   -4.60,    4.86
+  1999,   20.57,    8.56,  -31.77,  -27.89,   -8.44,    4.68
+  2000,  -17.60,    4.21,   39.69,   26.67,   32.74,    5.89
+  2001,  -15.20,   23.68,   19.52,   19.84,   11.79,    3.83
+  2002,  -22.76,    5.82,    7.47,   20.39,   14.47,    1.65
+  2003,   30.75,   24.49,    5.40,  -20.46,   17.26,    1.02
+  2004,   10.72,    7.34,    8.08,    8.34,   -7.86,    1.20
+  2005,    3.09,   -0.75,    8.33,    1.71,   -5.03,    2.98
+  2006,   10.60,    1.53,   14.11,    2.08,    8.19,    4.80
+  2007,    1.04,   -7.94,  -14.65,    4.37,   -7.80,    4.66
+  2008,  -38.34,    3.34,    0.82,   15.06,    4.16,    1.60
+  2009,   28.26,    7.99,   -9.17,    2.78,   -2.67,    0.10
+  2010,   17.37,   13.28,   -5.31,   -1.48,    9.93,    0.12
+  2011,    0.44,   -5.78,   -8.35,   12.60,   -0.88,    0.04
+  2012,   16.28,   -0.08,    9.68,   -4.71,    9.41,    0.06
+  2013,   35.20,    7.68,    1.33,   -4.44,    1.31,    0.02
+  2014,   11.70,   -8.10,   -1.74,    1.04,   -1.70,    0.02
+  2015,    0.07,   -5.85,   -9.65,    0.89,   -8.49,    0.02
+  2016,   13.30,    9.20,   23.02,    4.82,    9.77,    0.20
+  2017,   21.50,   -5.84,  -13.98,    6.81,  -11.80,    0.80
+  2018,   -6.93,   -5.35,   -9.16,   -1.37,    0.21,    1.81
+  2019,   28.28,   -6.22,  -11.67,    4.99,   -2.81,    2.14
diff --git a/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV
new file mode 100644
index 0000000..de66995
--- /dev/null
+++ b/Data/F-F_Research_Data_5_Factors_2x3_daily.CSV
@@ -0,0 +1,14374 @@
+This file was created by CMPT_ME_BEME_OP_INV_RETS_DAILY using the 202007 CRSP database.
+The 1-month TBill return is from Ibbotson and Associates, Inc.
+
+,Mkt-RF,SMB,HML,RMW,CMA,RF
+19630701,   -0.67,    0.00,   -0.32,   -0.01,    0.15,   0.012
+19630702,    0.79,   -0.27,    0.27,   -0.07,   -0.19,   0.012
+19630703,    0.63,   -0.17,   -0.09,    0.17,   -0.33,   0.012
+19630705,    0.40,    0.08,   -0.28,    0.08,   -0.33,   0.012
+19630708,   -0.63,    0.04,   -0.18,   -0.29,    0.13,   0.012
+19630709,    0.45,    0.00,    0.10,    0.14,   -0.04,   0.012
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+20200716,   -0.37,   -0.16,    0.81,    0.30,    0.34,   0.000
+20200717,    0.30,   -0.16,   -1.40,   -1.07,    0.00,   0.000
+20200720,    1.01,   -1.11,   -2.38,   -0.81,   -1.31,   0.000
+20200721,    0.16,    1.46,    3.24,    0.53,    0.91,   0.000
+20200722,    0.49,   -0.61,   -0.34,    0.50,    0.03,   0.000
+20200723,   -1.20,    1.21,    1.96,   -0.16,    0.21,   0.000
+20200724,   -0.75,   -0.78,    0.47,    0.29,    0.06,   0.000
+20200727,    0.88,    0.29,   -1.93,   -0.22,    0.07,   0.000
+20200728,   -0.81,   -0.45,    1.01,    0.29,    0.12,   0.000
+20200729,    1.35,    0.66,    0.84,    0.19,    0.15,   0.000
+20200730,   -0.29,    0.01,   -1.81,   -0.46,   -0.17,   0.000
+20200731,    0.61,   -1.77,   -0.65,    0.76,    0.73,   0.000
diff --git a/Data/F-F_Research_Data_Factors.CSV b/Data/F-F_Research_Data_Factors.CSV
new file mode 100644
index 0000000..c1069e4
--- /dev/null
+++ b/Data/F-F_Research_Data_Factors.CSV
@@ -0,0 +1,1231 @@
+This file was created by CMPT_ME_BEME_RETS using the 202007 CRSP database.
+The 1-month TBill return is from Ibbotson and Associates, Inc.
+
+,Mkt-RF,SMB,HML,RF
+192607,    2.96,   -2.30,   -2.87,    0.22
+192608,    2.64,   -1.40,    4.19,    0.25
+192609,    0.36,   -1.32,    0.01,    0.23
+192610,   -3.24,    0.04,    0.51,    0.32
+192611,    2.53,   -0.20,   -0.35,    0.31
+192612,    2.62,   -0.04,   -0.02,    0.28
+192701,   -0.06,   -0.56,    4.83,    0.25
+192702,    4.18,   -0.10,    3.17,    0.26
+192703,    0.13,   -1.60,   -2.67,    0.30
+192704,    0.46,    0.43,    0.60,    0.25
+192705,    5.44,    1.41,    4.93,    0.30
+192706,   -2.34,    0.47,   -1.53,    0.26
+192707,    7.26,   -3.23,   -1.16,    0.30
+192708,    1.97,   -0.72,   -3.69,    0.28
+192709,    4.76,   -3.57,   -0.71,    0.21
+192710,   -4.31,    2.13,   -4.33,    0.25
+192711,    6.58,    2.76,   -0.31,    0.21
+192712,    2.09,    0.93,   -1.06,    0.22
+192801,   -0.68,    4.25,   -0.72,    0.25
+192802,   -1.70,   -2.03,   -0.69,    0.33
+192803,    8.81,   -0.26,   -1.20,    0.29
+192804,    4.23,    3.82,    3.67,    0.22
+192805,    1.52,    2.98,   -3.46,    0.32
+192806,   -4.85,   -3.50,   -0.06,    0.31
+192807,    0.62,   -1.35,   -0.47,    0.32
+192808,    6.68,   -2.07,   -2.11,    0.32
+192809,    2.88,    2.33,    0.99,    0.27
+192810,    1.33,    2.27,   -2.26,    0.41
+192811,   11.81,   -1.81,    2.80,    0.38
+192812,    0.36,   -0.85,   -0.60,    0.06
+192901,    4.66,   -3.54,   -1.21,    0.34
+192902,   -0.34,   -0.39,    1.68,    0.36
+192903,   -0.89,   -4.74,    1.61,    0.34
+192904,    1.43,   -1.00,    0.60,    0.36
+192905,   -6.39,   -5.35,   -1.40,    0.44
+192906,    9.70,   -2.17,   -2.76,    0.52
+192907,    4.46,   -3.88,    2.66,    0.33
+192908,    8.18,   -9.52,    0.06,    0.40
+192909,   -5.47,    1.17,   -0.63,    0.35
+192910,  -20.12,   -4.08,    7.85,    0.46
+192911,  -12.74,   -1.90,    5.33,    0.37
+192912,    1.33,   -4.33,   -0.59,    0.37
+193001,    5.61,    3.54,   -1.01,    0.14
+193002,    2.50,    0.13,    0.39,    0.30
+193003,    7.10,    3.43,    0.15,    0.35
+193004,   -2.06,   -0.20,   -0.84,    0.21
+193005,   -1.66,   -2.04,   -0.63,    0.26
+193006,  -16.27,   -3.24,    2.00,    0.27
+193007,    4.12,   -0.37,   -1.56,    0.20
+193008,    0.30,   -2.22,   -0.78,    0.09
+193009,  -12.75,   -2.22,   -5.27,    0.22
+193010,   -8.78,   -0.10,   -1.35,    0.09
+193011,   -3.04,    2.21,   -3.53,    0.13
+193012,   -7.83,   -4.67,   -5.40,    0.14
+193101,    6.24,    3.77,    7.16,    0.15
+193102,   10.88,    3.38,    1.61,    0.04
+193103,   -6.43,    3.07,   -3.66,    0.13
+193104,   -9.98,   -4.62,   -3.94,    0.08
+193105,  -13.24,    5.17,   -6.58,    0.09
+193106,   13.90,   -5.38,   11.31,    0.08
+193107,   -6.62,    1.43,   -2.10,    0.06
+193108,    0.41,   -1.97,   -1.49,    0.03
+193109,  -29.13,    0.56,   -6.75,    0.03
+193110,    8.04,   -1.87,    1.70,    0.10
+193111,   -9.08,    4.30,   -5.05,    0.17
+193112,  -13.53,   -0.56,   -8.86,    0.12
+193201,   -1.58,    3.94,    9.04,    0.23
+193202,    5.46,   -2.77,   -1.45,    0.23
+193203,  -11.21,    2.27,   -2.32,    0.16
+193204,  -17.96,    1.44,    1.42,    0.11
+193205,  -20.51,    3.91,   -2.99,    0.06
+193206,   -0.70,    0.34,    5.30,    0.02
+193207,   33.84,   -4.44,   35.46,    0.03
+193208,   37.06,   14.29,   33.03,    0.03
+193209,   -2.94,   -2.44,   -6.82,    0.03
+193210,  -13.17,   -2.76,  -10.05,    0.02
+193211,   -5.88,    2.08,  -13.28,    0.02
+193212,    4.40,   -8.27,   -8.17,    0.01
+193301,    1.25,    0.69,    6.23,    0.01
+193302,  -15.24,   -2.75,   -2.73,   -0.03
+193303,    3.29,    3.90,    7.38,    0.04
+193304,   38.85,    4.56,   17.43,    0.10
+193305,   21.43,   36.70,   19.03,    0.04
+193306,   13.11,    8.56,   -2.05,    0.02
+193307,   -9.63,   -1.01,    3.27,    0.02
+193308,   12.05,   -5.45,    2.96,    0.03
+193309,  -10.65,   -0.32,  -11.77,    0.02
+193310,   -8.36,   -0.08,   -8.46,    0.01
+193311,    9.97,   -6.48,    2.33,    0.02
+193312,    1.83,    0.66,   -1.53,    0.02
+193401,   12.60,   12.53,   15.54,    0.05
+193402,   -2.50,    5.19,    2.12,    0.02
+193403,    0.09,    2.51,   -2.73,    0.02
+193404,   -1.79,    2.80,   -3.75,    0.01
+193405,   -7.25,   -0.26,   -5.91,    0.01
+193406,    2.64,   -2.14,   -2.92,    0.01
+193407,  -10.96,   -6.92,  -10.64,    0.01
+193408,    5.58,    5.39,    0.45,    0.01
+193409,   -0.23,   -1.50,   -1.22,    0.01
+193410,   -1.66,    1.25,   -5.11,    0.01
+193411,    8.33,    6.50,   -2.24,    0.01
+193412,    0.36,    3.06,   -3.20,    0.01
+193501,   -3.45,    1.10,   -1.86,    0.01
+193502,   -1.94,    0.41,   -7.27,    0.02
+193503,   -3.68,   -3.57,   -5.10,    0.01
+193504,    9.06,   -1.51,    4.35,    0.01
+193505,    3.47,   -3.38,    2.56,    0.01
+193506,    5.93,   -2.51,   -1.65,    0.01
+193507,    7.51,    1.47,    6.86,    0.01
+193508,    2.65,    6.28,    5.70,    0.01
+193509,    2.63,    1.57,   -4.05,    0.01
+193510,    7.03,    2.64,   -2.33,    0.01
+193511,    4.88,    4.28,   11.91,    0.02
+193512,    4.56,    0.23,    1.07,    0.01
+193601,    6.89,    5.14,   10.51,    0.01
+193602,    2.49,    1.14,    5.04,    0.01
+193603,    0.99,    0.65,   -1.66,    0.02
+193604,   -8.14,   -6.04,   -2.08,    0.02
+193605,    5.19,    0.83,    2.63,    0.02
+193606,    2.40,   -3.26,   -1.20,    0.03
+193607,    6.67,    1.14,    2.56,    0.01
+193608,    0.99,    0.72,    3.99,    0.02
+193609,    0.98,    3.07,    0.88,    0.01
+193610,    7.12,   -2.39,    2.52,    0.02
+193611,    3.27,    9.01,   -0.92,    0.01
+193612,    0.21,    3.61,    3.96,    0.00
+193701,    3.35,    4.31,    2.61,    0.01
+193702,    1.09,    1.23,    5.05,    0.02
+193703,   -0.27,   -1.78,    6.39,    0.01
+193704,   -7.36,   -3.77,   -3.61,    0.03
+193705,   -0.83,   -0.66,   -3.49,    0.06
+193706,   -4.21,   -3.65,   -3.21,    0.03
+193707,    8.91,    0.85,    0.82,    0.03
+193708,   -4.86,    0.42,   -2.25,    0.02
+193709,  -13.61,   -6.89,   -4.57,    0.04
+193710,   -9.61,    0.45,   -1.62,    0.02
+193711,   -8.31,   -3.65,    0.20,    0.02
+193712,   -4.24,   -7.76,   -0.39,    0.00
+193801,    0.49,    4.97,   -1.60,    0.00
+193802,    5.84,    0.35,   -2.02,    0.00
+193803,  -23.82,   -4.24,   -3.52,   -0.01
+193804,   14.51,    6.37,    0.18,    0.01
+193805,   -3.83,   -2.47,   -0.28,    0.00
+193806,   23.87,    4.05,    0.30,    0.00
+193807,    7.34,    6.66,    2.18,   -0.01
+193808,   -2.67,   -2.44,   -4.72,    0.00
+193809,    0.81,   -2.72,   -1.62,    0.02
+193810,    7.80,    5.83,    5.01,    0.01
+193811,   -1.72,   -2.56,   -1.23,   -0.06
+193812,    4.19,   -1.80,    0.50,    0.00
+193901,   -5.96,   -1.56,   -3.92,   -0.01
+193902,    3.51,    0.63,    2.95,    0.01
+193903,  -11.99,   -4.76,   -8.29,   -0.01
+193904,   -0.18,    1.66,   -0.30,    0.00
+193905,    6.80,    2.81,    0.47,    0.01
+193906,   -5.31,   -1.02,   -5.41,    0.01
+193907,   10.24,    4.32,   -0.03,    0.00
+193908,   -6.68,   -4.61,   -2.42,   -0.01
+193909,   16.88,   20.23,   22.22,    0.01
+193910,   -0.53,   -0.01,   -4.89,    0.00
+193911,   -3.62,   -5.07,   -6.46,    0.00
+193912,    3.03,    0.79,   -4.06,    0.00
+194001,   -2.41,    0.24,   -0.77,    0.00
+194002,    1.44,    2.51,   -0.32,    0.00
+194003,    2.05,    1.25,   -1.27,    0.00
+194004,    0.22,    3.92,   -0.13,    0.00
+194005,  -21.95,   -6.66,   -3.69,   -0.02
+194006,    6.67,   -2.13,    4.63,    0.00
+194007,    3.16,    1.01,   -0.74,    0.01
+194008,    2.19,   -0.11,    0.56,   -0.01
+194009,    2.39,    3.22,   -1.13,    0.00
+194010,    3.02,    0.28,    4.64,    0.00
+194011,   -1.61,    1.94,    0.12,    0.00
+194012,    0.69,   -2.15,   -0.89,    0.00
+194101,   -4.17,    1.00,    3.83,   -0.01
+194102,   -1.43,   -1.57,    0.89,   -0.01
+194103,    0.84,    0.10,    3.04,    0.01
+194104,   -5.46,   -1.69,    3.41,   -0.01
+194105,    1.39,   -0.66,    0.60,    0.00
+194106,    5.83,    1.32,    0.59,    0.00
+194107,    5.87,    5.70,    7.25,    0.03
+194108,   -0.17,   -0.41,   -1.10,    0.01
+194109,   -0.87,   -0.99,   -0.28,    0.01
+194110,   -5.25,   -2.02,    1.63,    0.00
+194111,   -1.92,   -1.21,   -0.64,    0.00
+194112,   -4.87,   -2.98,   -5.94,    0.01
+194201,    0.79,    7.52,   10.10,    0.02
+194202,   -2.46,    1.72,   -1.13,    0.01
+194203,   -6.58,    1.78,   -0.62,    0.01
+194204,   -4.37,   -0.60,    2.09,    0.01
+194205,    5.94,   -3.05,   -2.65,    0.03
+194206,    2.69,   -1.22,    0.54,    0.02
+194207,    3.51,   -0.16,    2.40,    0.03
+194208,    1.80,   -0.09,    1.33,    0.03
+194209,    2.61,    0.66,    2.39,    0.03
+194210,    6.82,    1.82,    6.47,    0.03
+194211,    0.15,   -1.54,   -4.24,    0.03
+194212,    5.12,   -2.49,    0.56,    0.03
+194301,    7.13,    8.78,    8.23,    0.03
+194302,    6.15,    4.84,    6.51,    0.03
+194303,    6.01,    5.03,    5.48,    0.03
+194304,    0.81,    2.06,    5.86,    0.03
+194305,    5.74,    4.41,    3.28,    0.03
+194306,    1.82,   -1.06,   -0.74,    0.03
+194307,   -4.77,   -2.41,   -2.27,    0.03
+194308,    1.30,   -0.62,   -0.47,    0.03
+194309,    2.40,    1.28,    1.43,    0.03
+194310,   -1.15,    0.58,    1.65,    0.03
+194311,   -5.91,   -1.64,   -4.04,    0.03
+194312,    6.36,    3.34,    3.22,    0.03
+194401,    1.74,    2.56,    2.17,    0.03
+194402,    0.37,   -0.10,    0.83,    0.03
+194403,    2.46,    1.71,    3.43,    0.02
+194404,   -1.69,   -1.35,   -1.13,    0.03
+194405,    5.07,    1.67,    1.02,    0.03
+194406,    5.49,    4.04,    1.82,    0.03
+194407,   -1.49,    0.55,   -0.43,    0.03
+194408,    1.57,    2.41,   -1.63,    0.03
+194409,    0.01,    0.47,   -1.21,    0.02
+194410,    0.16,   -0.14,   -0.34,    0.03
+194411,    1.71,    0.37,    2.31,    0.03
+194412,    4.03,    2.24,    5.91,    0.02
+194501,    2.01,    2.44,    0.71,    0.03
+194502,    6.23,    1.56,    4.40,    0.02
+194503,   -3.89,   -1.61,   -1.78,    0.02
+194504,    7.80,    0.32,    3.21,    0.03
+194505,    1.73,    1.51,    0.35,    0.03
+194506,    0.39,    3.10,    4.32,    0.02
+194507,   -2.17,   -1.46,   -2.71,    0.03
+194508,    6.20,    1.61,   -4.40,    0.03
+194509,    4.77,    1.71,    0.48,    0.03
+194510,    3.89,    2.46,    2.25,    0.03
+194511,    5.39,    4.16,    4.34,    0.02
+194512,    1.20,    2.12,   -2.32,    0.03
+194601,    6.24,    4.02,    2.21,    0.03
+194602,   -5.83,   -0.73,   -1.27,    0.03
+194603,    5.87,    0.31,   -0.36,    0.03
+194604,    4.23,    2.37,    0.45,    0.03
+194605,    3.93,    1.46,    1.09,    0.03
+194606,   -3.89,   -1.53,   -0.59,    0.03
+194607,   -2.69,   -2.06,    0.04,    0.03
+194608,   -6.44,   -1.78,    0.60,    0.03
+194609,  -10.17,   -4.41,   -1.96,    0.03
+194610,   -1.44,    0.09,    3.44,    0.03
+194611,   -0.01,   -0.40,    1.52,    0.03
+194612,    4.96,    0.06,   -1.38,    0.03
+194701,    1.25,    2.18,   -0.73,    0.03
+194702,   -1.08,    0.68,    0.14,    0.03
+194703,   -1.67,   -1.61,    0.61,    0.03
+194704,   -4.80,   -3.97,    0.85,    0.03
+194705,   -0.97,   -3.26,    0.34,    0.03
+194706,    5.29,   -0.31,   -0.60,    0.03
+194707,    4.14,    1.43,    2.82,    0.03
+194708,   -1.74,    0.30,    0.16,    0.03
+194709,   -0.54,    1.64,    1.36,    0.06
+194710,    2.47,    0.51,    0.08,    0.06
+194711,   -1.97,   -1.74,    1.06,    0.06
+194712,    3.00,   -2.48,    3.69,    0.08
+194801,   -3.93,    2.49,    1.38,    0.07
+194802,   -4.38,   -1.71,    0.07,    0.07
+194803,    8.07,    0.13,    4.49,    0.09
+194804,    3.65,   -1.66,    4.10,    0.08
+194805,    7.30,    0.93,   -1.23,    0.08
+194806,   -0.10,   -1.86,    2.75,    0.09
+194807,   -5.09,   -0.32,    0.07,    0.08
+194808,    0.25,   -1.10,    0.27,    0.09
+194809,   -2.97,   -1.23,   -1.79,    0.04
+194810,    5.96,   -1.50,    0.56,    0.04
+194811,   -9.30,   -0.62,   -4.14,    0.04
+194812,    3.26,   -2.80,   -1.94,    0.04
+194901,    0.23,    1.81,    1.17,    0.10
+194902,   -2.93,   -1.89,   -0.91,    0.09
+194903,    4.04,    2.48,    1.34,    0.10
+194904,   -1.87,   -0.89,   -1.18,    0.09
+194905,   -2.94,   -0.77,   -2.42,    0.10
+194906,    0.10,   -0.88,   -1.76,    0.10
+194907,    5.54,    0.57,    0.38,    0.09
+194908,    2.60,    0.13,   -0.57,    0.09
+194909,    3.09,    1.05,    0.23,    0.09
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+200104,    7.94,    0.54,   -4.72,    0.39
+200105,    0.72,    2.58,    3.17,    0.32
+200106,   -1.94,    6.03,   -1.03,    0.28
+200107,   -2.13,   -4.35,    5.57,    0.30
+200108,   -6.46,    2.50,    2.50,    0.31
+200109,   -9.25,   -6.12,    1.60,    0.28
+200110,    2.46,    7.63,   -8.10,    0.22
+200111,    7.54,   -0.41,    2.01,    0.17
+200112,    1.61,    4.57,    1.10,    0.15
+200201,   -1.44,    1.19,    3.33,    0.14
+200202,   -2.29,   -1.10,    2.50,    0.13
+200203,    4.24,    4.23,    1.10,    0.13
+200204,   -5.20,    5.94,    3.93,    0.15
+200205,   -1.38,   -3.21,    1.68,    0.14
+200206,   -7.21,    4.27,    0.12,    0.13
+200207,   -8.18,   -5.30,   -3.44,    0.15
+200208,    0.50,   -2.44,    2.53,    0.14
+200209,  -10.35,    2.57,    1.32,    0.14
+200210,    7.84,   -2.90,   -5.45,    0.14
+200211,    5.96,    2.84,   -1.12,    0.12
+200212,   -5.76,    0.01,    2.23,    0.11
+200301,   -2.57,    1.38,   -0.94,    0.10
+200302,   -1.88,   -0.34,   -1.45,    0.09
+200303,    1.09,    0.89,   -2.07,    0.10
+200304,    8.22,    0.56,    1.03,    0.10
+200305,    6.05,    4.69,   -0.29,    0.09
+200306,    1.42,    1.67,    0.69,    0.10
+200307,    2.35,    5.24,   -1.14,    0.07
+200308,    2.34,    2.60,    2.03,    0.07
+200309,   -1.24,    0.80,    0.01,    0.08
+200310,    6.08,    2.68,    1.77,    0.07
+200311,    1.35,    2.02,    1.85,    0.07
+200312,    4.29,   -3.00,    2.41,    0.08
+200401,    2.15,    2.80,    1.97,    0.07
+200402,    1.40,   -1.43,    0.50,    0.06
+200403,   -1.32,    1.75,    0.22,    0.09
+200404,   -1.83,   -2.06,   -2.62,    0.08
+200405,    1.17,   -0.21,   -0.39,    0.06
+200406,    1.86,    2.26,    1.39,    0.08
+200407,   -4.06,   -3.81,    4.11,    0.10
+200408,    0.08,   -1.62,    1.03,    0.11
+200409,    1.60,    3.03,   -0.25,    0.11
+200410,    1.43,    0.31,   -0.63,    0.11
+200411,    4.54,    3.90,    1.79,    0.15
+200412,    3.43,    0.11,   -0.08,    0.16
+200501,   -2.76,   -1.51,    1.96,    0.16
+200502,    1.89,   -0.50,    1.65,    0.16
+200503,   -1.97,   -1.37,    1.61,    0.21
+200504,   -2.61,   -3.98,   -0.35,    0.21
+200505,    3.65,    2.88,   -0.81,    0.24
+200506,    0.57,    2.62,    2.63,    0.23
+200507,    3.92,    2.93,   -0.52,    0.24
+200508,   -1.22,   -0.92,    1.27,    0.30
+200509,    0.49,   -0.58,    0.77,    0.29
+200510,   -2.02,   -1.21,    0.22,    0.27
+200511,    3.61,    0.90,   -1.19,    0.31
+200512,   -0.25,   -0.46,    0.44,    0.32
+200601,    3.04,    5.42,    1.13,    0.35
+200602,   -0.30,   -0.37,   -0.25,    0.34
+200603,    1.46,    3.54,    0.60,    0.37
+200604,    0.73,   -1.34,    2.61,    0.36
+200605,   -3.57,   -3.05,    2.54,    0.43
+200606,   -0.35,   -0.35,    0.87,    0.40
+200607,   -0.78,   -4.07,    2.94,    0.40
+200608,    2.03,    0.91,   -1.71,    0.42
+200609,    1.84,   -1.37,    0.04,    0.41
+200610,    3.23,    1.73,   -0.03,    0.41
+200611,    1.71,    0.86,    0.07,    0.42
+200612,    0.87,   -1.10,    3.16,    0.40
+200701,    1.40,    0.10,   -0.11,    0.44
+200702,   -1.96,    1.32,   -0.09,    0.38
+200703,    0.68,   -0.05,   -0.22,    0.43
+200704,    3.49,   -2.06,   -1.15,    0.44
+200705,    3.24,    0.02,   -0.04,    0.41
+200706,   -1.96,    0.77,   -1.12,    0.40
+200707,   -3.73,   -2.51,   -3.33,    0.40
+200708,    0.92,   -0.13,   -2.24,    0.42
+200709,    3.22,   -2.29,   -1.86,    0.32
+200710,    1.80,    0.22,   -2.59,    0.32
+200711,   -4.83,   -2.62,   -1.18,    0.34
+200712,   -0.87,    0.20,   -0.51,    0.27
+200801,   -6.36,   -0.90,    3.65,    0.21
+200802,   -3.09,   -0.24,   -0.94,    0.13
+200803,   -0.93,    0.95,   -0.14,    0.17
+200804,    4.60,   -1.64,   -0.95,    0.18
+200805,    1.86,    3.22,   -1.38,    0.18
+200806,   -8.44,    1.28,   -2.41,    0.17
+200807,   -0.77,    2.41,    5.93,    0.15
+200808,    1.53,    3.61,    1.55,    0.13
+200809,   -9.24,   -1.17,    6.32,    0.15
+200810,  -17.23,   -2.33,   -2.88,    0.08
+200811,   -7.86,   -2.96,   -6.03,    0.03
+200812,    1.74,    3.63,   -0.28,    0.00
+200901,   -8.12,    0.13,  -11.23,    0.00
+200902,  -10.10,    0.22,   -7.30,    0.01
+200903,    8.95,   -0.12,    3.64,    0.02
+200904,   10.19,    4.76,    5.53,    0.01
+200905,    5.21,   -2.34,   -0.21,    0.00
+200906,    0.43,    2.59,   -2.66,    0.01
+200907,    7.72,    2.09,    5.31,    0.01
+200908,    3.33,   -0.90,    7.76,    0.01
+200909,    4.08,    2.45,    0.91,    0.01
+200910,   -2.59,   -4.22,   -4.17,    0.00
+200911,    5.56,   -2.50,   -0.19,    0.00
+200912,    2.75,    6.10,   -0.01,    0.01
+201001,   -3.36,    0.38,    0.31,    0.00
+201002,    3.40,    1.21,    3.13,    0.00
+201003,    6.31,    1.43,    2.13,    0.01
+201004,    2.00,    4.97,    2.82,    0.01
+201005,   -7.89,    0.05,   -2.39,    0.01
+201006,   -5.56,   -1.97,   -4.48,    0.01
+201007,    6.93,    0.17,   -0.26,    0.01
+201008,   -4.77,   -3.00,   -1.96,    0.01
+201009,    9.54,    3.92,   -3.13,    0.01
+201010,    3.88,    1.13,   -2.61,    0.01
+201011,    0.60,    3.71,   -0.90,    0.01
+201012,    6.82,    0.68,    3.81,    0.01
+201101,    1.99,   -2.47,    0.81,    0.01
+201102,    3.49,    1.53,    1.09,    0.01
+201103,    0.45,    2.60,   -1.58,    0.01
+201104,    2.90,   -0.34,   -2.52,    0.00
+201105,   -1.27,   -0.70,   -2.07,    0.00
+201106,   -1.75,   -0.16,   -0.31,    0.00
+201107,   -2.36,   -1.35,   -1.21,    0.00
+201108,   -5.99,   -3.05,   -2.45,    0.01
+201109,   -7.59,   -3.52,   -1.39,    0.00
+201110,   11.35,    3.42,   -0.18,    0.00
+201111,   -0.28,   -0.17,   -0.34,    0.00
+201112,    0.74,   -0.70,    1.78,    0.00
+201201,    5.05,    2.15,   -1.14,    0.00
+201202,    4.42,   -1.74,    0.09,    0.00
+201203,    3.11,   -0.62,    0.91,    0.00
+201204,   -0.85,   -0.52,   -0.49,    0.00
+201205,   -6.19,    0.00,   -0.58,    0.01
+201206,    3.89,    0.76,    0.44,    0.00
+201207,    0.79,   -2.61,   -0.26,    0.00
+201208,    2.55,    0.40,    1.28,    0.01
+201209,    2.73,    0.49,    1.52,    0.01
+201210,   -1.76,   -1.15,    3.79,    0.01
+201211,    0.78,    0.59,   -0.98,    0.01
+201212,    1.18,    1.48,    3.58,    0.01
+201301,    5.57,    0.38,    0.91,    0.00
+201302,    1.29,   -0.45,    0.01,    0.00
+201303,    4.03,    0.81,   -0.28,    0.00
+201304,    1.55,   -2.43,    0.59,    0.00
+201305,    2.80,    1.66,    2.56,    0.00
+201306,   -1.20,    1.17,   -0.17,    0.00
+201307,    5.65,    1.85,    0.55,    0.00
+201308,   -2.71,    0.30,   -2.76,    0.00
+201309,    3.77,    2.92,   -1.20,    0.00
+201310,    4.18,   -1.49,    1.10,    0.00
+201311,    3.12,    1.25,    0.27,    0.00
+201312,    2.81,   -0.51,   -0.30,    0.00
+201401,   -3.32,    0.86,   -2.10,    0.00
+201402,    4.65,    0.31,   -0.46,    0.00
+201403,    0.43,   -1.88,    5.06,    0.00
+201404,   -0.19,   -4.26,    1.07,    0.00
+201405,    2.06,   -1.84,   -0.19,    0.00
+201406,    2.61,    3.07,   -0.76,    0.00
+201407,   -2.04,   -4.24,    0.01,    0.00
+201408,    4.24,    0.35,   -0.58,    0.00
+201409,   -1.97,   -3.83,   -1.23,    0.00
+201410,    2.52,    4.21,   -1.68,    0.00
+201411,    2.55,   -2.08,   -2.99,    0.00
+201412,   -0.06,    2.54,    2.06,    0.00
+201501,   -3.11,   -0.58,   -3.47,    0.00
+201502,    6.13,    0.51,   -1.83,    0.00
+201503,   -1.12,    3.03,   -0.46,    0.00
+201504,    0.59,   -2.97,    1.85,    0.00
+201505,    1.36,    0.91,   -1.36,    0.00
+201506,   -1.53,    2.81,   -0.79,    0.00
+201507,    1.54,   -4.15,   -4.14,    0.00
+201508,   -6.04,    0.50,    2.66,    0.00
+201509,   -3.08,   -2.65,    0.53,    0.00
+201510,    7.75,   -1.97,   -0.06,    0.00
+201511,    0.56,    3.66,   -0.50,    0.00
+201512,   -2.17,   -2.83,   -2.60,    0.01
+201601,   -5.77,   -3.35,    2.08,    0.01
+201602,   -0.07,    0.80,   -0.49,    0.02
+201603,    6.96,    0.86,    1.15,    0.02
+201604,    0.92,    0.69,    3.26,    0.01
+201605,    1.78,   -0.28,   -1.82,    0.01
+201606,   -0.05,    0.65,   -1.46,    0.02
+201607,    3.95,    2.63,   -1.09,    0.02
+201608,    0.50,    1.17,    3.37,    0.02
+201609,    0.25,    2.04,   -1.47,    0.02
+201610,   -2.02,   -4.40,    4.14,    0.02
+201611,    4.86,    5.46,    8.32,    0.01
+201612,    1.81,    0.12,    3.60,    0.03
+201701,    1.94,   -1.02,   -2.77,    0.04
+201702,    3.57,   -2.04,   -1.82,    0.04
+201703,    0.17,    1.19,   -3.17,    0.03
+201704,    1.09,    0.71,   -1.87,    0.05
+201705,    1.06,   -2.56,   -3.81,    0.06
+201706,    0.78,    2.16,    1.35,    0.06
+201707,    1.87,   -1.38,   -0.26,    0.07
+201708,    0.16,   -1.61,   -2.22,    0.09
+201709,    2.51,    4.52,    3.02,    0.09
+201710,    2.25,   -1.83,   -0.08,    0.09
+201711,    3.12,   -0.67,   -0.04,    0.08
+201712,    1.06,   -1.28,    0.11,    0.09
+201801,    5.58,   -2.92,   -1.55,    0.11
+201802,   -3.65,    0.28,   -1.09,    0.11
+201803,   -2.35,    3.88,    0.00,    0.12
+201804,    0.29,    1.13,    0.56,    0.14
+201805,    2.65,    5.35,   -3.21,    0.14
+201806,    0.48,    1.23,   -2.40,    0.14
+201807,    3.19,   -2.20,    0.47,    0.16
+201808,    3.44,    1.14,   -4.13,    0.16
+201809,    0.06,   -2.37,   -1.32,    0.15
+201810,   -7.68,   -4.78,    3.47,    0.19
+201811,    1.69,   -0.81,    0.27,    0.18
+201812,   -9.55,   -2.63,   -1.49,    0.19
+201901,    8.41,    3.00,   -0.60,    0.21
+201902,    3.40,    2.07,   -2.83,    0.18
+201903,    1.10,   -3.16,   -4.04,    0.19
+201904,    3.96,   -1.70,    1.96,    0.21
+201905,   -6.94,   -1.23,   -2.35,    0.21
+201906,    6.93,    0.33,   -1.08,    0.18
+201907,    1.19,   -2.05,    0.17,    0.19
+201908,   -2.58,   -2.42,   -5.05,    0.16
+201909,    1.43,   -0.96,    6.81,    0.18
+201910,    2.06,    0.26,   -2.04,    0.15
+201911,    3.87,    0.87,   -1.80,    0.12
+201912,    2.77,    0.68,    1.93,    0.14
+202001,   -0.11,   -3.10,   -6.37,    0.13
+202002,   -8.13,    0.98,   -4.05,    0.12
+202003,  -13.39,   -5.15,  -14.17,    0.12
+202004,   13.65,    2.76,   -1.18,    0.00
+202005,    5.58,    2.46,   -4.92,    0.01
+202006,    2.45,    2.68,   -2.19,    0.01
+202007,    5.77,   -2.31,   -1.27,    0.01
+
+ Annual Factors: January-December 
+,Mkt-RF,SMB,HML,RF
+  1927,   29.47,   -2.46,   -3.75,    3.12
+  1928,   35.39,    4.41,   -5.83,    3.56
+  1929,  -19.54,  -30.78,   11.96,    4.75
+  1930,  -31.23,   -5.19,  -12.29,    2.41
+  1931,  -45.11,    3.51,  -14.32,    1.07
+  1932,   -9.39,    4.91,   10.49,    0.96
+  1933,   57.05,   48.86,   28.15,    0.30
+  1934,    3.02,   25.43,  -27.38,    0.16
+  1935,   44.96,    9.99,    9.78,    0.17
+  1936,   32.07,   17.89,   35.86,    0.18
+  1937,  -34.96,  -14.00,   -3.97,    0.31
+  1938,   28.48,    9.35,  -12.18,   -0.02
+  1939,    2.70,    5.85,  -19.17,    0.02
+  1940,   -7.14,    0.79,   -0.82,    0.00
+  1941,  -10.53,   -4.04,   11.13,    0.06
+  1942,   16.20,    5.05,   19.87,    0.27
+  1943,   27.96,   33.35,   38.97,    0.35
+  1944,   20.97,   17.98,   15.61,    0.33
+  1945,   38.38,   25.56,   11.40,    0.33
+  1946,   -6.73,   -3.79,    2.98,    0.35
+  1947,    2.95,   -7.08,    9.76,    0.50
+  1948,    1.07,   -9.14,    3.52,    0.81
+  1949,   19.12,    3.93,   -4.55,    1.10
+  1950,   28.82,    0.93,   27.01,    1.20
+  1951,   19.22,   -4.93,   -5.93,    1.49
+  1952,   11.80,   -6.66,    3.34,    1.66
+  1953,   -1.05,   -1.16,   -7.71,    1.82
+  1954,   49.35,   -2.18,   26.21,    0.86
+  1955,   23.75,   -6.71,    5.77,    1.57
+  1956,    5.90,   -1.17,   -1.60,    2.46
+  1957,  -13.16,   -2.72,   -6.39,    3.14
+  1958,   43.45,   14.81,   13.16,    1.54
+  1959,    9.76,    5.43,    1.86,    2.95
+  1960,   -1.46,   -2.76,   -4.87,    2.66
+  1961,   24.81,    1.49,    5.19,    2.13
+  1962,  -12.90,   -8.25,    8.80,    2.73
+  1963,   17.84,   -5.88,   15.66,    3.12
+  1964,   12.54,   -0.99,    9.86,    3.54
+  1965,   10.52,   21.80,    7.36,    3.93
+  1966,  -13.51,    2.59,   -0.68,    4.76
+  1967,   24.49,   50.69,   -8.58,    4.21
+  1968,    8.79,   24.50,   18.49,    5.21
+  1969,  -17.54,  -13.98,   -9.81,    6.58
+  1970,   -6.49,  -11.79,   22.34,    6.52
+  1971,   11.78,    5.62,  -11.29,    4.39
+  1972,   13.05,  -11.95,    1.75,    3.84
+  1973,  -26.19,  -23.44,   18.08,    6.93
+  1974,  -35.75,   -0.60,    9.67,    8.00
+  1975,   32.44,   15.28,    9.49,    5.80
+  1976,   21.91,   14.69,   24.50,    5.08
+  1977,   -8.26,   22.95,    7.51,    5.12
+  1978,    1.03,   14.38,    0.37,    7.18
+  1979,   13.09,   21.12,   -2.12,   10.38
+  1980,   22.13,    5.57,  -25.06,   11.24
+  1981,  -18.13,    7.23,   25.01,   14.71
+  1982,   10.66,    8.89,   13.59,   10.54
+  1983,   13.74,   13.67,   20.85,    8.80
+  1984,   -6.05,   -8.31,   19.63,    9.85
+  1985,   24.91,    0.12,    1.35,    7.72
+  1986,   10.12,   -9.60,    9.58,    6.16
+  1987,   -3.87,  -11.00,   -1.64,    5.47
+  1988,   11.55,    5.90,   14.77,    6.35
+  1989,   20.49,  -12.72,   -4.29,    8.37
+  1990,  -13.95,  -14.18,   -9.72,    7.81
+  1991,   29.18,   16.13,  -14.41,    5.60
+  1992,    6.23,    7.58,   24.28,    3.51
+  1993,    8.21,    5.80,   18.91,    2.90
+  1994,   -4.10,   -1.06,   -0.69,    3.90
+  1995,   31.22,   -9.09,    5.30,    5.60
+  1996,   15.96,   -3.71,    6.16,    5.21
+  1997,   25.96,   -6.76,   17.46,    5.26
+  1998,   19.46,  -26.02,   -8.89,    4.86
+  1999,   20.57,   14.85,  -31.77,    4.68
+  2000,  -17.60,   -1.51,   39.69,    5.89
+  2001,  -15.20,   18.09,   19.52,    3.83
+  2002,  -22.76,    4.72,    7.47,    1.65
+  2003,   30.75,   26.18,    5.40,    1.02
+  2004,   10.72,    4.85,    8.08,    1.20
+  2005,    3.09,   -1.93,    8.33,    2.98
+  2006,   10.60,    0.24,   14.11,    4.80
+  2007,    1.04,   -7.14,  -14.65,    4.66
+  2008,  -38.34,    3.18,    0.82,    1.60
+  2009,   28.26,    9.39,   -9.17,    0.10
+  2010,   17.37,   13.77,   -5.31,    0.12
+  2011,    0.44,   -6.04,   -8.35,    0.04
+  2012,   16.28,   -1.28,    9.68,    0.06
+  2013,   35.20,    7.18,    1.33,    0.02
+  2014,   11.70,   -8.11,   -1.74,    0.02
+  2015,    0.07,   -4.03,   -9.65,    0.02
+  2016,   13.30,    6.65,   23.02,    0.20
+  2017,   21.50,   -4.68,  -13.98,    0.80
+  2018,   -6.93,   -3.40,   -9.16,    1.81
+  2019,   28.28,   -6.09,  -11.67,    2.14
+
+Copyright 2020 Kenneth R. French
diff --git a/Data/F-F_Research_Data_Factors_daily.CSV b/Data/F-F_Research_Data_Factors_daily.CSV
new file mode 100644
index 0000000..f502001
--- /dev/null
+++ b/Data/F-F_Research_Data_Factors_daily.CSV
@@ -0,0 +1,24797 @@
+This file was created by CMPT_ME_BEME_RETS_DAILY using the 202007 CRSP database.
+The Tbill return is the simple daily rate that, over the number of trading days
+in the month, compounds to 1-month TBill rate from Ibbotson and Associates Inc.
+
+,Mkt-RF,SMB,HML,RF
+19260701,    0.10,   -0.24,   -0.28,   0.009
+19260702,    0.45,   -0.32,   -0.08,   0.009
+19260706,    0.17,    0.27,   -0.35,   0.009
+19260707,    0.09,   -0.59,    0.03,   0.009
+19260708,    0.21,   -0.36,    0.15,   0.009
+19260709,   -0.71,    0.44,    0.56,   0.009
+19260710,    0.62,   -0.50,   -0.15,   0.009
+19260712,    0.04,    0.03,    0.54,   0.009
+19260713,    0.48,   -0.26,   -0.23,   0.009
+19260714,    0.04,    0.09,   -0.48,   0.009
+19260715,   -0.43,    0.54,   -0.30,   0.009
+19260716,    0.53,    0.01,   -0.57,   0.009
+19260717,    0.34,    0.43,   -0.63,   0.009
+19260719,   -0.01,    0.01,   -0.49,   0.009
+19260720,   -0.57,   -0.23,    0.16,   0.009
+19260721,   -0.60,    0.21,    0.31,   0.009
+19260722,   -0.73,   -0.30,   -0.17,   0.009
+19260723,   -0.02,    0.08,    0.06,   0.009
+19260724,   -0.14,    0.44,   -0.06,   0.009
+19260726,    0.53,   -0.38,   -0.25,   0.009
+19260727,    0.43,   -0.45,    0.26,   0.009
+19260728,    1.09,    0.04,   -0.28,   0.009
+19260729,    0.36,   -0.61,   -1.01,   0.009
+19260730,    0.14,   -0.47,    0.55,   0.009
+19260731,    0.46,   -0.12,   -0.17,   0.009
+19260802,    0.84,   -0.22,   -0.03,   0.010
+19260803,    0.47,   -0.27,   -0.60,   0.010
+19260804,   -0.36,    0.16,    0.25,   0.010
+19260805,   -0.09,    0.01,    0.73,   0.010
+19260806,    0.68,    0.08,    0.16,   0.010
+19260807,    0.46,    0.08,   -0.24,   0.010
+19260809,    0.21,   -0.04,   -0.26,   0.010
+19260810,   -1.40,    0.34,    0.21,   0.010
+19260811,   -0.57,    0.21,    0.34,   0.010
+19260812,    0.84,   -0.77,    0.42,   0.010
+19260813,    0.58,   -0.85,    0.25,   0.010
+19260814,    0.69,    0.15,   -0.36,   0.010
+19260816,    0.07,    0.23,    0.70,   0.010
+19260817,   -0.95,    0.34,    0.16,   0.010
+19260818,    0.26,    0.08,    0.04,   0.010
+19260819,   -0.60,    0.12,    0.10,   0.010
+19260820,   -0.20,   -0.36,    0.42,   0.010
+19260821,    0.32,   -0.06,    0.49,   0.010
+19260823,    0.49,   -0.48,    0.62,   0.010
+19260824,   -0.84,    0.50,   -0.09,   0.010
+19260825,   -0.32,   -0.43,    0.28,   0.010
+19260826,    0.53,    0.09,    0.50,   0.010
+19260827,    0.35,    0.13,   -0.39,   0.010
+19260828,    0.34,    0.15,   -0.06,   0.010
+19260830,    0.30,    0.00,    0.42,   0.010
+19260831,    0.58,   -0.52,   -0.04,   0.010
+19260901,    0.55,    0.52,   -0.08,   0.009
+19260902,   -0.06,   -0.07,    0.21,   0.009
+19260903,    0.50,   -0.30,    0.03,   0.009
+19260907,    0.59,   -0.25,   -0.24,   0.009
+19260908,   -0.51,   -0.10,    0.08,   0.009
+19260909,   -0.07,    0.16,    0.26,   0.009
+19260910,   -0.83,    0.09,   -0.13,   0.009
+19260911,   -0.44,    0.32,    0.13,   0.009
+19260913,   -0.03,   -0.11,    0.50,   0.009
+19260914,    0.90,   -0.62,    0.09,   0.009
+19260915,   -0.21,    0.40,   -0.19,   0.009
+19260916,   -0.18,   -0.33,    0.10,   0.009
+19260917,   -1.01,   -0.16,   -0.28,   0.009
+19260918,    0.70,   -0.28,   -0.11,   0.009
+19260920,   -0.64,    0.03,   -0.01,   0.009
+19260921,    0.30,   -0.42,    0.19,   0.009
+19260922,   -0.13,    0.05,   -0.01,   0.009
+19260923,    0.26,   -0.16,    0.06,   0.009
+19260924,    0.62,   -0.19,   -0.03,   0.009
+19260925,    0.21,    0.02,   -0.56,   0.009
+19260927,   -0.12,    0.14,    0.39,   0.009
+19260928,    0.00,   -0.02,   -0.35,   0.009
+19260929,   -0.30,    0.22,   -0.19,   0.009
+19260930,    0.30,   -0.22,    0.06,   0.009
+19261001,    0.73,   -0.27,   -0.23,   0.013
+19261002,   -0.10,   -0.02,   -0.08,   0.013
+19261004,   -1.40,   -0.10,   -0.10,   0.013
+19261005,   -1.10,    0.44,   -0.72,   0.013
+19261006,   -0.56,   -0.69,   -0.28,   0.013
+19261007,   -0.77,    0.32,    0.30,   0.013
+19261008,    0.59,   -0.36,    0.58,   0.013
+19261009,   -0.96,    0.44,   -0.05,   0.013
+19261011,   -0.69,   -0.08,   -0.16,   0.013
+19261013,    0.69,   -0.31,   -0.04,   0.013
+19261014,    0.97,   -0.47,    1.08,   0.013
+19261015,   -1.83,    0.13,   -0.29,   0.013
+19261016,   -0.73,   -0.34,    0.42,   0.013
+19261018,    0.70,   -0.16,    0.46,   0.013
+19261019,   -1.06,    0.40,   -0.54,   0.013
+19261020,   -0.25,   -0.59,   -1.17,   0.013
+19261021,    1.53,   -0.12,    0.55,   0.013
+19261022,   -0.40,    0.44,    0.20,   0.013
+19261023,    0.54,    0.50,    0.19,   0.013
+19261025,   -0.10,    0.35,   -0.03,   0.013
+19261026,    0.32,    0.02,    0.30,   0.013
+19261027,    1.00,   -0.47,    0.19,   0.013
+19261028,   -0.08,    0.69,    0.20,   0.013
+19261029,   -0.06,    0.01,   -0.33,   0.013
+19261030,   -0.25,    0.20,    0.25,   0.013
+19261101,    0.46,   -0.11,   -0.43,   0.013
+19261103,    0.20,   -0.24,   -0.28,   0.013
+19261104,    0.59,   -0.15,    0.69,   0.013
+19261105,    0.07,   -0.09,    0.23,   0.013
+19261106,    0.16,   -0.29,    0.05,   0.013
+19261108,    0.52,   -0.07,    0.08,   0.013
+19261109,    0.13,    0.29,   -0.49,   0.013
+19261110,   -0.60,    0.15,    0.09,   0.013
+19261111,    0.67,    0.03,    0.09,   0.013
+19261112,    0.24,   -0.03,   -0.37,   0.013
+19261113,   -0.24,    0.12,   -0.16,   0.013
+19261115,    0.37,    0.28,   -0.25,   0.013
+19261116,    0.15,   -0.31,   -0.06,   0.013
+19261117,   -0.35,   -0.35,    0.46,   0.013
+19261118,   -0.48,    0.17,    0.20,   0.013
+19261119,   -0.64,    0.56,    0.07,   0.013
+19261120,    0.40,    0.01,    0.12,   0.013
+19261122,    0.57,    0.16,   -0.20,   0.013
+19261123,    0.33,    0.02,    0.20,   0.013
+19261124,    0.08,   -0.22,    0.20,   0.013
+19261126,    0.20,   -0.02,    0.03,   0.013
+19261127,   -0.15,   -0.04,   -0.28,   0.013
+19261129,   -0.37,   -0.13,    0.06,   0.013
+19261130,    0.26,    0.03,   -0.50,   0.013
+19261201,    0.36,    0.05,   -0.34,   0.011
+19261202,    0.51,   -0.01,    0.10,   0.011
+19261203,    0.32,   -0.05,    0.06,   0.011
+19261204,   -0.17,    0.20,    0.10,   0.011
+19261206,    0.04,    0.32,   -0.37,   0.011
+19261207,   -0.04,    0.34,   -0.51,   0.011
+19261208,   -0.08,    0.21,    0.25,   0.011
+19261209,    0.45,   -0.15,    0.44,   0.011
+19261210,    0.35,   -0.17,    0.50,   0.011
+19261211,    0.09,   -0.10,   -0.50,   0.011
+19261213,    0.42,    0.64,    0.00,   0.011
+19261214,    0.10,   -0.09,    0.14,   0.011
+19261215,    0.17,   -0.22,    0.22,   0.011
+19261216,   -0.63,   -0.20,    0.05,   0.011
+19261217,    0.91,   -0.75,    0.68,   0.011
+19261218,    0.42,   -0.22,    0.20,   0.011
+19261220,   -0.31,    0.08,   -0.13,   0.011
+19261221,   -0.03,   -0.38,   -0.19,   0.011
+19261222,    0.02,   -0.19,   -0.53,   0.011
+19261223,    0.28,    0.01,   -0.47,   0.011
+19261224,    0.31,    0.21,    0.26,   0.011
+19261227,   -0.22,   -0.14,   -0.40,   0.011
+19261228,   -1.07,   -0.07,   -0.16,   0.011
+19261229,    0.44,   -0.16,   -0.06,   0.011
+19261230,   -0.35,    0.44,    0.10,   0.011
+19261231,    0.33,    0.30,    0.67,   0.011
+19270103,   -0.79,    0.19,    0.09,   0.010
+19270104,    0.31,    0.15,   -0.67,   0.010
+19270105,    0.14,    0.43,   -0.31,   0.010
+19270106,   -0.17,    0.00,    0.07,   0.010
+19270107,    0.30,   -0.25,    0.44,   0.010
+19270108,    0.39,   -0.18,    0.27,   0.010
+19270110,    0.14,    0.02,    0.16,   0.010
+19270111,   -0.28,   -0.36,    0.87,   0.010
+19270112,    0.07,   -0.06,    0.02,   0.010
+19270113,   -0.04,    0.20,   -0.30,   0.010
+19270114,    0.05,   -0.85,    0.92,   0.010
+19270115,    0.13,    0.10,    0.83,   0.010
+19270117,   -0.04,   -0.18,    0.77,   0.010
+19270118,   -0.06,   -0.22,    0.01,   0.010
+19270119,    0.61,   -0.01,   -0.77,   0.010
+19270120,   -0.17,   -0.30,    0.13,   0.010
+19270121,   -0.01,    0.60,    1.29,   0.010
+19270122,   -0.36,    0.26,    0.34,   0.010
+19270124,   -0.48,    0.47,    1.44,   0.010
+19270125,   -1.07,   -0.47,   -1.29,   0.010
+19270126,    0.19,    0.12,    0.48,   0.010
+19270127,   -0.35,    0.00,   -0.42,   0.010
+19270128,    0.16,    0.16,    0.76,   0.010
+19270129,    0.73,   -0.41,   -0.29,   0.010
+19270131,    0.61,    0.12,   -0.04,   0.010
+19270201,   -0.01,    0.53,    1.11,   0.012
+19270202,    0.70,   -0.21,    0.10,   0.012
+19270203,    0.17,    0.21,   -0.07,   0.012
+19270204,    0.37,    0.12,    1.38,   0.012
+19270205,   -0.18,    0.31,    0.45,   0.012
+19270207,   -0.13,    0.64,    0.47,   0.012
+19270208,    0.10,    0.19,    1.03,   0.012
+19270209,    0.04,   -0.88,   -0.57,   0.012
+19270210,    0.08,    0.28,   -0.69,   0.012
+19270211,    0.31,   -0.44,   -0.63,   0.012
+19270214,    0.49,   -0.14,   -0.29,   0.012
+19270215,    0.10,   -0.27,   -0.16,   0.012
+19270216,   -0.02,    0.01,    1.55,   0.012
+19270217,    0.24,    0.07,    0.86,   0.012
+19270218,    0.32,   -0.73,   -0.12,   0.012
+19270219,    0.01,    0.38,   -0.48,   0.012
+19270221,   -0.17,    0.19,   -0.90,   0.012
+19270223,    0.72,   -0.33,   -0.13,   0.012
+19270224,    0.14,    0.35,    0.09,   0.012
+19270225,    0.21,    0.24,   -0.33,   0.012
+19270226,    0.07,   -0.19,   -0.05,   0.012
+19270228,    0.55,   -0.38,    0.24,   0.012
+19270301,   -0.40,   -0.03,   -0.55,   0.011
+19270302,   -1.14,   -0.14,   -0.33,   0.011
+19270303,    1.02,   -0.05,    0.05,   0.011
+19270304,   -0.45,    0.22,   -0.16,   0.011
+19270305,   -0.24,    0.60,   -0.46,   0.011
+19270307,   -0.37,   -0.10,   -0.58,   0.011
+19270308,    0.12,   -0.53,   -0.34,   0.011
+19270309,    0.49,    0.17,    0.34,   0.011
+19270310,    0.46,    0.10,    0.46,   0.011
+19270311,    0.17,   -0.25,    0.24,   0.011
+19270312,   -0.04,   -0.06,   -0.19,   0.011
+19270314,    0.18,    0.04,   -0.78,   0.011
+19270315,   -0.81,   -0.10,   -0.44,   0.011
+19270316,    1.02,   -0.50,   -0.06,   0.011
+19270317,    0.42,   -0.31,   -0.41,   0.011
+19270318,   -0.79,    0.11,   -0.42,   0.011
+19270319,    0.04,    0.01,   -0.21,   0.011
+19270321,    0.17,   -0.54,    0.05,   0.011
+19270322,   -1.06,    0.17,   -0.54,   0.011
+19270323,    0.19,    0.01,    0.51,   0.011
+19270324,    0.68,   -0.04,    0.06,   0.011
+19270325,    0.16,    0.45,   -0.33,   0.011
+19270326,    0.34,    0.14,    0.18,   0.011
+19270328,    0.52,   -0.24,    0.07,   0.011
+19270329,   -0.23,   -0.09,   -0.41,   0.011
+19270330,   -0.62,    0.11,    1.25,   0.011
+19270331,    0.30,   -0.85,    0.55,   0.011
+19270401,   -0.05,   -0.19,   -0.31,   0.010
+19270402,   -0.08,    0.19,    0.02,   0.010
+19270404,    0.53,   -0.21,    1.12,   0.010
+19270405,    0.59,   -0.30,    0.01,   0.010
+19270406,    0.11,   -0.10,   -0.04,   0.010
+19270407,    0.24,   -0.35,    0.33,   0.010
+19270408,    0.31,    0.35,   -0.05,   0.010
+19270409,    0.03,   -0.13,    0.29,   0.010
+19270411,    0.19,    0.00,   -0.15,   0.010
+19270412,   -0.29,    0.57,   -0.12,   0.010
+19270413,    0.21,    0.13,   -1.14,   0.010
+19270414,    0.09,    0.37,   -0.20,   0.010
+19270416,    0.18,    0.55,    0.18,   0.010
+19270418,   -0.03,   -0.17,    0.75,   0.010
+19270419,    0.18,   -0.08,    0.32,   0.010
+19270420,    0.27,   -0.37,    0.94,   0.010
+19270421,    0.36,   -0.25,   -0.76,   0.010
+19270422,    0.07,   -0.34,    0.04,   0.010
+19270423,   -0.37,    0.24,    0.00,   0.010
+19270425,   -1.56,    0.08,    0.10,   0.010
+19270426,    0.58,    0.04,   -0.43,   0.010
+19270427,   -0.38,    0.07,    0.20,   0.010
+19270428,   -1.11,    0.19,   -0.35,   0.010
+19270429,    0.48,    0.00,   -0.18,   0.010
+19270430,   -0.01,    0.21,   -0.07,   0.010
+19270502,    0.27,    0.22,    0.48,   0.012
+19270503,    0.74,    0.02,   -0.64,   0.012
+19270504,    0.78,   -0.16,    0.15,   0.012
+19270505,    0.30,    0.30,   -0.28,   0.012
+19270506,    0.28,   -0.13,    0.87,   0.012
+19270507,    0.28,    0.26,   -0.45,   0.012
+19270509,    0.20,    0.08,    0.39,   0.012
+19270510,   -0.07,   -0.12,    0.13,   0.012
+19270511,   -0.14,   -0.03,    0.04,   0.012
+19270512,   -0.11,   -0.33,    0.37,   0.012
+19270513,    0.49,   -0.11,   -0.24,   0.012
+19270514,    0.02,    0.29,    0.04,   0.012
+19270516,   -0.72,    0.54,   -0.20,   0.012
+19270517,    0.47,    0.08,    0.21,   0.012
+19270518,    0.51,   -0.60,    0.08,   0.012
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+20190822,   -0.10,   -0.55,    0.52,   0.007
+20190823,   -2.66,   -0.55,   -0.21,   0.007
+20190826,    1.08,   -0.03,   -0.20,   0.007
+20190827,   -0.46,   -0.88,   -0.68,   0.007
+20190828,    0.68,    0.47,    0.64,   0.007
+20190829,    1.35,    0.32,    0.33,   0.007
+20190830,    0.03,   -0.38,    0.25,   0.007
+20190903,   -0.86,   -0.95,    0.07,   0.009
+20190904,    1.08,   -0.22,    0.42,   0.009
+20190905,    1.42,    0.41,    0.71,   0.009
+20190906,   -0.02,   -0.37,    0.06,   0.009
+20190909,    0.07,    0.74,    3.11,   0.009
+20190910,    0.17,    1.02,    1.24,   0.009
+20190911,    0.88,    1.33,   -0.03,   0.009
+20190912,    0.22,   -0.29,   -0.21,   0.009
+20190913,   -0.01,    0.00,    0.77,   0.009
+20190916,   -0.23,    0.93,    0.52,   0.009
+20190917,    0.17,   -0.60,   -1.24,   0.009
+20190918,   -0.05,   -0.74,    0.04,   0.009
+20190919,   -0.05,   -0.44,   -0.45,   0.009
+20190920,   -0.48,    0.31,    0.14,   0.009
+20190923,   -0.02,   -0.18,    0.52,   0.009
+20190924,   -1.01,   -0.74,   -0.01,   0.009
+20190925,    0.69,    0.37,    0.54,   0.009
+20190926,   -0.41,   -0.96,    0.11,   0.009
+20190927,   -0.62,   -0.36,    0.91,   0.009
+20190930,    0.50,   -0.15,   -0.49,   0.009
+20191001,   -1.31,   -0.47,   -0.62,   0.007
+20191002,   -1.73,    0.86,   -0.39,   0.007
+20191003,    0.80,   -0.22,   -0.91,   0.007
+20191004,    1.39,   -0.50,   -0.08,   0.007
+20191007,   -0.41,    0.16,   -0.05,   0.007
+20191008,   -1.61,   -0.14,   -0.11,   0.007
+20191009,    0.92,   -0.53,   -0.12,   0.007
+20191010,    0.59,   -0.40,    0.29,   0.007
+20191011,    1.23,    0.48,    0.21,   0.007
+20191014,   -0.18,   -0.32,   -0.06,   0.007
+20191015,    1.03,    0.25,   -0.23,   0.007
+20191016,   -0.24,    0.40,    0.22,   0.007
+20191017,    0.37,    0.80,   -0.28,   0.007
+20191018,   -0.49,   -0.38,    0.88,   0.007
+20191021,    0.71,    0.18,    0.35,   0.007
+20191022,   -0.34,    0.22,    0.91,   0.007
+20191023,    0.26,   -0.08,    0.23,   0.007
+20191024,    0.25,   -0.26,   -0.91,   0.007
+20191025,    0.50,    0.28,    0.13,   0.007
+20191028,    0.62,    0.26,   -0.23,   0.007
+20191029,   -0.13,    0.29,    0.42,   0.007
+20191030,    0.27,   -0.38,   -1.20,   0.007
+20191031,   -0.38,   -0.25,   -0.42,   0.007
+20191101,    1.08,    0.45,    0.84,   0.006
+20191104,    0.40,    0.05,    1.43,   0.006
+20191105,   -0.03,    0.25,    0.51,   0.006
+20191106,   -0.05,   -0.69,    0.16,   0.006
+20191107,    0.38,   -0.11,    0.56,   0.006
+20191108,    0.31,    0.09,   -0.36,   0.006
+20191111,   -0.19,   -0.02,   -0.21,   0.006
+20191112,    0.16,   -0.11,   -0.14,   0.006
+20191113,    0.01,   -0.23,   -0.81,   0.006
+20191114,    0.07,   -0.12,   -0.26,   0.006
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+20191206,    0.91,    0.25,    0.43,   0.007
+20191209,   -0.33,    0.36,    0.14,   0.007
+20191210,   -0.08,    0.29,   -0.09,   0.007
+20191211,    0.28,   -0.07,    0.08,   0.007
+20191212,    0.90,   -0.11,    1.18,   0.007
+20191213,   -0.03,   -0.31,   -0.53,   0.007
+20191216,    0.74,    0.07,   -0.07,   0.007
+20191217,    0.10,    0.42,    0.70,   0.007
+20191218,   -0.05,    0.18,    0.09,   0.007
+20191219,    0.43,   -0.02,   -0.44,   0.007
+20191220,    0.48,   -0.30,   -0.30,   0.007
+20191223,    0.10,    0.20,   -0.29,   0.007
+20191224,    0.01,    0.37,   -0.03,   0.007
+20191226,    0.49,   -0.53,    0.00,   0.007
+20191227,   -0.09,   -0.52,   -0.08,   0.007
+20191230,   -0.57,    0.18,    0.57,   0.007
+20191231,    0.28,    0.00,    0.12,   0.007
+20200102,    0.86,   -0.85,   -0.37,   0.006
+20200103,   -0.67,    0.37,    0.01,   0.006
+20200106,    0.36,   -0.07,   -0.51,   0.006
+20200107,   -0.19,   -0.01,   -0.20,   0.006
+20200108,    0.47,   -0.06,   -0.64,   0.006
+20200109,    0.65,   -0.63,   -0.49,   0.006
+20200110,   -0.34,   -0.16,   -0.36,   0.006
+20200113,    0.73,   -0.08,   -0.12,   0.006
+20200114,   -0.06,    0.37,   -0.13,   0.006
+20200115,    0.16,    0.46,   -0.84,   0.006
+20200116,    0.88,    0.50,   -0.12,   0.006
+20200117,    0.28,   -0.64,   -0.12,   0.006
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+20200122,    0.08,   -0.27,    0.00,   0.006
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+20200124,   -0.97,   -0.41,   -0.35,   0.006
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+20200130,    0.34,   -0.69,    0.64,   0.006
+20200131,   -1.74,   -0.44,   -0.34,   0.006
+20200203,    0.84,    0.62,   -0.69,   0.006
+20200204,    1.57,   -0.02,   -0.68,   0.006
+20200205,    0.97,    0.30,    1.52,   0.006
+20200206,    0.27,   -0.45,   -0.85,   0.006
+20200207,   -0.55,   -0.78,   -0.06,   0.006
+20200210,    0.73,   -0.02,   -0.91,   0.006
+20200211,    0.28,    0.05,    1.07,   0.006
+20200212,    0.66,    0.17,   -0.33,   0.006
+20200213,   -0.09,    0.17,   -0.04,   0.006
+20200214,    0.15,   -0.39,   -0.71,   0.006
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+20200219,    0.60,    0.22,   -0.06,   0.006
+20200220,   -0.35,    0.46,    0.66,   0.006
+20200221,   -1.12,   -0.10,    0.05,   0.006
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+20200225,   -3.09,   -0.09,   -0.74,   0.006
+20200226,   -0.52,   -0.41,   -1.24,   0.006
+20200227,   -4.22,    0.75,    0.09,   0.006
+20200228,   -0.78,    0.28,   -0.79,   0.006
+20200302,    4.31,   -1.77,   -0.42,   0.006
+20200303,   -2.79,    0.78,   -0.64,   0.006
+20200304,    4.02,   -1.04,   -1.10,   0.006
+20200305,   -3.38,    0.18,   -1.37,   0.006
+20200306,   -1.78,    0.04,   -1.39,   0.006
+20200309,   -7.79,   -0.47,   -4.73,   0.006
+20200310,    4.74,   -2.46,    0.75,   0.006
+20200311,   -5.05,   -0.86,   -0.89,   0.006
+20200312,   -9.63,   -0.54,   -1.24,   0.006
+20200313,    8.96,   -2.70,    3.07,   0.006
+20200316,  -12.00,   -0.35,   -0.88,   0.006
+20200317,    5.87,    0.71,   -0.97,   0.006
+20200318,   -5.56,   -3.66,   -4.68,   0.006
+20200319,    1.31,    5.53,    0.99,   0.006
+20200320,   -4.15,    0.39,   -0.86,   0.006
+20200323,   -2.56,    2.18,   -3.50,   0.006
+20200324,    9.34,   -1.16,    2.44,   0.006
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+20200326,    6.02,   -0.67,    1.23,   0.006
+20200327,   -3.47,   -0.81,   -0.70,   0.006
+20200330,    3.16,   -0.37,   -2.20,   0.006
+20200331,   -1.44,    1.81,   -0.48,   0.006
+20200401,   -4.51,   -1.58,   -1.49,   0.000
+20200402,    2.09,   -0.99,   -0.27,   0.000
+20200403,   -1.64,   -1.11,   -1.17,   0.000
+20200406,    7.06,    0.76,    0.30,   0.000
+20200407,   -0.11,   -0.49,    2.15,   0.000
+20200408,    3.40,    0.36,    1.34,   0.000
+20200409,    1.60,    1.96,    3.19,   0.000
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+20200414,    3.02,   -0.14,   -3.15,   0.000
+20200415,   -2.15,   -1.20,   -2.49,   0.000
+20200416,    0.62,   -0.23,   -3.01,   0.000
+20200417,    2.72,    0.73,    2.71,   0.000
+20200420,   -1.56,    0.77,   -0.92,   0.000
+20200421,   -3.08,    0.96,    0.06,   0.000
+20200422,    2.31,   -0.71,   -1.64,   0.000
+20200423,    0.13,    1.17,    0.56,   0.000
+20200424,    1.44,    0.32,   -0.07,   0.000
+20200427,    1.73,    1.65,    2.75,   0.000
+20200428,   -0.45,    0.89,    2.85,   0.000
+20200429,    2.92,    1.87,    2.07,   0.000
+20200430,   -1.18,   -1.41,   -1.64,   0.000
+20200501,   -2.91,   -0.57,   -1.02,   0.000
+20200504,    0.53,    0.16,   -1.40,   0.000
+20200505,    0.95,    0.08,   -1.97,   0.000
+20200506,   -0.52,    0.56,   -2.77,   0.000
+20200507,    1.33,   -0.05,    0.41,   0.000
+20200508,    1.90,    1.44,    2.49,   0.000
+20200511,    0.07,    0.44,   -3.72,   0.000
+20200512,   -2.06,   -0.88,   -1.35,   0.000
+20200513,   -1.89,   -0.96,   -2.18,   0.000
+20200514,    1.14,   -1.48,    0.95,   0.000
+20200515,    0.57,    1.60,   -1.23,   0.000
+20200518,    3.24,    1.54,    4.61,   0.000
+20200519,   -1.01,   -0.56,   -1.42,   0.000
+20200520,    1.80,    1.09,    1.28,   0.000
+20200521,   -0.70,    0.66,    0.45,   0.000
+20200522,    0.27,    0.48,   -0.86,   0.000
+20200526,    1.23,    0.09,    4.62,   0.000
+20200527,    1.54,    0.63,    3.67,   0.000
+20200528,   -0.41,   -1.48,   -2.41,   0.000
+20200529,    0.60,   -0.29,   -2.00,   0.000
+20200601,    0.52,   -0.01,    0.49,   0.000
+20200602,    0.81,   -0.03,    0.46,   0.000
+20200603,    1.41,    0.12,    2.67,   0.000
+20200604,   -0.34,   -0.23,    3.02,   0.000
+20200605,    2.50,    0.75,    2.70,   0.000
+20200608,    1.39,    0.38,    2.18,   0.000
+20200609,   -0.85,   -0.32,   -1.98,   0.000
+20200610,   -0.57,   -0.84,   -4.19,   0.000
+20200611,   -5.91,   -0.86,   -3.09,   0.000
+20200612,    1.29,    0.37,    1.95,   0.000
+20200615,    1.09,    1.41,   -0.40,   0.000
+20200616,    1.86,    0.64,    0.47,   0.000
+20200617,   -0.40,   -0.92,   -2.00,   0.000
+20200618,    0.19,    0.10,   -0.53,   0.000
+20200619,   -0.45,    0.06,   -0.65,   0.000
+20200622,    0.71,    0.82,   -1.42,   0.000
+20200623,    0.42,    0.14,   -0.55,   0.000
+20200624,   -2.61,   -0.51,   -1.32,   0.000
+20200625,    1.12,    0.25,    0.50,   0.000
+20200626,   -2.44,    0.18,   -1.42,   0.000
+20200629,    1.51,    1.24,    1.81,   0.000
+20200630,    1.58,    0.06,   -0.02,   0.000
+20200701,    0.41,   -1.23,   -2.54,   0.000
+20200702,    0.50,    0.00,   -0.09,   0.000
+20200706,    1.65,   -0.59,    0.33,   0.000
+20200707,   -1.03,   -0.55,   -1.51,   0.000
+20200708,    0.91,    0.29,   -0.47,   0.000
+20200709,   -0.53,   -0.93,   -2.58,   0.000
+20200710,    1.11,   -0.09,    3.05,   0.000
+20200713,   -1.20,   -0.81,    2.00,   0.000
+20200714,    1.35,    0.35,   -0.33,   0.000
+20200715,    1.14,    2.43,    1.29,   0.000
+20200716,   -0.37,   -0.37,    0.81,   0.000
+20200717,    0.30,    0.16,   -1.40,   0.000
+20200720,    1.01,   -0.47,   -2.38,   0.000
+20200721,    0.16,    0.86,    3.24,   0.000
+20200722,    0.49,   -0.58,   -0.34,   0.000
+20200723,   -1.20,    0.72,    1.96,   0.000
+20200724,   -0.75,   -0.80,    0.47,   0.000
+20200727,    0.88,    0.75,   -1.93,   0.000
+20200728,   -0.81,   -0.72,    1.01,   0.000
+20200729,    1.35,    0.54,    0.84,   0.000
+20200730,   -0.29,    0.47,   -1.81,   0.000
+20200731,    0.61,   -1.54,   -0.65,   0.000
+
+Copyright 2020 Kenneth R. French
diff --git a/Data/F-F_Research_Data_Factors_weekly.CSV b/Data/F-F_Research_Data_Factors_weekly.CSV
new file mode 100644
index 0000000..a6741c9
--- /dev/null
+++ b/Data/F-F_Research_Data_Factors_weekly.CSV
@@ -0,0 +1,4916 @@
+This file was created by CMPT_ME_BEME_RETS_WEEKLY using the 202007 CRSP database.
+The Tbill return is the weekly rate that, over four weeks, 
+compounds to 1-month TBill rate from Ibbotson and Associates Inc.
+
+,Mkt-RF,SMB,HML,RF
+19260702,    1.60,   -0.57,   -0.90,   0.056
+19260710,    0.36,   -0.86,    0.27,   0.056
+19260717,    1.01,    0.83,   -1.84,   0.056
+19260724,   -2.05,    0.15,   -0.25,   0.056
+19260731,    3.04,   -1.86,   -0.85,   0.056
+19260807,    2.01,    0.08,    0.53,   0.063
+19260814,    0.33,   -0.66,    0.76,   0.063
+19260821,   -1.11,    0.26,    1.95,   0.063
+19260828,    0.53,    0.07,    0.84,   0.063
+19260903,    1.87,   -0.41,    0.55,   0.057
+19260911,   -1.27,    0.16,   -0.01,   0.057
+19260918,    0.16,   -1.10,    0.32,   0.057
+19260925,    0.62,   -0.80,   -0.39,   0.057
+19261002,    0.48,   -0.08,   -0.48,   0.080
+19261009,   -4.14,   -0.31,   -0.35,   0.080
+19261016,   -1.62,   -1.10,    1.02,   0.080
+19261023,    1.05,    0.63,   -0.35,   0.080
+19261030,    0.83,    0.87,    0.54,   0.080
+19261106,    1.47,   -0.88,    0.37,   0.077
+19261113,    0.71,    0.46,   -0.96,   0.077
+19261120,   -0.56,    0.67,    0.76,   0.077
+19261127,    1.03,   -0.17,   -0.13,   0.077
+19261204,    0.90,    0.02,   -0.79,   0.069
+19261211,    0.81,    0.47,   -0.18,   0.069
+19261218,    1.38,   -0.99,    0.94,   0.069
+19261224,    0.25,   -0.25,   -0.90,   0.069
+19261231,   -0.89,    0.37,    0.24,   0.069
+19270108,    0.17,    0.59,   -0.03,   0.062
+19270115,    0.06,   -1.02,    2.53,   0.062
+19270122,   -0.04,    0.16,    1.74,   0.062
+19270129,   -0.83,   -0.12,    0.52,   0.062
+19270205,    1.66,    1.28,    2.86,   0.064
+19270211,    0.39,   -0.09,   -0.21,   0.064
+19270219,    1.15,   -0.56,    1.66,   0.064
+19270226,    0.96,    0.34,   -1.56,   0.064
+19270305,   -0.68,    0.15,   -1.54,   0.074
+19270312,    0.83,   -0.54,    0.00,   0.074
+19270319,    0.05,   -1.00,   -2.39,   0.074
+19270326,    0.47,    0.17,   -0.09,   0.074
+19270402,   -0.17,   -0.89,    1.17,   0.064
+19270409,    1.83,   -0.58,    1.92,   0.064
+19270416,    0.36,    1.71,   -1.73,   0.064
+19270423,    0.47,   -0.90,    1.21,   0.064
+19270430,   -2.01,    0.58,   -0.82,   0.064
+19270507,    2.69,    0.59,    0.01,   0.075
+19270514,    0.40,   -0.24,    0.64,   0.075
+19270521,    1.10,   -0.77,    0.24,   0.075
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+20190726,    1.75,   -0.20,    0.72,   0.048
+20190802,   -3.45,    0.31,   -0.98,   0.041
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+20190823,   -1.57,   -0.77,   -0.27,   0.041
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+20190906,    1.61,   -1.16,    1.26,   0.044
+20190913,    1.33,    2.86,    5.05,   0.044
+20190920,   -0.64,   -0.55,   -1.00,   0.044
+20190927,   -1.36,   -1.86,    2.00,   0.044
+20191004,   -0.40,   -0.46,   -2.46,   0.038
+20191011,    0.69,   -0.43,    0.22,   0.038
+20191018,    0.47,    0.76,    0.55,   0.038
+20191025,    1.38,    0.34,    0.70,   0.038
+20191101,    1.47,    0.38,   -0.60,   0.031
+20191108,    1.01,   -0.41,    2.31,   0.031
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+20191122,   -0.19,   -0.06,   -1.56,   0.031
+20191129,    1.13,    1.53,   -1.59,   0.031
+20191206,    0.10,    0.47,    0.85,   0.035
+20191213,    0.74,    0.16,    0.77,   0.035
+20191220,    1.71,    0.36,   -0.02,   0.035
+20191227,    0.49,   -0.49,   -0.40,   0.035
+20200103,   -0.12,   -0.29,    0.33,   0.032
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+20200228,  -11.50,    0.63,   -2.40,   0.030
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+20200409,   12.35,    2.84,    7.88,   0.001
+20200417,    3.24,   -1.67,   -8.58,   0.001
+20200424,   -0.85,    2.54,   -2.01,   0.001
+20200501,    0.01,    2.31,    4.85,   0.002
+20200508,    4.24,    2.26,   -3.52,   0.002
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+20200522,    3.59,    3.29,    4.01,   0.002
+20200529,    2.98,   -1.12,    3.73,   0.002
+20200605,    4.97,    0.59,    9.93,   0.003
+20200612,   -4.73,   -1.27,   -5.23,   0.003
+20200619,    2.29,    1.27,   -3.21,   0.003
+20200626,   -2.83,    0.84,   -4.13,   0.003
+20200702,    4.04,    0.67,   -1.66,   0.003
+20200710,    2.10,   -1.86,   -1.38,   0.003
+20200717,    1.20,    1.76,    2.38,   0.003
+20200724,   -0.31,   -0.23,    2.91,   0.003
+20200731,    1.73,   -0.51,   -2.53,   0.003
+
+Copyright 2020 Kenneth R. French

From 3e73e48d51e188552783db87513255e5741c26ac Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 18 Sep 2020 14:15:52 -0700
Subject: [PATCH 714/753] Adds 315 G-Score Investing

---
 .../01 Strategy Library.php                   |  9 +++
 .../1030 G-Score Investing/01 Abstract.html   |  3 +
 .../02 Introduction.html                      | 10 ++++
 .../1030 G-Score Investing/03 Method.html     | 57 +++++++++++++++++++
 .../1030 G-Score Investing/04 Algorithm.html  |  6 ++
 .../1030 G-Score Investing/05 Results.html    |  5 ++
 .../1030 G-Score Investing/06 References.html |  6 ++
 7 files changed, 96 insertions(+)
 create mode 100644 04 Strategy Library/1030 G-Score Investing/01 Abstract.html
 create mode 100644 04 Strategy Library/1030 G-Score Investing/02 Introduction.html
 create mode 100644 04 Strategy Library/1030 G-Score Investing/03 Method.html
 create mode 100644 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
 create mode 100644 04 Strategy Library/1030 G-Score Investing/05 Results.html
 create mode 100644 04 Strategy Library/1030 G-Score Investing/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..edba6dd 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'G-Score Investing',
+     'link' => 'strategy-library/g-score-investing',
+     'sources' => [
+       'SSRN' => 'https://papers.ssrn.com/sol3/papers.cfm?abstract_id=403180'
+     ],
+     'description' => "Applying G-Score Investing to Invest in a Portfolio of Technology Stocks",
+     'tags'=>'Fundamentals, Factor Investing, G-Score Investing, MorningStar data, equities'
   ]
 ];
 
diff --git a/04 Strategy Library/1030 G-Score Investing/01 Abstract.html b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html
new file mode 100644
index 0000000..248865d
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+    In this tutorial, we apply G-Score Investing to choose a Universe of stocks to invest in.
+</p>
diff --git a/04 Strategy Library/1030 G-Score Investing/02 Introduction.html b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html
new file mode 100644
index 0000000..b26b9f7
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/02 Introduction.html	
@@ -0,0 +1,10 @@
+<p>
+    Analyzing a company&rsquo;s fundamentals is a method of trading that doesn&rsquo;t
+    rely purely on price and volume data. We will apply the use of computers to automate
+    the analysis of this data, and we will do so using a method of
+    <a href="https://www.investopedia.com/terms/f/factor-investing.asp">Factor Investing</a>,
+    the process of using different attributes, in this case, fundamental data, to choose
+    stocks to purchase. More specifically, we will use G-Score investing, and evaluate companies
+    on seven factors that we will detail later. We specifically choose companies with Book-to-Market
+    due to abnormal returns as a result of the Risk Premium Effect.
+</p>
diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html
new file mode 100644
index 0000000..b6ef87b
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html	
@@ -0,0 +1,57 @@
+<p>
+    We first sort all companies that have fundamental data by their Book-to-Market ratio, and narrow our universe to the
+    bottom quartile. We measure the Book-to-Market ratio using
+    <strong>fine.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths</strong> divided by
+    <strong>fine.MarketCap</strong>. In this strategy, we will use Technology as the industry of choice, thus, we further
+    narrow this universe to Technology stocks only.
+</p>
+
+<p>
+    For each of the conditions that are described below, if met, one point will be added to the G-Score.
+    Thus, with seven factors, our G-Score can range from 0 to 7. We evaluate a company based on the following:
+</p>
+
+
+<ul>
+    <li>
+        <em>The Return on Assets (ROA) is greater than the contemporaneous industry median. In other words, the ROA for
+        the analyzed company is greater than the median of the ROAs of all companies in the same industry</em> <br>
+        We measure this value using <strong>fine.OperationRatios.ROA.OneYear</strong>
+    </li>
+    <li>
+        <em>The Cash Flow Return on Assets (CFROA) is greater than the contemporaneous industry median</em> <br>
+        We measure this value using <strong>fine.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths</strong>
+        divided by<strong> fine.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths</strong>
+    </li>
+    <li>
+        <em>The CFROA is greater than the ROA</em>
+    </li>
+    <li>
+        <em>The Variance of the ROA (VARROA) is lower than the contemporaneous industry median</em> <br>
+        We measure this by storing the past twelve values of <strong>fine.OperationRatios.ROA.ThreeMonths </strong>in a
+        <strong>RollingWindow</strong> and computing the variance of the values in the <strong>RollingWindow</strong>
+    </li>
+    <li>
+        <em>The Research and Development Expenditure (R&D) is higher than the contemporaneous industry median</em> <br>
+        We measure this using <strong>fine.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths</strong>
+    </li>
+    <li>
+        <em>The Capital Expenditure (CapEx) is higher than the contemporaneous industry median</em> <br>
+        We measure this using <strong>fine.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths</strong>
+    </li>
+    <li>
+        <em>The Advertisement Expenditure (Ad) is higher than the contemporaneous industry median</em> <br>
+        We measure this using <strong>fine.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths</strong>
+    </li>
+</ul>
+
+<p>
+    The fundamental data used in our algorithms is sourced from MorningStar, and to read more about our fundamental data,
+    please visit the <a href="https://www.quantconnect.com/docs/data-library/fundamentals">Fundamentals</a> section of our
+    documentation.
+</p>
+
+<p>
+    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher
+</p>
+
diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
new file mode 100644
index 0000000..108b8ca
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_a4ddf109c4ac44f27fd8567b5ba053be.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
new file mode 100644
index 0000000..c8570c2
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -0,0 +1,5 @@
+<p>
+    Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is
+    outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1030 G-Score Investing/06 References.html b/04 Strategy Library/1030 G-Score Investing/06 References.html
new file mode 100644
index 0000000..f21473a
--- /dev/null
+++ b/04 Strategy Library/1030 G-Score Investing/06 References.html	
@@ -0,0 +1,6 @@
+<ol>
+    <li>
+        Mohanram, Partha S., Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement
+        nalysis (April 2004). <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=403180">Online Copy</a>.
+    </li>
+</ol>
\ No newline at end of file

From 140b9612c017265b3e8d4720eff49b69e69e9ad0 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 18 Sep 2020 15:22:35 -0600
Subject: [PATCH 715/753] Fix strategy library map file

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   8 +
 .../02 Background.html                        |  14 ++
 .../03 Method.html                            | 183 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Conclusion.html                        |  26 +++
 .../06 References.html                        |  11 ++
 7 files changed, 257 insertions(+)
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html
 create mode 100644 04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..384bc60 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'Using News Sentiment to Predict Price Direction of Drug Manufacturers',
+     'link' => 'strategy-library/using-news-sentiment-to-predict-price-direction-of-drug-manufacturers',
+     'sources' => [
+         'arXiv' => 'https://arxiv.org/abs/1812.04199'
+     ],
+     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday'
   ]
 ];
 
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
new file mode 100644
index 0000000..ab35081
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
@@ -0,0 +1,8 @@
+<p>
+  Several studies have found that press releases and other media can impact the perspective of investors. In this 
+  tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug 
+  manufacturer following positive news releases. Our findings show that when combining the effect with the 
+  day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the 
+  trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is 
+  inspired by the work of Isah, Shah, & Zulkernine (2018).
+</p>
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html
new file mode 100644
index 0000000..60fe4eb
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/02 Background.html	
@@ -0,0 +1,14 @@
+<p>
+  The use of alternative data sets to forecast stock prices has increased in recent years as the fundamental and 
+  technical analysis spaces increase in competition. Utilizing Natural Language Processing (NLP) techniques to 
+  analyze the sentiment of news releases and other text related to publicly traded companies has caught the interest
+  of many quant researchers. Such online information is frequently released and can be interpreted in a virtually 
+  unlimited number of ways, leading to a novel approach to determining the "societal mood" (Isah et al, 2018, p. 2)
+  towards a company.
+</p>
+
+<p>
+  There are several ways to implement a NLP system. In this tutorial, we utilize a dictionary to quantify the 
+  sentiment of news releases. The dictionary provided herein was sourced from Isah et al (2018), where it's use 
+  achieved a 70% accuracy when targeting several hand-picked stocks in India's pharmaceutical industry.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html
new file mode 100644
index 0000000..ac080d3
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/03 Method.html	
@@ -0,0 +1,183 @@
+<h4>Universe Selection</h4>
+<p>
+  We implement a universe selection model that provides the trading system with companies classified by 
+  <a href="https://www.quantconnect.com/docs/data-library/fundamentals">MorningStar</a> as being in the drug 
+  manufacturing industry group. We narrow our universe to include only the drug manufacturers with the greatest PE 
+  ratios and dollar volume.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def SelectCoarse(self, algorithm, coarse):
+    has_fundamentals = [c for c in coarse if c.HasFundamentalData]
+    sorted_by_dollar_volume = sorted(has_fundamentals, key=lambda c: c.DollarVolume, reverse=True)
+    return [ x.Symbol for x in sorted_by_dollar_volume[:self.coarse_size] ]
+
+def SelectFine(self, algorithm, fine):
+    drug_manufacturers = [f for f in fine if f.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.DrugManufacturers]
+    sorted_by_pe = sorted(drug_manufacturers, key=lambda f: f.ValuationRatios.PERatio, reverse=True)
+    return [ x.Symbol for x in sorted_by_pe[:self.fine_size] ]
+</pre>
+</div>
+
+
+<h4>Alpha Construction</h4>
+<p>
+  The DrugNewsSentimentAlphaModel emits insights to take long intraday positions for securities that have positive 
+  news sentiment. During construction of the model, we:
+</p>
+
+<ul>
+  <li>Create a dictionary to store SymbolData for each symbol</li>
+  <li>Gather the sentiment dictionary provided by Isah et al (2018)</li>
+  <li>Determine the maximum number of grams we need to analyze news articles</li>
+  <li>Define a method to determine the sign of sentiment</li>
+  <li>Specify the value of `bars_before_insight`</li>
+</ul>
+
+<p>
+  The `bars_before_insight` parameter determines how many bars the alpha model should observe after the market opens
+  before emitting insights. Isah et al (2018) batch the news released by each company into 30-minute intervals 
+  before analyzing the sentiment of the batch. In this tutorial, we follow a similar procedure by setting 
+  `bars_before_insight` to 30.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class DrugNewsSentimentAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    sentiment_by_phrase = SentimentByPhrase.dictionary
+    max_phrase_words = max([len(phrase.split()) for phrase in sentiment_by_phrase.keys()])
+    sign = lambda _, x: int(x and (1, -1)[x < 0])
+    
+    def __init__(self, bars_before_insight=30):
+        self.bars_before_insight = bars_before_insight
+</pre>
+</div>
+
+<h4>Alpha Securities Management</h4>
+<p>
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  each security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+    
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            algorithm.RemoveSecurity(symbol_data.tiingo_symbol)
+</pre>
+</div>
+
+<p>
+  The definition of the SymbolData class is shown below. We add properties to it to track the cumulative sentiment 
+  of news releases over time and the number of bars the alpha model has received for each security since the market 
+  open. In the constructor, we save a reference to the security's exchange so we can access the market hours of the 
+  exchange when generating insights. This is also where we initialize the 
+  <a href="https://www.quantconnect.com/docs/alternative-data/tiingo-news">Tiingo news feed</a> for each security.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    cumulative_sentiment = 0
+    bars_seen_today = 0
+    
+    def __init__(self, security, algorithm):
+        self.exchange = security.Exchange
+        self.tiingo_symbol = algorithm.AddData(TiingoNews, security.Symbol).Symbol
+</pre>
+</div>
+
+
+<h4>Alpha Update</h4>
+<p>
+  As new Tiingo objects are provided to the alpha model's Update method, we update the cumulative sentiment for each
+  security. The cumulative sentiment counter is reset at each market close. Therefore, when we emit insights 
+  30-minutes after the open, we are considering the sentiment of the news articles released from the previous close 
+  to the current time. We employ the findings of Berument & Kiymaz (2001), restricting the alpha model's trading to 
+  Wednesday, the most profitable day of the week. Positions are entered 30-minutes after the open and exited at the 
+  close.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    insights = []
+        
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+    
+        # If it's after-hours or within 30-minutes of the open, update
+        # cumulative sentiment for each symbol    
+        if symbol_data.bars_seen_today < self.bars_before_insight:
+            tiingo_symbol = symbol_data.tiingo_symbol
+            if data.ContainsKey(tiingo_symbol) and data[tiingo_symbol] is not None:
+                article = data[tiingo_symbol]
+                symbol_data.cumulative_sentiment += self.CalculateSentiment(article)
+    
+        if data.ContainsKey(symbol) and data[symbol] is not None:
+            symbol_data.bars_seen_today += 1
+
+            # 30-mintes after the open, emit insights in the direction of the cumulative sentiment.
+            # Only emit insights on Wednesdays to capture the analomaly documented by Berument and 
+            # Kiymaz (2001).
+            if symbol_data.bars_seen_today == self.bars_before_insight and data.Time.weekday() == 2:
+                    next_close_time = symbol_data.exchange.Hours.GetNextMarketClose(data.Time, False)
+                    direction = self.sign(symbol_data.cumulative_sentiment)
+                    if direction == 0:
+                        continue
+                    insight = Insight.Price(symbol, 
+                                            next_close_time - timedelta(minutes=2),
+                                            direction)
+                    insights.append(insight)
+    
+            # At the close, reset the cumulative sentiment
+            if not symbol_data.exchange.DateTimeIsOpen(data.Time):
+                symbol_data.cumulative_sentiment = 0
+                symbol_data.bars_seen_today = 0
+    
+    return insights
+</pre>
+</div>
+
+
+<h4>Calculating Sentiment</h4>
+<p>
+  We define the following helper method to return the sentiment of a Tiingo news article by analyzing the article's 
+  title and description. The `sentiment_by_phrase` dictionary was retrieved from queensbamlab's 
+  <a href="https://github.com/queensbamlab/NewsSentiments">NewsSentiment GitHub repository</a>. Although we have 
+  adjusted the dictionary to lowercase and removed some redundancies, this is the same dictionary used by Isah et 
+  al (2018). "The dictionary was created by leveraging author's domain expertise and thorough analysis of news 
+  articles over the years" (p. 3).
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+def CalculateSentiment(self, article):
+    sentiment = 0
+    for content in (article.Title, article.Description):
+        words = content.lower().split()
+        for num_words in range(1, self.max_phrase_words + 1):
+            for gram in ngrams(words, num_words):
+                phrase = ' '.join(gram)
+                if phrase in self.sentiment_by_phrase.keys():
+                    sentiment += self.sentiment_by_phrase[phrase]
+    return sentiment
+</pre>
+</div>
+
+
+<h4>Portfolio Construction & Trade Execution</h4>
+<p>
+  Following the guidelines of <a href="https://www.quantconnect.com/market">Alpha Streams</a> and the 
+  <a href="https://www.quantconnect.com/competitions">Quant League competition</a>, we utilize the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Portfolio/EqualWeightingPortfolioConstructionModel.py">
+  EqualWeightingPortfolioConstructionModel</a> and the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm/Execution/ImmediateExecutionModel.py">
+  ImmediateExecutionModel</a>.
+</p>
+
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html
new file mode 100644
index 0000000..43dfb6f
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_3f19f0bed332488a044fd1c2eff0a656.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html
new file mode 100644
index 0000000..553c5a6
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
@@ -0,0 +1,26 @@
+<p>
+  We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide 
+  as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with 
+  a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting 
+  trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability 
+  over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated 
+  by LEAN.
+</p>
+
+<p>
+To continue the development of this strategy, future areas of research include:
+</p>
+
+<ul>
+  <li>Adding a threshold parameter the cumulative sentiment counter must pass to signal trades</li>
+  <li>
+    Only analyzing the sentiment of articles which contain keywords like "US, "USA", 'Q1', and others in their 
+    titles
+  </li>
+  <li>
+    <a href="https://www.datacamp.com/community/tutorials/stemming-lemmatization-python">Stemming</a> and removing 
+    punctuation from articles before calculating their sentiment
+  </li>
+  <li>Adding phrases to the sentiment dictionary or adjust the sentiment values</li>
+  <li>Adding other data feeds beside just Tiingo</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html
new file mode 100644
index 0000000..8f8f421
--- /dev/null
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/06 References.html	
@@ -0,0 +1,11 @@
+<ol>
+  <li>
+    Shah, Dev, Haruna Isah, and Farhana Zulkernine. “Predicting the Effects of News Sentiments on the Stock Market.”
+    2018 IEEE International Conference on Big Data (Big Data) (2018).
+    <a href="https://arxiv.org/abs/1812.04199">Online copy</a>
+  </li>
+  <li>
+    Berument, Hakan and Kiymaz, Halil, The Day of the Week Effect on Stock Market Volatility (2001). Journal of 
+    Economics and Finance, Vol.25, No.2, pp. 181-193. <a href="https://ssrn.com/abstract=3211399">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 0f88f5770ebc6f2b0f26aac80b0a1c6acd244c4c Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 18 Sep 2020 15:23:55 -0600
Subject: [PATCH 716/753] Fix typo

---
 .../01 Abstract.html                                            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
index ab35081..387cf48 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
@@ -1,7 +1,7 @@
 <p>
   Several studies have found that press releases and other media can impact the perspective of investors. In this 
   tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug 
-  manufacturer following positive news releases. Our findings show that when combining the effect with the 
+  manufacturers following positive news releases. Our findings show that when combining the effect with the 
   day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the 
   trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is 
   inspired by the work of Isah, Shah, & Zulkernine (2018).

From 6235e6b7a9661b0ee4c9435b785f69fb1c34764b Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 25 Sep 2020 12:54:30 -0700
Subject: [PATCH 717/753] Adds 321 SVM Wavelet Forecasting

---
 .../01 Strategy Library.php                   |  9 ++++
 .../01 Abstract.html                          |  3 ++
 .../02 Introduction.html                      | 10 ++++
 .../03 Method.html                            | 52 +++++++++++++++++++
 .../04 Algorithm.html                         |  6 +++
 .../05 Results.html                           |  8 +++
 .../06 References.html                        |  5 ++
 7 files changed, 93 insertions(+)
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
 create mode 100644 04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..e979478 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'SVM Wavelet Forecasting',
+     'link' => 'strategy-library/svm-wavelet-forecasting',
+     'sources' => [
+       'Academia' => 'https://www.academia.edu/37180223/SVR_Wavelet_Adaptive_Model_for_Forecasting_Financial_Time_Series'
+     ],
+     'description' => "Forecasting EURJPY prices with an SVM Wavelet model",
+     'tags'=>'Support Vector Machines (SVM), Forex, Forecasting, Wavelet, Discrete Wavelet Transform'
   ]
 ];
 
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html
new file mode 100644
index 0000000..fb88b27
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
@@ -0,0 +1,3 @@
+<p>
+    In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices.
+</p>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
new file mode 100644
index 0000000..168b024
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -0,0 +1,10 @@
+<p>
+    Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
+    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often
+    used for classification problems, such as classifying proteins, they can also be applied in regression problems, and
+    are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be
+    used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so
+    when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to
+    forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate
+    forecast, which we detail in the <em>Method</em> section.
+</p>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
new file mode 100644
index 0000000..1a923a5
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
@@ -0,0 +1,52 @@
+<p>
+    Given EURJPY data, our first step is to decompose our data into multiple resolutions.
+    We work with wavelets using the <strong>pywt</strong> package. For denoising, Daubechies and
+    Symlets are good choices for Wavelets, and we use Symlets 10 in our strategy. We create a Symlets 10 Wavelet
+    using the following:&nbsp;
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+    w = pywt.Wavelet('sym10')
+</pre>
+</div>
+<p>To determine the length of the data we&rsquo;d need for a certain number of levels after decomposition, we can solve for:</p>
+\[log_{2}(\frac{len(data)}{wavelength-1})=levels\]
+<p>
+    Given the length of a Symlet 10 wavelet is 20, if we want three levels, we
+    solve for <strong>len(data)</strong> to get <strong>len(data) = 152</strong>, which means <strong>data</strong> would need to have at least
+    152 values. Since we will denoise our components using thresholding,
+    we specify <strong>threshold = 0.5</strong> to indicate the strength of the thresholding.
+    This <strong>threshold</strong> value can be any number between 0 and 1.
+</p>
+<p>To decompose our data, we use:&nbsp;</p>
+
+<div class="section-example-container">
+<pre class="python">
+    coeffs = pywt.wavedec(data, w)
+</pre>
+</div>
+
+<p>For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:</p>
+
+<div class="section-example-container">
+<pre class="python">
+    for i in range(1, len(coeffs)):
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+        forecasted = __svm_forecast(coeffs[i])
+        coeffs[i] = np.roll(coeffs[i], -1)
+        coeffs[i][-1] = forecasted
+</pre>
+</div>
+
+<p>The <strong>__svm_forecast</strong> method fits partitioned data to an SVM model then predicts one value into the
+    future, and can be found under <strong>SVMWavelet.py</strong> file under <strong>Algorithm</strong>&nbsp;</p>
+<p>Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:</p>
+<div class="section-example-container">
+<pre class="python">
+    datarec = pywt.waverec(coeffs, w)
+</pre>
+</div>
+<p>Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or <strong>datarec[-1]</strong>.</p>
+
+<p>Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the <strong>InsightWeightPortfolioConstructionModel</strong> so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.</p>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
new file mode 100644
index 0000000..fc4ed47
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6b4b0b8ff160d4de43b6089b565c5573.html"></iframe>
+    </div>
+</div>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
new file mode 100644
index 0000000..d3cea27
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -0,0 +1,8 @@
+<p>The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163,
+    while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:</p>
+<ul>
+    <li>Testing different Wavelet types (e.g. Daubechies)</li>
+    <li>Trying out other time resolutions (e.g. Minute)</li>
+    <li>Using alternative Decomposition methods (e.g. Stationary Wavelet Transform)&nbsp;</li>
+</ul>
+<p>If a user comes across any interesting results with modifications of this algorithm, we&rsquo;d love to hear about it in the Community Forum.</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html
new file mode 100644
index 0000000..1cc8866
--- /dev/null
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/06 References.html	
@@ -0,0 +1,5 @@
+<ol>
+    <li>
+        M. S. Raimundo and J. Okamoto, "SVR-wavelet adaptive model for forecasting financial time series," 2018 International Conference on Information and Computer Technologies (ICICT), DeKalb, IL, 2018, pp. 111-114, doi: 10.1109/INFOCT.2018.8356851. <a href="https://www.academia.edu/37180223/SVR_Wavelet_Adaptive_Model_for_Forecasting_Financial_Time_Series">Online Copy</a>.
+    </li>
+</ol>
\ No newline at end of file

From e66d96aed7e3ee445d5c95e339392aa1883afbc5 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Mon, 28 Sep 2020 08:29:12 -0700
Subject: [PATCH 718/753] Adds 321 SVM Wavelet Forecasting

---
 .../1031 SVM Wavelet Forecasting/03 Method.html        | 10 +++++++---
 .../1031 SVM Wavelet Forecasting/04 Algorithm.html     |  2 +-
 .../1031 SVM Wavelet Forecasting/05 Results.html       |  2 +-
 3 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
index 1a923a5..6651dd7 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
@@ -27,12 +27,16 @@
 </pre>
 </div>
 
-<p>For each component beyond the first, we threshold/denoise the component, roll the values of the component one spot to the left, and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:</p>
+<p>For each component, we threshold/denoise the component (except for the first component, the approximation coefficients),
+    roll the values of the component one spot to the left,
+    and replace the last value of the component with a value forecasted from an SVM. This process looks like the following in code:</p>
 
 <div class="section-example-container">
 <pre class="python">
-    for i in range(1, len(coeffs)):
-        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    for i in range(len(coeffs)):
+        if i > 0:
+            # we don't want to threshold the approximation coefficients
+            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
         forecasted = __svm_forecast(coeffs[i])
         coeffs[i] = np.roll(coeffs[i], -1)
         coeffs[i][-1] = forecasted
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index fc4ed47..868c5d9 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_6b4b0b8ff160d4de43b6089b565c5573.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_c5da749bbf6d7b5dee1fa5509d6ad8e5.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index d3cea27..c80ae5c 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-<p>The performance of the algorithm was quite poor. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.163,
+<p>The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:</p>
 <ul>
     <li>Testing different Wavelet types (e.g. Daubechies)</li>

From 920bf30f1ad9e98a66db45d643fdcfceb72ad8db Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 2 Oct 2020 19:06:33 -0600
Subject: [PATCH 719/753] Update cointegration algorithm and write-up

---
 .../04 Part II - Cointegration Method.html    | 53 ++++++++++---------
 .../05 Summary.html                           | 18 +++----
 .../06 Algorithm.html                         |  4 +-
 3 files changed, 40 insertions(+), 35 deletions(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 7011faf..76bf605 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -9,39 +9,44 @@ <h3>Step 1: Generate the Spread Series</h3>
 
 \[spread_t=\log(price_t^y)-\beta \log(price_t^x)\]
 
-<h3>Step 2: Compute the Threshold</h3>
+<h3>Step 2: Compute the Signals</h3>
 
 <p>
-  Using the standard deviation of spread during the rolling formation period, a threshold of two standard deviations is set up for the trading strategy as indicated in the paper.
+  Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
+  is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  sizes are scaled by the coefficient β.
 </p>
 
 <div class="section-example-container">
 
-<pre class="python">price_x = pd.Series([float(i.Close) for i in self.symbols[0].hist_window],
-                     index = [i.Time for i in self.symbols[0].hist_window])
+<pre class="python">log_close_x = np.log(self.closes_by_symbol[self.x_symbol])
+log_close_y = np.log(self.closes_by_symbol[self.y_symbol])
+
+spread, beta = self.regr(log_close_x, log_close_y)
 
-price_y = pd.Series([float(i.Close) for i in self.symbols[1].hist_window],
-                     index = [i.Time for i in self.symbols[1].hist_window])
-if len(price_x) < 250: return
-spread = self.regr(np.log(price_x), np.log(price_y))
 mean = np.mean(spread)
 std = np.std(spread)
-ratio = floor(self.Portfolio[self.symbols[1]].Price / self.Portfolio[self.symbols[0]].Price)
-if spread[-1] > mean + self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity > 0 and not self.Portfolio[self.symbols[0]].Quantity < 0:
-        self.Sell(self.symbols[1], 100)
-        self.Buy(self.symbols[0],  ratio * 100)
-
-elif spread[-1] < mean - self.threshold * std:
-    if not self.Portfolio[self.symbols[0]].Quantity < 0 and not self.Portfolio[self.symbols[0]].Quantity > 0:
-        self.Sell(self.symbols[0], 100)
-        self.Buy(self.symbols[1], ratio * 100)
+
+x_holdings = self.Portfolio[self.x_symbol]
+
+if x_holdings.Invested:
+    if x_holdings.IsShort and spread[-1] <= mean or \
+        x_holdings.IsLong and spread[-1] >= mean:
+        self.Liquidate()
 else:
-    self.Liquidate()
+    if beta < 1:
+        x_weight = 0.5
+        y_weight = 0.5 / beta
+    else:
+        x_weight = 0.5 / beta
+        y_weight = 0.5
+    
+    if spread[-1] < mean - self.threshold * std:
+        self.SetHoldings(self.y_symbol, -y_weight) 
+        self.SetHoldings(self.x_symbol, x_weight)
+    if spread[-1] > mean + self.threshold * std:
+        self.SetHoldings(self.x_symbol, -x_weight)
+        self.SetHoldings(self.y_symbol, y_weight)
 </pre>
 </div>
-
-<h3>Step 3: Set up the Trading Signals</h3>
-<p>
-  On each trading day, we enter a trade whenever the spread moves more than two standard deviations away from its mean. In other words, we construct short positions in X and long positions in Y on the day that spread mean+2*std. We construct short positions in Y and long positions in X on the day that spread&lt;mean-2*std. The trade is exited if the spread reverts to its equilibrium (defined as less than half a standard deviation from zero spread). The value of mean and standard deviation are calculated from the rolling formation period and will be updated once a month.
-</p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html
index cd9ff46..44ab024 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/05 Summary.html	
@@ -15,21 +15,21 @@
 <tbody>
 <tr>
 <td style="text-align: center;">Copula</td>
-<td style="text-align: center;">346</td>
-<td style="text-align: center;">274.293%</td>
-<td style="text-align: center;">1.022</td>
-<td style="text-align: center;">19.4%</td>
+<td style="text-align: center;">493</td>
+<td style="text-align: center;">8.884%</td>
+<td style="text-align: center;">0.12</td>
+<td style="text-align: center;">26.1%</td>
 </tr>
 <tr>
 <td style="text-align: center;">Cointegration</td>
-<td style="text-align: center;">91</td>
-<td style="text-align: center;">26.358%</td>
-<td style="text-align: center;">0.298</td>
-<td style="text-align: center;">23.7%</td>
+<td style="text-align: center;">126</td>
+<td style="text-align: center;">4.517%</td>
+<td style="text-align: center;">0.196</td>
+<td style="text-align: center;">3.9%</td>
 </tr>
 </tbody>
 </table>
 
 <p>
-  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 39 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
+  Generally, ETFs are not very volatile and so mean-reversion did not provide many trading opportunities. There are only 91 trades during 5 years for cointegration method. It is observed that the use of copula in pairs trading provides more trading opportunities as it does not require any rigid assumptions according to Liew R Q, Wu Y. - Pairs trading A copula approach.
 </p>
diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html
index 21099b5..228f289 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/06 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_1affb5ffa1ff019c9db6b9eda1e9aecb.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f9402ae6732efa3242331f8e4619f762.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d4443ef5bd0d45da3d003fe8e49a966e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f069b55872c55c89b3cb9fc1449a7177.html"></iframe>
     </div>
 </div>

From d831869a62cc3858617fa9a275d6ab6492eee23d Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 2 Oct 2020 19:12:06 -0600
Subject: [PATCH 720/753] Fix typo

---
 .../04 Part II - Cointegration Method.html                      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html
index 76bf605..d36c895 100755
--- a/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
+++ b/04 Strategy Library/03 Pairs Trading-Copula vs Cointegration/04 Part II - Cointegration Method.html	
@@ -14,7 +14,7 @@ <h3>Step 2: Compute the Signals</h3>
 <p>
   Using the standard deviation of spread during the rolling formation period, a threshold of one standard deviation 
   is set up for the trading strategy. We enter a trade whenever the spread moves more than one standard deviations 
-  away from its mean. Trades are exited when the spread reverts back to the mean trailing stread value. The position
+  away from its mean. Trades are exited when the spread reverts back to the mean trailing spread value. The position
   sizes are scaled by the coefficient β.
 </p>
 

From adb03722c992d5267f8ecef3cc275e3b45c2c999 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Mon, 5 Oct 2020 16:56:27 -0700
Subject: [PATCH 721/753] update backtest

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 108b8ca..52a4b06 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_a4ddf109c4ac44f27fd8567b5ba053be.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_7210ca43137f0742e849b4359c7535fd.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index c8570c2..cefd93b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 <p>
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.713 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 </p>
\ No newline at end of file

From a11df07a89239053b1e37e3247ad47fe11101275 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 16 Oct 2020 15:54:15 -0600
Subject: [PATCH 722/753] Adds Gradient Boosting Strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  24 +++
 .../03 Method.html                            | 163 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  53 ++++++
 ...06 Market & Competition Qualification.html |  13 ++
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |   5 +
 9 files changed, 297 insertions(+)
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1033 Gradient Boosting Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 6144105..7e5dd34 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -678,6 +678,15 @@
      ],
      'description' => "Mathematically Deriving the Optimal Entry and Liquidation Values of a Pairs Trading Process",
      'tags'=>'Pairs Trading, Ornstein-Uhlenbeck Process, Optimal Stopping'
+  ],
+  [
+     'name' => 'Gradient Boosting Model',
+     'link' => 'strategy-library/gradient-boosting-model',
+     'sources' => [
+       'arXiv' => 'https://ssrn.com/abstract=2323899'
+     ],
+     'description' => "Forecasts future intraday returns with a gradient boosting model trained on technical indicators",
+     'tags'=>'Gradient Boost, Regression Trees, Equities, Machine Learning'
   ]
 ];
 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html
new file mode 100644
index 0000000..1d309c1
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+<p>
+  In this tutorial, we train a Gradient Boosting Model (GBM) to forecast the intraday price movements of the SPY ETF using a 
+  collection of technical indicators. The implementation is based on the research produced by Zhou et al (2013), where a GBM 
+  was found to produce an annualized Sharpe ratio greater than 20. Our research shows that throughout a 5 year backtest, the 
+  model underperforms the SPY with its current parameter set. However, we finish the tutorial with highlighting potential 
+  areas of further research to improve the model’s performance.
+</p>
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
new file mode 100644
index 0000000..49f45d3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -0,0 +1,24 @@
+<p>
+  A GBM is trained by setting the initial model prediction to the mean target value in the training set. The model then 
+  iteratively builds regression trees to predict the model’s pseudo-residuals on the training set to tighten the fit. The 
+  pseudo-residuals are the differences between the target value and the model’s prediction on the current training iteration 
+  for each sample. The model’s predictions are made by summing the mean target value and the products of the learning rate 
+  and the regression tree outputs. The full algorithm is shown here.
+</p>
+
+<div style='text-align:center'>
+  <img class="img-responsive" style='max-width:100%; max-height: 200px' src="https://cdn.quantconnect.com/i/tu/gradient-boost-algorithm.png" alt="Tutorial1033-gradient-boost-1" />
+</div>
+
+<p>
+  We provide technical indicator values as inputs to the GBM. The model is trained to predict the security’s return over the 
+  next 10 minutes and the performance of the model’s predictions are assessed using the mean squared error loss function.
+</p>
+
+\[ MSE = \frac{\Sigma_{i=1}^n(y_i - \hat{y}_i)^2}{n}  \]
+
+<p>
+  Zhou et al (2013) utilize custom loss functions to fit their GBM in a manner that aims to maximize the profit-and-loss or 
+  Sharpe ratio over the training data set. The attached notebook shows training the GBM with these custom loss functions 
+  leads to poor model predictions.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html
new file mode 100644
index 0000000..7e88ae2
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/03 Method.html	
@@ -0,0 +1,163 @@
+<h4>Universe Selection</h4>
+<p>
+  We use a ManualUniverseSelectionModel to subscribe to the SPY ETF. The algorithm is designed to work with minute and
+  second data resolutions. In our implementation, we use data on a minute resolution.
+</p>
+<div class="section-example-container">
+<pre class="python">
+symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
+self.SetUniverseSelection( ManualUniverseSelectionModel(symbols) )
+self.UniverseSettings.Resolution = Resolution.Minute
+</pre>
+</div>
+
+
+<h4>Alpha Construction</h4>
+<p>
+  The GradientBoostingAlphaModel predicts the direction of the SPY at each timestep. Each position taken is held for 
+  10 minutes, although this duration is customizable in the constructor. During construction of this alpha model, we 
+  simply set up a dictionary to hold a SymbolData object for each symbol in the universe. In the case where the 
+  universe consists of multiple securities, the alpha model holds each with equal weighting.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class GradientBoostingAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    
+    def __init__(self, hold_duration = 10):
+        self.hold_duration = hold_duration
+        self.weight = 1
+</pre>
+</div>
+
+
+<h4>Alpha Securities Management</h4>
+<p>
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def OnSecuritiesChanged(self, algorithm, changes):
+    for security in changes.AddedSecurities:
+        symbol = security.Symbol
+        self.symbol_data_by_symbol[symbol] = SymbolData(symbol, algorithm, self.hold_duration)
+            
+    for security in changes.RemovedSecurities:
+        symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+        if symbol_data:
+            symbol_data.dispose()
+
+    self.weight = 1 / len(self.symbol_data_by_symbol)
+
+</pre>
+</div>
+
+
+<h4>SymbolData Class</h4>
+<p>
+  The SymbolData class is used in this algorithm to manage indicators, train the GBM, and produce trading predictions. 
+  The constructor definition is shown below. The class is designed to train at the end of each month, using the 
+  previous 4 weeks of data to fit the GBM that consists of 20 stumps (regression trees with 2 leaves). To ensure 
+  overnight holds are avoided, the class uses 
+  <a href="https://www.quantconnect.com/docs/algorithm-reference/scheduled-events">Scheduled Events</a> to stop trading
+  near the market close.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:    
+    def __init__(self, symbol, algorithm, hold_duration, k_start=0.5, k_end=5,
+                    k_step=0.25, training_weeks=4, max_depth=1, num_leaves=2, num_trees=20,
+                    commission=0.02, spread_cost=0.03):
+        self.symbol = symbol
+        self.algorithm = algorithm
+        self.hold_duration = hold_duration
+        self.resolution = algorithm.UniverseSettings.Resolution
+        self.training_length = int(training_weeks * 5 * 6.5 * 60) # training_weeks in minutes
+        self.max_depth = max_depth
+        self.num_leaves = num_leaves
+        self.num_trees = num_trees
+        self.cost = commission + spread_cost
+
+        self.indicator_consolidators = []
+
+        # Train a model at the end of each month
+        self.model = None
+        algorithm.Train(algorithm.DateRules.MonthEnd(symbol),
+                        algorithm.TimeRules.BeforeMarketClose(symbol),
+                          self.train)
+
+        # Avoid overnight holds
+        self.allow_predictions = False
+        self.events = [
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.AfterMarketOpen(symbol, 0),
+                                  self.start_predicting),
+            algorithm.Schedule.On(algorithm.DateRules.EveryDay(symbol),
+                                  algorithm.TimeRules.BeforeMarketClose(symbol, hold_duration + 1),
+                                  self.stop_predicting)
+        ]
+
+        self.setup_indicators(k_start, k_end, k_step)
+        self.train()
+</pre>
+</div>
+
+
+<h4>GBM Predictions</h4>
+<p>
+  For brevity, we omit the model training logic. Although, the code can be seen in the attached backtest. To make 
+  predictions, we define the following method inside the SymbolData class. A position is held in the predicted 
+  direction only if the predicted return in that direction exceeds the cost of the trade.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def predict_direction(self):
+    if self.model is None or not self.allow_predictions:
+        return 0
+
+    input_data = [[]]
+    for _, indicators in self.indicators_by_indicator_type.items():
+        for indicator in indicators:
+            input_data[0].append(indicator.Current.Value)
+                
+    return_prediction = self.model.predict(input_data)
+    if return_prediction > self.cost:
+        return 1
+    if return_prediction < -self.cost:
+        return -1
+    return 0
+</pre>
+</div>
+
+
+<h4>Alpha Update</h4>
+<p>
+  As new TradeBars are provided to the alpha model's Update method, each SymbolData object makes a directional 
+  prediction for its security. If the prediction is not flat, the alpha model emits an insight in that direction with 
+  a duration of 10 minutes.
+</p>
+<div class="section-example-container">
+<pre class="python">
+def Update(self, algorithm, data):
+    insights = []
+    for symbol, symbol_data in self.symbol_data_by_symbol.items():
+        direction = symbol_data.predict_direction()
+        if direction:
+            hold_duration = timedelta(minutes=self.hold_duration) # Should match universe resolution
+            insights.append(Insight.Price(symbol, hold_duration, direction, None, None, None, self.weight))
+
+    return insights
+</pre>
+</div>
+
+
+<h4>Portfolio Construction & Trade Execution</h4>
+<p>
+  Following the guidelines of <a href="https://www.quantconnect.com/docs/alpha-streams/overview">Alpha Streams</a> 
+  and the <a href="https://www.quantconnect.com/competitions/quant-league-7">Quant League</a> competition, we 
+  utilize the <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Portfolio/InsightWeightingPortfolioConstructionModel.py">
+  InsightWeightingPortfolioConstructionModel</a> and the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm/Execution/ImmediateExecutionModel.py">
+  ImmediateExecutionModel</a>.
+</p>
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html
new file mode 100644
index 0000000..223e7f3
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e7bb0850d32f66c61861779fd1effc04.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
new file mode 100644
index 0000000..88d08bf
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -0,0 +1,53 @@
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>Variance</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">5 Year Backtest</td>
+            <td rowspan="2">9/1/2015</td>
+            <td rowspan="2">9/17/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_e7bb0850d32f66c61861779fd1effc04.html">Strategy</a></td>
+            <td>-0.716</td>
+            <td style="background-color: lightgrey;">0.006</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9769ceac3d261b1c9d8b75d95855bed8.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.845</td>
+            <td>0.036</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_eb5b1c009734de0a768500f6bd7fe9c0.html">Strategy</a></td>
+            <td>-2.879</td>
+            <td style="background-color: lightgrey;">0.101</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_cc2afe14cbcdef1c74d0f3718cac49fc.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">-1.243</td>
+            <td>0.628</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9bd3cc352c9f55cf557a39fc496fa4e2.html">Strategy</a></td>
+            <td>-2.329</td>
+            <td style="background-color: lightgrey;">0.027</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9f37bccb0732468f3f9ed2c09136bee4.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">13.761</td>
+            <td>0.149</td>
+        </tr>
+    </tbody>
+</table>
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d2ac60d
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,13 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-7#rules">metrics</a> required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+</p>
+
+
+<ul>
+  <li>Profitable</li>
+  <li>PSR >= 80%</li>
+  <li>Max drawdown duration <= 6 months</li>
+  <li>Handles dividends and splits</li>
+</ul>
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html
new file mode 100644
index 0000000..10701d6
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+<p>
+  The GBM implemented in this tutorial has a lower Sharpe ratio than the S&P 500 index ETF benchmark over the periods 
+  we tested. However, the strategy generates a lower annual variance over all the testing period, implying more 
+  consistent returns than buy-and-hold. To continue the development of this strategy, future areas of research 
+  include:
+</p>
+
+<ul>
+  <li>Adjusting parameters in the GradientBoostingAlphaModel and SymbolData classes</li>
+  <li>Testing other custom loss functions</li>
+  <li>Changing the data resolution from minutes to seconds</li>
+  <li>Using more/other technical indicators</li>
+  <li>
+    Adding a model to predict the cost of trading (slippage, commissions, market impact) for each security instead of 
+    prescribing a fixed amount
+  </li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/08 References.html b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html
new file mode 100644
index 0000000..385172c
--- /dev/null
+++ b/04 Strategy Library/1033 Gradient Boosting Model/08 References.html	
@@ -0,0 +1,5 @@
+<ol>
+  <li>
+    Zhou, Nan and Cheng, Wen and Qin, Yichen and Yin, Zongcheng, Evolution of High Frequency Systematic Trading: A Performance-Driven Gradient Boosting Model (September 10, 2013). <a href="https://ssrn.com/abstract=2323899">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 79fc67e91a61a0b9fc43f2859bb4ca797db73379 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 16 Oct 2020 16:41:39 -0700
Subject: [PATCH 723/753] fixed typos

---
 .../01 Abstract.html                          |  2 +-
 .../02 Introduction.html                      | 14 +++++------
 .../03 Method.html                            | 24 +++++++++----------
 3 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html
index fb88b27..a93480f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/01 Abstract.html	
@@ -1,3 +1,3 @@
 <p>
-    In this tutorial, we apply an SVM Wavelet model in attempt to forecast EURJPY prices.
+    In this tutorial, we apply an SVM Wavelet model in an attempt to forecast EURJPY prices.
 </p>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 168b024..986ebed 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -1,10 +1,10 @@
 <p>
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
-    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are most often
-    used for classification problems, such as classifying proteins, they can also be applied in regression problems, and
-    are able to handle non-linear data. Wavelets are often applied in Signal Processing applications, and they can be
-    used to decompose a time-series into multiple components, and each component can be denoised using thresholding, so
-    when they are recombined, the original time-series becomes cleaner. We can combine these models by applying SVMs to
-    forecast the individual components of a time-series decomposed by Wavelets, and recombine them for an aggregate
-    forecast, which we detail in the <em>Method</em> section.
+    combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
+    used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
+    leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
+    decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
+    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 </p>
diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html
index 6651dd7..5cd8e93 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/03 Method.html	
@@ -7,7 +7,7 @@
 
 <div class="section-example-container">
 <pre class="python">
-    w = pywt.Wavelet('sym10')
+w = pywt.Wavelet('sym10')
 </pre>
 </div>
 <p>To determine the length of the data we&rsquo;d need for a certain number of levels after decomposition, we can solve for:</p>
@@ -23,7 +23,7 @@
 
 <div class="section-example-container">
 <pre class="python">
-    coeffs = pywt.wavedec(data, w)
+coeffs = pywt.wavedec(data, w)
 </pre>
 </div>
 
@@ -33,24 +33,24 @@
 
 <div class="section-example-container">
 <pre class="python">
-    for i in range(len(coeffs)):
-        if i > 0:
-            # we don't want to threshold the approximation coefficients
-            coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
-        forecasted = __svm_forecast(coeffs[i])
-        coeffs[i] = np.roll(coeffs[i], -1)
-        coeffs[i][-1] = forecasted
+for i in range(len(coeffs)):
+    if i > 0:
+        # we don't want to threshold the approximation coefficients
+        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))
+    forecasted = __svm_forecast(coeffs[i])
+    coeffs[i] = np.roll(coeffs[i], -1)
+    coeffs[i][-1] = forecasted
 </pre>
 </div>
 
 <p>The <strong>__svm_forecast</strong> method fits partitioned data to an SVM model then predicts one value into the
-    future, and can be found under <strong>SVMWavelet.py</strong> file under <strong>Algorithm</strong>&nbsp;</p>
+    future, and can be found under <strong>SVMWavelet.py</strong> file under the <strong>Algorithm</strong>&nbsp;section</p>
 <p>Once we forecast one value into the future, we can aggregate the forecasts by recombining the components into a simple time-series. We do this with:</p>
 <div class="section-example-container">
 <pre class="python">
-    datarec = pywt.waverec(coeffs, w)
+datarec = pywt.waverec(coeffs, w)
 </pre>
 </div>
 <p>Since we want the aggregate forecast one time-step into the future, we return the last element of this time-series, or <strong>datarec[-1]</strong>.</p>
 
-<p>Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the <strong>InsightWeightPortfolioConstructionModel</strong> so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have larger allocation.</p>
+<p>Our trading rules are simple: feed in the past 152 points of daily closing prices of EURJPY into our SVM Wavelet forecasting method, and divide that number by the current close of EURJPY to get the forecasted percent change. Then, we emit an Insight based on the direction of the percent change with the weight of the Insight as the absolute value of the percent change. We use the <strong>InsightWeightPortfolioConstructionModel</strong> so that the weight of the Insight determines the portfolio allocation percentage, which means larger forecasted moves will have a larger allocation.</p>

From 09e9a54af61023974d291b481fa7cce0cfdeb8b9 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 16 Oct 2020 16:43:02 -0700
Subject: [PATCH 724/753] fixed typos

---
 .../1031 SVM Wavelet Forecasting/02 Introduction.html         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html
index 986ebed..6fe7274 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/02 Introduction.html	
@@ -2,9 +2,9 @@
     Several methods have been developed to forecast time-series, from ARIMA to Neural Networks. In this strategy, we
     combine a Support Vector Machine (SVM) and Wavelets in an attempt to forecast EURJPY. Although SVMs are generally
     used for classification problems, such as classifying proteins, they can also be applied in regression problems, valued
-    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allows us
+    for their ability to handle non-linear data. Furthermore, Wavelets are often applied in Signal Processing applications. Wavelets allow us
     to decompose a time-series into multiple components, where each individual component can be denoised using thresholding, and this
     leads to a cleaner time-series after the components are recombined. To use these two models in conjunction, we first
     decompose the EURJPY data into components using Wavelet decomposition, then we apply the SVM to forecast one time-step
-    ahead each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
+    ahead of each of the components. After we recombine the components, we get the aggregate forecast of our SVM-Wavelet model.
 </p>

From f1cfa4561d7fcba2cdd7d2a88794e1140eab2a69 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 23 Oct 2020 16:29:26 -0600
Subject: [PATCH 725/753] Add relative metrics to news sentiment strategy

---
 .../01 Abstract.html                                |  3 ++-
 .../05 Conclusion.html                              | 13 +++++++------
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html
index 387cf48..d02e534 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/01 Abstract.html	
@@ -3,6 +3,7 @@
   tutorial, we implement an intraday strategy to capitalize on the upward drift in the stock prices of drug 
   manufacturers following positive news releases. Our findings show that when combining the effect with the 
   day-of-the-week anomaly documented by Berument & Kiymaz (2001), there is enough directional accuracy for the 
-  trading system to remain profitable throughout the 2020 stock market crash. The algorithm we design here is 
+  trading system to remain profitable throughout the 2020 stock market crash. However, the algorithm 
+  underperforms the S&P 500 market index ETF, SPY, over the same time period. The algorithm we design here is 
   inspired by the work of Isah, Shah, & Zulkernine (2018).
 </p>
diff --git a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html
index 553c5a6..8f0a19e 100644
--- a/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
+++ b/04 Strategy Library/1027 Using News Sentiment to Predict Price Direction of Drug Manufacturers/05 Conclusion.html	
@@ -1,10 +1,11 @@
 <p>
-  We conclude that deploying the sentiment analysis strategy on the US drug manufacturer industry does not provide 
-  as accurate results as found by Isah et al (2018). The algorithm above achieves a win rate of 47%. However, with 
-  a 1.16 profit-loss ratio, it remains profitable throughout the 2020 stock market crash. Only after restricting 
-  trading to the most profitable day of the week (Berument & Kiymaz, 2001) does the strategy achieve profitability 
-  over this testing period. We attribute the decrease in performance to the commissions and spread costs simulated 
-  by LEAN.
+  We conclude that deploying the sentiment analysis strategy on the US drug manufacturing industry does not provide 
+  as accurate of results as found by Isah et al (2018). Only after restricting trading to the most profitable day of 
+  the week (Berument & Kiymaz, 2001) does the strategy achieve profitability over our testing period. Overall, the 
+  strategy produces a Sharpe ratio of 0.116, while the 
+  <a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f14f15cc975842304468786dc56601d7.html">SPY benchmark</a>
+  produces a 0.971 Sharpe ratio during the same period. We attribute the decrease in performance to the commissions 
+  and spread costs simulated by LEAN.
 </p>
 
 <p>

From b606a4f8f03eba2060a611875de59a9f1ab12de2 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 23 Oct 2020 16:45:26 -0600
Subject: [PATCH 726/753] Add missing bracket to gradient boosting algo image

---
 .../1033 Gradient Boosting Model/02 Background.html             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html
index 49f45d3..597b32d 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/02 Background.html	
@@ -7,7 +7,7 @@
 </p>
 
 <div style='text-align:center'>
-  <img class="img-responsive" style='max-width:100%; max-height: 200px' src="https://cdn.quantconnect.com/i/tu/gradient-boost-algorithm.png" alt="Tutorial1033-gradient-boost-1" />
+  <img class="img-responsive" style='max-width:100%; max-height: 200px' src="https://cdn.quantconnect.com/i/tu/gradient-boosting-algorithm.png" alt="Tutorial1033-gradient-boost-1" />
 </div>
 
 <p>

From 95e76f2ef3d3722d5e05de72f769bda4b8a5921f Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 23 Oct 2020 15:52:54 -0700
Subject: [PATCH 727/753] updated algo

---
 .../1031 SVM Wavelet Forecasting/04 Algorithm.html              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html
index 868c5d9..935af6f 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_c5da749bbf6d7b5dee1fa5509d6ad8e5.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_f24dfd9efc8b3a2a8fcce16a638b6281.html"></iframe>
     </div>
 </div>

From acb4f1ffd84421887cfceeafd8ad4d7ae2e738d4 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 23 Oct 2020 15:53:37 -0700
Subject: [PATCH 728/753] updated resuls

---
 .../1031 SVM Wavelet Forecasting/05 Results.html                | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html
index c80ae5c..96be5b5 100644
--- a/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
+++ b/04 Strategy Library/1031 SVM Wavelet Forecasting/05 Results.html	
@@ -1,4 +1,4 @@
-<p>The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.388,
+<p>The performance of the algorithm was decent. Over the past five years, the algorithm achieved a Sharpe Ratio of 0.252,
     while buying and holding SPY over the same period would have achieved a Sharpe Ratio of 0.713. Some ideas for improvement include:</p>
 <ul>
     <li>Testing different Wavelet types (e.g. Daubechies)</li>

From 37d0c973209b93184a39b7d0c4f04b2e89b1257c Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 23 Oct 2020 16:21:44 -0700
Subject: [PATCH 729/753] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 52a4b06..11f46e3 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_7210ca43137f0742e849b4359c7535fd.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_70307dadfbc7e7448cfc8386ccf91e84.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index cefd93b..f9af05b 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 <p>
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.5 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 </p>
\ No newline at end of file

From fc5ef6d18ae739434b1f8cf9056d93879e759f71 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 23 Oct 2020 16:22:54 -0700
Subject: [PATCH 730/753] updated updated algo

---
 04 Strategy Library/1030 G-Score Investing/03 Method.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/03 Method.html b/04 Strategy Library/1030 G-Score Investing/03 Method.html
index b6ef87b..3a493ab 100644
--- a/04 Strategy Library/1030 G-Score Investing/03 Method.html	
+++ b/04 Strategy Library/1030 G-Score Investing/03 Method.html	
@@ -52,6 +52,6 @@
 </p>
 
 <p>
-    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher
+    Once we have computed the G-Scores for each of the securities, we long the securities with G-Scores of 5 or higher.
 </p>
 

From ce40497bbf02d5587fe1229e825e93752c43adeb Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 23 Oct 2020 18:15:01 -0600
Subject: [PATCH 731/753] Make relative tables mobile friendly

---
 .../05 Relative Performance.html                               | 2 ++
 .../1026 Intraday ETF Momentum/05 Conclusion.html              | 2 ++
 .../05 Relative Performance.html                               | 2 ++
 .../1033 Gradient Boosting Model/05 Relative Performance.html  | 3 +++
 4 files changed, 9 insertions(+)

diff --git a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html
index fd71509..2d3e6c9 100644
--- a/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
+++ b/04 Strategy Library/1023 Intraday Arbitrage Between Index ETFs/05 Relative Performance.html	
@@ -6,6 +6,7 @@
   across all our testing periods is displayed in the table below.
 </p>
 
+<div style="overflow: auto">
 <table class="table qc-table">
     <thead>
         <tr>
@@ -72,6 +73,7 @@
         </tr>
     </tbody>
 </table>
+</div>
 
 
 <p>
diff --git a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html
index ff087ea..bad583b 100644
--- a/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
+++ b/04 Strategy Library/1026 Intraday ETF Momentum/05 Conclusion.html	
@@ -7,6 +7,7 @@
   A breakdown of the results from all of the testing periods can be seen in the table below.
 </p>
 
+<div style="overflow: auto">
 <table class="table qc-table">
     <thead>
         <tr>
@@ -73,6 +74,7 @@
         </tr>
     </tbody>
 </table>  
+</div>
 
 
 <p>
diff --git a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html
index a1d1610..07a20e4 100644
--- a/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
+++ b/04 Strategy Library/1028 Ichimoku Clouds in the Energy Sector/05 Relative Performance.html	
@@ -17,6 +17,7 @@
   frames, implying that the strategy has more consistent returns than the benchmark.
 </p>
 
+<div style="overflow: auto">
 <table class="table qc-table">
     <thead>
         <tr>
@@ -83,6 +84,7 @@
         </tr>
     </tbody>
 </table>
+</div>
 
 <p>
   We find the lack of performance for this strategy is not largely attributed to the transaction costs. After 
diff --git a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html
index 88d08bf..415eea9 100644
--- a/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
+++ b/04 Strategy Library/1033 Gradient Boosting Model/05 Relative Performance.html	
@@ -1,3 +1,4 @@
+<div style="overflow: auto">
 <table class="table qc-table">
     <thead>
         <tr>
@@ -51,3 +52,5 @@
         </tr>
     </tbody>
 </table>
+</div>
+

From dd941cd87883482bba20bc09a2fca642bfd8c263 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 28 Oct 2020 16:08:48 -0700
Subject: [PATCH 732/753] fixed algo

---
 04 Strategy Library/1030 G-Score Investing/04 Algorithm.html | 2 +-
 04 Strategy Library/1030 G-Score Investing/05 Results.html   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html
index 11f46e3..2c62d81 100644
--- a/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
+++ b/04 Strategy Library/1030 G-Score Investing/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_70307dadfbc7e7448cfc8386ccf91e84.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_4b575276a5c8789bd24b031e81e8cdf3.html"></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/1030 G-Score Investing/05 Results.html b/04 Strategy Library/1030 G-Score Investing/05 Results.html
index f9af05b..5f327af 100644
--- a/04 Strategy Library/1030 G-Score Investing/05 Results.html	
+++ b/04 Strategy Library/1030 G-Score Investing/05 Results.html	
@@ -1,5 +1,5 @@
 <p>
     Since we use Technology as the industry, we decided to use Nasdaq-100, or ^NDX, as the benchmark, which we track
-    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.491 from April 2016 to September 2020, and so it is
+    using the QQQ ETF. Our algorithm achieves a Sharpe Ratio of 0.778 from April 2016 to September 2020, and so it is
     outperformed by simply holding QQQ, which yielded a Sharpe Ratio of 1.22 over the same period.
 </p>
\ No newline at end of file

From 9f30601e58cecd646ecd46f69cce5a57b8750c3a Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Thu, 5 Nov 2020 17:36:42 -0800
Subject: [PATCH 733/753] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index bcf0bc2..60c8e6c 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_4f8946295bb7358518b35ecbd3d46f01.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_c09be8e7229e743e072e25b8e74a8905.html"></iframe>
     </div>
 </div>

From 9ef83b5ff7ad231b106bff7e7c0130719d0e3e94 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Fri, 6 Nov 2020 15:42:22 -0800
Subject: [PATCH 734/753] fixed algorithm

---
 .../04 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html
index 60c8e6c..8586edb 100755
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/?request=embedded_backtest_c09be8e7229e743e072e25b8e74a8905.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_d3027cf77a67e7519de875a6b9162a8f.html"></iframe>
     </div>
 </div>

From 65f9e86bf866688998d76599ce3b80df25d45fc9 Mon Sep 17 00:00:00 2001
From: Shile Wen <shilewen@cs.washington.edu>
Date: Wed, 11 Nov 2020 17:23:37 -0800
Subject: [PATCH 735/753] fixed algorithm

---
 .../05 Algorithm.html                                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html
index f47a119..645e3da 100755
--- a/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
+++ b/04 Strategy Library/01 CAPM Alpha Ranking Strategy on Dow 30 Companies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_7794e68c615e9be3c7144821923fcd7a.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_9e87249fbe109fdfd9ce156c269807fc.html"></iframe>
     </div>
 </div>
\ No newline at end of file

From 0f13ec60484bfa85c7097f358515b5717152dbf2 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Thu, 19 Nov 2020 18:37:06 -0700
Subject: [PATCH 736/753] Adds gaussian naive bayes model strategy

---
 .../01 Strategy Library.php                   |   9 +
 .../01 Abstract.html                          |   7 +
 .../02 Background.html                        |  44 ++++
 .../03 Method.html                            | 223 ++++++++++++++++++
 .../04 Algorithm.html                         |   6 +
 .../05 Relative Performance.html              |  56 +++++
 ...06 Market & Competition Qualification.html |  12 +
 .../07 Conclusion.html                        |  17 ++
 .../08 References.html                        |  12 +
 9 files changed, 386 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 2edbaff..9fdb414 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -714,6 +714,15 @@
      ],
      'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
      'tags'=>'Equities, NLP, News Sentiment, Drug Manufacturers, Tiingo, Intraday'
+  ],
+  [
+     'name' => 'Gaussian Naive Bayes Model',
+     'link' => 'strategy-library/gaussian-naive-bayes-model',
+     'sources' => [
+         'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
+     ],
+     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];
 
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html
new file mode 100644
index 0000000..9421bb8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/01 Abstract.html	
@@ -0,0 +1,7 @@
+<p>
+  Naïve Bayes models have become popular for their success in spam email filtering. In this tutorial, we train 
+  Gaussian Naïve Bayes (GNB) classifiers to forecast the daily returns of stocks in the technology sector given the 
+  historical returns of the sector. Our implementation shows the strategy has a greater Sharpe and lower variance 
+  than the SPY ETF over a 5 year backtest and during the 2020 stock market crash. The algorithm we build here follows 
+  the research done by Lu (2016) and Imandoust & Bolandraftar (2014).
+</p>
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
new file mode 100644
index 0000000..2643372
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -0,0 +1,44 @@
+<p>
+  Naïve Bayes models classify observations into a set of classes by utilizing 
+  <a href="https://www.youtube.com/watch?v=HZGCoVF3YvM">Bayes’ Theorem</a>
+</p>
+
+\[\text{posterior} = \frac{ \text{prior } * \text{ likelihood} } {\text{evidence}}\]
+
+<p>
+  In symbols, this translates to
+</p>
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
+where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+<p>
+  The Naïve Bayes model assumes the features are independent, so that 
+</p>
+
+\[P(c_i | x_1, ..., x_n) = \frac{P(c_i)\prod_{j=1}^{n} P(x_j | c_i)}{P(x_1, ..., x_n)} \propto P(c_i)\prod_{j=1}^{n} P(x_j|c_i)\]
+
+<p>
+  The class that is most probable given the observation is then determined by solving
+</p>
+
+\[\hat{c} = \arg\max_{i \in \{1, ..., m\}} P(c_i) \prod_{j=1}^{n} P(x_j | c_i)\]
+
+
+<p>
+  In our use case, the classes in the model are: positive, negative, or flat future return for a security. The features
+  are the last 4 daily returns of the universe constituents. Since we are dealing with continuous data, we extend the 
+  model to a GNB model by replacing \(P(x_j|c_i)\) in the equation above. First, we find the mean \(\mu_j\) and standard 
+  deviation \(\sigma_j^2\) of the \(x_j\) feature vector in the training set labeled class \(c_i\). A normal distribution 
+  parameterized by \(\mu_j\) and \(\sigma_j^2\) is then used to determine the likelihood of the observations. If \(o\) is the 
+  observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
+</p>
+
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+
+<p>
+  The mechanics of the GNB model can be seen visually in 
+  <a href="https://www.youtube.com/watch?v=H3EjCKtlVog">this video</a>. Note that the GNB model has 2 underlying 
+  assumptions: the feature vectors are independent and normally distributed. We do not test for these properties, but 
+  rather leave it as an area of future research.
+</p>
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
new file mode 100644
index 0000000..8ef11c2
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html	
@@ -0,0 +1,223 @@
+<h4>Universe Selection</h4>
+<p>
+  Following Lu (2016), we implement a custom universe selection model to select the largest stocks from the technology 
+  sector. We restrict our universe to have a size of 10, but this can be easily customized via the `fine_size` 
+  parameter in the constructor.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class BigTechUniverseSelectionModel(FundamentalUniverseSelectionModel):
+    def __init__(self, fine_size=10):
+        self.fine_size = fine_size
+        self.month = -1
+        super().__init__(True)
+
+    def SelectCoarse(self, algorithm, coarse):
+        if algorithm.Time.month == self.month:
+            return Universe.Unchanged
+        return [ x.Symbol for x in coarse if x.HasFundamentalData ]
+    
+    def SelectFine(self, algorithm, fine):
+        self.month = algorithm.Time.month
+        
+        tech_stocks = [ f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology ]
+        sorted_by_market_cap = sorted(tech_stocks, key=lambda x: x.MarketCap, reverse=True)
+        return [ x.Symbol for x in sorted_by_market_cap[:self.fine_size] ]
+</pre>
+</div>
+
+
+<h4>Alpha Construction</h4>
+<p>
+  The GaussianNaiveBayesAlphaModel predicts the direction each security will move from a given day’s open to the next 
+  day’s open. When constructing this alpha model, we set up a dictionary to hold a SymbolData object for each symbol 
+  in the universe and a flag to show the universe has changed. 
+</p>
+<div class="section-example-container">
+<pre class="python">
+class GaussianNaiveBayesAlphaModel(AlphaModel):
+    symbol_data_by_symbol = {}
+    new_securities = False
+</pre>
+</div>
+
+
+<h4>Alpha Securities Management</h4>
+<p>
+  When a new security is added to the universe, we create a SymbolData object for it to store information unique to 
+  the security. The management of the SymbolData objects occurs in the alpha model's OnSecuritiesChanged method. In 
+  this algorithm, since we train the Gaussian Naive Bayes classifier using the historical returns of the securities 
+  in the universe, we flag to train the model every time the universe changes.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def OnSecuritiesChanged(self, algorithm, changes):
+        for security in changes.AddedSecurities:
+            self.symbol_data_by_symbol[security.Symbol] = SymbolData(security, algorithm)
+            
+        for security in changes.RemovedSecurities:
+            symbol_data = self.symbol_data_by_symbol.pop(security.Symbol, None)
+            if symbol_data:
+                symbol_data.dispose()
+        
+        self.new_securities = True
+</pre>
+</div>
+
+
+<h4>SymbolData Class</h4>
+<p>
+  The SymbolData class is used to store training data for the GaussianNaiveBayesAlphaModel and manage a consolidator 
+  subscription. In the constructor, we specify the training parameters, setup the consolidator, and warm up the 
+  training data.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    def __init__(self, security, algorithm, num_days_per_sample=4, num_samples=100):
+        self.exchange = security.Exchange
+        self.symbol = security.Symbol
+        self.algorithm = algorithm
+        self.num_days_per_sample = num_days_per_sample
+        self.num_samples = num_samples
+        self.previous_open = 0
+        self.model = None
+        
+        # Setup consolidators
+        self.consolidator = TradeBarConsolidator(timedelta(days=1))
+        self.consolidator.DataConsolidated += self.CustomDailyHandler
+        algorithm.SubscriptionManager.AddConsolidator(self.symbol, self.consolidator)
+        
+        # Warm up ROC lookback
+        self.roc_window = np.array([])
+        self.labels_by_day = pd.Series()
+        
+        data = {f'{self.symbol.ID}_(t-{i})' : [] for i in range(1, num_days_per_sample + 1)}
+        self.features_by_day = pd.DataFrame(data)
+        
+        lookback = num_days_per_sample + num_samples + 1
+        history = algorithm.History(self.symbol, lookback, Resolution.Daily)
+        if history.empty or 'close' not in history:
+            algorithm.Log(f"Not enough history for {self.symbol} yet")    
+            return
+        
+        history = history.loc[self.symbol]
+        history['open_close_return'] = (history.close - history.open) / history.open
+        
+        start = history.shift(-1).open
+        end = history.shift(-2).open
+        history['future_return'] = (end - start) / start
+        
+        for day, row in history.iterrows():
+            self.previous_open = row.open
+            if self.update_features(day, row.open_close_return) and not pd.isnull(row.future_return):
+                row = pd.Series([np.sign(row.future_return)], index=[day])
+                self.labels_by_day = self.labels_by_day.append(row)[-self.num_samples:]
+</pre>
+</div>
+
+<p>
+  The update_features method is called to update our training features with the latest data passed to the algorithm. 
+  It returns True/False, representing if the features are in place to start updating the training labels.
+</p>
+
+<div class="section-example-container">
+<pre class="python">
+class SymbolData:
+    ...
+
+    def update_features(self, day, open_close_return):
+        self.roc_window = np.append(open_close_return, self.roc_window)[:self.num_days_per_sample]
+        
+        if len(self.roc_window) < self.num_days_per_sample:
+            return False
+            
+        self.features_by_day.loc[day] = self.roc_window
+        self.features_by_day = self.features_by_day[-(self.num_samples+2):]
+        return True
+</pre>
+</div>
+
+
+
+
+<h4>Model Training</h4>
+<p>
+  The GNB model is trained each day the universe has changed. By default, it uses 100 samples to train. The features 
+  are the historical open-to-close returns of the universe constituents. The labels are the returns from the open at 
+  T+1 to the open at T+2 at each time step for each security.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def train(self):
+        features = pd.DataFrame()
+        labels_by_symbol = {}
+        
+        # Gather training data
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                features = pd.concat([features, symbol_data.features_by_day], axis=1)
+                labels_by_symbol[symbol] = symbol_data.labels_by_day
+        
+        # Train the GNB model
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.IsReady:
+                symbol_data.model = GaussianNB().fit(features.iloc[:-2], labels_by_symbol[symbol])
+</pre>
+</div>
+
+
+<h4>Alpha Update</h4>
+<p>
+  As new TradeBars are provided to the alpha model's Update method, we collect the latest TradeBar’s open-to-close 
+  return for each security in the universe. We then predict the direction of each security using the security’s 
+  corresponding GNB model, and return insights accordingly.
+</p>
+<div class="section-example-container">
+<pre class="python">
+class GaussianNaiveBayesAlphaModel(AlphaModel):
+    ...
+
+    def Update(self, algorithm, data):
+        if self.new_securities:
+            self.train()
+            self.new_securities = False
+        
+        tradable_symbols = {}
+        features = [[]]
+        
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if data.ContainsKey(symbol) and data[symbol] is not None and symbol_data.IsReady:
+                tradable_symbols[symbol] = symbol_data
+                features[0].extend(symbol_data.features_by_day.iloc[-1].values)
+
+        insights = []
+        if len(tradable_symbols) == 0:
+            return []
+        weight = 1 / len(tradable_symbols)
+        for symbol, symbol_data in tradable_symbols.items():
+            direction = symbol_data.model.predict(features)
+            if direction:
+                insights.append(Insight.Price(symbol, data.Time + timedelta(days=1, seconds=-1), 
+                                              direction, None, None, None, weight))
+
+        return insights
+</pre>
+</div>
+
+
+<h4>Portfolio Construction & Trade Execution</h4>
+<p>
+  Following the guidelines of <a href="https://www.quantconnect.com/docs/alpha-streams/overview">Alpha Streams</a> 
+  and the <a href="https://www.quantconnect.com/competitions">Quant League</a> competition, we 
+  utilize the <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Portfolio/InsightWeightingPortfolioConstructionModel.py">
+  InsightWeightingPortfolioConstructionModel</a> and the 
+  <a href="https://github.com/QuantConnect/Lean/blob/master/Algorithm/Execution/ImmediateExecutionModel.py">
+  ImmediateExecutionModel</a>.
+</p>
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
new file mode 100644
index 0000000..7f48965
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html	
@@ -0,0 +1,6 @@
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_373d2d04f623f4a9e411e7a8008e662a.html"></iframe>
+    </div>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
new file mode 100644
index 0000000..3ddfffe
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html	
@@ -0,0 +1,56 @@
+<div style="overflow: auto">
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>Variance</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">5 Year Backtest</td>
+            <td rowspan="2">10/1/2015</td>
+            <td rowspan="2">10/13/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_373d2d04f623f4a9e411e7a8008e662a.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">0.97</td>
+            <td style="background-color: lightgrey;">0.016</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_138ca8556c9da681e5d339fb5b8054bd.html">Benchmark</a></td>
+            <td>0.805</td>
+            <td>0.029</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_5bb63f83456aadebb96fb58039f289e7.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">-0.981</td>
+            <td style="background-color: lightgrey;">0.353</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_daad9c3dfe51614997a4d0a3807f0834.html">Benchmark</a></td>
+            <td>-1.4</td>
+            <td>0.474</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c0ef3e655e74b41fd9845d5ebaf55ec1.html">Strategy</a></td>
+            <td>-2.011</td>
+            <td style="background-color: lightgrey;">0.035</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_a108dfbd0a7855c4ec5a77c58f14ca0f.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">8.765</td>
+            <td>0.103</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
new file mode 100644
index 0000000..d16c9d8
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html	
@@ -0,0 +1,12 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-12#rules">metrics</a> required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+</p>
+
+<ul>
+  <li>PSR >= 80%</li>
+  <li>Max drawdown duration <= 6 months</li>
+  <li>Handles dividends and splits</li>
+  <li>Minute or second data resolution</li>
+</ul>
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
new file mode 100644
index 0000000..99f052c
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html	
@@ -0,0 +1,17 @@
+<p>
+  The GNB model strategy implemented in this tutorial produced a greater Sharpe ratio and lower annual variance than 
+  buying and holding the S&P 500 index ETF benchmark over the backtesting period. In addition to outperforming during 
+  the entire backtest, the strategy also outperformed during the 2020 stock market crash. 
+</p>
+
+<p>
+  To continue the development of this strategy, future areas of research include:
+</p>
+
+<ul>
+  <li>Adjusting parameters in the SymbolData class</li>
+  <li>Trying other features and labels for the GNB model</li>
+  <li>Adjusting the universe parameters and targeted sector</li>
+  <li>Adding handlers for corporate actions</li>
+  <li>Filter for stocks with independent and normal returns</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
new file mode 100644
index 0000000..0320284
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html	
@@ -0,0 +1,12 @@
+<ol>
+  <li>
+    Imandoust, S. B., & Mohammad, B. (2014). Forecasting the direction of stock market index movement using three 
+    data mining techniques: the case of Tehran Stock Exchange. <i>Journal of Engineering Research and Applications, 
+    6</i>(2), 106-117. 
+    <a href="https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange">Online copy</a>
+  </li>
+  <li>
+    Lu, N. (2016). A Machine Learning Approach to Automated Trading.  
+    <a href="https://www.bc.edu/content/dam/files/schools/cas_sites/cs/pdf/academics/honors/16Lu.pdf">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 93cfc1f62b1904001eb57a767e167956284b646b Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Thu, 19 Nov 2020 18:39:43 -0700
Subject: [PATCH 737/753] Fix description

---
 04 Strategy Library/00 Strategy Library/01 Strategy Library.php | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php
index 9fdb414..9fc944f 100644
--- a/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
+++ b/04 Strategy Library/00 Strategy Library/01 Strategy Library.php	
@@ -721,7 +721,7 @@
      'sources' => [
          'Academia' => 'https://www.academia.edu/7677227/Forecasting_the_direction_of_stock_market_index_movement_using_three_data_mining_techniques_the_case_of_Tehran_Stock_Exchange'
      ],
-     'description' => "Analyzes the news releases of drug manufacturers and places intraday trades for the stocks with positive news.",
+     'description' => "Forecasts the next day's return of technology stocks by fitting a gaussian naive bayes model to the historical returns of the technology sector constituents.",
      'tags'=>'Equities, Machine Learning, Naive Bayes, Gaussian'
   ]
 ];

From 28e73de28c4e10b564278a805f5b34dc8ec725c8 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Thu, 19 Nov 2020 20:56:41 -0700
Subject: [PATCH 738/753] Fix latex error

---
 .../1036 Gaussian Naive Bayes Model/02 Background.html     | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html
index 2643372..f576900 100644
--- a/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/02 Background.html	
@@ -10,7 +10,10 @@
 </p>
 
 \[P(c_i | x_1, ..., x_n) = \frac{P(c_i)P(x_1, ..., x_n | c_i)}{P(x_1, ..., x_n)}\]
-where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+
+<p>
+  where \(c_i\) represents one of the \(m\) classes and \(x_1, ..., x_n\) are the features.
+</p>
 
 <p>
   The Naïve Bayes model assumes the features are independent, so that 
@@ -34,7 +37,7 @@
   observation for the \(j\)th feature. The likelihood of the observation given the class \(c_i\) is
 </p>
 
-\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2} \]
+\[P(x_j = o | c_i) = \frac{1} {\sqrt{2 \pi{} \sigma{}_j^2 }}e^{- \frac{(o - \mu{}_j)^2} {2 \sigma{}_j^2}} \]
 
 <p>
   The mechanics of the GNB model can be seen visually in 

From a8b4090d6a95dd4a58b59c3a55551e730a6c9590 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 20 Nov 2020 19:13:33 -0700
Subject: [PATCH 739/753] Add video walkthrough

---
 .../03 Video Walkthrough.html                                | 5 +++++
 .../{03 Method.html => 04 Method.html}                       | 0
 .../{04 Algorithm.html => 05 Algorithm.html}                 | 0
 ...elative Performance.html => 06 Relative Performance.html} | 0
 ...ation.html => 07 Market & Competition Qualification.html} | 0
 .../{07 Conclusion.html => 08 Conclusion.html}               | 0
 .../{08 References.html => 09 References.html}               | 0
 7 files changed, 5 insertions(+)
 create mode 100644 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{03 Method.html => 04 Method.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{04 Algorithm.html => 05 Algorithm.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{05 Relative Performance.html => 06 Relative Performance.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{06 Market & Competition Qualification.html => 07 Market & Competition Qualification.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{07 Conclusion.html => 08 Conclusion.html} (100%)
 rename 04 Strategy Library/1036 Gaussian Naive Bayes Model/{08 References.html => 09 References.html} (100%)

diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html
new file mode 100644
index 0000000..3a3ef40
--- /dev/null
+++ b/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Video Walkthrough.html	
@@ -0,0 +1,5 @@
+<iframe width="560" height="315"
+        src="https://www.youtube.com/embed/ilGYYQRsy6A?start=1098&end=1933"
+        frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+        allowfullscreen>
+</iframe>
\ No newline at end of file
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/03 Method.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Method.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/04 Algorithm.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Algorithm.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/05 Relative Performance.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Relative Performance.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/06 Market & Competition Qualification.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Market & Competition Qualification.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/07 Conclusion.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 Conclusion.html
diff --git a/04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html b/04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html
similarity index 100%
rename from 04 Strategy Library/1036 Gaussian Naive Bayes Model/08 References.html
rename to 04 Strategy Library/1036 Gaussian Naive Bayes Model/09 References.html

From 227973336f5633a840abd9953f1ae0734f471070 Mon Sep 17 00:00:00 2001
From: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
Date: Tue, 8 Dec 2020 21:47:33 +0000
Subject: [PATCH 740/753] Update Link to The Refernce

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 .../05 References.html                                          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html
index 145c7bf..4d6f9c3 100644
--- a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
+++ b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 References.html	
@@ -1,5 +1,5 @@
 <ol>
   <li>
-    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>
+    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf">Online Copy</a>
   </li>
 </ol>

From d204c4e0e21e02048a61ae7b4363b4c1234641bb Mon Sep 17 00:00:00 2001
From: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
Date: Tue, 8 Dec 2020 21:49:01 +0000
Subject: [PATCH 741/753] Update Reference in Chinese Version

Using https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf
---
 ...05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html" "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"
index 145c7bf..4d6f9c3 100644
--- "a/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
+++ "b/04 Strategy Library/09 Stock Selection Strategy Based on Fundamental Factors/05 \345\217\202\350\200\203\346\226\207\347\214\256.cn.html"	
@@ -1,5 +1,5 @@
 <ol>
   <li>
-    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="file:///Users/jingwu/Downloads/(2015-07-07)_Quant_Blocks_Factor_Based_Stock_Selection_Model%20(4).pdf">Online Copy</a>
+    Factor Based Stock Selection Model for Turkish Equities,<em> 2015, </em>Ayhan Yüksel <a href="https://www.dropbox.com/s/bfkvrggydf06rxq/Factor%20Based%20Stock%20Selection%20Model.pdf">Online Copy</a>
   </li>
 </ol>

From 55297e639875aa0a4ccf7f107e9c7b062355b099 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Wed, 10 Feb 2021 16:32:44 -0700
Subject: [PATCH 742/753] Update Short Term Reversal strategy tutorial and
 algorithm

---
 .../01 Abstract.html                          |  12 +-
 .../02 Method.html                            | 182 +++++++++++-------
 .../03 Summary.html                           |   5 -
 .../04 Algorithm.html                         |   2 +-
 .../05 References.html                        |   5 -
 .../05 Relative Performance.html              |  56 ++++++
 ...07 Market & Competition Qualification.html |  13 ++
 .../08 Conclusion.html                        |  16 ++
 .../09 References.html                        |   6 +
 9 files changed, 208 insertions(+), 89 deletions(-)
 delete mode 100755 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
 delete mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html
 create mode 100644 04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html

diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html
index eae0057..e1419ee 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/01 Abstract.html	
@@ -1,8 +1,8 @@
 <p>
-  This strategy is called Short-Term Reversal Strategy which is discussed in detail in the paper written by Wilma de Groot, Joop Huij and Weili Zhou (2011) titled "<strong><em>Another look at trading costs and short-term reversal profits</em></strong>". The standard reversal strategy takes the whole universe of stocks into consideration, while this paper limits the stock universe only to large cap stocks so that trading costs could be significantly reduced.
-</p>
-<p>
-  One simple version of this strategy could be described like this: The investment universe consists of 100 biggest companies by market capitalization. We go long on the 10% stocks which have the lowest performances in the last month while going short on the 10% stocks with the highest ones. The portfolio is rebalanced weekly.
-  In the paper, however, strategies with different investment universes and different rebalancing frequencies are all backtested. The results show that, the larger the size of the investment universe, the larger the trading costs caused by extensively trading in small cap stocks which are less liquid; and trading costs become substantially lower when the rebalancing frequency is decreased from daily to weekly, but so do gross returns.
-  In this tutorial, we only use 100 stocks with weekly rebalancing for illustration.
+  In this tutorial, we implement a version of the short-term reversal strategy published by De Groot, Huij, & Zhou 
+  (2012). The strategy works by observing the returns of each security in the universe over the previous month. Every 
+  week, the algorithm longs the worst performers and shorts the top performers. The original strategy outlined in the 
+  literature considers the entire universe of stocks when trading. To reduce trading costs, we limit our universe to 
+  the most liquid large cap stocks. Our analysis shows the strategy underperforms the S&P 500 index during all our 
+  backtest periods except the 2020 market crash.
 </p>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html
index a666b03..8b9f021 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/02 Method.html	
@@ -1,103 +1,141 @@
 <p>
-  The strategy code mainly consists of three parts: Initialization, Warm Up, and Weekly Rebalancing.
+  The strategy code mainly consists of four parts: Initialization, Universe Selection, OnData, and OnSecuritiesChanged.
 </p>
 
-<h3>Step 1: Initialization</h3>
+<h3>Algorithm Initialization</h3>
 <p>
-  In the Initialize function, we set up look-back period, beginning cash balance, the size of the investment universe, the number of traded stocks, etc. We use <em>self._numOfWeeks</em> to count?the number of weeks that have passed since the start date, and <em>self.</em>_LastDay to indicate whether it is a new week. <em>self._ifWarmUp</em> is true when the <em>self._numOfWeeks</em> is 3, which means as long as next week's data come, we can make our investment decisions.??<em>self._stocks</em> is a list containing all the symbols of the 100 stocks that are taken into consideration. <em>self._values</em> is a dictionary with keys the stock symbols and values the lists containing the prices of stock each week since 4 weeks ago.
+  When initializing the algorithm, we add a coarse universe selection method and specify several parameters to use 
+  when selecting securities.
 </p>
-
 <div class="section-example-container">
-
-<pre class="python">    def Initialize(self):
-        self.SetStartDate(2005, 1, 1)
-        self.SetEndDate(2017, 5, 10)
-        self.SetCash(1000000)
-        
+<pre class="python">
+class ShortTimeReversal(QCAlgorithm):
+    def Initialize(self):
+        # ...
+      
         self.UniverseSettings.Resolution = Resolution.Daily
-        self.AddUniverse(self.CoarseSelectionFunction)
-        self._numberOfSymbols = 100
-        self._numberOfTradings = int(0.1 * self._numberOfSymbols)
-        
-        self._numOfWeeks = 0
-        self._LastDay = -1
-        self._ifWarmUp = False
-        
-        self._stocks = []
-        self._values = {}
+        self.AddUniverse(self.SelectCoarse)
+      
+        self.dollar_volume_selection_size = 100
+        self.roc_selection_size = int(0.1 * self.dollar_volume_selection_size)
+      
+        self.lookback = 22
+        self.roc_by_symbol = {}
+        self.week = 0
+
 </pre>
 </div>
-<p>
-  Also, we need to use?<em>CoarseSelectionFunction</em> to select 100 qualified stocks from the total stock universe. Here, we sort the total stock universe by each stock's DollarVolume in decreasing order. Then, we select the first 100 stocks that have the largest DollarVolume among all the stocks in the universe.
-</p>
 
-<div class="section-example-container">
 
-<pre class="python">
-def CoarseSelectionFunction(self, coarse):
-     sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
-     top100 = sortedByDollarVolume[:self._numberOfSymbols]
-     return [i.Symbol for i in top100]
-</pre>
-</div>
-<h3>Step 2:Warm Up</h3>
+
+<h3>Universe Selection</h3>
 <p>
-  Before we are able to make our investment decisions, we must have at least 4 weeks' data to calculate the performance, i.e. the monthly return, of each stock. Hence, we need a warm up period as long as 3 weeks to accumulate price series, so that once the fourth week's data come we can calculate?the return of the whole month.
+  The coarse universe selection method creates a RateOfChange 
+  <a href="https://www.quantconnect.com/docs/algorithm-reference/indicators">indicator</a> for each of the top 100 
+  most liquid securities in the market. Upon creation, the indicator is manually warmed-up with historical closing 
+  prices. After the indicators are ready, the universe selects the securities with the 10 best and 10 worst 
+  RateOfChange values.
 </p>
 <div class="section-example-container">
+<pre class="python">
+class ShortTimeReversal(QCAlgorithm):
+    # ...
+
+    def SelectCoarse(self, coarse):
+        
+        # We should keep a dictionary for all securities that have been selected
+        for cf in coarse:
+            symbol = cf.Symbol
+            if symbol in self.roc_by_symbol:
+                self.roc_by_symbol[symbol].Update(cf.EndTime, cf.AdjustedPrice)
+
+        # Refresh universe each week
+        week_number = self.Time.date().isocalendar()[1]
+        if week_number == self.week:
+            return Universe.Unchanged
+        self.week = week_number
+
+        # sort and select by dollar volume
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+        selected = {cf.Symbol: cf for cf in sortedByDollarVolume[:self.dollar_volume_selection_size]} 
+        
+        # New selections need a history request to warm up the indicator
+        symbols = [k for k in selected.keys()
+            if k not in self.roc_by_symbol or not self.roc_by_symbol[k].IsReady]
+
+        if symbols:
+            history = self.History(symbols, self.lookback, Resolution.Daily)
+            if history.empty:
+                self.Log(f'No history for {", ".join([x.Value for x in symbols])}')
+            history = history.close.unstack(0)
+
+            for symbol in symbols:
 
-<pre class="python">self._stocks = []
-self.uni_symbol = None
-symbols = self.UniverseManager.Keys
-for i in symbols:
-        if str(i.Value) == "QC-UNIVERSE-COARSE-USA":
-                self.uni_symbol = i
-        for i in self.UniverseManager[self.uni_symbol].Members:
-                self._stocks.append(i.Value.Symbol)
-                self._values[i.Value.Symbol] = [self.Securities[i.Value.Symbol].Price]
+                if symbol not in history:
+                    continue
 
+                # Create and warm-up the RateOfChange indicator
+                roc = RateOfChange(self.lookback)
+                for time, price in history[symbol].dropna().iteritems():
+                    roc.Update(time, price)
+                
+                if roc.IsReady:
+                    self.roc_by_symbol[symbol] = roc
+        
+        # Sort the symbols by their ROC values
+        selectedRateOfChange = {}
+        for symbol in selected.keys():
+            if symbol in self.roc_by_symbol:
+                selectedRateOfChange[symbol] = self.roc_by_symbol[symbol]
+        sortedByRateOfChange = sorted(selectedRateOfChange.items(), key=lambda kv: kv[1], reverse=True)
+        
+        # Define the top and the bottom to buy and sell
+        self.rocTop = [x[0] for x in sortedByRateOfChange[:self.roc_selection_size]]
+        self.rocBottom = [x[0] for x in sortedByRateOfChange[-self.roc_selection_size:]]
+        
+        return self.rocTop + self.rocBottom
 </pre>
 </div>
-<p>
-  We get all the symbols of qualified stocks from UniverseManager and keep them in <em>self._stocks</em> which is a list. Then we create for each key in the dictionary <em>self._values</em> a list where its first week's price is stored. And every time new data come, we append the new price to the end of the list of each stock.
-</p>
 
-<div class="section-example-container">
 
-<pre class="python">for stock in self._stocks:
-        self._values[stock].append(self.Securities[stock].Price)</pre>
-</div>
-<h3>Step 3:Weekly Rebalancing</h3>
+<h3>The OnData Method</h3>
 <p>
-  After the warm-up period, we calculate monthly returns every week and based on the returns, we make our investment decisions.
+  As new data is passed to the OnData method, we issue orders to form a long-short portfolio. We long the securities 
+  with the lowest RateOfChange values and short those with the largest values. After rebalancing, we clear the 
+  `rocTop` and `rocBottom` lists to ensure we don’t trade again until the universe is refreshed.
 </p>
-
 <div class="section-example-container">
+<pre class="python">
+class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-<pre class="python">returns = {}
-for stock in self._stocks:
-        newPrice = self.Securities[stock].Price
-        oldPrice = self._values[stock].pop(0)
-        self._values[stock].append(newPrice)
-        returns[stock] = newPrice/oldPrice
+    def OnData(self, data):
+        # Rebalance
+        for symbol in self.rocTop:
+            self.SetHoldings(symbol, -0.5/len(self.rocTop))
+        for symbol in self.rocBottom:
+            self.SetHoldings(symbol, 0.5/len(self.rocBottom))
+        
+        # Clear the list of securities we have placed orders for
+        # to avoid new trades before the next universe selection
+        self.rocTop.clear() 
+        self.rocBottom.clear()
 </pre>
 </div>
+
+<h3>The OnSecuritiesChanged Method</h3>
 <p>
-  Every week when new data come, we use them along with the data four weeks ago to calculate the monthly returns. At the same time, we remove the oldest data from our lists. This step is essential to prevent memory size exceeding the limit.
+  We are rebalancing the portfolio on a weekly basis, but securities can leave our defined universe between rebalance 
+  days. To accommodate this, we liquidate any securities that are removed from the universe in the 
+  OnSecuritiesChanged method.
 </p>
-
 <div class="section-example-container">
+<pre class="python">
+class ShortTimeReversal(QCAlgorithm):
+    # ...
 
-<pre class="python">newArr = [(v,k) for k,v in returns.items()]
-newArr.sort()
-for ret, stock in newArr[self._numberOfTradings:-self._numberOfTradings]:
-        self.SetHoldings(stock, 0)
-for ret, stock in newArr[0:self._numberOfTradings]:
-        self.SetHoldings(stock, 0.5/self._numberOfTradings)
-for ret, stock in newArr[-self._numberOfTradings:]:
-        self.SetHoldings(stock, -0.5/self._numberOfTradings)
+    def OnSecuritiesChanged(self, changes):
+        for security in changes.RemovedSecurities:
+            self.Liquidate(security.Symbol, 'Removed from Universe')
 </pre>
-</div>
-<p>
-  Finally, we sort the returns in increasing order. For the stocks whose monthly returns fall into the first 10% (performed badly in last month), we long them; For those fall into the last 10% (performed well in last month), we short them. Others (between 10% and 90%) will be set to 0.
-</p>
+</div>
\ No newline at end of file
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html
deleted file mode 100755
index 0c2b46e..0000000
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/03 Summary.html	
+++ /dev/null
@@ -1,5 +0,0 @@
-<p>
-  In the paper, the look-back period is from 1990 to 2009. However, we want to test whether the strategy is still profitable in the new time period. Hence we use different look-back periods instead.
-  If we begin from 2005 and end in 2017, there will be a total return of 131.50%. Although to some extent the performance of this strategy is dependent on different market situations,?nevertheless, in either situation mentioned above, this strategy could significantly beat the S&amp;P 500 benchmark.
-  Further research and backtesting could be done on different look-back periods, rebalancing frequencies, investment universes, numbers of traded stocks, etc.
-</p>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html
index 009e778..c2e37d5 100755
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/04 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75595e3ad41ca49ebcc6ab95aeaa92b8.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_a0d4717262243c4d65984bf11f5403d8.html "></iframe>
     </div>
 </div>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html
deleted file mode 100644
index 6c1e761..0000000
--- a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 References.html	
+++ /dev/null
@@ -1,5 +0,0 @@
-<ol>
-  <li>
-    Groot, Wilma (2011). Another look at trading costs and short-term reversal profit, page 1,?<a href="http://www.sciencedirect.com/science/article/pii/S0378426611002263"> Online Copy</a>
-  </li>
-</ol>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html
new file mode 100644
index 0000000..3d18c34
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/05 Relative Performance.html	
@@ -0,0 +1,56 @@
+<div style="overflow: auto">
+<table class="table qc-table">
+    <thead>
+        <tr>
+            <th>Period Name</th>
+            <th>Start Date</th>
+            <th>End Date</th>
+            <th>Strategy</th>
+            <th>Sharpe</th>
+            <th>Variance</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan="2">5 Year Backtest</td>
+            <td rowspan="2">1/1/2016</td>
+            <td rowspan="2">1/1/2021</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_a0d4717262243c4d65984bf11f5403d8.html">Strategy</a></td>
+            <td>0.24</td>
+            <td>0.058</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_5c22465ee99150974a9400fadc38cbb6.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">0.825</td>
+            <td style="background-color: lightgrey;">0.028</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Crash</td>
+            <td rowspan="2">2/19/2020</td>
+            <td rowspan="2">3/23/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_c174d85b4a8f9e48bf84ba171b409b8c.html">Strategy</a></td>
+            <td style="background-color: lightgrey;">-1.025</td>
+            <td>0.917</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_ce71a8e3d973d6f4cc02f5e80dac3a2f.html">Benchmark</a></td>
+            <td>-1.4</td>
+            <td style="background-color: lightgrey;">0.474</td>
+        </tr>
+        <tr>
+            <td rowspan="2">2020 Recovery</td>
+            <td rowspan="2">3/23/2020</td>
+            <td rowspan="2">6/8/2020</td>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_127b5201b8eedd33fb6d8b8fb4242511.html">Strategy</a></td>
+            <td>1.688</td>
+            <td>0.16</td>
+        </tr>
+        <tr>
+            <td><a href="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_90278572f8bd15b0a776213aade8a088.html">Benchmark</a></td>
+            <td style="background-color: lightgrey;">8.765</td>
+            <td style="background-color: lightgrey;">0.103</td>
+        </tr>
+    </tbody>
+</table>
+</div>
+
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html
new file mode 100644
index 0000000..1c0cad5
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/07 Market & Competition Qualification.html	
@@ -0,0 +1,13 @@
+<p>
+  Although this strategy passes several of the 
+  <a href="https://www.quantconnect.com/competitions/quant-league-29#rules">metrics</a> required for Alpha Streams 
+  and the Quant League competition, it requires further work to pass the following requirements:
+</p>
+
+<ul>
+  <li>PSR >= 80%</li>
+  <li>Emits insights</li>
+  <li>Max drawdown <= 10%</li>
+  <li>Handles dividends and splits</li>
+  <li>Minute or second data resolution</li>
+</ul>
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html
new file mode 100644
index 0000000..ae81428
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/08 Conclusion.html	
@@ -0,0 +1,16 @@
+<p>
+  The short-term reversal strategy implemented in this tutorial produced a lower Sharpe ratio than the S&P 
+  500 index ETF benchmark over all our testing periods except during the 2020 market crash. To continue the 
+  development of this strategy, future areas of research include:
+</p>
+
+<p>
+  To continue the development of this strategy, future areas of research include:
+</p>
+
+<ul>
+  <li>Increasing the data resolution and adding risk management logic.</li>
+  <li>Applying the strategy to a different universe of securities.</li>
+  <li>Testing other lookback periods for the RateOfChange indicator. Some researchers exclude the most-recent month’s price action from the indicator’s calculation.</li>
+  <li>Adjusting the weight for each insight emitted from the alpha model. Perhaps the most extreme performer of all the securities the alpha model is about to long (short) should be given the largest weight of all the securities the model is about to long (short).</li>
+</ul>
\ No newline at end of file
diff --git a/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html
new file mode 100644
index 0000000..2a52089
--- /dev/null
+++ b/04 Strategy Library/10 Short-Term Reversal Strategy in Stocks/09 References.html	
@@ -0,0 +1,6 @@
+<ol>
+  <li>
+    de Groot, Wilma and Huij, Joop and Zhou, Weili, Another Look at Trading Costs and Short-Term Reversal 
+    Profits (July 1, 2011). <a href="https://ssrn.com/abstract=1605049">Online copy</a>
+  </li>
+</ol>
\ No newline at end of file

From 5aaf69e507ff2f368f5986d9a19257da90160f20 Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 5 Mar 2021 11:43:23 -0700
Subject: [PATCH 743/753] Updating fama french tutorial

---
 .../02 Fama-French Three-Factor Model.html    |  7 ++---
 .../05 Other Factors.html                     | 12 --------
 .../{06 Summary.html => 05 Summary.html}      |  6 ++--
 .../{07 Algorithm.html => 06 Algorithm.html}  | 30 +++++++++----------
 ...{08 References.html => 07 References.html} |  3 ++
 5 files changed, 23 insertions(+), 35 deletions(-)
 delete mode 100755 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html
 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{06 Summary.html => 05 Summary.html} (98%)
 mode change 100755 => 100644
 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{07 Algorithm.html => 06 Algorithm.html} (78%)
 mode change 100755 => 100644
 rename 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/{08 References.html => 07 References.html} (81%)

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html
index 21d0568..1f83f6d 100755
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/02 Fama-French Three-Factor Model.html	
@@ -1,8 +1,5 @@
 <p>
-  This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns.[ref] <strong>Fama, E F; French, K R</strong> (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. 33: 3. CiteSeerX 10.1.1.139.5892 Freely accessible. doi:10.1016/0304-405X(93)90023-5[/ref]
-</p>
-<p>
-  The 3-factor model is
+  This model was proposed in 1993 by <strong>Eugene Fama</strong> and <strong>Kenneth French</strong> to describe stock returns. The 3-factor model is
 </p>
 \[ R = \alpha + \beta_m MKT + \beta_s SMB + \beta_h HML \]
 
@@ -12,7 +9,7 @@
 <ul>
     <li>MKT is the excess return of the market. It's the value-weighted return of all CRSP firms incorporated in the US and listed on the NYSE, AMEX, or NASDAQ minus the 1-month Treasury Bill rate.</li>
     <li>SMB (Small Minus Big) measures the excess return of stocks with small market cap over those with larger market cap.</li>
-    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have high book to price ratio (B/P) than growth stocks.</li>
+    <li>HML (High Minus Low) measures the excess return of value stocks over growth stocks. Value stocks have a higher book to price ratio (B/P) than growth stocks.</li>
 </ul>
 
 <p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html
deleted file mode 100755
index 510c436..0000000
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Other Factors.html	
+++ /dev/null
@@ -1,12 +0,0 @@
-<p>
-  The Fama-French 5-Factor model comprises two more factors:
-</p>
-<ul>
-    <li>RMW (Robust Minus Weak) measures the excess returns of firms with high operating profit margins over those with lower profits.</li>
-    <li>CMA (Conservative Minus Aggressive) measures the excess returns of firms investing less over those investing more.</li>
-</ul>
-
-<p>
-  RMW was proposed by Novy-Marx (2013) who wrote that:
-  "Controlling for gross profitability explains most earnings related anomalies, and a wide range of seemingly unrelated profitable trading strategies." CMA was proposed by Fama and French (2014) who pointed out that: A five-factor model directed at capturing the size, value, profitability, and investment patterns in average stock returns is rejected on the GRS test, but for applied purposes it provides an acceptable description of average returns. Finally, momentum is another commonly used factor. It captures excess returns of stocks with highest returns over those with lowest returns
-</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html
old mode 100755
new mode 100644
similarity index 98%
rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html
rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html
index 549200f..d2b7e7a
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Summary.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/05 Summary.html	
@@ -1,3 +1,3 @@
-<p>
-  In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
-</p>
+<p>
+  In this chapter we expand Capital Asset Pricing Model (CAPM) into multi-factor models: the Fama-French factor models in particular. They are the most empirically successful multi-factor models by far, and are commonly used in practice.
+</p>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html
old mode 100755
new mode 100644
similarity index 78%
rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html
rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html
index e3c69c8..2fd2fa2
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html	
@@ -1,15 +1,15 @@
-<p>
-  Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.
-  The paper recommends picking stocks by their value, quality (profitability) and momentum.
-  The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
-  The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
-</P>
-<p>
-  In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every 2 months and our backtest period runs from Jan 2012 to Jan 2015. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
-</p>
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d0f89f7876841581a8eee32cf007cd75.html"></iframe>
-    </div>
-</div>
+<p>
+  Multi-factor strategies are <strong>stock picking</strong> strategies. Here we try to implement a 2013 paper published by AQR Capital Management.
+  The paper recommends picking stocks by their value, quality (profitability) and momentum.
+  The empirically successful measure of value is book-to-price ratio (B/P), but other measures can be used simultaneously to form a more robust and reliable view of a stock's value. The paper uses 5 measures: book-to-price, earnings-to-price ratio (EPS), forecasted EPS, cash flow-to-enterprise value and sales-to-enterprise value.
+  The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
+</P>
+<p>
+  In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache?request=embedded_backtest_45b5f4ad1eddc1d37543cc4199a34a40.html"></iframe>
+    </div>
+</div>
diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html
similarity index 81%
rename from 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html
rename to 05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html
index 79b3bcb..eb7216e 100644
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/08 References.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/07 References.html	
@@ -14,4 +14,7 @@
   <li>
     Robert Novy-Marx (2013). The Other Side of Value: The Gross Profitability Premium Journal of Financial Economics 108 (1), 1-28. Retrieved from <a href="http://rnm.simon.rochester.edu/research/OSoV.pdf" target="_blank">rnm.simon.rochester.edu/research/OSoV.pdf</a>
   </li> 
+  <li>
+    Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. <i>Journal of Financial Economics</i>, 33, 3-56. doi: 10.1016/0304-405X(93)90023-5. Retrieved from <a href="https://rady.ucsd.edu/faculty/directory/valkanov/pub/classes/mfe/docs/fama_french_jfe_1993.pdf">rady.ucsd.edu</a>.
+  </li>
 </ol>

From 304f01fe6fa9e01d15c3155cab4f3b03d5e251fc Mon Sep 17 00:00:00 2001
From: Derek Melchin <melchin.derek@gmail.com>
Date: Fri, 5 Mar 2021 11:44:46 -0700
Subject: [PATCH 744/753] Update value factor definition to match algo

---
 .../14 Fama-French Multi-Factor Models/06 Algorithm.html        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html
index 2fd2fa2..3eb4334 100644
--- a/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/14 Fama-French Multi-Factor Models/06 Algorithm.html	
@@ -5,7 +5,7 @@
   The paper suggested a few quality measures: total profit over assets, gross margin and free cash flow over assets. There are also various measures of momentum. 1-year momentum, fundamental momentum and returns around earnings announcement are good choices.
 </P>
 <p>
-  In our backtested strategy, we used operating profit margin to measure quality, P/B value to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
+  In our backtested strategy, we used operating profit margin to measure quality, book value per share to measure value, and 1-month momentum. The portfolio was rebalanced every month. You can build your own version by changing the factor, the weight of each factor, and the rebalance period based on the backtested strategy.
 </p>
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>

From b1fc9c2bb99abb67fdf008e118597f55bab491ba Mon Sep 17 00:00:00 2001
From: Jared <jaredbroad@gmail.com>
Date: Wed, 17 Mar 2021 10:17:08 -0700
Subject: [PATCH 745/753] Update 01 Definition.html

---
 07 Applied Options[]/02 Bull Call Spread/01 Definition.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html
index 0418abb..c361521 100755
--- a/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/01 Definition.html	
@@ -5,7 +5,7 @@
  This strategy creates a ceiling and floor for the profit. By purchasing a call and selling a call with higher strike simultaneously, traders can reduce the cost of just one long call option with the premium of a short call option. But the premium of ITM call is more expensive than the OTM call. The strategy limits the loss resulting from a drop in the price of the underlying stock but still creates a ceiling to the profit while the underlying price is increasing.
 </p>
 <p>
- Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 20 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
+ Take GOOG as an example. If the share price of GOOG is $950 at time 0, the premium of ITM call option is 50 with strike 900 and the premium of OTM call option is 2 with strike 1000. If we ignore the commission, dividends and other transaction fees, the payoff of Bull Call Spread strategy is as follows:
 </p>
 <div class="section-example-container">
 

From 6750665e5c3f051e4ab37bbafb7835d1bed848b3 Mon Sep 17 00:00:00 2001
From: Web Editor <web.editor@quantconnect.com>
Date: Mon, 11 Oct 2021 16:34:56 -0400
Subject: [PATCH 746/753] Automated push from server made by Alexandre Catarino

---
 .../02 Fetching Data.html                     | 196 +++++++++---------
 1 file changed, 98 insertions(+), 98 deletions(-)

diff --git a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html
index 325ecd6..181f0fc 100755
--- a/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
+++ b/05 Introduction to Financial Python[]/05 Pandas-Resampling and DataFrame/02 Fetching Data.html	
@@ -1,98 +1,98 @@
-<p>
-  Here we use the Quandl API to retrieve data.
-</p>
-<div class="section-example-container">
-
-<pre class="python">import quandl
-quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
-</pre>
-</div>
-<p>
-  We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
-</p>
-<div class="section-example-container">
-
-<pre class="python">aapl_table = quandl.get('WIKI/AAPL')
-aapl = aapl_table['Adj. Close']['2017']
-print aapl
-</pre>
-</div>
-
-<p>
-  Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
-</p>
-<div class="section-example-container">
-
-<pre class="python">print aapl['2017-3']
-Date
-2017-03-01    138.657681
-2017-03-02    137.834404
-2017-03-03    138.647762
-2017-03-06    138.211326
-2017-03-07    138.389868
-2017-03-08    137.874080
-2017-03-09    137.556672
-2017-03-10    138.012946
-2017-03-13    138.072460
-2017-03-14    137.864161
-2017-03-15    139.322254
-2017-03-16    139.550391
-2017-03-17    138.856061
-2017-03-20    140.314154
-2017-03-21    138.707276
-2017-03-22    140.274478
-2017-03-23    139.778528
-2017-03-24    139.500796
-2017-03-27    139.738852
-2017-03-28    142.635200
-2017-03-29    142.952608
-2017-03-30    142.764147
-2017-03-31    142.496334
-</pre>
-</div>
-
-<p>
-  Or in several consecutive months:
-</p>
-<div class="section-example-container">
-
-<pre class="python">aapl['2017-2':'2017-4']
-</pre>
-</div>
-
-<p>
-  .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
-</p>
-<div class="section-example-container">
-
-<pre class="python">print aapl.head()
-print aapl.tail(10)
-</pre>
-</div>
-<p>
-  The output:
-</p>
-<div class="section-example-container">
-
-<pre class="python">
-Date
-2017-01-03    114.715378
-2017-01-04    114.586983
-2017-01-05    115.169696
-2017-01-06    116.453639
-2017-01-09    117.520300
-Name: Adj. Close, dtype: float64
-Date
-2017-08-08    159.433108
-2017-08-09    160.409148
-2017-08-10    155.270000
-2017-08-11    157.480000
-2017-08-14    159.850000
-2017-08-15    161.600000
-2017-08-16    160.950000
-2017-08-17    157.870000
-2017-08-18    157.500000
-2017-08-21    157.210000
-Name: Adj. Close, dtype: float64
-</pre>
-</div>
+<p>
+  Here we use the Quandl API to retrieve data...
+</p>
+<div class="section-example-container">
+
+<pre class="python">import quandl
+quandl.ApiConfig.api_key = 'dRQxJ15_2nrLznxr1Nn4'
+</pre>
+</div>
+<p>
+  We will create a Series named "aapl" whose values are Apple's daily closing prices, which are of course indexed by dates:
+</p>
+<div class="section-example-container">
+
+<pre class="python">aapl_table = quandl.get('WIKI/AAPL')
+aapl = aapl_table['Adj. Close']['2017']
+print aapl
+</pre>
+</div>
+
+<p>
+  Recall that we can fetch a specific data point using series['yyyy-mm-dd']. We can also fetch the data in a specific month using series['yyyy-mm'].
+</p>
+<div class="section-example-container">
+
+<pre class="python">print aapl['2017-3']
+Date
+2017-03-01    138.657681
+2017-03-02    137.834404
+2017-03-03    138.647762
+2017-03-06    138.211326
+2017-03-07    138.389868
+2017-03-08    137.874080
+2017-03-09    137.556672
+2017-03-10    138.012946
+2017-03-13    138.072460
+2017-03-14    137.864161
+2017-03-15    139.322254
+2017-03-16    139.550391
+2017-03-17    138.856061
+2017-03-20    140.314154
+2017-03-21    138.707276
+2017-03-22    140.274478
+2017-03-23    139.778528
+2017-03-24    139.500796
+2017-03-27    139.738852
+2017-03-28    142.635200
+2017-03-29    142.952608
+2017-03-30    142.764147
+2017-03-31    142.496334
+</pre>
+</div>
+
+<p>
+  Or in several consecutive months:
+</p>
+<div class="section-example-container">
+
+<pre class="python">aapl['2017-2':'2017-4']
+</pre>
+</div>
+
+<p>
+  .head(N) and .tail(N) are methods for quickly accessing the first or last N elements.
+</p>
+<div class="section-example-container">
+
+<pre class="python">print aapl.head()
+print aapl.tail(10)
+</pre>
+</div>
+<p>
+  The output:
+</p>
+<div class="section-example-container">
+
+<pre class="python">
+Date
+2017-01-03    114.715378
+2017-01-04    114.586983
+2017-01-05    115.169696
+2017-01-06    116.453639
+2017-01-09    117.520300
+Name: Adj. Close, dtype: float64
+Date
+2017-08-08    159.433108
+2017-08-09    160.409148
+2017-08-10    155.270000
+2017-08-11    157.480000
+2017-08-14    159.850000
+2017-08-15    161.600000
+2017-08-16    160.950000
+2017-08-17    157.870000
+2017-08-18    157.500000
+2017-08-21    157.210000
+Name: Adj. Close, dtype: float64
+</pre>
+</div>

From eb15f94de0868141eb5bb55909c4e7ead648f79f Mon Sep 17 00:00:00 2001
From: Web Editor <web.editor@quantconnect.com>
Date: Fri, 11 Mar 2022 01:59:09 -0500
Subject: [PATCH 747/753] Automated push from server made by Louis Szeto

---
 .../02 QuantConnect Options API/04 Select Contracts.html  | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index 54084fb..e9ca2d1 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -65,12 +65,12 @@
 <pre class="python">def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-	optionchain = i.Value
-	self.Log("underlying price:" + str(optionchain.Underlying.Price))
-	df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+    optionchain = i.Value
+    self.Log("underlying price:" + str(optionchain.Underlying.Price))
+    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
 			   index=[x.Symbol.Value for x in optionchain],
 			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-        self.Log(str(df))
+    self.Log(str(df))
 </pre>
 </div>
 <table class="table qc-table">

From 7f51fe51a3dcbd4c46ac07aeefe2d678ed94bdde Mon Sep 17 00:00:00 2001
From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com>
Date: Fri, 11 Mar 2022 15:01:25 +0800
Subject: [PATCH 748/753] Update 04 Select Contracts.html

---
 .../04 Select Contracts.html                         | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index e9ca2d1..7e92e12 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -65,12 +65,12 @@
 <pre class="python">def OnData(self,slice):
     for i in slice.OptionChains:
         if i.Key != self.symbol: continue
-    optionchain = i.Value
-    self.Log("underlying price:" + str(optionchain.Underlying.Price))
-    df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
-			   index=[x.Symbol.Value for x in optionchain],
-			   columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
-    self.Log(str(df))
+        optionchain = i.Value
+        self.Log("underlying price:" + str(optionchain.Underlying.Price))
+        df = pd.DataFrame([[x.Right,float(x.Strike),x.Expiry,float(x.BidPrice),float(x.AskPrice)] for x in optionchain],
+                           index=[x.Symbol.Value for x in optionchain],
+                           columns=['type(call 0, put 1)', 'strike', 'expiry', 'ask price', 'bid price'])
+        self.Log(str(df))
 </pre>
 </div>
 <table class="table qc-table">

From 406089ace2b2521c0bd698e0120196c776346094 Mon Sep 17 00:00:00 2001
From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com>
Date: Fri, 11 Mar 2022 15:13:16 +0800
Subject: [PATCH 749/753] Update 04 Select Contracts.html

Correct indentation and variable names
---
 .../04 Select Contracts.html                  | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html
index 7e92e12..07ea589 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/04 Select Contracts.html	
@@ -160,18 +160,18 @@
 
 <pre class="python">for i in slice.OptionChains:
     if i.Key != self.symbol: continue
-    chain = i.Value
-# differentiate the call and put options
-call = [x for x in optionchain if chain.Right == 0]
-put = [x for x in optionchain if chain.Right == 1]
-# choose ITM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &gt; 0]
-# or choose ATM contracts
-contracts = sorted(optionchain, key = lambda x: abs(optionchain.Underlying.Price - x.Strike))[0]
-# or choose OTM contracts
-contracts = [x for x in call if call.UnderlyingLastPrice - x.Strike &lt; 0]
-# sort the contracts by their expiration dates
-contracts = sorted(contracts, key = lambda x:x.Expiry, reverse = True)
+    optionchain = i.Value
+    # differentiate the call and put options
+    call = [x for x in optionchain if x.Right == 0]
+    put = [x for x in optionchain if x.Right == 1]
+    # choose ITM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike &gt; 0]
+    # or choose ATM contracts
+    contracts = sorted(optionchain, key = lambda x: abs(x.UnderlyingLastPrice - x.Strike))[0]
+    # or choose OTM call contracts
+    contracts = [x for x in call if x.UnderlyingLastPrice - x.Strike &lt; 0]
+    # sort the contracts by their expiration dates
+    contracts = sorted(contracts, key = lambda x: x.Expiry, reverse = True)
 </pre>
 </div>
 <p>

From 86fba1494c5934965d6d08be3dfc2e7e77ebd1f0 Mon Sep 17 00:00:00 2001
From: Louis Szeto <56447733+LouisSzeto@users.noreply.github.com>
Date: Fri, 11 Mar 2022 15:14:13 +0800
Subject: [PATCH 750/753] Update 02 Add Options.html

Correct formatting
---
 .../02 QuantConnect Options API/02 Add Options.html           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html
index a1d5a37..44c5959 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/02 Add Options.html	
@@ -22,8 +22,8 @@
 <div class="section-example-container">
 
 <pre class="python">def Initialize(self):
-    self.SetStartDate(2017, 01, 01)  #Set Start Date
-    self.SetEndDate(2017, 06, 30)  #Set End Date
+    self.SetStartDate(2017, 1, 1)  #Set Start Date
+    self.SetEndDate(2017, 6, 30)  #Set End Date
     self.SetCash(50000)  #Set Strategy Cash
     equity = self.AddEquity("GOOG", Resolution.Minute) # Add the underlying stock: Google
     option = self.AddOption("GOOG", Resolution.Minute) # Add the option corresponding to underlying stock

From 25963e427c4cee06d7778fecca635126745242cf Mon Sep 17 00:00:00 2001
From: Alexandre Catarino <AlexCatarino@users.noreply.github.com>
Date: Thu, 19 May 2022 23:54:45 +0100
Subject: [PATCH 751/753] Move Workflow to Documentation Page

---
 README.md | 30 ++++++------------------------
 1 file changed, 6 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 5e2fd23..c9846d7 100644
--- a/README.md
+++ b/README.md
@@ -8,40 +8,22 @@ This repository is a collection of WordPress and Jupyter notebook tutorials for
 
 Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage. It was designed in Mono and operates in Windows, Linux and Mac platforms. For more information about the LEAN Algorithmic Trading engine see the [Lean][4] Engine repository.
 
-
-## New Tutorial Requests and Edits ##
-
-Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
-
-## Mailing List ##
-
-The mailing list for the project can be found on [Google Groups][6]
-
 ## Contributors and Pull Requests ##
 
 Contributions are warmly very welcomed but we ask you read the existing code to see how it is formatted, commented and ensure contributions match the existing style. All code submissions must include accompanying tests. Please see the [contributor guide lines][7].
 
 ## Strategy Library Development Workflow ##
-To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow these steps:
-1. Review filtered sources like SSRN, arxiv, and other academic journals/papers for a strategy to implement. Try to adhere to the [Quant League competition](https://www.quantconnect.com/competitions/quant-league-1) criteria and the Alpha Streams [minimum criteria](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Minimum-Criteria) and [review process](https://www.quantconnect.com/docs/alpha-streams/submitting-an-alpha#Submitting-an-Alpha-Subsequent-Review-Process).
-2. Post a 3-point development plan to [our Slack channel](https://www.quantconnect.com/slack) and wait for approval by @jaredbroad or @alexcatarino. See an example [here](https://cdn.quantconnect.com/i/tu/development-plan-example.png).
-3. Develop the strategy (add [license and imports](https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/BasicTemplateAlgorithm.py#L1) to main.py).
-4. Add an Issue to the [Tutorials repo](https://github.com/QuantConnect/Tutorials/issues) ([example](https://github.com/QuantConnect/Tutorials/issues/277)).
-5. Add @alexcatarino as a [collaborator](https://www.quantconnect.com/blog/collaborating-in-quantconnect/) to the project.
-6. Publish a strategy write-up in the Slack channel and wait for approval (see [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library) for examples).
-7. Convert the strategy write-up to HTML form ([examples](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library)).
-8. Make PR (following the [Contributor's Guidelines](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md)):
-    - If the write-up includes images, upload them [here](https://www.quantconnect.com/admin/cdnUpload).
-    - Add summary HTML files to [Strategy Library directory](https://github.com/QuantConnect/Tutorials/tree/master/04%20Strategy%20Library). If it's a non-Quantpedia strategy, set the ID number (in the directory name) to the next available after 1023.
-    - If the strategy is from Quantpedia, add strategy ID and backtest ID to [quantpedia.json](https://github.com/QuantConnect/Tutorials/blob/master/quantpedia.json).
-    - Add strategy metadata to [this file](https://github.com/QuantConnect/Tutorials/blob/master/04%20Strategy%20Library/00%20Strategy%20Library/01%20Strategy%20Library.php) (Currently semi-sorted by Quantpedia strategy ID).
-9. After the PR is merged, send @jaredbroad the URL and a 1-sentence summary of what the paper/strategy is about and post the strategy to the forum with the backtest of the algorithm and a short summary of the project ([example](https://www.quantconnect.com/forum/discussion/8608/strategy-library-addition-residual-momentum/p1)).
+
+To publish a strategy to our [Strategy Library](https://www.quantconnect.com/tutorials/strategy-library/strategy-library), follow the steps on the [documentation page](https://www.quantconnect.com/docs/v2/writing-algorithms/strategy-library#03-Contribute-Tutorials)
+
+## New Tutorial Requests and Edits ##
+
+Please submit new tutorial requests as an issue to the [Tutorials][5] repository. Before submitting an issue please read others to ensure it is not a duplicate. Edits and fixes for clarity are warmly welcomed!
 
 [1]: https://www.quantconnect.com/tutorials "Tutorials Viewer"
 [2]: https://www.quantconnect.com/lean/docs "Lean Documentation"
 [3]: https://github.com/QuantConnect/Lean/archive/master.zip
 [4]: https://github.com/QuantConnect/Lean
 [5]: https://github.com/QuantConnect/Tutorials/issues
-[6]: https://groups.google.com/forum/#!forum/lean-engine
 [7]: https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md
 [8]: https://www.quantconnect.com/slack

From 67580984b715b02d829840831b8bc487f29c7213 Mon Sep 17 00:00:00 2001
From: Gustavo Aviles <gustavo@quantconnect.com>
Date: Wed, 15 Jun 2022 17:35:12 -0700
Subject: [PATCH 752/753] Update backtest embed url.

---
 .../05 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../04 Algorithm.html                         |  4 +--
 .../05 Algorithm.html                         |  2 +-
 .../06 Algorithm.html                         |  2 +-
 .../06 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../04 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  2 +-
 .../113 January Barometer/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../13 Asset Class Momentum/03 Algorithm.html |  2 +-
 .../14 Sector Momentum/03 Algorithm.html      |  2 +-
 .../15 Short Term Reversal/03 Algorithm.html  |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../16 Overnight Anomaly/03 Algorithm.html    |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../17 Forex Momentum/03 Algorithm.html       |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../20 Forex Carry Trade/03 Algorithm.html    |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../04 Algorithm.html                         |  4 +--
 .../04 \347\256\227\346\263\225.cn.html"      |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  4 +--
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../32 Gold Market Timing/03 Algorithm.html   |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 \347\256\227\346\263\225.cn.html"      |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../03 Algorithm.html                         |  2 +-
 .../83 Pre-Holiday Effect/03 Algorithm.html   |  2 +-
 .../06 Algorithm.html                         | 18 ++++++-------
 .../13 Market Risk/06 Algorithm.html          | 26 +++++++++----------
 .../05 Algorithm.html                         |  2 +-
 .../05 Algorithm.html                         |  2 +-
 .../01 Covered Call/04 Algorithm.html         |  4 +--
 .../02 Bull Call Spread/04 Algorithm.html     |  4 +--
 .../03 Long Straddle/04 Algorithm.html        |  4 +--
 .../04 Long Strangle/04 Algorithm.html        |  4 +--
 .../05 Butterfly Spread/04 Algorithm.html     |  4 +--
 .../06 Iron Condor/04 Algorithm.html          |  4 +--
 .../07 Iron Butterfly/04 Algorithm.html       |  4 +--
 .../08 Protective Collar/04 Algorithm.html    |  4 +--
 65 files changed, 98 insertions(+), 98 deletions(-)

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index 5c357f4..9717146 100755
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index 58173b8..3358861 100755
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diff --git a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html
index 8d6082c..309e3c2 100755
--- a/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/12 Modern Portfolio Theory/06 Algorithm.html	
@@ -1,9 +1,9 @@
-<p>
-  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
-</p>
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_03b5af43deef78e3a81c9a8322069567.html"></iframe>
-    </div>
-</div>
+<p>
+  Mean-variance analysis is used to optimize portfolios with several strategies. Here we treat Dow 30 stocks as strategy and designed an algorithm to test mean-variance analysis:
+</p>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_03b5af43deef78e3a81c9a8322069567.html"></iframe>
+    </div>
+</div>
diff --git a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html
index 3069322..64474c1 100755
--- a/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
+++ b/05 Introduction to Financial Python[]/13 Market Risk/06 Algorithm.html	
@@ -1,13 +1,13 @@
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8bac244b346cbd9bc3b39dca76192919.html"></iframe>
-    </div>
-</div>
-
-<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
-    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
-    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5c75197259238b36a6cea72832c9e709.html"></iframe>
-    </div>
-</div>
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_8bac244b346cbd9bc3b39dca76192919.html"></iframe>
+    </div>
+</div>
+
+<div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
+    <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
+    <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_5c75197259238b36a6cea72832c9e709.html"></iframe>
+    </div>
+</div>
diff --git a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html
index 64b1d35..e485feb 100755
--- a/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
+++ b/06 Introduction to Options[]/02 QuantConnect Options API/05 Algorithm.html	
@@ -4,6 +4,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_6453fa45f6b98d880a4646a820265a62.html"></iframe>
     </div>
 </div>
diff --git a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html
index 12d6ce9..b196a80 100755
--- a/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
+++ b/06 Introduction to Options[]/03 Put-Call Parity and Arbitrage Strategies/05 Algorithm.html	
@@ -1,6 +1,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_b986091b5e1822b3d74f1231bf44eb2c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_b986091b5e1822b3d74f1231bf44eb2c.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/01 Covered Call/04 Algorithm.html b/07 Applied Options[]/01 Covered Call/04 Algorithm.html
index 9fa4e5e..6858e6e 100755
--- a/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
+++ b/07 Applied Options[]/01 Covered Call/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_9d07e52d5c6199a77597d5f48e68353e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_9d07e52d5c6199a77597d5f48e68353e.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_31a6d88a35d29c2bbe654a3e5ee5f80e.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_31a6d88a35d29c2bbe654a3e5ee5f80e.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html
index 0e1c81b..ef1b62b 100755
--- a/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/02 Bull Call Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_06ae7450577c1f0c80edc563c4cf06b8.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_06ae7450577c1f0c80edc563c4cf06b8.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_db61c966b616e8d1d8f289c4eed5525c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_db61c966b616e8d1d8f289c4eed5525c.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html
index 3e98c40..89e3d28 100755
--- a/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
+++ b/07 Applied Options[]/03 Long Straddle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_8f07db5f247cf314e82292b375e1cb77.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_8f07db5f247cf314e82292b375e1cb77.html"></iframe>
     </div>
 </div>
 <p>
@@ -13,6 +13,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_75edf3c5d0221d22e8927ffedbf116b2.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_75edf3c5d0221d22e8927ffedbf116b2.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html
index 78ab066..09a8a85 100755
--- a/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
+++ b/07 Applied Options[]/04 Long Strangle/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_89f1f8e5f1ed5e78bd1b4cdf3ab796a1.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_89f1f8e5f1ed5e78bd1b4cdf3ab796a1.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e3c07881318d864a9ae25637d6d2d9d3.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_e3c07881318d864a9ae25637d6d2d9d3.html"></iframe>
     </div>
 </div>
diff --git a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html
index 134f5b1..beb7ac0 100755
--- a/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
+++ b/07 Applied Options[]/05 Butterfly Spread/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d3c16b8ba4f7df0bebbd766b3dfa7c14.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_d3c16b8ba4f7df0bebbd766b3dfa7c14.html"></iframe>
     </div>
 </div>
 
@@ -14,6 +14,6 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_dab462cab9b3f5c8b2a23911f3e98843.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_dab462cab9b3f5c8b2a23911f3e98843.html"></iframe>
     </div>
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diff --git a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html
index cf2d154..7a08824 100755
--- a/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
+++ b/07 Applied Options[]/06 Iron Condor/04 Algorithm.html	
@@ -4,7 +4,7 @@
 <div class="qc-embed-frame" style="display: inline-block; position: relative; width: 100%; min-height: 100px; min-width: 300px;">
     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
     <div class="qc-embed-element" style="position: absolute; top: 0; bottom: 0; left: 0; right: 0;">
-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_c2a81c53ec674492e16d3e8da52baecd.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_c2a81c53ec674492e16d3e8da52baecd.html"></iframe>
     </div>
 </div>
 <p>
@@ -14,6 +14,6 @@
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     <div class="qc-embed-dummy" style="padding-top: 56.25%;"></div>
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_e496915dea9a5b0efcd70d0f77309261.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_e496915dea9a5b0efcd70d0f77309261.html"></iframe>
     </div>
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diff --git a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html
index cd42f7d..05d0e03 100755
--- a/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
+++ b/07 Applied Options[]/07 Iron Butterfly/04 Algorithm.html	
@@ -4,7 +4,7 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_a2fe60a67bc1c78532ba4a1a93211c6c.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_a2fe60a67bc1c78532ba4a1a93211c6c.html"></iframe>
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@@ -14,6 +14,6 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_d60e69c80aeb5719f06202a0c25b1274.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_d60e69c80aeb5719f06202a0c25b1274.html"></iframe>
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diff --git a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html
index 245885d..77dc3ce 100755
--- a/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
+++ b/07 Applied Options[]/08 Protective Collar/04 Algorithm.html	
@@ -4,7 +4,7 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_5e793524109c9230ca92970f050f119f.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_5e793524109c9230ca92970f050f119f.html"></iframe>
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@@ -14,6 +14,6 @@
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-        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/index.php?key=processCache&request=embedded_backtest_823c79dcfbae11dabb14ee9ea2365c13.html"></iframe>
+        <iframe class="qc-embed-backtest" height="100%" width="100%" style="border: 1px solid #ccc; padding: 0; margin: 0;" src="https://www.quantconnect.com/terminal/processCache/embedded_backtest_823c79dcfbae11dabb14ee9ea2365c13.html"></iframe>
     </div>
 </div>

From 144ea92bd184b1cd91f0af35c8aa288e97d1ed5f Mon Sep 17 00:00:00 2001
From: avorobiev <vorobiev.alexander@gmail.com>
Date: Mon, 28 Jul 2025 17:58:07 +0300
Subject: [PATCH 753/753] Correct intrinsic and time value calculation in AAPL
 example

---
 .../01 General Features of Options/04 The Value of Options.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html
index f0877cc..b392bbd 100755
--- a/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
+++ b/06 Introduction to Options[]/01 General Features of Options/04 The Value of Options.html	
@@ -12,5 +12,5 @@
 \[Time Value= Premium-Intrinsic Value\]
 </div>
 <p>
-For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $160. The intrinsic value of this contract is 160-143=$17, the time value is 17-10=$7. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
+For example, an AAPL call option contract which expires after 10 days has strike $143 and premium $10. now the market price of AAPL is $150. The intrinsic value of this contract is 150-143=$7, the time value is 10-7=$3. Although the intrinsic value of OTM and ATM options is zero, they have time values if they still have a certain amount of time until the option expires so for OTM and ATM options, their premiums equal their time values.
 </p>